diff --git "a/4204.jsonl" "b/4204.jsonl" new file mode 100644--- /dev/null +++ "b/4204.jsonl" @@ -0,0 +1,648 @@ +{"seq_id":"607488499","text":"\"\"\"Demo with MJPEG streaming\"\"\"\nfrom trueface.recognition import FaceRecognizer\nfrom trueface.video import VideoStream, QVideoStream\nfrom trueface.server import create_server\nfrom trueface.object_detection import ObjectRecognizer\nfrom multiprocessing import Process, Queue, Value\nimport os\n\n#init object recognizer\nobject_recognition = ObjectRecognizer(ctx='gpu',\n model_path=\"./tf-object_detection-mobilenet/model.trueface\",\n params_path=\"./tf-object_detection-mobilenet/model.params\",\n license=os.environ['TF_TOKEN'],\n classes=\"./tf-object_detection-mobilenet/classes.names\")\n\n#simple streaming server\ndef start_server(port, q):\n \"\"\"starts a simple MJPEG streaming server\"\"\"\n app = create_server()\n app.config['q'] = q\n p = Process(target=app.run, kwargs={\"host\":'0.0.0.0',\"port\":port, \"threaded\":True})\n p.daemon = True\n p.start()\n\n#initialize video capture from your webcam\ncap = VideoStream(src=0).start()\n\n#create a queue\nq = Queue(maxsize=10)\n\n#start streaming server\nstart_server(8086, q)\nprint('navigate to http://localhost:8086/ to view your stream.')\n\nwhile(True):\n\tframe = cap.read()\n\tresult = object_recognition.predict(frame)\n\tprint(result)\n\tfor i, box in enumerate(result['boxes']):\n\t\tobject_recognition.draw_label(frame, (int(box[0]), int(box[1])), result['classes'][i])\n\t\tobject_recognition.draw_box(frame, box)\n\tif q.full():\n\t\tq.get()\n\tq.put(frame)","sub_path":"python_sdk_deprecated/object-detection/demo_with_mjpeg_streaming.py","file_name":"demo_with_mjpeg_streaming.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"293524958","text":"from django.conf.urls import patterns, include, url\nfrom Library.views import *\nfrom Library import settings\n \n# Uncomment the next two lines to enable the admin:\n# from django.contrib import admin\n# admin.autodiscover()\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n # Examples:\n # url(r'^$', 'Library.views.home', name='home'),\n # url(r'^Library/', include('Library.foo.urls')),\n\n # Uncomment the admin/doc line below to enable admin documentation:\n # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n # url(r'^admin/', include(admin.site.urls)),\n url(r'^admin/', include(admin.site.urls)),\n (r'^home/$', show_book),\n (r'^search/$', search_book),\n (r'^Book/delete/$',delete_book),\n (r'^Book/change/$',change_book),\n (r'^Book/add/$',add_book),\n (r'^Author/$',add_author),\n (r'^Book/detail/$',detail_book),\n (r'^medias/(?P.*)$', 'django.views.static.serve', {'document_root': settings.MEDIA_ROOT}),\n)\n","sub_path":"Library/Library/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"179127529","text":"# This code is from openai baseline\n# https://github.com/openai/baselines/tree/master/baselines/common/vec_env\n\nimport numpy as np\nfrom multiprocessing import Process, Pipe\nimport pickle\nimport cloudpickle\nimport gym\n\n\ndef make_env(env_name):\n def _thunk():\n env = gym.make(env_name)\n return env\n return _thunk\n\n\nclass CloudpickleWrapper(object):\n def __init__(self, x):\n self.x = x\n\n def __getstate__(self):\n return cloudpickle.dumps(self.x)\n\n def __setstate__(self, ob):\n self.x = pickle.loads(ob)\n\n\ndef worker(remote, parent_remote, env_fn_wrapper):\n # remote로 할일과 데이터를 받아옴.\n # env에 일을 수행.\n # remote로 결과를 보냄\n parent_remote.close()\n\n env = env_fn_wrapper.x()\n\n while True:\n\n cmd, data = remote.recv()\n if cmd == 'step':\n ob, reward, done, info = env.step(data)\n if done:\n ob = env.reset()\n remote.send((ob, reward, done, info))\n\n elif cmd == 'reset':\n ob = env.reset()\n remote.send(ob)\n\n elif cmd == 'reset_task':\n ob = env.reset_task()\n remote.send(ob)\n\n elif cmd == 'close':\n remote.close()\n break\n\n elif cmd == 'get_spaces':\n remote.send((env.observation_space, env.action_space))\n\n elif cmd == 'set_max_episode_steps':\n env._max_episode_steps = data\n\n else:\n raise NotImplementedError\n\n\nclass VecEnv(object):\n def __init__(self, num_envs, observation_space, action_space):\n self.num_envs = num_envs\n self.observation_space = observation_space\n self.action_space = action_space\n\n def reset(self):\n pass\n\n def step_async(self, actions):\n pass\n\n def step_wait(self):\n pass\n\n def close(self):\n pass\n\n def step(self, actions):\n self.step_async(actions)\n return self.step_wait()\n\n\nclass SubprocVecEnv(VecEnv):\n def __init__(self, env_fns, spaces=None):\n self.waiting = False\n self.closed = False\n self.n_envs = len(env_fns)\n self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(self.n_envs)])\n\n self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))\n for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]\n\n for p in self.ps:\n p.daemon = True # if the main process crashes, we should not cause things to hang\n p.start()\n\n for remote in self.work_remotes:\n remote.close()\n\n self.remotes[0].send(('get_spaces', None))\n\n observation_space, action_space = self.remotes[0].recv()\n\n VecEnv.__init__(self, len(env_fns), observation_space, action_space)\n\n def step_async(self, actions):\n for remote, action in zip(self.remotes, actions):\n remote.send(('step', action))\n self.waiting = True\n\n def step_wait(self):\n results = [remote.recv() for remote in self.remotes]\n self.waiting = False\n ob_s, rew_s, done_s, info_s = zip(*results)\n return np.stack(ob_s), np.stack(rew_s), np.stack(done_s), info_s\n\n def reset(self):\n for remote in self.remotes:\n remote.send(('reset', None))\n return np.stack([remote.recv() for remote in self.remotes])\n\n def reset_task(self):\n for remote in self.remotes:\n remote.send(('reset_task', None))\n return np.stack([remote.recv() for remote in self.remotes])\n\n def set_max_episode_steps(self, max_steps):\n for remote in self.remotes:\n remote.send(('set_max_episode_steps', max_steps))\n\n def close(self):\n if self.closed:\n return\n if self.waiting:\n for remote in self.remotes:\n remote.recv()\n for remote in self.remotes:\n remote.send(('close', None))\n for p in self.ps:\n p.join()\n self.closed = True\n\n def __len__(self):\n return self.n_envs\n","sub_path":"actorcritic_pendulum/common/multiprocessing_env.py","file_name":"multiprocessing_env.py","file_ext":"py","file_size_in_byte":4102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"452099143","text":"# -*- coding: utf-8 -*-\n\"\"\"Test suite for all tests.\n\"\"\"\nimport unittest\n\n\ndef suite():\n '''Packing all tests.\n\n Returns:\n obj:`TestSuite`: testing suite object contained test cases.\n '''\n suite = unittest.TestSuite()\n\n suite.addTests((\n # Writing cheer prize@estar\n ))\n\n return suite\n","sub_path":"tests/test_all.py","file_name":"test_all.py","file_ext":"py","file_size_in_byte":324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"377250508","text":"import networkx as nx\nimport matplotlib.pyplot as plt\n'''\nG=nx.cubical_graph()\npos=nx.spring_layout(G) # positions for all nodes\n\n# nodes\nnx.draw_networkx_nodes(G,pos,\n nodelist=[0,1,2,3],\n node_color='r',\n node_size=500,\n alpha=0.8)\nnx.draw_networkx_nodes(G,pos,\n nodelist=[4,5,6,7],\n node_color='b',\n node_size=500,\n alpha=0.8)\n\n# edges\nnx.draw_networkx_edges(G,pos,width=1.0,alpha=0.5)\nnx.draw_networkx_edges(G,pos,\n edgelist=[(0,1),(1,2),(2,3),(3,0)],\n width=8,alpha=0.5,edge_color='r')\nnx.draw_networkx_edges(G,pos,\n edgelist=[(4,5),(5,6),(6,7),(7,4)],\n width=8,alpha=0.5,edge_color='b')\n\n\n# some math labels\nlabels={}\nlabels[0]=r'$a$'\nlabels[1]=r'$b$'\nlabels[2]=r'$c$'\nlabels[3]=r'$d$'\nlabels[4]=r'$\\alpha$'\nlabels[5]=r'$\\beta$'\nlabels[6]=r'$\\gamma$'\nlabels[7]=r'$\\delta$'\nnx.draw_networkx_labels(G,pos,labels,font_size=16)\n\nplt.axis('off')\nplt.savefig(\"labels_and_colors.png\") # save as png\nplt.show() # display\n'''\nG=nx.Graph()\n\nG.add_edge('a','b',weight=0.6)\nG.add_edge('a','c',weight=0.2)\nG.add_edge('c','d',weight=0.1)\nG.add_edge('c','e',weight=0.7)\nG.add_edge('c','f',weight=0.9)\nG.add_edge('a','d',weight=0.3)\n\nelarge=[(u,v) for (u,v,d) in G.edges(data=True) if d['weight'] >0.5]\nesmall=[(u,v) for (u,v,d) in G.edges(data=True) if d['weight'] <=0.5]\n\npos=nx.spring_layout(G) # positions for all nodes\n\n# nodes\nnx.draw_networkx_nodes(G,pos,node_size=700)\n\n# edges\nnx.draw_networkx_edges(G,pos,edgelist=elarge,\n width=6)\nnx.draw_networkx_edges(G,pos,edgelist=esmall,\n width=6,alpha=0.5,edge_color='b',style='dashed')\n\n# labels\nnx.draw_networkx_labels(G,pos,font_size=20,font_family='sans-serif')\n\nplt.axis('off')\nplt.savefig(\"weighted_graph.png\") # save as png\nplt.show() # display\nnx.write_gexf(G,\"migrafo.gexf\")","sub_path":"ServiciosTecnicos/pruebaGraph.py","file_name":"pruebaGraph.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"398132113","text":"import csv\nimport sys\nfrom person import Person\n\n\ndef open_csv(file_name):\n with open(file_name) as f:\n return[Person(i[0], i[1]) for i in list(csv.reader(f, delimiter=\",\"))]\n\n\ndef get_csv_file_name(argv_list):\n if len(argv_list) >= 2:\n return argv_list[1]\n\n\ndef format_output(person):\n if type(person) is Person:\n return(\"This number belongs to: \" + person.get_name())\n elif type(person) is list:\n return(\"Persons with similar number: \" + str([i.get_name() for i in person])[1:-1])\n else:\n return(\"No match found.\")\n\n\ndef get_person_by_phone_number(person_list, user_input_phone_number):\n match = [i for i in person_list if i.is_phone_number_matching(user_input_phone_number)]\n if len(match) > 1:\n return match\n elif len(match) == 1:\n return match[0]\n\n\ndef main():\n file_name = get_csv_file_name(sys.argv)\n if file_name is None:\n print('No database file was given.')\n sys.exit(0)\n\n person_list = open_csv(file_name)\n user_input_phone_number = input('Please enter the phone number: ')\n match_person = get_person_by_phone_number(person_list, user_input_phone_number)\n\n print(format_output(match_person))\n\nif __name__ == '__main__':\n main()\n","sub_path":"3-phone-numbers/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"531252221","text":"#!/usr/bin/env python3\n\n# ----------------\n# RangeIterable.py\n# ----------------\n\nclass Range_Iterable_1 :\n class iterator () :\n def __init__ (self, b, e) :\n self.b = b\n self.e = e\n\n def __iter__ (self) :\n return self\n\n def __next__ (self) :\n if self.b == self.e :\n raise StopIteration()\n v = self.b\n self.b += 1\n return v\n\n def __init__ (self, b, e) :\n self.b = b\n self.e = e\n\n def __iter__ (self) :\n return Range_Iterable_1.iterator(self.b, self.e)\n\nclass Range_Iterable_2 :\n def __init__ (self, b, e) :\n self.b = b\n self.e = e\n\n def __iter__ (self) :\n b = self.b\n e = self.e\n while b != e :\n yield b\n b += 1\n\ndef test (c) :\n x = c(2, 2)\n assert list(x) == []\n assert list(x) == []\n\n x = c(2, 3)\n assert list(x) == [2]\n assert list(x) == [2]\n\n x = c(2, 4)\n assert list(x) == [2, 3]\n assert list(x) == [2, 3]\n\n x = c(2, 5)\n assert list(x) == [2, 3, 4]\n assert list(x) == [2, 3, 4]\n\nprint(\"RangeIterable.py\")\n\ntest(Range_Iterable_1)\ntest(Range_Iterable_2)\ntest(range)\n\nprint(\"Done.\")\n","sub_path":"exercises/RangeIterable.py","file_name":"RangeIterable.py","file_ext":"py","file_size_in_byte":1226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"218630817","text":"#!/usr/bin/env python3\n#\n# ISC License (ISC)\n#\n# Copyright (c) 2016, Austin Hellyer \n#\n# Permission to use, copy, modify, and/or distribute this software for any\n# purpose with or without fee is hereby granted, provided that the above\n# copyright notice and this permission notice appear in all copies.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\n# REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\n# AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\n# INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\n# LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\n# OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\n# PERFORMANCE OF THIS SOFTWARE.\n\nfrom __future__ import unicode_literals\nfrom os.path import join, dirname\nfrom dotenv import load_dotenv\n\n# .env file _must_ contain:\n# - **DISCORD_EMAIL**: the user's email address.\n# - **DISCORD_PASS**: the user's password.\n#\n# The .env file _should_ contain:\n# - **UPDATE_INTERVAL**: number of seconds between updates; defaults to 300.\n#\n# Note that these should not be stored in a version control (such as git), but\n# instead should be edited in server-side in some shape or form.\ndotenv_path = join(dirname(__file__), '.env')\nload_dotenv(dotenv_path)\n\nimport asyncio\nimport discord\nfrom os import environ\nimport random\nimport sys\nfrom yaml import YAMLError\nimport yaml\n\nclient = discord.Client()\n\n@client.event\nasync def on_ready():\n print('ready')\n\n\nasync def status():\n await client.wait_until_ready()\n\n # Try to parse the statuses.yml file into a list of possible statuses.\n # This can error if the file does not exist or is not a valid YAML file, in\n # each case letting the user know and exiting.\n try:\n with open('./.statuses.yml') as f:\n try:\n choices = yaml.load(f)\n except YAMLError as e:\n print('Could not parse statuses; exiting')\n\n sys.exit(1)\n except FileNotFoundError:\n print('No .statuses.yml file present; exiting')\n\n sys.exit(1)\n\n # Retrieve the update interval from the .env file. If this is not present,\n # let them know and default to 300. If it's present but is not parsable to\n # an integer, let them know and exit.\n update_interval = environ.get('UPDATE_INTERVAL')\n\n if update_interval is None:\n print('no UPDATE_INTERVAL present in .env; defaulting to 300')\n\n update_interval = 300\n else:\n try:\n update_interval = int(update_interval)\n except TypeError:\n print('{} is not an integer; exiting')\n\n sys.exit(2)\n\n # Discord imposes a limit of 5 status updates per minute. Thus, an update\n # interval should not be less than 12 seconds per iteration.\n if update_interval < 12:\n print('Discord statuses can not be updated more than 5 times per')\n print('minute; pick an interval >= 12.')\n\n sys.exit(3)\n\n while 1:\n choice = random.choice(choices)\n\n await client.change_status(game=discord.Game(name=choice))\n await asyncio.sleep(update_interval)\n\n\nloop = asyncio.get_event_loop()\n\ntry:\n email = environ.get('DISCORD_EMAIL')\n pw = environ.get('DISCORD_PASS')\n\n loop.create_task(status())\n loop.run_until_complete(client.login(email, pw))\n loop.run_until_complete(client.connect())\nexcept Exception:\n loop.run_until_complete(client.close())\nfinally:\n loop.close()\n","sub_path":"__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":3577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"363657702","text":"from pathlib import Path\nimport json\nimport math\nimport os\nimport random\nimport time\n\nfrom torch import nn, optim\nimport torch\nimport torchvision.transforms as transforms\n\nfrom PIL import Image, ImageOps, ImageFilter\n\nimport resnet\nimport datasets\n \n\nclass GaussianBlur(object):\n def __init__(self, p):\n self.p = p\n\n def __call__(self, img):\n if random.random() < self.p:\n sigma = random.random() * 1.9 + 0.1\n return img.filter(ImageFilter.GaussianBlur(sigma))\n else:\n return img\n\n\nclass Solarization(object):\n def __init__(self, p):\n self.p = p\n\n def __call__(self, img):\n if random.random() < self.p:\n return ImageOps.solarize(img)\n else:\n return img\n\n\nclass Transform:\n def __init__(self):\n self.transform = transforms.Compose([\n transforms.RandomResizedCrop(96, interpolation=Image.BICUBIC),\n transforms.RandomHorizontalFlip(p=0.5),\n transforms.RandomApply(\n [transforms.ColorJitter(brightness=0.4, contrast=0.4,\n saturation=0.2, hue=0.1)],\n p=0.8\n ),\n transforms.RandomGrayscale(p=0.2),\n GaussianBlur(p=1.0),\n Solarization(p=0.0),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])\n ])\n self.transform_prime = transforms.Compose([\n transforms.RandomResizedCrop(96, interpolation=Image.BICUBIC),\n transforms.RandomHorizontalFlip(p=0.5),\n transforms.RandomApply(\n [transforms.ColorJitter(brightness=0.4, contrast=0.4,\n saturation=0.2, hue=0.1)],\n p=0.8\n ),\n transforms.RandomGrayscale(p=0.2),\n GaussianBlur(p=0.1),\n Solarization(p=0.2),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])\n ])\n\n def __call__(self, x):\n y1 = self.transform(x)\n y2 = self.transform_prime(x)\n return y1, y2\n \n\n \nclass LARS(optim.Optimizer):\n def __init__(self, params, lr, weight_decay=0, momentum=0.9, eta=0.001,\n weight_decay_filter=None, lars_adaptation_filter=None):\n defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum,\n eta=eta, weight_decay_filter=weight_decay_filter,\n lars_adaptation_filter=lars_adaptation_filter)\n super().__init__(params, defaults)\n\n @torch.no_grad()\n def step(self):\n for g in self.param_groups:\n for p in g['params']:\n dp = p.grad\n\n if dp is None:\n continue\n\n if g['weight_decay_filter'] is None or not g['weight_decay_filter'](p):\n dp = dp.add(p, alpha=g['weight_decay'])\n\n if g['lars_adaptation_filter'] is None or not g['lars_adaptation_filter'](p):\n param_norm = torch.norm(p)\n update_norm = torch.norm(dp)\n one = torch.ones_like(param_norm)\n q = torch.where(param_norm > 0.,\n torch.where(update_norm > 0,\n (g['eta'] * param_norm / update_norm), one), one)\n dp = dp.mul(q)\n\n param_state = self.state[p]\n if 'mu' not in param_state:\n param_state['mu'] = torch.zeros_like(p)\n mu = param_state['mu']\n mu.mul_(g['momentum']).add_(dp)\n\n p.add_(mu, alpha=-g['lr'])\n\n\ndef exclude_bias_and_norm(p):\n return p.ndim == 1\n\ndef off_diagonal(x):\n # return a flattened view of the off-diagonal elements of a square matrix\n n, m = x.shape\n assert n == m\n return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()\n\n\ndef adjust_learning_rate(totalEpochs, optimizer, loader, step):\n max_steps = totalEpochs * len(loader)\n warmup_steps = 10 * len(loader)\n base_lr = 0.2 * 1024 / 256\n if step < warmup_steps:\n lr = base_lr * step / warmup_steps\n else:\n step -= warmup_steps\n max_steps -= warmup_steps\n q = 0.5 * (1 + math.cos(math.pi * step / max_steps))\n end_lr = base_lr * 0.001\n lr = base_lr * q + end_lr * (1 - q)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr\n\n\nclass BarlowTwins(nn.Module):\n def __init__(self):\n super().__init__()\n self.backbone = resnet.get_custom_resnet34()\n self.backbone.fc = nn.Identity()\n\n # projector\n sizes = [512] + list(map(int, '1024-1024-1024'.split('-')))\n layers = []\n for i in range(len(sizes) - 2):\n layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=False))\n layers.append(nn.BatchNorm1d(sizes[i + 1]))\n layers.append(nn.ReLU(inplace=True))\n layers.append(nn.Linear(sizes[-2], sizes[-1], bias=False))\n self.projector = nn.Sequential(*layers)\n\n # normalization layer for the representations z1 and z2\n self.bn = nn.BatchNorm1d(sizes[-1], affine=False)\n\n def forward(self, y1, y2):\n z1 = self.projector(self.backbone(y1))\n z2 = self.projector(self.backbone(y2))\n # empirical cross-correlation matrix\n c = self.bn(z1).T @ self.bn(z2)\n # sum the cross-correlation matrix between all gpus\n c.div_(1024)\n # use --scale-loss to multiply the loss by a constant factor\n # see the Issues section of the readme\n on_diag = torch.diagonal(c).add_(-1).pow_(2).sum().mul(1/32)\n off_diag = off_diagonal(c).pow_(2).sum().mul(1/32)\n loss = on_diag + 3.9e-3 * off_diag\n return loss\n\n\ndef getTrainedBarlowModel(totalEpochs):\n # create a dataset from your image folder\n dataset = datasets.CustomDataset(root='/dataset', split='unlabeled', transform=Transform())\n trainDataset = datasets.CustomDataset(root='/dataset', split='train', transform=Transform())\n dataset = torch.utils.data.ConcatDataset((dataset, trainDataset))\n\n # build a PyTorch dataloader\n loader = torch.utils.data.DataLoader(dataset, batch_size=1024, shuffle=True, pin_memory=True, num_workers=4)\n\n torch.backends.cudnn.benchmark = True\n\n model = BarlowTwins().cuda()\n # model = nn.SyncBatchNorm.convert_sync_batchnorm(model)\n optimizer = LARS(model.parameters(), lr=0, weight_decay=1e-6,\n weight_decay_filter=exclude_bias_and_norm,\n lars_adaptation_filter=exclude_bias_and_norm)\n\n # automatically resume from checkpoint if it exists, RELOAD MODEL\n if os.path.isfile('./barlow-34/checkpoint.pth'):\n ckpt = torch.load('./barlow-34/checkpoint.pth',\n map_location='cpu')\n start_epoch = ckpt['epoch']\n model.load_state_dict(ckpt['model'])\n optimizer.load_state_dict(ckpt['optimizer'])\n else:\n start_epoch = 0\n \n least_loss = float('inf')\n running_loss = 0\n\n start_time = time.time()\n scaler = torch.cuda.amp.GradScaler()\n for epoch in range(start_epoch, totalEpochs):\n # sampler.set_epoch(epoch)\n for step, ((y1, y2), _) in enumerate(loader, start=epoch * len(loader)):\n y1 = y1.cuda()\n y2 = y2.cuda()\n lr = adjust_learning_rate(totalEpochs, optimizer, loader, step)\n optimizer.zero_grad()\n with torch.cuda.amp.autocast():\n loss = model.forward(y1, y2)\n \n running_loss += loss.item()\n scaler.scale(loss).backward()\n scaler.step(optimizer)\n scaler.update()\n if step % 10 == 0:\n stats = dict(epoch=epoch, step=step, learning_rate=lr,\n loss=loss.item(),\n time=int(time.time() - start_time))\n print(json.dumps(stats), flush=True)\n # print(json.dumps(stats), file=stats_file)\n # save checkpoint\n state = dict(epoch=epoch + 1, model=model.state_dict(),\n optimizer=optimizer.state_dict())\n if running_loss < least_loss:\n least_loss = running_loss\n torch.save(state, './barlow-34/best-checkpoint.pth')\n running_loss = 0\n # SAVE MODEL AFTER EVERY EPOCH\n torch.save(state, './barlow-34/checkpoint.pth')\n\n # FINAL MODEL SAVING\n torch.save(model.backbone.state_dict(),\n './barlow-34/resnet50.pth')\n \n return model.backbone.state_dict()","sub_path":"barlow.py","file_name":"barlow.py","file_ext":"py","file_size_in_byte":8806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"134913829","text":"# Now we want to encode and decode the label\n# Example:\n# Encode: 7abc --> [7,10,11,12] (IntTensor)\n# Docode: [11,12,13] --> [b,c,d]\n\n# Define alphabet in config.py\nimport torch\n\nclass LabelConverter(object):\n def __init__(self, alphabet, ignore_case = True):\n self.ignore_case = ignore_case\n if self.ignore_case:\n alphabet = alphabet.lower()\n self.alphabet = alphabet\n self.dict = {}\n for index, c in enumerate(self.alphabet):\n self.dict[c] = index\n \n def encode(self, labels):\n # input: labels: list of labels name\n # return: [labels encoded], [labels length]\n length = [len(c) for c in labels]\n text = ''.join(labels)\n encoded = []\n for c in text:\n encoded.append(self.dict[c.lower()] )\n return torch.IntTensor(encoded), torch.IntTensor(length)\n \n def decode(self, text, length):\n # decode to strs, batch mode\n # text: list of encodings length: list of length\n assert sum(length) == text.numel() # .numel used to calculate number of elements in text\n decodings = []\n pos = 0\n for str_len in length:\n encode = text[pos:pos+str_len]\n decode = ''\n for digit in encode:\n decode += self.alphabet[digit-1]\n decodings.append(decode)\n pos+= str_len\n return decodings\n\n","sub_path":"utils/convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"246645056","text":"from PyQt5.QtWidgets import QApplication, QPushButton, QDialog, QGroupBox, QGridLayout, QVBoxLayout\nfrom PyQt5 import QtGui, QtCore\nfrom PyQt5.QtCore import QRect\n\nimport sys\n\nclass Window(QDialog):\n def __init__(self):\n super().__init__()\n self.title = \"PyQt5 Grid Layout\";\n self.left = 500;\n self.top = 200;\n self.width = 400;\n self.height = 100;\n self.iconName = \"pic.jpg\";\n\n self.InitWindow();\n\n self.InitWindow();\n\n def InitWindow(self):\n self.setWindowTitle(self.title);\n self.setWindowIcon(QtGui.QIcon(self.iconName));\n self.setGeometry(self.left, self.top, self.width, self.height);\n \n self.CreateLayout();\n vbox = QVBoxLayout();\n vbox.addWidget(self.groupBox);\n\n self.setLayout(vbox);\n \n self.show()\n\n\n def CreateLayout(self):\n self.groupBox = QGroupBox(\"What is your Favorite Programming Language ?\");\n gridLayout = QGridLayout();\n\n btn1 = QPushButton(\"Python\", self);\n btn1.setIcon(QtGui.QIcon(\"../_Icons/python.jpg\")); # add icon to the left of button\n btn1.setIconSize(QtCore.QSize(40, 40)); # Change the size of the icon --> import needed: from PyQt5 import QtCore\n btn1.setMinimumHeight(40);\n gridLayout.addWidget(btn1, 0, 0);\n\n btn2 = QPushButton(\"C++\", self);\n btn2.setIcon(QtGui.QIcon(\"../_Icons/C_Plus_Plus.png\")); # add icon to the left of button\n btn2.setIconSize(QtCore.QSize(40, 40)); # Change the size of the icon --> import needed: from PyQt5 import QtCore\n btn2.setMinimumHeight(40);\n gridLayout.addWidget(btn2, 0, 1); # row, column\n\n btn3 = QPushButton(\"Fortran\", self);\n btn3.setIcon(QtGui.QIcon(\"../_Icons/fortran.png\")); # add icon to the left of button\n btn3.setIconSize(QtCore.QSize(40, 40)); # Change the size of the icon --> import needed: from PyQt5 import QtCore\n btn3.setMinimumHeight(40);\n gridLayout.addWidget(btn3, 1, 0); # row, column\n\n btn4 = QPushButton(\"C#\", self);\n btn4.setIcon(QtGui.QIcon(\"../_Icons/c_sharp.jpg\")); # add icon to the left of button\n btn4.setIconSize(QtCore.QSize(40, 40)); # Change the size of the icon --> import needed: from PyQt5 import QtCore\n btn4.setMinimumHeight(40);\n gridLayout.addWidget(btn4, 1, 1); # row, column\n\n self.groupBox.setLayout(gridLayout);\n\n\n\n\nif __name__==\"__main__\":\n App = QApplication(sys.argv);\n window = Window();\n sys.exit(App.exec());\n\n","sub_path":"06_Grid_Layout/gridLayout.py","file_name":"gridLayout.py","file_ext":"py","file_size_in_byte":2361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"484546042","text":"# -*- coding: utf-8 -*-\n# @Time : 2018/8/24 10:20\n# @Author : zhoujun\n\n# NOTE: -1 is reserved for 'blank' required by mxnet ctc\nfrom mxnet import nd\nfrom mxnet.gluon.data import ArrayDataset, DataLoader\n\n\nclass t:\n def __init__(self):\n data_set = [ArrayDataset(list(range(10))), ArrayDataset(list(range(20, 40)))]\n ratio = [3, 4]\n self.dataset_len = 0\n self.data_loader_list = []\n self.dataloader_iter_list = []\n for d, r in zip(data_set, ratio):\n self.data_loader_list.append(DataLoader(dataset=d, batch_size=r, last_batch='rollover',shuffle=True))\n self.dataset_len += len(d)\n for s in self.data_loader_list:\n self.dataloader_iter_list.append(iter(s))\n\n def __iter__(self):\n return self\n\n def __len__(self):\n return min([len(x) for x in self.data_loader_list])\n\n def __next__(self):\n balanced_batch_images = []\n for i, data_loader_iter in enumerate(self.dataloader_iter_list):\n try:\n image = next(data_loader_iter)\n balanced_batch_images.append(image)\n except StopIteration:\n self.dataloader_iter_list[i] = iter(self.data_loader_list[i])\n image = next(self.dataloader_iter_list[i])\n balanced_batch_images.append(image)\n batch = nd.concat(*balanced_batch_images, dim=0)\n return batch\n\n\na = t()\nepochs = 2\nprint(epochs, a.dataset_len)\nfor epoch in range(epochs):\n print('epoch', epoch)\n for i, data in enumerate(a):\n if i >= len(a):\n break\n print(data)\n","sub_path":"keys.py","file_name":"keys.py","file_ext":"py","file_size_in_byte":1621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"636048239","text":"import asyncio\nfrom logger import logger\n\n\ndef scheduled(interval: float):\n def wrapped(func):\n async def inner(*args, **kwargs):\n while True:\n try:\n await func(*args, **kwargs)\n except Exception: # noqa\n logger.exception(f'Failed to execute scheduled task')\n await asyncio.sleep(interval)\n\n return inner\n\n return wrapped\n","sub_path":"service/src/scheduled.py","file_name":"scheduled.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"582822098","text":"def find_closing(text, start, open='(', close=')'):\n assert(text[start] == open)\n count = 1\n current = start + 1\n while count:\n if text[current] == open:\n count += 1\n elif text[current] == close:\n count -= 1\n \n current += 1\n \n return current\n ","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"256532594","text":"from urlparse import urlparse\nfrom django.conf import settings\nfrom django.core.validators import URLValidator\nfrom django.core.exceptions import ValidationError\n\n\ndef check_if_url_is_valid(url):\n \"\"\" validates oEmbed url parameter\"\"\"\n\n val = URLValidator(verify_exists=False)\n try:\n val(url)\n except ValidationError:\n return False\n\n domain = urlparse(url).netloc\n if domain.split('.')[0] == 'www':\n domain = '.'.join(domain.split('.')[1:])\n\n if domain != settings.ORGANISATION_URL:\n return False\n return True","sub_path":"badges/oembed/procesors.py","file_name":"procesors.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"473970889","text":"import scipy.io as scio\nimport random\nimport pickle\nimport time\nfrom Classify.Utils import flatten_imgs\n\ntrain_data = scio.loadmat(\"../DataSet/train_32x32.mat\")\ntest_data = scio.loadmat(\"../DataSet/test_32x32.mat\")\nX_train = train_data['X'].transpose((3, 0, 1, 2))\ny_train = train_data['y']\nX_test = test_data['X'].transpose((3, 0, 1, 2))\ny_test = test_data['y']\n\ntrain_range = list(range(0, X_train.shape[0]))\ntest_range = list(range(0, X_test.shape[0]))\ntrain_idx = random.sample(train_range, 10000)\ntest_idx = random.sample(test_range, 5000)\n\nX_train = X_train[train_idx]\ny_train = y_train[train_idx]\n\nX_test = X_test[test_idx]\ny_test = y_test[test_idx]\n\n\nprint(X_train.shape, X_test.shape)\nprint(y_train.shape, y_test.shape)\n\nf = open(\"../DataSet/SampleTrainData\", \"wb\")\npickle.dump((X_train, y_train), f)\nf.close()\n\nf = open(\"../DataSet/SampleTestData\", \"wb\")\npickle.dump((X_test, y_test), f)\nf.close()\n\nexit(0)\ntime.sleep(1)\n\n\nX_train = flatten_imgs(X_train)\nX_test = flatten_imgs(X_test)\nprint(X_train.shape, X_test.shape)\n\n\nf = open(\"../DataSet/TrainData\", \"wb\")\npickle.dump((X_train, y_train), f)\nf.close()\n\nf = open(\"../DataSet/TestData\", \"wb\")\npickle.dump((X_test, y_test), f)\nf.close()","sub_path":"Classify/ExtractSubDataSet.py","file_name":"ExtractSubDataSet.py","file_ext":"py","file_size_in_byte":1197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"462710270","text":"from radvel import driver\nimport os\nimport emcee\nif not emcee.__version__ == \"2.2.1\":\n raise AssertionError('radvel requires emcee v2')\n\nclass args_object(object):\n \"\"\"\n a minimal version of the \"parser\" object that lets you work with the\n high-level radvel API from python. (without directly using the command line\n interface)\n \"\"\"\n def __init__(self, setupfn, outputdir):\n # return args object with the following parameters set\n self.setupfn = setupfn\n self.outputdir = outputdir\n self.decorr = False\n self.plotkw = {}\n self.gp = False\n\n# setupfn = \"/home/luke/Dropbox/proj/timmy/drivers/radvel_drivers/TOI837.py\" # planet fits\nsetupfn = \"/home/luke/Dropbox/proj/timmy/drivers/radvel_drivers/TOI837_fpscenario_limits.py\" # fpscenarios\n\n# outputdir = \"/home/luke/Dropbox/proj/timmy/results/radvel_fitting/20200525_simple_planet\"\n# outputdir = \"/home/luke/Dropbox/proj/timmy/results/radvel_fitting/20200525_fpscenario\"\n# outputdir = \"/home/luke/Dropbox/proj/timmy/results/radvel_fitting/20200624_simple_planet\"\noutputdir = \"/home/luke/Dropbox/proj/timmy/results/radvel_fitting/20200624_fpscenario\"\n\nif not os.path.exists(outputdir):\n os.mkdir(outputdir)\n\nargs = args_object(setupfn, outputdir)\n\n# # perform max-likelihood fit. usually needed to be done first.\n# radvel fit -s $basepath\ndriver.fit(args)\n\n# # plot the maxlikelihood fit\n# radvel plot -t rv -s $basepath\nargs.type = ['rv']\ndriver.plots(args)\n\n# # perform mcmc to get uncertainties\n# radvel mcmc -s $basepath\nargs.nsteps = 10000 # Number of steps per chain [10000]\nargs.nwalkers = 50 # Number of walkers. [50]\nargs.ensembles = 16 # Number of ensembles. Will be run in parallel on separate CPUs [8]\nargs.maxGR = 1.01 # Maximum G-R statistic for chains to be deemed well-mixed and halt the MCMC run [1.01]\nargs.burnGR = 1.03 # Maximum G-R statistic to stop burn-in period [1.03]\nargs.minTz = 1000 # Minimum Tz to consider well-mixed [1000]\nargs.minsteps = 1000 # Minimum number of steps per walker before convergence tests are performed [1000].\n # Convergence checks will start after the minsteps threshold or the minpercent threshold has been hit.\nargs.minpercent = 5 # Minimum percentage of steps before convergence tests are performed [5]\n # Convergence checks will start after the minsteps threshold or the minpercent threshold has been hit.\nargs.thin = 1 # Save one sample every N steps [default=1, save all samples]\nargs.serial = False # If True, run MCMC in serial instead of parallel. [False]\ndriver.mcmc(args)\n\n# # corner plot the samples\n# radvel plot -t rv corner trend -s $basepath\nargs.type = ['rv','corner','trend']\ndriver.plots(args)\n\n# # make a sick pdf report\n# radvel report -s $basepath\nargs.comptype= 'ic' # Type of model comparison table to include. Default: ic\nargs.latex_compiler = 'pdflatex' # path to latex compiler\n# driver.report(args)\n\n# # optionally, include stellar parameters to derive physical parameters for the\n# # planetary system\n# radvel derive -s $basepath\ndriver.derive(args)\n\n# # optionally, make corner plot for derived parameters\n# radvel plot -t derived -s $basepath\nargs.type = ['derived']\ndriver.plots(args)\n\n# # do model comparison. valid choices: ['nplanets', 'e', 'trend', 'jit', 'gp']\n# radvel ic -t nplanets e trend -s $basepath\nargs.type = ['nplanets', 'e', 'trend', 'jit']\nargs.mixed = True # flag to compare all models with the fixed parameters mixed and matched rather than treating each model comparison separately. This is the default.\nargs.unmixed = False # flag to treat each model comparison separately (without mixing them) rather than comparing all models with the fixed parameters mixed and matched.\nargs.fixjitter = False # flag to fix the stellar jitters at the nominal model best-fit value\nargs.verbose = True # get more details\n\ndriver.ic_compare(args)\n\n# # make the final report\n# radvel report -s $basepath\ndriver.report(args)\n","sub_path":"drivers/radvel_drivers/run_rv_fitting.py","file_name":"run_rv_fitting.py","file_ext":"py","file_size_in_byte":4025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"322410284","text":"# -*- coding: utf-8 -*-\n\nimport datetime\nfrom django.db import models\nfrom django.contrib import admin\nfrom django.utils.translation import ugettext as _\n\nfrom koalixcrm.crm.contact.postal_address import PostalAddress\nfrom koalixcrm.crm.contact.phone_address import PhoneAddress\nfrom koalixcrm.crm.contact.email_address import EmailAddress\nfrom koalixcrm.crm.documents.pdf_export import PDFExport\nfrom koalixcrm.djangoUserExtension.const.purpose import *\nfrom koalixcrm.djangoUserExtension.exceptions import *\nfrom koalixcrm.global_support_functions import xstr\nfrom koalixcrm.crm.reporting.work import Work\n\n\nclass UserExtension(models.Model):\n user = models.ForeignKey(\"auth.User\", blank=False, null=False)\n default_template_set = models.ForeignKey(\"TemplateSet\")\n default_currency = models.ForeignKey(\"crm.Currency\")\n\n @staticmethod\n def objects_to_serialize(object_to_create_pdf, reference_user):\n from koalixcrm.crm.contact.phone_address import PhoneAddress\n from koalixcrm.crm.contact.email_address import EmailAddress\n from django.contrib import auth\n objects = list(auth.models.User.objects.filter(id=reference_user.id))\n user_extension = UserExtension.objects.filter(user=reference_user.id)\n if len(user_extension) == 0:\n raise UserExtensionMissing(_(\"During \"+str(object_to_create_pdf)+\" PDF Export\"))\n phone_address = UserExtensionPhoneAddress.objects.filter(\n userExtension=user_extension[0].id)\n if len(phone_address) == 0:\n raise UserExtensionPhoneAddressMissing(_(\"During \"+str(object_to_create_pdf)+\" PDF Export\"))\n email_address = UserExtensionEmailAddress.objects.filter(\n userExtension=user_extension[0].id)\n if len(email_address) == 0:\n raise UserExtensionEmailAddressMissing(_(\"During \"+str(object_to_create_pdf)+\" PDF Export\"))\n objects += list(user_extension)\n objects += list(EmailAddress.objects.filter(id=email_address[0].id))\n objects += list(PhoneAddress.objects.filter(id=phone_address[0].id))\n return objects\n\n @staticmethod\n def get_user_extension(django_user):\n user_extensions = UserExtension.objects.filter(user=django_user)\n if len(user_extensions) > 1:\n raise TooManyUserExtensionsAvailable(_(\"More than one User Extension define for user \") + django_user.__str__())\n elif len(user_extensions) == 0:\n raise UserExtensionMissing(_(\"No User Extension define for user \") + django_user.__str__())\n return user_extensions[0]\n\n def create_pdf(self, template_set, printed_by, *args, **kwargs):\n return PDFExport.create_pdf(self, template_set, printed_by, *args, **kwargs)\n\n def get_template_set(self, template_set):\n if template_set == self.default_template_set.work_report_template:\n if self.default_template_set.work_report_template:\n return self.default_template_set.work_report_template\n else:\n raise TemplateSetMissingForUserExtension((_(\"Template Set for work report \" +\n \"is missing for User Extension\" + str(self))))\n\n def get_fop_config_file(self, template_set):\n template_set = self.get_template_set(template_set)\n return template_set.get_fop_config_file()\n\n def get_xsl_file(self, template_set):\n template_set = self.get_template_set(template_set)\n return template_set.get_xsl_file()\n\n def serialize_to_xml(self, **kwargs):\n date_from = kwargs.get('date_from', datetime.date.today()-datetime.timedelta(days=60))\n date_to = kwargs.get('date_to', datetime.date.today())\n date_first_of_the_month = date_from.replace(day=1)\n date_to_month = date_to.month\n date_end_of_the_month = date_from.replace(day=1).replace(month=date_to_month+1) - datetime.timedelta(days=1)\n date = date_first_of_the_month\n days = dict()\n weeks = dict()\n months = dict()\n projects = self.user_contribution_project(date_from, date_to)\n objects = [self, self.user]\n objects.extend(projects)\n main_xml = PDFExport.write_xml(objects)\n while date < date_from:\n project_efforts = dict()\n for project in projects:\n project_efforts[project] = {'effort': \"-\",\n 'project': project.id.__str__()}\n days[date] = {'effort': \"-\",\n \"day\": str(date.day),\n \"week\": str(date.isocalendar()[1]),\n \"week_day\": str(date.isoweekday()),\n \"month\": str(date.month),\n \"year\": str(date.year),\n \"project_efforts\": project_efforts}\n date += datetime.timedelta(days=1)\n while date <= date_to:\n project_efforts_day = dict()\n project_efforts_week = dict()\n project_efforts_month = dict()\n for project in projects:\n project_efforts_day[project] = {'effort': 0,\n 'project': project.id.__str__()}\n project_efforts_week[project] = {'effort': 0,\n 'project': project.id.__str__()}\n project_efforts_month[project] = {'effort': 0,\n 'project': project.id.__str__()}\n days[date] = {'effort': 0,\n \"day\": str(date.day),\n \"week\": str(date.isocalendar()[1]),\n \"week_day\": str(date.isoweekday()),\n \"month\": str(date.month),\n \"year\": str(date.year),\n \"project_efforts\": project_efforts_day}\n month_key = str(date.month)+\"/\"+str(date.year)\n week_key = str(date.isocalendar()[1])+\"/\"+str(date.year)\n if not (week_key in weeks):\n weeks[week_key] = {'effort': 0,\n 'week': str(date.isocalendar()[1]),\n 'year': str(date.year),\n \"project_efforts\": project_efforts_week}\n if not (month_key in months):\n months[month_key] = {'effort': 0,\n 'month': str(date.month),\n 'year': str(date.year),\n \"project_efforts\": project_efforts_month}\n date += datetime.timedelta(days=1)\n while date < date_end_of_the_month:\n project_efforts = dict()\n for project in projects:\n project_efforts[project] = {'effort': \"-\",\n 'project': project.id.__str__()}\n days[date] = {'effort': \"-\",\n \"day\": str(date.day),\n \"week\": str(date.isocalendar()[1]),\n \"week_day\": str(date.isoweekday()),\n \"month\": str(date.month),\n \"year\": str(date.year),\n \"project_efforts\": project_efforts}\n date += datetime.timedelta(days=1)\n main_xml = PDFExport.append_element_to_pattern(main_xml,\n \".\",\n \"range_from\",\n date_from.__str__(),\n attributes={\"day\": str(date_from.day),\n \"week\": str(date_from.isocalendar()[1]),\n \"week_day\": str(date_from.isoweekday()),\n \"month\": str(date_from.month),\n \"year\": str(date_from.year)})\n main_xml = PDFExport.append_element_to_pattern(main_xml,\n \".\",\n \"range_to\",\n date_to.__str__(),\n attributes={\"day\": str(date_to.day),\n \"week\": str(date_to.isocalendar()[1]),\n \"week_day\": str(date_to.isoweekday()),\n \"month\": str(date_to.month),\n \"year\": str(date_to.year)})\n works = Work.objects.filter(employee=self, date__range=(date_from, date_to))\n for work in works:\n days[work.date]['effort'] += work.effort_hours()\n days[work.date]['project_efforts'][work.task.project]['effort'] += work.effort_hours()\n month_key = str(work.date.month)+\"/\"+str(work.date.year)\n week_key = str(work.date.isocalendar()[1])+\"/\"+str(work.date.year)\n weeks[week_key]['effort'] += work.effort_hours()\n weeks[week_key]['project_efforts'][work.task.project]['effort'] += work.effort_hours()\n months[month_key]['effort'] += work.effort_hours()\n months[month_key]['project_efforts'][work.task.project]['effort'] += work.effort_hours()\n work_xml = work.serialize_to_xml()\n main_xml = PDFExport.merge_xml(main_xml, work_xml)\n for day_key in days.keys():\n main_xml = PDFExport.append_element_to_pattern(main_xml,\n \"object/[@model='djangoUserExtension.userextension']\",\n \"Day_Work_Hours\",\n str(days[day_key]['effort']),\n attributes={\"day\": days[day_key]['day'],\n \"week\": days[day_key]['week'],\n \"week_day\": days[day_key]['week_day'],\n \"month\": days[day_key]['month'],\n \"year\": days[day_key]['year']})\n for project_key in days[day_key]['project_efforts'].keys():\n main_xml = PDFExport.append_element_to_pattern(main_xml,\n \"object/[@model='djangoUserExtension.userextension']\",\n \"Day_Project_Work_Hours\",\n str(days[day_key]['project_efforts'][project_key]['effort']),\n attributes={\"day\": days[day_key]['day'],\n \"week\": days[day_key]['week'],\n \"week_day\": days[day_key]['week_day'],\n \"month\": days[day_key]['month'],\n \"year\": days[day_key]['year'],\n \"project\": days[day_key]['project_efforts'][project_key]['project']})\n for week_key in weeks.keys():\n main_xml = PDFExport.append_element_to_pattern(main_xml,\n \"object/[@model='djangoUserExtension.userextension']\",\n \"Week_Work_Hours\",\n str(weeks[week_key]['effort']),\n attributes={\"week\": weeks[week_key]['week'],\n \"year\": weeks[week_key]['year']})\n for project_key in weeks[week_key]['project_efforts'].keys():\n main_xml = PDFExport.append_element_to_pattern(main_xml,\n \"object/[@model='djangoUserExtension.userextension']\",\n \"Week_Project_Work_Hours\",\n str(weeks[week_key]['project_efforts'][project_key]['effort']),\n attributes={\"week\": weeks[week_key]['week'],\n \"year\": weeks[week_key]['year'],\n \"project\": weeks[week_key]['project_efforts'][project_key]['project']})\n for month_key in months.keys():\n main_xml = PDFExport.append_element_to_pattern(main_xml,\n \"object/[@model='djangoUserExtension.userextension']\",\n \"Month_Work_Hours\",\n str(months[month_key]['effort']),\n attributes={\"month\": months[month_key]['month'],\n \"year\": months[month_key]['year']})\n for project_key in months[month_key]['project_efforts'].keys():\n main_xml = PDFExport.append_element_to_pattern(main_xml,\n \"object/[@model='djangoUserExtension.userextension']\",\n \"Month_Project_Work_Hours\",\n str(months[month_key]['project_efforts'][project_key]['effort']),\n attributes={\"month\": months[month_key]['month'],\n \"year\": months[month_key]['year'],\n \"project\": months[month_key]['project_efforts'][project_key]['project']})\n return main_xml\n\n def user_contribution_project(self, date_from, date_to):\n works = Work.objects.filter(employee=self, date__range=(date_from, date_to))\n projects = []\n for work in works:\n if not work.task.project in projects:\n projects.append(work.task.project)\n return projects\n\n class Meta:\n app_label = \"djangoUserExtension\"\n verbose_name = _('User Extension')\n verbose_name_plural = _('User Extension')\n\n def __str__(self):\n return xstr(self.id) + ' ' + xstr(self.user.__str__())\n\n\nclass UserExtensionPostalAddress(PostalAddress):\n purpose = models.CharField(verbose_name=_(\"Purpose\"), max_length=1, choices=PURPOSESADDRESSINUSEREXTENTION)\n userExtension = models.ForeignKey(UserExtension)\n\n def __str__(self):\n return xstr(self.name) + ' ' + xstr(self.pre_name)\n\n class Meta:\n app_label = \"djangoUserExtension\"\n verbose_name = _('Postal Address for User Extension')\n verbose_name_plural = _('Postal Address for User Extension')\n\n\nclass UserExtensionPhoneAddress(PhoneAddress):\n purpose = models.CharField(verbose_name=_(\"Purpose\"), max_length=1, choices=PURPOSESADDRESSINUSEREXTENTION)\n userExtension = models.ForeignKey(UserExtension)\n\n def __str__(self):\n return xstr(self.phone)\n\n class Meta:\n app_label = \"djangoUserExtension\"\n verbose_name = _('Phone number for User Extension')\n verbose_name_plural = _('Phone number for User Extension')\n\n\nclass UserExtensionEmailAddress(EmailAddress):\n purpose = models.CharField(verbose_name=_(\"Purpose\"), max_length=1, choices=PURPOSESADDRESSINUSEREXTENTION)\n userExtension = models.ForeignKey(UserExtension)\n\n def __str__(self):\n return xstr(self.email)\n\n class Meta:\n app_label = \"djangoUserExtension\"\n verbose_name = _('Email Address for User Extension')\n verbose_name_plural = _('Email Address for User Extension')\n\n\nclass InlineUserExtensionPostalAddress(admin.StackedInline):\n model = UserExtensionPostalAddress\n extra = 1\n classes = ('collapse-open',)\n fieldsets = (\n (_('Basics'), {\n 'fields': (\n 'prefix',\n 'pre_name',\n 'name',\n 'address_line_1',\n 'address_line_2',\n 'address_line_3',\n 'address_line_4',\n 'zip_code',\n 'town',\n 'state',\n 'country',\n 'purpose')\n }),\n )\n allow_add = True\n\n\nclass InlineUserExtensionPhoneAddress(admin.StackedInline):\n model = UserExtensionPhoneAddress\n extra = 1\n classes = ('collapse-open',)\n fieldsets = (\n (_('Basics'), {\n 'fields': ('phone',\n 'purpose',)\n }),\n )\n allow_add = True\n\n\nclass InlineUserExtensionEmailAddress(admin.StackedInline):\n model = UserExtensionEmailAddress\n extra = 1\n classes = ('collapse-open',)\n fieldsets = (\n (_('Basics'), {\n 'fields': ('email',\n 'purpose',)\n }),\n )\n allow_add = True\n\n\nclass OptionUserExtension(admin.ModelAdmin):\n list_display = ('id',\n 'user',\n 'default_template_set',\n 'default_currency')\n list_display_links = ('id',\n 'user')\n list_filter = ('user',\n 'default_template_set',)\n ordering = ('id',)\n search_fields = ('id',\n 'user')\n fieldsets = (\n (_('Basics'), {\n 'fields': ('user',\n 'default_template_set',\n 'default_currency')\n }),\n )\n\n def create_work_report_pdf(self, request, queryset):\n from koalixcrm.crm.views.create_work_report import create_work_report\n\n return create_work_report(self, request, queryset)\n\n create_work_report_pdf.short_description = _(\"Work Report PDF\")\n\n save_as = True\n actions = [create_work_report_pdf]\n inlines = [InlineUserExtensionPostalAddress,\n InlineUserExtensionPhoneAddress,\n InlineUserExtensionEmailAddress]","sub_path":"koalixcrm/djangoUserExtension/user_extension/user_extension.py","file_name":"user_extension.py","file_ext":"py","file_size_in_byte":18928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"110080842","text":"# -*- coding: utf-8 -*-\n\"\"\"\nRandom Walk 2D\n\nCreated on Fri Nov 7 11:27:07 2014\n\n@author: sbroad\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef move(c,p=[0.25,0.25,0.25,0.25]):\n \"\"\"\n Move one step on a rectangular grid.\n \n c ---> an array [x,y] where x is the previous x position\n and y is the previous y position\n p ---> an array with four elements that sum to at most 1\n p[0] ---> the probability of moving right\n p[1] ---> the prob of moving up\n p[2] ---> the prob of moving left\n p[3] ---> the prob of moving down\n 1 - sum(p) ---> the prob of not moving\n \"\"\"\n v = np.random.uniform()\n \n if v <= p[0]:\n return c + [1,0] # move right\n elif v <= sum(p[0:2]):\n return c + [0,1] # move up\n elif v <= sum(p[0:3]):\n return c + [-1,0]\n elif v <= sum(p):\n return c + [0,-1]\n \n # else do not move\n return c\n\nplt.figure(0)\nfinal = []\nfor j in range(1000):\n pos = [np.array([0,0])]\n \n for i in range(100):\n pos.append(move(pos[i]))\n \n pos = np.array(pos)\n plt.plot(pos[:,0],pos[:,1])\n final.append([pos[-1,0],pos[-1,1]])\n\nfinal = np.array(final)\nfinal_x = final[:,0]\nfinal_y = final[:,1]\n\nx_min = min(final_x)\nx_max = max(final_x)\ny_min = min(final_y)\ny_max = max(final_y)\nbin_sq = 2\nshift = 0.5\n#x_bins = np.linspace(x_min-shift,x_max+1-shift,np.ceil((x_max-x_min)/bin_sq))\n#y_bins = np.linspace(x_min-shift,x_max+1-shift,np.ceil((x_max-x_min)/bin_sq))\n\nplt.hist2d(final_x, final_y, bins=15, cmap=plt.cm.YlOrRd_r)\n#plt.hist2d(final_x, final_y, bins=[x_bins, y_bins])\n#plt.hexbin(final_x,final_y,gridsize=15, cmap=plt.cm.YlOrRd_r)\nplt.xticks([])\nplt.yticks([])","sub_path":"ProbStoch/rw2d.py","file_name":"rw2d.py","file_ext":"py","file_size_in_byte":1727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"109966644","text":"\"\"\"\nurls.py\n\nURL dispatch route mappings and error handlers\n\n\"\"\"\nfrom flask import render_template\n\nfrom application import app\nfrom application import views\n\n\n## URL dispatch rules\n# App Engine warm up handler\n# See http://code.google.com/appengine/docs/python/config/appconfig.html#Warming_Requests\napp.add_url_rule('/_ah/warmup', 'warmup', view_func=views.warmup)\n\n# Home page\napp.add_url_rule('/', 'home', view_func=views.home)\n\n# List all questions for anonymous users\napp.add_url_rule('/questions', 'list_questions', view_func=views.list_questions, methods=['GET'])\n\n# Displays the user profile\napp.add_url_rule('/user', 'user_profile', view_func=views.user_profile, methods=['GET', 'POST'])\n\n# List all questions for logged in user\napp.add_url_rule('/user/questions', 'list_questions_for_user', view_func=views.list_questions_for_user, methods=['GET'])\n\n# Ask a new question\napp.add_url_rule('/new_question', view_func=views.new_question, methods=['POST'])\n\n# Edit a question\napp.add_url_rule('/questions//edit', 'edit_question', view_func=views.edit_question, methods=['GET', 'POST'])\n\n# Delete a question\napp.add_url_rule('/questions//delete', view_func=views.delete_question, methods=['POST'])\n\n# List all answers related to a question\napp.add_url_rule('/questions//answers', 'answers_for_question', view_func=views.answers_for_question, methods=['GET'])\n\n# Get a single answer, used for AJAX calls\napp.add_url_rule('/answers/', 'answer', view_func=views.answer, methods=['GET'])\n\n# Provide a new answer\napp.add_url_rule('/questions//new_answer', view_func=views.new_answer, methods=['POST'])\n\n# Accept answer for a question\napp.add_url_rule('/answers//accept', view_func=views.accept_answer, methods=['POST'])\n\n# Search Questions list page\napp.add_url_rule('/questions/search', 'search_questions', view_func=views.search_questions, methods=['POST'])\n\napp.add_url_rule('/admin/rebuild_question_search_index', 'rebuild_question_search_index', view_func=views.rebuild_question_search_index, methods=['GET'])\n\n# Logout\napp.add_url_rule('/logout', 'authenticate', view_func=views.authenticate, methods=['GET'])\n\n# Login\napp.add_url_rule('/login', 'login', view_func=views.login, methods=['GET'])\n\n# List all subscriptions\napp.add_url_rule('/subscriptions', 'list_subscriptions', view_func=views.list_subscriptions, methods=['GET'])\n\n# Match all subscriptions\napp.add_url_rule('/_ah/prospective_search', view_func=views.match_prospective_search, methods=['POST'])\n\n# Channel Presence\napp.add_url_rule('/_ah/channel/connected/', view_func=views.channel_connected, methods=['POST'])\napp.add_url_rule('/_ah/channel/disconnected/', view_func=views.channel_disconnected, methods=['POST'])\n\napp.add_url_rule('/_get_questions', view_func=views.get_questions, methods=['GET'])\n\n\n## Error handlers\n# Handle 404 errors\n@app.errorhandler(404)\ndef page_not_found(e):\n return render_template('404.html'), 404\n\n# Handle 500 errors\n@app.errorhandler(500)\ndef server_error(e):\n return render_template('500.html'), 500\n\n","sub_path":"megaphone/application/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"505337609","text":"import urllib.request\nfrom bs4 import BeautifulSoup\nimport json\nimport re\n\ndef weather_img(conditions):\n return {\n 'Clear Sky':'sun.png',\n 'Sunny':'sun.png',\n 'Sunny Intervals':'sunny_intervals.png',\n\n 'Partly Cloudy':'light_cloud.png',\n 'Light Cloud':'light_cloud.png',\n 'Thick Cloud':'thick_cloud.png',\n 'Grey Cloud':'light_cloud.png',\n 'Mist':'fog.png',\n 'Fog':'fog.png',\n\n 'Thundery Shower':'heavy_thunder_showers.png',\n 'Heavy Thunder':'heavy_thunder.png',\n 'Drizzle':'light_rain.png',\n 'Light Rain':'light_rain.png',\n 'Light Rain Shower':'light_rain_showers.png',\n\n 'Heavy Rain':'heavy_rain.png',\n 'Heavy Rain Shower':'heavy_rain_showers.png',\n\n 'Sleet':'sleet.png',\n 'Light Snow':'light_snow.png',\n 'Light Snow Shower':'light_snow_showers.png',\n 'Light Snow Rain Showers':'light_snow_rain_showers.png',\n 'Heavy Snow':'heavy_snow.png',\n 'Heavy Hail':'heavy_hail.png',\n }.get(conditions, None)\n\ndef wind_direction(windDir):\n return{\n 'Northerly':'wind/N.png',\n 'North North Easterly':'wind/NE.png',\n 'North East':'wind/NE.png',\n 'North East Easterly':'wind/NE.png',\n 'Easterly':'wind/E.png',\n 'East South Easterly':'wind/SE.png',\n 'South Easterly':'wind/SE.png',\n 'South South Easterly':'wind/SE.png',\n 'Southerly':'wind/S.png',\n 'South South Westerly':'wind/SW.png',\n 'South Westerly':'wind/SW.png',\n 'Sount West Westerly':'wind/SW.png',\n 'Westerly':'wind/W.png',\n 'West North Westerly':'wind/NW.png',\n 'North West':'wind/NW.png',\n 'North North Westerly':'wind/NW.png',\n '':'wind/none.png'\n }.get(windDir, None)\n\n\nurl = \"http://www.bbc.co.uk/weather/en/6296559/?day1\"\npage = urllib.request.urlopen(url)\nsoup = BeautifulSoup(page, \"html5lib\")\n\n\nweekWeather = soup.find('div', {'class':'daily-window'})\n\nday = [x.text.strip() for x in weekWeather.findAll('div', {'class':'daily__day-header'})]\n\ncondition = [x[\"title\"] for x in weekWeather.findAll('span', {'class':'weather-type-image weather-type-image-40'})]\n\n\nmax_temp = [x.text[:-2].strip() for x in weekWeather.findAll('span', {'class':'units-value temperature-value temperature-value-unit-c'})][::2]#select every other element from array\n\nmin_temp = [x.text[:-2].strip() for x in weekWeather.findAll('span', {'class':'units-value temperature-value temperature-value-unit-c'})][1::2]#selects every other element starting with first\n\nwindSpeed = [x.text[:-4] for x in weekWeather.findAll('span', {'class': 'units-value windspeed-value windspeed-value-unit-mph'})]\n\nwindDirT = [x.text for x in weekWeather.findAll('span', {'class':'description blq-hide'})]\n\nwindDir = [\"\".join([item[0] for item in x.text.split()])\n for x in weekWeather.select('span.description.blq-hide')]\n\n\nwk = [0,1,2,3,4,5]\n\ndCond = dict(zip(wk,condition))\ndWind = dict(zip(wk,windDir))\nwImg = {}\nfor wk in dCond:\n wImg[wk] = (weather_img(condition[wk]))\n\nwindImg={}\nfor wk in dWind:\n windImg[wk] = (wind_direction(windDirT[wk]))\n\nwkForecast = {}\nwks = [0,1,2,3]\nfor i in wks:\n wkForecast[i] = day[i], condition[i], max_temp[i], min_temp[i], windSpeed[i], windDir[i], wImg[i], windImg[i]\n\nwith open(\"./jsonData/ll_wkforecast.json\", 'w') as outfile:\n json.dump(wkForecast, outfile, indent=4, sort_keys=True)\n","sub_path":"pythonScripts/LL_weekly_forcast.py","file_name":"LL_weekly_forcast.py","file_ext":"py","file_size_in_byte":3596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"259758837","text":"# coding:utf-8\n\nimport math\nimport torch\nfrom torch.optim.optimizer import Optimizer\nfrom . import _temafunctional as tF\n\nclass TAdamW(Optimizer):\n\n def __init__(self, params, lr=1e-3, betas=(0.9, 0.999),\n eps=1e-8, weight_decay=0, warmup=0,\n k_dof=1.0, beta_dof=0.999):\n if not 0.0 <= lr:\n raise ValueError(\"Invalid learning rate: {}\".format(lr))\n if not 0.0 <= eps:\n raise ValueError(\"Invalid epsilon value: {}\".format(eps))\n if not 0.0 <= betas[0] < 1.0:\n raise ValueError(\"Invalid beta parameter at index 0: {}\".format(betas[0]))\n if not 0.0 <= betas[1] < 1.0:\n raise ValueError(\"Invalid beta parameter at index 1: {}\".format(betas[1]))\n if not (0.0 < k_dof or math.inf == k_dof):\n raise ValueError(\"Invalid degrees of freedom scale factor: {}\".format(k_dof))\n if not 0.0 <= beta_dof <= 1.0:\n raise ValueError(\"Invalid beta parameter for dof optimisation: {}\".format(beta_dof))\n defaults = dict(lr=lr, betas=betas, eps=eps,\n weight_decay=weight_decay, warmup=warmup,\n k_dof=k_dof, beta_dof=beta_dof, optim_dof=beta_dof < 1.0)\n super(TAdamW, self).__init__(params, defaults)\n\n def __setstate__(self, state):\n super(TAdamW, self).__setstate__(state)\n\n @torch.no_grad()\n def step(self, closure=None):\n loss = None\n if closure is not None:\n loss = closure()\n\n for group in self.param_groups:\n\n for p in group['params']:\n if p.grad is None:\n continue\n grad = p.grad.data.float()\n if grad.is_sparse:\n raise RuntimeError('TAdamW does not support sparse gradients, please consider SparseAdam instead')\n\n p_data_fp32 = p.data.float()\n\n state = self.state[p]\n\n if len(state) == 0:\n state['step'] = 0\n state['exp_avg'] = torch.zeros_like(p_data_fp32)\n state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)\n else:\n state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)\n state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)\n\n exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n beta1, beta2 = group['betas']\n beta_dof = group[\"beta_dof\"]\n optim_dof = group[\"optim_dof\"]\n\n state['step'] += 1\n\n # Weights computation\n betaw = tF.get_tema_decay_factor(grad=grad,\n state=state,\n group=group,\n exp_avg=exp_avg,\n exp_var=exp_avg_sq,\n beta=beta1,\n beta_dof=beta_dof,\n optim_dof=optim_dof)\n ###\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\n exp_avg.mul_(betaw).add_(1 - betaw, grad)\n\n denom = exp_avg_sq.sqrt().add_(group['eps'])\n bias_correction1 = 1 - beta1 ** state['step']\n bias_correction2 = 1 - beta2 ** state['step']\n\n if group['warmup'] > state['step']:\n scheduled_lr = 1e-8 + state['step'] * group['lr'] / group['warmup']\n else:\n scheduled_lr = group['lr']\n\n step_size = scheduled_lr * math.sqrt(bias_correction2) / bias_correction1\n\n if group['weight_decay'] != 0:\n p_data_fp32.add_(-group['weight_decay'] * scheduled_lr, p_data_fp32)\n\n p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size)\n\n p.data.copy_(p_data_fp32)\n\n return loss\n","sub_path":"tmomentum/optimizers/tadamw.py","file_name":"tadamw.py","file_ext":"py","file_size_in_byte":4049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"176700797","text":"from main.models import Article, Comments, FavoriteArticle\nfrom django.test import TestCase\nfrom datetime import datetime\nfrom django.contrib.auth.models import User\n\n\nclass TestModels(TestCase):\n def setUp(self) -> None:\n self.user = User.objects.create(username='testuser', password='password')\n self.article = Article.objects.create(user_id=self.user, title='testTitle', body='testBody')\n self.comment = Comments.objects.create(user_id=self.user, article_id=self.article, body='testComment')\n self.favorite_article = FavoriteArticle.objects.create(user_id=self.user, article_id=self.article)\n\n def test_article_model(self):\n self.assertIsInstance(self.article.title, str)\n self.assertIsInstance(self.article.body, str)\n self.assertIsInstance(self.article.create_date, datetime)\n self.assertEquals(str(self.article), self.article.title)\n\n def test_comments_model(self):\n self.assertIsInstance(self.comment.body, str)\n self.assertIsInstance(self.comment.create_date, datetime)\n\n def test_favorite_article_model(self):\n self.assertEquals(str(self.favorite_article), str(self.favorite_article.id))\n","sub_path":"backend/BlogBackend/main/tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"79362177","text":"#!/usr/bin/env python3.6\nimport glob\nimport os\nimport shutil\n\n# Cache paths\n# /home/vagrant/.cache/pip\n# /home/vagrant/.npm/_cacache\n# /home/vagrant/.nvm/.cache\n# /var/tmp\n# /var/cache\n\n# /home/vagrant/.nvm/versions/node/\n# /ebs If swap is not used??\n\n\ndef _only_dirs(items):\n return [item for item in items if os.path.isdir(item)]\n\n\ndef _in_use_artifact_dirs():\n paths = [\n os.path.realpath(deploy_link)\n for deploy_link in glob.glob('/opt/energysavvy/*/*/*/deploy')\n ]\n return set(_only_dirs(paths))\n\n\ndef _all_artifact_dirs():\n return set(_only_dirs(\n os.path.realpath(path)\n for path in glob.glob('/opt/energysavvy/artifacts/*')\n ))\n\n\ndef _artifact_tar_files():\n return set(\n os.path.realpath(path)\n for path in glob.glob('/opt/energysavvy/artifacts/*.tar.xz')\n )\n\n\ndef _archived_logs():\n return (\n set(glob.glob('/var/log/**/*.gz')) |\n set(glob.glob('/var/log/**/*.log.????-??-??'))\n )\n\n\ndef _clean_artifacts():\n files_to_remove = _artifact_tar_files()\n in_use_artifact_dirs = _in_use_artifact_dirs()\n all_artifact_dirs = _all_artifact_dirs()\n\n dirs_to_remove = all_artifact_dirs - in_use_artifact_dirs\n if len(files_to_remove) == 0:\n print('No atrifact files to clean up.')\n return\n print(\n f'Will remove {len(files_to_remove)} artifact tar files and ' +\n f'{len(dirs_to_remove)} artifact directories. ' +\n f'{len(in_use_artifact_dirs)} in-use artifact directories will remain.'\n )\n answer = input('Continue: [y/N]?: ')\n if answer.strip().lower() in ['y', 'yes']:\n for filename in files_to_remove:\n os.remove(filename)\n print('Tar files successfully removed')\n for dirname in (dirs_to_remove):\n shutil.rmtree(dirname)\n print('Artifact directories successfully removed')\n else:\n print('Aborted')\n\n\ndef _clean_logs():\n logs_to_remove = _archived_logs()\n\n if len(logs_to_remove) == 0:\n print('No archived log files to clean up.')\n return\n print(\n f'Will remove {len(logs_to_remove)} archived log files.'\n )\n answer = input('Continue: [y/N]?: ')\n if answer.strip().lower() in ['y', 'yes']:\n for log_to_remove in logs_to_remove:\n os.remove(log_to_remove)\n print('Archived log files successfully removed')\n\n\ndef main():\n _clean_artifacts()\n _clean_logs()\n\n\nmain()\n","sub_path":"es-clean-artifacts.py","file_name":"es-clean-artifacts.py","file_ext":"py","file_size_in_byte":2442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"285486623","text":"#!/usr/bin/env python\n\nimport pyexotica as exo\nfrom numpy import array\nfrom pyexotica.publish_trajectory import *\nfrom time import sleep\n\nexo.Setup.initRos()\n(sol, prob)=exo.Initializers.loadXMLFull('{exotica_examples}/resources/configs/example_ik_trajectory.xml')\nprint(prob)\nprint(sol)\nproblem = exo.Setup.createProblem(prob)\nsolver = exo.Setup.createSolver(sol)\nsolver.specifyProblem(problem)\n\ndt=0.002\nt=0.0\nq=array([0.0]*7)\nprint('Publishing IK')\nsignal.signal(signal.SIGINT, sigIntHandler)\nwhile True:\n try:\n problem.startState = q\n problem.startTime = t\n q = solver.solve()[0]\n publishPose(q, problem, t)\n sleep(dt)\n t=(t+dt)%7.0\n except KeyboardInterrupt:\n break\n\n","sub_path":"examples/exotica_examples/scripts/example_ik_trajectory.py","file_name":"example_ik_trajectory.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"42688335","text":"import asyncio\nimport datetime as date\nimport time\nimport re\nimport discord as ds\nimport JukeBox as juke\n\n#TODO:\n#Fix process permissions to work on raspberry pi\n\n#Integrate me by copying https://discordapp.com/oauth2/authorize?&client_id=168310783900385280&scope=bot&permissions=0 into your browser!\nready = False\nnow = date.datetime.now()\nclient = ds.Client()\nschedule = []\nsubscribers = []\njukebox = juke.JukeBox(client)\ntimeRE = r'^([0-9]|0[0-9]|1?[0-9]|2[0-3]):[0-5]?[0-9]$'\ndateRE = r'^(?:(?:31(\\/|-|\\.)(?:0?[13578]|1[02]))\\1|(?:(?:29|30)(\\/|-|\\.)(?:0?[1,3-9]|1[0-2])\\2))(?:(?:1[6-9]|[2-9]\\d)?\\d{2})$|^(?:29(\\/|-|\\.)0?2\\3(?:(?:(?:1[6-9]|[2-9]\\d)?(?:0[48]|[2468][048]|[13579][26])|(?:(?:16|[2468][048]|[3579][26])00))))$|^(?:0?[1-9]|1\\d|2[0-8])(\\/|-|\\.)(?:(?:0?[1-9])|(?:1[0-2]))\\4(?:(?:1[6-9]|[2-9]\\d)?\\d{2})$'\nwith open('Schedule') as f:\n content = f.readlines()\n content = [x.strip('\\n') for x in content]\n for line in content:\n segment = line.split(' ')\n schedule.append((segment[0], segment[1], segment[2])) \n\n#opus.dll for windows, libopus.so for linux\nif not ds.opus.is_loaded():\n ds.opus.load_opus('D:\\Personal projects\\Python\\LiliaBot\\LiliaBot\\Liliabot\\opus.dll')\nprint(ds.opus.is_loaded())\n\n\n\n@client.event\nasync def on_ready():\n print('Logged in as')\n print(client.user.name)\n print(client.user.id)\n print(client.user.avatar) \n print (client.token);\n with open('Subscribers') as f:\n content = f.readlines()\n content = [x.strip('\\n') for x in content]\n for line in content:\n subscribers.append(client.get_channel(line)) \n print('------') \n while(True): \n now = date.datetime.now()\n if(now.hour == 10):\n for channel in subscribers: \n print(channel.name)\n await client.send_message(channel, \"Ohaiyo everyone! Did you have a good night of sleep? I'll check any tasks for today now!\")\n await CheckToday(channel, False) \n print('Checking the hour.. nyaaaa..')\n await asyncio.sleep(3600)\n\n@client.event\nasync def on_message(message):\n if message.author == client.user:\n return\n if message.content.lower().startswith('!test'):\n counter = 0\n tmp = await client.send_message(message.channel, 'Calculating messages...')\n async for log in client.logs_from(message.channel, limit=100):\n if log.author == message.author:\n counter += 1\n await client.edit_message(tmp, 'You have {} messages.'.format(counter))\n \n elif message.content.lower().startswith('!join '):\n if len(message.content[6:]) > 0:\n await JoinVoiceChannel(message.channel, message.content[6:])\n\n elif message.content.lower().startswith('!disconnect voice'):\n await DisconnectVoice(message.channel)\n\n elif message.content.lower().startswith('!addsong '): \n await jukebox.AddSong(client, message, message.content[9:])\n\n elif message.content.lower().startswith('!play'):\n await jukebox.PlaySong(client, message.channel)\n\n elif message.content.lower().startswith('!skip'):\n await jukebox.SkipSong(client, message.channel)\n\n elif message.content.lower().startswith('!pause'):\n await jukebox.PauseSong(client, message.channel)\n\n elif message.content.lower().startswith('!resume'):\n await jukebox.ResumeSong(client, message.channel)\n \n elif message.content.lower().startswith('!stop'):\n await jukebox.StopPlayer(client, message.channel)\n\n elif message.content.startswith('!sleep'):\n await asyncio.sleep(5)\n await client.send_message(message.channel, 'Done sleeping')\n\n elif message.content.lower().startswith('!subscribe'):\n if message.channel in subscribers:\n subscribers.remove(message.channel)\n await client.send_message(message.channel, \"Unsubscribed you for now... will you be back to play with me soon?\")\n else:\n subscribers.append(message.channel) \n await client.send_message(message.channel, \"Subscribed you! Let's have a lot of fun, okay?\")\n f = open('Subscribers', 'w')\n for channel in subscribers:\n f.write(channel.id)\n f.close()\n elif message.content.lower().startswith('!tableflip'): \n await client.send_message(message.channel, '/tableflip')\n\n elif message.content.lower().startswith('!putthetableback'):\n await client.send_message(message.channel, '/unflip')\n\n elif message.content.lower().startswith('!yes'): \n await client.send_message(message.channel, 'https://www.youtube.com/watch?v=P3ALwKeSEYs')\n\n elif message.content.lower().startswith(\"!don't be mean to lily\") or message.content.lower().startswith(\"!don't be mean to lilychan\") or message.content.lower().startswith(\"!dont' be mean to lilia\"):\n await client.send_message(message.channel, \"Please don't be mean to me! I'll be a good girl!\")\n\n elif message.content.lower().startswith('!cute') or message.content.lower().startswith('!kawaii'): \n await client.send_message(message.channel, ('♡＾▽＾♡'))\n\n elif message.content.lower().startswith('!marco'): \n await client.send_message(message.channel, 'polo!')\n\n elif message.content.lower().startswith('!clearschedule'):\n schedule.clear()\n f = open('Schedule', 'w')\n f.close()\n await client.send_message(message.channel, \"Cleared the schedule.. break time for me too now?\") \n\n elif message.content.lower().startswith('!setschedule'):\n line = message.content.split(\" \")\n if(len(line) < 4):\n await client.send_message(message.channel, \"No, no, not like that! Do it like this: !setschedule name_of_event DD/MM/YYYY HH:MM.\")\n return\n if(re.match(dateRE, line[2]) != None):\n if(re.match(timeRE, line[3]) != None):\n schedule.append((line[1], line[2], line[3]))\n f = open('Schedule', 'a')\n f.write('%s %s %s\\n' %(line[1], line[2], line[3]))\n f.close()\n await client.send_message(message.channel, \"Set '%s' at %s, %sST!\" % (line[1], line[2], line[3]))\n else: \n await client.send_message(message.channel, \"Baka! You didnt fill in a valid time! Try it like so: HH:MM\")\n else:\n await client.send_message(message.channel, \"You didn't do it right! You should fill in a valid date either DD/MM/YYYY, DD-MM-YYYY or DD.MM.YYYY. Now do it correctly!\")\n elif message.content.lower().startswith('!schedule'):\n if(schedule == []):\n if(message.author.name == \"Oblition\"):\n await client.send_message(message.channel, \"Nothing set yet, today is free! Do you want to go out with me, Onii-chan?\")\n else:\n await client.send_message(message.channel, \"Nothing set yet, today is free!\")\n else:\n line = message.content.split()\n response = \"\"\n if(len(line) >= 2):\n triggered = False \n for (name, day, time) in schedule:\n if(line[1].lower() in name.lower()):\n if(not(triggered)):\n triggered = True \n await client.send_message(message.channel, \"I found events planned for things like '%s'!\" % (line[1]))\t\t\t\t\t\t\n response += \"\\n'%s' at %s, %sST!\" %(name, day, time)\n if(not(triggered)): \n await client.send_message(message.channel, \"Couldn't find anything with that name. Aren't you lucky and free?\")\n else:\n await client.send_message(message.channel, response)\n elif(len(line) == 1): \n await client.send_message(message.channel, \"All the events that have been planned, coming right up!\")\n for (name, day, time) in schedule:\n response += \"\\n'%s' at %s, %sST!\" % (name, day, time)\n await client.send_message(message.channel, response)\n elif message.content.lower().startswith('!today'):\n await CheckToday(message.channel, message.author.name == \"Oblition\")\n \nasync def CheckToday(channel, isObli): \n if(schedule == []): \n if(isObli): \n await client.send_message(channel, \"Nothing set for today, Obli-chan! Spend the day with me!\")\n else:\n await client.send_message(channel, \"Nothing set for today! Maybe you can spend the day with me instead?\")\n else:\n triggered = False\n for(name, day, time) in schedule:\n if((date.datetime.today() - date.datetime.strptime(day, \"%d/%m/%Y\")).days == 0):\n if(not(triggered)):\n triggered = True \n await client.send_message(channel, \"There is stuff to do today! It seems the following is set:\")\n await client.send_message(channel, \"'%s' at %sST!\" %(name, time))\n if(not(triggered)): \n if(isObli): \n await client.send_message(channel, \"Nothing set for today, Obli-chan! Spend the day with me!\")\n else:\n await client.send_message(channel, \"Nothing set for today! Maybe you can spend the day with me instead?\")\n\nasync def JoinVoiceChannel(source, requestedChannel):\n channel = None\n print(\"Attempting voice channel joining!\")\n for c in source.server.channels:\n if c.name == requestedChannel and c.type == ds.ChannelType.voice:\n channel = c\n break\n if channel == None:\n await client.send_message(source, \"Couldn't find a voice channel with that name on this server... are you sure you did it right? Text channels don't count!\")\n else:\n if client.voice == None or not(client.voice.is_connected()): \n await client.send_message(source, \"I'll try to join that voice channel, okai? Give me a moment!\")\n await client.join_voice_channel(channel)\n else:\n await client.send_message(source, \"I'm already connected to voice! If you want me to join that one, you should have me leave first.\")\n \nasync def DisconnectVoice(source):\n print(\"Attempting disconnecting voice channel!\")\n if client.voice != None and not(client.voice.is_connected()):\n await client.send_message(source, \"You silly, how can I leave a voice channel I'm not even connected to?\")\n else:\n await client.send_message(source, \"Disconnecting from voice now.. come listen to me soon, please?\")\n await client.voice.disconnect()\n\ntoken = \"MTY4MzEwODIxMjUwNjYyNDAw.CepvKg.6Nw2Rkj7D8YF7QLnnhzlCRJ6Ayw\"\nclient.run(token);\n","sub_path":"Liliabot.py","file_name":"Liliabot.py","file_ext":"py","file_size_in_byte":10950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"317850156","text":"#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n\r\nfrom roblib import *\r\n\r\n\r\nplt.close(\"all\")\r\nplt.ion()\r\nfig = plt.figure(\"Kalman 3 equations\")\r\nax = fig.add_subplot(1, 1, 1)\r\nax.set_xlim(-100, 100)\r\nax.set_ylim(-100, 100)\r\n\r\nGalphas = zeros(2)\r\nA = eye(2)\r\n\r\nC0 = array([[2, 3]])\r\nC1 = array([[3, 2]])\r\nC2 = array([[1, -1]])\r\n\r\ny0 = 8\r\nu = 0\r\nxhat0 = array([[0],[0]])\r\nGx0 = 1000 * eye(2)\r\ndraw_ellipse(xhat0, Gx0, 0.9, ax, \"red\")\r\n\r\nxhat1, Gx1 = kalman(xhat0, Gx0, u, 8, Galphas, 1, A, C0)\r\ndraw_ellipse(xhat1, Gx1, 0.9, ax, \"blue\")\r\n\r\nxhat2, Gx2 = kalman(xhat1, Gx1, u, 7, Galphas, 4, A, C1)\r\ndraw_ellipse(xhat2, Gx2, 0.9, ax, \"magenta\")\r\n\r\nxhat3, Gx3 = kalman(xhat2, Gx2, u, 0, Galphas, 4, A, C2)\r\ndraw_ellipse(xhat3, Gx3, 0.9, ax, \"black\")\r\n\r\n\r\ny = array([[8], [7], [0]])\r\nGbeta = diag([1, 4, 4])\r\nC = np.row_stack((C0, C1, C2))\r\nxhat, Gx = kalman(xhat0, Gx0, u, y, Galphas, Gbeta, A, C)\r\ndraw_ellipse(xhat, Gx, 0.9, ax, (0.7, .8, .5))\r\n\r\n\r\nplt.show()","sub_path":"python/LeconE/ex_kalm3eq.py","file_name":"ex_kalm3eq.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"13676755","text":"import numpy as np\r\nimport pandas as pd\r\nimport streamlit as st\r\n\r\nclass Projects():\r\n \r\n def __init__(self):\r\n \r\n self.codebase_url = 'https://docs.google.com/spreadsheets/d/1seBg9Gu5S4xDxUjEwduufSQWTA15V3N-IdpzB5PEOnk/edit#gid=0'\r\n self.codebase_url = self.codebase_url.replace('/edit#gid=', '/export?format=csv&gid=')\r\n self.codebase_library = pd.read_csv(self.codebase_url)\r\n \r\n self.open_source_project_url = \"https://docs.google.com/spreadsheets/d/1F1QzElHO0dz4t8JPOEmEPDq13aa60Lbz9ildPm_KTnU/edit#gid=0\"\r\n self.open_source_project_url = self.open_source_project_url.replace('/edit#gid=', '/export?format=csv&gid=')\r\n self.open_source_project = pd.read_csv(self.open_source_project_url)\r\n \r\n self.packages_url = \"https://docs.google.com/spreadsheets/d/1BVYK4qmgZaUJHw0x1Xq8G8EvuS-5-JIK009Ll1WeDNE/edit#gid=0\"\r\n self.packages_url = self.packages_url.replace('/edit#gid=', '/export?format=csv&gid=')\r\n self.packages = pd.read_csv(self.packages_url)\r\n \r\n def get_open_source_list(self):\r\n \r\n st.title(\"Open Source Projects\")\r\n st.write(\"If you are interested in working on any of these projects please fill out our form [here](https://forms.gle/oTh3nZJ4ffcd33KfA)\")\r\n\r\n for i in range(len(self.open_source_project)):\r\n st.header(self.open_source_project[self.open_source_project.columns[0]][i])\r\n st.write(self.open_source_project[self.open_source_project.columns[1]][i])\r\n \r\n st.write('Backend Infrastructure list: [here](https://docs.google.com/spreadsheets/d/1falnY478mZZAY3lVGpDhpechekfTQvQK-F2DoxIvLyk/edit?usp=sharing)')\r\n st.write('Bugs list: [here](https://docs.google.com/spreadsheets/d/1h0mNp1qTaz2XPqChcETDdqLye0v_oXdT1v5dsbdSTig/edit?usp=sharing)')\r\n\r\n def get_packages_list(self):\r\n \r\n st.title(\"Integration of Packages:\")\r\n st.write(\"If you are interested in working on any of these projects please fill out our form [here](https://forms.gle/9s5PyHr46ArMPuTG6)\")\r\n\r\n for i in range(len(self.packages)):\r\n \r\n st.header(self.packages[self.packages.columns[0]][i])\r\n st.write(self.packages[self.packages.columns[1]][i])\r\n link = self.packages[self.packages.columns[2]][i]\r\n st.write(\"see examples:\", link)\r\n \r\n def get_list(self):\r\n \r\n st.title(\"Projects and backend\")\r\n st.write(\"If you are interested in working on any of these projects please fill out our form [here](https://forms.gle/rPC4N9WmpjPJ5Kxa6)\")\r\n st.write(self.codebase_library) \r\n self.get_open_source_list()\r\n \r\n\r\n self.get_packages_list()\r\n \r\n def get_prepared(self):\r\n \r\n for i in range(len(self.codebase_library)):\r\n \r\n st.write(\"problem here\")\r\n row = self.codebase_library.iloc[i]\r\n st.write(row[1], row[2], row[3], row[4])\r\n break\r\n \r\n\r\n ","sub_path":"projects.py","file_name":"projects.py","file_ext":"py","file_size_in_byte":3036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"178062406","text":"import tweepy\r\nfrom textblob import TextBlob\r\nimport statistics\r\nfrom typing import List\r\nimport preprocessor as p\r\n\r\nconsumer_key = \"\"\r\nconsumer_secret = \"\"\r\n\r\nauth = tweepy.AppAuthHandler(consumer_key, consumer_secret)\r\napi = tweepy.API(auth)\r\n\r\ndef get_tweets(keyword: str) -> List[str]: # get tweets and -> returns all_tweets as a list & as a string\r\n all_tweets = []\r\n for tweet in tweepy.Cursor(api.search, q=keyword, tweet_mode='extended', lang='en').items(10):\r\n all_tweets.append(tweet.full_text) # Prints the full text of tweet\r\n return all_tweets\r\n\r\ndef clean_tweets(all_tweets: List[str]) -> List[str]: # once we get tweets we clean them since we have stuff like mentions, urls..etc. We want clean pure text. so we use preprocessor library\r\n tweets_clean = []\r\n for tweet in all_tweets:\r\n tweets_clean.append(p.clean(tweet))\r\n return tweets_clean\r\n\r\ndef get_sentiment(all_tweets: List[str]) -> List[float]: # then we pass tweets onto sentiment function\r\n sentiment_scores = []\r\n for tweet in all_tweets:\r\n blob = TextBlob(tweet)\r\n sentiment_scores.append(blob.sentiment.polarity)\r\n return sentiment_scores\r\n\r\ndef generate_average_sentiment_score(keyword: str) -> int:# generates average sentiment score from all the download tweets\r\n tweets = get_tweets(keyword)\r\n tweets_clean = clean_tweets(tweets)\r\n sentiment_scores = get_sentiment(tweets_clean)\r\n\r\n average_score = statistics.mean(sentiment_scores)\r\n\r\n return average_score\r\n\r\nif __name__ == \"__main__\":\r\n print(\"Which football team do people feel more positive about right now:\")\r\n first_thing = input()\r\n print(\"...or...\")\r\n second_thing = input()\r\n print(\"\")\r\n\r\n first_score = generate_average_sentiment_score(first_thing)\r\n second_score = generate_average_sentiment_score(second_thing)\r\n\r\n if (first_score > second_score):\r\n print(f\"People are feeling more positive about {first_thing} over {second_thing}\")\r\n else:\r\n print(f\"People are feeling more positive about {second_thing} over {first_thing}\")","sub_path":"Main/word_sentiment.py","file_name":"word_sentiment.py","file_ext":"py","file_size_in_byte":2082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"295361974","text":"from . import *\nfrom pya import *\n\nclass TestStruct_DoubleBus_Ring2(pya.PCellDeclarationHelper):\n \"\"\"\n The PCell declaration for the DoubleBus_Ring test structure with grating couplers and waveguides\n \"\"\"\n\n def __init__(self):\n\n # Important: initialize the super class\n super(TestStruct_DoubleBus_Ring2, self).__init__()\n TECHNOLOGY = get_technology_by_name('EBeam')\n\n # declare the parameters\n self.param(\"silayer\", self.TypeLayer, \"Layer\", default = TECHNOLOGY['Waveguide'])\n self.param(\"s\", self.TypeShape, \"\", default = DPoint(0, 0))\n self.param(\"r\", self.TypeDouble, \"Radius\", default = 10)\n self.param(\"w\", self.TypeDouble, \"Waveguide Width\", default = 0.5)\n self.param(\"g\", self.TypeDouble, \"Gap\", default = 0.2)\n self.param(\"npoints\", self.TypeInt, \"Number of points\", default = 500) \n self.param(\"textpolygon\", self.TypeInt, \"Draw text polygon label? 0/1\", default = 1)\n self.param(\"textlayer\", self.TypeLayer, \"Text Layer\", default = LayerInfo(10, 0))\n self.param(\"pinrec\", self.TypeLayer, \"PinRec Layer\", default = TECHNOLOGY['PinRec'])\n self.param(\"devrec\", self.TypeLayer, \"DevRec Layer\", default = TECHNOLOGY['DevRec'])\n\n def display_text_impl(self):\n # Provide a descriptive text for the cell\n return \"TestStruct_DoubleBus_Ring2(R=\" + ('%s' % self.r) + \",g=\" + ('%s' % (1000*self.g)) + \")\"\n\n def can_create_from_shape_impl(self):\n return False\n\n \n def produce_impl(self):\n # This is the main part of the implementation: create the layout\n\n # fetch the parameters\n dbu = self.layout.dbu\n ly = self.layout\n cell = self.cell\n shapes = self.cell.shapes\n \n LayerSi = self.silayer\n LayerSiN = ly.layer(LayerSi)\n TextLayerN = ly.layer(self.textlayer)\n\n # Import cells from the SiEPIC GDS Library, and instantiate them\n \n # Ring resonator PCell\n r = self.r\n wg_width = self.w\n g = self.g\n y_ring = 127*3/2+r\n\n pcell = ly.create_cell(\"DoubleBus_Ring\", \"EBeam-dev\", {\"r\": r, \"w\": wg_width, \"g\": g, \"silayer\": LayerSi, \"devrec\": self.devrec, \"pinrec\": self.pinrec })\n #print( \"pcell: %s, %s\" \\ % (pcell.cell_index(), ly.cell_name(pcell.cell_index()) ) )\n t = Trans(Trans.R270, 10 / dbu, y_ring / dbu) \n instance = cell.insert(CellInstArray(pcell.cell_index(), t))\n #print(instance.cell_index)\n\n\n # Grating couplers, Ports 1, 2, 3, 4 (top-down):\n GC_name = \"ebeam_gc_te1550\"\n GC_imported = ly.cell(GC_name)\n if GC_imported == None:\n GC_imported = ly.create_cell(GC_name, \"SiEPIC-EBeam\").cell_index()\n else:\n GC_imported = GC_imported.cell_index() \n print(\"Cell: GC_imported: #%s\" % GC_imported )\n t = Trans(Trans.R0, 0, 0)\n instance = cell.insert(CellInstArray(GC_imported, t, Point(0,127/dbu), Point(0,0), 4, 1))\n print(instance.cell_index)\n\n # Label for automated measurements, laser on Port 2, detectors on Ports 1, 3, 4\n t = Trans(Trans.R0, 0, 127*2/dbu)\n text = Text (\"opt_in_TE_1550_device_DoubleBusRing\", t)\n shape = cell.shapes(TextLayerN).insert(text)\n shape.text_size = 3/dbu\n\n # Create paths for waveguides\n wg_bend_radius = 10\n\n # GC3 to bottom-left of ring\n points = [ [0, 127], [10,127], [10, y_ring-2*r-wg_width] ] \n layout_waveguide_abs(cell, LayerSi, points, wg_width, wg_bend_radius)\n\n # GC4 to bottom-right of ring\n points = [ [0, 0], [10+2*r+2*g+2*wg_width,0], [10+2*r+2*g+2*wg_width, y_ring-2*r-wg_width] ] \n layout_waveguide_abs(cell, LayerSi, points, wg_width, 20)\n\n # GC2 to top-right of ring\n points = [ [10,y_ring], [10, 127*2], [0,127*2] ] \n layout_waveguide_abs(cell, LayerSi, points, wg_width, wg_bend_radius)\n\n # GC1 to top-left of ring\n points = [ [0, 127*3], [10+2*r+2*g+2*wg_width,127*3], [10+2*r+2*g+2*wg_width, y_ring] ] \n layout_waveguide_abs(cell, LayerSi, points, wg_width, 20)\n\n print( \"Done drawing the layout for - TestStruct_DoubleBus_Ring2: %.3f-%g\" % (r, g) )\n","sub_path":"klayout_dot_config/tech/EBeam/pymacros/pcells_EBeam_Beta/TestStruct_DoubleBus_Ring2.py","file_name":"TestStruct_DoubleBus_Ring2.py","file_ext":"py","file_size_in_byte":3941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"432017529","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\nfrom renderer import Renderer\nfrom pallette import XTERM_PALLETTE, BINARY_PALLETTE\n\n\ndef isLink(test_string):\n return test_string.startswith('http')\n\ndef main():\n import argparse\n\n parser = argparse.ArgumentParser(\n description='Show images directly on terminal.')\n parser.add_argument(\"Image\", help=\"the directory of the image which will be opened\")\n parser.add_argument(\n \"-w\", \"--width\",\n help=\"image width on the terminal\",\n type=int\n )\n parser.add_argument(\n \"-i\", \"--interactive\",\n default=False, action='store_true',\n help=\"open image in interactive mode\",\n )\n\n args = parser.parse_args()\n\n r = Renderer(args.Image, XTERM_PALLETTE, wsize=args.width)\n if r.error==None:\n r.render()\n r.show(interactive=args.interactive)\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"img2sh/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"78727159","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.template import loader,Context\n# Create your views here.\n\nstore_detail = {'1':{'store_name':'Cafe Ansi','store_no':'12345',},\n '2':{'store_name':'Street Cafe','store_no':'56789',},\n '3':{'store_name':'Joe\\'s Pizzaria','store_no':'12345',}\n }\n\ndef index(request):\n #store_index = store_detail\n return render(request,'stores/index.html',{'stores':store_detail})\n\n\ndef detail(request,store_id='1',location=None):\n info = store_detail[store_id]\n hours = request.GET.get('hours','')\n mapi = request.GET.get('map','')\n value = {'ansi':info,'location':location,'hours':hours,'mapi':mapi}\n #response = HttpResponse()\n t = loader.get_template('stores/detail.html')\n #c = Context(value)\n return HttpResponse(t.render(value))\n #return TemplateResponse(request,'stores/detail.html',{'ansi':info,'location':location,'hours':hours,'mapi':mapi})\n","sub_path":"coffeehouse/CoffeeHouse/stores/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"552721152","text":"from app import flaskapp\nfrom flask import request, Response\nfrom bson import json_util\nfrom bson.objectid import ObjectId\nimport json\nimport requests\nimport dateutil.parser\nfrom .login import *\nimport datetime\nfrom .ravello import *\nfrom .azure.functions import delete_azure_by_username\n\n@flaskapp.route('/api/groups', methods=['POST'])\n@login_required(role=['admin', 'instructor'])\ndef create_group():\n try:\n lab = json.loads(request.form['lab'])\n except:\n lab = {}\n lab['name'] = \"\"\n flaskapp.config['HOT_COLLECTION'].insert_one(\n {\"lab\": lab,\n \"hotenvs\": request.form['hotenvs'],\n \"endTime\": dateutil.parser.parse(request.form['endTime']),\n \"startTime\": dateutil.parser.parse(request.form['startTime']),\n \"lastPublished\": datetime.datetime.utcnow(),\n \"blockSize\": int(request.form['block']),\n \"blockDelay\": int(request.form['delay']),\n \"buffer\": int(request.form['buffer']),\n \"tz\": request.form['tz'],\n \"createdEnvs\": 0,\n \"usedEnvs\": 0,\n \"envs\": []\n })\n\n return Response('OK')\n\n\n@flaskapp.route('/api/groups//delete', methods=['POST'])\n@login_required(role=['admin', 'instructor'])\ndef delete_group(id):\n\n group = flaskapp.config['HOT_COLLECTION'].find_one({\"_id\":ObjectId(id)})\n for item in group['envs']:\n if group['lab']['type'] == \"ravello\":\n delete_env(item['env'])\n delete_token(item['tokenID'])\n else:\n delete_azure_by_username(item['azure_name'])\n flaskapp.config['HOT_COLLECTION'].delete_one({\"_id\":ObjectId(id)})\n\n return Response('OK')\n\n\n@flaskapp.route('/api/groups')\n@login_required(role=['admin', 'instructor'])\ndef get_groups():\n cursor = flaskapp.config['HOT_COLLECTION'].find()\n results = []\n for item in cursor:\n item['time'] = {\n \"start\": item['startTime'],\n \"end\": item['endTime'],\n \"tz\": item['tz'],\n \"deploy\":item['startTime'] - datetime.timedelta(minutes=item['buffer'])\n }\n\n item['startTz'] = {\n \"start\": item['startTime'],\n \"tz\": item['tz']\n }\n item['endTz'] = {\n \"end\": item['endTime'],\n \"tz\": item['tz']\n }\n\n results.append(item)\n return json.dumps(results, default=json_util.default)\n","sub_path":"app/hot.py","file_name":"hot.py","file_ext":"py","file_size_in_byte":2348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"556207765","text":"import comet_ml\nimport matplotlib.pyplot as plt\nimport csv\nimport numpy as np\nimport os\nimport utils\nimport argparse\n\nparser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter)\nparser.add_argument(\"--env\", required=True,\n help=\"name of the environment to train on (REQUIRED)\")\nparser.add_argument(\"--model\", default=None,\n help=\"name of the model (default: {ENV}_{ALGO}_{TIME})\")\nparser.add_argument(\"--seed\", type=int, required=True,\n help=\"random seed (default: 1)\")\nparser.add_argument(\"--dense-reward\", action=\"store_true\", default=False,\n help=\"Use dense reward during training.\")\nargs = parser.parse_args()\n\n# Get model directory\nmodel_dir = \"storage/\" + args.model +\"_seed_\"+str(args.seed)\nif args.dense_reward: model_dir += \"_denser\"\nutils.create_folders_if_necessary(model_dir)\n\ndef prep_list(filename):\n with open(model_dir+filename, 'r') as f:\n reader = csv.reader(f)\n your_list = list(reader)\n\n all_vals = []\n for val in your_list[0]:\n all_vals.append(float(val))\n\n new_list = []\n for i in range(0, len(all_vals), 100):\n new_list.append(np.mean(all_vals[i:i+100]))\n\n return new_list\n\n\nif not os.path.isdir(model_dir+\"/plots/\"):\n os.mkdir(model_dir+\"/plots/\")\nreward_file = \"/rewards.csv\"\nsuccess_file = \"/episode_success.csv\"\nloss_file = \"/losses.csv\"\n\nfig = plt.figure()\nplt.plot(prep_list(reward_file))\nfig.suptitle('Sum of Rewards', fontsize=20)\nplt.xlabel('trajectories (x100)', fontsize=16)\nplt.ylabel('Reward', fontsize=16)\nfig.savefig(model_dir+'/plots/sum_rewards.jpg')\n\nfig = plt.figure()\nplt.plot(prep_list(success_file))\nfig.suptitle('Success Rate', fontsize=20)\nplt.xlabel('trajectories (x100)', fontsize=16)\nplt.ylabel('Average final reward', fontsize=16)\nfig.savefig(model_dir+'/plots/success_rate.jpg')\n\nfig = plt.figure()\nplt.plot(prep_list(loss_file))\nfig.suptitle('Q-Value difference', fontsize=20)\nplt.xlabel('trajectories (x100)', fontsize=16)\nplt.ylabel('Loss', fontsize=16)\nfig.savefig(model_dir+'/plots/losses.jpg')\n","sub_path":"scripts/plot_results.py","file_name":"plot_results.py","file_ext":"py","file_size_in_byte":2097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"451411191","text":"def flip(p):\n new_p = \"\"\n for c in p:\n new_p += \"+\" if c == \"-\" else \"-\"\n return new_p[::-1]\n\n\ndef solve(n):\n stack = n[:n.rfind('-')+1]\n n_flips = 0\n while '-' in stack:\n if stack[0] == '-':\n stack = flip(stack)\n stack = stack[:stack.rfind('-')+1]\n else:\n flip_point = stack.find('-')\n stack = flip(stack[:flip_point]) + stack[flip_point:]\n n_flips += 1\n # print(stack)\n return n_flips\n\n\nn = int(input())\nfor i in range(n):\n print(\"Case #{}: {}\".format(i+1, solve(input())))\n","sub_path":"codes/CodeJamCrawler/16_0_2/hallfox/p2.py","file_name":"p2.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"296243605","text":"from platform import platform\nfrom typing import Any, List\nimport os\nimport sys\nimport time\n\nimport urwid\nimport zulip\n\nfrom zulipterminal.helper import asynch\nfrom zulipterminal.model import Model, GetMessagesArgs\nfrom zulipterminal.ui import View, Screen\nfrom zulipterminal.ui_tools.utils import create_msg_box_list\nfrom zulipterminal.ui_tools.views import HelpView\n\n\nclass Controller:\n \"\"\"\n A class responsible for setting up the model and view and running\n the application.\n \"\"\"\n\n def __init__(self, config_file: str, theme: str) -> None:\n self.show_loading()\n self.client = zulip.Client(config_file=config_file,\n client='ZulipTerminal/0.1.0 ' + platform())\n # Register to the queue before initializing Model or View\n # so that we don't lose any updates while messages are being fetched.\n self.register_initial_desired_events()\n self.model = Model(self)\n self.view = View(self)\n # Start polling for events after view is rendered.\n self.model.poll_for_events()\n self.theme = theme\n self.editor_mode = False # type: bool\n self.editor = None # type: Any\n\n @asynch\n def show_loading(self) -> None:\n\n def spinning_cursor() -> Any:\n while True:\n for cursor in '|/-\\\\':\n yield cursor\n\n spinner = spinning_cursor()\n sys.stdout.write(\"\\033[92mWelcome to Zulip.\\033[0m\\n\")\n while not hasattr(self, 'view'):\n next_spinner = \"Loading \" + next(spinner)\n sys.stdout.write(next_spinner)\n sys.stdout.flush()\n time.sleep(0.1)\n sys.stdout.write('\\b'*len(next_spinner))\n\n sys.stdout.write('\\n')\n self.capture_stdout()\n\n def capture_stdout(self, path: str='debug.log') -> None:\n if hasattr(self, '_stdout'):\n return\n\n self._stdout = sys.stdout\n sys.stdout = open(path, 'a')\n\n def restore_stdout(self) -> None:\n if not hasattr(self, '_stdout'):\n return\n\n sys.stdout.flush()\n sys.stdout.close()\n sys.stdout = self._stdout\n sys.stdout.write('\\n')\n del self._stdout\n\n def update_screen(self) -> None:\n # Write something to update pipe to trigger draw_screen\n os.write(self.update_pipe, b'1')\n\n def draw_screen(self, *args: Any, **kwargs: Any) -> None:\n self.loop.draw_screen()\n\n def show_help(self) -> None:\n self.loop.widget = urwid.LineBox(urwid.Overlay(\n HelpView(self),\n self.view,\n align='center',\n width=('relative', 100),\n valign='middle',\n height=('relative', 100)\n ))\n\n def exit_help(self) -> None:\n self.loop.widget = self.view\n\n def search_messages(self, text: str) -> None:\n # Search for a text in messages\n self.update = False\n self.model.set_narrow(search=text)\n self.model.get_messages(num_after=0, num_before=30, anchor=10000000000)\n msg_id_list = self.model.index['search']\n w_list = create_msg_box_list(self.model, msg_id_list)\n self.model.msg_view.clear()\n self.model.msg_view.extend(w_list)\n focus_position = 0\n if focus_position >= 0 and focus_position < len(w_list):\n self.model.msg_list.set_focus(focus_position)\n\n def narrow_to_stream(self, button: Any) -> None:\n already_narrowed = self.model.set_narrow(stream=button.caption)\n if already_narrowed:\n return\n\n self.update = False\n # store the steam id in the model\n self.model.stream_id = button.stream_id\n # get the message ids of the current narrow\n msg_id_list = self.model.index['all_stream'][button.stream_id]\n # if no messages are found get more messages\n if len(msg_id_list) == 0:\n get_msg_opts = dict(num_before=30, num_after=10,\n anchor=None) # type: GetMessagesArgs\n if hasattr(button, 'message'):\n get_msg_opts['anchor'] = button.message['id']\n self.model.get_messages(**get_msg_opts)\n msg_id_list = self.model.index['all_stream'][button.stream_id]\n if hasattr(button, 'message'):\n w_list = create_msg_box_list(\n self.model, msg_id_list, button.message['id'])\n else:\n w_list = create_msg_box_list(self.model, msg_id_list)\n\n self._finalize_show(w_list)\n\n def narrow_to_topic(self, button: Any) -> None:\n already_narrowed = self.model.set_narrow(stream=button.caption,\n topic=button.title)\n if already_narrowed:\n return\n\n self.update = False\n self.model.stream_id = button.stream_id\n msg_id_list = self.model.index['stream'][button.stream_id].get(\n button.title, [])\n if len(msg_id_list) == 0:\n get_msg_opts = dict(num_before=30, num_after=10,\n anchor=None) # type: GetMessagesArgs\n if hasattr(button, 'message'):\n get_msg_opts['anchor'] = button.message['id']\n self.model.get_messages(**get_msg_opts)\n msg_id_list = self.model.index['stream'][button.stream_id].get(\n button.title, [])\n if hasattr(button, 'message'):\n w_list = create_msg_box_list(\n self.model, msg_id_list, button.message['id'])\n else:\n w_list = create_msg_box_list(self.model, msg_id_list)\n\n self._finalize_show(w_list)\n\n def narrow_to_user(self, button: Any) -> None:\n if hasattr(button, 'message'):\n emails = [recipient['email']\n for recipient in button.message['display_recipient']\n if recipient['email'] != self.model.client.email]\n user_emails = ', '.join(emails)\n user_ids = {user['id']\n for user in button.message['display_recipient']}\n else:\n user_emails = button.email\n user_ids = {self.model.user_id, button.user_id}\n\n already_narrowed = self.model.set_narrow(pm_with=user_emails)\n if already_narrowed:\n return\n\n self.update = False\n recipients = frozenset(user_ids)\n self.model.recipients = recipients\n msg_id_list = self.model.index['private'].get(recipients, [])\n\n if len(msg_id_list) == 0:\n get_msg_opts = dict(num_before=30, num_after=10,\n anchor=None) # type: GetMessagesArgs\n if hasattr(button, 'message'):\n get_msg_opts['anchor'] = button.message['id']\n self.model.get_messages(**get_msg_opts)\n msg_id_list = self.model.index['private'].get(recipients, [])\n\n if hasattr(button, 'message'):\n w_list = create_msg_box_list(\n self.model, msg_id_list, button.message['id'])\n else:\n w_list = create_msg_box_list(self.model, msg_id_list)\n\n self._finalize_show(w_list)\n\n def show_all_messages(self, button: Any) -> None:\n already_narrowed = self.model.set_narrow()\n if already_narrowed:\n return\n\n self.update = False\n msg_list = self.model.index['all_messages']\n if hasattr(button, 'message'):\n w_list = create_msg_box_list(\n self.model, msg_list, button.message['id'])\n else:\n w_list = create_msg_box_list(self.model, msg_list)\n\n self._finalize_show(w_list)\n\n def show_all_pm(self, button: Any) -> None:\n already_narrowed = self.model.set_narrow(pm_with='')\n if already_narrowed:\n return\n\n self.update = False\n msg_list = self.model.index['all_private']\n if len(msg_list) == 0:\n self.model.get_messages(num_before=30, num_after=10, anchor=None)\n msg_list = self.model.index['all_private']\n w_list = create_msg_box_list(self.model, msg_list)\n\n self._finalize_show(w_list)\n\n def _finalize_show(self, w_list: List[Any]) -> None:\n focus_position = self.model.get_focus_in_current_narrow()\n\n if focus_position == set():\n focus_position = len(w_list) - 1\n assert not isinstance(focus_position, set)\n self.model.msg_view.clear()\n self.model.msg_view.extend(w_list)\n if focus_position >= 0 and focus_position < len(w_list):\n self.model.msg_list.set_focus(focus_position)\n\n @asynch\n def register_initial_desired_events(self) -> None:\n event_types = [\n 'message',\n 'update_message',\n 'reaction',\n 'typing',\n 'update_message_flags',\n ]\n response = self.client.register(event_types=event_types,\n apply_markdown=True)\n self.max_message_id = response['max_message_id']\n self.queue_id = response['queue_id']\n self.last_event_id = response['last_event_id']\n\n def main(self) -> None:\n try:\n screen = Screen()\n screen.set_terminal_properties(colors=256)\n self.loop = urwid.MainLoop(self.view,\n self.view.palette[self.theme],\n screen=screen)\n self.update_pipe = self.loop.watch_pipe(self.draw_screen)\n except KeyError:\n print('Following are the themes available:')\n for theme in self.view.palette.keys():\n print(theme,)\n return\n\n try:\n self.loop.run()\n\n except Exception:\n self.restore_stdout()\n raise\n\n finally:\n self.restore_stdout()\n","sub_path":"zulipterminal/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":9905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"255234856","text":"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n\nfrom abc import ABCMeta\nfrom typing import List\nimport json\n\nimport pygments\nfrom pygments.console import colorize\nfrom pygments.formatters.terminal import TerminalFormatter\nfrom pygments.lexers.markup import RstLexer\n\nfrom airflow.upgrade.problem import RuleStatus\nfrom airflow.upgrade.rules.base_rule import BaseRule\nfrom airflow.utils.cli import header, get_terminal_size\n\n\nclass BaseFormatter(object):\n __metaclass__ = ABCMeta\n\n def start_checking(self, all_rules):\n # type: (List[BaseRule]) -> None\n pass\n\n def end_checking(self, rule_statuses):\n # type: (List[RuleStatus]) -> None\n\n pass\n\n def on_next_rule_status(self, rule_status):\n # type: (RuleStatus) -> None\n pass\n\n\nclass ConsoleFormatter(BaseFormatter):\n def start_checking(self, all_rules):\n print()\n header(\"STATUS\", \"=\")\n print()\n\n def end_checking(self, rule_statuses):\n messages_count = sum(\n len(rule_status.messages)\n for rule_status in rule_statuses\n )\n if messages_count == 1:\n print(\"Found {} problem.\".format(messages_count))\n else:\n print(\"Found {} problems.\".format(messages_count))\n print()\n\n if messages_count > 0:\n header(\"RECOMMENDATIONS\", \"=\")\n print()\n self.display_recommendations(rule_statuses)\n else:\n print(\"Not found any problems. World is beautiful. \")\n print(\"You can safely update Airflow to the new version.\")\n\n @staticmethod\n def display_recommendations(rule_statuses):\n for rule_status in rule_statuses:\n # Show recommendations only if there are any messaged\n if not rule_status.messages:\n continue\n\n rule = rule_status.rule\n lines = [rule.title, \"-\" * len(rule.title)]\n if rule_status.skipped:\n lines.extend([rule_status.messages[0]])\n else:\n if rule.description:\n lines.extend([rule.description])\n lines.extend([\n \"\",\n \"Problems:\",\n \"\",\n ])\n lines.extend(['{:>3}. {}'.format(i, m) for i, m in enumerate(rule_status.messages, 1)])\n msg = \"\\n\".join(lines)\n\n formatted_msg = pygments.highlight(\n code=msg, formatter=TerminalFormatter(), lexer=RstLexer()\n )\n print(formatted_msg)\n\n def on_next_rule_status(self, rule_status):\n if rule_status.skipped:\n status = colorize(\"yellow\", \"SKIPPED\")\n elif rule_status.is_success:\n status = colorize(\"green\", \"SUCCESS\")\n else:\n status = colorize(\"red\", \"FAIL\")\n status_line_fmt = self.prepare_status_line_format()\n print(status_line_fmt.format(rule_status.rule.title, status))\n\n @staticmethod\n def prepare_status_line_format():\n _, terminal_width = get_terminal_size()\n\n return \"{:.<\" + str(terminal_width - 10) + \"}{:.>10}\"\n\n\nclass JSONFormatter(BaseFormatter):\n def __init__(self, output_path):\n self.filename = output_path\n\n def start_checking(self, all_rules):\n print(\"Start looking for problems.\")\n\n @staticmethod\n def _info_from_rule_status(rule_status):\n return {\n \"rule\": type(rule_status.rule).__name__,\n \"title\": rule_status.rule.title,\n \"messages\": rule_status.messages,\n }\n\n def end_checking(self, rule_statuses):\n formatted_results = [self._info_from_rule_status(rs) for rs in rule_statuses]\n with open(self.filename, \"w+\") as output_file:\n json.dump(formatted_results, output_file, indent=2)\n print(\"Saved result to: {}\".format(self.filename))\n","sub_path":"airflow/upgrade/formatters.py","file_name":"formatters.py","file_ext":"py","file_size_in_byte":4608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"204328087","text":"#-------------------------------------------------#\r\n# Title: CustomerApp\r\n# Dev: Gabe\r\n# Date: 12/4/2016\r\n# Desc: This application manages customer data\r\n# ChangeLog:\r\n#\r\n#-------------------------------------------------#\r\nif __name__ == \"__main__\":\r\n import DataProcessors, Persons, Customers\r\n\r\n#---Data----#\r\nobjC = None #a Customer object\r\nintId = 0 #customer ID\r\nstrFirstName = \"\"#Customer's first name\r\nstrLastName = \"\"#Customer's last name\r\nstrInput = \"\" #temp user input\r\n\r\n#--Processing--#\r\ndef newCustomerdata(Id, FirstName, LastName):\r\n try:\r\n objC = Customers.Customer()\r\n objC.Id = Id\r\n objC.FirstName = FirstName\r\n objC.LastName = LastName\r\n Customers.CustomerList.AddCustomer(objC)\r\n except Exception as e:\r\n print(\"There was an error in creating new Customer: \" + str(e))\r\n\r\ndef SaveDataToFile():\r\n try:\r\n objFile = DataProcessors.File()\r\n objFile.FileName = \"CustomerData.txt\"\r\n objFile.TextData = Customers.CustomerList.ToString()\r\n objFile.SaveData()\r\n except Exception as e:\r\n print(\"There was an error in saving data to file: \"+str(e))\r\n\r\ndef getLastId():\r\n try:\r\n objFile = DataProcessors.File()\r\n objFile.FileName = \"CustomerData.txt\"\r\n objFile.TextData = objFile.GetData()\r\n listEntries = objFile.TextData.split(\"\\n\")\r\n lastId = int(listEntries[(len(listEntries)-2)][0])\r\n return lastId\r\n except:\r\n return 0\r\n\r\ndef getPrevCust():\r\n try:\r\n objFile = DataProcessors.File()\r\n objFile.FileName = \"CustomerData.txt\"\r\n objFile.TextData = objFile.GetData()\r\n return objFile.TextData\r\n except:\r\n pass\r\n\r\n\r\n#--Presentation--#\r\ngIntLastId = getLastId()\r\nwhile(True):\r\n strUserInput = input(\"Would you like to add customer data? (y/n)\")\r\n if(strUserInput ==\"y\"):\r\n intId = int(input(\"Enter a Customer ID (Last ID was \"+str(gIntLastId)+\"): \"))\r\n gIntLastId = intId\r\n strFirstName = str(input(\"Enter Customer's First Name: \"))\r\n strLastName = str(input(\"Enter Customer's Last Name: \"))\r\n newCustomerdata(intId,strFirstName,strLastName)\r\n else:\r\n break\r\nprint(\"The Previous Data is: \")\r\nprint(\"------------------------\")\r\nprint(getPrevCust())\r\nprint(\"The New Data is: \")\r\nprint(\"------------------------\")\r\nprint(Customers.CustomerList.ToString())\r\n\r\n#get user input\r\nstrInput = input(\"Would you like to save this data to the dat file?(y/n)\")\r\nif(strInput == \"y\"):\r\n SaveDataToFile()\r\n #send program output\r\n print(\"data saved in file\")\r\nelse:\r\n print(\"data was not saved\")\r\n\r\nprint(\"This application has ended. Thank you!\")\r\n","sub_path":"Assignment9/CustomerApp.py","file_name":"CustomerApp.py","file_ext":"py","file_size_in_byte":2692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"223079206","text":"__author__ = \"Lei Wang\"\n\nimport urllib, http.client\nimport hmac, hashlib, base64\nfrom collections import OrderedDict\nfrom datetime import datetime\ntry:\n from urllib.parse import urlparse\nexcept:\n from urlparse import urlparse\n\ntest_headers = \"(request-target) date\"\nAuthorization = 'Signature key=\"{key}\",algorithm=\"{algorithm}\",headers=\"{headers}\",signature=\"{signature}\"'\nGMT_FORMAT = '%a, %d %b %Y %H:%M:%S GMT'\n\ndef app_secret_coder(api_secret, msg, algorithm=\"hmac-sha256\"):\n algo, hash_scheme = algorithm.split(\"-\")\n if algo == \"hmac\" and hash_scheme == \"sha256\":\n digest_obj = hmac.new(api_secret.encode('ascii'), msg=msg.encode('ascii'), digestmod=hashlib.sha256).digest()\n else:\n raise Exception(\"(%s, %s) Not Be Supported Yet!\" % (algo, hash_scheme))\n return base64.b64encode(digest_obj).decode()\n\ndef _proc_item(o):\n key, val = o\n return ': '.join((key.lower(), ', '.join(val) )) if isinstance(val, (list, tuple)) and len(val) > 1 else \\\n ': '.join((key.lower(), val[0] if isinstance(val, (list, tuple)) else val))\n\ndef compose_signature(method, path, signed_headers):\n \"\"\"\n Signing HTTP Messages draft-cavage-http-signature-signatures-05 Chapter 2.3(Construct a Signature)\n :return: `signature string`\n \"\"\"\n signature_string = \"(request-target): {method} {path}\\n{ret}\"\n\n def encode_signed_headers(signed_headers):\n # Signing HTTP Messages draft-cavage-http-signature-signatures-05 Rule 2.3.2\n\n map_ob = map(_proc_item, signed_headers.items())\n return '\\n'.join(map_ob)\n\n ret = encode_signed_headers(signed_headers)\n return signature_string.format(method=method, path=path, ret=ret)\n\n\ndef client(url, keyId, secret, algorithm, headers=None):\n parsed = urlparse(url)\n signature_string = compose_signature('get', parsed.path,\n OrderedDict({\n 'Date': 'Tue, 08 Nov 2016 15:58:03 GMT'#datetime.utcnow().strftime(GMT_FORMAT)\n }))\n print(\"Signature string:\")\n print(signature_string)\n signature = app_secret_coder(secret, signature_string)\n\n # x-www-form-urlencoded\n headers = {\"Content-Type\":\"application/json\",\n \"Connection\":\"Keep-Alive\",\n \"Authorization\":Authorization.format(key=keyId,\n algorithm=algorithm,\n headers=headers or test_headers,\n signature=signature),\n \"Cache-Control\": \"no-cache\",\n 'date': \"Tue, 08 Nov 2016 15:58:03 GMT\"}\n\n print(\"HTTPConnection Host:\")\n print(parsed.netloc)\n conn = http.client.HTTPConnection(parsed.netloc)\n print(\"Request Headers:\")\n print(headers)\n conn.request(method=\"GET\",url=parsed.path ,headers=headers)\n response = conn.getresponse()\n print(\"\\n\")\n print(\"%s: %s\" %(response.reason, response.status))\n print(response.msg)\n\n\ndef test(*args):\n print(\"begin test... *******\\n\")\n url = input()#\"http://127.0.0.1:8000/api/0.1.4/user/\"\n keyId = input()#\"http://127.0.0.1:8000/api/0.1.4/user/\"\n secret = input()#\"aca8b32823db20ec542f004e273c12771028b0f4716a957f04c2bcc9cb5c98bd\"#\n algorithm = \"hmac-sha256\"\n client(url, keyId, secret, algorithm)\n print(\"****** end of test\")\n\nif __name__ == \"__main__\":\n import sys\n test(*sys.argv[1:])","sub_path":"SDK/sign5py3k.py","file_name":"sign5py3k.py","file_ext":"py","file_size_in_byte":3484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"34740642","text":"# vim:ts=4:et\n# ##### BEGIN GPL LICENSE BLOCK #####\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software Foundation,\n# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# ##### END GPL LICENSE BLOCK #####\n\n# \n\nfrom struct import unpack, pack\nfrom mathutils import Vector\n\nMaxPath = 64\n\nclass MD3Frame:\n MaxFrameName = 16\n Size = (12 * 3) + 4 + MaxFrameName\n\n def __init__(self, name=''):\n self.min_bounds = [ 0, 0, 0 ]\n self.max_bounds = [ 0, 0, 0 ]\n self.local_origin = [ 0, 0, 0 ]\n self.radius = 0\n self.name = name\n def read(self, mdl):\n self.min_bounds = mdl.read_float(3)\n self.max_bounds = mdl.read_float(3)\n self.local_origin = mdl.read_float(3)\n self.radius = mdl.read_float()\n self.name = mdl.read_path(MD3Frame.MaxFrameName)\n return self\n def write(self, mdl):\n mdl.write_float(self.min_bounds)\n mdl.write_float(self.max_bounds)\n mdl.write_float(self.local_origin)\n mdl.write_float(self.radius)\n mdl.write_path(self.name, MD3Frame.MaxFrameName)\n\nclass MD3Tag:\n Size = MaxPath + (4 * 3) + (4 * 9)\n\n def __init__(self, name=''):\n self.name = name\n self.origin = [ 0, 0, 0 ]\n self.axis = [ 1, 0, 0, 0, 1, 0, 0, 0, 1 ]\n def read(self, mdl):\n self.name = mdl.read_path(MaxPath)\n self.origin = mdl.read_float(3)\n self.axis = mdl.read_float(9)\n return self\n def write(self, mdl):\n mdl.write_path(self.name, MaxPath)\n mdl.write_float(self.origin)\n mdl.write_float(self.axis)\n\nclass MD3Shader:\n Size = MaxPath + 4\n\n def __init__(self, name='', index=0):\n self.name = name\n self.index = index\n def read(self, mdl):\n self.name = mdl.read_path(MaxPath)\n self.index = mdl.read_int()\n return self\n def write(self, mdl):\n mdl.write_path(self.name, MaxPath)\n mdl.write_int(self.index)\n\nclass MD3Triangle:\n Size = 4 * 3\n\n def __init__(self, v=None):\n self.v = ( 0, 0, 0 ) if not v else v\n def read(self, mdl):\n self.v = mdl.read_int(3)\n return self\n def write(self, mdl):\n mdl.write_int(self.v)\n\nclass MD3TexCoord:\n Size = 4 * 2\n\n def __init__(self, st=None):\n self.st = ( 0, 0 ) if not st else st\n def read(self, mdl):\n self.st = mdl.read_float(2)\n return self\n def write(self, mdl):\n mdl.write_float(self.st)\n\nclass MD3Vertex:\n Size = 2 * 4\n Scale = 64.0\n\n def __init__(self, xyz=None, normal=0):\n self.xyz = (0, 0, 0) if not xyz else xyz\n self.normal = normal\n def read(self, mdl):\n self.xyz = mdl.read_short(3)\n self.normal = mdl.read_ushort()\n return self\n def write(self, mdl):\n mdl.write_short(self.xyz)\n mdl.write_ushort(self.normal)\n\nclass MD3Surface:\n BaseSize = MaxPath + (4 * 11)\n\n def __init__(self, name=''):\n self.name = name\n self.flags = 0\n self.shaders = []\n self.triangles = []\n self.texcoords = []\n self.verts = []\n def read(self, mdl):\n ident = mdl.read_string(4)\n if ident != \"IDP3\":\n self.file.close()\n return None\n self.name = mdl.read_path(MaxPath)\n self.flags = mdl.read_int()\n\n num_frames = mdl.read_int()\n num_shaders = mdl.read_int()\n num_verts = mdl.read_int()\n num_triangles = mdl.read_int()\n\n ofs_triangles = mdl.read_int()\n ofs_shaders = mdl.read_int()\n ofs_st = mdl.read_int()\n ofs_xyznormal = mdl.read_int()\n ofs_eof = mdl.read_int()\n\n for _ in range(num_triangles):\n self.triangles.append(MD3Triangle().read(mdl))\n for _ in range(num_shaders):\n self.shaders.append(MD3Shader().read(mdl))\n for _ in range(num_verts):\n self.texcoords.append(MD3TexCoord().read(mdl))\n for _ in range(num_verts * num_frames):\n self.verts.append(MD3Vertex().read(mdl))\n return self\n def write(self, mdl):\n mdl.write_string(mdl.ident, 4)\n mdl.write_path(self.name, MaxPath)\n mdl.write_int(self.flags)\n\n mdl.write_int(len(mdl.frames))\n mdl.write_int(len(self.shaders))\n mdl.write_int(len(self.verts) // len(mdl.frames))\n mdl.write_int(len(self.triangles))\n\n ofs_triangles = MD3Surface.BaseSize\n ofs_shaders = ofs_triangles + (MD3Triangle.Size * len(self.triangles))\n ofs_st = ofs_shaders + (MD3Shader.Size * len(self.shaders))\n ofs_xyznormal = ofs_st + (MD3TexCoord.Size * len(self.texcoords))\n ofs_eof = ofs_xyznormal + (MD3Vertex.Size * len(self.verts))\n\n mdl.write_int(ofs_triangles)\n mdl.write_int(ofs_shaders)\n mdl.write_int(ofs_st)\n mdl.write_int(ofs_xyznormal)\n mdl.write_int(ofs_eof)\n\n for tri in self.triangles:\n tri.write(mdl)\n for shader in self.shaders:\n shader.write(mdl)\n for tc in self.texcoords:\n tc.write(mdl)\n for v in self.verts:\n v.write(mdl)\n\n def calculate_size(self):\n return MD3Surface.BaseSize + (MD3Shader.Size * len(self.shaders)) + (MD3Triangle.Size * len(self.triangles)) + (MD3TexCoord.Size * len(self.texcoords)) + (MD3Vertex.Size * len(self.verts))\n\nclass MD3:\n def read_byte(self, count=1):\n size = 1 * count\n data = self.file.read(size)\n data = unpack(\"<%dB\" % count, data)\n if count == 1:\n return data[0]\n return data\n\n def read_int(self, count=1):\n size = 4 * count\n data = self.file.read(size)\n data = unpack(\"<%di\" % count, data)\n if count == 1:\n return data[0]\n return data\n\n def read_short(self, count=1):\n size = 2 * count\n data = self.file.read(size)\n data = unpack(\"<%dh\" % count, data)\n if count == 1:\n return data[0]\n return data\n\n def read_ushort(self, count=1):\n size = 2 * count\n data = self.file.read(size)\n data = unpack(\"<%dH\" % count, data)\n if count == 1:\n return data[0]\n return data\n\n def read_float(self, count=1):\n size = 4 * count\n data = self.file.read(size)\n data = unpack(\"<%df\" % count, data)\n if count == 1:\n return data[0]\n return data\n\n def read_bytes(self, size):\n return self.file.read(size)\n\n def read_string(self, size):\n data = self.file.read(size)\n s = \"\"\n for c in data:\n s = s + chr(c)\n return s\n\n def write_byte(self, data):\n if not hasattr(data, \"__len__\"):\n data = (data,)\n self.file.write(pack((\"<%dB\" % len(data)), *data))\n\n def write_int(self, data):\n if not hasattr(data, \"__len__\"):\n data = (data,)\n self.file.write(pack((\"<%di\" % len(data)), *data))\n\n def write_short(self, data):\n if not hasattr(data, \"__len__\"):\n data = (data,)\n self.file.write(pack((\"<%dh\" % len(data)), *data))\n\n def write_ushort(self, data):\n if not hasattr(data, \"__len__\"):\n data = (data,)\n self.file.write(pack((\"<%dH\" % len(data)), *data))\n\n def write_float(self, data):\n if not hasattr(data, \"__len__\"):\n data = (data,)\n self.file.write(pack((\"<%df\" % len(data)), *data))\n\n def write_bytes(self, data, size=-1):\n if size == -1:\n size = len(data)\n self.file.write(data[:size])\n if size > len(data):\n self.file.write(bytes(size - len(data)))\n\n def write_string(self, data, size=-1):\n data = data.encode()\n self.write_bytes(data, size)\n\n def read_path(self, len):\n name = self.read_string(len)\n if \"\\0\" in name:\n name = name[:name.index(\"\\0\")]\n return name\n\n def write_path(self, path, len):\n self.write_string(path, len)\n\n def __init__(self, name=\"md3\"):\n self.ident = \"IDP3\"\n self.version = 15\n self.name = name\n self.flags = 0\n self.frames = []\n self.tags = []\n self.surfaces = []\n\n def calculate_surface_size(self):\n size = 0\n for surf in self.surfaces:\n size += surf.calculate_size()\n return size\n\n def read(self, filepath):\n self.file = open(filepath, \"rb\")\n self.ident = self.read_string(4)\n self.version = self.read_int()\n if self.ident != \"IDP3\" or self.version != 15:\n self.file.close()\n return None\n\n self.name = self.read_path(MaxPath)\n self.flags = self.read_int()\n\n num_frames = self.read_int()\n num_tags = self.read_int()\n num_surfs = self.read_int()\n self.read_int()\n\n ofs_frames = self.read_int()\n ofs_tags = self.read_int()\n ofs_surfaces = self.read_int()\n ofs_eof = self.read_int()\n\n self.file.seek(ofs_frames)\n\n for _ in range(num_frames):\n self.frames.append(MD3Frame().read(self))\n\n self.file.seek(ofs_tags)\n\n for _ in range(num_tags):\n self.tags.append(MD3Tag().read(self))\n\n self.file.seek(ofs_surfaces)\n\n for _ in range(num_surfs):\n self.surfaces.append(MD3Surface().read(self))\n\n self.file.close()\n return self\n \n def write(self, filepath):\n self.file = open(filepath, \"wb\")\n self.write_string(self.ident, 4)\n self.write_int(self.version)\n self.write_path(self.name, MaxPath)\n self.write_int(self.flags)\n\n self.write_int(len(self.frames))\n self.write_int(len(self.tags))\n self.write_int(len(self.surfaces))\n self.write_int(0)\n\n ofs_frames = self.file.tell() + (4 * 4)\n ofs_tags = ofs_frames + (MD3Frame.Size * len(self.frames))\n ofs_surfaces = ofs_tags + (MD3Tag.Size * len(self.tags))\n ofs_eof = ofs_surfaces + self.calculate_surface_size()\n\n self.write_int(ofs_frames)\n self.write_int(ofs_tags)\n self.write_int(ofs_surfaces)\n self.write_int(ofs_eof)\n\n for frame in self.frames:\n frame.write(self)\n for tag in self.tags:\n tag.write(self)\n for surf in self.surfaces:\n surf.write(self)\n\n self.file.close()","sub_path":"io_mesh_qfmd/md3/md3.py","file_name":"md3.py","file_ext":"py","file_size_in_byte":11002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"27006504","text":"#\n# @lc app=leetcode.cn id=23 lang=python3\n#\n# [23] 合并K个排序链表\n#\n# https://leetcode-cn.com/problems/merge-k-sorted-lists/description/\n#\n# algorithms\n# Hard (43.30%)\n# Total Accepted: 16.7K\n# Total Submissions: 37.8K\n# Testcase Example: '[[1,4,5],[1,3,4],[2,6]]'\n#\n# 合并 k 个排序链表，返回合并后的排序链表。请分析和描述算法的复杂度。\n#\n# 示例:\n#\n# 输入:\n# [\n# 1->4->5,\n# 1->3->4,\n# 2->6\n# ]\n# 输出: 1->1->2->3->4->4->5->6\n#\n#\n# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\n\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\n def __str__(self):\n tmp = []\n node = self\n while node:\n tmp.append(str(node.val))\n node = node.next\n return ' -> '.join(tmp)\n\n\ndef build_list_node(nums):\n head = node = ListNode(None)\n for i in nums:\n node.next = ListNode(i)\n node = node.next\n return head.next\n\n\nclass Solution:\n def mergeKLists(self, lists: List[ListNode]) -> ListNode:\n \"\"\"\n 采用分治法\n \"\"\"\n\n if len(lists) == 0:\n return None\n if len(lists) == 1:\n return lists[0]\n if len(lists) == 2:\n return self.mergeTwoLists(lists[0], lists[1])\n else:\n l1 = self.mergeKLists(lists[:len(lists)//2])\n l2 = self.mergeKLists(lists[len(lists)//2:])\n return self.mergeTwoLists(l1, l2)\n\n def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:\n \"\"\"\n 参考 插入排序,将一个链表插入到另一个链表中\n \"\"\"\n\n if not l1:\n return l2\n if not l2:\n return l1\n\n p1 = l1\n p2 = l2\n p = head = ListNode(None)\n\n while p1 and p2:\n if p1.val < p2.val:\n p.next = p1\n p1 = p1.next\n else:\n p.next = p2\n p2 = p2.next\n p = p.next\n p1 = p1 or p2 or None\n\n while p1:\n p.next = p1\n p = p.next\n p1 = p1.next\n return head.next\nif __name__ == \"__main__\":\n pass\n","sub_path":"23.合并k个排序链表.py","file_name":"23.合并k个排序链表.py","file_ext":"py","file_size_in_byte":2253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"388330731","text":"# -*- coding: utf-8 -*-\r\nfrom django.shortcuts import render\r\nfrom django.http import HttpResponse, Http404\r\nfrom guides.models.guide_title import GuideTitle\r\nfrom guides.models.guide_content import Guidebody\r\nfrom utils import switch_path_relative\r\nimport datetime, pytz, Image, math, json, os\r\n\r\n\r\ntry:\r\n from django.apps import apps as models\r\nexcept ImportError: # django < 1.7\r\n from django.db import models\r\n\r\n\r\ndef Guide(request, *args, **kwargs):\r\n page = kwargs.get(\"page\")\r\n page = int(page)\r\n page_size = 10\r\n essay_count = GuideTitle.objects.all().count()\r\n new_obj = GuideTitle.objects.all().order_by(\"-write_date\")[((page - 1) * page_size):(page * page_size)]\r\n essay_data = []\r\n t_ids = []\r\n for item in new_obj:\r\n t_ids.append(item.id)\r\n\r\n def get_img_path(ids):\r\n body_obj = Guidebody.objects.filter(title_id__in=t_ids).exclude(image_path=None, image_name=None)\r\n path_dict = {}\r\n for id in ids:\r\n for item in body_obj:\r\n if os.path.isfile(item.image_path):\r\n img_size = Image.open(item.image_path).size\r\n if img_size[0] > 400 and img_size[1] > 300 and item.title_id == id:\r\n path_dict[id] = switch_path_relative(item.image_path, \"static\")\r\n break;\r\n return path_dict\r\n\r\n path_dict = get_img_path(t_ids)\r\n for item in new_obj:\r\n assay_dict = {}\r\n assay_dict[\"title\"] = item.title[0:50]\r\n utc_date = item.write_date\r\n new_date = utc_date.astimezone(pytz.timezone('Asia/Shanghai'))\r\n assay_dict[\"date\"] = str(new_date)[0:19]\r\n assay_dict[\"user\"] = item.write_user.username if item.write_user else u\"不详\"\r\n assay_dict[\"abstract\"] = item.abstract[0:500] if item.abstract else \"\"\r\n id = str(item.id)\r\n url = '/guide/view/' + id + \"/\"\r\n assay_dict[\"url\"] = url\r\n assay_dict[\"img_path\"] = path_dict[item.id] if path_dict.has_key(item.id) else ''\r\n essay_data.append(assay_dict)\r\n all_page = math.ceil(float(essay_count) / page_size)\r\n context = {\r\n 'essay_data': essay_data,\r\n 'page': page,\r\n 'all_page': int(all_page),\r\n }\r\n if page > 1:\r\n context[\"previous_page\"] = page - 1\r\n if page < all_page:\r\n context[\"next_page\"] = page + 1\r\n return render(request, 'guide_index.html', context)\r\n\r\n\r\ndef view_essay(request, *args, **kwargs):\r\n id = kwargs.get(\"id\")\r\n title = {}\r\n title_obj = GuideTitle.objects.filter(id=id)\r\n if not title_obj:\r\n # 重定向到404\r\n pass\r\n title[\"name\"] = title_obj[0].title\r\n title[\"abstract\"] = title_obj[0].abstract\r\n title[\"user\"] = title_obj[0].write_user.username if title_obj[0].write_user else u\"不详\"\r\n utc_date = title_obj[0].write_date\r\n new_date = utc_date.astimezone(pytz.timezone('Asia/Shanghai'))\r\n title[\"date\"] = str(new_date)[0:19]\r\n img_obj = title_obj[0].title_img\r\n if not img_obj:\r\n # 写一个默认标题图\r\n pass\r\n else:\r\n title_path = os.path.join(img_obj.new_path, img_obj.alias)\r\n title_path = switch_path_relative(title_path, \"static\")\r\n title[\"title_path\"] = title_path\r\n\r\n body_obj = Guidebody.objects.filter(title_id=title_obj[0].id).order_by(\"numbers\")\r\n bodys = []\r\n for item in body_obj:\r\n body_dict = {\r\n \"id\": item.id,\r\n }\r\n if item.s_title:\r\n body_dict[\"action\"] = \"s_title\"\r\n body_dict[\"text\"] = item.s_title\r\n elif item.image_path:\r\n body_dict[\"action\"] = \"b_img\"\r\n body_dict[\"path\"] = switch_path_relative(item.image_path, \"static\")\r\n body_dict[\"image_msg\"] = item.image_msg\r\n body_dict[\"image_name\"] = item.image_name\r\n elif item.s_body:\r\n body_dict[\"action\"] = \"s_body\"\r\n body_dict[\"text\"] = item.s_body\r\n bodys.append(body_dict)\r\n context = {\r\n 'title': title,\r\n \"bodys\": bodys,\r\n }\r\n return render(request, 'view_essay.html', context)\r\n","sub_path":"guides/views/guide.py","file_name":"guide.py","file_ext":"py","file_size_in_byte":4107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"105437303","text":"import sys\r\nfrom PyQt5.QtWidgets import *\r\nfrom PyQt5.QtGui import *\r\n\r\n\r\nclass Example(QMainWindow):\r\n def __init__(self):\r\n super().__init__()\r\n self.init_ui()\r\n\r\n def init_ui(self):\r\n menubar = self.menuBar()\r\n\r\n file_menu = menubar.addMenu(\"File\")\r\n\r\n new_act = QAction(\"New\", self)\r\n\r\n file_menu.addAction(new_act)\r\n\r\n imp_menu = QMenu(\"Edit\", self)\r\n imp_act1 = QAction(\"Copy\", self)\r\n imp_act2 = QAction(\"Paste\", self)\r\n\r\n file_menu.addMenu(imp_menu)\r\n imp_menu.addAction(imp_act1)\r\n imp_menu.addAction(imp_act2)\r\n\r\n save_act = QAction(\"Save\", self)\r\n save_act.setShortcut(\"Crit+S\")\r\n\r\n exit_act = QAction(QIcon(\"6YToyEF.png\"), \"Quit\", self)\r\n exit_act.setShortcut(\"Crit+Q\")\r\n exit_act.setStatusTip(\"Exit application\")\r\n exit_act.triggered.connect(QApplication.instance().quit)\r\n\r\n file_menu.addAction(save_act)\r\n file_menu.addAction(exit_act)\r\n\r\n self.setGeometry(300, 300, 250, 150)\r\n self.setWindowTitle(\"Exercise 2\")\r\n self.statusBar().showMessage(\"By Manee\")\r\n self.statusBar().addPermanentWidget(QLabel(\"By Manee\"), 1)\r\n self.show()\r\n\r\n\r\ndef main():\r\n app = QApplication(sys.argv)\r\n ex = Example()\r\n sys.exit(app.exec_())\r\n\r\n\r\nif __name__ == '__main__':\r\n main()","sub_path":"Krit-623040184-8-lab10/P2.py","file_name":"P2.py","file_ext":"py","file_size_in_byte":1380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"473974332","text":"import math\nimport os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib import patches\n\nfrom genetics.extract import calibrate_data, build_params, find_extend_a, calculate_distance_sigmoid\nfrom genetics.graph import calibrate_data_check\nfrom test import read_accel_csv\n\ncurrent_path = os.path.dirname(os.path.realpath(__file__))\ncurrent_path = os.path.join(current_path, '../genetics')\nprint(current_path)\n\n\ndef sigmoid_standard(a=1, b=1, c=0, d=0, t=0):\n denominator = 1 + (np.float128(math.e) ** ((-a) * (t - c)))\n return b * ((1 / denominator) + d)\n\n\ndef sin_function(f=1, t=0):\n return np.sin(2 * np.pi * f * t)\n\n\ndef integration_example():\n frequency = 1 / 10\n x = np.linspace(0, 10, num=1000)\n sample = np.linspace(0, 10, num=11)\n\n plt.figure(1)\n ax = plt.subplot(111)\n plt.ylabel(\"Acceleration Data\")\n plt.plot(x, sin_function(f=frequency, t=x), \"r\", label=\"Analog Acceleration\")\n plt.plot(sample, sin_function(f=frequency, t=sample), \"bo\", linestyle=\"-\", linewidth=2,\n label=\"Discrete Acceleration\")\n # plt.plot(sample, sin_function(f=frequency, t=sample), \"b\")\n # plt.xlim([-1, 11])\n # plt.ylim([-2, 2])\n plt.legend(loc=0)\n plt.axvline(x=0, color='black')\n plt.axhline(y=0, color='black')\n\n previous_a = 0\n rectangle = np.linspace(1, 10, num=10)\n for time in rectangle:\n a = sin_function(f=frequency, t=time)\n ax.add_patch(patches.Rectangle(\n (1 * time - 1, 0.0), # (x,y)\n 1, # width\n (a + previous_a) / 2, # height\n hatch='\\\\',\n fill=False # remove background\n ))\n previous_a = a\n # plt.subplot(312)\n # plt.ylabel(\"Velocity Data\")\n # plt.plot(x, velocity, \"g\", label=\"Velocity\")\n # plt.legend(loc=0)\n # plt.axvline(x=0, color='black')\n # plt.axhline(y=0, color='black')\n #\n # plt.subplot(313)\n # plt.ylabel(\"Distance Data\")\n # plt.plot(x, velocity_traz, \"r\", label=\"Distance\")\n # plt.legend(loc=0)\n # plt.axvline(x=0, color='black')\n # plt.axhline(y=0, color='black')\n plt.show()\n\n\ndef experiment_figure_original_adjust_v_zero():\n d, data, time = read_accel_csv(\"%s/experiment/android/10cm/normal/10.csv\" % current_path, is_calibrate=False)\n\n if False:\n y = []\n for d in data[1]:\n y.append(d * -1)\n data[1] = y\n\n data = calibrate_data(data)\n\n individual = [39, 84, 92, 78, 9, 58, 21, 18, 64, 52]\n params = build_params(individual)\n\n a1 = params['a1']\n c1 = params['c1']\n a4 = params['a4']\n c4 = params['c4']\n\n map_a = find_extend_a()\n calibration_check_value = calibrate_data_check(data, time, c1 - map_a[a1], c4 + map_a[a4])\n print(\"calibration check : %s\" % calibration_check_value)\n calibrate_y = []\n for data_y in data[1]:\n calibrate_y.append(data_y + calibration_check_value)\n data[1] = calibrate_y\n\n y_list, y_velocity, y_distance, vy, dy = calculate_distance_sigmoid(time, individual=individual,\n calibrate_check=calibration_check_value,\n offset=0)\n\n y_list_1, y_velocity_1, y_distance_1, vy, dy = calculate_distance_sigmoid(time, individual=individual,\n calibrate_check=calibration_check_value,\n offset=0.0147)\n\n plt.figure(1)\n plt.subplot(211)\n plt.ylabel('Accel Data')\n plt.plot(time, data[1], color='blue', label=r'original data (10cm)')\n plt.plot(time, [0] * len(time), color='black')\n plt.plot(time, y_list, color='red', label=r'before adjust velocity', linewidth=1.5)\n plt.legend(loc=1, prop={'size': 10})\n\n plt.subplot(212)\n plt.xlabel('Time')\n plt.plot(time, y_velocity, color='green', label=r'velocity')\n plt.plot(time, y_distance, color='brown', label=r'distance')\n plt.legend(loc=2, prop={'size': 10})\n\n # plt.subplot(222)\n # plt.ylabel('Accel Data')\n # plt.plot(time, data[1], color='red', label=r'original data (10cm)')\n # plt.plot(time, [0] * len(time), color='black')\n # plt.plot(time, y_list_1, color='blue', label=r'proposed adjust velocity', linewidth=1.5)\n # plt.legend(loc=1, prop={'size': 10})\n #\n # plt.subplot(224)\n # plt.xlabel('Time')\n # plt.plot(time, y_velocity_1, color='green', label=r'velocity')\n # plt.plot(time, y_distance_1, color='brown', label=r'distance')\n # plt.legend(loc=2, prop={'size': 10})\n plt.show()\n\n\ndef experiment_figure_adjust_v_zero():\n d, data, time = read_accel_csv(\"%s/experiment/android/10cm/normal/10.csv\" % current_path, is_calibrate=False)\n\n if False:\n y = []\n for d in data[1]:\n y.append(d * -1)\n data[1] = y\n\n data = calibrate_data(data)\n\n individual = [39, 84, 92, 78, 9, 58, 21, 18, 64, 52]\n params = build_params(individual)\n\n a1 = params['a1']\n c1 = params['c1']\n a4 = params['a4']\n c4 = params['c4']\n\n map_a = find_extend_a()\n calibration_check_value = calibrate_data_check(data, time, c1 - map_a[a1], c4 + map_a[a4])\n print(\"calibration check : %s\" % calibration_check_value)\n calibrate_y = []\n for data_y in data[1]:\n calibrate_y.append(data_y + calibration_check_value)\n data[1] = calibrate_y\n\n y_list_1, y_velocity_1, y_distance_1, vy, dy = calculate_distance_sigmoid(time, individual=individual,\n calibrate_check=calibration_check_value,\n offset=0.0147)\n y_list_2, y_velocity_2, y_distance_2, vy, dy = calculate_distance_sigmoid(time, individual=individual,\n calibrate_check=calibration_check_value,\n offset=0.024, adjust_v_1=True)\n\n plt.figure(1)\n plt.subplot(221)\n plt.ylabel('Accel Data')\n plt.plot(time, data[1], color='blue', label=r'original data (10cm)')\n plt.plot(time, [0] * len(time), color='black')\n plt.plot(time, y_list_1, color='red', label=r'proposed adjust velocity', linewidth=1.5)\n plt.legend(loc=1, prop={'size': 10})\n\n plt.subplot(223)\n plt.xlabel('Time')\n plt.plot(time, y_velocity_1, color='green', label=r'velocity')\n plt.plot(time, y_distance_1, color='brown', label=r'distance')\n plt.legend(loc=2, prop={'size': 10})\n\n plt.subplot(222)\n plt.ylabel('Accel Data')\n plt.plot(time, data[1], color='blue', label=r'original data (10cm)')\n plt.plot(time, [0] * len(time), color='black')\n plt.plot(time, y_list_2, color='red', label=r'peak adjust velocity', linewidth=1.5)\n plt.ylim([-0.20, 0.25])\n plt.legend(loc=1, prop={'size': 10})\n\n plt.subplot(224)\n plt.xlabel('Time')\n plt.plot(time, y_velocity_2, color='green', label=r'velocity')\n plt.plot(time, y_distance_2, color='brown', label=r'distance')\n plt.legend(loc=2, prop={'size': 10})\n plt.show()\n\n\ninclude_wrong = {\n 'raw': {\n 'iphone': {\n 'rmse': [3, 5.11, 9.57],\n 'mean': [11.68, 18.61, 23.78],\n 'std': [2.48, 4.91, 7.27]\n },\n 'android': {\n 'rmse': [4.14, 9.68, 16.07],\n 'mean': [8.49, 19.59, 25.16],\n 'std': [3.86, 9.68, 15.32]\n }\n },\n 'fit': {\n 'iphone': {\n 'rmse': [5.16, 5.98, 13.13],\n 'mean': [10.61, 22.45, 24.54],\n 'std': [5.12, 5.46, 11.94]\n },\n 'android': {\n 'rmse': [3.85, 5.7, 8.94],\n 'mean': [9.9, 18.63, 27],\n 'std': [3.85, 5.54, 8.42]\n }\n },\n 'all': {\n 'iphone': {\n 'rmse': [1.51, 3.14, 8.2],\n 'mean': [9.55, 19.26, 24.88],\n 'std': [1.44, 3.05, 6.42]\n },\n 'android': {\n 'rmse': [2.09, 5.12, 7.4],\n 'mean': [8.65, 15.91, 24.7],\n 'std': [1.6, 3.08, 5.17]\n }\n }\n}\n\nexclude_wrong = {\n 'raw': {\n 'iphone': {\n 'rmse': [2.68, 5.13, 9.63],\n 'mean': [11.4, 18.66, 23.98],\n 'std': [2.29, 4.95, 7.52]\n },\n 'android': {\n 'rmse': [4.22, 9.29, 15.96],\n 'mean': [8.42, 19.29, 25.48],\n 'std': [3.92, 9.26, 15.31]\n }\n },\n 'fit': {\n 'iphone': {\n 'rmse': [4.83, 5.95, 12.71],\n 'mean': [10.96, 22.63, 25.25],\n 'std': [4.74, 5.34, 11.8]\n },\n 'android': {\n 'rmse': [3.88, 5.27, 8.57],\n 'mean': [9.82, 19, 27.34],\n 'std': [3.87, 5.18, 8.14]\n }\n },\n 'all': {\n 'iphone': {\n 'rmse': [1.27, 2.84, 5.59],\n 'mean': [9.66, 19.4, 26.3],\n 'std': [1.22, 2.78, 4.18]\n },\n 'android': {\n 'rmse': [1.65, 4.31, 6.24],\n 'mean': [8.84, 16.31, 25.18],\n 'std': [1.18, 2.23, 3.96]\n }\n }\n}\n\n\ndef experiment_meanstd_result():\n N = 3\n\n data = exclude_wrong\n device = 'iphone'\n\n ind = np.arange(N) # the x locations for the groups\n width = 0.2 # the width of the bars\n raw_mean = data['raw'][device]['mean']\n velocity_mean = data['fit'][device]['mean']\n all_mean = data['all'][device]['mean']\n\n raw_std = data['raw'][device]['std']\n velocity_std = data['fit'][device]['std']\n all_std = data['all'][device]['std']\n\n fig, ax = plt.subplots()\n rects1 = ax.bar(ind, raw_mean, width, color='red', yerr=raw_std)\n rects2 = ax.bar(ind + width, velocity_mean, width, color='yellow', yerr=velocity_std)\n rects3 = ax.bar(ind + width + width, all_mean, width, color='blue', yerr=all_std)\n\n # add some text for labels, title and axes ticks\n ax.set_ylabel('distance')\n ax.set_xticks(ind + width + width / 2)\n ax.set_xticklabels(('10cm', '20cm', '30cm'))\n\n ax.legend((rects1[0], rects2[0], rects3[0]), ('Raw + Calibration', 'Before Velocity Adjustment', 'All'), loc=2)\n\n def auto_label(rects):\n # attach some text labels\n for rect in rects:\n height = rect.get_height()\n ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * height,\n '%.2f' % height,\n ha='center', va='bottom')\n\n auto_label(rects1)\n auto_label(rects2)\n auto_label(rects3)\n plt.show()\n\n\ndef experiment_rmse_result():\n N = 3\n\n data = exclude_wrong\n device = 'iphone'\n\n ind = np.arange(N) # the x locations for the groups\n width = 0.2 # the width of the bars\n raw = data['raw'][device]['rmse']\n velocity = data['fit'][device]['rmse']\n all = data['all'][device]['rmse']\n\n fig, ax = plt.subplots()\n rects1 = ax.bar(ind, raw, width, color='red')\n rects2 = ax.bar(ind + width, velocity, width, color='yellow')\n rects3 = ax.bar(ind + width + width, all, width, color='blue')\n\n # add some text for labels, title and axes ticks\n ax.set_ylabel('rmse (cm)')\n ax.set_xticks(ind + width + width / 2)\n ax.set_xticklabels(('10cm', '20cm', '30cm'))\n\n ax.legend((rects1[0], rects2[0], rects3[0]), ('Raw + Calibration', 'Before Velocity Adjustment', 'All'), loc=2)\n\n def auto_label(rects):\n # attach some text labels\n for rect in rects:\n height = rect.get_height()\n ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * height,\n '%.2f' % height,\n ha='center', va='bottom')\n\n auto_label(rects1)\n auto_label(rects2)\n auto_label(rects3)\n plt.show()\n\n\ndef experiment_velocity_rmse_result():\n\n velocity_exclude_wrong = {\n 'iphone': {\n 'proposed': [1.27, 2.84, 5.59],\n 'peak': [1.62, 3.25, 6.77]\n },\n 'android': {\n 'proposed': [1.65, 4.31, 6.24],\n 'peak': [2.02, 4.35, 7.13]\n }\n }\n\n velocity_include_wrong = {\n 'iphone': {\n 'proposed': [1.51, 3.14, 8.2],\n 'peak': [1.76, 3.49, 8.94]\n },\n 'android': {\n 'proposed': [2.09, 5.12, 7.4],\n 'peak': [2.39, 5.14, 8.14]\n }\n }\n\n N = 3\n\n data = velocity_include_wrong\n\n ind = np.arange(N) # the x locations for the groups\n width = 0.2 # the width of the bars]\n\n proposed_iphone = data['iphone']['proposed']\n peak_iphone = data['iphone']['peak']\n proposed_android = data['android']['proposed']\n peak_android = data['android']['peak']\n\n fig, ax = plt.subplots()\n rects1 = ax.bar(ind, proposed_iphone, width, color='red')\n rects2 = ax.bar(ind + width, peak_iphone, width, color='yellow')\n rects3 = ax.bar(ind + width + width, proposed_android, width, color='blue')\n rects4 = ax.bar(ind + width + width + width, peak_android, width, color='green')\n\n # add some text for labels, title and axes ticks\n ax.set_ylabel('rmse (cm)')\n ax.set_xticks(ind + width + width / 2 + width / 2)\n ax.set_xticklabels(('10cm', '20cm', '30cm'))\n\n ax.legend((rects1[0], rects2[0], rects3[0], rects4[0]), ('Proposed (iPhone)', 'Peak (iPhone)', 'Proposed (Android)', 'Peak (Android)'), loc=2)\n\n def auto_label(rects):\n # attach some text labels\n for rect in rects:\n height = rect.get_height()\n ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * height,\n '%.2f' % height,\n ha='center', va='bottom')\n\n auto_label(rects1)\n auto_label(rects2)\n auto_label(rects3)\n auto_label(rects4)\n plt.show()\n\n\n# integration_example()\n# experiment_figure_original_adjust_v_zero()\nexperiment_figure_adjust_v_zero()\n\n# experiment_meanstd_result()\n# experiment_rmse_result()\n# experiment_velocity_rmse_result()\n","sub_path":"figure/generate_figure.py","file_name":"generate_figure.py","file_ext":"py","file_size_in_byte":13927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"464963921","text":"from ckeditor_uploader.fields import RichTextUploadingField\nfrom django.contrib.auth.models import User\nfrom django.db import models\nfrom django.template import defaultfilters\nfrom meta.models import ModelMeta\nfrom unidecode import unidecode\n\nfrom app.core.models import Product\n\n\nclass Category(models.Model):\n name = models.CharField(verbose_name=\"Название категории\", max_length=400)\n slug = models.SlugField(default='')\n\n class Meta:\n verbose_name = \"Категория\"\n verbose_name_plural = \"Категории\"\n\n def save(self, *args, **kwargs):\n self.slug = defaultfilters.slugify(unidecode(self.name))\n models.Model.save(self, *args, **kwargs)\n\n def __str__(self):\n return self.name\n\n\nclass Article(ModelMeta, models.Model):\n title = models.CharField(verbose_name=\"Название статьи\", max_length=400)\n short_content = RichTextUploadingField(verbose_name=\"Коротрое Описание\", default='')\n content = RichTextUploadingField(verbose_name=\"Описание\")\n product = models.ForeignKey(Product, verbose_name=\"Платформа\", null=True, blank=True)\n category = models.ForeignKey(Category, verbose_name=\"Категория\", null=True, blank=True)\n author = models.ForeignKey(User, verbose_name=\"Автор\", null=True, blank=True)\n datetime_create = models.DateTimeField(auto_now_add=True)\n slug = models.SlugField()\n seo_description = models.TextField(verbose_name=\"CEO\")\n seo_image = models.ImageField(default='', verbose_name=\"Изображение для соц. сетей\")\n\n class Meta:\n verbose_name = \"Пост\"\n verbose_name_plural = \"Посты\"\n ordering = (\"-datetime_create\",)\n\n _metadata = {\n 'title': 'get_title',\n 'description': 'get_description',\n 'use_title_tag': 'True',\n 'use_og' : 'True',\n 'type': 'article',\n 'locale': 'ru_RU',\n 'use_twitter': 'True',\n 'use_facebook': 'True',\n 'use_googleplus': 'True',\n 'image': 'get_seo_image',\n 'site_name': \"CyberTech\",\n 'url': 'get_absolute_url',\n }\n\n def get_seo_image(self):\n return self.seo_image.url\n\n def get_title(self):\n return self.title + \" | CyberTech\"\n\n def get_description(self):\n return self.seo_description\n\n def __str__(self):\n return self.title\n\n def save(self, *args, **kwargs):\n self.slug = defaultfilters.slugify(unidecode(self.title))\n\n models.Model.save(self, *args, **kwargs)\n\n def get_absolute_url(self):\n return \"/post/%s/\" % self.slug\n","sub_path":"app/article/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"458778700","text":"from sys import version_info\nfrom os import system\nfrom showImages import show\nfrom loadFeatures import *\nfrom CCA_search import textToImageSearch\n\nuser_input = input if version_info[0] > 2 else raw_input\n\n\ndef load(num=1, name='COCO'):\n print(\"Loading CCA %d (%s DB)...\" % (num, name))\n load_features(name)\n imIds = get_images_id()\n W_T, W_V, phi_T, phi_V, D = np.load('Computed_CCA/CCA_{0}.npy'.format(num), encoding='latin1')\n return W_T, W_V, phi_T, phi_V, D, imIds\n\n\nW_T, W_V, phi_T, phi_V, D, imIds = load()\n\nsearch = ''\nwhile(search != 'EXIT'):\n system(\"clear\")\n search = user_input(\"Search Terms: \")\n\n if (search[:2] == 'DB'):\n W_T, W_V, phi_T, phi_V, D, imIds = load(int(search[2:]))\n elif (search != 'EXIT'):\n res_IDs, similarities = textToImageSearch(search, W_T, D, 10, phi_V, W_V, imIds)\n show(res_IDs.tolist())\n","sub_path":"T2I.py","file_name":"T2I.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"232516879","text":"from conexionDB import conexionDB\n# conexion a la base de datos GALBA (servidor mirrow)\ngalba = conexionDB()\ndef buscarDatosGalba(servidor):\n\t\n\t# galba = conexionDB()\n\tgalba.parametrosConexion(\"GALBA\",\"postgres\",\"root\",servidor)\n\tgalba.conectar()\n\n\t# Entrada de los puntos selecccioados por el administrador operativo\n\t# puntos = {\"point_id\":\"36554433\"},{\"point_id\":\"36554434\"},{\"point_id\":\"36554435\"},\\\n\t# \t\t{\"point_id\":\"36554436\"},{\"point_id\":\"36554437\"}\n\ndef imprimirValores(arreglo):\n\ti = 0\t\n\n\twhile i int:\n \n if grid is None:\n return -1\n \n fresh_count, time, queue, dirs = 0, 0, deque(), [[0, -1], [0, 1], [-1, 0], [1, 0]]\n \n for row in range(len(grid)):\n for col in range(len(grid[0])):\n if grid[row][col] == 1:\n fresh_count += 1\n \n if grid[row][col] == 2:\n queue.append((row, col))\n \n if fresh_count == 0:\n return 0\n \n while len(queue) > 0:\n size = len(queue)\n while size > 0:\n pos = queue.popleft()\n for nei in dirs:\n if pos[0] + nei[0] >= 0 and pos[0] + nei[0] < len(grid) and pos[1] + nei[1] >= 0 and pos[1] + nei[1] < len(grid[0]) and grid[pos[0] + nei[0]][pos[1] + nei[1]] == 1:\n grid[pos[0] + nei[0]][pos[1] + nei[1]] = 2\n queue.append((pos[0] + nei[0], pos[1] + nei[1]))\n fresh_count -= 1\n size -= 1\n \n time += 1\n \n if fresh_count != 0:\n return -1\n else:\n return time - 1\n \n \n","sub_path":"Problem-2_rotting_oranges.py","file_name":"Problem-2_rotting_oranges.py","file_ext":"py","file_size_in_byte":1999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"89915201","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nimport mac_say\nimport plural_ru\nimport my_apps\n\n\nclass Rest(my_apps.App):\n def run(self):\n self.growlnotify(t=\"Перерыв\")\n mac_say.say(\"Перерыв начался\")\n self.sleep(60)\n i = 1\n while True:\n ru = plural_ru.ru(i, [\"минуту\", \"минуты\", \"минут\"])\n mac_say.say(\"Перерыв длится %s %s\" % (i, ru))\n self.growlnotify(t=\"%s Перерыв\" % i)\n self.sleep(60)\n i += 1\n","sub_path":"Dock/apps/Workout/rest.py","file_name":"rest.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"58668116","text":"#!/usr/bin/env python\nimport rospy\nimport rosparam\nimport numpy as np\nfrom math import pi\nimport math\nimport tf\nimport cv2\nimport ipdb \nimport geometry_msgs.msg\nfrom std_msgs.msg import String\nfrom std_msgs.msg import UInt16\nfrom std_msgs.msg import Int16\nfrom geometry_msgs.msg import Quaternion\nfrom cv_bridge import CvBridge, CvBridgeError\nfrom visualization_msgs.msg import Marker\nfrom utils import dxl_move as DXL\nfrom utils import handeye_sub as HandEye\nfrom utils import conbe_ik as CONBE\nfrom utils import client_trajectory\nfrom utils import arm_state_commander as arm_master\nfrom utils import target_sub\nfrom utils import dolly_sub as DollyFB\n\n\n# def create_marker(axis):\n# marker = Marker()\n# marker.header.frame_id = 'Llink0'\n# marker.header.stamp = rospy.Time.now()\n# marker.ns = \"visulize-eef-orientation\"\n# marker.id = 0 if(axis=='x') else 1 if(axis=='y') else 2\n# marker.action = Marker.ADD\n# marker.color.r = 1.0 if(axis=='x') else 0.0\n# marker.color.g = 1.0 if(axis=='y') else 0.0\n# marker.color.b = 1.0 if(axis=='z') else 0.0\n# marker.color.a = 1.0 \n# marker.scale.x = 0.1\n# marker.scale.y = 0.01\n# marker.scale.z = 0.01\n# return marker\n\n# def set_marker_param(marker,axis,px,py,pz,quaternion):\n# ##offest the orientation\n# ## conversion to represent each axis. since arrow marker is along with x-axis as a default,\n# ## this is the conversion quaternion \n# orientation = {'x' :[0.0,0.0,0.0,1],\n# 'y' :[0.0,0.0,0.7071068,0.707168],\n# 'z' :[0.0,-0.7071068,0.0,0.7071068]}\n# ###marker to visualize eef arrow\n# marker.pose.position.x = px\n# marker.pose.position.y = py\n# marker.pose.position.z = pz\n# #since the original arrow is along with x-axis,need to offset make it same as link0 frame.\n# #after this, put the one\n# link0_to_EEF = np.asarray(orientation[axis], dtype=np.float32)\n# q_new = tf.transformations.quaternion_multiply(quaternion, link0_to_EEF)\n# marker.pose.orientation.x=q_new[0]\n# marker.pose.orientation.y=q_new[1]\n# marker.pose.orientation.z=q_new[2]\n# marker.pose.orientation.w=q_new[3]\n# marker.lifetime = rospy.Duration()\n# marker.type = 0\n# return marker\n\n# def set_pre_position(q_orientation,px,py,pz,L):\n# ##convert to matrix\n# matrix = tf.transformations.quaternion_matrix(q_orientation)\n# # print('matrix: ',matrix)\n# ##set the point\n# point = np.matrix([0, 0, L, 1], dtype='float32')\n# point.resize((4, 1))\n# ##get the point along with eef_frame\n# rotated = matrix*point\n# ##get the pre-grip position\n# pre_position = np.array([ px + rotated.item(0), py + rotated.item(1), pz + rotated.item(2)])\n# return pre_position\n \n# def calc_orientation(px,py,pz,offset_z):\n# yaw= math.atan2(py,px)\n# z_offset = math.fabs(pz-offset_z)\n# Beta = math.atan2(z_offset,math.sqrt(px*px+py*py))\n# pitch = Beta - pi/2 if(pz-offset_z > 0) else - Beta - pi/2\n# return pitch,yaw\n\n\ndef dolly_mode_interpreter(command):\n if(command == 49):\n print('stop')\n elif(command < 49):\n print('left')\n else:\n print('right')\n \ndef key_control():\n background = np.zeros((200,300,3), np.uint8)\n cv2.namedWindow('input_test')\n\n while not rospy.is_shutdown():\n try: \n cv2.imshow('input_test',background)\n print('Reference distance: ',dolly_sub.get_state())\n input = cv2.waitKey(0)\n if input == ord('r'): # if key 'z' is pressed \n print('r-pressed: Go to right')\n dolly_pub.publish(59)\n elif input == ord('l'): # if key 'x' is pressed \n print('l-pressed: Go to left')\n dolly_pub.publish(39)\n elif input == ord('q'): # break\n cv2.destroyAllWindows()\n break\n elif input == ord('s'):\n print('stop')\n dolly_pub.publish(49)\n print('Reference distance: ',dolly_sub.get_state())\n except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException):\n continue\n\n\ndef move_dolly(dolly_error):\n ## DORY control msg\n ##msg can be interpret as below\n # HERE, use dolly_mode_interpreter func \n # start-R : 50~99\n # start-L : 0~48\n # stop : 49\n\n test = 1\n while not rospy.is_shutdown():\n try: \n target_msg = target_marker_sub.get()\n #target point ref Llink0 frame\n # print('get target position in main*****')\n target_ref_link0_point = target_msg.points\n px = target_ref_link0_point[0].x\n py = target_ref_link0_point[0].y \n pz = target_ref_link0_point[0].z\n if(test == 1 ):\n print('******************PY : ', py) \n test = 2\n \n print('Reference distance: ',dolly_sub.get_state())\n \n ####################################\n ## DORRY MOVE\n ####################################\n ##in case tomato is not recongnized \n if(px == -1 and py == -1 and pz == -1):\n ##Go to right\n # print('*****go right in no detection')\n dolly_pub.publish(50)\n elif(py > dolly_error):\n # print('L')\n dolly_pub.publish(40)\n elif(-dolly_error > py ):\n # print('R')\n dolly_pub.publish(60)\n else:\n # print('S')\n dolly_pub.publish(49)\n rospy.sleep(0.3)\n break\n rospy.sleep(0.3)\n except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException):\n continue\n\ndef move_dolly_by_tracking(max_distance,move_flag):\n thred = 0.05\n thred_over_cnt = 0\n dolly_pub.publish(49)\n while(thred_over_cnt < 5):\n print('thred_over_cnt',thred_over_cnt)\n target_msg = target_marker_sub.get()\n target_ref_link0_point = target_msg.points\n px = target_ref_link0_point[0].x\n py = target_ref_link0_point[0].y \n pz = target_ref_link0_point[0].z\n current_distance = dolly_sub.get_state()\n if(py != -1):\n target_y = py\n else:\n target_y = 1\n print(target_y)\n D_isOK = bool(math.fabs(target_y) < thred)\n ###########################################\n ## when dolly was about to out of the lane \n ###########################################\n if (current_distance < -0.01):\n print('got to righr for a while')\n move_flag['L'] = False\n move_flag['R'] =True\n dolly_pub.publish(49)\n for i in range(10):\n dolly_pub.publish(59)\n rospy.sleep(0.5)\n dolly_pub.publish(49)\n break\n\n if (current_distance > max_distance):\n print('dolly is about to off the rane')\n print('got to left for a while')\n move_flag['L'] = True\n move_flag['R'] = False\n dolly_pub.publish(49)\n for i in range(10):\n dolly_pub.publish(39)\n rospy.sleep(0.5)\n dolly_pub.publish(49)\n break\n\n if (move_flag['Force'] and move_flag['L'] and not move_flag['R']):\n print('got to left for a while')\n move_flag['L'] = True\n move_flag['R'] =True\n move_flag['Force'] = False\n dolly_pub.publish(49)\n for i in range(5):\n dolly_pub.publish(59)\n rospy.sleep(0.2)\n dolly_pub.publish(49)\n break\n elif (move_flag['Force'] and move_flag['R'] and not move_flag['L']):\n print('got to righr for a while')\n move_flag['L'] = True\n move_flag['R'] =True\n move_flag['Force'] = False\n dolly_pub.publish(49)\n for i in range(5):\n dolly_pub.publish(39)\n rospy.sleep(0.2)\n dolly_pub.publish(49)\n break\n\n ###########################################\n ## usual state\n ###########################################\n if(D_isOK):\n print('stop')\n thred_over_cnt += 1\n speed_control = 49\n dolly_pub.publish(49)\n rospy.sleep(0.2)\n if(thred_over_cnt >= 5):\n break\n else:\n d_err = -target_y\n if (d_err > 0.5):\n d_err = 0.5\n elif (d_err < -0.5):\n d_err = -0.5\n\n ###############################################\n ##check move flag\n ###############################################\n if(not move_flag['L'] and d_err<0):\n d_err = -d_err\n if(not move_flag['R'] and d_err>0):\n d_err = -d_err\n ##########################################\n\n speed_control = 49 + 20 * d_err \n \n dolly_pub.publish(UInt16(speed_control))\n rospy.sleep(0.15)\n return move_flag\n\ndef move_dolly_right():\n thred = 0.05\n thred_over_cnt = 0\n dolly_pub.publish(49)\n while(thred_over_cnt < 3):\n print('thred_over_cnt',thred_over_cnt)\n target_msg = target_marker_sub.get()\n target_ref_link0_point = target_msg.points\n px = target_ref_link0_point[0].x\n py = target_ref_link0_point[0].y \n pz = target_ref_link0_point[0].z\n current_distance = dolly_sub.get_state()\n if(py != -1):\n target_y = py\n else:\n target_y = -1\n print(target_y)\n\n D_isOK = bool(math.fabs(target_y) < thred)\n ###########################################\n ## usual state\n ###########################################\n if(D_isOK):\n print('stop')\n thred_over_cnt += 1\n speed_control = 49\n dolly_pub.publish(49)\n rospy.sleep(0.2)\n\n else:\n d_err = -target_y\n if (d_err > 0.5):\n d_err = 0.5\n elif (d_err < -0.5):\n d_err = -0.5\n\n speed_control = 49 + 20 * d_err \n \n dolly_pub.publish(UInt16(speed_control))\n rospy.sleep(0.15)\n\ndef move_dolly_left():\n thred = 0.05\n thred_over_cnt = 0\n dolly_pub.publish(49)\n while(thred_over_cnt < 3):\n print('thred_over_cnt',thred_over_cnt)\n target_msg = target_marker_sub.get()\n target_ref_link0_point = target_msg.points\n py = target_ref_link0_point[0].y \n current_distance = dolly_sub.get_state()\n if(py != -1):\n d_err = -py\n if(py < -0.10):\n d_err = -1\n else:\n d_err = -1\n #ipdb.set_trace()()()()()()()\n\n\n D_isOK = bool(math.fabs(d_err) < thred)\n ###########################################\n ## usual stated_err\n ###########################################\n if(D_isOK):\n print('stop')\n thred_over_cnt += 1\n speed_control = 49\n dolly_pub.publish(49)\n rospy.sleep(0.2)\n\n else:\n if (d_err > 0.5):\n d_err = 0.5\n elif (d_err < -0.5):\n d_err = -0.5\n\n speed_control = 49 + 20 * d_err \n \n dolly_pub.publish(UInt16(speed_control))\n rospy.sleep(0.15)\n\ndef move_dolly_by_fb():\n thred = 0.03\n thred_over_cnt = 0\n target_y = 1\n ##########IMPORTANT#############\n #### initialize the offset of distance \n #### to check get reference movement in this loop\n while(thred_over_cnt < 5):\n print('Reference distance: ',dolly_sub.get_local_state())\n if(target_y == 1):\n ############################\n #get target-msg only 1time\n target_msg = target_marker_sub.get()\n target_ref_link0_point = target_msg.points\n px = target_ref_link0_point[0].x\n py = target_ref_link0_point[0].y \n pz = target_ref_link0_point[0].z\n if(py != -1):\n target_y = py\n print('target_y: ',target_y)\n D_isOK = bool(math.fabs(target_y + dolly_sub.get_local_state()) < thred)\n if(D_isOK):\n print('stop')\n thred_over_cnt += 1\n speed_control = 49\n dolly_pub.publish(49)\n else:\n d_err = -(target_y + dolly_sub.get_local_state() )\n print(d_err)\n if (d_err > 0.6):\n d_err = 0.6\n elif (d_err < -0.6):\n d_err = -0.6\n speed_control = 49 + 22 * d_err \n dolly_pub.publish(UInt16(speed_control))\n rospy.sleep(0.15)\n\nif __name__ == '__main__': \n ######################################\n ## init_node & create arm commander\n ######################################\n rospy.init_node('Arm_main',anonymous=True)\n LArm = arm_master.Arm_state_commander('L')\n RArm = arm_master.Arm_state_commander('R')\n\n ######################################\n # here describe the listener of target\n ######################################\n target_marker_node = \"/target_marker_Llink0_frame\"\n target_marker_sub = target_sub.target_subscriber('L')\n\n ######################################\n ## instance to get feedback from Dolly\n ######################################\n dolly_pub = rospy.Publisher(\"Dolly/command\", UInt16, queue_size = 1)\n dolly_sub = DollyFB.Dolly_feedback()\n\n ####################\n ## define max dist of dolly\n ####################\n dolly_max_distance = 1.2\n move_direction_flag = {'L':True,'R':True,'Force':False}\n\n\n while not rospy.is_shutdown():\n try:\n # key_control()\n # continue\n\n print('start main loop')\n # print('move_direction_flag: ',move_direction_flag)\n # move_dolly(0.05)\n # move_dolly_by_fb()\n # move_direction_flag = move_dolly_by_tracking(dolly_max_distance,move_direction_flag)\n\n #move dolly\n move_dolly_left()\n\n print('start_moving')\n \n #wait until the state of arm is changed\n while(LArm.arm_response == 'WAIT'):\n ##command to move arm\n LArm.start_moving()\n rospy.sleep(0.1)\n\n #wait until the arm process finish\n while (LArm.arm_response == 'OTW'):\n rospy.sleep(0.1)\n\n #get the result of arm to decide next plan\n arm_result = LArm.arm_response \n print('arm_result ** ', arm_result) \n print('*******************[main.py]stop_moving')\n\n #stop arm\n while(LArm.arm_response != 'WAIT'):\n LArm.stop_moving()\n rospy.sleep(0.1)\n\n except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException):\n continue\n","sub_path":"dynamixel_motor/conbe/scripts/not_currently_used/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":15203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"363757511","text":"from .base import (\n CPObject,\n TextField,\n CollectionField,\n ObjectField,\n BooleanField,\n RangeField\n)\nfrom .dimensional_restrictions import DimensionalRestrictions\nfrom .option import Option\n\n\nclass Restrictions(CPObject):\n _name = 'restrictions'\n\n _fields = {\n \"weight-restriction\": RangeField('weight-restriction'),\n \"dimensional_restrictions\": ObjectField(\n 'dimensional-restrictions', format=DimensionalRestrictions\n ),\n \"options\": CollectionField(\n 'options', child_name='option', format=Option\n ),\n \"density_factor\": TextField('density-factor'),\n \"can_ship_in_mailing_tube\": BooleanField('can-ship-in-mailing-tube'),\n \"can_ship_unpackaged\": BooleanField('can-ship-unpackaged'),\n \"allowed_as_return_service\": BooleanField('allowed-as-return-service')\n }\n","sub_path":"src/canadapost/objects/restrictions.py","file_name":"restrictions.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"532870949","text":"import sys\nsys.path.append('/home/thanatos/lala/riqingrijie/')\n\nimport os\nos.environ['DJANGO_SETTINGS_MODULE'] = 'riqingrijie.settings'\n\nimport django\ndjango.setup()\n\nfrom django.core.mail import send_mail\nfrom django.utils import timezone\n#from UserProfile.models import UserProfile\nfrom django.contrib.auth.models import User\nfrom task.models import Task\n#from django.shortcuts import rend\nfrom django.template.loader import render_to_string\n\n\n# get today\ntoday = timezone.now().today().date()\ntoday_tasks = Task.objects.filter(post_time=today)\n\n# get all users\nusers = User.objects.filter(is_active=True)\n\nfor user in users:\n # drop users that does'nt have email address\n if user.email:\n mail_txt = ''\n try:\n user_tasks = today_tasks.filter(owner=user)\n except Exception as e:\n pass\n else:\n # some users may not write any tasks fot today\n if user_tasks.count()>0:\n # sum total time\n total_time = 0\n for t in user_tasks:\n total_time += t.spend_time\n\n # get bhd\n bhd = float(total_time / 7.5)\n bhd = round(bhd, 2)\n\n mail_txt = render_to_string('everyday_report.html',\n {'user': user,\n 'user_tasks': user_tasks,\n 'today': today,\n 'bhd': bhd,\n 'total_time': total_time})\n if user.userprofile.leader:\n user_leader_email = user.userprofile.leader.email\n send_mail(\n subject='[日清日结系统-%s小结]' % today,\n message=\"today's works summary\",\n html_message=mail_txt,\n from_email='xxx@xxx.com',\n recipient_list=[user.email, user_leader_email, 'zhangchi160104@credithc.com'],\n fail_silently=False,\n )\n else:\n send_mail(\n subject='[日清日结系统-%s小结]'%today,\n message=\"today's works summary\",\n html_message=mail_txt,\n from_email='xxx@xxx.com',\n recipient_list=[user.email,'zhangchi160104@credithc.com'],\n fail_silently=False,\n )","sub_path":"haha/everyday_report.py","file_name":"everyday_report.py","file_ext":"py","file_size_in_byte":2560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"46203345","text":"import logging\nimport uuid\nfrom twisted.internet import defer\nfrom twisted.internet import reactor\nfrom twisted.names import srvconnect\nfrom twisted.python import log\nfrom twisted.words.xish import domish\nfrom twisted.words.protocols.jabber import client\nfrom twisted.words.protocols.jabber import jid\nfrom twisted.words.protocols.jabber import xmlstream\n\nNS_TYRION = 'http://tyrion.org/protocol/1.0/service'\n\nclass Error(Exception): pass\n\nclass Iq(object):\n\n def __init__(self, jid_=None):\n self._id = None\n self.jid = jid_\n\n def del_id(self):\n self._id = None\n\n def get_id(self):\n if self._id is None:\n self._id = uuid.uuid4().get_hex()\n return self._id\n\n def set_id(self, value):\n if self._id is not None and self._id != value:\n raise Error('The id variable has already been set')\n else:\n self._id = value\n self._id = value\n\n id = property(get_id, set_id, del_id)\n\nclass Request(object):\n\n def __init__(self):\n self.iq = Iq()\n self.id = None\n self.service = None\n self.timeout = None\n self.user = None\n self.group = None\n self.input = None\n\nclass Response(object):\n\n def __init__(self):\n self.iq = Iq()\n self.id = None\n self.code = None\n self.service = None\n self.output = None\n self.error = None\n\nclass ClientConnector(srvconnect.SRVConnector):\n\n def __init__(self, reactor, domain, factory):\n srvconnect.SRVConnector.__init__(\n self,\n reactor,\n 'xmpp-client',\n domain,\n factory,\n )\n\n def pickServer(self):\n host, port = srvconnect.SRVConnector.pickServer(self)\n if not self.servers and not self.orderedServers:\n port = 5222\n return host, port\n\nclass Base(object):\n\n def __init__(self, jid_, password, host=None, port=5222, debug=False):\n if isinstance(jid_, jid.JID):\n self.jid = jid_\n else:\n self.jid = jid.JID(jid_)\n self.password = password\n self.host = host\n self.port = port\n self.debug = debug\n self.setupFactory()\n self.connect()\n self.connector = None\n\n def connect(self, connect=True):\n if connect:\n if self.host:\n self.connector = reactor.connectTCP(\n self.host,\n self.port,\n self.factory,\n )\n else:\n self.connector = ClientConnector(\n reactor,\n self.jid.host,\n self.factory,\n )\n self.connector.connect()\n\n def setupFactory(self):\n self.factory = client.XMPPClientFactory(self.jid, self.password)\n self.factory.addBootstrap(\n xmlstream.STREAM_CONNECTED_EVENT,\n self.connected,\n )\n self.factory.addBootstrap(\n xmlstream.STREAM_END_EVENT,\n self.disconnected,\n )\n self.factory.addBootstrap(\n xmlstream.STREAM_AUTHD_EVENT,\n self.authenticated,\n )\n self.factory.addBootstrap(xmlstream.INIT_FAILED_EVENT, self.initFailed)\n\n def rawDataIn(self, buffer):\n log.msg('RECV: %s' %\n unicode(buffer, 'utf-8').encode('ascii', 'replace'),\n logLevel=logging.DEBUG,\n )\n\n def rawDataOut(self, buffer):\n log.msg('SEND: %s' %\n unicode(buffer, 'utf-8').encode('ascii', 'replace'),\n logLevel=logging.DEBUG,\n )\n\n def connected(self, xs):\n self.xmlstream = xs\n if self.debug:\n self.xmlstream.rawDataInFn = self.rawDataIn\n self.xmlstream.rawDataOutFn = self.rawDataOut\n\n def authenticated(self, xs):\n self.run()\n\n def initFailed(self, failure):\n self.xmlstream.sendFooter()\n\n def disconnected(self, xs):\n pass\n\n def handleResponse(self, xml):\n response = Response()\n response.iq.id = xml.attributes.get('id')\n response.iq.jid = xml.attributes.get('from')\n for e1 in xml.elements():\n if e1.name == 'service':\n response.id = e1.attributes.get('id')\n response.code = e1.attributes.get('code')\n response.service = e1.attributes.get('type')\n for e2 in e1.elements():\n if e2.name == 'output':\n response.output = unicode(e2).strip()\n elif e2.name == 'error':\n response.error = unicode(e2).strip()\n return response\n\n def run(self):\n pass\n\n def request(self, value, type, input='', user=None, group=None,\n timeout=None):\n if isinstance(value, Iq):\n iq_ = value\n else:\n iq_ = Iq(value)\n if '/' not in iq_.jid:\n raise Error('Resource is required in JID (ex: user@host/resource)')\n iq = xmlstream.IQ(self.xmlstream)\n service = iq.addElement('service', NS_TYRION)\n service.attributes['id'] = iq_.id\n service.attributes['type'] = type\n if user is not None:\n service.attributes['user'] = user\n if group is not None:\n service.attributes['group'] = group\n if timeout is not None:\n service.attributes['timeout'] = unicode(timeout)\n if input and not input.endswith('\\n'):\n input = input + '\\n'\n service.addElement('input', content=input)\n d = iq.send(iq_.jid)\n d.addCallback(self.handleResponse)\n return d\n\nclass Client(Base):\n\n def __init__(self, jid_, *args, **kwargs):\n if '/' in jid_:\n raise Error('Resource should not be in client JID (ex: user@host)')\n super(Client, self).__init__(jid_, *args, **kwargs)\n\nclass Node(Base):\n\n def __init__(self, jid_, *args, **kwargs):\n if not '/' in jid_:\n raise Error('Resource is required in node '\n 'JID (ex: user@host/resource)')\n super(Node, self).__init__(jid_, *args, **kwargs)\n\n def handleRequest(self, xml):\n request = Request()\n request.iq.id = xml.attributes.get('id')\n request.iq.jid = xml.attributes.get('from')\n for e1 in xml.elements():\n if e1.name == 'service':\n request.timeout = e1.attributes.get('timeout')\n request.id = e1.attributes.get('id')\n request.service = e1.attributes.get('type')\n request.user = e1.attributes.get('user')\n request.group = e1.attributes.get('group')\n for e2 in e1.elements():\n if e2.name == 'input':\n request.input = unicode(e2).strip()\n self.handle(request)\n\n def connected(self, xs):\n super(Node, self).connected(xs)\n self.xmlstream.addObserver(\n '/iq/service[@xmlns=\"' + NS_TYRION + '\"]',\n self.handleRequest,\n )\n\n def handle(self, request):\n pass\n\n def respond(self, request, code=0, output='', error=''):\n if '/' not in request.iq.jid:\n raise Error('Resource is required in JID (ex: user@host/resource)')\n iq = xmlstream.IQ(self.xmlstream)\n iq['type'] = 'result'\n iq['to'] = request.iq.jid\n iq['from'] = self.jid.full()\n iq['id'] = request.iq.id\n service = iq.addElement('service', NS_TYRION)\n service.attributes['code'] = unicode(code)\n service.attributes['id'] = request.id\n service.attributes['type'] = request.service\n service.addElement('output', content=output)\n service.addElement('error', content=error)\n d = iq.send(request.iq.jid)\n d.addCallback(self.handleResponse)\n return d\n\n__all__ = ['Base', 'Client', 'Error', 'Node', 'Request', 'Response']\n","sub_path":"python-txtyrion/txtyrion.py","file_name":"txtyrion.py","file_ext":"py","file_size_in_byte":7944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"223114477","text":"T = int(input())\n\nfor i in range(T):\n S = input().split()\n if S[0] == S[1]:\n R = \"De novo!\"\n elif ((S[0] == \"tesoura\" and S[1] in [\"papel\", \"lagarto\"]) or (S[0] == \"papel\" and S[1] in [\"pedra\", \"Spock\"])\n or (S[0] == \"pedra\" and S[1] in [\"lagarto\", \"tesoura\"]) or (S[0] == \"lagarto\" and S[1] in [\"Spock\", \"papel\"])\n or (S[0] == \"Spock\" and S[1] in [\"tesoura\", \"pedra\"])):\n R = \"Bazinga!\"\n else:\n R = \"Raj trapaceou!\"\n print(f\"Caso #{i + 1}: {R}\")","sub_path":"beginner/1828.py","file_name":"1828.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"332217098","text":"import webbrowser\n\n\nclass Movie:\n \"\"\"This class provides a way to store movie related information\"\"\"\n\n def __init__(self, movie_title,\n movie_storyline,\n movie_poster_image,\n movie_trailer_url,\n movie_rating,\n movie_genres,\n movie_actors):\n \"\"\"Create a new instance of a movie.\n\n title: Movie title.\n storyline: Movie storyline\n rating: Rating out of 10.\n poster_image_url: URL of the movie poster.\n trailer_youtube_url: URL of the trailer on YouTube.\n genres: List of genres as strings.\n actors: List of actors as string.\n \"\"\"\n self.title = movie_title\n self.storyline = movie_storyline\n self.rating = movie_rating\n self.poster_image_url = movie_poster_image\n self.trailer_youtube_url = movie_trailer_url\n self.genres = movie_genres\n self.actors = movie_actors\n\n def show_trailer(self):\n webbrowser.open(self.trailer_youtube_url)\n\n def genres_list(self):\n \"\"\"Get a comma separated string of genres.\"\"\"\n list = \"\"\n for genre in self.genres:\n list += genre + \", \"\n\n return list.rstrip(\", \")\n\n def actors_list(self):\n \"\"\"Get a comma separated string of actors.\"\"\"\n list = \"\"\n for actor in self.actors:\n list += actor.name + \", \"\n\n return list.rstrip(\", \")\n\n\nclass Actors:\n \"\"\"This class provides a way to store information about actors\"\"\"\n\n def __init__(self, actor_name, actor_image):\n self.movie_actor = actor_name\n self.movie_actor_image = actor_image\n","sub_path":"movie.py","file_name":"movie.py","file_ext":"py","file_size_in_byte":1745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"467794500","text":"sta_file = '/home/zhouyj/Desktop/California/preprocess/station.dat'\nf=open(sta_file); lines=f.readlines(); f.close()\nout = open('station.dat','w')\n\nfor line in lines:\n net, sta, chn, lon, lat, ele = line.split(',')\n lon = float(lon)\n lat = float(lat)\n out.write('{} {} {}\\n'.format(sta, lat, lon))\nout.close()\n","sub_path":"hypodd/mk_sta.py","file_name":"mk_sta.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"59461046","text":"\"\"\"\n94. Binary Tree Inorder Traversal\n\nGiven a binary tree, return the inorder traversal of its nodes' values.\n\nExample:\n\nInput: [1,null,2,3]\n 1\n \\\n 2\n /\n 3\n\nOutput: [1,3,2]\nFollow up: Recursive solution is trivial, could you do it iteratively?\n\n\n\"\"\"\n\n# recursive solution\n\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n def inorderTraversal(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: List[int]\n \"\"\"\n res = []\n if root == None: return res\n self.helper(root, res)\n return res\n\n def helper(self, root, res):\n if root == None: return\n self.helper(root.left, res)\n res.append(root.val)\n self.helper(root.right, res)\n\n# 2020/04/08, divide and conquer\n'''\nRuntime: 28 ms, faster than 66.10% of Python3 online submissions for Binary Tree Inorder Traversal.\nMemory Usage: 13.7 MB, less than 6.56% of Python3 online submissions for Binary Tree Inorder Traversal.\n'''\n\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n def inorderTraversal(self, root: TreeNode) -> List[int]:\n res = self.div_conq(root)\n return res\n\n def div_conq(self, root):\n if not root: return []\n left = self.div_conq(root.left)\n right = self.div_conq(root.right)\n curr = []\n for val in left: curr.append(val)\n curr.append(root.val)\n for val in right: curr.append(val)\n return curr\n\n# iterative way\n\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n def inorderTraversal(self, root: TreeNode) -> List[int]:\n dummy = TreeNode(None)\n dummy.right = root\n stack = [dummy]\n res = []\n while stack:\n top = stack.pop()\n if top.right:\n top = top.right\n while top:\n stack.append(top)\n top = top.left\n if stack: res.append(stack[-1].val)\n return res\n\n\n# Morris traversal, too hard\n\n'''\nRuntime: 28 ms, faster than 66.29% of Python3 online submissions for Binary Tree Inorder Traversal.\nMemory Usage: 13.9 MB, less than 6.56% of Python3 online submissions for Binary Tree Inorder Traversal.\n'''\n\n\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n def inorderTraversal(self, root: TreeNode) -> List[int]:\n res = []\n while root:\n if not root.left:\n res.append(root.val)\n root = root.right\n else:\n curr = root\n root = root.left\n while root.right and root.right != curr:\n root = root.right\n if not root.right:\n root.right = curr\n root = curr.left\n else:\n root.right = None\n res.append(curr.val)\n root = curr.right\n return res","sub_path":"0094. Binary Tree Inorder Traversal.py","file_name":"0094. Binary Tree Inorder Traversal.py","file_ext":"py","file_size_in_byte":3390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"554651376","text":"import cell\nimport sys\nimport threading\nimport time\n\nif len(sys.argv) < 4:\n\tprint(\"usage: motherCell.py \")\n\n\nc=cell.cell(sys.argv[1],sys.argv[2],sys.argv[3])\n\ndef startCellThread():\n\t\t#lock=Lock()\n\tt=threading.Thread(target=c.cellLoop)\n\tt.daemon = True\t\t\t\t\t\t\t#permit ctr+c and the like\n\tt.start()\n\nc.startCellThread()\nc.startAPIThread()\nwhile(1):\n\ttime.sleep(0.1)\n","sub_path":"2 - Embyronic Development Platform/motherCell.py","file_name":"motherCell.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"108865485","text":"# NOTE: This example uses the next generation Twilio helper library - for more\n# information on how to download and install this version, visit\n# https://www.twilio.com/docs/libraries/python\nimport os\nfrom twilio.rest import Client\n\n# Your Account Sid and Auth Token from twilio.com/user/account\n# To set up environmental variables, see http://twil.io/secure\naccount = os.environ['TWILIO_ACCOUNT_SID']\ntoken = os.environ['TWILIO_AUTH_TOKEN']\nclient = Client(account, token)\n\nservice = client.notify.services(\"ISXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\").update(\n friendly_name=\"Another Awesome Service\",\n facebook_messenger_page_id=\"your_page_id\",\n messaging_service_sid=\"your_twilio_messaging_service_sid\"\n)\n\nprint(service.friendly_name)\n","sub_path":"notifications/rest/services/update-service/update-service.8.x.py","file_name":"update-service.8.x.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"391845484","text":"import random\r\n\r\narr = [3,2,6,3,15,15,5,3,4,7]\r\n\r\n#for x in range(1000000):\r\n # arr.append(random.randint(0,1000000))\r\n\r\n#print(\"zacel\")\r\n\r\ndef sort(arraycek):\r\n if len(arraycek) <= 1:\r\n return arraycek\r\n pivot = arraycek[0]\r\n arr1 = []\r\n arr2 = []\r\n for i in range(1,len(arraycek)):\r\n if arraycek[i] <= pivot:\r\n arr1.append(arraycek[i])\r\n else:\r\n arr2.append(arraycek[i])\r\n arr1 = sort(arr1)\r\n arr2 = sort(arr2)\r\n return arr1 + [pivot] + arr2\r\n\r\n#print(arr)\r\nprint(sort(arr))\r\n#print(\"koncal\")\r\n\r\ndef sort2(arraycek):\r\n if len(arraycek) <= 1:\r\n return arraycek\r\n pivot = arraycek[0]\r\n arr1 = []\r\n arr2 = []\r\n for i in arraycek[1:]:\r\n if i <= pivot:\r\n arr1.append(i)\r\n else:\r\n arr2.append(i)\r\n arr1 = sort(arr1)\r\n arr2 = sort(arr2)\r\n return arr1 + [pivot] + arr2\r\n\r\n\r\nprint(sort2(arr))","sub_path":"programerski/quick_sort_algoritem/quick_sort.py","file_name":"quick_sort.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"506309850","text":"from PIL import Image\n\nclass pictureToStr():\n def __init__(self):\n self.width = 80\n self.height = 80\n self.charList = list(\"$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\\|()1{}[]?-_+~<>i!lI;:,\\\"^`'. \")\n #self.charList = list(\".#\")\n\n self.txt=\"\"\n\n # 将256灰度映射到70个字符上\n #主要就是这个函数的结构，值得深思\n def get_char(self,r, g, b, alpha=256):\n if alpha == 0:\n return ' '\n length = len(self.charList)\n gray = int(0.2126 * r + 0.7152 * g + 0.0722 * b)\n\n unit = (256.0 + 1) / length\n return self.charList[int(gray / unit)]\n\n#重置图片尺寸\n# PIL.Image.NEAREST：最低质量， PIL.Image.BILINEAR：双线性，PIL.Image.BICUBIC：三次样条插值，Image.ANTIALIAS：最高质量\n def convert(self,openFileName):\n #打开图片，返回一个image对象\n image = Image.open(openFileName)\n image = image.resize((self.width, self.height), Image.ANTIALIAS)\n for i in range(self.height):\n for j in range(self.width):\n #遍历每一个像素，使用闭包函数get_char()\n #*image.getpixel((j,i))，*的意思是动态参数（参数个数不定）\n #对应RGBA\n a=image.getpixel((i,j))\n #print(a)\n self.txt += self.get_char(*image.getpixel((j, i)))\n\n self.txt +='\\n'\n\n def saveStr(self,saveFileName):\n with open(saveFileName,\"w\") as f :\n f.write(self.txt)\n\n def start(self):\n openFileName=\"ascii_dora.png\"\n #openFileName = input(\"请输入需要打印图片的名称，并加上后缀名：\")\n print(\"开始将图片转化为字符！\")\n self.convert(openFileName)\n saveFileName=\"123.txt\"\n #saveFileName = input(\"请输入文件保存名称，并加上后缀名:\")\n print(\"正在保存文件到本地！\")\n self.saveStr(saveFileName)\n\np = pictureToStr()\np.start()","sub_path":"untitled/Funny/ascii.py","file_name":"ascii.py","file_ext":"py","file_size_in_byte":2009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"237957863","text":"import os\nimport re\nimport datetime\nimport os.path\nimport time\nimport math\nimport numpy\nfrom os_parameters_define import *\nfrom utility_function import *\nfrom nm_ipmi_raw_to_str import *\nfrom error_messages_define import *\n\n## Function : 0xC8H, Get Platform Power Read\ndef platform_power( mode, domain, power_domain, policy_id):\n # Read Platform power 5 times\n read_count = 1\n power_average = 0\n energy_acc = 0\n for read_count in range(1, 5):\n #IPMI command 0xC8 read currect power status\n c8h_raw = c8h_raw_to_str( mode, domain, power_domain, policy_id)\n ipmisend1 = ipmi_network_bridge_raw_cmd_header + c8h_raw\n DEBUG(ipmisend1)\n resp = os.popen(ipmisend1).read()\n DEBUG(resp)\n sts = ipmi_resp_analyst( resp, OEM )\n # Check if rsp data correct\n if(sts != SUCCESSFUL ):\n return ERROR\n # Calculate Byte5 value, low byte\n val_high = int('0x'+resp[10],0)*16\n val_low = int('0x'+resp[11],0)\n power = val_high + val_low\n # Calculate Byte6 value high byte\n val_high = int('0x'+resp[13],0)*16*16*16\n val_low = int('0x'+resp[14],0)*16*16\n power = power + val_high + val_low\n # Calculate average power\n energy_acc = energy_acc + power\n power_average = energy_acc / read_count\n DEBUG(power_average)\n read_count = read_count + 1\n\n return power_average\n\n## Function : 0xC9H, Get NM capability and platform power Draw Range\ndef get_platform_power_draw_range(c9h_domain, c9h_policy_trigger_type, c9h_policy_type, c9h_power_domain):\n c9h_raw = c9h_raw_to_str(c9h_domain, c9h_policy_trigger_type, c9h_policy_type, c9h_power_domain)\n # Send 0xC9h to know Power Draw Rnage\n ipmisend1 = ipmi_network_bridge_raw_cmd_header + c9h_raw\n DEBUG('send c9h cmd: ' + ipmisend1)\n resp = os.popen(ipmisend1).read()\n DEBUG(resp)\n # Check if rsp data correct\n sts = ipmi_resp_analyst( resp, OEM )\n if(sts != SUCCESSFUL ):\n return ERROR, ERROR, ERROR, ERROR\n # Calculate Max power draw range Byte[7:6] value, total 2 bytes\n max_draw_range = calculate_byte_value(resp, 6, 2)\n DEBUG('max_draw_range = %6d' %max_draw_range)\n # Calculate Min power draw range Byte[9:8] value, total 2 bytes\n min_draw_range = calculate_byte_value(resp, 8, 2)\n DEBUG('min_draw_range = %6d' %min_draw_range)\n if(max_draw_range > 0):\n DEBUG('Get Power Draw Range OK')\n else:\n DEBUG('!!! Get Power Draw Range Fail !!!')\n return ERROR, ERROR, ERROR, ERROR\n # Calculate Min correcction time Byte[13:10] value, total 4 bytes\n min_correction_time = calculate_byte_value(resp, 10, 4)\n DEBUG('minimun correction time = %6d' %min_correction_time)\n # Calculate Max correcction time Byte[17:14] value, total 4 bytes\n max_correction_time = calculate_byte_value(resp, 14, 4)\n DEBUG('maxmun correction time = %6d' %max_correction_time)\n if(min_correction_time > 0):\n DEBUG('Get correction time value OK')\n else:\n DEBUG('!!! Get correction time Fail !!!')\n return ERROR, ERROR, ERROR, ERROR\n\n\n return max_draw_range, min_draw_range, min_correction_time, max_correction_time\n\n## Function : 0xC1H, Set NM Policy\ndef set_nm_power_policy( domain, policy_enable, policy_id, policy_trigger_type, policy_add, aggressive, storage_mode, alert, shutdown, power_domain, limit_value, min_correction_time, trigger_limit, report_period ):\n # Coverter Limit int value to hex value for byte[9:8]- Policy Target Limit\n limit = int_to_hex( limit_value, 2 )\n # Coverter Correction time Setting from int to hex for byte[13:10]- Correction Time Limit\n correction = int_to_hex( min_correction_time, 4 )\n # Trigger limit Byte[15:14]\n trigger_point = int_to_hex( trigger_limit, 2 )\n if(policy_trigger_type == 0 or policy_trigger_type == 4 or policy_trigger_type == 6 ):\n #trigger_limit = c1h_trigger_limit_null\n trigger_limit = c1h_trigger_limit_null\n DEBUG('set_nm_power_policy note: This policy trigger type %2d is no need to input trigger limit point , so force use default settings = 0' %policy_trigger_type)\n tirgger_point = int_to_hex( trigger_limit, 2 )\n elif(policy_trigger_type == 2 or policy_trigger_type == 3):\n DEBUG('set_nm_power_policy: This trigger type %2d will use 0.1sec be single unit for policy trigger point, so policy trigger point =%2d secs ' %(policy_trigger_type, trigger_limit/10))\n trigger_point = int_to_hex( trigger_limit, 2 )\n elif(policy_trigger_type == 1):\n DEBUG('set_nm_power_policy: This trigger type %2d will use 1 Celsiu be single unit for policy trigger point, so policy trigger point =%2d secs ' %(policy_trigger_type, trigger_limit))\n trigger_point = int_to_hex( trigger_limit, 2 )\n else:\n DEBUG('set_nm_power_policy: ERROR !!! No support this policy trigger type %d' %policy_trigger_type)\n return ERROR\n # Byte[17:16] = Statistics Reporting Period in second = 1sec\n if(report_period > 65535):\n DEBUG('set_nm_power_policy: ERROR!!! report_period settings value %6d to hurge !!' %report_period)\n return ERROR\n statistic_period = int_to_hex( report_period, 2 )\n # Send 0xC1h cmd\n c1h_raw = c1h_raw_to_str(domain, policy_enable, policy_id, policy_trigger_type, policy_add, aggressive, storage_mode, alert, shutdown, power_domain, limit, correction, trigger_point, statistic_period )\n ipmisend1 = ipmi_network_bridge_raw_cmd_header + c1h_raw\n DEBUG('send c1h cmd: ' + ipmisend1)\n resp = os.popen(ipmisend1).read()\n DEBUG(resp)\n # Check if rsp data correct\n sts = ipmi_resp_analyst( resp, OEM )\n if(sts != SUCCESSFUL ):\n return ERROR\n\n return SUCCESSFUL\n\n\n## Function : NM_010 Test Process: Verify that power limiting is working correctly in CPU power domain.\ndef NM_010(os_ip_addr, bmc_ip_addr, user, password ):\n # Run load on host system with PTU 100% loading for 20secs\n os.system('ssh howard@'+ os_ip_addr +' -t sudo /home/howard/PTU/PURLEY/ptumon -t 3')\n os.system('nohup ssh howard@'+ os_ip_addr+' -t sudo /home/howard/PTU/PURLEY/ptugen -p 100 -t 30 &')\n time.sleep(5)\n # Read CPU Power via 0xC8h cmd\n power_average_stress = platform_power(global_power_mode , cpu_domain, AC_power_side, 0)\n if(power_average_stress == ERROR):\n print(NM_010.__name__ + ':CPU power reading error!!!')\n return ERROR\n # Power Reading OK\n print(NM_010.__name__ + ':CPU Power Reading OK')\n print(NM_010.__name__ + ':Currently Full Stress Average CPU Power Reading = %6d watts' %power_average_stress)\n # Read Power Draw Range and Correction time via 0xC9h cmd\n max_draw_range, min_draw_range, min_correction_time, max_correction_time = get_platform_power_draw_range( cpu_domain, 0, 1, 0)\n # Check if Power Draw Range data are correct\n if(max_draw_range > (0.8*power_average_stress) and (0.8*power_average_stress) > min_draw_range):\n # Power Draw Range OK\n print(NM_010.__name__ + ':CPU Power Draw Range Setting OK')\n print(NM_010.__name__ + \":Power Capping value = %6d watt\" % (power_average_stress*4/5))\n else:\n print(NM_010.__name__ + ':Error ! CPU Power Draw Rnage Setting Error!!!')\n return ERROR\n # Check if Correction time data are correct\n if(min_correction_time == ERROR):\n print(NM_010.__name__ + ':Error! Correction Time value not correct !!!')\n return ERROR\n # Correction Time OK\n print(NM_010.__name__ + ':Correction Time Value OK')\n print(NM_010.__name__ + \":Set correction time value = %6d msec\" % (min_correction_time))\n # Set Power Capping via 0xC1h cmd in police id #3, target limit = 80% of full stress value, correction time = minimum support correction time\n sts = set_nm_power_policy( c1h_cpu_domain, c1h_policy_enable, 3, c1h_no_policy_trigger, c1h_add_policy, c1h_auto_aggressive, c1h_presistent, c1h_alert_enable, c1h_shutdown_disable, AC_power_side, (power_average_stress*4/5), min_correction_time, c1h_trigger_limit_null, c1h_minimum_report_period )\n if(sts == ERROR):\n print(NM_010.__name__ + ':set_nm_power_policy fail !!!')\n return ERROR\n # Read CPU Power via 0xC8h cmd again to make sure power drop\n time.sleep(3) # Wait for power drop after capping time longer than correction time settings\n power_average_cap = platform_power(global_power_mode , cpu_domain, AC_power_side, 0)\n if(power_average_cap == ERROR):\n print(NM_010.__name__ + ':CPU power reading error!!!')\n return ERROR\n # Below is check the power reading value to make sure if power capping really work. Make sure power drop to capping value.\n elif(power_average_cap > power_average_stress*85/100):\n print(NM_010.__name__ + ':Power limit error!!! CPU power reading still higher than capping value !!!')\n print(NM_010.__name__ + ':Expected limit value = %6d , But currecnt CPU power reading %6d' %((power_average_stress*4/5) , power_average_cap))\n return ERROR\n # Power Reading OK\n print(NM_010.__name__ + ':CPU Power Reading OK')\n print(NM_010.__name__ + ':After NM Capping average platform power reading = %6d watts' %power_average_cap)\n\n return SUCCESSFUL\n\n\n## Below is __Main__\nsts = NM_010(os_ip_addr, bmc_ip_addr, user, password)\nif(sts == SUCCESSFUL):\n print('NM_010 CPU Power Limit Test: Pass')\nelse:\n print('NM_010 CPU Power Limit Test: Fail !!!')\n print('Please enable debug mode for more detail test information')\n\n","sub_path":"NM_010.py","file_name":"NM_010.py","file_ext":"py","file_size_in_byte":9687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"233530730","text":"###################################################################################\n# \n# This study definition:\n# - Takes the paramenter `step`\n# - Selects patients with >= repeat_events_steps$lower[step] of any event type\n# - Reads the vairables out_date_{name}_{repeat_events_steps$upper[step-1]}\n# - Extracts out_date_{name}_{repeat_events_steps$lower[step]} to\n# out_date_{name}_{repeat_events_steps$upper[step]}\n# \n###################################################################################\n\n# Import statements\n\n## Set seed\nimport numpy as np\nnp.random.seed(123456)\n\n## Cohort extractor\nfrom cohortextractor import (\n StudyDefinition,\n patients,\n codelist_from_csv,\n codelist,\n filter_codes_by_category,\n combine_codelists,\n params\n)\n\n## Codelists from codelist.py (which pulls them from the codelist folder)\nfrom codelists import *\n\n# import json module\nimport json\n\n# import clinical_event_date_X function\nfrom common_variables import clinical_event_date_X\n\n# repeat_events_steps\nimport pandas as pd\nrepeat_events_steps = pd.read_csv(\n filepath_or_buffer='./lib/repeat_events_steps.csv',\n dtype=int\n)\n\n# max events for each outcome\nwith open(\"output/repeat_events/max_events.json\") as f:\n max_events = json.load(f)\n\n#study_dates\nwith open(\"output/study_dates.json\") as f:\n study_dates = json.load(f)\n\n# params\nstep=int(params[\"step\"])\n# remember indexing starts at 0 in python, so take an extra -1 from step compared to R\nindex_event=repeat_events_steps[\"upper\"][step-2] \nn_lower=repeat_events_steps[\"lower\"][step-1]\nn_upper=repeat_events_steps[\"upper\"][step-1]\n\n# extract repeat events\ndef out_date_n(\n name, \n index_event=index_event,\n n_lower=n_lower, \n n_upper=n_upper, \n max_events=max_events\n ):\n # redefine n_upper to be the maximum of n_upper and max_events[\"name\"]\n n_upper=min(n_upper, int(max_events[name]))\n # function for creating the out_date_5 variable\n def out_date_index(name, index_event):\n return {\n f\"out_date_{name}_{index_event}\": patients.with_value_from_file(\n f_path=f\"output/repeat_events/out_date_{step}.csv.gz\",\n returning=f\"out_date_{name}_{index_event}\", \n returning_type=\"date\", \n date_format='YYYY-MM-DD',\n )\n }\n variables=out_date_index(name, index_event)\n variables.update(\n clinical_event_date_X(\n name=name,\n start_date=f\"out_date_{name}_{index_event}\",\n end_date=study_dates[\"omicron_date\"],\n n=n_upper,\n index_from=n_lower,\n )\n )\n return variables\n\n# define study definition\nstudy = StudyDefinition(\n\n # Configure the expectations framework\n default_expectations={\n \"date\": {\"earliest\": study_dates[\"earliest_expec\"], \"latest\": \"today\"},\n \"rate\": \"uniform\",\n \"incidence\": 0.5,\n },\n\n population=patients.which_exist_in_file(\n f_path=f\"output/repeat_events/out_date_{step}.csv.gz\"\n ), \n\n **out_date_n(name=\"breathless\"),\n **out_date_n(name=\"asthma_exac\"),\n **out_date_n(name=\"copd_exac\"),\n **out_date_n(name=\"cough\"),\n **out_date_n(name=\"urti\"), \n\n)","sub_path":"analysis/study_definition_repeat_events_x.py","file_name":"study_definition_repeat_events_x.py","file_ext":"py","file_size_in_byte":3198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"115632408","text":"# Linked list class and support classes.\n#\nclass ListNode(object):\n '''Represents an item on a linked list.\n\n There are two attributes, one to hold the value stored in\n the list, the next \"points\" to the next node on the list.\n The last node on the list always has its link set to some\n \"sentinel\" value, such as None in Python or null in Java.\n '''\n def __init__(self, value = None):\n '''Construct a list node.'''\n self.value = value\n self.link = None\n\nclass SinglyLinkedList(object):\n '''Class that defines a relatively simple linked list, with a\n single link leading from a predecessor node to a successor node.\n\n Implements several methods, modeled on those in the standard\n list() class. In addition, implements a \"prepend()\" method that\n illustrates adding an element to the beginning of a list.\n\n Methods of the standard list class that we do not implement include:\n clear(), copy(), extend(), __add__(), __mul__(), reverse(), and sort().\n '''\n def __init__(self, iterable = None):\n '''Creates a new list, initialized to the contents of \n 'iterable'.'''\n self.head = None\n if iterable != None:\n previous = None\n for value in iterable:\n newnode = ListNode(value)\n if previous == None:\n self.head = newnode\n else:\n previous.link = newnode\n previous = newnode\n\n def prepend(self, value):\n '''Add an element to the beginning of the list.\n\n This is the easiest case. All we need to do is:\n 1) create the new list node\n 2) set the link field of the new node to the\n current list head.\n 3) set the current list head to this node.\n '''\n node = ListNode(value)\n node.link = self.head\n self.head = node\n\n def get(self, index):\n '''Get the value stored at a particular index\n of the linked list.\n\n To accomplish this, we have to look through every node\n on the list, counting up as we go. When the count reaches\n the index, we return the value found there.'''\n count = 0\n node = self.head\n while node != None:\n if count == index:\n return node.value\n node = node.link\n count += 1\n raise IndexError('list index out of range')\n\n def count(self, value):\n '''Count the number of list nodes with values equal to the\n 'value'.'''\n count = 0\n node = self.head\n while node != None:\n if node.value == value:\n count += 1\n node = node.link\n return count\n\n def index(self, value):\n '''Return the index of the first list element that matches\n the given 'value'.'''\n count = 0\n node = self.head\n while node != None:\n if node.value == value:\n return count\n count += 1\n node = node.link\n raise ValueError('Value ' + str(value) + ' not found.')\n \n def append(self, value):\n '''Add an element to the end of the linked list.\n\n This method has to be a bit more complex than the\n prepend() method, in that we have to search to the\n end of the list, and insert the new node there.\n '''\n node = self.head\n if node == None: # Empty list\n self.head = ListNode(value)\n else:\n while node.link != None:\n node = node.link\n node.link = ListNode(value)\n\n def insert(self, index, value):\n '''Add an element at a particular index of a linked\n list.\n If the index is greater than the length of the list, the\n new node is just appended to the list.'''\n newnode = ListNode(value)\n node = self.head\n if index == 0:\n newnode.link = self.head\n self.head = newnode\n else:\n count = 1\n while node.link != None and count < index:\n node = node.link\n count += 1\n newnode.link = node.link\n node.link = newnode\n \n def remove(self, value):\n '''Remove the first element that matches 'value' from the list.'''\n previous = None\n node = self.head\n while node != None and node.value != value:\n previous = node\n node = node.link\n if node == None:\n raise ValueError('Value ' + str(value) + ' not found.')\n elif previous != None:\n previous.link = node.link\n else:\n self.head = node.link\n\n def pop(self, index = -1):\n '''Removes an node from the list based on a given\n index. If the index is -1, the last node on list is\n removed. Returns the value associated with the deleted\n node.'''\n count = 0\n previous = None\n node = self.head\n if index >= 0:\n while node != None and count != index:\n count += 1\n previous = node\n node = node.link\n else:\n while node != None and node.link != None:\n previous = node\n node = node.link\n if node == None:\n raise ValueError('Position ' + str(index) + ' not found.')\n \n if previous != None:\n previous.link = node.link\n else:\n self.head = node.link\n\n return node.value\n\n def __bool__(self):\n '''Returns True if the list is non-empty.\n\n This method is called implicitly when a value of our class\n is converted to a boolean value.'''\n return self.head != None\n\n def __len__(self):\n '''Returns the total number of nodes on the list.\n\n This method is called implicitly when the len() function\n is used with values of this class.'''\n count = 0\n node = self.head\n while node != None:\n count += 1 # count the node\n node = node.link # go to the next node\n return count\n\n def __str__(self):\n '''Convert a linked list to a string representation.\n\n This method is called implicitly when the str() function\n is used with values of this class.\n '''\n node = self.head\n r = '['\n while node != None:\n r += str(node.value)\n if node.link != None: # If not at the end,\n r += ', ' # add a comma and space.\n node = node.link\n r += ']'\n return r\n\n def __eq__(self, other):\n '''Compare two linked lists for equality.\n\n This method is called when a SinglyLinkedList is\n compared with '==' or '!='.\n '''\n node1 = self.head\n node2 = other.head\n while node1 != None and node2 != None:\n if node1.value != node2.value:\n return False\n node1 = node1.link\n node2 = node2.link\n return node1 == None and node2 == None\n\n### Testing code ###\n#\n# As of now, the testing code is quite haphazard, but it attempts\n# to exercise all of the features of the class, as well as running a\n# simple performance check to illustrate some of the performance differences\n# compared with the Python list() class.\n#\nif __name__ == \"__main__\":\n print(\"Testing the SinglyLinkedList class.\")\n r = SinglyLinkedList()\n for x in range(1, 10, 2):\n r.prepend(x)\n for v in r:\n print(v)\n","sub_path":"a5/a5ex2b.py","file_name":"a5ex2b.py","file_ext":"py","file_size_in_byte":7560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"543658515","text":"\"\"\"\nMain game engine driver. Handles coordination between the player objects,\nthe view object, and the game object.\n\"\"\"\n\nimport game\n\nclass Driver(object):\n def __init__(self, view, game, player1, player2):\n self.view = view\n self.game = game\n self.player1 = player1\n self.player2 = player2\n\n def get_initial_player(self):\n # Ask each if he wants to start\n if self.player1.choose_first():\n return self.player1\n elif self.player2.choose_first():\n return self.player2\n else:\n # Just pick player 1, then\n return self.player1\n\n def play_game(self):\n # initialize the view\n self.view.start_game(self.game)\n\n # get the initial game state\n state = self.game.initial_state()\n\n # get the initial player\n player = self.get_initial_player()\n self.game.set_initial_player(player)\n\n # determine the game state\n (cont, winner) = self.game.eval(state)\n\n # loop until game stops\n while cont:\n # get the current player and display\n player = self.game.next_player(state)\n self.view.display_state(state, player)\n\n # get the current actions for the state\n actions = state.actions(player)\n\n # get player choice and display\n action = player.choose_action(state, actions)\n self.view.display_action(player, action)\n\n # update game state\n state = state.act(player, action)\n\n # Check if anyone won\n (cont, winner) = self.game.eval(state)\n\n # display game end\n self.view.display_winner(winner, state)\n\n","sub_path":"ai/project/engine.py","file_name":"engine.py","file_ext":"py","file_size_in_byte":1521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"370436287","text":"# Get the PC for all the basic block first instruction\n# and branches\n\nfrom pprint import pprint\nimport re\nfrom collections import OrderedDict\nfrom sys import argv\n\nscript, dump_file, output = argv\nbranch_insts = []\ncount = 0\nstart_count = 0 \nbb_offset_out = open(output, 'w')\n\"\"\"def write_file(bb, offset, count):\n\tstr = ''.join(\"%s %s %d %s\" %(bb, offset, count, '\\n'))\n\tbb_offset_out.write(str)\n\"\"\"\n\ndef write_file():\n\tfor func, bb in bb_offset_out_dict.keys():\n\t\tkey = func, bb\n\t\toffset,count = bb_offset_out_dict[key]\n\t\tstr = ''.join(\"%s %s %s %s %s\" %(func, bb, offset, count, '\\n'))\n\t\tbb_offset_out.write(str)\n\n\n\"\"\"f = open(branch_file)\nfor line in f:\n\tbranch_insts.append(line.rstrip())\nf.close()\n\"\"\"\n\nfunctions = [] \nBB = {}\ncurr_basic_block = ''\nbb_offset_out_dict = OrderedDict()\nbasic_blocks = []\nfunc_boundary = 0\nbr = 0\nbb_offset = 0\nbr_type = ''\n\nf = open(dump_file)\nfor line in f:\n\ttext = line.split()\n\tif text:\n\t\t# Line of the form xxxxxx :\n\t\tif (len(text) == 2):\n\t\t\tif re.match(\"<\\w+>:+(?:\\s|$)\", text[1]):\n\t\t\t\tfunc_boundary = 0\n\t\t\t\tfunc_to_add = re.findall(r'<(.+?)>:',text[1])\n\t\t\tif \"@function\" in text[1]:\n\t\t\t\tfunc_boundary = 1\n\t\t\t\tfunctions.append(func_to_add[0].replace(\"'\",\"\"))\n\n\t\t# Line of the form # BB#x # %BB NAME\n\t\tif len(text) > 1 and re.match('#', text[0]) and \\\n\t\t\t re.match('BB#[0-9]+:', text[1]) and \\\n\t\t\t func_boundary and \\\n\t\t\t len(text) >=4 :\n\n\t\t\t# Store the PC of the previous basic block \n\t\t\tif (curr_basic_block):\n\t\t\t\t#write_file(curr_basic_block, bb_offset, count)\t\n\t\t\t\tif curr_basic_block in bb_offset_out_dict:\n\t\t\t\t\tcurr_off, curr_count = bb_offset_out_dict[curr_basic_block]\n\t\t\t\t\tcount = count + curr_count\n\t\t\t\t\tbb_offset_out_dict[curr_basic_block] = curr_off, count\n\t\t\t\telse:\t\n\t\t\t\t\tbb_offset_out_dict[curr_basic_block] = bb_offset, count\n\n\t\t\t# Change the basic block to current one\t\n\t\t\tcurr_basic_block = func_to_add[0].replace(\":\",\"\"), text[3].replace(\"%\",\"\")\n\t\t\tif not (curr_basic_block in basic_blocks):\n\t\t\t\t# print curr_basic_block\n\t\t\t\tbasic_blocks.append(curr_basic_block)\n\t\t\t\tbb_start = 1\n\t\t\telse:\n\t\t\t\tbb_start = 0\n\n\t\t\tcount = 0\n\t\t\tstart_count = 1\n\n\t\t# Line of the form # LBB#_# # %BB NAME\n\t\telif re.match('.LBB\\d+(_)\\d+:', text[0]) and \\\n\t\t\t\t func_boundary and \\\n\t\t\t\t len(text) >=3 :\n\t\t\t#Store the PC of the previous basic block here\n\t\t\tif (curr_basic_block):\n\t\t\t\t#write_file(curr_basic_block, bb_offset, count)\n\t\t\t\tif curr_basic_block in bb_offset_out_dict:\n\t\t\t\t\tcurr_off, curr_count = bb_offset_out_dict[curr_basic_block]\n\t\t\t\t\tcount = count + curr_count\n\t\t\t\t\tbb_offset_out_dict[curr_basic_block] = curr_off, count\n\t\t\t\telse:\t\n\t\t\t\t\tbb_offset_out_dict[curr_basic_block] = bb_offset, count\n\n\t\t\tcurr_basic_block = func_to_add[0].replace(\":\",\"\"), text[2].replace(\"%\",\"\")\n\t\t\tif curr_basic_block not in basic_blocks:\n\t\t\t\t# print curr_basic_block\n\t\t\t\tbasic_blocks.append(curr_basic_block)\n\t\t\t\tbb_start = 1\n\t\t\telse:\n\t\t\t\tbb_start = 0\n\n\t\t\tcount = 0\n\t\t\tstart_count = 1\n\t\t\n\t\t# Track the instruction in the basic block\n\t\t# It is of the form offset: \tinstruction \t\tllvm_instruction\n\t\telif re.match(\"[0-9A-Fa-f]+:+(?:\\s|$)\", text[0]):\n\t\t\n\t\t\tif(start_count == 1):\n\t\t\t\tif not text[0].startswith('@'):\n\t\t\t\t\tcount = count + 1\t\n\t\t\t# If tracking a valid basic block and it has just started,\n\t\t\t# store the next offset as the basic block's start offset\n\t\t\tif(curr_basic_block) and func_boundary and bb_start:\n\t\t\t\tbb_offset = text[0].replace(\":\", \"\")\n\t\t\t\tbb_start = 0\n\t\t\n\t\t\t# If tracking a valid basic block and 'br' is set,\n\t\t\t# store the offset of the next instruction as the \n\t\t\t# branch instruction's offset\n\t\t\tif(curr_basic_block) and func_boundary and br:\n\t\t\t\t# print \"Writing for branch\"\n\t\t\t\t# str = ''.join(\"%s %s %s\" %(\" \", text[0].replace(\":\", \"\"), '\\n'))\n\t\t\t\t# bb_offset_out.write(str)\n\t\t\t\t\n\t\t\t\t# bb_offset_out_dict[br_type] = text[0].replace(\":\",\"\")\n\t\t\t\tbr = 0\n\t\t\n\t\telif text[0].startswith('b'):\n\t\t\t# print \"BR found\"\n\t\t\tby_type = text[0]\n\t\t\t# bb_offset_out.write(text[0])\n\t\t\tbr = 1\n\nf.close()\n\nwrite_file()\n","sub_path":"scripts/x86_scripts/bb_offset.py","file_name":"bb_offset.py","file_ext":"py","file_size_in_byte":3962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"150981549","text":"import re\r\nimport os\r\npunct = '[.!;_,-]'\r\n\r\ndef num_files(search):\r\n files = [r for r in os.listdir('.') if os.path.isfile(r) and len(re.findall(search, r))>1] \r\n return files\r\n\r\ndef files_only(files):\r\n names = [i.rsplit('.', 1)[0] for i in files]\r\n return names\r\n\r\ndef result(search):\r\n f = num_files(search)\r\n n = files_only(f)\r\n print(len(f), ', '.join(f))\r\n print(', '.join(set(n)))\r\n\r\nresult(punct)\r\n\r\n","sub_path":"python/variant6dz13/arpijandz13.py","file_name":"arpijandz13.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"235339077","text":"import cv2\nimport glob\nimport numpy as np\n\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport time\nimport pickle\n\nfrom random import shuffle\n\nfrom skimage.feature import hog\nfrom sklearn.svm import LinearSVC\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom scipy.ndimage.measurements import label\n\nfrom moviepy.editor import VideoFileClip\nfrom IPython.display import HTML\nfrom lesson_functions import *\nfrom params import *\n\n# =============================================================================\n# =============================================================================\n\n# read in the data sets\ncar_images = glob.glob('data/cars/*/*.png')\nshuffle(car_images)\nnotcar_images = glob.glob('data/notcars/*/*.png')\nshuffle(notcar_images)\ntest_images = glob.glob('test_images/*.jpg')\nshuffle(test_images)\n\n# PRINT INFORMATION ABOUT THE LOADED DATA SETS\nprint(len(car_images), ' images of vehicles')\nprint(len(notcar_images), ' images of non-vehicles')\nprint(len(test_images), ' test images')\n\nexample_img = mpimg.imread(car_images[0])\nprint(example_img.shape, ' image shape')\nprint(example_img.dtype, ' image data type')\n\nexample_img = mpimg.imread(notcar_images[0])\nprint(example_img.shape, ' image shape')\nprint(example_img.dtype, ' image data type')\n\nexample_img = mpimg.imread(test_images[0])\nprint(example_img.shape, ' image shape')\nprint(example_img.dtype, ' image data type')\n\n\nfig, axs = plt.subplots(1,8, figsize=(16, 2))\nfor i in np.arange(4):\n img = mpimg.imread(car_images[np.random.randint(0,len(car_images))])\n axs[i].axis('off')\n axs[i].set_title('car', fontsize=15)\n axs[i].imshow(img)\nfor i in np.arange(4,8):\n img = mpimg.imread(notcar_images[np.random.randint(0,len(notcar_images))])\n axs[i].axis('off')\n axs[i].set_title('not car', fontsize=15)\n axs[i].imshow(img)\nplt.savefig('explore/data_visualization.png', bbox_inches=\"tight\")\n\n# =============================================================================\n# =============================================================================\n\n# LOAD A RANDOM IMAGE FROM THE CAR DATA SET\ni = np.random.randint(0, len(car_images))\nimage = mpimg.imread(car_images[i])\n\n# GET THE HOG FEATURES FOR CHANNEL 0\nch1 = image[:,:,0]\nhot_features, hog_image = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, vis=True, feature_vec=False)\n\nfig = plt.figure(figsize = (15,20))\nplt.subplot(121)\nplt.imshow(image, cmap='gray')\nplt.title('Example Car Image')\nplt.subplot(122)\nplt.imshow(hog_image, cmap='gray')\nplt.title('HOG Visualization')\nplt.savefig('explore/car_hog_visualization.png', bbox_inches=\"tight\")\n\n# =============================================================================\n\n# LOAD A RANDOM IMAGE FROM THE NOTCAR DATA SET\ni = np.random.randint(0, len(notcar_images))\nimage = mpimg.imread(notcar_images[i])\n\n# GET THE HOG FEATURES FOR CHANNEL 0\nch1 = image[:,:,0]\nhot_features, hog_image = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, vis=True, feature_vec=False)\n\nfig = plt.figure(figsize = (15,20))\nplt.subplot(121)\nplt.imshow(image, cmap='gray')\nplt.title('Example Non-Car Image')\nplt.subplot(122)\nplt.imshow(hog_image, cmap='gray')\nplt.title('HOG Visualization')\nplt.savefig('explore/notcar_hog_visualization.png', bbox_inches=\"tight\")\n\n\n# =============================================================================\n# =============================================================================\n\n# LOAD A RANDOM IMAGE FROM THE CAR DATA SET\ni = np.random.randint(0, len(test_images))\nimage = mpimg.imread(test_images[i])\n\nwindows = []\n\nfor p in window_params:\n ystart = p[0]\n ystop = p[1]\n scale = p[2] \n windows.append(slide_window(image,\n x_start_stop=[None, None],\n y_start_stop=[ystart, ystop], #tune the parameters\n xy_window=(int(64*scale), int(64*scale)),\n xy_overlap=(0.5, 0.5)))\n\nwindows = [item for sublist in windows for item in sublist] \n\nwindow_img = draw_boxes(image, windows, random_color=True, thick=2)\n\nfig = plt.figure()\nplt.imshow(window_img);\nmatplotlib.rc('xtick', labelsize=15) \nmatplotlib.rc('ytick', labelsize=15)\nplt.title('Sliding Windows Technique:', fontsize=15);\nplt.savefig('explore/sliding_windows.png', bbox_inches=\"tight\")","sub_path":"explore.py","file_name":"explore.py","file_ext":"py","file_size_in_byte":4399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"157119041","text":"import boilerplatebase\nimport lan\nimport lan.ast_buildingblock as ast_bb\nfrom processing import collect_device as cd\nfrom processing import collect_gen as cg\nfrom processing import collect_id as ci\n\n\nclass KernelArgs(boilerplatebase.BoilerplateBase):\n def __init__(self, ast, file_ast):\n super(KernelArgs, self).__init__(ast, file_ast)\n\n def set_kernel_args(self):\n\n dev_func_id = cd.get_dev_func_id(self.ast)\n\n set_arguments_kernel = ast_bb.EmptyFuncDecl(self._set_arguments_name + dev_func_id)\n self.file_ast.append(set_arguments_kernel)\n arg_body = set_arguments_kernel.compound.statements\n self.__set_arg_misc(arg_body)\n\n kernel_args = cg.get_kernel_args(self.ast)\n\n kernel_id = self._get_kernel_id()\n types = ci.get_types(self.ast)\n err_name = self._err_name\n dict_n_to_dev_ptr = cd.get_dev_ids(self.ast)\n\n name_swap = boilerplatebase.BPNameSwap(self.ast)\n\n lval = lan.Id(err_name)\n op = '|='\n for n in sorted(kernel_args):\n arg_type = types[n]\n if boilerplatebase.is_type_pointer(arg_type):\n rval = self._create_cl_set_kernel_arg(kernel_id, boilerplatebase.count_id(), self._cl_mem_name,\n dict_n_to_dev_ptr[n])\n else:\n n = name_swap.try_swap(n)\n cl_type = arg_type[0]\n if cl_type == 'size_t':\n cl_type = 'unsigned'\n rval = self._create_cl_set_kernel_arg(kernel_id, boilerplatebase.count_id(), cl_type, n)\n arg_body.append(lan.Assignment(lval, rval, op))\n\n err_check = self._err_check_function(self._cl_set_kernel_arg_name)\n arg_body.append(err_check)\n\n def __set_arg_misc(self, arg_body):\n arg_body.append(self._cl_success())\n\n lval = lan.TypeId(['int'], boilerplatebase.count_id())\n rval = lan.Constant(0)\n arg_body.append(lan.Assignment(lval, rval))\n\n def _create_cl_set_kernel_arg(self, kernel_id, cnt_name, ctype, var_ref):\n arglist = [kernel_id,\n lan.Increment(cnt_name, '++'),\n boilerplatebase.func_call_sizeof(ctype),\n boilerplatebase.void_pointer_ref(var_ref)]\n return ast_bb.FuncCall(self._cl_set_kernel_arg_name, arglist)\n","sub_path":"src/framework/host/kernel_args.py","file_name":"kernel_args.py","file_ext":"py","file_size_in_byte":2356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"37065495","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('ui', '0002_auto_20150120_0439'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='userprofile',\n name='phone_confirmed',\n field=models.BooleanField(default=False),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='userprofile',\n name='role',\n field=models.CharField(default='USR', choices=[('USR', 'User'), ('MGR', 'Manager'), ('ADM', 'Administrator')], max_length=3),\n preserve_default=True,\n ),\n ]\n","sub_path":"apps/ui/migrations/0003_auto_20150122_0242.py","file_name":"0003_auto_20150122_0242.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"504669631","text":"import json\nimport math\nimport csv\nimport argparse\nimport os\n\n\nif __name__==\"__main__\":\n\t#create train/valid/test file from AF output\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument(\"--data_path\", type=str, default='Data/WP/WP_Eval/', help='data path for WP_Eval data')\n\tparser.add_argument(\"--output_AF\", type=str, default='AF_output.json', help='The result file from AF')\n\tparser.add_argument(\"--train_output\", type=str, default='AF_ManPlts_train.tsv', help='The training file resulted from AF applied on WP manipulated plots to be used for training the evaluator')\n\tparser.add_argument(\"--valid_output\", type=str, default='AF_ManPlts_valid.tsv', help='The validation file resulted from AF applied on WP manipulated plots to be used for validating the evaluator')\n\tparser.add_argument(\"--test_output\", type=str, default='AF_ManPlts_test.tsv', help='The testing file resulted from AF applied on WP manipulated plots to be used for testing the evaluator')\n\targs = parser.parse_args()\n\n\tfr = open(os.path.join(args.data_path, args.output_AF), 'r')\n\tfw_train = open(os.path.join(args.data_path, 'adv_ManPlts/'+args.train_output), 'w')\n\tfw_valid = open(os.path.join(args.data_path, 'adv_ManPlts'+args.valid_output), 'w')\n\tfw_test = open(os.path.join(args.data_path, 'adv_ManPlts'+args.test_output), 'w')\n\ttsv_train = csv.writer(fw_train, delimiter='\\t', lineterminator='\\n')\n\ttsv_valid = csv.writer(fw_valid, delimiter='\\t', lineterminator='\\n')\n\ttsv_test = csv.writer(fw_test, delimiter='\\t', lineterminator='\\n')\n\n\tlist_ctx = []\n\tlist_gt = []\n\tlist_gens = []\n\tnum_stories= 0\n\tfor line in fr:\n\t\tline = json.loads(line)\n\t\tlist_ctx.append(line['ctx'])\n\t\tlist_gt.append(line['gt_detok'])\n\t\tgens = []\n\t\t#line['assignment'][-1] shows the index of most challenging generated stories based on the applied AF\n\t\tsel_inds = line['assignment'][-1]\n\t\tfor ind in sel_inds:\n\t\t\tgens.append(line['gens'][ind])\n\t\tlist_gens.append(gens)\n\n\tnum_stories=len(list_ctx)\n\tnum_train_stories = math.ceil((60*num_stories)/100)\n\tnum_valid_stories = math.ceil((20*num_stories)/100)\n\n\tstart_ind={'train':0, 'valid':num_train_stories, 'test': num_train_stories + num_valid_stories}\n\tfor mode in ['train', 'valid', 'test']:\n\t\tst_ind = start_ind[globals()['{}'.format(mode)]]\n\t\tglobals()['{}_ctx'.format(mode)] = list_ctx[globals()[st_ind:st_ind+'num_{}_stories'.format(mode)]]\n\t\tglobals()['{}_gt'.format(mode)] = list_gt[globals()[st_ind:st_ind+'num_{}_stories'.format(mode)]]\n\t\tglobals()['{}_gens'.format(mode)] = list_gens[globals()[st_ind:st_ind+'num_{}_stories'.format(mode)]]\n\n\t\n\tfor mode in ['train', 'valid', 'test']:\n\t\tline_ind = 0\n\t\tfor ind, gt in enumerate(globals()['{}_gt'.format(mode)]):\n\t\t\tglobals()['tsv_{}'.format(mode)].writerow([line_ind, 1, line_ind , gt])\n\t\t\tline_ind+=1\n\t\t\tfor gen in globals()['{}_gens'.format(mode)][ind]:\n\t\t\t\tglobals()['tsv_{}'.format(mode)].writerow([line_ind, 0, line_ind, gen])\n\t\t\t\tline_ind+=1\n\t\t\t\n\n\n\n","sub_path":"make_tsv_input_WP.py","file_name":"make_tsv_input_WP.py","file_ext":"py","file_size_in_byte":2919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"523786168","text":"# Copyright 2017 Cloudbase Solutions SRL\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\n\"\"\"\nUnit tests for the Neutron HNV L2 Agent.\n\"\"\"\n\nimport sys\nfrom unittest import mock\n\n\nfrom networking_hyperv.neutron.agent import hnv_neutron_agent as hnv_agent\nfrom networking_hyperv.neutron import constants\nfrom networking_hyperv.tests import base as test_base\n\n\nclass TestHNVAgent(test_base.HyperVBaseTestCase):\n\n _autospec_classes = [\n hnv_agent.neutron_client.NeutronAPIClient,\n ]\n\n @mock.patch.object(hnv_agent.HNVAgent, \"_setup\")\n @mock.patch.object(hnv_agent.HNVAgent, \"_setup_rpc\")\n @mock.patch.object(hnv_agent.HNVAgent, \"_set_agent_state\")\n def _get_agent(self, mock_set_agent_state, mock_setup_rpc, mock_setup):\n return hnv_agent.HNVAgent()\n\n def setUp(self):\n super(TestHNVAgent, self).setUp()\n\n self.agent = self._get_agent()\n\n def test_get_agent_configurations(self):\n self.config(logical_network=mock.sentinel.logical_network,\n group=\"HNV\")\n self.agent._physical_network_mappings = mock.sentinel.mappings\n\n agent_configurations = self.agent._get_agent_configurations()\n\n expected_keys = [\"logical_network\", \"vswitch_mappings\",\n \"devices\", \"l2_population\", \"tunnel_types\",\n \"bridge_mappings\", \"enable_distributed_routing\"]\n self.assertEqual(sorted(expected_keys),\n sorted(agent_configurations.keys()))\n self.assertEqual(mock.sentinel.mappings,\n agent_configurations[\"vswitch_mappings\"])\n self.assertEqual(str(mock.sentinel.logical_network),\n agent_configurations[\"logical_network\"])\n\n @mock.patch.object(hnv_agent.HNVAgent, \"_get_vswitch_name\")\n def test_provision_network(self, mock_get_vswitch_name):\n self.agent._provision_network(mock.sentinel.port_id,\n mock.sentinel.net_uuid,\n mock.sentinel.network_type,\n mock.sentinel.physical_network,\n mock.sentinel.segmentation_id)\n\n mock_get_vswitch_name.assert_called_once_with(\n mock.sentinel.network_type,\n mock.sentinel.physical_network)\n\n vswitch_map = self.agent._network_vswitch_map[mock.sentinel.net_uuid]\n self.assertEqual(mock.sentinel.network_type,\n vswitch_map['network_type'])\n self.assertEqual(mock_get_vswitch_name.return_value,\n vswitch_map['vswitch_name'])\n self.assertEqual(mock.sentinel.segmentation_id,\n vswitch_map['vlan_id'])\n\n @mock.patch.object(hnv_agent.hyperv_base.Layer2Agent, '_port_bound')\n def test_port_bound(self, mock_super_port_bound):\n self.agent._port_bound(\n mock.sentinel.port_id, mock.sentinel.network_id,\n mock.sentinel.network_type, mock.sentinel.physical_network,\n mock.sentinel.segmentation_id, mock.sentinel.port_security_enabled,\n mock.sentinel.set_port_sriov)\n\n mock_super_port_bound.assert_called_once_with(\n mock.sentinel.port_id, mock.sentinel.network_id,\n mock.sentinel.network_type, mock.sentinel.physical_network,\n mock.sentinel.segmentation_id, mock.sentinel.port_security_enabled,\n mock.sentinel.set_port_sriov)\n mock_neutron_client = self.agent._neutron_client\n mock_neutron_client.get_port_profile_id.assert_called_once_with(\n mock.sentinel.port_id)\n self.agent._utils.set_vswitch_port_profile_id.assert_called_once_with(\n switch_port_name=mock.sentinel.port_id,\n profile_id=mock_neutron_client.get_port_profile_id.return_value,\n profile_data=constants.PROFILE_DATA,\n profile_name=constants.PROFILE_NAME,\n net_cfg_instance_id=constants.NET_CFG_INSTANCE_ID,\n cdn_label_id=constants.CDN_LABEL_ID,\n cdn_label_string=constants.CDN_LABEL_STRING,\n vendor_id=constants.VENDOR_ID,\n vendor_name=constants.VENDOR_NAME)\n\n\nclass TestMain(test_base.BaseTestCase):\n\n @mock.patch.object(hnv_agent, 'HNVAgent')\n @mock.patch.object(hnv_agent, 'common_config')\n @mock.patch.object(hnv_agent, 'neutron_config')\n def test_main(self, mock_config, mock_common_config, mock_hnv_agent):\n hnv_agent.main()\n\n mock_config.register_agent_state_opts_helper.assert_called_once_with(\n hnv_agent.CONF)\n mock_common_config.init.assert_called_once_with(sys.argv[1:])\n mock_config.setup_logging.assert_called_once_with()\n mock_hnv_agent.assert_called_once_with()\n mock_hnv_agent.return_value.daemon_loop.assert_called_once_with()\n","sub_path":"networking_hyperv/tests/unit/neutron/agent/test_hnv_neutron_agent.py","file_name":"test_hnv_neutron_agent.py","file_ext":"py","file_size_in_byte":5387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"447516865","text":"\"\"\"Main module starts here\"\"\"\nimport sys\nimport pygame\nfrom settings import Settings\nfrom racoon import Racoon\n\n\ndef run_game():\n \"\"\"Initialize the game and create a screen object\"\"\"\n pygame.init()\n rf_settings = Settings()\n screen = pygame.display.set_mode(\n (rf_settings.screen_width, rf_settings.screen_height))\n pygame.display.set_caption(\"The Racoon Survival\")\n\n # Make a racoon.\n racoon = Racoon(screen)\n\n while True:\n \"\"\"Create The main loop and check for mouse and keyboard events.\"\"\"\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n sys.exit()\n\n # Redraw the screen during each pass through the loop.\n screen.fill(rf_settings.bg_color)\n racoon.blitme()\n # Return The most recently drawn screen\n pygame.display.flip()\n\n\nrun_game()\n\n\"\"\"Main module ends here\"\"\"\n","sub_path":"Chapter 12/12-2. Game Character.py","file_name":"12-2. Game Character.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"277947910","text":"import sys\n\nsys.stdin = open('숫자만들기.txt','r')\n\ndef cal(A,p,s,m,d,k):\n global Min,Max\n if k == N:\n Min = min(Min,A)\n Max = max(Max,A)\n return\n B = number[k]\n if p > 0:\n cal(A+B,p-1,s,m,d,k+1)\n if s > 0:\n cal(A-B,p,s-1,m,d,k+1)\n if m > 0:\n cal(A*B,p,s,m-1,d,k+1)\n if d > 0:\n cal(int(A/B),p,s,m,d-1,k+1)\n \nT = int(input())\nfor tc in range(1,T+1):\n N = int(input())\n board = list(map(int,input().split()))\n number = list(map(int,input().split()))\n start = number[0]\n Min = 10**9\n Max = -10**9\n cal(start,board[0],board[1],board[2],board[3],1)\n print('#{} {}'.format(tc,Max-Min))\n","sub_path":"1113/숫자만들기.py","file_name":"숫자만들기.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"58987388","text":"import requests\n\nfrom P2MT_App.models import FacultyAndStaff\n\nGOOGLE_DISCOVERY_URL = \"https://accounts.google.com/.well-known/openid-configuration\"\n\n# Included for reference. AuthLib handles retrieval of authorization endpoint.\n# See https://realpython.com/flask-google-login/\ndef get_google_provider_cfg():\n google_provider_cfg = requests.get(GOOGLE_DISCOVERY_URL).json()\n authorization_endpoint = google_provider_cfg[\"authorization_endpoint\"]\n token_endpoint = google_provider_cfg[\"token_endpoint\"]\n userinfo_endpoint = google_provider_cfg[\"userinfo_endpoint\"]\n print(\"authorization_endpoint:\", authorization_endpoint)\n print(\"token_endpoint:\", token_endpoint)\n print(\"userinfo_endpoint:\", userinfo_endpoint)\n return google_provider_cfg\n\n\ndef updateProfilePic(user_id, profilePicUrl):\n user = FacultyAndStaff.query.get(user_id)\n print(\"user_id =\", user_id)\n print(\"user =\", user)\n print(\"profilePicUrl =\", profilePicUrl)\n user.google_picture = profilePicUrl\n return\n\n\ndef updateGoogleSub(user_id, googleSubId):\n user = FacultyAndStaff.query.get(user_id)\n user.google_sub = googleSubId\n return\n","sub_path":"P2MT_App/googleAPI/googleLogin.py","file_name":"googleLogin.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"160801","text":"import functools\nimport unittest\n\nimport numpy as np\n\nfrom timemachine.lib import custom_ops\nfrom timemachine.potentials import bonded\n\nimport jax\nfrom jax.config import config; config.update(\"jax_enable_x64\", True)\n\n\nclass ReferenceLangevin():\n\n def __init__(self, dt, ca, cb, cc):\n self.dt = dt\n self.coeff_a = ca\n self.coeff_bs = cb\n self.coeff_cs = cc\n\n def step(self, x_t, v_t, dE_dx):\n noise = np.random.normal(x_t.shape[0], x_t.shape[1])\n # (ytz): * operator isn't defined for sparse grads (resulting from tf.gather ops), hence the tf.multiply\n v_t_1 = self.coeff_a*v_t - np.expand_dims(self.coeff_bs, axis=-1)*dE_dx + np.expand_dims(self.coeff_cs, axis=-1)*noise\n x_t_1 = x_t + v_t_1*self.dt\n return x_t_1, v_t_1\n\n\nclass TestOptimizers(unittest.TestCase):\n\n def setup_system(self):\n\n masses = np.array([1.0, 12.0, 4.0, 3.0])\n x0 = np.array([\n [1.0, 0.5, -0.5],\n [0.2, 0.1, -0.3],\n [0.5, 0.4, 0.3],\n [0.8, 0.3, 0.4],\n ], dtype=np.float64)\n x0.setflags(write=False)\n\n num_atoms = x0.shape[0]\n\n params = np.array([100.0, 2.0, 75.0, 1.81,], np.float64)\n\n bond_idxs = np.array([[0, 1], [1, 2], [0, 3]], dtype=np.int32)\n bond_param_idxs = np.array([[0, 1], [0, 1], [1, 0]], dtype=np.int32)\n\n angle_idxs = np.array([[0,1,2]], dtype=np.int32)\n angle_param_idxs = np.array([[2,3]], dtype=np.int32)\n\n # 1. Reference integration.\n ref_hb = functools.partial(bonded.harmonic_bond,\n bond_idxs=bond_idxs,\n param_idxs=bond_param_idxs,\n box=None\n )\n\n ref_ha = functools.partial(bonded.harmonic_angle,\n angle_idxs=angle_idxs,\n param_idxs=angle_param_idxs,\n box=None\n )\n\n def total_nrg(conf, params):\n return ref_hb(conf, params) + ref_ha(conf, params)\n \n\n test_hb = custom_ops.HarmonicBond_f64(\n bond_idxs,\n bond_param_idxs\n )\n\n test_ha = custom_ops.HarmonicAngle_f64(\n angle_idxs,\n angle_param_idxs\n )\n\n return total_nrg, x0, params, masses, [test_hb, test_ha]\n\n def test_inference_context(self):\n\n ref_total_nrg_fn, x0, params, masses, test_energies = self.setup_system()\n\n num_atoms = len(masses)\n ref_dE_dx_fn = jax.grad(ref_total_nrg_fn, argnums=(0,))\n ref_dE_dx_fn = jax.jit(ref_dE_dx_fn)\n\n dt = 0.002\n ca = 0.95\n cb = np.random.rand(num_atoms)\n cc = np.zeros(num_atoms, dtype=np.float64)\n\n intg = ReferenceLangevin(dt, ca, cb, cc)\n\n # set random velocities\n v0 = np.random.rand(x0.shape[0], x0.shape[1])\n\n def integrate(x_t, v_t, params):\n for _ in range(100):\n x_t, v_t = intg.step(x_t, v_t, ref_dE_dx_fn(x_t, params)[0])\n return x_t, v_t\n\n x_f, v_f = integrate(x0, v0, params)\n\n # 2. Custom Ops Integration\n lo = custom_ops.LangevinOptimizer_f64(\n dt,\n 3,\n ca,\n cb,\n cc\n )\n\n dp_idxs = np.arange(len(params)).astype(dtype=np.int32)\n\n ctxt = custom_ops.InferenceContext_f64(\n test_energies,\n lo,\n params,\n x0,\n v0\n )\n\n for i in range(100):\n ctxt.step()\n\n np.testing.assert_almost_equal(x_f, ctxt.get_x())\n np.testing.assert_almost_equal(v_f, ctxt.get_v())\n\n def test_context(self):\n\n ref_total_nrg_fn, x0, params, masses, test_energies = self.setup_system()\n\n num_atoms = len(masses)\n ref_dE_dx_fn = jax.grad(ref_total_nrg_fn, argnums=(0,))\n ref_dE_dx_fn = jax.jit(ref_dE_dx_fn)\n\n dt = 0.002\n ca = 0.95\n cb = np.random.rand(num_atoms)\n cc = np.zeros(num_atoms, dtype=np.float64)\n\n intg = ReferenceLangevin(dt, ca, cb, cc)\n\n # set random velocities\n v0 = np.random.rand(x0.shape[0], x0.shape[1])\n\n def integrate(x_t, v_t, params):\n for _ in range(100):\n x_t, v_t = intg.step(x_t, v_t, ref_dE_dx_fn(x_t, params)[0])\n return x_t, v_t\n\n x_f, v_f = integrate(x0, v0, params)\n\n grad_fn = jax.jacfwd(integrate, argnums=(2))\n\n dx_dp_f, dv_dp_f = grad_fn(x0, v0, params)\n # jax returns a different shape than the timemachine so we have to transpose\n # asarray is so we can index into them\n dx_dp_f = np.asarray(np.transpose(dx_dp_f, (2,0,1)))\n dv_dp_f = np.asarray(np.transpose(dv_dp_f, (2,0,1)))\n\n # 2. Custom Ops Integration\n lo = custom_ops.LangevinOptimizer_f64(\n dt,\n 3,\n ca,\n cb,\n cc\n )\n\n dp_idxs = np.arange(len(params)).astype(dtype=np.int32)\n\n ctxt = custom_ops.Context_f64(\n test_energies,\n lo,\n params,\n x0,\n v0,\n dp_idxs\n )\n\n for i in range(100):\n ctxt.step()\n\n np.testing.assert_almost_equal(x_f, ctxt.get_x())\n np.testing.assert_almost_equal(v_f, ctxt.get_v())\n\n np.testing.assert_almost_equal(dx_dp_f, ctxt.get_dx_dp())\n np.testing.assert_almost_equal(dv_dp_f, ctxt.get_dv_dp())\n\n # test partial indices\n dp_idxs = np.random.permutation(np.arange(len(params)))[:np.random.randint(len(params))]\n\n ctxt = custom_ops.Context_f64(\n test_energies,\n lo,\n params.astype(np.float64),\n x0.astype(np.float64),\n v0.astype(np.float64),\n dp_idxs.astype(np.int32)\n )\n\n for i in range(100):\n ctxt.step()\n\n np.testing.assert_almost_equal(x_f, ctxt.get_x())\n np.testing.assert_almost_equal(v_f, ctxt.get_v())\n\n np.testing.assert_almost_equal(dx_dp_f[dp_idxs], ctxt.get_dx_dp())\n np.testing.assert_almost_equal(dv_dp_f[dp_idxs], ctxt.get_dv_dp())\n\n # test a second set of integration steps\n\n dt2 = 0.01\n ca2 = 0.5\n cb2 = np.random.rand(num_atoms)\n cc2 = np.zeros(num_atoms, dtype=np.float64)\n\n # re-initialize just for safety\n intg = ReferenceLangevin(dt, ca, cb, cc)\n intg2 = ReferenceLangevin(dt2, ca2, cb2, cc2)\n ctxt = custom_ops.Context_f64(\n test_energies,\n lo,\n params.astype(np.float64),\n x0.astype(np.float64),\n v0.astype(np.float64),\n dp_idxs.astype(np.int32)\n )\n\n # 3. test mixed integration, swap out coefficients mid-way\n def integrate_mixed(x_t, v_t, params):\n for _ in range(25):\n x_t, v_t = intg.step(x_t, v_t, ref_dE_dx_fn(x_t, params)[0])\n for _ in range(25):\n x_t, v_t = intg2.step(x_t, v_t, ref_dE_dx_fn(x_t, params)[0])\n return x_t, v_t\n\n x_f, v_f = integrate_mixed(x0, v0, params)\n grad_fn = jax.jacfwd(integrate_mixed, argnums=(2))\n dx_dp_f, dv_dp_f = grad_fn(x0, v0, params)\n\n for i in range(25):\n ctxt.step()\n\n lo.set_dt(dt2)\n lo.set_coeff_a(ca2)\n lo.set_coeff_b(cb2)\n lo.set_coeff_c(cc2)\n\n for i in range(25):\n ctxt.step()\n\n np.testing.assert_almost_equal(x_f, ctxt.get_x())\n np.testing.assert_almost_equal(v_f, ctxt.get_v())\n\n dx_dp_f, dv_dp_f = grad_fn(x0, v0, params)\n dx_dp_f = np.asarray(np.transpose(dx_dp_f, (2,0,1)))\n dv_dp_f = np.asarray(np.transpose(dv_dp_f, (2,0,1)))\n\n np.testing.assert_almost_equal(dx_dp_f[dp_idxs], ctxt.get_dx_dp())\n np.testing.assert_almost_equal(dv_dp_f[dp_idxs], ctxt.get_dv_dp())\n\n def test_langevin_step(self):\n \"\"\"\n Test that we correctly step through a couple of langevin steps.\n \"\"\"\n\n # num_params = 23\n # num_atoms = 68\n\n num_params = 5\n num_atoms = 4\n\n coeff_a = 0.95\n coeff_bs = np.random.rand(num_atoms)\n coeff_cs = np.random.rand(num_atoms)\n\n for _ in range(10):\n\n dE_dx = np.random.rand(num_atoms, 3)\n d2E_dx2 = np.random.rand(num_atoms*3, num_atoms*3)\n d2E_dx2 = np.tril(d2E_dx2) + np.tril(d2E_dx2, -1).T\n d2E_dx2 = np.reshape(d2E_dx2, (num_atoms, 3, num_atoms, 3))\n d2E_dxdp = np.random.rand(num_params, num_atoms, 3)\n\n dt = 1e-3\n\n lo = custom_ops.LangevinOptimizer_f64(\n dt,\n 3,\n coeff_a,\n coeff_bs,\n coeff_cs\n )\n\n x_t = np.random.rand(num_atoms, 3)\n v_t = np.random.rand(num_atoms, 3)\n\n dx_dp_t = np.random.rand(num_params, num_atoms, 3)\n dv_dp_t = np.random.rand(num_params, num_atoms, 3)\n\n noise = np.random.rand(num_atoms, 3)\n\n ref_v_t = coeff_a*v_t - np.expand_dims(coeff_bs, axis=-1)*dE_dx + np.expand_dims(coeff_cs, axis=-1)*noise\n ref_x_t = x_t + ref_v_t*dt\n\n hmp = np.einsum('ijkl,mkl->mij', d2E_dx2, dx_dp_t) + d2E_dxdp\n ref_dv_dp_t = coeff_a*dv_dp_t - np.reshape(coeff_bs, (1, -1, 1))*hmp\n ref_dx_dp_t = dx_dp_t + dt*ref_dv_dp_t\n\n lo.step(\n dE_dx,\n d2E_dx2,\n d2E_dxdp,\n x_t,\n v_t,\n dx_dp_t,\n dv_dp_t,\n noise\n )\n\n np.testing.assert_almost_equal(ref_v_t, v_t)\n np.testing.assert_almost_equal(ref_x_t, x_t)\n\n np.testing.assert_almost_equal(ref_dv_dp_t, dv_dp_t)\n np.testing.assert_almost_equal(ref_dx_dp_t, dx_dp_t)\n","sub_path":"timemachine/cpp/tests/test_integrator.py","file_name":"test_integrator.py","file_ext":"py","file_size_in_byte":9795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"386808948","text":"# Copyright (c) 2013 Mirantis, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport ply.lex as lex\nfrom yaql.exceptions import YaqlLexicalException\n\nkeywords = {\n 'true': 'TRUE',\n 'false': 'FALSE',\n 'null': 'NULL'\n}\n\nkeywords_to_val = {\n 'TRUE': True,\n 'FALSE': False,\n 'NULL': None\n}\n\ntokens = [\n 'SYMBOL',\n 'STRING',\n 'QUOTED_STRING',\n 'NUMBER',\n 'FUNC',\n 'GE',\n 'LE',\n 'NE',\n 'FILTER',\n 'TUPLE',\n 'OR',\n 'AND',\n 'NOT',\n 'IS',\n 'IN',\n 'DOLLAR'\n] + list(keywords.values())\n\nliterals = \"+-*/.()]><=,\"\n\nt_ignore = ' \\t'\n\n\nt_GE = '>='\nt_LE = '<='\nt_NE = '!='\n\nt_TUPLE = '=>'\n\n\ndef t_SYMBOL(t):\n \"\"\"\n \\\\b\\\\w+\\\\:\\\\w+\\\\b\n \"\"\"\n return t\n\n\ndef t_DOLLAR(t):\n \"\"\"\n \\\\$\\\\w*\n \"\"\"\n return t\n\n\ndef t_AND(t):\n \"\"\"\n \\\\band\\\\b\n \"\"\"\n return t\n\n\ndef t_OR(t):\n \"\"\"\n \\\\bor\\\\b\n \"\"\"\n return t\n\n\ndef t_NOT(t):\n \"\"\"\n \\\\bnot\\\\b\n \"\"\"\n return t\n\n\ndef t_IS(t):\n \"\"\"\n \\\\bis\\\\b\n \"\"\"\n return t\n\n\ndef t_IN(t):\n \"\"\"\n \\\\bin\\\\b\n \"\"\"\n return t\n\n\ndef t_NUMBER(t):\n \"\"\"\n \\\\b\\\\d+(\\\\.?\\\\d+)?\\\\b\n \"\"\"\n if '.' in t.value:\n t.value = float(t.value)\n else:\n t.value = int(t.value)\n return t\n\n\ndef t_FUNC(t):\n \"\"\"\n \\\\b\\\\w+\\\\(\n \"\"\"\n t.value = t.value[:-1]\n return t\n\n# (?<=\\\\w)\\\\[|(?<=\\\\])\\\\[|(?<=\\\\$)\\\\[\n\n\ndef t_FILTER(t):\n \"\"\"\n (?46-49\n\n # ---------- Initialization -------------------\n E = np.asarray([0,0,0])\n dE = np.asarray([0,0,0])\n delta_F = np.zeros((3,3)) # x[N, N-1, N-2],\n dE_list = np.zeros((3,3))\n E_list = np.zeros((3,3)) # y[N, N-1, N-2],\n # z[N, N-1, N-2] \n sensor_readings = np.zeros((6,max_num_it))\n x_c_list = np.zeros((3,max_num_it))\n x_list = np.zeros((3,max_num_it))\n x_d_list = np.zeros((3,max_num_it))\n F_d_list = np.zeros((3,max_num_it))\n \n for i in range(max_num_it):\n #for plotting\n sensor_readings[:,i]=np.append(robot.endpoint_effort()['force'],robot.endpoint_effort()['torque'])\n x_d_list[:,i] = x_d\n x_c_list[:,i] = x_d + E\n x_list[:,i] = robot.endpoint_pose()['position']\n F_d_list[:,i] = F_d\n \n \n if i ==2000: \n x_d = np.asarray([0.6,0,0.48]) #move 20 cm in the x direction \n \n \n if i%3==0:\n update_temp_lists(delta_F,dE_list,x_d,F_d,E)\n dE = compute_compliant_position(omega[0],omega[1],omega[2],delta_F, dE_list,T) #update compliant position\n E = E+dE\n update_dE_list(dE_list,dE,E)\n \n \n position_control_loop(x_d,E,goal_ori) #control x_c = x_d + E\n \n \n #printing and plotting\n if i%100==0:\n print(i,', pos:',robot.endpoint_pose()['position'],' F_e: ', np.array([delta_F[2][0]]))#' force measured: ',robot.endpoint_effort()['force'])\n plot_result(sensor_readings,x_c_list,x_list,F_d_list,x_d_list)\n\n\n","sub_path":"panda_simulator_examples/scripts/Old_scripts/Admittance_faulty2018.py","file_name":"Admittance_faulty2018.py","file_ext":"py","file_size_in_byte":8584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"355201448","text":"from __future__ import unicode_literals\nfrom django.http import HttpResponse\nfrom django.shortcuts import render, redirect, render_to_response\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.template import RequestContext\nfrom django.contrib import auth\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django import forms\nfrom django.http import HttpResponseRedirect\nfrom django.contrib.auth import authenticate, login, logout\nfrom vilina.models import UserProfile, UserTask\nfrom django.core.mail import send_mail\n\n@csrf_exempt\ndef index(request):\n context = RequestContext(request)\n context_dict = {'boldmessage': \"I am a bold font from the context\"}\n if request.user is not None and request.user.is_active:\n context_dict = {'task': UserTask.objects.get(user = request.user).take_task(), 'time': UserTask.objects.get(user = request.user).take_date()}\n if request.method == 'POST':\n user = request.user\n if user is not None and request.user.is_active:\n UserTask.objects.get(user = request.user).make_task(request.POST['task'])\n UserTask.objects.get(user = request.user).make_date(request.POST['date'])\n else:\n return redirect('../login')\n return render_to_response('site/index.html', context_dict, context) #need to redirect to new page\n\n@csrf_exempt\ndef log(request):\n context = RequestContext(request)\n if request.method == 'POST':\n a = request.POST['login']\n b = request.POST['password']\n user = authenticate(username=a, password=b)\n if user is not None:\n if user.is_active:\n login(request, user)\n return redirect('../index')\n else:\n context_dict = {'boldmessage': \"Данный аккаунт неактивен\"}\n return render_to_response('site/login.html', context_dict, context)\n else:\n context_dict = {'boldmessage': \"Неверный логин или пароль\"}\n return render_to_response('site/login.html', context_dict, context)\n context_dict = {'boldmessage': \"\"}\n return render_to_response('site/login.html', context_dict, context)\n\n@csrf_exempt\ndef logout_view(request):\n auth.logout(request)\n return render(request,'site/index.html')\n\n","sub_path":"vilina/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"98842206","text":"# ***** BEGIN LICENSE BLOCK *****\n# Version: MPL 1.1/GPL 2.0/LGPL 2.1\n#\n# The contents of this file are subject to the Mozilla Public License Version\n# 1.1 (the \"License\"); you may not use this file except in compliance with\n# the License. You may obtain a copy of the License at\n# http://www.mozilla.org/MPL/\n#\n# Software distributed under the License is distributed on an \"AS IS\" basis,\n# WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License\n# for the specific language governing rights and limitations under the\n# License.\n#\n# The Original Code is l10n django site.\n#\n# The Initial Developer of the Original Code is\n# Mozilla Foundation.\n# Portions created by the Initial Developer are Copyright (C) 2010\n# the Initial Developer. All Rights Reserved.\n#\n# Contributor(s):\n# Peter Bengtsson \n#\n# Alternatively, the contents of this file may be used under the terms of\n# either the GNU General Public License Version 2 or later (the \"GPL\"), or\n# the GNU Lesser General Public License Version 2.1 or later (the \"LGPL\"),\n# in which case the provisions of the GPL or the LGPL are applicable instead\n# of those above. If you wish to allow use of your version of this file only\n# under the terms of either the GPL or the LGPL, and not to allow others to\n# use your version of this file under the terms of the MPL, indicate your\n# decision by deleting the provisions above and replace them with the notice\n# and other provisions required by the GPL or the LGPL. If you do not delete\n# the provisions above, a recipient may use your version of this file under\n# the terms of any one of the MPL, the GPL or the LGPL.\n#\n# ***** END LICENSE BLOCK *****\n\nfrom mock import patch\nfrom django.test import TestCase\nfrom django.core.urlresolvers import reverse\nfrom django.conf import settings\nfrom django.http import Http404\nfrom django.test.client import RequestFactory\nfrom django.core.urlresolvers import Resolver404\nfrom nose.tools import eq_, ok_\nfrom life.models import Locale\nfrom commons.tests.mixins import EmbedsTestCaseMixin\n\n\nclass HomepageTestCase(TestCase, EmbedsTestCaseMixin):\n\n def setUp(self):\n super(HomepageTestCase, self).setUp()\n\n # SESSION_COOKIE_SECURE has to be True for tests to work.\n # The reason this might be switched off is if you have set it to False\n # in your settings/local.py so you can use http://localhost:8000/\n settings.SESSION_COOKIE_SECURE = True\n\n # side-step whatever authentication backend has been set up otherwise\n # we might end up trying to go online for some sort of LDAP lookup\n self._original_auth_backends = settings.AUTHENTICATION_BACKENDS\n settings.AUTHENTICATION_BACKENDS = (\n 'django.contrib.auth.backends.ModelBackend',\n )\n\n # make sure this is always set to something and iff the mocking of\n # django_arecibo was to fail at least it won't send anything to a real\n # arecibo server\n settings.ARECIBO_SERVER_URL = 'http://arecibo/'\n\n def tearDown(self):\n super(HomepageTestCase, self).tearDown()\n settings.AUTHENTICATION_BACKENDS = self._original_auth_backends\n\n def test_handler404(self):\n # import the root urlconf like django does when it starts up\n root_urlconf = __import__(settings.ROOT_URLCONF,\n globals(), locals(), ['urls'], -1)\n # ...so that we can access the 'handler404' defined in there\n par, end = root_urlconf.handler404.rsplit('.', 1)\n # ...which is an importable reference to the real handler404 function\n views = __import__(par, globals(), locals(), [end], -1)\n # ...and finally we the handler404 function at hand\n handler404 = getattr(views, end)\n\n # to call this view function we need a mock request object\n fake_request = RequestFactory().request(**{'wsgi.input': None})\n\n # the reason for first causing an exception to be raised is because\n # the handler404 function is only called by django when an exception\n # has been raised which means sys.exc_info() is something.\n try:\n raise Http404(\"something bad\")\n except Http404:\n # mock the django_arecibo wrapper so it doesn't actually\n # call out on the network\n with patch('django_arecibo.wrapper') as m:\n # do this inside a frame that has a sys.exc_info()\n response = handler404(fake_request)\n eq_(response.status_code, 404)\n ok_('Page not found' in response.content)\n eq_(m.post.call_count, 1)\n\n try:\n # raise an error but this time withou a message\n raise Http404\n except Http404:\n with patch('django_arecibo.wrapper') as m:\n response = handler404(fake_request)\n eq_(response.status_code, 404)\n ok_('Page not found' in response.content)\n eq_(m.post.call_count, 1)\n\n try:\n # Resolver404 is a subclass of Http404 that is raised by django\n # when it can't match a URL to a view\n raise Resolver404(\"/never/heard/of/\")\n except Resolver404:\n with patch('django_arecibo.wrapper') as m:\n response = handler404(fake_request)\n eq_(response.status_code, 404)\n ok_('Page not found' in response.content)\n eq_(m.post.call_count, 0)\n\n def test_handler500(self):\n # import the root urlconf like django does when it starts up\n root_urlconf = __import__(settings.ROOT_URLCONF,\n globals(), locals(), ['urls'], -1)\n # ...so that we can access the 'handler500' defined in there\n par, end = root_urlconf.handler500.rsplit('.', 1)\n # ...which is an importable reference to the real handler500 function\n views = __import__(par, globals(), locals(), [end], -1)\n # ...and finally we the handler500 function at hand\n handler500 = getattr(views, end)\n\n # to make a mock call to the django view functions you need a request\n fake_request = RequestFactory().request(**{'wsgi.input': None})\n\n # the reason for first causing an exception to be raised is because\n # the handler500 function is only called by django when an exception\n # has been raised which means sys.exc_info() is something.\n try:\n raise NameError(\"sloppy code!\")\n except NameError:\n # do this inside a frame that has a sys.exc_info()\n with patch('django_arecibo.wrapper') as m:\n response = handler500(fake_request)\n eq_(response.status_code, 500)\n ok_('Oops' in response.content)\n eq_(m.post.call_count, 1)\n\n def test_secure_session_cookies(self):\n \"\"\"secure session cookies should always be 'secure' and 'httponly'\"\"\"\n url = reverse('accounts.views.login')\n # run it as a mocked AJAX request because that's how elmo does it\n response = self.client.post(url,\n {'username': 'peterbe',\n 'password': 'secret'},\n **{'X-Requested-With': 'XMLHttpRequest'})\n eq_(response.status_code, 200)\n ok_('class=\"errorlist\"' in response.content)\n\n from django.contrib.auth.models import User\n user = User.objects.create(username='peterbe',\n first_name='Peter')\n user.set_password('secret')\n user.save()\n\n response = self.client.post(url,\n {'username': 'peterbe',\n 'password': 'secret',\n 'next': '/foo'},\n **{'X-Requested-With': 'XMLHttpRequest'})\n # even though it's\n eq_(response.status_code, 302)\n ok_(response['Location'].endswith('/foo'))\n\n # if this fails it's because settings.SESSION_COOKIE_SECURE\n # isn't true\n assert settings.SESSION_COOKIE_SECURE\n ok_(self.client.cookies['sessionid']['secure'])\n\n # if this fails it's because settings.SESSION_COOKIE_HTTPONLY\n # isn't true\n assert settings.SESSION_COOKIE_HTTPONLY\n ok_(self.client.cookies['sessionid']['httponly'])\n\n # should now be logged in\n url = reverse('accounts.views.user_json')\n response = self.client.get(url)\n eq_(response.status_code, 200)\n # \"Hi Peter\" or something like that\n ok_('Peter' in response.content)\n\n def test_index_page(self):\n \"\"\"load the current homepage index view\"\"\"\n url = reverse('homepage.views.index')\n response = self.client.get(url)\n eq_(response.status_code, 200)\n self.assert_all_embeds(response.content)\n\n def test_teams_page(self):\n \"\"\"check that the teams page renders correctly\"\"\"\n Locale.objects.create(\n code='en-US',\n name='English',\n )\n Locale.objects.create(\n code='sv-SE',\n name='Swedish',\n )\n\n url = reverse('homepage.views.teams')\n response = self.client.get(url)\n eq_(response.status_code, 200)\n self.assert_all_embeds(response.content)\n ok_(-1 < response.content.find('English')\n < response.content.find('Swedish'))\n\n def test_team_page(self):\n \"\"\"test a team (aka. locale) page\"\"\"\n Locale.objects.create(\n code='sv-SE',\n name='Swedish',\n )\n url = reverse('homepage.views.locale_team', args=['xxx'])\n response = self.client.get(url)\n # XXX would love for this to be a 404 instead (peterbe)\n eq_(response.status_code, 302)\n url = reverse('homepage.views.locale_team', args=['sv-SE'])\n response = self.client.get(url)\n eq_(response.status_code, 200)\n self.assert_all_embeds(response.content)\n ok_('Swedish' in response.content)\n","sub_path":"apps/homepage/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":9927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"157585320","text":"from time import perf_counter\nfrom fastapi import Request\n\n\nclass RequestTimingMiddleware:\n async def __call__(self, request: Request, call_next):\n start_time = perf_counter()\n # process the request and get the response \n response = await call_next(request)\n process_time = perf_counter() - start_time\n response.headers[\"X-Process-Time\"] = f'{process_time:0.4f} sec'\n return response\n","sub_path":"app/utils/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"412124105","text":"from random import choice\n\narray = [\"pierre\", \"papier\", \"ciseaux\"]\nrep = None\n\nuser = None\n\nwhile rep != \"n\":\n choose = input(\"Faites vos jeux (pierre, papier, ciseaux) : \")\n\n if choose in array:\n # user = input(\"Faites vos jeux : \")\n print(\"vous : \" + str(choose))\n\n ordi = choice(list(array))\n print(\"ordi : \" + str(ordi))\n\n if ordi == \"pierre\" and user == \"ciseaux\" or ordi == \"ciseaux\" and user == \"papier\" or ordi == \"papier\" and user == \"pierre\":\n print(\" - perdu\")\n rep = input(\"Voulez vous recommencé n/o : \")\n\n if ordi == user:\n print(\"égalité\")\n\n # elif ordi == \"pierre\" and user == \"papier\" or ordi == \"ciseaux\" and user == \"pierre\" or ordi == \"papier\" and user == \"ciseaux\":\n # print(\"gagné\")\n # rep = input(\"Voulez vous recommencé n/o : \")\n\n else:\n print(\"égalité\")\n rep = input(\"Voulez vous recommencé n/o : \")\n\nprint(\"fin de jeu\")\n\n\"\"\"\narray = {\n \"pierre\": \"👊\",\n \"papier\": \"🤚\",\n \"ciseaux\": \"✌\",\n}\n\n\nuser = input(\"Faites vos jeux : \")\n\nwhile user not in array.keys():\n user = input(\"Faites vos jeux : \")\nprint(\"vous : \" + str(array[user])) # récupére valeur \n\nordi = choice(list(array.keys()))\nprint(\"ordi : \" + str(array[ordi]))\n\n\nif ordi == \"pierre\" and user == \"ciseaux\" or ordi == \"ciseaux\" and user == \"papier\" or ordi == \"papier\" and user == \"pierre\": \n print(\" - perdu\")\n\nelif ordi == \"pierre\" and user == \"papier\" or ordi == \"ciseaux\" and user == \"pierre\" or ordi == \"papier\" and user == \"ciseaux\": \n print(\"gagné\")\n\nelse : \n print(\"égalité\")\n \"\"\"","sub_path":"Chifoumi/chifoumi.py","file_name":"chifoumi.py","file_ext":"py","file_size_in_byte":1655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"13600659","text":"import cherrypy\nimport main\nfrom main import addWebsite, testWebsite, cost, curwords, xs, theta, y\n\nhtml = \"\"\"\n \n \n
\n \n \n \n
\n
\n \n \n
\n \n \"\"\"\n\nclass Test(object):\n @cherrypy.expose\n def index(self):\n return html\n\n @cherrypy.expose\n def add(self,site=\"http://www.archlinux.org\"):\n addWebsite(site,1)\n return html + (\"Cost: \" + str(cost()) + \", amount of words: \" + str(len(curwords)))\n\n @cherrypy.expose\n def addNeg(self,site):\n addWebsite(site,0)\n return html + (\"Cost: \" + str(cost()) + \", amount of words: \" + str(len(curwords)))\n\n @cherrypy.expose\n def test(self,site):\n odds = testWebsite(site)\n return html + \"Odds that you'll enjoy \" + site + \": \" + str(100*odds) + \"%\"\ncherrypy.quickstart(Test())\n","sub_path":"bootstrap/public/webtest.py","file_name":"webtest.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"586957154","text":"import sys\n\ndef mergeSort(someList):\n if len(someList)>1:\n midpoint = len(someList)//2\n leftSide = someList[:midpoint]\n rightSide = someList[midpoint:]\n\n mergeSort(leftSide)\n mergeSort(rightSide)\n\n i =0\n j =0\n k =0\n\n while i < len(leftSide) and j < len(rightSide):\n if leftSide[i] < rightSide[j]:\n someList[k] = leftSide[i]\n i = i+1\n else:\n someList[k] = rightSide[j]\n j = j +1\n k = k +1\n\n while i 0):\n trees = int(input())\n fruits = [int(i) for i in input().split()]\n mergeSort(fruits)\n tasks = 1\n if len(fruits) == 1:\n result += str(tasks) + \"\\n\"\n continue\n for i in range(1,len(fruits)):\n if fruits[i] == fruits[i-1]:\n continue\n else:\n tasks += 1\n\n result += str(tasks) + \"\\n\"\n n -=1\n\nprint(result)\n","sub_path":"Practice005/B.py","file_name":"B.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"581865543","text":"#\n# hw9pr2.py\n# Name: Brian Richardson & Courtney Gutherie\n#\nclass Board:\n def __init__(self, width, height):\n self.data = [[' ' for _ in range(width)] for _ in range(height)]\n self.width = width\n self.height = height\n self.winScore = 4\n def __repr__(self):\n out = ''\n for y,row in enumerate(self.data):\n for x,e in enumerate(self.data[y]):\n out += ('|%s' % e)\n out+='|\\n'\n for _ in range(len(out.split('\\n')[0])):\n out+='-'\n out += '\\n'\n for i in range(self.width):\n out += (\" %s\" % (i % 10))\n return out\n\n def addMove(self,col,player):\n '''\n >>> board = Board(7, 6)\n >>> board.addMove(0, 'X')\n >>> board.addMove(0, 'O')\n >>> board.addMove(0, 'X')\n >>> board.addMove(3, 'O')\n >>> board.addMove(4, 'O') # Cheat and let O go again!\n >>> board.addMove(5, 'O')\n >>> board.addMove(6, 'O')\n >>> board\n | | | | | | | |\n | | | | | | | |\n | | | | | | | |\n |X| | | | | | |\n |O| | | | | | |\n |X| | |O|O|O|O|\n ---------------\n 0 1 2 3 4 5 6\n '''\n assert(player == 'X' or player == 'O')\n for y in reversed(range(self.height)):\n if (self.data[y][col] == ' '):\n self.data[y][col] = player\n break\n\n def clear(self):\n self.data = [[' ' for _ in range(self.width)] for _ in range(self.height)]\n\n def setBoard(self,moveString):\n nextChar = 'X'\n for colString in moveString:\n col = int(colString)\n if 0 <= col < self.width:\n self.addMove(col,nextChar)\n if nextChar == 'X':\n nextChar = 'O'\n else:\n nextChar = 'X'\n\n def isMoveLegal(self,col):\n '''\n >>> board = Board(2, 2)\n >>> print(board)\n | | |\n | | |\n -----\n 0 1\n >>> board.addMove(0, 'X')\n >>> board.addMove(0, 'O')\n >>> print(board)\n |O| |\n |X| |\n -----\n 0 1\n >>> board.isMoveLegal(-1)\n False\n >>> board.isMoveLegal(0)\n False\n >>> board.isMoveLegal(1)\n True\n >>> board.isMoveLegal(2)\n False\n '''\n if col >= self.width or col < 0:\n return False\n if (self.data[0][col] == ' '):\n return True\n else:\n return False\n\n def isFull(self):\n '''\n >>> board = Board(2, 2)\n >>> board.isFull()\n False\n >>> board.setBoard('0011')\n >>> print(board)\n |O|O|\n |X|X|\n -----\n 0 1\n >>> board.isFull()\n True\n '''\n for y,row in enumerate(self.data):\n for x,e in enumerate(row):\n if e == ' ':\n return False\n return True\n def deleteMove(self,col):\n '''\n >>> board = Board(2, 2)\n >>> board.setBoard('0011')\n >>> board.deleteMove(1)\n >>> board.deleteMove(1)\n >>> board.deleteMove(1)\n >>> board.deleteMove(0)\n >>> print(board)\n | | |\n |X| |\n -----\n 0 1\n '''\n for y in range(self.height):\n if (self.data[y][col] != ' '):\n self.data[y][col] = ' '\n break\n def isWinFor(self,player):\n '''\n >>> board = Board(7, 6)\n >>> board.setBoard('00102030')\n >>> print(board)\n | | | | | | | |\n |O| | | | | | |\n |O| | | | | | |\n |O| | | | | | |\n |O| | | | | | |\n |X|X|X|X| | | |\n ---------------\n 0 1 2 3 4 5 6\n >>> board.isWinFor('X')\n True\n >>> board.isWinFor('O')\n True\n >>> board.clear()\n >>> board.setBoard('23344545515')\n >>> board\n | | | | | | | |\n | | | | | | | |\n | | | | | |X| |\n | | | | |X|X| |\n | | | |X|X|O| |\n | |O|X|O|O|O| |\n ---------------\n 0 1 2 3 4 5 6\n >>> board.isWinFor('X') # diagonal\n True\n >>> board.isWinFor('O')\n False\n '''\n for y,row in enumerate(self.data):\n for x,e in enumerate(row):\n score = 0\n #Horizontal Check\n if x <= self.width - self.winScore:\n for i in range(self.winScore):\n if (self.data[y][x + i] == player):\n score += 1\n if score >= 4:\n return True\n score = 0\n #Vertial check\n if y <= self.height - self.winScore:\n for i in range(self.winScore):\n if self.data[y + i][x] == player:\n score += 1\n if score >= 4:\n return True\n score = 0\n #Diag (up to down)\n if y <= self.height - self.winScore and x <= self.width - self.winScore:\n for i in range(self.winScore):\n if self.data[y+i][x+i] == player:\n score += 1\n if score >= 4:\n return True\n score = 0\n #Diag (down to up)\n if y >= self.winScore - 1 and x <= self.width - self.winScore:\n for i in range(self.winScore):\n if self.data[y-i][x+i] == player:\n score += 1\n if score >= 4:\n return True\n score = 0\n return False\n\n def hostGame(self):\n winner = 'Tie'\n turn = 'X'\n print(\"Welcome to Connect Four\\n\")\n while (self.isFull() == False):\n print(self)\n col = int(input(\"%s's choice: \" % turn))\n if (self.isMoveLegal(col)):\n self.addMove(col,turn)\n if self.isWinFor(turn):\n winner = turn\n break\n if turn == 'X':\n turn = 'O'\n else:\n turn = 'X'\n else:\n print(\"Not valid move\")\n print(self)\n if winner == 'Tie':\n print(\"Tie Game\")\n else:\n print(\"%s Won!\" % winner)\n\ndef connect4():\n b = Board(7,6)\n b.hostGame()\nimport doctest\ndoctest.testmod()\n","sub_path":"hw09/hw9pr2.py","file_name":"hw9pr2.py","file_ext":"py","file_size_in_byte":6561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"509992977","text":" # coding=UTF-8\nimport string\nimport io\nimport StringIO\n\nclass ZdaniaIter:\n\tdef __init__(self, strumien):\n\t\tself.strumien = strumien\n\tlitery = string.letters + 'ąęśćńóżźł' + 'ĄĘŚĆŃÓŻŹŁ'\n\t\n\tdef czytaj_slowo(self):\n\t\tslowo = ''\n\t\twhile True:\n\t\t\tznak = self.strumien.read(1)\n\t\t\tif not znak: break\n\t\t\tif znak in ZdaniaIter.litery: \n\t\t\t\tslowo = slowo + znak\n\t\t\telse: \n\t\t\t\tif znak == '-':\n\t\t\t\t\tn_znak = self.strumien.read(1)\n\t\t\t\t\tif n_znak != '\\n':\n\t\t\t\t\t\tself.strumien.seek(-1, io.SEEK_CUR)\n\t\t\t\t\t\tif slowo != '':\n\t\t\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tif slowo != '':\n\t\t\t\t\t\tbreak\n\t\treturn slowo\n\t\n\tdef __iter__(self):\n\t\treturn self\n\t\n\tdef next(self):\n\t\tznak = self.strumien.read(1)\n\t\tif not znak: \n\t\t\tself.strumien.close()\n\t\t\traise StopIteration \n\t\tself.strumien.seek(-1, io.SEEK_CUR)\n\t\treturn self.czytaj_slowo()\n\n\ndef slowa(nazwa_pliku):\n\tslowa = []\n\tfor slowo in ZdaniaIter(nazwa_pliku):\n\t\tslowa.append(slowo)\n\treturn slowa\n\ndef statystyki(nazwa_pliku):\n\tslownik = {}\n\tfor slowo in ZdaniaIter(nazwa_pliku):\n\t\ttry:\n\t\t\tslownik[len(slowo)] = slownik[len(slowo)] + 1\n\t\texcept KeyError:\n\t\t\tslownik[len(slowo)] = 1\n\tstat = ''\n\tfor key, value in slownik.iteritems():\n\t\tstat = stat + 'słów o długości %i bylo: %i\\n' %(key, value)\n\treturn stat\n\n\n","sub_path":"z4.4.py","file_name":"z4.4.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"236036533","text":"import logging\nfrom datetime import datetime\nfrom typing import Callable\n\nfrom quant_candles.controllers import ExchangeS3, use_s3\nfrom quant_candles.models import Symbol\n\nfrom .base import BybitS3Mixin\n\nlogger = logging.getLogger(__name__)\n\n\ndef bybit_trades(\n symbol: Symbol,\n timestamp_from: datetime,\n timestamp_to: datetime,\n on_data_frame: Callable,\n retry: bool = False,\n verbose: bool = False,\n) -> None:\n \"\"\"Get Bybit trades.\"\"\"\n max_timestamp_to = use_s3()\n if timestamp_to > max_timestamp_to:\n logger.info(\n \"Bybit no longer provides a paginated REST API for trades, \"\n f\"{timestamp_to} modified to {max_timestamp_to}\"\n )\n timestamp_to = max_timestamp_to\n if timestamp_from < max_timestamp_to:\n BybitTradesS3(\n symbol,\n timestamp_from=timestamp_from,\n timestamp_to=timestamp_to,\n on_data_frame=on_data_frame,\n retry=retry,\n verbose=verbose,\n ).main()\n\n\nclass BybitTradesS3(BybitS3Mixin, ExchangeS3):\n \"\"\"Bybit trades S3.\"\"\"\n","sub_path":"quant_candles/exchanges/bybit/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"460795551","text":"import os\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\n\n\n# GPU validation\ndef validation(args, val_batch, models, ep):\n\n # Writing accuracy\n if not os.path.exists('./acc'):\n os.makedirs('./acc')\n \n acc_path = './acc/lr{}_ps{}_acc'.format(args.learning_rate, args.patch_size)\n if os.path.isfile(acc_path):\n file_acc = open(acc_path, 'a')\n file_acc.write('\\n\\n------------------------------------------------------------\\n\\n')\n else:\n file_acc = open(acc_path, 'w')\n\n ac = 0.0\n total = 0\n dsc_total = 0\n sum_out = 0\n ac_zero = 0.0\n thsd = 0.5\n print('\\nValidation start...')\n resnet_s = models[0]\n resnet_b = models[1]\n classifier = models[2]\n\n for img,_,p in val_batch:\n mid = int(args.patch_size/2)\n\n x1 = Variable(img[:,:,:mid]).cuda()\n x2 = Variable(img[:,:,mid:]).cuda()\n\n out_s = resnet_s.forward(x1)\n out_b = resnet_b.forward(x2)\n\n concat_out = torch.cat([out_s,out_b],dim=1)\n\n out = classifier.forward(concat_out)\n out = nn.Sigmoid()(out)\n out = out.view(args.batch_size,-1)\n\n target = Variable(_).float().cuda()\n target = target.view(args.batch_size,-1)\n\n # for accuracy calc\n for b in range(args.batch_size):\n\n out_val = out.data.cpu().numpy()[b,0]\n target_val = target.data.cpu().numpy()[b,0]\n \n if target_val == 1:\n dsc_total += 1\n if out_val > thsd:\n ac += 1\n else:\n if out_val <= thsd:\n ac_zero += 1\n if out_val > thsd:\n dsc_total += 1\n sum_out += out_val\n \n\n print('predict avg = {}, dsc = {}%, accuracy = {}%'.format(sum_out/(args.batch_size*len(val_batch)), (2*ac)/dsc_total*100, (ac+ac_zero)/(args.batch_size*len(val_batch))*100))\n file_acc.write('Epoch {} : predict avg = {}, dsc = {}%, accuracy = {}%\\n'.format(ep, sum_out/(args.batch_size*len(val_batch)), (2*ac)/dsc_total*100, (ac+ac_zero)/(args.batch_size*len(val_batch))*100))\n print('Validation done.\\n') \n","sub_path":"validation.py","file_name":"validation.py","file_ext":"py","file_size_in_byte":2166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"451191504","text":"from django.shortcuts import render_to_response\nfrom django.http import HttpResponseBadRequest\nfrom django.template import RequestContext\nfrom django.contrib.auth.models import AnonymousUser\nfrom landing.models import BetaRegisteredUser\nfrom django.conf import settings\nfrom datetime import datetime\nfrom giviu.views import home as main_home\n\n\ndef home(request):\n\n data = {}\n if request.method == 'POST':\n if 'name' not in request.POST or 'email' not in request.POST:\n return HttpResponseBadRequest()\n try:\n real_ip = request.META['HTTP_X_FORWARDED_FOR']\n except KeyError:\n real_ip = request.META['REMOTE_ADDR']\n\n email = request.POST['email']\n name = request.POST['name']\n try:\n user = BetaRegisteredUser.objects.get(email__exact=email)\n data['user_already_exist'] = True\n return render_to_response('landing.html',\n data,\n context_instance=RequestContext(request))\n except BetaRegisteredUser.DoesNotExist:\n user = BetaRegisteredUser(\n email=email,\n name=name,\n ip=real_ip,\n comment=request.POST.get('comment', None)\n )\n user.save()\n data['user_created'] = True\n\n return render_to_response('landing.html',\n data,\n context_instance=RequestContext(request))\n","sub_path":"giviu/landing/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"89863250","text":"import os\nfrom whoosh.index import create_in\nfrom whoosh.fields import Schema, TEXT, ID\nfrom whoosh.analysis import StemmingAnalyzer\nimport sys\n\n#indexdir = full corpus, no stemming no stopwords\n#indexdir1 = full corpus, stemming, stopwords\n#indexdir2 = small corpus, stemming, stopwords, for testing\n\ndef createSearchableData(root):\n '''\n Schema definition: title(name of file), path(as ID), content(indexed\n but not stored),textdata (stored text content)\n '''\n schema = Schema(title=TEXT(stored=True),path=ID(stored=True),content=TEXT(analyzer=StemmingAnalyzer()),textdata=TEXT(stored=True))\n if not os.path.exists(\"indexdir2\"):\n os.mkdir(\"indexdir2\")\n\n # Creating a index writer to add document as per schema\n ix = create_in(\"indexdir2\",schema)\n writer = ix.writer()\n\n filepaths = [os.path.join(root,i) for i in os.listdir(root)]\n for path in filepaths:\n fp = open(path,'r')\n print(path)\n text = fp.read()\n writer.add_document(title=path.split(\"/\")[1], path=path,content=text,textdata=text)\n fp.close()\n writer.commit()\n\nroot = \"corpus2\"\ncreateSearchableData(root)\n","sub_path":"Tugas 2 Preprocessing Term Document Incidence Index/Tugas Stop-Word Removal Stemming Whoosh/indexing.py","file_name":"indexing.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"11960668","text":"def pair_of_parentheses(n_pairs):\n\tif n_pairs == 1:\n\t\treturn set(['()'])\n\tpairs = pair_of_parentheses(n_pairs-1)\n\tnew_pairs = set()\n\tfor p in pairs:\n\t\tnew_pairs.add(f'(){p}')\n\t\tfor i, ch in enumerate(p):\n\t\t\tif ch == '(':\n\t\t\t\tnew_pairs.add(p[:i+1]+'()'+p[i+1:])\t\t\n\treturn new_pairs\n\n\ndef pair_of_parentheses_fast(output, remained_left, remained_right, substring=\"\"):\n\tif remained_left == remained_right == 0:\n\t\toutput.append(substring)\n\t\treturn\n\t\n\tif remained_left > 0:\n\t\tpair_of_parentheses_fast(output, remained_left-1, remained_right, substring + '(')\n\tif remained_right > remained_left:\n\t\tpair_of_parentheses_fast(output, remained_left, remained_right-1, substring + ')')\n\nif __name__ == '__main__':\n\timport sys, time\n\tpairs = int(sys.argv[1])\n\ts1 = time.time()\n\tout1 = pair_of_parentheses(pairs)\n\te1 = time.time()\t\n\tout2 = []\n\ts2 = time.time()\n\tpair_of_parentheses_fast(out2, pairs, pairs)\n\te2 = time.time()\n\t# print(out1)\n\t# print(out2)\t\t\n\tprint(len(out1), len(out2))\n\tprint(f'method-1 time: {e1-s1:.2f}s, method-2 time:{e2-s2:.2f}s')","sub_path":"CtCI/Chapter 8/8_9PariOfParentheses.py","file_name":"8_9PariOfParentheses.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"650149385","text":"from django.shortcuts import render, redirect\n\n\ndef cart(request):\n return render(request, 'cart/cart.html')\n\n\ndef add_to_cart(request, item_id):\n quantity = int(request.POST['quantity'])\n redirect_url = request.POST['redirect_url']\n my_cart = request.session.get('cart', {})\n\n if item_id in list(my_cart.keys()):\n my_cart[item_id] += quantity\n else:\n my_cart[item_id] = quantity\n\n request.session['cart'] = my_cart\n print(request.session['cart'])\n return redirect(cart)\n\n\ndef remove_from_cart(request, item_id):\n redirect_url = request.POST['redirect_url']\n cart = request.session.get('cart', {})\n cart.pop(item_id)\n request.session['cart'] = cart\n return redirect(redirect_url)\n","sub_path":"cart/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"453711801","text":"import contextlib\nimport logging\nimport urllib\n\nfrom hat import juggler\nfrom hat import util\nfrom hat.util import aio\n\nmlog = logging.getLogger(__name__)\n\n\nasync def create(conf, path, components):\n \"\"\"Create ui for monitoring and controlling components\n\n Args:\n conf (hat.json.Data): configuration defined by\n ``hat://orchestrator.yaml#/definitions/ui``\n path (pathlib.Path): web ui directory path\n components (List[hat.orchestrator.component.Component]): components\n\n Returns:\n WebServer\n\n \"\"\"\n srv = WebServer()\n srv._async_group = aio.Group()\n srv._components = components\n srv._change_registry = util.CallbackRegistry()\n srv._cb_handles = [\n component.register_change_cb(srv._change_registry.notify)\n for component in components]\n addr = urllib.parse.urlparse(conf['address'])\n juggler_srv = await juggler.listen(\n f'ws://{addr.hostname}:{addr.port}/ws',\n lambda conn: srv._async_group.spawn(srv._conn_loop, conn),\n static_path=path)\n srv._async_group.spawn(aio.call_on_cancel, juggler_srv.async_close)\n return srv\n\n\nclass WebServer:\n \"\"\"WebServer\n\n For creating new instance of this class see :func:`create`\n\n \"\"\"\n\n @property\n def closed(self):\n \"\"\"asyncio.Future: closed future\"\"\"\n return self._async_group.closed\n\n async def async_close(self):\n \"\"\"Close web server and all active connections\"\"\"\n for handle in self._cb_handles:\n handle.cancel()\n await self._async_group.async_close()\n\n def _set_data(self, conn):\n data = {\n 'components': [{\n 'id': idx,\n 'name': component.name,\n 'delay': component.delay,\n 'revive': component.revive,\n 'status': component.status.name\n } for idx, component in enumerate(self._components)]}\n with contextlib.suppress(juggler.ConnectionClosedError):\n conn.set_local_data(data)\n\n async def _conn_loop(self, conn):\n try:\n self._set_data(conn)\n\n with self._change_registry.register(lambda: self._set_data(conn)):\n while True:\n msg = await conn.receive()\n fn = {'start': self._process_msg_start,\n 'stop': self._process_msg_stop,\n 'revive': self._process_msg_revive}.get(msg['type'])\n if not fn:\n raise Exception('received invalid message type')\n fn(msg['payload'])\n except juggler.ConnectionClosedError:\n pass\n finally:\n await conn.async_close()\n\n def _process_msg_start(self, payload):\n component = self._components[payload['id']]\n component.start()\n\n def _process_msg_stop(self, payload):\n component = self._components[payload['id']]\n component.stop()\n\n def _process_msg_revive(self, payload):\n component = self._components[payload['id']]\n component.set_revive(bool(payload['value']))\n","sub_path":"src_py/hat/orchestrator/ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":3106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"14722455","text":"import os, sys\nimport yaml\n\n\ndef createVolume(name, path):\n\n return {'name': name, 'path': path}\n\n\ndef createEnv(name, secret):\n\n return {name: {'from_secret': secret}}\n\n\ndef createStep(\n name,\n image,\n volumes=None,\n settings=None,\n environment=None,\n commands=None\n ):\n\n PAYLOAD = {}\n PAYLOAD['name'] = name\n PAYLOAD['image'] = image\n if volumes:\n PAYLOAD['volumes'] = volumes\n if settings:\n PAYLOAD['settings'] = settings\n if environment:\n PAYLOAD['environment'] = environment\n if commands:\n PAYLOAD['commands'] = commands\n\n return PAYLOAD\n\n\ndef addBackPush(files, commands):\n\n if len(files) > 0:\n input_files = \"\"\n for filename in files:\n input_files += \"'{}' \".format(filename)\n\n commands.append('TMPLOC=`mktemp -d`')\n commands.append(('mv {} \"$TMPLOC\"').format(input_files))\n\n commands.append('git checkout --orphan gin-proc')\n commands.append('git reset --hard')\n commands.append('mkdir \"$DRONE_BUILD_NUMBER\"')\n\n input_files = ''\n for filename in files:\n input_files += '\"$TMPLOC\"/\"{}\" '.format(filename)\n\n commands.append('mv {} \"$DRONE_BUILD_NUMBER\"/'.format(\n input_files))\n\n commands.append('git add \"$DRONE_BUILD_NUMBER\"/')\n commands.append('git commit \"$DRONE_BUILD_NUMBER\"/ -m \"Back-Push\"')\n commands.append('git push origin gin-proc')\n commands.append('git annex sync --content')\n\n return commands\n\n\ndef addAnnex(files, commands):\n\n if len(files) > 0:\n input_files = ''\n for filename in files:\n input_files += '{} '.format(filename)\n\n commands.append(\"git annex get {}\".format(input_files))\n else:\n commands.append(\"git annex sync --content\")\n\n return commands\n\n\ndef createWorkflow(workflow, commands, user_commands=None):\n\n if workflow == 'snakemake':\n commands.append('snakemake')\n commands.append('echo \".snakemake/\" > .gitignore')\n\n else:\n for command in user_commands[:]:\n commands.append(command)\n\n return commands\n\n\ndef integrateVolumes(volumes):\n\n PAYLOAD = []\n\n for volume in volumes:\n PAYLOAD.append(\n {\n 'name': volume[0],\n 'host': {'path': volume[1]}\n }\n )\n\n return PAYLOAD\n\n\ndef generateConfig(\n workflow,\n commands,\n annexFiles,\n backPushFiles,\n notifications\n ):\n\n try:\n\n print(\"Writing fresh configuration.\")\n\n data = {\n 'kind': 'pipeline',\n 'name': 'gin-proc',\n\n 'clone': {\n 'disable': 'true'\n },\n\n 'steps': [\n createStep(\n name='execute',\n image='falconshock/gin-proc:micro-test',\n volumes=[createVolume('repo', '/repo')],\n environment=[createEnv('SSH_KEY', 'DRONE_PRIVATE_SSH_KEY')],\n commands=[\n 'eval $(ssh-agent -s)',\n 'mkdir -p /root/.ssh && echo \"$SSH_KEY\" > /root/.ssh/id_rsa && chmod 0600 /root/.ssh/id_rsa',\n 'mkdir -p /etc/ssh',\n 'echo \"StrictHostKeyChecking no\" >> /etc/ssh/ssh_config',\n 'ssh-add /root/.ssh/id_rsa',\n 'git clone \"$DRONE_GIT_SSH_URL\"',\n 'cd \"$DRONE_REPO_NAME\"/',\n 'pip3 install -r requirements.txt',\n 'git annex init \"$DRONE_REPO_NAME\"-drone-annexe',\n ]\n ),\n ],\n 'volumes': integrateVolumes([\n ('cache', '/gin-proc/cache'),\n ('repo', '/gin-proc/repo')\n ]),\n 'trigger': {\n 'branch': ['master'],\n 'event': ['push'],\n 'status': ['success']\n }\n }\n\n data['steps'][0]['commands'] = modifyConfigFiles(\n workflow=workflow,\n annexFiles=annexFiles,\n backPushFiles=backPushFiles,\n commands=commands,\n data=data['steps'][0]['commands']\n )\n\n data['steps'] = addNotifications(\n notifications=notifications,\n data=data['steps']\n )\n\n print(\"Configuration complete.\")\n\n return data\n\n except Exception as e:\n print(e)\n print(\"Exiting...\")\n sys.exit()\n\n\ndef modifyConfigFiles(\n data,\n annexFiles,\n workflow,\n backPushFiles,\n commands\n ):\n\n try:\n print(\"Adding user's files.\")\n\n data = addAnnex(annexFiles, data)\n\n data = createWorkflow(workflow, data, commands)\n\n data = addBackPush(backPushFiles, data)\n\n return data\n\n except Exception as e:\n print(e)\n print(\"Exiting...\")\n sys.exit()\n\n\ndef addNotifications(notifications, data):\n\n notifications = [n for n in notifications if n['value']]\n\n for notification in notifications:\n if notification['name'] == 'Slack':\n\n print(\"Adding notification: {}\".format(notification['name']))\n\n data.append(\n createStep(\n name='notification',\n image='plugins/slack',\n settings={\n 'webhook': 'https://hooks.slack.com/services/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'\n }\n )\n )\n\n return data\n\n\ndef ensureConfig(\n config_path,\n commands,\n workflow='snakemake',\n annexFiles=[],\n backPushFiles=[],\n notifications=[]\n ):\n\n try:\n if not os.path.exists(os.path.join(config_path, '.drone.yml')):\n print(\"CI Configuration file not found in repo.\")\n\n with open(os.path.join(config_path, '.drone.yml'), 'w') \\\n as new_config:\n\n yaml.dump(\n generateConfig(\n workflow=workflow,\n commands=commands,\n annexFiles=annexFiles,\n backPushFiles=backPushFiles,\n notifications=notifications\n ),\n new_config,\n default_flow_style=False)\n\n return True\n\n else:\n print(\"CI Configuration exists in repo.\")\n\n config = []\n\n with open(os.path.join(config_path, '.drone.yml'), 'r') as stream:\n config = yaml.load(stream, Loader=yaml.FullLoader)\n\n with open(os.path.join(config_path, '.drone.yml'), 'w') as stream:\n config['steps'][0]['commands'] = modifyConfigFiles(\n workflow=workflow,\n annexFiles=annexFiles,\n backPushFiles=backPushFiles,\n commands=commands,\n data=config['steps'][0]['commands'][:8]\n )\n\n config['steps'] = addNotifications(\n notifications=notifications,\n data=config['steps']\n )\n\n yaml.dump(\n config,\n stream,\n default_flow_style=False)\n\n return True\n\n except Exception as e:\n print(str(e))","sub_path":"back-end/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":7482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"103056784","text":"n=int(input()) # 단어의 개수\n#-----------\n'''\n맨 앞에서부터 pop: catrtee일 때 c,a 다음에 tree의 t가 빠지지 않고 cat의 t가 빠지면 no가 뜸\n동시에 t t인경우 어떤 t를 뺄지 정하는것 어떻게 고려? -> 이러한 상황 있을 때 저장하고 돌아가\n---\naa bab babaa-> 오류 -> copy하는데에서 문제 있었음\n---\naba aab abaaba->오류-> 모든 같게 되는 지점을 고려\n---\n메모리초과 -> index로 접근 -> 시간초과 -> pypy3 -> 메모리초과 -->저장 하면 안될듯? 갈아엎자 ㅠ \n---\ndp 이용 -> 단어 1에서 i만큼 단어 2에서 j만큼 썼을 때 가능한가 살펴본다. word1길이xword2길이만큼의 배열 필요\n'''\ndef find(word1,word2,make_word,check,num):\n ## 단어 길이\n w1len=len(word1)\n w2len=len(word2)\n \n for i in range(w1len+1):\n for j in range(w2len+1):\n\n #0: 불가능 1:가능\n if i==j==0:\n check[i][j]=1 # 초기지점 설정\n\n if i>0 and make_word[i+j-1]==word1[i-1] and check[i-1][j]: #만들고자 하는 단어의 현재 필요한 철자가 현재 word1 인덱스 자리에 있음, 이전 지점에 가능했었음\n check[i][j]=1 #check[i-1][j] \n\n if j>0 and make_word[i+j-1]==word2[j-1] and check[i][j-1]: # t t 인경우 체크해야하니까 elif아니고 if임 \n check[i][j]=1 #check[i][j-1]\n\n if check[w1len][w2len]: #1 이면\n print('Data set %d: yes' %num)\n\n else: # 0 이면\n print('Data set %d: no' %num)\n\n\n#---------실행\nfor i in range(n): #n회 반복\n word1, word2, make_word=map(str,input().split()) #단어 1, 단어 2, 만들 단어 \n\n check=[[0 for col in range(len(word2)+1)] for row in range(len(word1)+1)] #배열[word1 최대길이+1][word2 최길이+1] (+1은 0부터 필요하므로) \n #print(check)\n find(word1,word2,make_word,check,i+1)\n","sub_path":"week6/9177_단어 섞기_minji-o-j.py","file_name":"9177_단어 섞기_minji-o-j.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"183833734","text":"from django.contrib import admin\n\nclass ReadOnlyAdminMixIn(admin.ModelAdmin):\n def get_readonly_fields(self, request, obj=None):\n if self.declared_fieldsets:\n return flatten_fieldsets(self.declared_fieldsets)\n else:\n readonly_fields = set(\n [field.name for field in self.opts.local_fields] +\n [field.name for field in self.opts.local_many_to_many]\n ) - set(getattr(self, 'exclude_from_read_only', []))\n\n return list(readonly_fields)","sub_path":"polling_stations/apps/data_finder/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"164792568","text":"import logging\nimport httplib\nimport socket\nimport sys\nimport StringIO\nimport gzip\nimport fnmatch\nimport time\nimport gevent\n\nfrom .direct import Proxy\nfrom .. import networking\nfrom .. import ip_substitution\n\nLOGGER = logging.getLogger(__name__)\n\nSO_MARK = 36\n\nNO_DIRECT_PROXY_HOSTS = {\n 'hulu.com',\n '*.hulu.com',\n 'huluim.com',\n '*.huluim.com',\n 'netflix.com',\n '*.netflix.com',\n 'skype.com',\n '*.skype.com',\n 'radiotime.com',\n '*.radiotime.com'\n 'myfreecams.com',\n '*.myfreecams.com'\n}\n\nWHITE_LIST = {\n 'www.google.com',\n 'google.com',\n 'www.google.com.hk',\n 'google.com.hk',\n}\n\n\ndef is_no_direct_host(client_host):\n return any(fnmatch.fnmatch(client_host, host) for host in NO_DIRECT_PROXY_HOSTS)\n\n\nclass HttpTryProxy(Proxy):\n\n host_black_list = {} # host => count\n host_slow_list = set()\n host_slow_detection_enabled = True\n dst_black_list = {} # (ip, port) => count\n\n def __init__(self):\n super(HttpTryProxy, self).__init__()\n self.flags.add('DIRECT')\n\n def do_forward(self, client):\n try:\n self.try_direct(client)\n if client.host and self.host_black_list.get(client.host, 0) > 3:\n LOGGER.error('remove host %s from blacklist' % client.host)\n del self.host_black_list[client.host]\n except NotHttp:\n raise\n except:\n if client.host and client.host not in WHITE_LIST:\n self.host_black_list[client.host] = self.host_black_list.get(client.host, 0) + 1\n if self.host_black_list[client.host] == 4:\n LOGGER.error('blacklist host %s' % client.host)\n raise\n\n def try_direct(self, client):\n is_payload_complete = recv_and_parse_request(client)\n # check host\n if client.host in self.host_slow_list:\n client.fall_back(reason='%s was too slow to direct connect' % client.host, silently=True)\n failed_count = self.host_black_list.get(client.host, 0)\n if failed_count > 3 and (failed_count % 10) != 0:\n client.fall_back(reason='%s tried before' % client.host, silently=True)\n if is_no_direct_host(client.host):\n client.fall_back(reason='%s blacklisted for direct access' % client.host, silently=True)\n # check ip\n ip_substitution.substitute_ip(client, self.dst_black_list)\n failed_count = self.dst_black_list.get((client.dst_ip, client.dst_port), 0)\n if failed_count and (failed_count % 10) != 0:\n client.fall_back(reason='%s:%s tried before' % (client.dst_ip, client.dst_port), silently=True)\n # start trying\n try:\n upstream_sock = client.create_tcp_socket(client.dst_ip, client.dst_port, 3)\n except:\n if LOGGER.isEnabledFor(logging.DEBUG):\n LOGGER.debug('[%s] http try connect failed' % (repr(client)), exc_info=1)\n client.fall_back(reason='http try connect failed')\n return\n client.headers['Host'] = client.host\n request_data = self.before_send_request(client, upstream_sock, is_payload_complete)\n request_data += '%s %s HTTP/1.1\\r\\n' % (client.method, client.path)\n request_data += ''.join('%s: %s\\r\\n' % (k, v) for k, v in client.headers.items())\n request_data += '\\r\\n'\n try:\n upstream_sock.sendall(request_data + client.payload)\n except:\n client.fall_back(reason='send to upstream failed: %s' % sys.exc_info()[1])\n self.after_send_request(client, upstream_sock)\n if is_payload_complete:\n http_response = try_receive_response_header(\n client, upstream_sock, rejects_error=('GET' == client.method))\n response = self.detect_slow_host(client, http_response)\n try:\n response = self.process_response(client, upstream_sock, response, http_response)\n except client.ProxyFallBack:\n raise\n except:\n LOGGER.exception('process response failed')\n client.forward_started = True\n client.downstream_sock.sendall(response)\n if not is_payload_complete and client.method and 'GET' != client.method.upper():\n client.forward(upstream_sock, timeout=360)\n else:\n client.forward(upstream_sock)\n\n def detect_slow_host(self, client, http_response):\n if self.host_slow_detection_enabled:\n greenlet = gevent.spawn(\n try_receive_response_body, http_response)\n try:\n return greenlet.get(timeout=5)\n except gevent.Timeout:\n self.host_slow_list.add(client.host)\n LOGGER.error('host %s is too slow to direct access' % client.host)\n client.fall_back('too slow')\n finally:\n greenlet.kill()\n else:\n return try_receive_response_body(http_response)\n\n def before_send_request(self, client, upstream_sock, is_payload_complete):\n return ''\n\n def after_send_request(self, client, upstream_sock):\n pass\n\n def process_response(self, client, upstream_sock, response, http_response):\n return response\n\n def is_protocol_supported(self, protocol, client=None):\n return 'HTTP' == protocol\n\n def __repr__(self):\n return 'HttpTryProxy'\n\n\nclass GoogleScrambler(HttpTryProxy):\n def do_forward(self, client):\n dst = (client.dst_ip, client.dst_port)\n try:\n super(GoogleScrambler, self).do_forward(client)\n if dst in self.dst_black_list:\n LOGGER.error('removed dst %s:%s from blacklist' % dst)\n del self.dst_black_list[dst]\n except NotHttp:\n raise\n except:\n google_scrambler_hacked = getattr(client, 'google_scrambler_hacked', False)\n if google_scrambler_hacked:\n if dst not in self.dst_black_list:\n LOGGER.error('blacklist dst %s:%s' % dst)\n self.dst_black_list[dst] = self.dst_black_list.get(dst, 0) + 1\n raise\n\n def before_send_request(self, client, upstream_sock, is_payload_complete):\n client.google_scrambler_hacked = is_payload_complete and is_blocked_google_host(client.host)\n if client.google_scrambler_hacked:\n client.headers['Connection'] = 'close'\n if 'Referer' in client.headers:\n del client.headers['Referer']\n LOGGER.info('[%s] scramble google traffic' % repr(client))\n return 'GET http://www.google.com/ncr HTTP/1.1\\r\\n\\r\\n\\r\\n'\n return ''\n\n def after_send_request(self, client, upstream_sock):\n google_scrambler_hacked = getattr(client, 'google_scrambler_hacked', False)\n if google_scrambler_hacked:\n try_receive_response_body(try_receive_response_header(client, upstream_sock), reads_all=True)\n\n def process_response(self, client, upstream_sock, response, http_response):\n google_scrambler_hacked = getattr(client, 'google_scrambler_hacked', False)\n if not google_scrambler_hacked:\n return response\n response = response.replace('Connection: keep-alive', 'Connection: close')\n if len(response) < 10:\n client.fall_back('response is too small: %s' % response)\n if http_response:\n if httplib.FORBIDDEN == http_response.status:\n client.fall_back(reason='403 forbidden')\n if httplib.NOT_FOUND == http_response.status:\n client.fall_back(reason='404 not found')\n if http_response.content_length \\\n and httplib.PARTIAL_CONTENT != http_response.status \\\n and 0 < http_response.content_length < 10:\n client.fall_back('content length is too small: %s' % http_response.msg.dict)\n fallback_if_youtube_unplayable(client, http_response)\n return response\n\n def __repr__(self):\n return 'GoogleScrambler'\n\nclass TcpScrambler(HttpTryProxy):\n def __init__(self):\n super(TcpScrambler, self).__init__()\n self.bad_requests = {} # host => count\n self.dst_black_list = {}\n\n def do_forward(self, client):\n if is_blocked_google_host(client.host):\n LOGGER.info('[%s] tcp scramble youtube' % repr(client))\n dst = (client.dst_ip, client.dst_port)\n try:\n super(TcpScrambler, self).do_forward(client)\n if dst in self.dst_black_list:\n LOGGER.error('removed dst %s:%s from blacklist' % dst)\n del self.dst_black_list[dst]\n except NotHttp:\n raise\n except:\n if dst not in self.dst_black_list:\n LOGGER.error('blacklist dst %s:%s' % dst)\n self.dst_black_list[dst] = self.dst_black_list.get(dst, 0) + 1\n raise\n\n def before_send_request(self, client, upstream_sock, is_payload_complete):\n client.headers['Connection'] = 'close'\n if 'Referer' in client.headers:\n del client.headers['Referer']\n upstream_sock.setsockopt(socket.SOL_SOCKET, SO_MARK, 0xbabe)\n return ''\n\n def after_send_request(self, client, upstream_sock):\n pass\n\n def process_response(self, client, upstream_sock, response, http_response):\n upstream_sock.setsockopt(socket.SOL_SOCKET, SO_MARK, 0)\n if httplib.BAD_REQUEST == http_response.status:\n LOGGER.info('[%s] bad request to %s' % (repr(client), client.host))\n self.bad_requests[client.host] = self.bad_requests.get(client.host, 0) + 1\n if self.bad_requests[client.host] >= 3:\n LOGGER.critical('!!! too many bad requests, disable tcp scrambler !!!')\n self.died = True\n client.fall_back('tcp scrambler bad request')\n else:\n if client.host in self.bad_requests:\n LOGGER.info('[%s] reset bad request to %s' % (repr(client), client.host))\n del self.bad_requests[client.host]\n response = response.replace('Connection: keep-alive', 'Connection: close')\n fallback_if_youtube_unplayable(client, http_response)\n return response\n\n def __repr__(self):\n return 'TcpScrambler'\n\n\nHTTP_TRY_PROXY = HttpTryProxy()\nGOOGLE_SCRAMBLER = GoogleScrambler()\nTCP_SCRAMBLER = TcpScrambler()\n\n\ndef fallback_if_youtube_unplayable(client, http_response):\n if not http_response:\n return\n if 'youtube.com' not in client.host:\n return\n if http_response.body and 'gzip' == http_response.msg.dict.get('content-encoding'):\n stream = StringIO.StringIO(http_response.body)\n gzipper = gzip.GzipFile(fileobj=stream)\n http_response.body = gzipper.read()\n if http_response.body and (\n 'id=\"unavailable-message\" class=\"message\"' in http_response.body or 'UNPLAYABLE' in http_response.body):\n client.fall_back(reason='youtube player not available in China')\n\n\ndef is_blocked_google_host(client_host):\n if not client_host:\n return False\n return 'youtube.com' in client_host or 'ytimg.com' in client_host or 'googlevideo.com' in client_host \\\n or '.c.android.clients.google.com' in client_host # google play apk\n\n\ndef try_receive_response_header(client, upstream_sock, rejects_error=False):\n try:\n upstream_rfile = upstream_sock.makefile('rb', 0)\n client.add_resource(upstream_rfile)\n capturing_sock = CapturingSock(upstream_rfile)\n http_response = httplib.HTTPResponse(capturing_sock)\n http_response.capturing_sock = capturing_sock\n http_response.body = None\n http_response.begin()\n content_length = http_response.msg.dict.get('content-length')\n if content_length:\n http_response.content_length = int(content_length)\n else:\n http_response.content_length = 0\n if LOGGER.isEnabledFor(logging.DEBUG):\n LOGGER.debug('[%s] http try read response header: %s %s' %\n (repr(client), http_response.status, http_response.content_length))\n if http_response.chunked:\n if LOGGER.isEnabledFor(logging.DEBUG):\n LOGGER.debug('[%s] skip try reading response due to chunked' % repr(client))\n return http_response\n if not http_response.content_length:\n if LOGGER.isEnabledFor(logging.DEBUG):\n LOGGER.debug('[%s] skip try reading response due to no content length' % repr(client))\n return http_response\n if rejects_error and not (200 <= http_response.status < 400):\n raise Exception('http try read response status %s not in [200, 400)' % http_response.status)\n return http_response\n except NotHttp:\n raise\n except:\n if LOGGER.isEnabledFor(logging.DEBUG):\n LOGGER.debug('[%s] http try read response failed' % (repr(client)), exc_info=1)\n client.fall_back(reason='http try read response failed: %s' % sys.exc_info()[1])\n\ndef try_receive_response_body(http_response, reads_all=False):\n content_type = http_response.msg.dict.get('content-type')\n if content_type and 'text/html' in content_type:\n reads_all = True\n if reads_all:\n http_response.body = http_response.read()\n else:\n http_response.body = http_response.read(min(http_response.content_length, 128 * 1024))\n return http_response.capturing_sock.rfile.captured\n\nclass CapturingSock(object):\n def __init__(self, rfile):\n self.rfile = CapturingFile(rfile)\n\n def makefile(self, mode='r', buffersize=-1):\n if 'rb' != mode:\n raise NotImplementedError()\n return self.rfile\n\n\nclass CapturingFile(object):\n def __init__(self, fp):\n self.fp = fp\n self.captured = ''\n\n def read(self, *args, **kwargs):\n chunk = self.fp.read(*args, **kwargs)\n self.captured += chunk\n return chunk\n\n def readline(self, *args, **kwargs):\n chunk = self.fp.readline(*args, **kwargs)\n self.captured += chunk\n return chunk\n\n def close(self):\n self.fp.close()\n\n\ndef recv_and_parse_request(client):\n client.peeked_data, client.payload = recv_till_double_newline(client.peeked_data, client.downstream_sock)\n if 'Host:' not in client.peeked_data:\n if LOGGER.isEnabledFor(logging.DEBUG):\n LOGGER.debug('[%s] not http' % (repr(client)))\n raise NotHttp()\n try:\n client.method, client.path, client.headers = parse_request(client.peeked_data)\n client.host = client.headers.pop('Host', '')\n if not client.host:\n raise Exception('missing host')\n if client.path[0] == '/':\n client.url = 'http://%s%s' % (client.host, client.path)\n else:\n client.url = client.path\n if 'youtube.com/watch' in client.url:\n LOGGER.info('[%s] %s' % (repr(client), client.url))\n if LOGGER.isEnabledFor(logging.DEBUG):\n LOGGER.debug('[%s] parsed http header: %s %s' % (repr(client), client.method, client.url))\n if 'Content-Length' in client.headers:\n more_payload_len = int(client.headers.get('Content-Length', 0)) - len(client.payload)\n if more_payload_len > 1024 * 1024:\n client.peeked_data += client.payload\n LOGGER.info('[%s] skip try reading request payload due to too large: %s' %\n (repr(client), more_payload_len))\n return False\n if more_payload_len > 0:\n client.payload += client.downstream_rfile.read(more_payload_len)\n if client.payload:\n client.peeked_data += client.payload\n return True\n except:\n LOGGER.error('[%s] failed to parse http request:\\n%s' % (repr(client), client.peeked_data))\n raise\n\n\ndef recv_till_double_newline(peeked_data, sock):\n for i in range(16):\n if peeked_data.find(b'\\r\\n\\r\\n') != -1:\n header, crlf, payload = peeked_data.partition(b'\\r\\n\\r\\n')\n return header + crlf, payload\n more_data = sock.recv(8192)\n if not more_data:\n return peeked_data, ''\n peeked_data += more_data\n raise Exception('http end not found')\n\n\nclass NotHttp(Exception):\n pass\n\n\ndef parse_request(request):\n lines = request.splitlines()\n method, path = lines[0].split()[:2]\n headers = dict()\n for line in lines[1:]:\n keyword, _, value = line.partition(b':')\n keyword = keyword.title()\n value = value.strip()\n if keyword and value:\n headers[keyword] = value\n return method, path, headers\n\n\ndef detect_if_ttl_being_ignored():\n try:\n LOGGER.info('detecting if ttl being ignored')\n baidu_ip = networking.resolve_ips('www.baidu.com')[0]\n sock = socket.socket(family=socket.AF_INET, type=socket.SOCK_STREAM)\n if networking.OUTBOUND_IP:\n sock.bind((networking.OUTBOUND_IP, 0))\n sock.setblocking(0)\n sock.settimeout(2)\n sock.setsockopt(socket.SOL_IP, socket.IP_TTL, 3)\n try:\n sock.connect((baidu_ip, 80))\n finally:\n sock.close()\n LOGGER.info('ttl 3 should not connect baidu, disable fqting')\n return True\n except:\n LOGGER.exception('detected if ttl being ignored')\n return False\n","sub_path":"fqsocks/proxies/http_try.py","file_name":"http_try.py","file_ext":"py","file_size_in_byte":17418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"412929445","text":"# -*- encoding: utf-8\n\"\"\"\nText viewer component subclassed by Editor.\n\"\"\"\n\nimport os\nimport sys\nimport imp\nimport curses\n\nfrom line import *\nfrom cursor import *\nfrom helpers import *\nfrom themes import scope_to_pair\n\n\nclass Viewer:\n def __init__(self, app, window):\n \"\"\"\n Handle Viewer initialization\n\n :param App app: The main App class of Suplemon\n :param Window window: The ui window to use for the viewer\n \"\"\"\n self.app = app\n self.window = window\n self.config = {}\n self.data = \"\"\n self.lines = [Line()]\n self.file_extension = \"\"\n\n # Map special extensions to generic ones for highlighting\n self.extension_map = {\n \"scss\": \"css\",\n \"less\": \"css\",\n }\n self.show_line_ends = True\n\n self.cursor_style = curses.A_UNDERLINE\n\n self.y_scroll = 0\n self.x_scroll = 0\n self.cursors = [Cursor()]\n\n self.syntax = None\n if self.app.config[\"editor\"][\"show_highlighting\"]:\n self.setup_highlight()\n else:\n self.setup_linelight()\n\n def log(self, s):\n \"\"\"Log to the app.\"\"\"\n # TODO: log types: ERROR | WARNING | NOTICE\n self.app.log(s)\n\n def setup_linelight(self):\n \"\"\"Setup line based highlighting.\"\"\"\n ext = self.file_extension\n # Check if a file extension is redefined\n # Maps e.g. 'scss' to 'css'\n if ext in self.extension_map.keys():\n ext = self.extension_map[ext] # Use it\n curr_path = os.path.dirname(os.path.realpath(__file__))\n\n filename = ext + \".py\"\n path = os.path.join(curr_path, \"linelight\", filename)\n module = False\n if os.path.isfile(path):\n self.app.log(\"Syntax file found...\", LOG_INFO)\n try:\n module = imp.load_source(ext, path)\n self.app.log(\"File loaded...\", LOG_INFO)\n except:\n self.app.log(get_error_info())\n else:\n return False\n\n if not module or \"Syntax\" not in dir(module):\n self.app.log(\"File doesn't match API!\")\n return False\n self.syntax = module.Syntax()\n\n def setup_highlight(self):\n \"\"\"Setup word based highlighting.\"\"\"\n ext = self.file_extension\n # Check if a file extension is redefined\n # Maps e.g. 'scss' to 'css'\n if ext in self.extension_map.keys():\n ext = self.extension_map[ext] # Use it\n curr_path = os.path.dirname(os.path.realpath(__file__))\n\n filename = ext + \".py\"\n path = os.path.join(curr_path, \"highlight\", filename)\n module = False\n if os.path.isfile(path):\n self.app.log(\"Syntax file found...\", LOG_INFO)\n try:\n module = imp.load_source(ext, path)\n self.app.log(\"File loaded...\", LOG_INFO)\n except:\n self.app.log(get_error_info())\n else:\n return False\n\n if not module or \"Syntax\" not in dir(module):\n self.app.log(\"File doesn't match API!\")\n return False\n self.syntax = module.Syntax()\n\n def size(self):\n \"\"\"Get editor size (x,y). (Deprecated, use get_size).\"\"\"\n self.log(\"size() is deprecated, please use get_size()\")\n return self.get_size()\n\n def cursor(self):\n \"\"\"Return the main cursor. (Deprecated, use get_cursor)\"\"\"\n self.log(\"cursor() is deprecated, please use get_cursor()\")\n return self.get_cursor()\n\n def get_size(self):\n \"\"\"Get editor size (x,y).\"\"\"\n y, x = self.window.getmaxyx()\n return (x, y)\n\n def get_cursor(self):\n \"\"\"Return the main cursor.\"\"\"\n return self.cursors[0]\n\n def get_cursors(self):\n \"\"\"Return list of all cursors.\"\"\"\n return self.cursors[0]\n\n def get_first_cursor(self):\n \"\"\"Get the first (primary) cursor.\"\"\"\n highest = None\n for cursor in self.cursors:\n if highest is None or cursor.y < highest.y:\n highest = cursor\n return highest\n\n def get_last_cursor(self):\n \"\"\"Get the last cursor.\"\"\"\n lowest = None\n for cursor in self.cursors:\n if lowest is None:\n lowest = cursor\n elif cursor.y > lowest.y:\n lowest = cursor\n elif cursor.y == lowest.y and cursor.x > lowest.x:\n lowest = cursor\n return lowest\n\n def get_cursors_on_line(self, line_no):\n \"\"\"Return all cursors on a specific line.\"\"\"\n cursors = []\n for cursor in self.cursors:\n if cursor.y == line_no:\n cursors.append(cursor)\n return cursors\n\n def get_lines_with_cursors(self):\n \"\"\"Return all line indices that have cursors.\n\n :return: A list of line numbers that have cursors.\n :rtype: list\n \"\"\"\n line_nums = []\n for cursor in self.cursors:\n if cursor.y not in line_nums:\n line_nums.append(cursor.y)\n line_nums.sort()\n return line_nums\n\n def get_line_color(self, raw_line):\n \"\"\"Return a color based on line contents.\n\n :param str raw_line: The line from which to get a color value.\n :return: A color value for given raw_data.\n :rtype: int\n \"\"\"\n if self.syntax:\n try:\n return self.syntax.get_color(raw_line)\n except:\n return 0\n return 0\n\n def get_word_scope(self, raw_word):\n \"\"\"Return the scope name based on the word.\"\"\"\n if self.syntax:\n try:\n return self.syntax.get_scope(raw_word)\n except:\n return 0\n return 0\n\n def get_data(self):\n \"\"\"Get editor contents.\n\n :return: Editor contents.\n :rtype: str\n \"\"\"\n str_lines = []\n for line in self.lines:\n if isinstance(line, str):\n str_lines.append(line)\n else:\n str_lines.append(line.get_data())\n data = str(self.config[\"end_of_line\"].join(str_lines))\n return data\n\n def set_data(self, data):\n \"\"\"Set editor data or contents.\n\n :param str data: Set the editor contents to data.\n \"\"\"\n self.data = data\n self.lines = []\n lines = self.data.split(self.config[\"end_of_line\"])\n for line in lines:\n self.lines.append(Line(line))\n\n def set_config(self, config):\n \"\"\"Set the viewer configuration dict.\n\n :param dict config: Editor config dict with any supported fields. See config.py.\n \"\"\"\n self.config = config\n self.set_cursor_style(self.config[\"cursor\"])\n\n def set_cursor_style(self, cursor):\n \"\"\"Set cursor style.\n\n :param str cursor: Cursor type, either 'underline' or 'reverse'.\n \"\"\"\n if cursor == \"underline\":\n self.cursor_style = curses.A_UNDERLINE\n elif cursor == \"reverse\":\n self.cursor_style = curses.A_REVERSE\n else:\n return False\n return True\n\n def set_cursor(self, cursor):\n self.log(\"set_cursor is deprecated, use set_cursor_style instead.\")\n return self.set_cursor_style(cursor)\n\n def set_single_cursor(self, cursor):\n \"\"\"Discard all cursors and place a new one.\"\"\"\n self.cursors = [Cursor(cursor)]\n\n def set_cursors(self, cursors):\n \"\"\"Replace cursors with new cursor list.\"\"\"\n self.cursors = cursors\n\n def set_file_extension(self, ext):\n \"\"\"Set the file extension.\"\"\"\n ext = ext.lower()\n if ext and ext != self.file_extension:\n self.file_extension = ext\n if self.app.config[\"editor\"][\"show_highlighting\"]:\n self.setup_highlight()\n else:\n self.setup_linelight()\n\n def add_cursor(self, cursor):\n \"\"\"Add a new cursor. Accepts a x,y tuple or a Cursor instance.\"\"\"\n self.cursors.append(Cursor(cursor))\n\n def pad_lnum(self, n):\n \"\"\"Pad line number with zeroes.\"\"\"\n # TODO: move to helpers\n s = str(n)\n while len(s) < self.line_offset()-1:\n s = \"0\" + s\n return s\n\n def max_line_length(self):\n \"\"\"Get maximum line length that fits in the editor.\"\"\"\n return self.get_size()[0]-self.line_offset()-1\n\n def line_offset(self):\n \"\"\"Get the x coordinate of beginning of line.\"\"\"\n if not self.config[\"show_line_nums\"]:\n return 0\n return len(str(len(self.lines)))+1\n\n def whitespace(self, line):\n \"\"\"Return index of first non whitespace character on a line.\"\"\"\n i = 0\n for char in line:\n if char != \" \":\n break\n i += 1\n return i\n\n def toggle_line_nums(self):\n \"\"\"Toggle display of line numbers.\"\"\"\n self.config[\"show_line_nums\"] = not self.config[\"show_line_nums\"]\n self.render()\n\n def toggle_line_ends(self):\n \"\"\"Toggle display of line ends.\"\"\"\n self.show_line_ends = not self.show_line_ends\n self.render()\n\n def toggle_highlight(self):\n \"\"\"Toggle syntax highlighting.\"\"\"\n return False\n\n def render(self):\n \"\"\"Render the editor curses window.\"\"\"\n self.window.clear()\n max_y = self.get_size()[1]\n i = 0\n max_len = self.max_line_length()\n # Iterate through visible lines\n while i < max_y:\n x_offset = self.line_offset()\n lnum = i + self.y_scroll\n if lnum >= len(self.lines): # Make sure we have a line to show\n break\n\n # Get line for current row\n line = self.lines[lnum]\n if self.config[\"show_line_nums\"]:\n self.window.addstr(i, 0, self.pad_lnum(lnum+1)+\" \", curses.color_pair(line.number_color))\n\n # Normal rendering\n line_part = line[min(self.x_scroll, len(line)):]\n if self.show_line_ends:\n line_part += self.config[\"line_end_char\"]\n if len(line_part) >= max_len:\n # Clamp line length to view width\n line_part = line_part[:max_len]\n\n # Replace unsafe whitespace with normal space or visible\n # replacement. For example tab characters make cursors\n # go out of sync with line contents\n for key in self.config[\"white_space_map\"].keys():\n char = \" \"\n if self.config[\"show_white_space\"]:\n char = self.config[\"white_space_map\"][key]\n line_part = line_part.replace(key, char)\n\n if self.app.config[\"editor\"][\"show_highlighting\"]:\n words = line_part.split(\" \")\n for raw_word in words:\n word = raw_word\n # Use unicode support on Python 3.3 and higher\n if sys.version_info[0] == 3 and sys.version_info[1] > 2:\n word = word.encode(\"utf-8\")\n try:\n if self.config[\"show_line_colors\"]:\n scope = self.get_word_scope(raw_word)\n settings = self.app.themes.get_scope(scope)\n pair = scope_to_pair.get(scope)\n if settings is not None and pair is not None:\n fg = int(settings.get(\"foreground\") or -1)\n bg = int(settings.get(\"background\") or -1)\n curses.init_pair(pair, fg, bg)\n self.window.addstr(i, x_offset, word, curses.color_pair(pair))\n else:\n self.window.addstr(i, x_offset, word)\n else:\n self.window.addstr(i, x_offset, word)\n except Exception as inst:\n self.log(type(inst)) # the exception instance\n self.log(inst.args) # arguments stored in .args\n self.log(inst) # __str__ allows args to be printed\n x_offset += len(word) + 1\n else:\n # Use unicode support on Python 3.3 and higher\n if sys.version_info[0] == 3 and sys.version_info[1] > 2:\n line_part = line_part.encode(\"utf-8\")\n try:\n if self.config[\"show_line_colors\"]:\n self.window.addstr(i, x_offset, line_part, curses.color_pair(self.get_line_color(line)))\n else:\n self.window.addstr(i, x_offset, line_part)\n except Exception as inst:\n self.log(type(inst)) # the exception instance\n self.log(inst.args) # arguments stored in .args\n self.log(inst) # __str__ allows args to be printed\n i += 1\n self.render_cursors()\n\n def render_cursors(self):\n \"\"\"Render editor window cursors.\"\"\"\n max_x, max_y = self.get_size()\n for cursor in self.cursors:\n x = cursor.x - self.x_scroll + self.line_offset()\n y = cursor.y - self.y_scroll\n if y < 0:\n continue\n if y >= max_y:\n break\n if x < self.line_offset():\n continue\n if x > max_x-1:\n continue\n self.window.chgat(y, cursor.x+self.line_offset()-self.x_scroll, 1, self.cursor_style)\n\n def refresh(self):\n \"\"\"Refresh the editor curses window.\"\"\"\n self.window.refresh()\n\n def resize(self, yx=None):\n \"\"\"Resize the UI.\"\"\"\n if not yx:\n yx = self.window.getmaxyx()\n self.window.resize(yx[0], yx[1])\n self.move_cursors()\n self.refresh()\n\n def move_win(self, yx):\n \"\"\"Move the editor window to position yx.\"\"\"\n # Must try & catch since mvwin might\n # crash with incorrect coordinates\n try:\n self.window.mvwin(yx[0], yx[1])\n except:\n self.app.log(get_error_info(), LOG_WONTFIX)\n\n def move_y_scroll(self, delta):\n \"\"\"Add delta the y scroll axis scroll\"\"\"\n self.y_scroll += delta\n\n def scroll_up(self):\n \"\"\"Scroll view up if neccesary.\"\"\"\n cursor = self.get_first_cursor()\n if cursor.y - self.y_scroll < 0:\n # Scroll up\n self.y_scroll = cursor.y\n\n def scroll_down(self):\n \"\"\"Scroll view up if neccesary.\"\"\"\n cursor = self.get_last_cursor()\n size = self.get_size()\n if cursor.y - self.y_scroll >= size[1]:\n # Scroll down\n self.y_scroll = cursor.y - size[1]+1\n\n def move_cursors(self, delta=None, noupdate=False):\n \"\"\"Move all cursors with delta. To avoid refreshing the screen set noupdate to True.\"\"\"\n for cursor in self.cursors:\n if delta:\n if delta[0] != 0 and cursor.x >= 0:\n cursor.move_right(delta[0])\n if delta[1] != 0 and cursor.y >= 0:\n cursor.move_down(delta[1])\n\n if cursor.x < 0:\n cursor.x = 0\n if cursor.y < 0:\n cursor.y = 0\n if cursor.y >= len(self.lines)-1:\n cursor.y = len(self.lines)-1\n if cursor.x >= len(self.lines[cursor.y]):\n cursor.x = len(self.lines[cursor.y])\n\n cur = self.get_cursor() # Main cursor\n size = self.get_size()\n offset = self.line_offset()\n # Check if we should scroll horizontally\n if cur.x - self.x_scroll+offset > size[0] - 1:\n # -1 to allow space for cursor at line end\n self.x_scroll = len(self.lines[cur.y]) - size[0]+offset+1\n if cur.x - self.x_scroll < 0:\n self.x_scroll -= abs(cur.x - self.x_scroll) # FIXME\n if cur.x - self.x_scroll+offset < offset:\n self.x_scroll -= 1\n if not noupdate:\n self.purge_cursors()\n\n def scroll_to_line(self, line_no):\n \"\"\"Center the viewport on line_no.\"\"\"\n if line_no >= len(self.lines):\n line_no = len(self.lines)-1\n new_y = line_no - int(self.get_size()[1] / 2)\n if new_y < 0:\n new_y = 0\n self.y_scroll = new_y\n\n def move_x_cursors(self, line, col, delta):\n \"\"\"Move all cursors starting at line and col with delta on the x axis.\"\"\"\n for cursor in self.cursors:\n if cursor.y == line:\n if cursor.x > col:\n cursor.move_right(delta)\n\n def move_y_cursors(self, line, delta, exclude=None):\n \"\"\"Move all cursors starting at line and col with delta on the y axis.\n Exlude a cursor by passing it via the exclude argument.\"\"\"\n for cursor in self.cursors:\n if cursor == exclude:\n continue\n if cursor.y > line:\n cursor.move_down(delta)\n\n def cursor_exists(self, cursor):\n \"\"\"Check if a given cursor exists.\"\"\"\n return cursor.tuple() in [cur.tuple() for cur in self.cursors]\n\n def remove_cursor(self, cursor):\n \"\"\"Remove a cursor object from the cursor list.\"\"\"\n try:\n index = self.cursors.index(cursor)\n except:\n return False\n self.cursors.pop(index)\n return True\n\n def purge_cursors(self):\n \"\"\"Remove duplicate cursors that have the same position.\"\"\"\n new = []\n # This sucks: can't use \"if .. in ..\" for different instances (?)\n # Use a reference list instead. FIXME: use a generator\n ref = []\n for cursor in self.cursors:\n if not cursor.tuple() in ref:\n ref.append(cursor.tuple())\n new.append(cursor)\n self.cursors = new\n self.render()\n\n def purge_line_cursors(self, line_no):\n \"\"\"Remove all but first cursor on given line.\"\"\"\n line_cursors = []\n for cursor in self.cursors:\n if cursor.y == line_no:\n line_cursors.append(cursor)\n if len(line_cursors) < 2:\n return False\n\n # Leave the first cursor out\n line_cursors.pop(0)\n # Remove the rest\n for line_cursors in cursor:\n self.remove_cursor(cursor)\n return True\n","sub_path":"viewer.py","file_name":"viewer.py","file_ext":"py","file_size_in_byte":18490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"589297589","text":"from tkinter import Toplevel, Label, LEFT, W\nfrom PIL import Image, ImageTk\n\n\ndef about_me():\n about_me_window = Toplevel()\n about_me_window.geometry(\"400x160\")\n about_me_window.title(\"Hooman Hesamyan - Developer\")\n try:\n load = Image.open(\"info/hooman.png\")\n render = ImageTk.PhotoImage(load)\n img = Label(about_me_window, image=render)\n img.image = render\n img.place(x=250, y=0)\n except FileNotFoundError as e:\n print(\"--->\", e)\n Label(about_me_window, anchor=W, justify=LEFT, text=\"\"\"\n .: Hooman Hesamyan :.\n\n\n Web: hooman.hesamian.com\n Tell: +37477281774\n GitHub: github.com/hooman734\n\n \"\"\").pack(anchor=W)\n","sub_path":"functions/about_me.py","file_name":"about_me.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"259654322","text":"import math\nimport pandas as pd\n#metodos : 1- trapecio simple, 2- trapecio compuesto\n#metodos : 3- simpson 1/3 simple, 4- simpson 1/3 complejo\n#metodos : 5- simpson 3/8 simple, 6- simpson 3/8 complejo\n\n\ndef funcion(a, f):\n av = f.evalf(subs={\"x\":a, \"e\" : math.e})\n #print(\"datos a: \",a, \"funcion: \",f, \"valor: \",av)\n return av\n\nclass IntegracionNum:\n def __init__(self, lstX,lstFx, metodo):\n self.lstX = lstX\n self.lstFx = lstFx\n self.metodo = metodo\n\n def distancia(lstX):\n resu = lstX[0]-lstX[1]\n iguales = False\n n = 1\n for x in range(1, len(lstX)-1):\n resu2 = lstX[x]-lstX[x+1]\n n += 1\n if resu == resu2:\n iguales = True\n else:\n iguales = False\n salida = {\"puntos\":n,\"iguales\":iguales}\n return salida\n\n def resultado(self):\n opcion = IntegracionNum.distancia(self.lstX)\n if self.metodo == 1:\n if opcion[\"puntos\"] < 2:\n print(\"Simple\")\n else:\n print(\"Compuesto\")\n sum = 0\n for datos in range(1, len(self.lstFx)-1):\n sum = sum + self.lstFx[datos] \n integral = (self.lstX[-1] - self.lstX[0])*(self.lstFx[0]+2*(sum)+self.lstFx[-1])/(2*opcion[\"puntos\"])\n salida = {\"tabla\":integral} #grafica\n return salida\n \n elif self.metodo == 3:\n if opcion[\"puntos\"] % 2 == 0: \n print(\"Compuesto\")\n else:\n integral = list()\n print(\"1/3 Simple\")\n lst = list()\n iteracion = 0\n num = 0\n while num < len(self.lstX):\n lst.append(self.lstX[num])\n iteracion+=1\n if iteracion == 3:\n integral.append((lst[2] - lst[0])*(self.lstFx[self.lstX.index(lst[0])]+4*(self.lstFx[self.lstX.index(lst[1])])+self.lstFx[self.lstX.index(lst[2])])/6)\n num -= 1\n iteracion = 0\n lst = list()\n print(\"\")\n\n if iteracion == 2 and num == (len(self.lstX)-1):\n print((float(lst[1]) - float(lst[0])))\n integral.append((lst[1] - lst[0])*(self.lstFx[self.lstX.index(lst[0])] + self.lstFx[self.lstX.index(lst[1])])/2)\n num +=1\n sum = 0\n for i in integral:\n sum += i\n print(sum)\n\n elif self.metodo == 6:\n if opcion[\"puntos\"] % 3 == 0: \n print(\"Compuesto\")\n else:\n integral = list()\n print(\"3/8 Simple\")\n lst = list()\n iteracion = 0\n num = 0\n while num < len(self.lstX):\n lst.append(self.lstX[num])\n iteracion+=1\n if iteracion == 4:\n integral.append((lst[-1] - lst[0])*(self.lstFx[self.lstX.index(lst[0])]+3*(self.lstFx[self.lstX.index(lst[1])])+3*(self.lstFx[self.lstX.index(lst[2])])+self.lstFx[self.lstX.index(lst[3])])/8)\n num -= 1\n iteracion = 0\n lst = list()\n print(\"\")\n\n if iteracion == 3 and num == (len(self.lstX)-1):\n integral.append((lst[-1] - lst[0])*(self.lstFx[self.lstX.index(lst[0])]+4*(self.lstFx[self.lstX.index(lst[1])])+self.lstFx[self.lstX.index(lst[2])])/6)\n num +=1\n sum = 0\n for i in integral:\n sum += i\n print(sum)\n\n\n","sub_path":"Calculadora/clases/unidad4/integracionListas.py","file_name":"integracionListas.py","file_ext":"py","file_size_in_byte":3796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"479494200","text":"#Write a program to shift every element of an array to circularly right.\n\nnumber_array = list()\nnumber = input(\"Enter the number of elements you want: \")\nprint ('Enter numbers in array: ')\n\nfor i in range(int(number)):\n n = input(f\"number {i+1} : \")\n number_array.append(int(n))\n\nprint('original array is: ',number_array)\nprint ('circulatory right array is: ',number_array[::-1])\n","sub_path":"python/Array/circular right.py","file_name":"circular right.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"507570941","text":"from core.models.user import User, Merchant\n\nfrom mongoengine import DoesNotExist\n\n\ndef add_merchants_to_user(user_id, merchant_ids):\n try:\n u = User.objects.get(id=user_id)\n for m_id in merchant_ids:\n m = Merchant.objects.get(id=m_id)\n u.update(add_to_set__merchants=m)\n except DoesNotExist:\n return \"User doesnt exist\"\n\n\ndef get_user_by_id(user_id):\n try:\n u = User.objects.get(id=user_id)\n return u\n except DoesNotExist:\n return \"User doesnt exist\"\n","sub_path":"backend/core/api/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"211880242","text":"from sqlalchemy import create_engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import sessionmaker\n\n\nBase = declarative_base()\nSession = sessionmaker()\n\n\ndef init_database(url: str):\n engine = create_engine(url)\n Base.metadata.bind = engine\n Base.metadata.create_all()\n Session.configure(bind=engine)\n","sub_path":"chapter_2/queue/worker/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"429059848","text":"# coding:utf8\n# 任务管理器\nimport spider_dbo\nimport md5\n# from DBmodel import mysqldbhand\n\n\nclass url_manager(object):\n global dbo\n global finished_urls\n\n def __init__(self):\n # 初始化组件\n self.dbo = spider_dbo.dbo()\n self.finished_urls = set()\n # 加载已经采集列表 防止重复采集\n urls_collected = self.dbo.url_get_collected()\n for i in range(len(urls_collected)):\n self.finished_urls.add(urls_collected[i]['url_md5'])\n\n # 此URL是否需要后续采集,如果是新 url 则加入待采区\n def need_collect(self, url):\n url_info = self.dbo.url_get_info(url)\n if url_info:\n if url_info[0]['collected'] == 1:\n return False\n else:\n return True\n else:\n # 保存页面\n self.dbo.url_add_new(url)\n return True\n\n # 缓存当前已经采集的 URL 列表的 md5值\n def collected_list(self, url=''):\n if url:\n url_md5 = md5.md5(url).hexdigest()\n self.finished_urls.add(url_md5)\n return self.finished_urls\n\n # 检索是否已采集\n def url_not_collected(self, url):\n # print self.finished_urls\n url_md5 = md5.md5(url).hexdigest()\n if url_md5 not in self.finished_urls:\n return True\n else:\n return False\n","sub_path":"bbs.auto.sina/spider_url_manager.py","file_name":"spider_url_manager.py","file_ext":"py","file_size_in_byte":1391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"245581062","text":"import warnings\n\nimport numpy as np\n\nfrom . import StochasticMomentumOptimizer\n\n\nclass AdaMax(StochasticMomentumOptimizer):\n\n def __init__(self,\n f,\n x=None,\n batch_size=None,\n eps=1e-6,\n tol=1e-8,\n epochs=1000,\n step_size=0.002,\n momentum_type='none',\n momentum=0.9,\n beta1=0.9,\n beta2=0.999,\n offset=1e-8,\n callback=None,\n callback_args=(),\n shuffle=True,\n random_state=None,\n verbose=False):\n super(AdaMax, self).__init__(f=f,\n x=x,\n step_size=step_size,\n momentum_type=momentum_type,\n momentum=momentum,\n batch_size=batch_size,\n eps=eps,\n tol=tol,\n epochs=epochs,\n callback=callback,\n callback_args=callback_args,\n shuffle=shuffle,\n random_state=random_state,\n verbose=verbose)\n if not 0 <= beta1 < 1:\n raise ValueError('beta1 has to lie in [0, 1)')\n self.beta1 = beta1\n self.est_mom1 = 0 # initialize 1st moment vector\n if not 0 <= beta2 < 1:\n raise ValueError('beta2 has to lie in [0, 1)')\n self.beta2 = beta2\n self.est_mom2 = 0 # initialize the exponentially weighted infinity norm\n if not self.beta1 < np.sqrt(self.beta2):\n warnings.warn('constraint from convergence analysis for adam not satisfied')\n if not offset > 0:\n raise ValueError('offset must be > 0')\n self.offset = offset\n\n def minimize(self):\n\n self._print_header()\n\n for batch in self.batches:\n\n if self.momentum_type == 'nesterov':\n step_m1 = self.step\n step1 = next(self.momentum) * step_m1\n self.x += step1\n\n self.f_x, self.g_x = self.f.function_jacobian(self.x, *batch)\n\n self._print_info()\n\n try:\n self.callback(batch)\n except StopIteration:\n break\n\n if self.is_batch_end():\n self.epoch += 1\n\n if self.epoch >= self.epochs:\n self.status = 'stopped'\n break\n\n t = self.iter + 1\n\n # compute search direction\n d = -self.g_x\n\n est_mom1_m1 = self.est_mom1\n est_mom2_m1 = self.est_mom2\n\n # update biased 1st moment estimate\n self.est_mom1 = self.beta1 * est_mom1_m1 + (1. - self.beta1) * d\n # update the exponentially weighted infinity norm\n self.est_mom2 = np.maximum(self.beta2 * est_mom2_m1, np.abs(self.g_x))\n\n est_mom1_crt = self.est_mom1 / (1. - self.beta1 ** t) # compute bias-corrected 1st moment estimate\n\n step2 = next(self.step_size(*batch)) * est_mom1_crt / (self.est_mom2 + self.offset)\n\n if self.momentum_type == 'polyak':\n\n step_m1 = self.step\n self.step = next(self.momentum) * step_m1 + step2\n self.x += self.step\n\n elif self.momentum_type == 'nesterov':\n\n self.x += step2\n self.step = step1 + step2\n\n elif self.momentum_type == 'none':\n\n self.step = step2\n self.x += self.step\n\n try:\n self.check_lagrangian_dual_optimality()\n except StopIteration:\n break\n\n self.iter += 1\n\n self.check_lagrangian_dual_conditions()\n\n if self.verbose:\n print('\\n')\n\n return self\n","sub_path":"optiml/opti/unconstrained/stochastic/adamax.py","file_name":"adamax.py","file_ext":"py","file_size_in_byte":4075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"628987811","text":"import re\nimport sys\nfrom datetime import date, datetime\nfrom django import forms\nfrom django.db import models\nfrom django.core.validators import RegexValidator\nfrom django.template.loader import render_to_string\nfrom django.utils.translation import ugettext_lazy as _\n\n\nclass ForeignKeyRawIdWidgetWrapper(forms.Widget):\n \"\"\"\n Admin widget that will show a thumbnail if the item is set instead of just\n a string\n \"\"\"\n default_thumbnail = ''\n\n def __init__(self, widget):\n self.needs_multipart_form = widget.needs_multipart_form\n self.attrs = widget.attrs\n self.choices = widget.choices\n self.widget = widget\n self.rel = widget.rel\n self.admin_site = widget.admin_site\n self.db = widget.db\n self.widget.label_for_value = self.label_for_value\n\n def __deepcopy__(self, memo):\n import copy\n obj = copy.copy(self)\n obj.widget = copy.deepcopy(self.widget, memo)\n obj.attrs = self.widget.attrs\n memo[id(self)] = obj\n return obj\n\n @property\n def media(self):\n return self.widget.media\n\n def render(self, name, value, *args, **kwargs):\n from django.utils.safestring import mark_safe\n key = self.rel.get_related_field().name\n try:\n obj = self.rel.to._default_manager.using(self.db).get(**{key: value})\n if obj.thumbnail:\n img_str = ' ' % obj.thumbnail\n else:\n img_str = '
%s
' % (self.default_thumbnail % obj.background_color)\n except (ValueError, self.rel.to.DoesNotExist):\n img_str = self.default_thumbnail % '#FFF'\n return mark_safe(img_str + self.widget.render(name, value, *args, **kwargs))\n\n def build_attrs(self, extra_attrs=None, **kwargs):\n \"Helper function for building an attribute dictionary.\"\n self.attrs = self.widget.build_attrs(extra_attrs=None, **kwargs)\n return self.attrs\n\n def value_from_datadict(self, data, files, name):\n return self.widget.value_from_datadict(data, files, name)\n\n def id_for_label(self, id_):\n return self.widget.id_for_label(id_)\n\n def label_for_value(self, value):\n from django.core.urlresolvers import reverse\n from django.utils.html import escape\n from django.utils.text import Truncator\n\n rel_to = self.rel.to\n key = self.rel.get_related_field().name\n try:\n obj = self.rel.to._default_manager.using(self.db).get(**{key: value})\n related_url = reverse('admin:%s_%s_change' %\n (rel_to._meta.app_label,\n rel_to._meta.model_name),\n args=(value, ),\n current_app=self.admin_site.name)\n edit_str = '  View %s' % (related_url, rel_to._meta.model_name)\n metadata = \"
\".join([\n escape(obj.date_string),\n escape(Truncator(obj.headline).words(14, truncate='...')),\n escape(Truncator(obj.text).words(14, truncate='...')),\n ])\n return \"\".join([\n \"
\",\n '%s' % escape(Truncator(obj).words(14, truncate='...')),\n edit_str,\n \"

\",\n metadata,\n \"

\",\n \"
\"\n ])\n\n except (ValueError, self.rel.to.DoesNotExist):\n return ''\n\n\ndef get_month_choices():\n \"\"\"\n Get the choices for months using the locale\n \"\"\"\n import locale\n locale.setlocale(locale.LC_ALL, '')\n output = [('00', 'No month')]\n for i in range(1, 13):\n output.append((\"%02d\" % i,\n locale.nl_langinfo(getattr(locale, 'ABMON_%s' % i)))\n )\n return output\n\n\ndef get_day_choices():\n \"\"\"\n Return the choices for days\n \"\"\"\n output = [('00', 'No day')]\n for i in range(1, 32):\n output.append((\"%02d\" % i, str(i)))\n return output\n\n\nclass HistoricalDate(object):\n \"\"\"\n A field that stores a historical date as an integer with different resolutions\n\n The field format is +/-YYYYYYYYMMDD\n\n Parsed as last 2 digits are the day, with 00 meaning not specified\n second-to-last 2 digits are month, with 00 meaning not specified\n remaining digits are year.\n\n Maximum year in the past is 214748 BC\n\n Examples:\n 10000 = 1AD\n -10000 = 1BC\n 0 = Undefined\n 99999999 = \"the present\"\n 19631122 = Nov 22, 1963\n -45611200 = Dec 4561BC\n \"\"\"\n def __init__(self, value=None):\n if value and not isinstance(value, (int, date, datetime)):\n raise ValueError(\"HistoricalDate must be an int, date or datetime.\")\n if value:\n if isinstance(value, (date, datetime)):\n self.from_date(value)\n return\n elif abs(value) < 10000:\n raise ValueError(\"HistoricalDate must have at least 5 digits.\")\n elif value == 99999999:\n self.value = sys.maxint\n self.year = sys.maxint\n self.month = sys.maxint\n self.day = sys.maxint\n return\n numstr = str(value)\n day = int(numstr[-2:])\n month = int(numstr[-4:-2])\n year = int(numstr[:-4])\n if month < 1:\n month = None\n if day < 1:\n day = None\n else:\n year = month = day = None\n self.value = value\n self.year = year\n self.month = month\n self.day = day\n self.validate_month()\n\n def validate_month(self):\n if self.month is not None and (self.month < 1 or self.month > 12):\n raise ValueError(\"Invalid month in value. %s is not between 00 and 12\" % self.month)\n\n def validate_day(self):\n if self.month is None and self.day is not None:\n raise ValueError(\"Day specified without month.\")\n if self.day is not None and (self.day < 1):\n raise ValueError(\"Day less than 01\")\n if self.day is not None:\n if self.year < 1:\n date(2012, self.month, self.day) # Use a leap year for that possibility\n else:\n self.to_date()\n\n def to_dict(self):\n output = {'year': str(self.year)}\n if self.month:\n output['month'] = str(self.month)\n\n # There shouldn't be a \"day\" if there isn't a month, but including\n # this in the if self.month condition to make sure.\n if self.day:\n output['day'] = str(self.day)\n return output\n\n def to_date(self):\n if self.value == sys.maxint:\n return date.today()\n return date(self.year, self.month, self.day)\n\n def from_date(self, value):\n \"\"\"\n Populate the values from a date.\n \"\"\"\n self.year = value.year\n self.month = value.month\n self.day = value.day\n self.value = (self.year * 10000) + (self.month * 100) + self.day\n\n def __repr__(self):\n return \"HistoricalDate(%r)\" % self.value\n\n def __str__(self):\n import locale\n locale.setlocale(locale.LC_ALL, '')\n output = []\n if self.day is not None:\n output.append(str(self.day))\n if self.month is not None:\n output.append(locale.nl_langinfo(getattr(locale, 'ABMON_%s' % self.month)))\n if self.year is not None:\n output.append(str(abs(self.year)))\n if self.year:\n if self.year < 0:\n output.append('BCE')\n else:\n output.append('CE')\n return \" \".join(output)\n\n def __unicode__(self):\n return self.__str__()\n\n def __int__(self):\n return self.value\n\n def __cmp__(self, other):\n return cmp(self.value, int(other))\n\n\nclass HistoricalDateWidget(forms.MultiWidget):\n \"\"\"\n A widget for a Historical Date field. Includes 3 text entry widgets and\n optional \"to present\" checkbox\n \"\"\"\n def __init__(self, attrs=None):\n if attrs:\n new_attrs = attrs.copy()\n else:\n new_attrs = {'class': 'vHistoricDateField'}\n year_attrs = new_attrs.copy()\n year_attrs['size'] = \"10\"\n widgets = (forms.TextInput(attrs=year_attrs),\n forms.Select(attrs=new_attrs, choices=(('-', 'BCE'), ('+', 'CE'))),\n forms.Select(attrs=new_attrs, choices=get_month_choices()),\n forms.Select(attrs=new_attrs, choices=get_day_choices()))\n return super(HistoricalDateWidget, self).__init__(widgets, attrs)\n\n def decompress(self, value):\n if value:\n hdate = HistoricalDate(value)\n if hdate.year < 0:\n era = '-'\n else:\n era = '+'\n return [abs(hdate.year), era,\n \"%02d\" % (hdate.month or 0), \"%02d\" % (hdate.day or 0)]\n return ['', '+', '00', '00']\n\n def format_output(self, rendered_widgets):\n return \" \".join(rendered_widgets)\n\n\nclass HistoricalDateFormField(forms.MultiValueField):\n widget = HistoricalDateWidget\n\n def __init__(self, *args, **kwargs):\n kwargs['widget'] = HistoricalDateWidget\n fields = (\n forms.IntegerField(min_value=1),\n forms.ChoiceField(choices=(('-', 'BCE'), ('+', 'CE'))),\n forms.ChoiceField(choices=get_month_choices()),\n forms.ChoiceField(choices=get_day_choices())\n )\n super(HistoricalDateFormField, self).__init__(fields, *args, **kwargs)\n\n def compress(self, data_list):\n \"\"\"\n Convert the value list into an integer\n \"\"\"\n if not data_list:\n return None\n if data_list[0] is None:\n return None\n out = int(\"\".join([data_list[1], str(data_list[0]), data_list[2], data_list[3]]))\n return out\n\n def formfield(self, **kwargs):\n # don't call super, as that overrides default widget if it has choices\n defaults = {'required': not self.blank, 'label': self.verbose_name,\n 'help_text': self.help_text}\n if self.has_default():\n defaults['initial'] = self.get_default()\n defaults.update(kwargs)\n return HistoricalDateWidget(**defaults)\n\n\nclass HistoricalDateField(models.IntegerField):\n \"\"\"\n A subclass of integer that stores a HistoricalDate value.\n \"\"\"\n def get_internal_type(self):\n return \"IntegerField\"\n\n def value_to_string(self, obj):\n value = self._get_val_from_obj(obj)\n return str(self.get_db_prep_value(value))\n\n def formfield(self, **kwargs):\n defaults = {'form_class': HistoricalDateFormField}\n defaults.update(kwargs)\n return super(HistoricalDateField, self).formfield(**defaults)\n\n def get_db_prep_value(self, value, *args, **kwargs):\n if isinstance(value, basestring):\n return int(value)\n elif isinstance(value, int):\n return value\n elif isinstance(value, list):\n return int(\"\".join(value))\n\n\ncolor_re = re.compile('^#([A-Fa-f0-9]{6}|[A-Fa-f0-9]{3})$')\nvalidate_color = RegexValidator(color_re, _('Enter a valid color.'), 'invalid')\n\n\nclass ColorWidget(forms.Widget):\n class Media:\n js = ['timelines/jscolor.min.js', ]\n\n def render(self, name, value, attrs=None):\n return render_to_string('timelines/color.html', {'name': name, 'value': value, 'attrs': attrs})\n\n\nclass ColorField(models.CharField):\n default_validators = [validate_color]\n\n def __init__(self, *args, **kwargs):\n kwargs['max_length'] = 10\n super(ColorField, self).__init__(*args, **kwargs)\n\n def formfield(self, **kwargs):\n kwargs['widget'] = ColorWidget\n return super(ColorField, self).formfield(**kwargs)\n","sub_path":"timelines/fields.py","file_name":"fields.py","file_ext":"py","file_size_in_byte":12646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"46255514","text":"#!/usr/bin/env python3\n''' Lab4 from CS162 '''\n\n# Making heavy use of tuple packing/unpacking\n# Could have used objects, used namedtuple package instead\n# Advantages: More succinct code. \n# Disadvantage: \"structs\" are immutable here. \n\n\nfrom collections import namedtuple \n#student struct:\n #first\n #last\n #CRN\n #Course Designator (e.g. CS162) \n #Section: (Integer) \n\n# Define the new struct as a tuple \nstudent = namedtuple('student', 'first, last, course, crn, designator, section')\n\n\n# Have to refactor functions to return values \ndef get_name():\n # Initialize variables \n first, last = \"\", \"\"\n first = input(\"Please enter your first name: \")\n last = input(\"Please enter your last name: \")\n return first, last \n \n # Returns (first, last) as a tuple, need to unpack that later\n\n\ndef read_in_course():\n course, crn, designator, section = \"\",\"\",\"\",0\n course = input(\"Please enter a course description?: \") \n crn = input(\"CRN?: \") \n designator = input(\"Designator? e.g. CS162: \") \n section = input(\"Course section?\" ) \n return course, crn, designator, section\n\n\ndef again():\n '''Function prompts user if they would like to continue'''\n answer = input(\"Would you like to add another? y/n: \")\n # Has to be a more elegant way to do it\n if answer == 'n':\n return False \n else:\n return True\n\n\ndef display_info(s):\n '''Prints all elements in f'''\n for x in s:\n print(x)\n\n\n\ndef write_info(s):\n f = open(\"Schedule.txt\", \"w\")\n # Writes information from struct p into the file.\n # Uses dollarsign as delimeter\n for x in s:\n f.write(x + '$') \n \n f.close()\n\n \n\n# Main program body\ndef main():\n\n goagain = True\n\n # We're returning multiple values from get_name()\n # Values get returned from get_name() as tuple, so unpack aftercall \n first, last = get_name()\n\n # Unpack returned tuple into respective variables \n course, crn, designator, section = read_in_course()\n\n # Create new student 'struct' as p\n p = student(first, last, course, crn, designator, section)\n\n display_info(p)\n write_info(p)\n\n # Continue adding information? \n while goagain: \n goagain = again() \n\n\n# Start things rollin!\nmain()\n\n# TODO add error and type checking \n# Refactor the hell out of this, it works, but can't help but feel it's sloppy. \n# Work out the CHallenge as well: Use array of structs to hold multiple courses student enrolls in. \n# Create a way to recover saved data.\n\n","sub_path":"lab4.py","file_name":"lab4.py","file_ext":"py","file_size_in_byte":2522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"446375732","text":"\"\"\"Test cases for running mypy programs by compiling to C.\n\nEach test cases translates a mypy program to C and then compiles and runs it.\nThe output (stdout) of the program is compared to expected output. The\ntranslation uses the C builtins (lib/builtins.py).\n\nNote: These test cases are not included in the main test suite yet.\n\"\"\"\n\nimport os.path\nimport re\nimport subprocess\nimport sys\n\nimport build\nimport errors\nfrom myunit import Suite, run_test\nfrom testconfig import test_data_prefix, test_temp_dir\nfrom testdata import parse_test_cases\nfrom testhelpers import assert_string_arrays_equal\n\n\n# Files which contain test case descriptions.\ncgen_files = ['cgen-basic.test',\n 'cgen-intops.test']\n\n\nclass CGenSuite(Suite):\n def cases(self):\n c = []\n for f in cgen_files:\n c += parse_test_cases(os.path.join(test_data_prefix, f),\n test_cgen, test_temp_dir, True)\n return c\n\n\ndef test_cgen(testcase):\n # Build the program.\n text = '\\n'.join(testcase.input)\n program = '_program.py'\n try:\n build.build(text, program, target=build.C, alt_lib_path='lib')\n # Run the program.\n outfile = './_program'\n outb = subprocess.check_output([outfile], stderr=subprocess.STDOUT)\n # Split output into lines.\n out = [s.rstrip('\\n\\r') for s in str(outb, 'utf8').splitlines()]\n # Remove temp file.\n os.remove(outfile)\n except errors.CompileError as e:\n out = e.messages\n # Include line-end comments in the expected output.\n # Note: # characters in string literals can confuse this.\n for s in testcase.input:\n m = re.search(' #(.*)', s)\n if m:\n testcase.output.append(m.group(1).strip())\n # Verify output.\n assert_string_arrays_equal(testcase.output, out,\n 'Invalid output ({}, line {})'.format(\n testcase.file, testcase.line))\n\n\nif __name__ == '__main__':\n run_test(CGenSuite(), sys.argv[1:])\n","sub_path":"testcgen.py","file_name":"testcgen.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"359919017","text":"import sys\n\n[n, w] = list(map(int, sys.stdin.readline().strip().split()))\n\nv = list(map(int, sys.stdin.readline().strip().split()))\n\nv.sort()\n\ncount = [1]\n\ndef backtrack(n, v, a, sum):\n for i in range(a, n):\n if sum + v[i] > w:\n return\n else:\n count[0] += 1\n backtrack(n, v, i + 1, sum + v[i])\n\nif sum(v) < w:\n print(2 ** n)\nelse:\n backtrack(n, v, 0, 0)\n print(count[0])\n\n\"\"\"\n4 10\n1 2 4 5\n\"\"\"\n\n\"\"\"\n8\n\"\"\"","sub_path":"nowcoder/apr/08/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"228115064","text":"\"\"\"Default view.\"\"\"\n########################\n# imports #\n########################\nfrom project.models import Symbol # pragma: no cover\nfrom flask import render_template, Blueprint, jsonify, \\\n Response # pragma: no cover\nfrom flask_login import login_required # pragma: no cover\nfrom project.ml import ml as ml\nimport json\nimport os\nimport pandas as pd\nfrom sklearn.externals import joblib\n\n# import os # pragma: no cover\n\n##########################\n# config #\n##########################\n\nsymbols_blueprint = Blueprint(\n 'symbols', __name__,\n template_folder='templates'\n)\n\n########################\n# routes #\n########################\n# use decorators to link the function to a url\n\n\n@symbols_blueprint.route('/symbols')\n@login_required\ndef symbols():\n \"\"\"Json for symbols.\"\"\"\n return render_template('symbols.html')\n\n\n@symbols_blueprint.route('/symbols.json')\n# @login_required\ndef symbols_json():\n \"\"\"Json for symbols.\"\"\"\n symbols = Symbol.query.all()\n return jsonify(symbols_list=[symbol.serialize for symbol in symbols])\n\n\n@symbols_blueprint.route('/symbol/')\n@login_required\ndef symbol_show(symbol):\n \"\"\"Show symbol info.\"\"\"\n symbol = Symbol.query.filter(symbol == symbol).first()\n file_path = os.getcwd() + \\\n \"/project/static/datasets/news_quotes/{}.json\".format(symbol.symbol)\n table = pd.read_json(\n file_path).sort_values(by='Date', ascending=False)\n table = table[(table['compound'].notnull())]\n # table = table[['Open', 'Close', 'High', 'Low', 'Volume',\n # 'compound', 'neu', 'pos', 'neg',\n # 'Next_Open', 'Next_Close']]\n\n orig = table.copy()\n file_path = os.getcwd() + \"/project/static/datasets/models/all.sav\"\n all_dic = {}\n if os.path.exists(file_path):\n all_dic = joblib.load(file_path)\n lr_model = all_dic['lr_model']\n table = table[['Close', 'High', 'Low', 'Open', 'Volume', 'compound',\n 'neg', 'neu', 'pos', 'Next_Open', 'Prev_Slope']]\n pred = lr_model.predict(table)\n table = table.reset_index()\n table['pred_difference'] = pred[0]\n table = table.set_index('index')\n table['Next_Close'] = orig['Next_Close']\n table['pred_N_Close'] = table['pred_difference'] + 2*table['Close'] - \\\n orig['Prev_Close']\n table['Date'] = orig['Date']\n\n table = table.set_index('Date')\n table = table[['Open', 'Close', 'High', 'Low', 'Volume',\n 'compound', 'neu', 'pos', 'neg',\n 'Next_Close', 'pred_N_Close']]\n\n table = table.to_html(\n classes='table table-striped table-bordered table-hover')\n return render_template('show.html', symbol=symbol, table=table)\n\n\n@symbols_blueprint.route(\"/Symbol/update/\")\n@login_required\ndef symbol_update(symbol):\n \"\"\"Update symbol info.\"\"\"\n return render_template('update_symbol.html', symbol=symbol)\n\n\n@symbols_blueprint.route('/symbol/.json')\n# @login_required\ndef show_symbol_json(symbol):\n \"\"\"Json for symbols.\"\"\"\n file_path = os.getcwd() + \\\n \"/project/static/datasets/news_quotes/{}.json\".format(symbol)\n with open(file_path) as data_file:\n data = json.load(data_file)\n\n resp = Response(json.dumps(data), status=200, mimetype='application/json')\n # resp.headers['Link'] = ''\n\n return resp\n","sub_path":"project/symbols/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"512182470","text":"items = {\n 1: \"matches\",\n 2: \"lantern\",\n 3: \"flashlight\",\n 4: \"key_to_door_1\",\n 5: \"letter_from_bedroom\",\n 6: \"calendar\",\n 7: \"blade\",\n 8: \"books_off_shelf\",\n 9: \"bag_of_corn\",\n 10: \"key_to_door_2\",\n 11: \"key_to_door_3\",\n 12: \"globe\",\n 13: \"incomplete_whiskey_set\",\n 14: \"three_candles_on_wall\",\n 15: \"umbrella\",\n 16: \"keypad\",\n 17: \"fuse_switches\",\n 18: \"note\",\n 19: \"chicken\",\n 20: \"paper_from_kitchen\",\n 21: \"bag_of_sugar\",\n 22: \"bag_of_flour\",\n 23: \"dustpan\",\n 24: \"note_1\",\n 25: \"note_2\",\n 26: \"note_3\",\n}\n\n# bathroom items: black light, box, lock(?), toilet, note, matches, key for door 1, lantern, flashlight\n# bedroom items: table(?), key to study, letter, calendar, bed, box, books, key to door 3, key to door 2, bag of corn\n# hall items: doors in here, but no items (makes sense)\n# study items: blade, globe, whiskey set, glasses(?)\n# stairs items: candles, tape recorder\n# foyer items: keypad, umbrella,\n# kitchen items: fuse switches, schematics, dustpan, ","sub_path":"src/items.py","file_name":"items.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"158095148","text":"import math\nimport random\n\nDISPLACEMENT_OPTION = 0\nINVERSMENT_OPTION = 1\nDISPLACED_INVERSION_OPTION = 2\n\ndef mutation(option, chromosome, length) :\n chromosome = chromosome[:]\n if (option == 0) :\n choice = random.randint(0, 1)\n if (choice == 0) :\n allele_flip_M(chromosome, length)\n elif (choice == 1) :\n insertion_M(chromosome, length)\n elif (option == 1) :\n allele_flip_M(chromosome, length)\n elif (option == 2) :\n insertion_M(chromosome, length)\n elif (option == 3) :\n inv_and_or_disp_M(DISPLACEMENT_OPTION, chromosome, length)\n elif (option == 4) :\n inv_and_or_disp_M(INVERSMENT_OPTION, chromosome, length)\n elif (option == 5) :\n inv_and_or_disp_M(DISPLACED_INVERSION_OPTION, chromosome, length)\n elif (option == 6) :\n invasive_allele_flip_M(chromosome, length)\n elif (option == 7) :\n chromosome = rand_displacement_M(chromosome)\n elif (option == 8) :\n chromosome = rand_displaced_inversion_M(chromosome)\n return chromosome\n\ndef allele_flip_M(chromosome, length):\n rand = random.randint(0, length - 1)\n chromosome[rand] = random.randint(0, length - 1)\n\ndef invasive_allele_flip_M(chromosome, length) :\n index = 0\n index_plus_one = 0\n while (index < length) :\n index_rand = random.randint(0, length - 1)\n chromosome[index_rand] = random.randint(0, length - 1)\n index_plus_one += 1\n index += index_plus_one\n \n\ndef insertion_M(chromosome, length):\n rand_from = random.randint(0, length - 1)\n temp = chromosome[rand_from]\n chromosome.pop(rand_from)\n rand_to = random.randint(0, length - 2) # already poped out one element \n chromosome.insert(rand_to, temp) \n\ndef inv_and_or_disp_M(option, chromosome, length) :\n rand_1 = random.randint(0, length - 1)\n rand_2 = random.randint(0, length - 1)\n rand_start = min(rand_1, rand_2)\n rand_end = max(rand_1, rand_2)\n length_change = rand_end - rand_start + 1\n rand_to = random.randint(0, length - length_change)\n #print(rand_start)\n #print(rand_end)\n #print(rand_to)\n if (option == DISPLACEMENT_OPTION) :\n displacement_M(chromosome, length_change, rand_start, rand_to)\n elif (option == INVERSMENT_OPTION) :\n inversion_M(chromosome, length_change, rand_start, rand_end) \n elif (option == DISPLACED_INVERSION_OPTION) : \n inversion_M(chromosome, length_change, rand_start, rand_end) \n displacement_M(chromosome, length_change, rand_start, rand_to)\n\ndef inversion_M(chromosome, length_change, rand_start, rand_end):\n for i in range(math.floor(length_change/2)) :\n tmp = chromosome[rand_end - i]\n chromosome[rand_end - i] = chromosome[rand_start + i]\n chromosome[rand_start + i] = tmp\n\ndef displacement_M(chromosome, length_change, rand_start, rand_to) :\n temp_list = chromosome[rand_start:rand_start + length_change]\n del chromosome[rand_start:rand_start + length_change]\n for i in range(length_change) :\n chromosome.insert(rand_to + i, temp_list[i])\n\ndef rand_displacement_M(individual):\n\t\n\tmy_individual = individual\n\tfirst_rnd = random.randint(0,len(my_individual )-1)\n\tsec_rnd = random.randint(0,len(my_individual )-1)\n\tt_rnd = random.randint(0,len(my_individual )-1)\n\t\"\"\" print ('R1 : ' + str(first_rnd))\n\tprint ('R2 : ' + str(sec_rnd))\n\tprint ('R3 : ' + str(t_rnd))\n\tprint ('Array Length : ' + str(len(my_individual ))) \"\"\"\n\tif sec_rnd != first_rnd:\n\t\tif sec_rnd > first_rnd:\n\t\t\t#print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%')\n\t\t\t#print('R2 > R1')\n\t\t\treturn shift(my_individual,first_rnd,sec_rnd, t_rnd)\n\t\telse:\n\t\t\t#print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%')\n\t\t\t\n\t\t\t#print('R1 > R2')\n\t\t\treturn shift(my_individual,sec_rnd,first_rnd, t_rnd)\n\treturn my_individual\n\ndef rand_displaced_inversion_M(individual):\n\n\tmy_individual = individual\n\tfirst_rnd = random.randint(0,len(my_individual )-1)\n\tsec_rnd = random.randint(0,len(my_individual )-1)\n\tt_rnd = random.randint(0,len(my_individual )-1)\n\t\"\"\" print ('R1 : ' + str(first_rnd))\n\tprint ('R2 : ' + str(sec_rnd))\n\tprint ('R3 : ' + str(t_rnd))\n\tprint ('Array Length : ' + str(len(my_individual ))) \"\"\"\n\tif sec_rnd != first_rnd:\n\t\tif sec_rnd > first_rnd:\n\t\t\t#print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%')\n\t\t\t#print('R2 > R1')\n\t\t\treturn shift_dis_inversion(my_individual,first_rnd,sec_rnd, t_rnd)\n\t\telse:\n\t\t\t#print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%')\n\t\t\t#print('R1 > R2')\n\t\t\treturn shift_dis_inversion(my_individual,sec_rnd,first_rnd, t_rnd)\n\treturn my_individual\n\ndef shift(L, start, end, insert_at):\n\t#transindividual = []\n\tcutrray = []\n\trem_array =[]\n\t#print('Genes of Individual')\n\t#for i in range(len(L)):\n\t\t#print(L[i])\n\tfor i in range(start, end + 1 ,1):\n\t\tcutrray.append(L[i])\n\tfor i in range(start-1,-1,-1):\n\t\trem_array.append(L[i])\n\tif start != len(L) - 1:\n\t\tfor i in range(len(L) - 1,end,-1):\n\t\t\trem_array.append(L[i])\n\telse:\n\t\tfor i in range(end,len(L) - 1,1):\n\t\t\trem_array.append(L[i])\n\t#print('Cut array'+ str(cutrray))\n\t#print('Rem array'+ str(rem_array))\n\tpos = insert_at\n\tfor i in range(len(cutrray)):\n\t\trem_array.insert(pos, cutrray[i])\n\t\tpos = pos + 1\n\t#print('Trans array'+ str(rem_array))\n\treturn rem_array\n\ndef shift_dis_inversion(L, start, end, insert_at):\n\t#transindividual = []\n\tcutrray = []\n\trem_array =[]\n\t#print('Genes of Individual')\n\t#for i in range(len(L)):\n\t\t#print(L[i])\n\tfor i in range(start, end + 1 ,1):\n\t\tcutrray.append(L[i])\n\tfor i in range(start-1,-1,-1):\n\t\trem_array.append(L[i])\n\tif start != len(L) - 1:\n\t\tfor i in range(len(L) - 1,end,-1):\n\t\t\trem_array.append(L[i])\n\telse:\n\t\tfor i in range(end,len(L) - 1,1):\n\t\t\trem_array.append(L[i])\n\t#print('Cut array'+ str(cutrray))\n\t#print('Rem array'+ str(rem_array))\n\tpos = insert_at\n\tfor i in range(len(cutrray)):\n\t\tcutrray.reverse()\n\t\trem_array.insert(pos, cutrray[i])\n\t\tpos = pos + 1\n\t#print('Trans array'+ str(rem_array))\n\treturn rem_array\n","sub_path":"imt_or/proj/py/mutation.py","file_name":"mutation.py","file_ext":"py","file_size_in_byte":5707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"619746911","text":"# Exercise 4: Using a while loop, iterate over the elements and print all those names.\n\nstuff = [\"apples\", \"bananas\", \"junk\", \"yolo\"]\nnumberz = 0\nlengthz = len(stuff)\n#print(\"length is \", lengthz)\nprint(\"Here is your list:\")\nwhile numberz < lengthz:\n print(stuff[numberz])\n numberz = numberz + 1\n \n","sub_path":"class-5/Carl/exercise4.py","file_name":"exercise4.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"143508571","text":"from typing import Dict, Tuple\nfrom configparser import ConfigParser, SectionProxy\nfrom abc import ABCMeta, abstractmethod\nfrom tkinter import LabelFrame\n\n\nclass BaseManager(LabelFrame, metaclass=ABCMeta):\n \"\"\"\" Базовый класс с загрузкой/сохранением для модуля (Manager) \"\"\"\n\n @abstractmethod\n def load(self, config: ConfigParser, module_list: Dict[str, 'BaseManager']) -> None:\n \"\"\" Загрузка данных модуля из ini файла (ConfigParser) \"\"\"\n\n @abstractmethod\n def save(self, config: ConfigParser) -> None:\n \"\"\" Сохранение данных модуля в ini файл (ConfigParser) \"\"\"\n\n def load_font(self, section: SectionProxy, name: str,\n font_name_def: str = \"Helvetica\",\n font_size_def: int = 32,\n is_bold_def: bool = False,\n is_italic_def: bool = False) -> Tuple[str, int, bool, bool]:\n \"\"\" Загрузка шрифта \"\"\"\n if not isinstance(section, SectionProxy):\n raise TypeError(\"section\")\n font_name = section.get(name + \"Name\", fallback=font_name_def)\n font_size = section.getint(name + \"Size\", fallback=font_size_def)\n is_bold = section.getboolean(name + \"Bold\", fallback=is_bold_def)\n is_italic = section.getboolean(name + \"Italic\", fallback=is_italic_def)\n return (font_name, font_size, is_bold, is_italic)\n","sub_path":"ext/base_manager.py","file_name":"base_manager.py","file_ext":"py","file_size_in_byte":1459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"373796476","text":"\"\"\"\nLongest String - Vertically/Horizontally\nThe program must accept a character matrix of size RxC and the position of a cell (X, Y) as the input. The program must form four string values based on the following conditions.\n- The string S1 must be formed by traversing the cells from the given cell vertically towards top.\n- The string S2 must be formed by traversing the cells from the given cell horizontally towards right.\n- The string S3 must be formed by traversing the cells from the given cell vertically towards bottom.\n- The string S4 must be formed by traversing the cells from the given cell horizontally towards left.\nFinally, the program must print the longest string among the four string values as the output. If two or more string values have the same maximum length, the program must print the first occurring string as the output.\nBoundary Condition(s):\n2 <= R, C <= 50\n1 <= X <= R\n1 <= Y <= C\nInput Format:\nThe first line contains R and C separated by a space.\nThe next R lines, each contains C characters separated by a space.\nThe (R+2)nd line contains X and Y separated by a space.\nOutput Format:\nThe first line contains the longest string among the four string values.\nExample Input/Output 1:\nInput:\n7 6\ns C G o f a\nw j o t F i\nb j f g k t\nk G p j E p\nf y w r r G\nB m h h g m\ne y w b m k\n3 3\nOutput:\nfpwhw\nExplanation:\nThe position of the given cell is (3, 3).\nS1 = foG.\nS2 = fgkt.\nS3 = fpwhw.\nS4 = fjb.\nThe string fpwhw is the longest string. So it is printed as the output.\nExample Input/Output 2:\nInput:\n9 5\no u w t f\na B g b D\nl o k a r\nx w F m C\ni d k p w\nj f r k p\nc v x e a\no w o d E\ny o b g G\n5 2\nOutput:\ndwoBu\n\"\"\"\n\nROW,COL = map(int,input().split())\nMATRIX = [input().split() for element in range(ROW)]\nX,Y = map(int,input().split())\nX,Y = X-1,Y-1\nA1,A2,A3,A4 = '','','',''\nfor index in range(X,-1,-1):\n A1 += MATRIX[index][Y]\nfor index2 in range(Y,COL):\n A2 += MATRIX[X][index2]\nfor index in range(X,ROW):\n A3 += MATRIX[index][Y]\nfor index2 in range(Y,-1,-1):\n A4 += MATRIX[X][index2]\nprint(max([A1,A2,A3,A4],key = len))","sub_path":"Python Programs/Longest String - Vertically-Horizontally.py","file_name":"Longest String - Vertically-Horizontally.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"84528449","text":"#django\nfrom django.http import *\nfrom django.shortcuts import render\nfrom django.contrib.auth import *\nfrom django.contrib import messages as ms\n\n#django class based\nfrom django.contrib.auth.decorators import login_required\nfrom django.utils.decorators import method_decorator\nfrom django.views import View\nfrom django.views.generic.base import TemplateView\n\n#models\nfrom cooggerapp.models import Content\n\n#views\nfrom cooggerapp.views.tools import paginator\n\nclass Hashtag(TemplateView):\n template_name = \"card/blogs.html\"\n ctof = Content.objects.filter\n info = \"konu etiketi\"\n\n def get_context_data(self, hashtag, **kwargs):\n if hashtag != \"\":\n queryset = self.ctof(tag__contains = hashtag,status = \"approved\")\n info_of_cards = paginator(self.request,queryset)\n context = super(Hashtag, self).get_context_data(**kwargs)\n html_head = dict(\n title = hashtag+\" | coogger\",\n keywords = hashtag,\n description = hashtag +\" {} altında ki bütün coogger bilgileri\".format(self.info),\n )\n context[\"content\"] = info_of_cards\n context[\"nameofhashtag\"] = hashtag\n context[\"head\"] = html_head\n return context\n\nclass Userlist(TemplateView):\n template_name = \"card/blogs.html\"\n info = \"liste\"\n ctof = Content.objects.filter\n\n def get_context_data(self, list_, **kwargs):\n if list_ != \"\":\n queryset = self.ctof(content_list__contains = list_,status = \"approved\")\n info_of_cards = paginator(self.request,queryset)\n context = super(Userlist, self).get_context_data(**kwargs)\n html_head = dict(\n title = list_+\" | coogger\",\n keywords = list_,\n description = list_ +\" {} altında ki bütün coogger bilgileri\".format(self.info),\n )\n context[\"content\"] = info_of_cards\n context[\"nameofhashtag\"] = list_\n context[\"head\"] = html_head\n return context\n","sub_path":"coogger/cooggerapp/views/explorer.py","file_name":"explorer.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"371517260","text":"#3.3\r\ncontin=10\r\nskill=1\r\nuper=0\r\nti=0\r\nskit=1\r\nfor i in range(1,365):\r\n ti+=1\r\n if 4<=ti<=7:\r\n uper+=0.01\r\n if i % contin == 0 :\r\n uper=0\r\n ti=0\r\n skit=skill\r\n skill*=(1+uper)\r\nprint(\"工作{}，休息一天的方式，365天后的能力值为：{:.2f}\".format(contin,skill))\r\n \r\n \r\n \r\n","sub_path":"Unit3/T3.3.py","file_name":"T3.3.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"345840125","text":"# From Django\nfrom django.utils.translation import ugettext_lazy as _ # To mark strings for translations\nfrom django.db import models\nfrom django.db.models.signals import pre_save, post_save, pre_delete\n\n# Our modules, from models must import from individual files, not models\n# to avoid circular import dependency errors\nfrom infra.models.record_owner import RecordOwner\nfrom infra.custom.fields import CodeField, DescriptionField\nfrom infra.custom.audit_handlers import audit_update_handler, audit_add_handler, audit_delete_handler\n\nclass FieldRestriction(RecordOwner):\n \"\"\"\n Field Restriction is the first model used by FilterManager to\n implement Automatic Filtering, ie row level filtering by User.\n Each Model which needs Automatic Filtering should override\n its default manager to FilterManager.\n\n The other two Models used are ModelRestriction and UserRestriction.\n Needless to say, these Models cannot use FilterManager as default\n manager!\n\n Each entry here is for one Field to be restricted, but each Field may\n have many entries in this table. But it is common for one Field to \n have only one Restriction entry, hence they are named similarly.\n \"\"\"\n # Each Restriction has a Unique Code\n restriction_code = CodeField(verbose_name=_(\"Restriction Code\"), unique=True,\n help_text=_(\"Please enter a unique code\"))\n # and a description\n restriction_description = DescriptionField(verbose_name=_(\"Description\"), null=False, blank=False,\n help_text=_(\"A helpful description of this Restriction\"))\n # This restriction is for this field\n field_name = models.CharField(verbose_name=_(\"Field Name\"), max_length=60,\n help_text=_(\"Field to use for predicate to be appended to filter()\"))\n\n class Meta:\n ordering = ['restriction_code']\n verbose_name = _(\"Field Restriction\")\n app_label = 'infra'\n db_table = 'if_field_restriction'\n\n def __unicode__(self):\n return unicode(self.restriction_code)\n\n# Register the audit update handler\npre_save.connect(audit_update_handler, sender=FieldRestriction)\n# Register the audit add handler\npost_save.connect(audit_add_handler, sender=FieldRestriction)\n# Register the audit delete handler\npre_delete.connect(audit_delete_handler, sender=FieldRestriction)\n","sub_path":"infra/models/field_restriction.py","file_name":"field_restriction.py","file_ext":"py","file_size_in_byte":2348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"322664318","text":"import pygame\nimport sys\nfrom pygame.locals import *\n\npygame.init()\n\nwhite = (255,255,255)\nblack = (0,0,0)\nbg = black\n\nfps = 1\ndispWidth = 800\ndispHeight = 600\ncellSize = 10\n\nUP = \"up\"\nDOWN = \"down\"\nRIGHT = \"right\"\nLEFT = \"left\"\n\ndef runGame():\n startx = 3\n starty = 3\n coords = [{'x': startx, 'y': starty}]\n\n \n direction = \"RIGHT\"\n \n \n while True:\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n \n elif event.type == KEYDOWN:\n if event.key == K_LEFT:\n direction = 'LEFT'\n elif event.key == K_RIGHT:\n direction = 'RIGHT'\n elif event.key == K_UP:\n direction = 'UP'\n \n elif event.key == K_DOWN:\n direction = 'DOWN'\n \n \n print(direction)\n if direction == 'UP':\n \n newCell = {'x':coords[0]['x'],'y':coords[0]['y']-1 } \n \n elif direction == 'DOWN':\n \n newCell = {'x':coords[0]['x'],'y':coords[0]['y']+1 }\n \n elif direction == 'LEFT':\n \n newCell = {'x':coords[0]['x']-1,'y':coords[0]['y']}\n \n elif direction == 'RIGHT':\n \n newCell = {'x':coords[0]['x']+1,'y':coords[0]['y']}\n \n print(newCell) \n del coords[-1]\n \n coords.insert(0, newCell)\n setDisplay.fill(bg)\n drawCell(coords)\n pygame.display.update()\n fpsTime.tick(fps)\n \n \ndef drawCell(coords):\n \n for coord in coords:\n x = coord['x']*cellSize\n y = coord['y']*cellSize\n makeCell = pygame.Rect(x,y,cellSize,cellSize)\n pygame.draw.rect(setDisplay,white,makeCell)\n \n\nwhile True:\n global fpsTime\n global setDisplay\n fpsTime = pygame.time.Clock()\n setDisplay = pygame.display.set_mode((dispWidth,dispHeight))\n pygame.display.set_caption(\"Mein Spiel\")\n runGame()\n ","sub_path":"Spieleentwicklung/Grundlagen_Tasten_eingabe.py","file_name":"Grundlagen_Tasten_eingabe.py","file_ext":"py","file_size_in_byte":2152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"630228797","text":"\"\"\"\n* Going to add a sample at a time\n* Need to keep a mask? End index? of whose samples have finished their games\n* \n\"\"\"\nimport numpy as np\nimport torch\nimport torch.testing\nfrom rebar import arrdict\n\ndef update_indices(starts, current):\n # This bit of madness is to generate the indices that need to be updated,\n # ie if starts[2] is 7 and current is 10, then the output will have \n # (7, 8, 9) somewhere in `ts`, and `bs` will have (2, 2, 2) in the \n # corresponding cells.\n #\n # It'd be a lot easier if PyTorch had a forward-fill.\n counts = current - starts\n ones = starts.new_ones((counts.sum(),))\n cumsums = counts.cumsum(0)[:-1]\n ones[cumsums] = -counts[:-1]+1\n\n ds = ones.cumsum(0)-1\n\n bs = torch.zeros_like(ds)\n bs[counts[:-1].cumsum(0)] = starts.new_ones((len(starts)-1,))\n bs = bs.cumsum(0)\n\n ts = starts[bs] + ds\n return ts, bs\n\nclass Buffer:\n\n def __init__(self, length, keep=1.):\n self.length = length\n self._buffer = None\n self.keep = keep\n\n def update_targets(self, terminal, rewards):\n if terminal.any():\n ts, bs = update_indices(self._ready[terminal], self.current)\n bs = terminal.nonzero(as_tuple=False).squeeze(1)[bs]\n\n self._buffer.targets[ts % self.length, bs] = rewards[bs]\n self._ready[terminal] = self.current\n\n def add_raw(self, subset, terminal, rewards):\n if self._buffer is None:\n self.device = terminal.device\n self.n_envs = terminal.size(0)\n self.n_seats = rewards.shape[-1]\n self.ts = torch.arange(self.length, device=self.device)\n self.bs = torch.arange(self.n_envs, device=self.device)\n\n self._buffer = subset.map(lambda x: x.new_zeros((self.length, *x.shape)))\n self._buffer['targets'] = torch.zeros((self.length, self.n_envs, self.n_seats), device=self.device, dtype=torch.half)\n\n self.current = 0\n self._ready = torch.zeros((self.n_envs,), device=self.device, dtype=torch.long)\n\n if np.random.rand() <= self.keep:\n self._buffer[self.current % self.length] = arrdict.arrdict(\n **subset,\n targets=torch.zeros((self.n_envs, self.n_seats), device=self.device, dtype=torch.half))\n self.current = self.current + 1\n self.update_targets(terminal, rewards)\n\n def add(self, sample):\n \"\"\"Expects the obs to precede the transition\"\"\"\n # Conversions here take a 1600B sample down to a 600B sample \n subset = arrdict.arrdict(\n obs=sample.worlds.obs.byte(),\n valid=sample.worlds.valid,\n seats=sample.worlds.seats.byte(),\n logits=sample.decisions.logits.half())\n return self.add_raw(subset, sample.transitions.terminal, sample.transitions.rewards.half())\n\n def ready(self):\n return (self._ready > 0).all()\n\n def sample(self, size):\n bs = torch.randint(0, self.n_envs, device=self.device, size=(size,))\n\n rs = torch.rand(device=self.device, size=(size,))\n start = max(self.current - self.length, 0)\n ends = self._ready[bs]\n if (ends == start).any():\n raise ValueError('No ready trajectories to draw from')\n ts = rs*(ends - start) + start\n\n sample = self._buffer[ts.long() % self.length, bs]\n return arrdict.arrdict(\n obs=sample.obs.float(),\n valid=sample.valid,\n seats=sample.seats.int(),\n logits=sample.logits.float(),\n targets=sample.targets.float())\n\ndef test_update_indices():\n starts = torch.tensor([7])\n current = 10\n ts, bs = update_indices(starts, current)\n torch.testing.assert_allclose(ts, torch.tensor([7, 8, 9]))\n torch.testing.assert_allclose(bs, torch.tensor([0, 0, 0]))\n\n starts = torch.tensor([7, 5])\n current = 10\n ts, bs = update_indices(starts, current)\n torch.testing.assert_allclose(ts, torch.tensor([7, 8, 9, 5, 6, 7, 8, 9]))\n torch.testing.assert_allclose(bs, torch.tensor([0, 0, 0, 1, 1, 1, 1, 1]))\n\n starts = torch.tensor([9])\n current = 10\n ts, bs = update_indices(starts, current)\n torch.testing.assert_allclose(ts, torch.tensor([9]))\n torch.testing.assert_allclose(bs, torch.tensor([0]))\n\ndef test_add_raw():\n # Check linear behaviour\n buffer = Buffer(3)\n buffer.add_raw(\n arrdict.arrdict(),\n torch.tensor([False]),\n torch.tensor([[0.]]))\n buffer.add_raw(\n arrdict.arrdict(),\n torch.tensor([True]),\n torch.tensor([[1.]]))\n torch.testing.assert_allclose(\n buffer._buffer.targets,\n torch.tensor([[[1.]], [[1.]], [[0.]]]))\n\n # Check wrapped behaviour\n buffer = Buffer(3)\n buffer.add_raw(\n arrdict.arrdict(),\n torch.tensor([False]),\n torch.tensor([[0.]]))\n buffer.add_raw(\n arrdict.arrdict(),\n torch.tensor([True]),\n torch.tensor([[0.]]))\n buffer.add_raw(\n arrdict.arrdict(),\n torch.tensor([False]),\n torch.tensor([[0.]]))\n buffer.add_raw(\n arrdict.arrdict(),\n torch.tensor([True]),\n torch.tensor([[1.]]))\n torch.testing.assert_allclose(\n buffer._buffer.targets, \n torch.tensor([[[1.]], [[0.]], [[1.]]]))\n\ndef test_buffer():\n n_envs = 3\n bs = torch.arange(n_envs)\n ts = torch.zeros_like(bs)\n durations = bs+1\n\n buffer = Buffer(5)\n\n for _ in range(8):\n terminal = (ts+1) % durations == 0\n rewards = (ts+1)*terminal.float()\n buffer.add_raw(\n arrdict.arrdict(ts=ts, bs=bs),\n terminal,\n rewards[..., None])\n ts = ts + 1\n\n torch.testing.assert_allclose(\n buffer._buffer.targets,\n torch.tensor([[6., 6., 6.],\n [7., 8., 0.],\n [8., 8., 0.],\n [4., 4., 6.],\n [5., 6., 6.]])[..., None])\n\n for _ in range(10):\n sample = buffer.sample(3)\n ds = durations[sample.bs]\n expected = (sample.ts // ds + 1)*ds\n torch.testing.assert_allclose(sample.targets, expected[..., None].float())\n","sub_path":"boardlaw/buffering.py","file_name":"buffering.py","file_ext":"py","file_size_in_byte":6142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"119037158","text":"# -*- coding: utf-8 -*-\n\nfrom openerp import models, fields, api\n\nclass SaleOrder(models.Model):\n\t_inherit = 'sale.order'\n\n\ttax_ids = fields.Many2many(\n\t\t'account.tax',\n\t\tstring='Taxes',\n\t\thelp='Note: this will be put on all Order Lines below.',\n\t\tstates={'draft': [('readonly', False)]},\n\t\tdomain=['|', ('active', '=', False), ('active', '=', True)]\n\t)\n\n\t@api.onchange(\"tax_ids\")\n\tdef onchange_tax_ids(self):\n\t\tfor o in self.order_line:\n\t\t\to.tax_id = self.tax_ids\n\n\nclass AccountInvoice(models.Model):\n\t_inherit = 'account.invoice'\n\n\ttax_ids = fields.Many2many(\n\t\t'account.tax',\n\t\tstring='Taxes',\n\t\thelp='Note: this will be put on all Invoice Lines below.',\n\t\tstates={'draft': [('readonly', False)]},\n\t\tdomain=[('parent_id', '=', False), '|', ('active', '=', False), ('active', '=', True)]\n\t)\n\n\t@api.onchange(\"tax_ids\")\n\tdef onchange_tax_ids(self):\n\t\tfor o in self.invoice_line:\n\t\t\to.invoice_line_tax_id = self.tax_ids","sub_path":"so_tax/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"297634569","text":"def Is_cross_str(s1,s2,s3):\n\tlength_1=len(s1)\n\tlength_2=len(s2)\n\tlength_3=len(s3)\n\tif length_1+length_2!=length_3:\n\t\tprint('exception parameter')\n\telse:\n\t\tdp=[[False for i in range(0,len(s1))] for j in range(0,len(s2))]\n\t\tdp[0][0]=True\n\t\t\n\t\tfor c in range(1,len(s1)+1):\n\t\t\tdp[c][0]=dp[c-1][0] and s1[c-1]==s3[c-1]\n\t\tfor c in range(1,len(s2)+1):\n\t\t\tdp[0][c]=dp[0][c-1] and s2[c-1]==s3[c-1]\n\t\tfor i in range(1,len(s1)+1):\n\t\t\tfor j in rangr(1,le(s2)+1):\n\t\t\t\tdp[i][j]=(dp[i][j-1] and s1[i-1]==s3[i+j-1]) or (dp[j-1][i] and s2[j-1]==s3[i+j-1])\n\tprint(str(dp[-1][-1]))\n\t\n\ns1=\"acc\"\ns2=\"da\"\ns3=\"adacc\"\nIs_cross_str(s1,s2,s3)\n\n\n\t","sub_path":"python/Day03/T13.py","file_name":"T13.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"328055456","text":"# -*- coding: utf-8 -*-\r\n__author__ = 'baio'\r\n\r\nimport unittest\r\nfrom storage import mongo_storage as mongo\r\nfrom converters.contrib2gexf import merge_xml_elements\r\nfrom server.server_post_links import update_links\r\n\r\nclass TestServer(unittest.TestCase):\r\n\r\n @unittest.skip(\"demonstrating skipping\")\r\n def test_gen_gexf(self):\r\n edges = list(mongo.get_edges())\r\n nodes = list(mongo.get_nodes())\r\n merge_xml_elements(edges, nodes, \"gephi/layout.gexf\")\r\n\r\n def test_update_links(self):\r\n update_links([\"елена скрынник-виктор христенко-брат.служба,http://goo.gl/ohEX4\"])\r\n\r\nif __name__ == '__main__':\r\n unittest.main()\r\n\r\n\r\n","sub_path":"py/links/tests/server_test.py","file_name":"server_test.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"227593719","text":"from CSVParser import CSVParser\nimport numpy as np\n# import numpy\nimport matplotlib.pyplot as plt\nfrom scipy.fftpack import fft\n\ncsvParser = CSVParser()\ndata = csvParser.getDataForKey(csvParser.getPositiveData(),\"zeroBasedPupilList\")\n\nsamplingRate = 1000\nwindowSize = 40\nN = windowSize\nT = 1 / samplingRate\nx = np.linspace(0, (1/(2 * T)), int(N/2))\n\nyr = fft(data[0][0:windowSize])\ny = 2/N * np.abs(yr[0:np.int(N/2)])\nmaxY = max(y)\nresult = np.where(y == [maxY])\n# print(len(data[0][0:150]))\nprint(result[0])\nplt.plot(x,y)\nplt.show()\n","sub_path":"DataExtractor/FFTTest.py","file_name":"FFTTest.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"633928539","text":"import sqlite3\n\ndef create_connection(db_file):\n \"\"\" create a database connection to the SQLite database\n specified by db_file\n :param db_file: database file\n :return: Connection object or None\n \"\"\"\n try:\n conn = sqlite3.connect(db_file)\n return conn\n except Error as e:\n print(e)\n \n return None\n\ndef create_table(conn, create_table_sql):\n \"\"\" create a table from the create_table_sql statement\n :param conn: Connection object\n :param create_table_sql: a CREATE TABLE statement\n :return:\n \"\"\"\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)\n\ndef main():\n database = \"pythonsqlite.db\"\n \n sql_create_user_table = \"\"\"CREATE TABLE IF NOT EXISTS utilizador (\n id integer PRIMARY KEY,\n \t nome text NOT NULL\n \t); \"\"\"\n\n sql_create_conta_table = \"\"\"CREATE TABLE IF NOT EXISTS conta (\n id integer PRIMARY KEY,\n tipo text NOT NULL,\n montante integer NOT NULL,\n utilizador_id integer NOT NULL,\n FOREIGN KEY (utilizador_id) REFERENCES utilizador (id)\n );\"\"\"\n \n # create a database connection\n conn = create_connection(database)\n if conn is not None:\n\n # create tables\n create_table(conn, sql_create_user_table)\n create_table(conn, sql_create_conta_table)\n \n else:\n print(\"Error! cannot create the database connection.\")","sub_path":"db_create.py","file_name":"db_create.py","file_ext":"py","file_size_in_byte":1689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"179289021","text":"#!/usr/bin/env python3\n\n##########################################################################\n# #\n# PYCC #\n# #\n# This program is free software; you can redistribute it and/or modify #\n# it under the terms of the GNU Library General Public License as #\n# published by the Free Software Foundation; version 2 or later of the #\n# License. #\n# #\n# This program is distributed in the hope that it will be useful, #\n# but WITHOUT ANY WARRANTY; without even the implied warranty of #\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #\n# GNU General Public License for more details. #\n# #\n# Copyright 2020 Paul RASCLE www.openfields.fr #\n# #\n##########################################################################\n\nimport os\nimport sys\nimport math\nfrom gendata import getSampleCloud, getSampleCloud2, dataDir, isCoordEqual, createSymbolicLinks\nimport cloudComPy as cc\ncreateSymbolicLinks() # required for tests on build, before cc.initCC\n\ncc.initCC() # to do once before using plugins or dealing with numpy\n\ncloud1 = cc.loadPointCloud(getSampleCloud2(3.0,0, 0.1))\ncloud1.setName(\"cloud1\")\ntr1 = cc.ccGLMatrix()\ntr1.initFromParameters(0.2*math.pi, (1., 1., 1.), (0.0, 0.0, 10.0))\ncloud1.applyRigidTransformation(tr1)\n\nplane = cc.ccPlane.Fit(cloud1)\nequation = plane.getEquation()\neqRef = [0.4032580554485321, -0.2757962644100189, 0.8725361824035645, 8.782577514648438]\nfor i in range(4):\n if not math.isclose(equation[i], eqRef[i], rel_tol=1.e-3):\n raise RuntimeError\n\ncc.SaveEntities([cloud1, plane], os.path.join(dataDir, \"cloudsFit.bin\"))\n\n\n\n\n","sub_path":"tests/test012.py","file_name":"test012.py","file_ext":"py","file_size_in_byte":2141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"181285546","text":"import boto3\nfrom boto3.dynamodb.conditions import Key\n\n# connect to Dynamo\ntry:\n dynamodb = boto3.resource('dynamodb')\n table = dynamodb.Table('answersnow_prompts')\nexcept:\n print('unable to connect to db')\n\ndef decipher_intent(prompt_name, user_response):\n print(prompt_name,user_response)\n\n #node = monty_response[prompt_name]\n node = table.query(\n KeyConditionExpression=Key('prompt_now').eq(prompt_name)\n )['Items'][0]['prompt']\n \n # Essentially, return next prompt\n if not user_response:\n return prompt_name, node['text'], True if len(node['responses']) == 0 else False\n else:\n for resp in node['responses'].keys():\n response = node['responses'][resp]\n for word in response['utterances'].split(\",\"):\n if word.strip() in user_response.lower():\n return decipher_intent(response['next_prompt'],None)\n return (prompt_name,'-> ' + node['text'],False)\n \n","sub_path":"slackbot_tipy/di/intent.py","file_name":"intent.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"109686672","text":"# https://projecteuler.net/problem=89\nimport csv\nfrom time import time\nT = time()\n\n\n# get the numerical value of the roman numeral in string s\n# s has to be valid in this implementation\ndef roman_numeral_value(s):\n d = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}\n val = 0\n for i in range(len(s)-1):\n if d[s[i]] < d[s[i+1]]:\n val -= d[s[i]]\n else:\n val += d[s[i]]\n\n val += d[s[-1]]\n\n return val\n\n\ndef shortest_roman_numeral(a):\n numbers = {1000: 'M', 900: 'CM', 500: 'D', 400: 'CD', 100: 'C',\n 90: 'XC', 50: 'L', 40: 'XL', 10: 'X', 9: 'IX', 5: 'V', 4: 'IV', 1: 'I'}\n res = ''\n r = a\n while r != 0:\n for i in sorted(numbers)[::-1]:\n if i <= r:\n res += numbers[i]\n break\n\n r -= i\n\n return res\n\ndata = []\nwith open('data/p089_roman.txt') as csvfile:\n reader = csv.reader(csvfile)\n for row in reader:\n data.append(row[0])\n\nsaved = 0\nfor roman in data:\n val = roman_numeral_value(roman)\n short = shortest_roman_numeral(val)\n saved += len(roman) - len(short)\n\nprint(saved)\nprint(\"time elapsed:\", time() - T)\n","sub_path":"089.py","file_name":"089.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"407590212","text":"import glob\nimport os\nimport librosa\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport numpy as np\nfrom audioSignal import AudioSignal\nimport matplotlib.pyplot as plt\nplt.style.use('ggplot')\n\ndef windows(data, window_size):\n start = 0\n while start < len(data):\n yield start, start + window_size\n start += (window_size // 2)\ndef completArray():\n d=0\ndef extract_features(parent_dir,sub_dirs,file_ext=\"*.wav\",bands = 20, frames = 11):\n mfccs = []\n labels = []\n for l, sub_dir in enumerate(sub_dirs):\n for fn in glob.glob(os.path.join(parent_dir, sub_dir, file_ext)):\n sound_clip,s = librosa.load(fn)\n label = fn.split('fold')[1].split('/')[0]\n tmp = AudioSignal(fn)\n tmp.normalizeEnergy() \n tmp.signalFeatures(0.01, 0.025)\n mfccs.append(tmp.featuresMFCC[0:11] ) \n labels.append(label) \n features = np.asarray(mfccs).reshape(len(mfccs),frames,bands)\n return np.array(features), np.array(labels,dtype = np.int)\n\ndef one_hot_encode(labels):\n n_labels = len(labels)\n n_unique_labels = len(np.unique(labels))\n one_hot_encode = np.zeros((n_labels,10))\n #print(one_hot_encode)\n #one_hot_encode[np.arange(n_labels), labels] = 1\n for i in range(n_labels):\n one_hot_encode[i][labels[0]-1] = 1\n \n return one_hot_encode\n\n # use this to process the audio files into numpy arrays\ndef save_folds(data_dir):\n for k in range(1,11):\n fold_name = 'fold' + str(k)\n print(\"\\nSaving \" + fold_name)\n features, labels = extract_features(parent_dir, [fold_name])\n labels = one_hot_encode(labels)\n \n #print(\"Features of\", fold_name , \" = \", features.shape)\n #print(\"Labels of\", fold_name , \" = \", labels.shape)\n \n feature_file = os.path.join(data_dir, fold_name + '_x.npy')\n labels_file = os.path.join(data_dir, fold_name + '_y.npy')\n np.save(feature_file, features)\n #print(\"Saved \" + feature_file)\n np.save(labels_file, labels)\n #print(\"Saved \" + labels_file)\n #print(labels)\n\ndef assure_path_exists(path):\n mydir = os.path.join(os.getcwd(), path)\n if not os.path.exists(mydir):\n os.makedirs(mydir)\n \n#uncomment this to recreate and save the feature vectors\nparent_dir = \"/home/ariel/TEC/patrones/proyecto2/audioRNN/\" # Where you have saved the UrbanSound8K data set\" \nsave_dir = \"/home/ariel/TEC/patrones/proyecto2/audioRNN/data\"\nassure_path_exists(save_dir)\nsave_folds(save_dir)\n\n# this is used to load the folds incrementally\ndef load_folds(folds):\n subsequent_fold = False\n for k in range(len(folds)):\n fold_name = 'fold' + str(folds[k])\n feature_file = os.path.join(data_dir, fold_name + '_x.npy')\n labels_file = os.path.join(data_dir, fold_name + '_y.npy')\n loaded_features = np.load(feature_file)\n loaded_labels = np.load(labels_file)\n print (fold_name, \"features: \", loaded_features.shape)\n\n if subsequent_fold:\n features = np.concatenate((features, loaded_features))\n labels = np.concatenate((labels, loaded_labels))\n else:\n features = loaded_features\n labels = loaded_labels\n subsequent_fold = True\n \n return features, labels\ndata_dir = \"/home/ariel/TEC/patrones/proyecto2/audioRNN/data\"\n\ndef extract_feature_array(filename, bands = 20, frames = 11):\n mfccs = []\n sound_clip,s = librosa.load(filename)\n tmp = AudioSignal(filename)\n tmp.normalizeEnergy() \n tmp.signalFeatures(0.01, 0.025)\n mfccs.append(tmp.featuresMFCC[0:11])\n \n features = np.asarray(mfccs)\n return np.array(features)\n\nsample_filename = \"samples/us8k/music.wav\"\nfeatures = extract_feature_array(sample_filename)\ndata_points, _ = librosa.load(sample_filename)\nprint (\"IN: Initial Data Points =\", len(data_points))\nprint (\"OUT: Total features =\", np.shape(features)) \n\ntf.set_random_seed(0)\nnp.random.seed(0)\n\ndef evaluate(model):\n y_prob = model.predict_proba(test_x, verbose=0)\n y_pred = y_prob.argmax(axis=-1)\n y_true = np.argmax(test_y, 1)\n\n #roc = roc_auc_score(test_y, y_prob)\n #print (\"ROC:\", round(roc,3))\n\n # evaluate the model\n score, accuracy = model.evaluate(test_x, test_y, batch_size=32)\n print(\"\\nAccuracy = {:.2f}\".format(accuracy))\n\n # the F-score gives a similiar value to the accuracy score, but useful for cross-checking\n p,r,f,s = precision_recall_fscore_support(y_true, y_pred, average='micro')\n print (\"F-Score:\", round(f,2))\n \n return accuracy\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Dense, Dropout\nfrom sklearn.metrics import precision_recall_fscore_support, roc_auc_score\nfrom keras.utils import np_utils\nfrom keras.callbacks import EarlyStopping\n\ndata_dim = 20\ntimesteps = 11\nnum_classes = 10\n\n# expected input data shape: (batch_size, timesteps, data_dim)\nmodel = Sequential()\n\n# returns a sequence of vectors of dimension 512\nmodel.add(LSTM(300, return_sequences=True, input_shape=(timesteps, data_dim))) \n\nmodel.add(Dropout(0.2))\n\n# return a single vector of dimension 128\nmodel.add(LSTM(16)) \n\nmodel.add(Dropout(0.2))\n\n# apply softmax to output\nmodel.add(Dense(num_classes, activation='softmax'))\n\n\n# compile the model for multi-class classification\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n\ndef getTest(num,datax,datay):\n test_x=[]\n test_y=[]\n idx = np.random.randint(len(datax), size=num)\n test_x=datax[idx,:]\n test_y=datay[idx,:]\n train_x= np.delete(datax, idx,0)\n train_y= np.delete(datay, idx,0)\n print(\"shape train\",train_x.shape,train_y.shape)\n return train_x,train_y,test_x,test_y\n\n# load fold1 for testing\ndatax, datay = load_folds([1,2,3,4,5,6,7,8,9,10])\nprint(datax.shape,datay.shape)\ntrain_x,train_y,test_x,test_y=getTest(30,datax,datay)\nprint(\"wwwwwww\")\n#print(train_x.shape)\n#print(train_x)\n# load fold2 for validation\nvalid_x, valid_y = load_folds([9])\n\n# a stopping function to stop training before we excessively overfit to the training set\nearlystop = EarlyStopping(monitor='val_loss', patience=0, verbose=1, mode='auto')\n\nmodel.fit(train_x, train_y, batch_size=128, nb_epoch=30, validation_data=(test_x, test_y)) \n\nprint(\"Evaluating model...\")\nacc = evaluate(model)\n\nfrom sklearn.metrics import confusion_matrix\nimport seaborn as sn\nimport pandas as pd\n\nlabels = [\"uno\",\"dos\",\"tres\",\"cuatro\",\"cinco\",\"seis\",\"siete\",\"ocho\",\"nueve\",\"diez\"]\nprint (\"Showing Confusion Matrix\")\ny_prob = model.predict_proba(test_x, verbose=0)\ny_pred = y_prob.argmax(axis=-1)\ny_true = np.argmax(test_y, 1)\ncm = confusion_matrix(y_true, y_pred)\n#df_cm = pd.DataFrame(cm, labels, labels)\nplt.figure(figsize = (16,8))\nsn.heatmap(cm, annot=True, annot_kws={\"size\": 14}, fmt='g', linewidths=.5)\ntick_marks = np.arange(len(labels))\nplt.xticks(tick_marks, labels, rotation=45)\nplt.yticks(tick_marks, labels)\nplt.show()\nsound_file_paths = [\"uno_22_1.wav\",\"dos_11_2.wav\",\"tres_26_2.wav\",\"cuatro_18_2.wav\",\"cinco_30_3.wav\", \n\"seis_27_3.wav\",\"siete_7_3.wav\",\"ocho_10_3.wav\",\"nueve_25_3.wav\",\"diez_24_1.wav\"]\nsound_names = [\"uno\",\"dos\",\"tres\",\"cuatro\",\"cinco\",\"seis\",\n \"siete\",\"ocho\",\"nueve\",\"diez\"]\nparent_dir = 'samples/digits/'\n\n\n# create predictions for each of the sound classes\nfor s in range(len(sound_file_paths)):\n\n print (\"\\n----- \", sound_names[s], \"-----\")\n # load audio file and extract features\n predict_file = parent_dir + sound_file_paths[s]\n predict_x = extract_feature_array(predict_file)\n \n # generate prediction, passing in just a single row of features\n predictions = model.predict(predict_x)\n \n if len(predictions) == 0: \n print (\"No prediction\")\n continue\n \n #for i in range(len(predictions[0])):\n # print sound_names[i], \"=\", round(predictions[0,i] * 100, 1)\n \n # get the indices of the top 2 predictions, invert into descending order\n ind = np.argpartition(predictions[0], -2)[-2:]\n ind[np.argsort(predictions[0][ind])]\n ind = ind[::-1]\n \n print (\"Top guess: \", sound_names[ind[0]], \" (\",round(predictions[0,ind[0]],3),\")\")\n print (\"2nd guess: \", sound_names[ind[1]], \" (\",round(predictions[0,ind[1]],3),\")\")","sub_path":"audioClass2.py","file_name":"audioClass2.py","file_ext":"py","file_size_in_byte":8308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"458494877","text":"from flask import Flask, redirect\n\napp = Flask(__name__)\n\n\npathsToUrls = {\n \"test-google\": \"https://google.com\",\n \"test-face\": \"https://facebook.com\",\n \"main-landing\": \"https://bloomberg.com\"\n}\n\n\n@app.route('/')\ndef index():\n return '

Hello

'\n\n\n@app.route(\"/\")\ndef shorter_url(short_url):\n redirected_url = pathsToUrls.get(short_url, \"https://bloomberg.com\")\n return redirect(redirected_url)\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n","sub_path":"urlshort/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"153010504","text":"# -*-coding:utf-8-*-\n#Project:CH-02,statistical learning model\n#author:JumpingBear12138\nimport numpy as np\nimport argparse\nclass Perceptron:\n def __init__(self,max_iter = 50,eta = 0.01,verbose = False):\n self.max_iter = max_iter#max number of the iteration\n self.eta = eta#learning rate\n self.verbose = verbose#Decide whether to print the result after every step.\n self.w = 0#parameters waiting to be trained\n def trainer(self,input_path):\n data = np.loadtxt(input_path)\n X = data[:,:-1]\n Y = data[:,-1]\n self.w = np.zeros(X.shape[1]+1)#combine w and b, putting b at the last index of w\n iterCount = 0\n while iterCount < self.max_iter:\n flag = True\n for i in range(X.shape[0]):\n x = np.hstack([X[i],1])\n y = 2*Y[i]-1#chang 0,1 into -1,1.\n if x.dot(self.w)*y<=0:#find a wrong case\n flag = False\n self.w += self.eta*y*x#use SGD to train the model\n iterCount += 1\n if self.verbose:\n s = [str(self.w[i]) for i in range(len(self.w)-1)]\n print(\"iterations: \"+str(iterCount)+\" \\nw:\"+\" \".join(s)+\" \\nb:\"+str(self.w[-1]))\n else:\n print(\"iterations: \"+str(iterCount))\n break\n if flag:\n break\n print(self.w)\n print(\"Finish\")\n #dual form to train\n def trainer_dual_form(self,input_path):\n data = np.loadtxt(input_path)\n X = data[:,:-1]\n xx = X.dot(X.T)\n alpha = np.zeros(X.shape[0])\n Y = data[:,-1]\n Y = 2*Y-1\n b = 0\n iterCount = 0\n while iterCount < self.max_iter:\n flag = True\n self.w = np.zeros(X.shape[1]+1)\n for i in range(X.shape[0]):\n alpha_y = np.multiply(alpha,Y)\n if Y[i]*(alpha_y.dot(xx[:,i])+b)<=0:\n iterCount+=1\n flag = False\n alpha[i] = alpha[i] + self.eta\n b = b + self.eta*Y[i]\n if self.verbose:\n s = [str(alpha[i]) for i in range(X.shape[0])]\n print(\"iterations: \"+str(iterCount)+\" \\nw:\"+\" \".join(s)+\" \\nb:\"+str(b))\n #print(\"iterations: \"+str(iterCount)+\" \\nw:\"+\" \".join(s)+\" \\nb:\"+str(self.w[-1]))\n else:\n print(\"iterations: \"+str(iterCount))\n break\n if flag:\n break\n for i in range(X.shape[0]):\n self.w[:-1] += alpha[i]*Y[i]*X[i]\n self.w[-1] = b\n print(self.w)\n print(\"Finish\")\n\n def predictor(self,X):\n X = np.hstack((X,np.ones((X.shape[0],1))))\n result = [1 if rst else -1 for rst in X.dot(self.w) > 0]\n return result\nif __name__ == \"__main__\":\n p = Perceptron()\n p.trainer(\"data/train.txt\")\n p.trainer_dual_form(\"data/train.txt\")\n\n \n\n\n\n","sub_path":"algo1-perceptron/Perceptron.py","file_name":"Perceptron.py","file_ext":"py","file_size_in_byte":3070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"650057494","text":"import heapq\n\ndef make_edge():\n for i in range(N):\n for j in range(i+1,N):\n d = ((X[i] - X[j])**2 + (Y[i] - Y[j])**2)*E\n heapq.heappush(distance, (d,i,j))\n\ndef find_set(v):\n if v == p[v]:\n return p[v]\n return find_set(p[v])\n\ndef union(v,u):\n root1 = find_set(v)\n root2 = find_set(u)\n\n if root1 > root2:\n p[root1] = root2\n else:\n p[root2] = root1\n\nT = int(input())\n\nfor tc in range(1,T+1):\n N = int(input())\n X = list(map(int, input().split()))\n Y = list(map(int, input().split()))\n E = float(input())\n p = [i for i in range(N)]\n distance = []\n make_edge()\n\n cnt = 0\n ans = 0\n while cnt != N-1:\n d, a, b = heapq.heappop(distance)\n if find_set(a) != find_set(b):\n ans += d\n cnt += 1\n union(a,b)\n ans = round(ans)\n print(\"#{} {}\".format(tc, ans))","sub_path":"swea/하나로.py","file_name":"하나로.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"22765147","text":"import tempfile\nimport os\nimport os.path as op\nimport enum\nimport logging\n\nimport numpy as np\n\nimport dipy.tracking.streamlinespeed as dps\n\nimport AFQ.viz.utils as vut\n\ntry:\n import plotly\n import plotly.graph_objs as go\n import plotly.io as pio\nexcept ImportError:\n raise ImportError(vut.viz_import_msg_error(\"plotly\"))\n\nscope = pio.kaleido.scope\nviz_logger = logging.getLogger(\"AFQ.viz\")\n\n\ndef _inline_interact(figure, show, show_inline):\n \"\"\"\n Helper function to reuse across viz functions\n \"\"\"\n if show:\n viz_logger.info(\"Creating interactive figure in HTML file...\")\n plotly.offline.plot(figure)\n\n if show_inline:\n viz_logger.info(\"Creating interactive figure inline...\")\n plotly.offline.init_notebook_mode()\n plotly.offline.iplot(figure)\n\n return figure\n\n\ndef _to_color_range(num):\n if num < 0:\n num = 0\n if num >= 0.999:\n num = 0.999\n return num\n\n\ndef _color_arr2str(color_arr, opacity=1.0):\n return (\n f\"rgba({_to_color_range(color_arr[0])}, \"\n f\"{_to_color_range(color_arr[1])}, \"\n f\"{_to_color_range(color_arr[2])}, \"\n f\"{_to_color_range(opacity)})\"\n )\n\n\ndef set_layout(figure, color=None):\n if color is None:\n color = f\"rgba(0,0,0,0)\"\n\n figure.update_layout(\n plot_bgcolor=color,\n scene=dict(\n xaxis=dict(\n showbackground=False, showticklabels=False, title=''),\n yaxis=dict(\n showbackground=False, showticklabels=False, title=''),\n zaxis=dict(\n showbackground=False, showticklabels=False, title='')\n )\n )\n\n\ndef _draw_streamlines(figure, sls, dimensions, color, name, cbv=None,\n cbv_lims=[None, None], flip_axes=[False, False, False]):\n color = np.asarray(color)\n\n if len(sls._offsets) > 1:\n plotting_shape = (sls._data.shape[0] + sls._offsets.shape[0])\n else:\n plotting_shape = sls._data.shape[0]\n # dtype object so None can be stored\n x_pts = np.zeros(plotting_shape)\n y_pts = np.zeros(plotting_shape)\n z_pts = np.zeros(plotting_shape)\n\n if cbv is not None:\n if cbv_lims[0] is None:\n cbv_lims[0] = 0\n if cbv_lims[1] is None:\n cbv_lims[1] = cbv.max()\n\n customdata = np.zeros(plotting_shape)\n line_color = np.zeros((plotting_shape, 3))\n color_constant = (color / color.max())\\\n * (1.4 / (cbv_lims[1] - cbv_lims[0])) + cbv_lims[0]\n else:\n customdata = np.zeros(plotting_shape)\n line_color = np.zeros((plotting_shape, 3))\n color_constant = color\n\n for sl_index, plotting_offset in enumerate(sls._offsets):\n sl_length = sls._lengths[sl_index]\n sl = sls._data[plotting_offset:plotting_offset + sl_length]\n\n # add sl to lines\n total_offset = plotting_offset + sl_index\n x_pts[total_offset:total_offset + sl_length] = sl[:, 0]\n y_pts[total_offset:total_offset + sl_length] = sl[:, 1]\n z_pts[total_offset:total_offset + sl_length] = sl[:, 2]\n\n # don't draw between streamlines\n if len(sls._offsets) > 1:\n x_pts[total_offset + sl_length] = np.nan\n y_pts[total_offset + sl_length] = np.nan\n z_pts[total_offset + sl_length] = np.nan\n\n if cbv is not None:\n brightness = cbv[\n sl[:, 0].astype(int),\n sl[:, 1].astype(int),\n sl[:, 2].astype(int)\n ]\n\n line_color[total_offset:total_offset + sl_length, :] = \\\n np.outer(brightness, color_constant)\n customdata[total_offset:total_offset + sl_length] = brightness\n else:\n line_color[total_offset:total_offset + sl_length, :] = \\\n color_constant\n customdata[total_offset:total_offset + sl_length] = 1\n\n if len(sls._offsets) > 1:\n line_color[total_offset + sl_length, :] = [0, 0, 0]\n customdata[total_offset + sl_length] = 0\n\n if flip_axes[0]:\n x_pts = dimensions[0] - x_pts\n if flip_axes[1]:\n y_pts = dimensions[1] - y_pts\n if flip_axes[2]:\n z_pts = dimensions[2] - z_pts\n figure.add_trace(\n go.Scatter3d(\n x=x_pts,\n y=y_pts,\n z=z_pts,\n name=name,\n marker=dict(\n size=0.0001,\n color=_color_arr2str(color)\n ), # this is necessary to add color to legend\n line=dict(\n width=8,\n color=line_color,\n ),\n hovertext=customdata,\n hoverinfo='all'\n )\n )\n\n\ndef visualize_bundles(sft, affine=None, n_points=None, bundle_dict=None,\n bundle=None, colors=None, color_by_volume=None,\n cbv_lims=[None, None], flip_axes=[False, False, False],\n figure=None, background=(1, 1, 1), interact=False,\n inline=False):\n \"\"\"\n Visualize bundles in 3D\n\n Parameters\n ----------\n sft : Stateful Tractogram, str\n A Stateful Tractogram containing streamline information\n or a path to a trk file.\n In order to visualize individual bundles, the Stateful Tractogram\n must contain a bundle key in it's data_per_streamline which is a list\n of bundle `'uid'`.\n\n affine : ndarray, optional\n An affine transformation to apply to the streamlines before\n visualization. Default: no transform.\n\n n_points : int or None\n n_points to resample streamlines to before plotting. If None, no\n resampling is done.\n\n bundle_dict : dict, optional\n Keys are names of bundles and values are dicts that should include\n a key `'uid'` with values as integers for selection from the sft\n metadata. Default: bundles are either not identified, or identified\n only as unique integers in the metadata.\n\n bundle : str or int, optional\n The name of a bundle to select from among the keys in `bundle_dict`\n or an integer for selection from the sft metadata.\n\n colors : dict or list\n If this is a dict, keys are bundle names and values are RGB tuples.\n If this is a list, each item is an RGB tuple. Defaults to a list\n with Tableau 20 RGB values if bundle_dict is None, or dict from\n bundles to Tableau 20 RGB values if bundle_dict is not None.\n\n color_by_volume : ndarray or str, optional\n 3d volume use to shade the bundles. If None, no shading\n is performed. Only works when using the plotly backend.\n Default: None\n\n cbv_lims : ndarray\n Of the form (lower bound, upper bound). Shading based on\n color_by_volume will only differentiate values within these bounds.\n If lower bound is None, will default to 0.\n If upper bound is None, will default to the maximum value in\n color_by_volume.\n Default: [None, None]\n\n flip_axes : ndarray\n Which axes to flip, to orient the image as RAS, which is how we\n visualize.\n For example, if the input image is LAS, use [True, False, False].\n Default: [False, False, False]\n\n background : tuple, optional\n RGB values for the background. Default: (1, 1, 1), which is white\n background.\n\n figure : Plotly Figure object, optional\n If provided, the visualization will be added to this Figure. Default:\n Initialize a new Figure.\n\n interact : bool\n Whether to open the visualization in an interactive window.\n Default: False\n\n inline : bool\n Whether to embed the interactivevisualization inline in a notebook.\n Only works in the notebook context. Default: False.\n\n Returns\n -------\n Plotly Figure object\n \"\"\"\n\n if color_by_volume is not None:\n color_by_volume = vut.load_volume(color_by_volume)\n\n if figure is None:\n figure = go.Figure()\n\n set_layout(figure, color=_color_arr2str(background))\n\n for (sls, color, name, dimensions) in vut.tract_generator(\n sft, affine, bundle, bundle_dict, colors, n_points):\n _draw_streamlines(\n figure,\n sls,\n dimensions,\n color,\n name,\n cbv=color_by_volume,\n cbv_lims=cbv_lims,\n flip_axes=flip_axes)\n\n figure.update_layout(legend=dict(itemsizing=\"constant\"))\n return _inline_interact(figure, interact, inline)\n\n\ndef create_gif(figure,\n file_name,\n n_frames=30,\n zoom=2.5,\n z_offset=0.5,\n size=(600, 600)):\n \"\"\"\n Convert a Plotly Figure object into a gif\n\n Parameters\n ----------\n figure: Plotly Figure object\n Figure to be converted to a gif\n\n file_name: str\n File to save gif to.\n\n n_frames: int, optional\n Number of frames in gif.\n Will be evenly distributed throughout the rotation.\n Default: 60\n\n zoom: float, optional\n How much to magnify the figure in the fig.\n Default: 2.5\n\n size: tuple, optional\n Size of the gif.\n Default: (600, 600)\n \"\"\"\n tdir = tempfile.gettempdir()\n\n for i in range(n_frames):\n theta = (i * 6.28) / n_frames\n camera = dict(\n eye=dict(x=np.cos(theta) * zoom,\n y=np.sin(theta) * zoom, z=z_offset)\n )\n figure.update_layout(scene_camera=camera)\n figure.write_image(tdir + f\"/tgif{i}.png\")\n scope._shutdown_kaleido() # temporary fix for memory leak\n\n vut.gif_from_pngs(tdir, file_name, n_frames,\n png_fname=\"tgif\", add_zeros=False)\n\n\ndef _draw_roi(figure, roi, name, color, opacity, dimensions, flip_axes):\n roi = np.where(roi == 1)\n pts = []\n for i, flip in enumerate(flip_axes):\n if flip:\n pts.append(dimensions[i] - (roi[i] + 1))\n else:\n pts.append(roi[i] + 1)\n figure.add_trace(\n go.Scatter3d(\n x=pts[0],\n y=pts[1],\n z=pts[2],\n name=name,\n marker=dict(color=_color_arr2str(color, opacity=opacity)),\n line=dict(color=f\"rgba(0,0,0,0)\")\n )\n )\n\n\ndef visualize_roi(roi, affine_or_mapping=None, static_img=None,\n roi_affine=None, static_affine=None, reg_template=None,\n name='ROI', figure=None, flip_axes=[False, False, False],\n color=np.array([0.9999, 0, 0]),\n opacity=1.0, interact=False, inline=False):\n \"\"\"\n Render a region of interest into a volume\n\n Parameters\n ----------\n roi : str or Nifti1Image\n The ROI information\n\n affine_or_mapping : ndarray, Nifti1Image, or str, optional\n An affine transformation or mapping to apply to the ROIs before\n visualization. Default: no transform.\n\n static_img: str or Nifti1Image, optional\n Template to resample roi to.\n Default: None\n\n roi_affine: ndarray, optional\n Default: None\n\n static_affine: ndarray, optional\n Default: None\n\n reg_template: str or Nifti1Image, optional\n Template to use for registration.\n Default: None\n\n name: str, optional\n Name of ROI for the legend.\n Default: 'ROI'\n\n color : ndarray, optional\n RGB color for ROI.\n Default: np.array([0.9999, 0, 0])\n\n opacity : float, optional\n Opacity of ROI.\n Default: 1.0\n\n flip_axes : ndarray\n Which axes to flip, to orient the image as RAS, which is how we\n visualize.\n For example, if the input image is LAS, use [True, False, False].\n Default: [False, False, False]\n\n figure : Plotly Figure object, optional\n If provided, the visualization will be added to this Figure. Default:\n Initialize a new Figure.\n\n interact : bool\n Whether to open the visualization in an interactive window.\n Default: False\n\n inline : bool\n Whether to embed the interactive visualization inline in a notebook.\n Only works in the notebook context. Default: False.\n\n Returns\n -------\n Plotly Figure object\n \"\"\"\n roi = vut.prepare_roi(roi, affine_or_mapping, static_img,\n roi_affine, static_affine, reg_template)\n\n if figure is None:\n figure = go.Figure()\n\n set_layout(figure)\n\n _draw_roi(figure, roi, name, color, opacity, roi.shape, flip_axes)\n\n return _inline_interact(figure, interact, inline)\n\n\nclass Axes(enum.IntEnum):\n X = 0\n Y = 1\n Z = 2\n\n\ndef _draw_slice(figure, axis, volume, opacity=0.3, step=None, n_steps=0):\n if step is None:\n height = volume.shape[axis] // 2\n visible = True\n else:\n height = (volume.shape[axis] * step) // n_steps\n visible = False\n\n v_min = volume.min()\n sf = volume.max() - v_min\n\n if axis == Axes.X:\n X, Y, Z = np.mgrid[height:height + 1,\n :volume.shape[1], :volume.shape[2]]\n values = volume[height, :, :].flatten()\n elif axis == Axes.Y:\n X, Y, Z = np.mgrid[:volume.shape[0],\n height:height + 1, :volume.shape[2]]\n values = volume[:, height, :].flatten()\n elif axis == Axes.Z:\n X, Y, Z = np.mgrid[:volume.shape[0],\n :volume.shape[1], height:height + 1]\n values = volume[:, :, height].flatten()\n\n values = 1 - (values - v_min) / sf\n\n figure.add_trace(\n go.Volume(\n x=X.flatten(),\n y=Y.flatten(),\n z=Z.flatten(),\n value=values,\n colorscale='greys',\n surface_count=1,\n showscale=False,\n opacity=opacity,\n visible=visible,\n name=_name_from_enum(axis),\n hoverinfo='skip'\n )\n )\n\n\ndef _name_from_enum(axis):\n if axis == Axes.X:\n return \"Sagittal\"\n elif axis == Axes.Y:\n return \"Coronal\"\n elif axis == Axes.Z:\n return \"Axial\"\n\n\ndef _draw_slices(figure, axis, volume,\n opacity=0.3, sliders=[], n_steps=0, y_loc=0):\n if n_steps == 0:\n _draw_slice(figure, axis, volume, opacity=opacity)\n else:\n active = n_steps // 2\n name = _name_from_enum(axis) + \" Plane\"\n steps = []\n for step in range(n_steps):\n _draw_slice(figure, axis, volume, opacity=opacity,\n step=step, n_steps=n_steps)\n\n for step in range(n_steps):\n step_dict = dict(\n method=\"update\",\n args=[{\"visible\": [True] * len(figure.data)}],\n label=\"\"\n )\n\n step_dict[\"args\"][0][\"visible\"][-n_steps:] = [False] * n_steps\n step_dict[\"args\"][0][\"visible\"][step] = True\n steps.append(step_dict)\n\n figure.data[-active].visible = True\n sliders.append(dict(\n active=active,\n currentvalue=dict(visible=True, prefix=name, xanchor='center'),\n pad=dict(t=50),\n steps=steps,\n y=y_loc,\n x=0.2,\n lenmode='fraction',\n len=0.6\n ))\n\n\ndef visualize_volume(volume, figure=None, show_x=True, show_y=True,\n show_z=True, interact=False, inline=False, opacity=0.3,\n flip_axes=[False, False, False],\n slider_definition=20, which_plane=None):\n \"\"\"\n Visualize a volume\n\n Parameters\n ----------\n volume : ndarray or str\n 3d volume to visualize.\n\n figure : Plotly Figure object, optional\n If provided, the visualization will be added to this Figure. Default:\n Initialize a new Figure.\n\n show_x : bool, optional\n Whether to show Coronal Slice.\n Default: True\n\n show_x : bool, optional\n Whether to show Sagittal Slice.\n Default: True\n\n show_x : bool, optional\n Whether to show Axial Slice.\n Default: True\n\n opacity : float, optional\n Opacity of slices.\n Default: 1.0\n\n flip_axes : ndarray\n Which axes to flip, to orient the image as RAS, which is how we\n visualize.\n For example, if the input image is LAS, use [True, False, False].\n Default: [False, False, False]\n\n slider_definition : int, optional\n How many discrete positions the slices can take.\n If 0, slices are stationary.\n Default: 50\n\n which_plane : str, optional\n Which plane can be moved with a slider.\n Should be 'Coronal', 'Axial', 'Sagittal', or None.\n If None, slices are stationary.\n Note: If slices are not stationary,\n do not add any more traces to the figure.\n Default: 'Coronal'\n\n interact : bool\n Whether to open the visualization in an interactive window.\n Default: False\n\n inline : bool\n Whether to embed the interactive visualization inline in a notebook.\n Only works in the notebook context. Default: False.\n\n Returns\n -------\n Plotly Figure object\n \"\"\"\n volume = vut.load_volume(volume)\n for i, flip in enumerate(flip_axes):\n if flip:\n volume = np.flip(volume, axis=i)\n\n if figure is None:\n figure = go.Figure()\n\n set_layout(figure)\n sliders = []\n\n # draw stationary slices first\n if show_x:\n if (which_plane is None) or which_plane.lower() != 'sagittal':\n _draw_slices(figure, Axes.X, volume, opacity=opacity, y_loc=0)\n if show_y:\n if (which_plane is None) or which_plane.lower() != 'coronal':\n _draw_slices(figure, Axes.Y, volume, opacity=opacity, y_loc=0)\n if show_z:\n if (which_plane is None) or which_plane.lower() != 'axial':\n _draw_slices(figure, Axes.Z, volume, opacity=opacity, y_loc=0)\n\n # Then draw interactive slices\n if show_x:\n if (which_plane is not None) and which_plane.lower() == 'sagittal':\n _draw_slices(figure, Axes.X, volume, sliders=sliders,\n opacity=opacity, n_steps=slider_definition,\n y_loc=0)\n if show_y:\n if (which_plane is not None) and which_plane.lower() == 'coronal':\n _draw_slices(figure, Axes.Y, volume, sliders=sliders,\n opacity=opacity, n_steps=slider_definition,\n y_loc=0)\n if show_z:\n if (which_plane is not None) and which_plane.lower() == 'axial':\n _draw_slices(figure, Axes.Z, volume, sliders=sliders,\n opacity=opacity, n_steps=slider_definition,\n y_loc=0)\n\n if slider_definition > 0 and which_plane is not None:\n figure.update_layout(sliders=tuple(sliders))\n\n return _inline_interact(figure, interact, inline)\n","sub_path":"AFQ/viz/plotly_backend.py","file_name":"plotly_backend.py","file_ext":"py","file_size_in_byte":18841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"111691012","text":"# Take in sequence of numbers and multiplier. Filter all non-numeric values multiply the rest by given multiplier \n\ndef mult_and_filter(sequence, multipler):\n # works for numbers only\n # return [val * multipler for val in sequence]\n string_list = list(map(str,sequence))\n nums_string = [value for value in string_list if value.isdigit()]\n nums_int = list(map(int, nums_string))\n multiplied = [value * multipler for value in nums_int]\n return multiplied\n\nprint(mult_and_filter([1,2,3,4],3)) # [3,6,9,12]\nprint(mult_and_filter([True, 5, \"hi\", {}, 10],5)) # [25,50]\n\n\n# BEST SOLUTION\ndef mult_and_filter2(sequence, multipler):\n return [num * multipler for num in sequence if type(num) in (int,float)]\n\n\nprint(mult_and_filter([1,2,3,4],3)) # [3,6,9,12]\nprint(mult_and_filter([True, 5, \"hi\", {}, 10],5)) # [25,50]\n","sub_path":"CodeWars/7kyu/arrays/mult_and_filter.py","file_name":"mult_and_filter.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"291139830","text":"from bs4 import BeautifulSoup\nfrom nltk.tokenize import sent_tokenize\nfrom nltk.stem.snowball import EnglishStemmer\nfrom pirat import synon\nimport re\nimport pdb\n\n\ndsn = 'dbname=essaydb user=dor password=4071505 host=138.68.136.117'\nstemmer = EnglishStemmer()\n\n\ndef clean_text(code):\n return BeautifulSoup(code, 'html.parser').get_text().replace('\\n', ' ').strip().replace('\"', '')\n\n\ndef synonimize(code):\n return re.sub(r\"(?<=[>])(.|\\n)*?(?=[<])\", synon.synonymize_en, code)\n\n\ndef get4sent(code, keyword):\n sents = sent_tokenize(clean_text(code))\n data = {}\n for s in sents[1:-2]:\n data[s] = len(s.split())\n if keyword.lower() in s.lower():\n data[s] += 100\n for w in [stemmer.stem(k) for k in keyword.split()]:\n if w.lower() in s.lower():\n data[s] += 50\n best = sorted(data.items(), key=lambda x: x[1], reverse=True)[0]\n index = sents.index(best[0])\n part = sents[index-1: index+2]\n return ' '.join(part)\n","sub_path":"dbessays.com/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"118776900","text":"# -*- encoding: utf-8 -*- \n\"\"\"\n@Author : zYx.Tom\n@Contact : 526614962@qq.com\n@site : https://zhuyuanxiang.github.io\n---------------------------\n@Software : PyCharm\n@Project : deep-learning-with-python-notebooks\n@File : ch0701_keras_functional_api.py\n@Version : v0.1\n@Time : 2019-11-26 17:27\n@License : (C)Copyright 2018-2019, zYx.Tom\n@Reference : 《Python 深度学习，Francois Chollet》, Sec0701，P196\n@Desc : 高级的深度学习最佳实践，使用Keras Functional API\n\"\"\"\nimport os\nimport sys\n\nimport keras\nimport matplotlib.pyplot as plt\nimport numpy as np # pip install numpy<1.17，小于1.17就不会报错\nimport winsound\nfrom keras import Input, Model\nfrom keras.activations import relu, sigmoid, softmax\nfrom keras.applications import Xception\nfrom keras.layers import (AveragePooling2D, concatenate, Conv1D, Conv2D, Embedding, GlobalMaxPooling1D, LSTM,\n MaxPooling1D, MaxPooling2D, )\nfrom keras.layers import Dense\nfrom keras.losses import binary_crossentropy, categorical_crossentropy, mse\nfrom keras.models import Sequential\nfrom keras.optimizers import rmsprop\n\n# 屏蔽警告：Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n# 设置数据显示的精确度为小数点后3位\nnp.set_printoptions(precision = 3, suppress = True, threshold = np.inf, linewidth = 200)\n# to make this notebook's output stable across runs\nseed = 42\nnp.random.seed(seed)\n# Python ≥3.5 is required\nassert sys.version_info >= (3, 5)\n# numpy 1.16.4 is required\nassert np.__version__ in [\"1.16.5\", \"1.16.4\"]\n\n# ----------------------------------------------------------------------\nepochs = 10\nbatch_size = 128\nverbose = 2\n\n\n# ----------------------------------------------------------------------\ndef sequential_realize():\n print(\"使用 Keras Functional API 使用 Sequential\")\n x_train = np.random.random((1000, 64))\n y_train = np.random.random((1000, 10))\n seq_title = \"使用Sequential实现的模型\"\n seq_model = Sequential(name = seq_title)\n seq_model.add(Dense(32, activation = relu, input_shape = (64,)))\n seq_model.add(Dense(32, activation = relu))\n seq_model.add(Dense(10, activation = softmax))\n seq_model.summary()\n seq_model.compile(optimizer = rmsprop(), loss = categorical_crossentropy)\n seq_model.fit(x_train, y_train, epochs = epochs, batch_size = batch_size,\n verbose = verbose, use_multiprocessing = True)\n print(seq_title + \":\", seq_model.evaluate(x_train, y_train))\n\n api_title = \"使用 Functional API 实现的模型\"\n input_tensor = Input(shape = (64,))\n x = Dense(32, activation = relu)(input_tensor)\n x = Dense(32, activation = relu)(x)\n output_tensor = Dense(10, activation = softmax)(x)\n api_model = Model(input_tensor, output_tensor, name = api_title)\n api_model.summary()\n api_model.compile(optimizer = rmsprop(), loss = categorical_crossentropy)\n api_model.fit(x_train, y_train, epochs = epochs, batch_size = batch_size)\n print(api_title + \":\", api_model.evaluate(x_train, y_train))\n\n\ndef sequential_realize_wrong():\n print(\"不相关的输入与输出连接\")\n input_tensor = Input(shape = (64,))\n x = Dense(32, activation = relu)(input_tensor)\n x = Dense(32, activation = relu)(x)\n output_tensor = Dense(10, activation = softmax)(x)\n unrelated_input = Input(shape = (32,))\n bad_model = Model(unrelated_input, output_tensor)\n pass\n\n\ndef multi_input_realize():\n print(\"Listing 7.1 使用 Functional API 实现的双输入问答模型\")\n text_vocabulary_size = 10000\n question_vocabulary_size = 10000\n answer_vocabulary_size = 500\n\n num_samples = 10000\n max_length = 100\n epochs = 20\n text = np.random.randint(1, text_vocabulary_size, size = (num_samples, max_length))\n question = np.random.randint(1, question_vocabulary_size, size = (num_samples, max_length))\n answers = np.random.randint(answer_vocabulary_size, size = (num_samples,))\n answers = keras.utils.to_categorical(answers, answer_vocabulary_size)\n\n text_input = Input(shape = (None,), dtype = 'int32', name = 'text')\n embedded_text = Embedding(text_vocabulary_size, 64)(text_input)\n encoded_text = LSTM(32)(embedded_text)\n\n question_input = Input(shape = (None,), dtype = 'int32', name = 'question')\n embedded_question = Embedding(question_vocabulary_size, 32)(question_input)\n encoded_question = LSTM(16)(embedded_question)\n\n concatenated = concatenate([encoded_text, encoded_question], axis = -1)\n output = Dense(answer_vocabulary_size, activation = softmax)(concatenated)\n model = Model([text_input, question_input], output, name = \"使用 Functional API 实现的双输入问答模型\")\n model.summary()\n model.compile(optimizer = rmsprop(), loss = categorical_crossentropy, metrics = ['acc'])\n # 使用输入组成的列表来输入数据\n # model.fit([text, question], answers, epochs = epochs, batch_size = batch_size,\n # verbose = verbose,use_multiprocessing = True)\n # 使用输入组成的字典来输入数据\n model.fit({'text': text, 'question': question}, answers, epochs = epochs, batch_size = batch_size,\n verbose = verbose, use_multiprocessing = True)\n\n\ndef multi_output_realize():\n vocabulary_size = 50000\n num_income_groups = 10\n posts_input = Input(shape = (None,), dtype = 'int32', name = 'posts')\n embedded_posts = Embedding(256, vocabulary_size)(posts_input)\n x = Conv1D(128, 5, activation = relu)(embedded_posts)\n x = MaxPooling1D(5)(x)\n x = Conv1D(256, 6, activation = relu)(x)\n x = Conv1D(256, 6, activation = relu)(x)\n x = MaxPooling1D(5)(x)\n x = Conv1D(256, 6, activation = relu)(x)\n x = Conv1D(256, 6, activation = relu)(x)\n x = GlobalMaxPooling1D()(x)\n x = Dense(128, activation = relu)(x)\n\n age_prediction = Dense(1, name = 'age')(x)\n income_prediction = Dense(num_income_groups, activation = softmax, name = 'income')(x)\n gender_prediction = Dense(1, activation = sigmoid, name = 'gender')(x)\n\n model = Model(posts_input, [age_prediction, income_prediction, gender_prediction])\n model.compile(optimizer = rmsprop(), loss = [mse, categorical_crossentropy, binary_crossentropy])\n # 使用name定义损失函数\n model.compile(optimizer = rmsprop(), loss = {\n 'age': mse, 'income': categorical_crossentropy, 'gender': binary_crossentropy\n })\n # Listing 7.5：多输出模型的损失加权\n model.compile(optimizer = rmsprop(), loss = {\n 'age': mse, 'income': categorical_crossentropy, 'gender': binary_crossentropy\n }, loss_weights = {'age': 0.25, 'income': 1., 'gender': 10.})\n\n model.fit(posts, [age_targets, income_targets, gender_targets], epochs = epochs, batch_size = batch_size)\n # 使用name定义标签\n model.fit(posts, {\n 'age': age_targets, 'income': income_targets, 'gender': gender_targets\n }, epochs = epochs, batch_size = batch_size)\n\n\ndef directed_acyclic_graphs_realize():\n print(\"图7-8：Inception模块的实现\")\n input_data = Input(shape = (None,))\n branch_a = Conv2D(128, 1, activation = relu, strides = 2)(input_data)\n\n branch_b = Conv2D(128, 1, activation = relu)(input_data)\n branch_b = Conv2D(128, 2, activation = relu, strides = 2)(branch_b)\n\n branch_c = AveragePooling2D(3, strides = 2)(input_data)\n branch_c = Conv2D(128, 3, activation = relu)(branch_c)\n\n branch_d = Conv2D(128, 1, activation = relu)(input_data)\n branch_d = Conv2D(128, 3, activation = relu)(branch_d)\n branch_d = Conv2D(128, 3, activation = relu, strides = 2)(branch_d)\n\n output = concatenate([branch_a, branch_b, branch_c, branch_d], axis = -1)\n\n\ndef resnet_realize():\n input_data = Input(shape = (None,))\n\n # 恒等残差连接（Identity Residual Connection）\n # 两个数据维度相同，直接相加\n y = Conv2D(128, 3, activation = relu, padding = 'same')(input_data)\n y = Conv2D(128, 3, activation = relu, padding = 'same')(y)\n y = Conv2D(128, 3, activation = relu, padding = 'same')(y)\n y = keras.layers.add([y, input_data])\n\n # 线性残差连接（Linear Residual Connection）\n # 两个数据维度不同，先使用卷积将其中一个数据整理成与另一个数据相同的形状\n y = Conv2D(128, 3, activation = relu, padding = 'same')(input_data)\n y = Conv2D(128, 3, activation = relu, padding = 'same')(y)\n y = MaxPooling2D(2, strides = 2)(y)\n residual = Conv2D(128, 1, strides = 2, padding = 'same')(input_data)\n y = keras.layers.add([y, residual])\n\n\n# ToSee：同时基于两组数据进行学习，那么两组数据相互之间是否应该有个数学关系（例如：相加、加权、或者轮流输入）\ndef layer_weight_sharing_realize():\n # 将一个 LSTM 层实例化一次\n lstm = LSTM(32)\n\n # 构建模型的左分支，输入是长度为 128 的向量组成的变长序列\n left_input = Input(shape = (None, 128))\n left_output = lstm(left_input)\n\n # 构建模型的右分支，如果调用已经存在的层实例，那么就会重复使用它的权重\n right_input = Input(shape = (None, 128))\n right_output = lstm(right_input)\n\n # 在上面构建一个分类器\n merged = concatenate([left_output, right_output], axis = -1)\n predictions = Dense(1, activation = sigmoid)(merged)\n\n # 将模型实例化并且训练，训练这种模型时，基于两个输入对LSTM层的权重进行更新\n model = Model([left_input, right_input], predictions)\n model.summary()\n # model.fit([left_data, right_data], targets)\n\n\ndef models_as_layers_realize():\n # 图像处理基础模型是 Xception 网络（只包括卷积基）\n xception_base = Xception(weights = None, include_top = False)\n # 输入是 250 x 250 的 RGB 图像\n left_input = Input(shape = (250, 250, 3))\n right_input = Input(shape = (250, 250, 3))\n\n # 对相同的视觉模型调用两次\n left_features = xception_base(left_input)\n right_features = xception_base(right_input)\n\n # 合并后的特征包含来自左右两个视觉输入中的信息\n merged_features = concatenate([left_features, right_features], axis = -1)\n\n\n# ----------------------------------------------------------------------\n# print('*' * 50)\n# sequential_realize()\n# print('*' * 50)\n# sequential_realize_wrong()\n# print('*' * 50)\n# multi_input_realize()\nprint('*' * 50)\nlayer_weight_sharing_realize()\n# ----------------------------------------------------------------------\n# 运行结束的提醒\nwinsound.Beep(600, 500)\nif len(plt.get_fignums()) != 0:\n plt.show()\npass\n","sub_path":"ch07/ch0701_keras_functional_api.py","file_name":"ch0701_keras_functional_api.py","file_ext":"py","file_size_in_byte":10711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"214796503","text":"import typing\n\nfrom betterconf.exceptions import ImpossibleToCastError\n\nVT = typing.TypeVar(\"VT\")\n\n\nclass AbstractCaster:\n def cast(self, val: str) -> typing.Any:\n \"\"\"Try to cast or return val\"\"\"\n raise NotImplementedError()\n\n\nclass ConstantCaster(AbstractCaster, typing.Generic[VT]):\n\n ABLE_TO_CAST: typing.Dict[\n typing.Union[str, typing.Tuple[str, ...]], typing.Any\n ] = {}\n\n def cast(self, val: str) -> typing.Union[VT, typing.NoReturn]:\n \"\"\"Cast using ABLE_TO_CAST dictionary as in BoolCaster\"\"\"\n if val in self.ABLE_TO_CAST:\n converted = self.ABLE_TO_CAST.get(val.lower())\n converted = typing.cast(VT, converted)\n return converted\n else:\n for key in self.ABLE_TO_CAST:\n if isinstance(key, tuple) and val.lower() in key:\n return self.ABLE_TO_CAST[key]\n elif isinstance(key, str) and val.lower() == key:\n return self.ABLE_TO_CAST[key]\n raise ImpossibleToCastError(val, self)\n\n\nclass BoolCaster(ConstantCaster):\n\n ABLE_TO_CAST = {\n \"true\": True,\n \"1\": True,\n \"yes\": True,\n \"ok\": True,\n \"on\": True,\n \"false\": False,\n \"0\": False,\n \"no\": False,\n \"off\": False,\n }\n\n\nclass IntCaster(AbstractCaster):\n def cast(self, val: str) -> typing.Union[int, typing.NoReturn]:\n try:\n as_int = int(val)\n return as_int\n except ValueError:\n raise ImpossibleToCastError(val, self)\n\n\nclass NothingCaster(AbstractCaster):\n \"\"\"Caster who does nothing\"\"\"\n\n def cast(self, val: str) -> str:\n return val\n\n\nto_bool = BoolCaster()\nto_int = IntCaster()\nDEFAULT_CASTER = NothingCaster()\n\n__all__ = (\"to_bool\", \"to_int\", \"AbstractCaster\", \"ConstantCaster\", \"DEFAULT_CASTER\")\n","sub_path":"betterconf/caster.py","file_name":"caster.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"134021110","text":"from module.testsuite.benchmark.l2l3.rfc3918.rfc3918_command import Rfc3918Command\nfrom framework.rom import meta\nfrom framework.smart_scripter.control_commands import *\nfrom framework.smart_scripter import smart_scripter\nfrom .group_command import AggregatedMulticastThroughputGroupCommand\nfrom .write_db_command import AggregatedMulticastThroughputWriteDbCommand\nfrom module.testsuite.suite_base.enum_types import *\n\n\n@meta.rom(description='Rfc3918 Aggregated Multicast Throughput command')\nclass AggregatedMulticastThroughputCommand(Rfc3918Command):\n def __init__(self):\n super().__init__()\n self.cancel = False\n self._load_size_index = 0\n self._frame_size_index = 0\n self._change_load = False\n\n def _create_test_loops(self):\n super()._create_test_loops()\n self.iteration_load_size_command = self._build_iteration_throughput_load_size_command()\n self.iteration_load_size_command.command = self\n self.iteration_group_count_command = self._build_iteration_group_count_command()\n self.iteration_group_count_command.command = self\n\n def publish_internal_commands(self):\n if smart_scripter.SmartScripter.instance().State != smart_scripter.EnumSmartScripterState.IDLE:\n log.Logger.CL.Error('SS is not idle')\n return False\n ss_global_group = smart_scripter.SmartScripter.instance().get_global_group()\n\n #load_loop\n load_loop = LoopCommand()\n load_loop.Name = 'Load Loop' #? need?\n load_loop.LoopCount = -1\n load_loop.append_command(self.iteration_load_size_command)\n load_loop.append_commands(self.commands)\n\n #frame_loop\n frame_size_loop = LoopCommand()\n frame_size_loop.Name = 'Frame Size Loop'\n frame_size_loop.append_command(self.iteration_frame_size_command)\n frame_size_loop.append_command(load_loop)\n\n # multicast group count loop\n multicast_group_count_loop = self._create_group_count_loop_command(frame_size_loop)\n\n #trial_loop\n trial_loop = LoopCommand()\n trial_loop.Name = 'Trial Loop'\n trial_loop.append_command(self.iteration_trial_command)\n trial_loop.append_command(multicast_group_count_loop)\n\n group_command = AggregatedMulticastThroughputGroupCommand()\n group_command.Name = 'RFC3918: Aggregated Multicast Throughput Group Test'\n group_command.append_command(self.test_start_command)\n group_command.append_command(self.set_duration_command)\n group_command.append_command(self.verify_link_delay)\n group_command.append_command(self.if_verify_link_command)\n group_command.append_command(trial_loop)\n group_command.append_command(self.test_stop_command)\n group_command.establish_relation(self)\n\n ss_global_group.append_command(group_command)\n return True\n\n def _build_write_iteration_result_command(self):\n command = AggregatedMulticastThroughputWriteDbCommand()\n command.Name = 'Save Results'\n self._populate_write_iteration_result_command(command)\n return command\n\n def _populate_iteration_load_size_command(self, cmd):\n cmd.IgnoreMinMaxLimit = self.config.IgnoreTrafficLoadLimit\n cmd.SearchMode = self.config.TrafficLoadSearchMode\n cmd.InitValue = self.config.InitRate\n cmd.CurrentLoad = self.config.InitRate\n cmd.LowerBoundLimit = self.config.MinimumRate\n cmd.Resolution = self.config.RateResolution\n cmd.UpperBoundLimit = self.config.MaximumRate\n cmd.ValueStep = self.config.RateStep\n cmd.Backoff = self.config.TrafficLoadBackoff\n cmd.AcceptableFrameLoss = self.config.AcceptableFrameLoss\n cmd.EnableMaxLatencyThreshold = self.config.EnableMaxLatency\n #cmd.EnableOutOfSeqThreshold = self.config.EnableOutOfSeqThreshold\n cmd.MaxLatencyThreshold = self.config.MaxLatencyThreshold\n #cmd.OutOfSeqThreshold = self.config.OutOfSeqThreshold\n cmd.reset_current_iteration()\n cmd.StreamTemplateList.clear()\n cmd.StreamTemplateList.extend(self.StreamTemplateHandles)\n\n def execute_post_load_iteration_command_fun(self):\n self.get_all_ports_online(True)\n if self.iteration_load_size_command:\n self._current_load = self.iteration_load_size_command.CurrentLoad\n if self.iteration_load_size_command and self.iteration_load_size_command.tput_found:\n return SSExecutionEnum.EXECUTE_BREAK\n return SSExecutionEnum.EXECUTE_NONE","sub_path":"CL/module/testsuite/benchmark/l2l3/rfc3918/aggregated_multicast_throughput/command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":4546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"414546585","text":"# CMPT 145 - Algorithms\n# The Make Change Problem\n#\n# Given a number D in the range 0-99\n# Find the smallest collection of coins whose value is D\n\n\n\n# a greedy solution, adding coins one at a time\ndef change_v1(cents):\n \"\"\"\n Make change for the given cents value.\n Assumes coin values 25c, 10c, 5c, 1c\n :param cents: an integer\n :return: a list of counts for the coins used.\n \"\"\"\n coins = [25, 10, 5, 1]\n coin_index = 0\n counts = [0, 0, 0, 0]\n remaining = cents\n while remaining > 0:\n if coins[coin_index] <= remaining:\n counts[coin_index] += 1\n remaining -= coins[coin_index]\n else:\n coin_index += 1\n return counts\n\n\n# a greedy solution, calculating each quantity\n# of coins exactly\ndef change_v2(cents):\n \"\"\"\n Make change for the given cents value.\n Assumes coin values 25c, 10c, 5c, 1c\n :param cents: an integer\n :return: a list of counts for the coins used.\n \"\"\"\n coins = [25, 10, 5, 1]\n coin_index = 0\n counts = [0] * len(coins)\n remaining = cents\n while remaining > 0:\n n = remaining // coins[coin_index]\n counts[coin_index] = n\n remaining = remaining % coins[coin_index]\n coin_index += 1\n return counts\n\n\n# A brute force solution\n# Find the smallest of all combinations of coins\ndef change_v3(cents):\n \"\"\"\n Make change for the given cents value.\n Assumes coin values 25c, 10c, 5c, 1c\n :param cents: an integer\n :return: a list of counts for the coins used.\n \"\"\"\n\n coins = [25, 10, 5, 1]\n\n def combinations(counts):\n \"\"\"\n Cycle through every possible combination, looking\n for a combo that adds up to the right value, and has the\n smallest number of coins\n :param counts: a 4-tuple\n :return: a pair (True, list) if list is the best combination\n \"\"\"\n value = sum([counts[i] * v for i, v in enumerate(coins)])\n if value == cents:\n return True, counts\n elif value > cents:\n # don't add more coins to this combination\n # because it's already too big\n return False, None\n else:\n (c0, c1, c2, c3) = counts\n # add 1 to each number of coins separately\n trying = [(c0 + 1, c1, c2, c3), (c0, c1, c2 + 1, c3),\n (c0, c1 + 1, c2, c3), (c0, c1, c2, c3 + 1)]\n # try to find the best combination, by using\n # the lowest number of coins\n best_size = 100\n best_counts = None\n for combo in trying:\n flag, res = combinations(combo)\n if flag and sum(res) < best_size:\n best_size = sum(res)\n best_counts = res\n if best_size == 100:\n # nothing worked!\n return False, None\n else:\n return True, best_counts\n\n flag, result = combinations((0, 0, 0, 0))\n if flag:\n return result\n else:\n return None\n\n\nif __name__ == '__main__':\n examples = [0, 25, 37, 49, 51, 87, 99]\n\n for e in examples:\n print('Version 1:', e, change_v1(e))\n print('Version 2:', e, change_v2(e))\n print('Version 3:', e, change_v3(e))\n\n print()\n","sub_path":"examples/ch19/change.py","file_name":"change.py","file_ext":"py","file_size_in_byte":3284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"360155241","text":"import datetime\r\nimport os.path\r\nimport shutil\r\n\r\nimport unittest\r\n\r\nfrom pathlib import Path\r\nfrom sevenZipFile import SevenZipFile\r\n\r\n\r\nclass BaseTestCase(unittest.TestCase):\r\n def setUp(self):\r\n base_test_dir = os.path.join(os.getcwd(), \"unittest\") # be careful if modifying this line, check teardown rmtree.\r\n base_p = Path(base_test_dir)\r\n base_p.mkdir()\r\n doc_test_dir = os.path.join(base_test_dir, \"Doc\")\r\n doc_p = Path(doc_test_dir)\r\n doc_p.mkdir()\r\n fin_test_dir = os.path.join(doc_test_dir, \"Fin\")\r\n fin_p = Path(fin_test_dir)\r\n fin_p.mkdir()\r\n bank_test_dir = os.path.join(doc_test_dir, \"Bank\")\r\n bank_p = Path(bank_test_dir)\r\n bank_p.mkdir()\r\n other_fin_dir = os.path.join(base_test_dir, \"Fin\")\r\n other_fin_p = Path(other_fin_dir)\r\n other_fin_p.mkdir()\r\n\r\n self.base_path = base_test_dir\r\n self.doc_path = doc_test_dir\r\n self.fin_path = fin_test_dir\r\n self.bank_path = bank_test_dir\r\n self.other_fin_path = other_fin_dir\r\n\r\n def tearDown(self):\r\n shutil.rmtree(self.base_path)\r\n\r\n\r\nclass TestReadConfig(BaseTestCase):\r\n def testParse(self):\r\n lines = [self.fin_path, self.bank_path]\r\n conf_path = os.path.join(self.base_path, \"config.txt\")\r\n with open(conf_path, \"w\") as fh:\r\n fh.write(\"\\n\".join(lines))\r\n\r\n reader = ReadConfig(conf_path)\r\n paths = reader.read()\r\n self.assertEqual(2, len(paths))\r\n self.assertTrue(self.fin_path in paths)\r\n self.assertTrue(self.bank_path in paths)\r\n for path in paths:\r\n self.assertTrue(os.path.exists(path))\r\n\r\n\r\nclass TestMakeZip(BaseTestCase):\r\n\r\n def testMakeSimpleArchiveZipDir(self):\r\n file_path = os.path.join(self.fin_path, \"testfile.txt\")\r\n with open(file_path, \"w\") as fh:\r\n lines = [\"toto\", \"test\", \"852.90$\", \"Truc\"]\r\n fh.writelines(lines)\r\n\r\n s_zip = SevenZipFile(self.base_path)\r\n zip_file_path = os.path.join(self.base_path, \"test.7z\")\r\n ret = s_zip.archive_dirs([self.fin_path], \"test\")\r\n self.assertTrue(ret)\r\n self.assertTrue(os.path.exists(zip_file_path))\r\n\r\n unzip_path = os.path.join(self.base_path, \"unzip\")\r\n s_zip.extract_all(zip_file_path, unzip_path)\r\n another_list = os.listdir(unzip_path)\r\n self.assertEqual(2, len(another_list))\r\n fin_dir_found = False\r\n for root, dirs, files in os.walk(unzip_path):\r\n dirname = os.path.split(root)[1]\r\n if dirname == \"Fin\":\r\n fin_dir_found = True\r\n self.assertEqual(1, len(files))\r\n self.assertEqual(files[0], \"testfile.txt\")\r\n self.assertTrue(fin_dir_found)\r\n\r\n def testCopyFolders(self):\r\n \"\"\"\r\n Simple, copies two folders with each one having a file and verifies that both folders and their file are copied.\r\n :return:\r\n \"\"\"\r\n folders = [self.fin_path, self.bank_path]\r\n for folder in folders:\r\n file_path = os.path.join(folder, \"testfile.doc\")\r\n with open(file_path, \"w\") as fh:\r\n fh.write(\"test\")\r\n\r\n dest_dir_p = Path(self.base_path, \"dest\")\r\n dest_dir_p.mkdir()\r\n\r\n for folder in folders:\r\n dirname = os.path.split(folder)[1]\r\n to_copy_dir_dest_p = dest_dir_p.joinpath(dirname)\r\n shutil.copytree(folder, str(to_copy_dir_dest_p))\r\n\r\n folder_list = os.listdir(str(dest_dir_p))\r\n\r\n for folder in folders:\r\n dirname = os.path.split(folder)[1]\r\n self.assertTrue(dirname in folder_list)\r\n file_path = os.path.join(str(dest_dir_p), dirname, \"testfile.doc\")\r\n self.assertTrue(os.path.exists(file_path))\r\n\r\n def testCopyFoldersConflict(self):\r\n \"\"\"\r\n Test copytree duplicate error management when two folders from two different drives have the same name.\r\n :return:\r\n \"\"\"\r\n folders = [self.fin_path, self.bank_path, self.other_fin_path]\r\n for folder in folders:\r\n file_path = os.path.join(folder, \"testfile.doc\")\r\n with open(file_path, \"w\") as fh:\r\n fh.write(\"test\")\r\n\r\n make = MakeArchive(None)\r\n final_dest_p = Path(self.base_path, \"final_dest\")\r\n final_dest_p.mkdir()\r\n final_dest_path = str(final_dest_p)\r\n\r\n make.create_with_dirs([self.fin_path, self.bank_path, self.other_fin_path],\r\n self.base_path, final_dest_path)\r\n dir_list = os.listdir(final_dest_path)\r\n dir_found_dict = {\"Fin\" : False, \"Bank\" : False, \"unittest_Fin\": False}\r\n\r\n for file in dir_list:\r\n ext = os.path.splitext(file)[1]\r\n if ext == \".7z\":\r\n unzip_path = os.path.join(self.base_path, \"unzip\")\r\n zip_file_path = os.path.join(final_dest_path, file)\r\n s_zip = SevenZipFile(zip_file_path)\r\n ret = s_zip.extract_all(zip_file_path, unzip_path)\r\n self.assertTrue(ret)\r\n another_list = os.listdir(unzip_path)\r\n self.assertEqual(1+len(folders), len(another_list))\r\n for root, dirs, files in os.walk(unzip_path):\r\n dirname = os.path.split(root)[1]\r\n if dirname == \"Fin\" or dirname == \"Bank\" or dirname == \"unittest_Fin\":\r\n dir_found_dict[dirname] = True\r\n self.assertEqual(1, len(files))\r\n for doc_file in files:\r\n self.assertEqual(\"testfile.doc\", doc_file)\r\n else:\r\n self.assertFalse(True)\r\n\r\n self.assertTrue(dir_found_dict[\"Fin\"])\r\n self.assertTrue(dir_found_dict[\"Bank\"])\r\n self.assertTrue(dir_found_dict[\"unittest_Fin\"])\r\n\r\n def testFullMake(self):\r\n folders = [self.fin_path, self.bank_path]\r\n for folder in folders:\r\n file_path = os.path.join(folder, \"testfile.doc\")\r\n with open(file_path, \"w\") as fh:\r\n fh.write(\"test\")\r\n\r\n make = MakeArchive(None)\r\n final_dest_p = Path(self.base_path, \"final_dest\")\r\n final_dest_p.mkdir()\r\n final_dest_path = str(final_dest_p)\r\n\r\n make.create_with_dirs([self.fin_path, self.bank_path], self.base_path, final_dest_path)\r\n dir_list = os.listdir(final_dest_path)\r\n dir_found_dict = {\"Fin\" : False, \"Bank\" : False}\r\n\r\n for file in dir_list:\r\n ext = os.path.splitext(file)[1]\r\n if ext == \".7z\":\r\n unzip_path = os.path.join(self.base_path, \"unzip\")\r\n zip_file_path = os.path.join(final_dest_path, file)\r\n s_zip = SevenZipFile(zip_file_path)\r\n ret = s_zip.extract_all(zip_file_path, unzip_path)\r\n self.assertTrue(ret)\r\n another_list = os.listdir(unzip_path)\r\n self.assertEqual(1+len(folders), len(another_list))\r\n for root, dirs, files in os.walk(unzip_path):\r\n dirname = os.path.split(root)[1]\r\n if dirname == \"Fin\" or dirname == \"Bank\":\r\n dir_found_dict[dirname] = True\r\n self.assertEqual(1, len(files))\r\n for doc_file in files:\r\n self.assertEqual(\"testfile.doc\", doc_file)\r\n else:\r\n self.assertFalse(True)\r\n\r\n self.assertTrue(dir_found_dict[\"Fin\"])\r\n self.assertTrue(dir_found_dict[\"Bank\"])\r\n\r\n\r\nclass ReadConfig:\r\n def __init__(self, conf_path):\r\n self.file_path = conf_path\r\n\r\n def read(self):\r\n res_list = []\r\n with open(self.file_path, \"r\") as fh:\r\n lines = fh.readlines()\r\n for line in lines:\r\n res_list.append(line.strip())\r\n\r\n return res_list\r\n\r\n\r\nclass DirectoryNotFoundError(Exception):\r\n def __init__(self, msg):\r\n self.message = msg\r\n\r\n def __str__(self):\r\n return self.message\r\n\r\n\r\nclass DirectoryConflictError(Exception):\r\n def __init__(self, msg):\r\n self.message = msg\r\n\r\n def __str__(self):\r\n return self.message\r\n\r\n\r\nclass CleanUpError(Exception):\r\n def __init__(self, msg):\r\n self.message = msg\r\n\r\n def __str__(self):\r\n return self.message\r\n\r\n\r\nclass MakeArchive:\r\n def __init__(self, pwd):\r\n self._password = pwd\r\n\r\n def create_with_dirs(self, dir_list, work_folder_path, copy_zip_to_path):\r\n if not os.path.exists(work_folder_path):\r\n raise DirectoryNotFoundError(\"work folder doesn't exist, it needs to exist so we can copy files.\")\r\n if not os.path.exists(copy_zip_to_path):\r\n raise DirectoryNotFoundError(\"Final folder path doesn't exist.\")\r\n if not os.path.isdir(copy_zip_to_path):\r\n raise NotADirectoryError(\"copy to path is not a directory\")\r\n\r\n dest_root_path = os.path.join(work_folder_path, \"work_temp\") # be careful with that line, a rmtree will be done on it.\r\n dest_dir_p = Path(dest_root_path)\r\n dest_dir_p.mkdir()\r\n\r\n dest_dir_list = []\r\n for folder in dir_list:\r\n split_path = os.path.split(folder)\r\n dirname = split_path[1]\r\n to_copy_dir_dest_p = dest_dir_p.joinpath(dirname)\r\n if os.path.exists(str(to_copy_dir_dest_p)):\r\n parent_dirname = os.path.split(split_path[0])[1]\r\n to_copy_dir_dest_p = dest_dir_p.joinpath(parent_dirname + \"_\" + dirname)\r\n if os.path.exists(str(to_copy_dir_dest_p)):\r\n raise DirectoryConflictError(\"Cannot find a proper name for {folder}\".format(folder=dirname))\r\n try:\r\n shutil.copytree(folder, str(to_copy_dir_dest_p)) # can throw Error\r\n except shutil.Error:\r\n raise DirectoryConflictError(\"Error during copy for folder: {dirname}\".format(\r\n dirname=dirname\r\n ))\r\n\r\n dest_dir_list.append(str(to_copy_dir_dest_p))\r\n\r\n timestamp = datetime.datetime.now().strftime(\"%Y_%m_%d__%H_%M_%S\")\r\n archive_name = \"backup_{stamp}\".format(stamp=timestamp)\r\n zip_file_path = os.path.join(dest_root_path, archive_name + \".7z\")\r\n s_zip = SevenZipFile(dest_root_path)\r\n is_use_pwd = False\r\n if self._password is not None:\r\n is_use_pwd = True\r\n s_zip.set_pwd(self._password)\r\n\r\n s_zip.archive_dirs(dest_dir_list, archive_name, is_use_pwd)\r\n\r\n shutil.copy(zip_file_path, copy_zip_to_path)\r\n files = 0\r\n folders = 0\r\n try:\r\n files, folders = s_zip.archive_info(zip_file_path, is_use_pwd)\r\n except Exception:\r\n files = folders = -1\r\n\r\n try:\r\n shutil.rmtree(dest_root_path)\r\n except PermissionError as ex:\r\n raise CleanUpError(\"Couldn't remove work folder.\") from ex\r\n\r\n return files, folders\r\n\r\n\r\nif __name__ == \"__main__\":\r\n unittest.main()\r\n\r\n","sub_path":"MakeArchive.py","file_name":"MakeArchive.py","file_ext":"py","file_size_in_byte":11178,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"624665542","text":"\nfrom aws_cdk import (\n aws_lambda,\n core\n)\n\nclass CustomLambda(aws_lambda.Function):\n def __init__(self, scope: core.Construct, id, handler, function_name, memory_size):\n super().__init__(scope=scope, id=id, \n code=aws_lambda.InlineCode(open('serverlessbackend\\libs\\dummy_lambda\\lambda_handler.py', encoding=\"utf-8\").read()),\n handler=handler, \n runtime=aws_lambda.Runtime.PYTHON_3_7, \n function_name=function_name, \n memory_size=memory_size)\n ","sub_path":"serverlessbackend/libs/custom_lambda.py","file_name":"custom_lambda.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"181093591","text":"#!/usr/bin/python3\n# -*- coding=utf8 -*-\n\nimport redis\n\nclass RedisPoolBase:\n\tredisPool = None\n\tdef __new__(cls, *args, **kwrgs):\n\t\tif not cls.redisPool:\n\t\t\tcls.redisPool = redis.ConnectionPool(host='localhost', port=6379, db=0)\n\t\treturn cls.redisPool\n\n\nclass RedisPool:\n\t\n\tdef __init__(self):\n\t\tself.redisPool = RedisPoolBase() \n\t\tself.conn = redis.StrictRedis(connection_pool=self.redisPool)\n","sub_path":"pythonitem/redisfunc/rediscon.py","file_name":"rediscon.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"527476684","text":"#!/usr/bin/env python3\n# encoding: utf-8\n\nfrom setuptools import setup\n\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetup(\n name=\"distest\",\n version=\"0.3.1\",\n description=\"Automate the testing of discord bots... With discord bots!\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"http://github.com/JakeCover/distest\",\n author=\"Jake Cover\",\n author_email=\"python@jakecover.me\",\n license=\"MIT\",\n packages=[\"distest\"],\n install_requires=[\"discord.py>=1.0.0\"],\n zip_safe=False,\n classifiers=[\"Topic :: Software Development :: Testing :: Unit\"],\n keywords=[\n \"Discord\",\n \"Discord.py\",\n \"Unit Test\",\n \"Test\",\n \"Distest\",\n \"Discord Testing\",\n ],\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"263228287","text":"# (1) Attempt to build a single string containing all the digits in the file\n\n## Define the absolute path for the test file\nfile_path = ('/Users/blackice02/Documents/self_development/python/book_' +\n\t\t\t'python_crash_course/source_files/chapter_10/'\n\t\t\t)\n## Define the file name to use\n# file_name = str(file_path + 'pi_digits.txt')\nfile_name = str(file_path + 'pi_million_digits.txt')\n\n\n\n## Read the file\nwith open(file_name) as file_object:\n\tlines = file_object.readlines()\n\n## Create variable to hold the pi string...\npi_string = ''\n\n## Loop over the content lines and add each to the string...\nfor line in lines:\n\tpi_string += line.strip()\n\n## Display contents; limit to 51\n# print(pi_string)\nprint(pi_string[:52] + '...')\nprint(len(pi_string))","sub_path":"book_python_crash_course/ch10/pi_string.py","file_name":"pi_string.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"512815722","text":"from rest_framework import renderers,status\nimport json\n\n\nclass Renderer(renderers.JSONRenderer):\n charset = 'utf-8'\n\n def render(self, data, accepted_media_type=None, renderer_context=None):\n response = ''\n if 'ErrorDetail' in str(data):\n response = json.dumps({\n 'code': 1,\n 'errors': data,\n 'message': \"please retry again...\",\n 'success': \"fail\",\n 'status': status.HTTP_400_BAD_REQUEST\n })\n else:\n response = json.dumps({\n 'code': 0,\n 'data': data,\n 'message': \"Successfully fetched the data\",\n 'success': \"success\",\n 'status': status.HTTP_200_OK\n })\n return response\n\n\n","sub_path":"custom_project/auth_token/renderers.py","file_name":"renderers.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"559543956","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom .models import Assistance\nfrom .forms import AssistanceForm\n# Create your views here.\n\ndef index(request):\n if request.user.is_authenticated:\n context={\n 'assistances':Assistance.objects.filter(owner=request.user)\n\n }\n return render(request, 'assistance/index.html',context)\n else:\n return render(request, 'assistance/index.html')\n\ndef assistance_detail(request,pk,year,month,day,assistance):\n if request.method == 'POST':\n pass\n else:\n assistance = get_object_or_404(Assistance,\n slug=assistance,\n owner=pk,\n #owner=request.user,\n\n created_at__year=year,\n created_at__month=month,\n created_at__day=day)\n\n return render(request, 'assistance/detail.html',{'assistance':assistance})\n\ndef assistance(request):\n if request.method == 'POST':\n form=AssistanceForm(request.POST)\n if form.is_valid:\n assistance=Assistance()\n assistance.owner=request.user\n assistance.message=request.POST.get('message')\n assistance.service=request.POST.get('service')\n brut=request.POST.get('service')\n brut=brut.replace(' ','-')\n assistance.slug=brut\n assistance.save()\n return redirect('assistance')\n else:\n return redirect('new_assistance')\n\n else:\n context={\n 'form':AssistanceForm()\n }\n return render(request, 'assistance/assistance.html', context)\n","sub_path":"assistance/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"311319063","text":"import requests\nimport re\nfrom bs4 import BeautifulSoup\n\n#A easy way to fetch the soul_plus_information.\n#you should change the pwuser and pwpwd to login the website,and \n#copy the cookies from the buying site then you can fetch the dowload\n#information from the website,then use another program to dowload it.\n\n\npath=re.compile('')\n\n\nclass Soul_plus:\n\tpost_headers = {\n\t\t'Cookie': 'your Cookie',\n\t\t'Host': 'bbs.north-plus.net',\n\t\t'Referer': 'http://bbs.north-plus.net/read.php?tid-192808.html',\n\t\t'Upgrade-Insecure-Requests': '1',\n\t\t'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.106 Safari/537.36',\n\t}\n\n\tpost_data={\n\t'jumpurl':'http://bbs.north-plus.net/index.php?',\n\t'step':'2',\n\t'cktime':'31536000',\n\t'lgt':'0',\n\t'pwuser':'your name',\n\t'pwpwd':'your password',\n\t}\n\n\tdef __init__(self):\n\t\tself.session=requests.session()\n\t\tself.login_url='http://bbs.north-plus.net/login.php'\n\t\tself.base_book_url='http://bbs.north-plus.net/thread.php?fid-36.html'\n\t\tself.base_job_url='http://bbs.north-plus.net/job.php?action=buytopic&tid={}&pid=tpc&verify=b7bee9ce'\n\t\tself.page_url=[]\n\t\tself.page_name=[]\n\n\n\tdef login(self):\n\t\tself.session.post(url=self.login_url,data=self.post_data)\n\n\tdef get_book_url(self):\n\t\tfor i in range(1,10):\n\t\t\tdata=self.session.get('http://bbs.north-plus.net/thread.php?fid-36-page-{}.html'.format(i))\n\t\t\turl_list=path.findall(data.text)\n\t\t\tprint(url_list)\n\t\t\tfor element in url_list:\n\t\t\t\tself.page_url.append('http://bbs.north-plus.net/'+element)\n\n\tdef get_book_information(self):\n\t\tfor element in self.page_url:\n\t\t\tinformation=self.session.get(element)\n\t\t\ttid=re.findall(r'http://bbs.north-plus.net/read.php\\?tid-(.+?).html',element)\n\t\t\tsoup=BeautifulSoup(information.text,'lxml')\n\t\t\ttitle=soup.find(\"span\",{\"class\":\"crumbs-item current\"})\n\t\t\tblock_quote=soup.find('blockquote',{\"class\":\"blockquote\"})\n\t\t\tif block_quote is None:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tif '若发现会员采用欺骗的方法获取财富,请立刻举报,我们会对会员处以2-N倍的罚金,严重者封掉ID!' in str(block_quote):\n\t\t\t\t\t\tif \"此帖售价 0 SP币\" in soup.find('h6',{\"class\",\"quote\"}).span.string:\n\t\t\t\t\t\t\tjob_url=self.base_job_url.format(tid[0])\n\t\t\t\t\t\t\tprint(job_url)\n\t\t\t\t\t\t\tself.session.get(job_url,headers=self.post_headers)\n\t\t\t\t\t\t\tdata=self.session.get(element)\n\t\t\t\t\t\t\tnew_soup=BeautifulSoup(data.text,'lxml')\n\t\t\t\t\t\t\tnew_block_quote=new_soup.find('blockquote',{\"class\":\"blockquote\"})\n\t\t\t\t\t\t\tprint(title.strong.a.string)\n\t\t\t\t\t\t\tprint(new_block_quote)\n\t\t\t\t\t\t\twith open(\"book.txt\",\"a\") as f:\n\t\t\t\t\t\t\t\tf.write(title.strong.a.string+\"\\n\")\n\t\t\t\t\t\t\t\tf.write(str(new_block_quote)+\"\\n\")\n\t\t\t\t\t\t\t\tf.write(\"\\n\")\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"售价不为0,跳过\")\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\telse:\n\t\t\t\t\t\tprint(title.strong.a.string)\n\t\t\t\t\t\tprint(block_quote)\n\t\t\t\t\t\twith open(\"book.txt\",\"a\") as f:\n\t\t\t\t\t\t\tf.write(title.strong.a.string+\"\\n\")\n\t\t\t\t\t\t\tf.write(str(block_quote)+\"\\n\")\n\t\t\t\t\t\t\tf.write('\\n')\n\t\t\t\texcept Exception as e:\n\t\t\t\t\tprint(e)\n\t\t\t\t\tcontinue\n\t\t#to check and do\n\tdef start(self):\n\t\tself.login()\n\t\tself.get_book_url()\n\t\tself.get_book_information()\n\nif __name__==\"__main__\":\n\tsoul_plus=Soul_plus()\n\tsoul_plus.start()\n\n\n\n\n\n\n","sub_path":"soul_plus_fetch_the_book_information.py","file_name":"soul_plus_fetch_the_book_information.py","file_ext":"py","file_size_in_byte":3249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"114298452","text":"import os\n\n\ndef get_size(start_path='C:\\\\'):\n total_size = 0\n total_modified_size = 0\n num_files = 0\n num_modified_files = 0\n\n for dirpath, dirnames, filenames in os.walk(start_path):\n for f in filenames:\n fp = os.path.join(dirpath, f)\n # skip if it is symbolic link\n if os.path.exists(fp):\n if not os.path.islink(fp):\n total_size += os.path.getsize(fp)\n num_files += 1\n if os.path.getctime(fp) != os.path.getmtime(fp):\n total_modified_size += os.path.getsize(fp)\n num_modified_files += 1\n\n print('Total file number: ', num_files)\n print('Modified file number: ', num_modified_files)\n print('Total bytes: ', total_size)\n print('Total bytes of modified file: ', total_modified_size)\n\n\nget_size()\n","sub_path":"time_work/cop4610_hw8_1_Abbey.py","file_name":"cop4610_hw8_1_Abbey.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"342810705","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 30 12:14:38 2022\n\n@author: Mohammad Asif Zaman\n\"\"\"\n\nimport numpy as np\nimport scipy.ndimage.filters as filters\nimport scipy.ndimage.morphology as morphology\n\n\nfrom critical_points_lines import critical_point_type\n\ndef detect_local_minima(arr):\n # Code taken from the following sources:\n # https://stackoverflow.com/questions/3986345/how-to-find-the-local-minima-of-a-smooth-multidimensional-array-in-numpy-efficie\n # https://stackoverflow.com/questions/3684484/peak-detection-in-a-2d-array/3689710#3689710\n \"\"\"\n Takes an array and detects the troughs using the local maximum filter.\n Returns a boolean mask of the troughs (i.e. 1 when\n the pixel's value is the neighborhood maximum, 0 otherwise)\n \"\"\"\n # define an connected neighborhood\n # http://www.scipy.org/doc/api_docs/SciPy.ndimage.morphology.html#generate_binary_structure\n neighborhood = morphology.generate_binary_structure(len(arr.shape),2)\n # apply the local minimum filter; all locations of minimum value \n # in their neighborhood are set to 1\n # http://www.scipy.org/doc/api_docs/SciPy.ndimage.filters.html#minimum_filter\n local_min = (filters.minimum_filter(arr, footprint=neighborhood)==arr)\n # local_min is a mask that contains the peaks we are \n # looking for, but also the background.\n # In order to isolate the peaks we must remove the background from the mask.\n # \n # we create the mask of the background\n background = (arr==0)\n # \n # a little technicality: we must erode the background in order to \n # successfully subtract it from local_min, otherwise a line will \n # appear along the background border (artifact of the local minimum filter)\n # http://www.scipy.org/doc/api_docs/SciPy.ndimage.morphology.html#binary_erosion\n \n eroded_background = morphology.binary_erosion(\n background, structure=neighborhood, border_value=1)\n # \n # we obtain the final mask, containing only peaks, \n # by removing the background from the local_min mask\n detected_minima = local_min ^ eroded_background\n \n \n \n return np.where(detected_minima) \n\n\n\n\n# ====================================================================================================\n# Correct for corner points\n# ====================================================================================================\ndef adj_local_minima(x,y,F):\n \n ind_lm = detect_local_minima(F)\n # ind_lm[1][..] is the x indices and ind_lm[0][...] are the y indices\n \n # Adjust to see if the indices do not point to corner points\n # =========================================================================\n xL = min(x)\n xH = max(x)\n yL = min(y)\n yH = max(y)\n \n adj_ind = np.where( (x[ind_lm[1]] != xL) & (x[ind_lm[1]] != xH) & (y[ind_lm[0]] != yH) & (y[ind_lm[0]] != yL) )\n \n # =========================================================================\n \n #ind_out is an array of arrrays. ind_out[1][..] is the x indices and ind_out[0][...] are the y indices\n \n ind_out = ind_lm[0][adj_ind],ind_lm[1][adj_ind] \n \n return ind_out\n\n\n# ====================================================================================================\n\n\n\n# ====================================================================================================\n# Correct for multiple critical points close to each other\n# ====================================================================================================\ndef thin_local_minima(x,y,ind,th):\n \n counter = 0\n ox = []\n oy = []\n \n # Loop over all the points except the last one\n for m in range(len(ind[0])-1):\n flag = 0\n x1 = x[ind[1][m]]\n y1 = y[ind[0][m]]\n \n # For each point, loop over the other right hand points in the list \n for n in range(m+1,len(ind[0])):\n \n x2 = x[ind[1][n]]\n y2 = y[ind[0][n]]\n \n r2 = (x2-x1)**2 + (y2-y1)**2\n \n if r2 < th**2: # check if any of the n loop points are close to the m loop point\n flag = 1 # if so, we will ignore the m point\n break # break with the flag value 1\n \n if flag == 0: # if the m point was found to be sufficiently far away from all the n points (i.e. flag == 0), we store it\n ox.append( ind[1][m] )\n oy.append( ind[0][m] )\n \n \n # Note that the loop starts eliminating points from the left of the list. The right most point (end of the list) is thus\n # always kept\n \n # Add the right most point to the list\n ox.append( ind[1][-1] )\n oy.append( ind[0][-1] )\n \n \n return np.array(oy), np.array(ox)\n# ====================================================================================================\n\n\n# ====================================================================================================\n# Find the average of the function value near a potential critical point\n# ====================================================================================================\ndef local_average(F,ix,iy,Nx,Ny):\n \n \n \n # Find the local average of F around F[iy,ix] taking into account Nx and Ny neigboring points\n \n # Note, np.size(F,1) = len(x) and np.size(F,0) = len(y)\n \n \n # The region over which the averaging will be performed. Checking for boundary crossings and setting the \n # limits appropriately\n ix_low = ix - Nx if (ix-Nx) >=0 else 0\n iy_low = iy - Ny if (iy-Ny) >=0 else 0\n \n ix_high = ix + Nx if (ix+Nx) < np.size(F,1) else np.size(F,1)-1\n iy_high = iy + Ny if (iy+Ny) < np.size(F,0) else np.size(F,0)-1\n \n # F_mean_x = np.zeros(iy_high-iy_low)\n counter = 0\n F_mean = 0\n for m in range(iy_low,iy_high+1):\n F_mean = F_mean + np.mean(F[m][ix_low:ix_high])\n counter = counter + 1\n \n \n # return ix_low, ix_high, iy_low, iy_high,counter, F_mean\n return abs(F_mean/counter)\n# ====================================================================================================\n \n\n\n# ====================================================================================================\n# Calculate local average for all the potential critical points \n# ==================================================================================================== \ndef avgF_at_local_minima(F,ind,Nx,Ny):\n \n avgF = np.zeros(len(ind[0]))\n \n for m in range(len(ind[0])):\n avgF[m] = local_average( F, ind[1][m], ind[0][m] ,Nx,Ny)\n \n \n return avgF\n# ==================================================================================================== \n \n\n\n\n# ==================================================================================================== \n# Estimate the number of critical points from local averages\n# ==================================================================================================== \ndef Npoint_estimate(avgF_sort,th):\n # Estimate the number of critical points\n # Select the points with high local average values, meaning it was surrounded by zeros. \n # As avF_sort is sorted, the [0] element is the maximum value. For points with local average th times\n # lower than avF, we disregard them.\n Npoints = 1\n for m in range(len(avgF_sort)):\n temp = avgF_sort[m]/avgF_sort[0]\n Npoints = m if Npoints < m else Npoints\n if temp < th:\n break\n \n return Npoints\n \n# ==================================================================================================== \n\n\n# ====================================================================================================\n# Sort the critical points according to the indices ind_sort\n# ==================================================================================================== \ndef adj_with_rank(ind_adj, ind_sort, Npoints):\n ind_adj2_x = np.zeros(Npoints, dtype = int)\n ind_adj2_y = np.zeros(Npoints, dtype = int)\n\n for m in range(Npoints):\n ind_adj2_x[m] = int( ind_adj[1][ind_sort[m]] )\n ind_adj2_y[m] = int( ind_adj[0][ind_sort[m]] )\n \n ind_adj2 = ind_adj2_y, ind_adj2_x\n \n return ind_adj2\n# ====================================================================================================\n\n\n# ====================================================================================================\n# Print data for the critical points\n# ====================================================================================================\ndef print_list(x,y,Fx,Fy,ind_adj,Navg_x,Navg_y):\n \n F = np.sqrt(Fx**2 + Fy**2) # Vector field magnitude \n avF = avgF_at_local_minima(F,ind_adj,Navg_x,Navg_y)\n \n c_type = critical_point_type(x, y, Fx, Fy, ind_adj)\n text_map = ['Repl N', 'Attr N', 'Saddle', 'Repl F','Attr F', 'Center']\n \n print('Number of potential CP = %2i \\n' % len(ind_adj[0]))\n \n for n in range(len(ind_adj[0])):\n \n xc = np.array([x[ind_adj[1][n]]])\n yc = np.array([y[ind_adj[0][n]]])\n \n txt = text_map[c_type[n]]\n \n print('CP %i: (%+1.2f,%+1.2f), type = %s, LA=%1.2e ' % (n,xc,yc,txt,avF[n]) )\n \n\n print('\\n') \n \n return 0\n# ====================================================================================================\n \n \n \n \n \n \n \n \n ","sub_path":"Codes/Python codes/Version 1.0/local_minima.py","file_name":"local_minima.py","file_ext":"py","file_size_in_byte":9581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"320924277","text":"#!/usr/bin/env python\n#\n# License: BSD\n# https://raw.githubusercontent.com/stonier/py_trees/devel/LICENSE\n#\n##############################################################################\n# Imports\n##############################################################################\n\n# enable some python3 compatibility options:\n# (unicode_literals not compatible with python2 uuid module)\nfrom __future__ import absolute_import, print_function\n\nimport py_trees\nimport py_trees.console as console\n\n##############################################################################\n# Logging Level\n##############################################################################\n\npy_trees.logging.level = py_trees.logging.Level.DEBUG\nlogger = py_trees.logging.Logger(\"Nosetest\")\n\n\n##############################################################################\n# Helpers\n##############################################################################\n\ndef create_impostered_composite():\n return py_trees.meta.failure_is_running(py_trees.composites.Sequence)(\"Impostered Composite\")\n\n\ndef create_impostered_behaviour():\n return py_trees.meta.success_is_failure(py_trees.behaviours.Success)(\"Impostered Behaviour\")\n\n\ndef has_child_with_name(parent, child_name):\n return child_name if child_name in [c.name for c in parent.children] else None\n\n\n##############################################################################\n# Tests\n##############################################################################\n\ndef test_imposter_has_add_child_method():\n print(console.bold + \"\\n****************************************************************************************\" + console.reset)\n print(console.bold + \"* Test Imposter has add_child_method\" + console.reset)\n print(console.bold + \"****************************************************************************************\\n\" + console.reset)\n tuples = []\n tuples.append((create_impostered_behaviour(), False))\n tuples.append((create_impostered_composite(), True))\n for b, asserted_result in tuples:\n print(\"%s has add_child: %s [%s]\" % (b.name, hasattr(b, 'add_child'), asserted_result))\n assert(hasattr(b, 'add_child') == asserted_result)\n\n\ndef test_parent_chain():\n print(console.bold + \"\\n****************************************************************************************\" + console.reset)\n print(console.bold + \"* Test Parent Chain\" + console.reset)\n print(console.bold + \"****************************************************************************************\\n\" + console.reset)\n root = py_trees.composites.Parallel(\"Root\")\n sequence_failure_is_running = create_impostered_composite()\n success_is_failure = create_impostered_behaviour()\n\n sequence_failure_is_running.add_child(success_is_failure)\n root.add_child(sequence_failure_is_running)\n\n tuples = []\n tuples.append((success_is_failure, sequence_failure_is_running.name))\n tuples.append((sequence_failure_is_running, root.name))\n for child, asserted_result in tuples:\n print(\"%s's parent: %s [%s]\" % (child.name, child.parent.name, asserted_result))\n assert(child.parent.name == asserted_result)\n\n\ndef test_parent_chain_with_add_children():\n print(console.bold + \"\\n****************************************************************************************\" + console.reset)\n print(console.bold + \"* Test Parent Chain with add_children\" + console.reset)\n print(console.bold + \"****************************************************************************************\\n\" + console.reset)\n root = py_trees.composites.Parallel(\"Root\")\n sequence_failure_is_running = create_impostered_composite()\n success_is_failure = create_impostered_behaviour()\n\n sequence_failure_is_running.add_children([success_is_failure])\n root.add_children([sequence_failure_is_running])\n\n tuples = []\n tuples.append((success_is_failure, sequence_failure_is_running.name))\n tuples.append((sequence_failure_is_running, root.name))\n for child, asserted_result in tuples:\n print(\"%s's parent: %s [%s]\" % (child.name, child.parent.name, asserted_result))\n assert(child.parent.name == asserted_result)\n\n\ndef test_child_chain():\n print(console.bold + \"\\n****************************************************************************************\" + console.reset)\n print(console.bold + \"* Test Child Chain\" + console.reset)\n print(console.bold + \"****************************************************************************************\\n\" + console.reset)\n root = py_trees.composites.Parallel(\"Root\")\n sequence_failure_is_running = create_impostered_composite()\n success_is_failure = create_impostered_behaviour()\n\n sequence_failure_is_running.add_child(success_is_failure)\n root.add_child(sequence_failure_is_running)\n\n tuples = []\n tuples.append((root, sequence_failure_is_running.name))\n tuples.append((sequence_failure_is_running, success_is_failure.name))\n for parent, asserted_result in tuples:\n print(\"%s's child: %s [%s]\" % (parent.name, has_child_with_name(parent, asserted_result), asserted_result))\n assert(has_child_with_name(parent, asserted_result) == asserted_result)\n","sub_path":"tests/test_imposter.py","file_name":"test_imposter.py","file_ext":"py","file_size_in_byte":5242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"53321478","text":"# -*- coding: utf-8 -*-\n# Author: Guillaume Chaslot\n\n# Global Imports\nimport os\nimport json\nimport time\nimport re\nimport pickle\nimport argparse\nimport sys\nimport xlsxwriter\nimport collections\nimport datetime\n\nfrom bs4 import BeautifulSoup\nfrom urllib.request import urlopen\n\n# Google Imports\nimport google.oauth2.credentials\nimport google_auth_oauthlib.flow\n\nfrom googleapiclient.discovery import build\nfrom googleapiclient.errors import HttpError\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.oauth2 import service_account\n\n\n# Number of videos that we need to scrap to understand a channel\nREQUIRED_RECOS = 10\nESTIMATED_RECOS_PER_VIDEO = 15\n\n# If True, reuse the latest channel stats that were obtained for that day.\nREUSE_CHANNEL_STATS = True\n\n# If True, only use API calls, but no scrapping\nNO_SCRAPPING = False\n\n# Max number of latest videos for each channel \n# (if number of recommendations needed is low, only a few of these latest videos will be considered)\nLATEST_VIDEOS = 50\n\n# The directory in which the data will be stored\nDATA_DIRECTORY = 'channel-stats/'\n\n# The CLIENT_SECRETS_FILE variable specifies the name of a file that contains\n# the OAuth 2.0 information for this application, including its client_id and\n# client_secret.\nCLIENT_SECRETS_FILE = \"client_secret_other.json\"\nAPI_SERVICE_NAME = 'youtube'\nAPI_VERSION = 'v3'\n\nclass YouTubeApiClient():\n \"\"\" This class is a client that interfaces with the YouTube API.\"\"\"\n\n def __init__(self):\n os.environ['OAUTHLIB_INSECURE_TRANSPORT'] = '1'\n self._client = self.get_authenticated_service()\n\n def get_authenticated_service(self):\n \"\"\" Create an authentificated client for YouTube API. \"\"\"\n credentials = service_account.Credentials.from_service_account_file(\"client_secret.json\")\n\n return build(API_SERVICE_NAME, API_VERSION, credentials=credentials)\n\n def remove_empty_kwargs(self, **kwargs):\n \"\"\" Removes keyword arguments that are not set. \"\"\"\n good_kwargs = {}\n if kwargs is not None:\n for key, value in kwargs.items():\n if value:\n good_kwargs[key] = value\n return good_kwargs\n\n def try_to_do(self, the_function, **kwargs):\n \"\"\" Tries to perform a function, and in case of exception, sleep for 30 seconds.\n \n :param the_function: function we want to use\n :param kwargs: arguments that it will use\n :returns: the return value of the function\n \"\"\"\n while True:\n try:\n return the_function(**kwargs)\n except Exception as e:\n # In case of exception, we print it and sleep 30 seconds.\n print(e)\n print('Sleeping 30 seconds')\n time.sleep(30)\n\n def channels_list_by_id(self, **kwargs):\n \"\"\" A wrapper for channels_list_by_id,\n that repeatedly tries to call this function. \"\"\"\n return self.try_to_do(self.channels_list_by_id_try, **kwargs)\n\n def channels_list_by_id_try(self, **kwargs):\n \"\"\" Gets information about a channel from its id and parts required,\n that are passed in kwargs. \"\"\"\n kwargs = self.remove_empty_kwargs(**kwargs)\n response = self._client.channels().list(\n **kwargs\n ).execute()\n\n return response\n\n def playlists_list_by_id(self, **kwargs):\n \"\"\" A wrapper for playlists_list_by_id \n that repeatedly tries to call this function. \"\"\"\n return self.try_to_do(self.playlists_list_by_id_try, **kwargs)\n\n def playlists_list_by_id_try(self, **kwargs):\n \"\"\" Gets information about a playlist from its id and parts required,\n that are passed in kwargs. \"\"\"\n kwargs = self.remove_empty_kwargs(**kwargs)\n\n response = self._client.playlistItems().list(\n **kwargs\n ).execute()\n\n return response\n\n def videos_list_multiple_ids(self, **kwargs):\n \"\"\" A wrapper for list_multiple_ids,\n that repeatedly tries to call this function. \"\"\"\n return self.try_to_do(self.videos_list_multiple_ids_try, **kwargs)\n\n def videos_list_multiple_ids_try(self, **kwargs):\n \"\"\" Gets information about videos from its id and parts required,\n that are passed in kwargs. \"\"\"\n kwargs = self.remove_empty_kwargs(**kwargs)\n\n response = self._client.videos().list(\n **kwargs\n ).execute()\n\n return response\n\n def search_list_related_videos(self, **kwargs):\n \"\"\" A wrapper for related videos,\n that repeatedly tries to call this function. \"\"\"\n return self.try_to_do(self.search_list_related_videos_try, **kwargs)\n\n def search_list_related_videos_try(self, **kwargs):\n \"\"\" Gets information about related videos from its id and parts required,\n that are passed in kwargs. \"\"\"\n kwargs = self.remove_empty_kwargs(**kwargs)\n\n response = self._client.search().list(\n **kwargs\n ).execute()\n\n return response\n\n def search_list_by_keyword(self, **kwargs):\n \"\"\" A wrapper for list_by_keyword,\n that repeatedly tries to call this function. \"\"\"\n return self.try_to_do(self.search_list_by_keyword_try, **kwargs)\n\n def search_list_by_keyword_try(self, **kwargs):\n \"\"\" Gets information about videos from a keyword search,\n from its id and parts required, that are passed in kwargs. \"\"\"\n kwargs = self.remove_empty_kwargs(**kwargs)\n\n response = self._client.search().list(\n **kwargs\n ).execute()\n\n return response\n\nclass YoutubeChannelScrapper():\n \"\"\" Class that scraps YouTube channels. \"\"\"\n\n def __init__(self, youtube_client, folder):\n try:\n os.mkdir(DATA_DIRECTORY + folder)\n except:\n pass\n self._youtube_client = youtube_client\n self._folder = folder\n\n # File names.\n self._channel_file = DATA_DIRECTORY + folder + '/all_channels'\n self._scrapped_videos_file = DATA_DIRECTORY + folder + '/scrapped_videos'\n self._api_video_file = DATA_DIRECTORY + folder + '/api_videos'\n self._video_to_chan_file = 'channel-stats/video_to_chan'\n\n # Data structures\n self._channel_stats = self.loadFromFile(self._channel_file)\n self._api_videos = self.loadFromFile(self._api_video_file)\n self._scrapped_videos = self.loadFromFile(self._scrapped_videos_file)\n self._video_to_chan_map = self.loadFromFile(self._video_to_chan_file)\n\n # Total number of recommendations for one channel\n self._total_channel_stats = collections.defaultdict(int)\n self._do_not_expand_channel_ids = set()\n\n # Channels to IDs mappings\n self._channel_name_to_id = {}\n self._channel_id_to_name = {}\n for video_id in self._api_videos:\n self._channel_name_to_id[self._api_videos[video_id]['snippet']['channelTitle']] = self._api_videos[video_id]['snippet']['channelId']\n self._channel_id_to_name[self._api_videos[video_id]['snippet']['channelId']] = self._api_videos[video_id]['snippet']['channelTitle']\n\n self.make_video_to_chan_map()\n\n def make_video_to_chan_map(self):\n \"\"\" Creates the mapping between videos and their channels. \"\"\"\n\n print('Making video to chan map, current has length '+ repr(len(self._video_to_chan_map)))\n\n video_to_get_by_api = ''\n video_to_get_by_api_nb = 0\n total_videos_got = 0\n\n # First looking at all scrapped videos, and calling YouTube API to get more info about them\n for video in self._scrapped_videos:\n # Looking for video if not in the api_videos\n if video not in self._api_videos and video not in self._video_to_chan_map:\n # The API calls allow to have information about 50 videos, so we call it when\n # we reach that number\n if video_to_get_by_api_nb == 50:\n self.getVideosFromYouTubeAPI(video_to_get_by_api)\n video_to_get_by_api = ''\n video_to_get_by_api_nb = 0\n\n if video_to_get_by_api != '':\n video_to_get_by_api += ','\n video_to_get_by_api += video\n video_to_get_by_api_nb += 1\n total_videos_got += 1\n\n # Getting api information about all recommendations\n for reco in self._scrapped_videos[video]['recommendations']:\n if total_videos_got % 1000 == 0 and total_videos_got > 0:\n self.saveToFile(self._video_to_chan_map, self._video_to_chan_file)\n print('Video to chan saved with length ' + repr(len(self._video_to_chan_map)))\n total_videos_got += 1\n\n if reco not in self._api_videos and reco not in self._video_to_chan_map:\n # The API calls allow to have information about 50 videos, so we call it when\n # we reach that number\n if video_to_get_by_api_nb == 50:\n self.getVideosFromYouTubeAPI(video_to_get_by_api)\n video_to_get_by_api = ''\n video_to_get_by_api_nb = 0\n\n if video_to_get_by_api != '':\n video_to_get_by_api += ','\n video_to_get_by_api += reco\n video_to_get_by_api_nb += 1\n total_videos_got += 1\n \n # Get the remaining videos if there are some.\n if video_to_get_by_api != '':\n self.getVideosFromYouTubeAPI(video_to_get_by_api)\n\n # Update the video to channel map.\n for video in self._api_videos:\n self._video_to_chan_map[video] = self._api_videos[video]['snippet']['channelId']\n self.saveToFile(self._video_to_chan_map, self._video_to_chan_file)\n print('Video to chan made with length ' + repr(len(self._video_to_chan_map)))\n\n def loadFromFile(self, filename):\n \"\"\" Loads a dictionary from a given json file. \n \n :param filename: filename without the json extension\n :returns: extracted dictionary\n \"\"\"\n\n print('Loading ' + filename + ' ...')\n try:\n with open(filename + '.json', \"r\") as json_file:\n my_dict = json.load(json_file)\n except:\n my_dict = {}\n print('Loaded ' + filename + ' with length: ' + repr(len(my_dict)))\n return my_dict\n\n def saveToFile(self, my_dict, filename):\n \"\"\" Saves an object to a given filename. \n\n :param my_dict: dictionary to save\n :param filename: filename without json extension\n :returns: nothing\n \"\"\"\n\n with open(filename + '.json', 'w') as fp:\n json.dump(my_dict, fp)\n\n def save_videos(self):\n \"\"\" Write files containing channel statistics, api data on videos,\n and data coming from scrapped videos. \"\"\"\n\n # First we print the top views videos, for information.\n sorted_videos = sorted(self._api_videos, key=lambda k: int(self._api_videos[k].get('statistics', {}).get('viewCount', -1)), reverse=True)\n print('\\n\\n\\n')\n print('Stats: ')\n for video in sorted_videos[0:100]:\n try:\n print(repr(self._api_videos[video].get('statistics', {})['viewCount']) + ' - ' + self._api_videos[video]['snippet']['title'])\n except:\n print('WARNING, A VIDEO IN THE TOP 100 HAS NO VIEWCOUNT')\n self.printGeneralStats()\n\n # Now we save the videos\n print('saving...')\n self.saveToFile(self._channel_stats, self._channel_file)\n self.saveToFile(self._api_videos, self._api_video_file)\n self.saveToFile(self._scrapped_videos, self._scrapped_videos_file)\n self.saveToFile(self._video_to_chan_map, self._video_to_chan_file)\n print('Saved! ')\n print('')\n\n def clean_count(self, text_count):\n \"\"\" From a text that represent a count, extracts its integer value\n \n :param text_count: a string\n :returns: the count as an integer\n \"\"\"\n\n # Ignore non ascii\n ascii_count = text_count.encode('ascii', 'ignore')\n # Ignore non numbers\n p = re.compile(r'[\\d,]+')\n return int(p.findall(ascii_count.decode('utf-8'))[0].replace(',', ''))\n\n def getChannelForVideo(self, video):\n \"\"\" Returns the channel id for a given video. \n \n :param: video id we want the channel of\n :returns: channel id for that given video\n \"\"\"\n if video in self._api_videos:\n return self._api_videos[video]['snippet']['channelId']\n else:\n if video in self._video_to_chan_map:\n return self._video_to_chan_map[video]\n\n # If we don't have the video in the API, let's make a call to get it.\n self.getVideosFromYouTubeAPI(reco)\n return self._api_videos[reco]['snippet']['channelId']\n\n def get_recommendations(self, video_id):\n \"\"\" Returns the recommendations for a given video. If it was not scrapped before,\n the video will be scrapped, and its information added to self._scrapped_videos\n\n :param video_id: the id of the video\n :returns: recommendations from that video\n \"\"\"\n\n if video_id in self._scrapped_videos:\n # This video was seen, returning recommendations that we stored\n return self._scrapped_videos[video_id]['recommendations']\n\n # Else, we scrap the video:\n\n url = \"https://www.youtube.com/watch?v=\" + video_id\n\n # Until we succeed, try to access the video page:\n while True:\n try:\n html = urlopen(url)\n break\n except Exception as e:\n print(repr(e))\n time.sleep(1)\n soup = BeautifulSoup(html, \"lxml\")\n\n # Getting views\n views = -1\n for watch_count in soup.findAll('div', {'class': 'watch-view-count'}):\n try:\n views = self.clean_count(watch_count.contents[0])\n except IndexError:\n pass\n\n # Getting likes\n likes = -1\n for like_count in soup.findAll('button', {'class': 'like-button-renderer-like-button'}):\n try:\n likes = self.clean_count(like_count.contents[0].text)\n except IndexError:\n pass\n\n # Getting dislikes\n dislikes = -1\n for like_count in soup.findAll('button', {'class': 'like-button-renderer-dislike-button'}):\n try:\n dislikes = self.clean_count(like_count.contents[0].text)\n except IndexError:\n pass\n\n # Getting duration\n duration = -1\n for time_count in soup.findAll('meta', {'itemprop': 'duration'}):\n try:\n dur = time_count['content'].replace('PT', '')\n duration = 0\n if 'H' in dur:\n contents = dur.split('H')\n duration += int(contents[0]) * 3600\n dur = contents[1]\n if 'M' in dur:\n contents = dur.split('M')\n duration += int(contents[0]) * 60\n dur = contents[1]\n if 'S' in dur:\n contents = dur.split('S')\n duration += int(contents[0])\n except IndexError:\n pass\n\n # Getting publication date\n pubdate = \"\"\n for datefield in soup.findAll('meta', {'itemprop': 'datePublished'}):\n try:\n pubdate = datefield['content']\n except IndexError:\n pass\n\n # Getting Channel\n channel = ''\n channel_id = ''\n for item_section in soup.findAll('a', {'class': 'yt-uix-sessionlink'}):\n if item_section['href'] and '/channel/' in item_section['href'] and item_section.contents[0] != '\\n':\n channel = item_section.contents[0]\n channel_id = item_section['href'].split('/channel/')[1]\n break\n\n if channel == '':\n print('WARNING: We could not find the channel of the video ' + video_id)\n\n # Getting recommendations\n recos = []\n # Up next\n for video_list in soup.findAll('li', {'class':\"video-list-item related-list-item show-video-time\"}):\n try:\n recos.append(video_list.contents[1].contents[1]['href'].replace('/watch?v=', ''))\n except IndexError:\n print ('WARNING Could not get a UP NEXT RECOMMENDATION')\n pass\n # Others\n for video_list in soup.findAll('li', {'class':\"video-list-item related-list-item show-video-time related-list-item-compact-video\"}): \n try:\n recos.append(video_list.contents[1].contents[1]['href'].replace('/watch?v=', ''))\n except IndexError:\n print ('WARNING Could not get a RECOMMENDATION')\n pass\n\n # Getting title\n title = ''\n for eow_title in soup.findAll('span', {'id': 'eow-title'}):\n title = eow_title.text.strip()\n\n if title == '':\n print ('WARNING: title not found')\n\n if video_id not in self._scrapped_videos:\n self._scrapped_videos[video_id] = {\n 'views': views,\n 'likes': likes,\n 'dislikes': dislikes,\n 'recommendations': recos,\n 'title': title,\n 'id': video_id,\n 'channel': channel,\n 'pubdate': pubdate,\n 'duration': duration,\n 'scrapDate': time.strftime('%Y%m%d-%H%M%S'),\n 'channel_id': channel_id}\n\n video = self._scrapped_videos[video_id]\n print(video_id + ': ' + video['title'] + ' [' + channel + ']' + str(video['views']) + ' views and ' + repr(len(video['recommendations'])) + ' recommendations')\n return recos\n\n def getVideosFromYouTubeAPI(self, video_to_get_by_api):\n \"\"\" From a list of YouTube video ids separated by commas, this video will get\n meta data on up to 50 videos, and store it.\n\n :param video_to_get_by_api: string with video ids comma separated\n :returns: nothing\n \"\"\"\n \n # API call to YouTube.\n video_infos = self._youtube_client.videos_list_multiple_ids(\n part='snippet,contentDetails,statistics',\n id=video_to_get_by_api)\n\n # Storing the date of scrapping up to the second\n scrapDate = time.strftime('%Y%m%d-%H%M%S')\n\n # Converting format and updating the video to channel map\n for video in video_infos['items']:\n video['scrapDate'] = scrapDate\n self._api_videos[video['id']] = video\n self._video_to_chan_map[video['id']] = video['snippet']['channelId']\n if 'snippet' not in video:\n video['snippet'] = {}\n if 'channelTitle' not in video['snippet']:\n video['snippet']['channelTitle'] = ''\n\n try:\n name = video['snippet']['channelTitle']\n self._channel_id_to_name[video['snippet']['channelId']] = name\n except:\n print('UNKNOWN CHANNEL FOUND FROM API CALL, CHANNEL WAS PROBABLY DELETED')\n self._channel_id_to_name[video['snippet']['channelId']] = 'unknown channel'\n\n try:\n id_ = video['snippet']['channelId']\n self._channel_name_to_id[video['snippet']['channelTitle']] = id_\n except:\n print('UNKNOWN CHANNEL FOUND FROM API CALL, CHANNEL WAS PROBABLY DELETED')\n self._channel_name_to_id[video['snippet']['channelTitle']] = 'unknown channel'\n\n def getChannelToCountFromUploads(self, response_list, required_recos):\n \"\"\" From a list of uploads of a video from a given channel,\n scraps the number of required videos and update the stats of each channel. \n (Channels that were the most recommended will be the next to be scrapped)\n \"\"\"\n\n channel_to_counts = {}\n videos_to_get = []\n\n # First pass: looking for videos allready scrapped: we want to get them from cache.\n for video in response_list['items']:\n video_id = video['contentDetails']['videoId']\n if video_id in self._scrapped_videos:\n videos_to_get.append(video_id)\n\n # How many recommendations did we get from them? Let's see:\n nb_recos = 0\n for video in videos_to_get:\n nb_recos += len(self._scrapped_videos[video]['recommendations'])\n\n total_video_needed = len(videos_to_get)\n if nb_recos < required_recos:\n total_video_needed = len(videos_to_get) + int((required_recos - nb_recos) / ESTIMATED_RECOS_PER_VIDEO)\n\n # Second pass: if not enought, adding more videos\n for video in response_list['items']:\n video_id = video['contentDetails']['videoId']\n if video_id not in videos_to_get:\n videos_to_get.append(video_id)\n if len(videos_to_get) >= total_video_needed:\n break\n\n # For each video that we got, we get its recommendations\n for video_id in videos_to_get:\n self.scrap_the_video(video_id, channel_to_counts)\n\n return channel_to_counts\n\n def scrap_the_video(self, video_id, channel_to_counts):\n \"\"\" Scraps an individual video.\n\n :param video_id: string with id of the video.\n :param channel_to_counts: number of times each channel was recommended.\n \"\"\"\n\n # Get recommendations for video id either from scrapping or memory.\n recos = self.get_recommendations(video_id)\n\n # Now we get all the recommendations. If we don't have info on the video, we need to get some.\n video_to_get_by_api = ''\n\n for reco in recos:\n if reco not in self._api_videos and reco not in self._video_to_chan_map:\n if video_to_get_by_api != '':\n video_to_get_by_api += ','\n video_to_get_by_api += reco\n if video_to_get_by_api != '':\n self.getVideosFromYouTubeAPI(video_to_get_by_api)\n\n for reco in recos:\n # Sometimes we are skipping videos that we can't get access to.\n try:\n reco_channel = self.getChannelForVideo(reco)\n except KeyError:\n continue\n channel_to_counts[reco_channel] = channel_to_counts.get(reco_channel, 0) + 1\n\n def scrap_the_channel(self, channel, required_recos, scrap_only_featuring_channels=None):\n \"\"\" Get information on a given channel with YouTube API, and launch scrapping on its videos.\n \n :param channel: the id of the channel\n :param required_recos: how many recommendations we want to have for each video of that channel\n :param scrap_only_featuring_channels: None or a list of channels.\n if not None, we only scrap channels that are featuring one of those \n \"\"\"\n\n print('Scrapping channel: ' + channel)\n if channel in self._do_not_expand_channel_ids or NO_SCRAPPING:\n return\n self._do_not_expand_channel_ids.add(channel)\n\n # If we already got the api info for this channel and we want to reuse it, do so\n if channel in self._channel_stats and REUSE_CHANNEL_STATS:\n listResponse = self._channel_stats[channel]['uploads']\n # Otherwise, query it\n else:\n channelResponse = self._youtube_client.channels_list_by_id(\n part='snippet,contentDetails,statistics,brandingSettings',\n id=channel)\n\n # If the channel has no items, immediatly return\n if len(channelResponse['items']) == 0:\n print('ERROR SCRAPPING CHANNEL ' + channel)\n return\n\n listResponse = self._youtube_client.playlists_list_by_id(\n part='snippet,contentDetails',\n playlistId=channelResponse['items'][0]['contentDetails']['relatedPlaylists']['uploads'],\n maxResults=LATEST_VIDEOS)\n self._channel_stats[channel] = {\n 'uploads' : listResponse,\n 'statistics' : channelResponse['items'][0].get('statistics', {}),\n 'snippet': channelResponse['items'][0]['snippet'],\n 'featuredChannelsUrls': channelResponse['items'][0].get('brandingSettings', {}).get('channel', {}).get('featuredChannelsUrls', '')}\n\n # If scrap_only_featuring_channels is not None, check that the new channel\n # features one of the channels in scrap_only_featuring_channels\n if scrap_only_featuring_channels:\n channel_suscribed_not_found = True\n if channel in scrap_only_featuring_channels:\n channel_suscribed_not_found = False\n for c in self._channel_stats[channel]['featuredChannelsUrls']:\n if c in scrap_only_featuring_channels:\n channel_suscribed_not_found = False\n if channel_suscribed_not_found:\n self._do_not_expand_channel_ids.add(channel)\n return\n\n # Scraps the required videos and update the number of times each channel was recommended.\n channel_to_counts = self.getChannelToCountFromUploads(listResponse, required_recos)\n\n for channel_recommended in channel_to_counts:\n self._total_channel_stats[channel_recommended] += channel_to_counts[channel_recommended]\n\n def getChannelsWithEnoughRecos(self):\n \"\"\" Returns channels that have more than 50 recommendations. \"\"\"\n channels_to_recos = {}\n for unused_id, video in self._scrapped_videos.items():\n channels_to_recos[video.get('channel', 'unknown')] = channels_to_recos.get(video.get('channel', 'unknown'), 0) + len(video['recommendations'])\n return channels_to_recos, list(filter(lambda channel: channels_to_recos[channel] > 50, channels_to_recos))\n\n def printGeneralStats(self):\n \"\"\" Print statistics on how many videos and channels were obtained via scrapping\n and API calls\n \"\"\"\n total_views = 0\n for unused_video_id, video in self._api_videos.items():\n if 'viewCount' in video.get('statistics', {}):\n vc = int(video.get('statistics', {})['viewCount'])\n total_views += vc\n\n print('\\n\\n\\n\\n\\n\\n')\n print(' Number of api videos: ' + repr(len(self._api_videos)) + ' total views ' + repr(total_views))\n channels_to_recos, channels_with_enough_recos = self.getChannelsWithEnoughRecos()\n print(' Number of scrapped videos: ' + repr(len(self._scrapped_videos)) + ' total channels ' + repr(len(channels_to_recos)) + ' which have more than 50 recos ' + repr(len(channels_with_enough_recos)))\n return channels_with_enough_recos\n\n def add_channels_from_searches(self, searches, channels):\n \"\"\" Perform search API call for different searches\n\n :param searches: array with search queries\n :param channels: list were the new channels will be appended\n \"\"\"\n if searches == []:\n return\n all_searches = {}\n for search in searches:\n list_of_results = self._youtube_client.search_list_by_keyword(\n part='snippet',\n maxResults=50,\n q=search,\n type='video')\n\n all_searches[search] = list_of_results\n\n for result in list_of_results['items']:\n chan_id = result['snippet']['channelId']\n if chan_id not in channels:\n channels.append(chan_id)\n print('Adding channel ' + chan_id)\n self.scrap_the_video(result['id']['videoId'], {})\n\n self.saveToFile(all_searches, DATA_DIRECTORY+ self._folder + '/' + self._folder + '-searches')\n\n def get_all_api_data(self):\n \"\"\" For all videos that were scrapped, get more information with the YouTube API. \"\"\"\n \n # First we try all the API data present in channel-stats\n video_to_get_by_api = ''\n video_to_get_by_api_nb = 0\n total_videos_got = 0\n\n for video in self._scrapped_videos:\n if video not in self._api_videos:\n # YouTube API takes 50 videos max.\n if video_to_get_by_api_nb == 50:\n print('Calling YouTube API to collect info about 50 videos...')\n self.getVideosFromYouTubeAPI(video_to_get_by_api)\n video_to_get_by_api = ''\n video_to_get_by_api_nb = 0\n if video_to_get_by_api != '':\n video_to_get_by_api += ','\n video_to_get_by_api += video\n video_to_get_by_api_nb += 1\n total_videos_got += 1\n\n for reco in self._scrapped_videos[video]['recommendations']:\n if total_videos_got % 1000 == 0 and total_videos_got > 0:\n self.saveToFile(self._video_to_chan_map, self._video_to_chan_file)\n print('Video to chan made with length ' + repr(len(self._video_to_chan_map)))\n total_videos_got += 1\n\n if reco not in self._api_videos:\n if video_to_get_by_api_nb == 50:\n self.getVideosFromYouTubeAPI(video_to_get_by_api)\n video_to_get_by_api = ''\n video_to_get_by_api_nb = 0\n\n if video_to_get_by_api != '':\n video_to_get_by_api += ','\n video_to_get_by_api += reco\n video_to_get_by_api_nb += 1\n total_videos_got += 1\n \n if video_to_get_by_api != '':\n self.getVideosFromYouTubeAPI(video_to_get_by_api)\n\n for video in self._api_videos:\n self._video_to_chan_map[video] = self._api_videos[video]['snippet']['channelId']\n self.saveToFile(self._video_to_chan_map, self._video_to_chan_file)\n print('New video to chan made with length ' + repr(len(self._video_to_chan_map)))\n\n\n def write_result_file(self):\n \"\"\" Write file with videos that were recommended the most. \"\"\"\n\n # 1 compute number of recos per video\n videos_to_recos = collections.defaultdict(int)\n for video in self._scrapped_videos:\n for reco in self._scrapped_videos[video]['recommendations']:\n videos_to_recos[reco] += 1\n\n nb_ok_vids = 0\n nb_not_ok_vids = 0\n final_dict = {'info_channels':[]}\n for video in sorted(videos_to_recos, key=videos_to_recos.get, reverse=True):\n\n # We only save videos that have been viewed more than once in order to save space.\n if videos_to_recos[video] > 1:\n if video in self._api_videos:\n nb_ok_vids += 1\n video_info = self._api_videos[video]\n final_dict['info_channels'].append({\n \"id\": video,\n 'pdate': video_info.get('snippet', {}).get('publishedAt', ''),\n \"views\": video_info.get('statistics', {}).get('viewCount', -1),\n \"dislikes\": video_info.get('statistics', {}).get('dislikeCount', -1),\n \"likes\": video_info.get('statistics', {}).get('likeCount', -1),\n \"views\": video_info.get('statistics', {}).get('viewCount', -1),\n \"nb_recommendations\": videos_to_recos[video],\n \"title\": video_info['snippet']['title'],\n \"channel\": video_info['snippet']['channelTitle'],\n \"comments\": int(video_info.get('statistics', {}).get('commentCount', 0))\n })\n # If the video is only in scrapped videos:\n elif video in self._scrapped_videos: \n nb_ok_vids += 1\n video_info = self._scrapped_videos[video]\n final_dict['info_channels'].append({\n \"id\": video,\n 'pdate': video_info['pubdate'],\n \"views\": video_info['views'],\n \"dislikes\": video_info['dislikes'],\n \"likes\": video_info['likes'],\n \"views\": video_info['views'],\n \"nb_recommendations\": videos_to_recos[video],\n \"title\": video_info['title'],\n \"channel\": video_info['channel']\n })\n else:\n nb_not_ok_vids += 1\n print('WARNING: one video not in API VIDEOS will be ignored despite beeing recommended ' + str(videos_to_recos[video]) + ' out of ' + str(nb_ok_vids))\n\n print(' Videos with info ok '+ repr(nb_ok_vids) + ' not ok '+ repr(nb_not_ok_vids))\n self.saveToFile(final_dict, DATA_DIRECTORY+ self._folder)\n print('Result file written! ')\n\n def describe_channels(self):\n \"\"\" Print the 500 top channels by recommendations. \"\"\"\n\n # Computing number of recommendations\n total_channel_stats = collections.defaultdict(int)\n for unused_vid, info in self._scrapped_videos.items():\n for reco in info['recommendations']:\n total_channel_stats[self._video_to_chan_map.get(reco, 'unknown')] += 1\n\n for chan in sorted(total_channel_stats, key=total_channel_stats.get, reverse=True)[0:500]:\n try:\n print('\\n\\n\\n' + str(total_channel_stats[chan]))\n print(self._channel_stats[chan]['snippet']['title'] + ' ' + chan)\n print(self._channel_stats[chan]['snippet']['description'] + ' ')\n except:\n print(' ' + str(total_channel_stats[chan]) + ' ' + chan)\n\n def scrap_from_base(self, base_channels, max_channels, required_recos, only_scrap_chans_featuring_base=False):\n \"\"\" This function start the snowball mechanism from a base of channels.\n \n :param base_channels: a list of channel ids to start from\n :param max_channels: the max amount of channels we want to get to\n :param required_recos: how many recommendations per channel we want\n :only_scrap_chans_featuring_base: if true, we'll only expand channels\n that feature one of the base channels\n \"\"\"\n\n suscribed_to = None\n if only_scrap_chans_featuring_base:\n suscribed_to = base_channels\n\n number_of_saved_videos = len(self._api_videos)\n\n # Scrapping the base channels\n for channel in base_channels:\n self.scrap_the_channel(channel, required_recos)\n if len(self._api_videos) > number_of_saved_videos + 100:\n self.save_videos()\n number_of_saved_videos = len(self._api_videos)\n\n # Saving all stats, in case the program is interupted.\n self.save_videos()\n\n # Load the list of channels not to be expanded\n nb_channels_not_to_scrap = 0\n with open('blacklisted_youtube_channels.txt', 'r', encoding='utf-8') as infile:\n for line in infile:\n nb_channels_not_to_scrap += 1\n self._do_not_expand_channel_ids.add(line.strip())\n print(str(nb_channels_not_to_scrap) + ' channels were added from youtube_channels_not_info.txt to not be scrapped')\n\n # We snowball here for extra channels.\n nb_extra_channels = 0\n while nb_extra_channels + len(base_channels) < max_channels:\n nb_extra_channels += 1\n print('\\n\\n\\n')\n print('Sorting channels ... ')\n sorted_top_channels = sorted(self._total_channel_stats, key=self._total_channel_stats.get, reverse=True)\n print('Sorted. Getting channels with enough recos ... ')\n unused_v, channels_with_enough_recos = self.getChannelsWithEnoughRecos()\n print('Done. Checking that all channels have been scrapped:')\n for channel in sorted_top_channels[0:nb_extra_channels]:\n if channel not in channels_with_enough_recos and channel not in self._do_not_expand_channel_ids:\n self.scrap_the_channel(channel, required_recos, scrap_only_featuring_channels=suscribed_to)\n\n # Display some of the channels were most recommended \n print('\\n\\n\\n')\n print('General Stats after computing ' + repr(nb_extra_channels) + ' channels')\n for channel in sorted_top_channels[0:20]:\n try:\n print(' - ' + channel + '( ' + self._channel_id_to_name[channel] + ' ) - ( ' + repr(self._total_channel_stats[channel]) + ' )' )\n except:\n print('- Channel that we will discover or that we do not care about -')\n\n print('...')\n for channel in sorted_top_channels[nb_extra_channels-2:nb_extra_channels + 2]:\n try:\n print(' - ' + channel + '( ' + self._channel_id_to_name[channel] + ' ) - ( ' + repr(self._total_channel_stats[channel]) + ' )' )\n except:\n print('- Channel that we will discover or that we do not care about -')\n\n # Saving if we have enough new videos.\n if len(self._api_videos) > number_of_saved_videos + 100:\n self.save_videos()\n number_of_saved_videos = len(self._api_videos)\n\n # Final saving all statistics\n self.get_all_api_data()\n self.save_videos()\n self.write_result_file()\n\ndef compute_recent_files(base_domain, original_channels, max_dates=31):\n \"\"\" Compute the files that are used \n \n :param base_domain: filename base\n :param original channels: the base channels that were used\n :param max_dates: the number of dates that will be loaded in memory\n if it is too big, reduce it, but you won't be able to obtain the bigger files.\n \"\"\"\n\n if base_domain == 'france-':\n file_name = 'france-info-'\n else:\n file_name = base_domain\n\n filenames = os.listdir(DATA_DIRECTORY)\n\n # Swapping dates from dd-mm-yyyy format to yyyy-mm-dd\n def invert_date(date):\n return date[6:10] + '-' + date[3:5] + '-' + date[0:2]\n\n # Swapping dates from yyyy-mm-dd format to dd-mm-yyyy\n def revert_date(date):\n return date[8:10] + '-' + date[5:7] + '-' + date[0:4]\n\n # Sort dates\n good_name_size = len(base_domain + '14-10-2018')\n dates_set = set()\n for filename in filenames:\n if '.json' not in filename and base_domain in filename and len(filename) == good_name_size:\n dates_set.add(filename.replace(base_domain,''))\n rdates_set = set(map(invert_date, dates_set))\n dates= list(map(revert_date, sorted(rdates_set, reverse=True)))[0:max_dates]\n\n def makeFolder(date):\n \"\"\" Creates folder name \n \n :param date: date of the scrapping\n :returns: folder name\n \"\"\"\n return base_domain + date\n\n folders = list(map(makeFolder, dates))\n print(folders)\n\n def loadFromFile(filename):\n \"\"\" Loads a dictionary from a file, and returns an empty dictionary if file\n is not there.\n\n :param filename: the filename to load\n :returns: dictionary, or empty dict if no file \n \"\"\"\n print('Loading ' + filename + ' ...')\n try:\n with open(filename, \"r\") as json_file:\n my_dict = json.load(json_file)\n except Exception as e:\n print(e)\n my_dict = {}\n return my_dict\n print(filename + ' loaded!')\n\n v_to_channame = {}\n\n # Loading files\n channel_stats = {}\n scrapped_videos = collections.defaultdict(dict)\n all_scrapped_vids = set()\n for date in dates:\n folder = makeFolder(date)\n channel_stats_loc = loadFromFile(DATA_DIRECTORY + folder + '/all_channels.json')\n for chan in channel_stats_loc:\n if chan not in channel_stats:\n channel_stats[chan] = channel_stats_loc[chan]\n scrapped_videos[date] = loadFromFile(DATA_DIRECTORY + folder + '/scrapped_videos.json')\n for vid in scrapped_videos[date]:\n all_scrapped_vids.add(vid)\n v_to_channame[vid] = scrapped_videos[date][vid]['channel']\n\n VIDEO_TO_CHAN_FILE = DATA_DIRECTORY + 'video_to_chan.json'\n video_to_chan = loadFromFile(VIDEO_TO_CHAN_FILE)\n\n api_videos = {}\n api_videos_date = collections.defaultdict(dict)\n\n for date in dates:\n folder = makeFolder(date)\n api_videos_date[date] = loadFromFile(DATA_DIRECTORY + folder + '/api_videos.json')\n api_videos.update(api_videos_date[date])\n for vid in api_videos_date[date]:\n v_to_channame[vid] = api_videos_date[date][vid]['snippet']['channelTitle']\n\n all_videos = set(all_scrapped_vids).union(set(api_videos.keys()))\n\n # Delete scrapped videos that returned empty data.\n nb_deleted = 0\n for date in dates:\n for v in list(scrapped_videos[date].keys()):\n if scrapped_videos[date][v]['title'] == '':\n del scrapped_videos[date][v]\n\n # Computing the number of videos to recommendations.\n video_to_recos = collections.defaultdict(int)\n video_to_recos_date = {}\n video_to_top_recs = collections.defaultdict(int)\n rdates = list(reversed(dates))\n inc = 0\n dec = 0\n\n print(len(all_scrapped_vids))\n\n # Computing the number of estimated recommendations for all videos.\n for v in all_scrapped_vids:\n first_index = 0\n while (first_index < len(rdates) - 1):\n # Find first date\n if v not in scrapped_videos[rdates[first_index]]:\n first_index +=1\n continue\n second_index = first_index + 1\n while True:\n # Couldn't find a second date. Let's break both loops.\n if second_index >= len(rdates):\n first_index = len(rdates)\n break\n \n if v not in scrapped_videos[rdates[second_index]]:\n second_index +=1\n continue\n\n # Video is both in first date and second date\n # We look at all recommendations at first date.\n # If the video is also recommended at the second date,\n # we assume that the video has been recommended in between the two dates\n # so the number of recommendations for this video is the increase of view of the original video\n first_video = scrapped_videos[rdates[first_index]][v]\n second_video = scrapped_videos[rdates[second_index]][v]\n\n view_inc = second_video['views'] - first_video['views']\n\n if view_inc > 0:\n for reco in first_video['recommendations']:\n if reco in second_video['recommendations']:\n # We approximate that only half of the recos were from that video\n video_to_recos[reco] += int(view_inc)\n nb_index_to_retribute = int(1 + second_index - first_index)\n for delta in range(nb_index_to_retribute):\n if reco not in video_to_recos_date:\n video_to_recos_date[reco] = collections.defaultdict(int)\n video_to_recos_date[reco][rdates[first_index + delta]] += int(view_inc/(nb_index_to_retribute))\n\n first_index = second_index\n break\n\n # Computing information to display about each video\n # Which channels were recommending a given video \n video_to_chans = collections.defaultdict(set)\n # Which channels were recommending a given video, per date \n video_date_to_chans = collections.defaultdict(dict)\n # Maximum number of channel recommending the video\n video_to_max_chans = collections.defaultdict(int)\n # For each chan title, number of time each other chan is recommended\n chans_title_to_chan_to_recos = {}\n total_recos = 0\n for date in dates:\n for v in scrapped_videos[date]:\n total_recos += len(scrapped_videos[date][v]['recommendations'])\n for reco in scrapped_videos[date][v]['recommendations']:\n video_to_chans[reco].add(scrapped_videos[date][v]['channel'])\n if reco not in video_date_to_chans[date]:\n video_date_to_chans[date][reco] = set()\n video_date_to_chans[date][reco].add(scrapped_videos[date][v]['channel'])\n if reco in api_videos:\n reco_chan = api_videos[reco]['snippet']['channelTitle']\n if reco_chan not in chans_title_to_chan_to_recos:\n chans_title_to_chan_to_recos[reco_chan] = collections.defaultdict(int)\n chans_title_to_chan_to_recos[reco_chan][scrapped_videos[date][v]['channel']] +=1\n\n video_to_chans_length = {}\n for v in video_to_chans:\n video_to_chans_length[v] = len(video_to_chans[v])\n \n for v in video_to_chans:\n for date in video_date_to_chans:\n if v in video_date_to_chans[date] and len(video_date_to_chans[date][v]) > video_to_max_chans[v]:\n video_to_max_chans[v] = len(video_date_to_chans[date][v])\n\n # Computing channel stats\n chaname_to_subs = {}\n for c in channel_stats: \n chaname_to_subs[channel_stats[c]['snippet']['title']] = int(channel_stats[c].get('statistics', {}).get('subscriberCount',0))\n \n # Computing channel names\n original_channels_names = set()\n for c in original_channels:\n original_channels_names.add(channel_stats.get(c, {}).get('snippet', {}).get('channelTitle', 'Unknown channel name'))\n\n def getSortedChannels(chanset):\n \"\"\" Get the sorted list of channels that have more than 100000 subscribers.\n \n :param chanset: a set of channels\n :returns: list of channel titles\n \"\"\"\n chanmap = {chan: chaname_to_subs.get(chan, 0) for chan in chanset if chaname_to_subs.get(chan, 0) > 100000}\n return sorted(chanmap, key=chanmap.get, reverse=True)\n\n # Returns dates in chronological order\n ordered_dates = list(map(invert_date, reversed(dates)))\n\n def make_video_history(v, days=60, view_increase=None, include_history=False):\n \"\"\" For a video, build the history of views, likes, etc...\n\n :param days: number of days considered\n :view_increase: dictionary with video to view increase\n :include history: if we want to include the view history in the output\n :returns: a dict with all interesting data for that video\n \"\"\"\n dates_considered = dates[::-1][-days:]\n max_views = max(int(api_videos_date[date].get(v, {'statistics':{'viewCount':0}}).get('statistics', {}).get('viewCount', -1)) for date in dates)\n max_likes = max(int(api_videos_date[date].get(v, {'statistics':{'likeCount':0}}).get('statistics', {}).get('likeCount', 0)) for date in dates)\n max_dislikes = max(int(api_videos_date[date].get(v, {'statistics':{'dislikeCount':0}}).get('statistics', {}).get('dislikeCount', 0)) for date in dates)\n video_data = {\n 'title': api_videos[v]['snippet']['title'],\n 'pdate': api_videos[v].get('snippet', {}).get('publishedAt', ''),\n 'id': v,\n 'views': max_views,\n 'likes': max_likes,\n 'dislikes': max_dislikes,\n 'top_chans_rec': getSortedChannels(video_to_chans[v]),\n 'nb_chans_rec': len(video_to_chans[v]),\n 'observed_recos': video_to_recos[v],\n 'channel': api_videos[v]['snippet']['channelTitle'],\n 'comments': int(api_videos[v].get('statistics', {}).get('commentCount', 0))\n }\n if include_history:\n chan_history = [len(video_date_to_chans[date].get(v,[])) for date in dates_considered]\n view_history = [api_videos_date[date].get(v, {'statistics':{'viewCount':-1}}).get('statistics', {}).get('viewCount', -1) for date in dates_considered]\n like_history = [api_videos_date[date].get(v, {'statistics':{'likeCount':-1}}).get('statistics', {}).get('likeCount', -1) for date in dates_considered] \n dislike_history = [api_videos_date[date].get(v, {'statistics':{'dislikeCount':-1}}).get('statistics', {}).get('dislikeCount', -1) for date in dates_considered]\n reco_history = [video_to_recos_date[v].get(date, 0) for date in dates_considered] if v in video_to_recos_date else []\n video_data['chan_history'] = chan_history\n video_data['view_history'] = view_history\n video_data['like_history'] = like_history\n video_data['dislike_history'] = dislike_history\n video_data['reco_history'] = reco_history\n\n if view_increase and view_increase > 1000 and video_data['observed_recos']/view_increase > 100:\n print(' WEIRDLY RECOMMENDED VIDEO' )\n print(video_data)\n\n if view_increase:\n video_data['view_inc'] = view_increase\n return video_data\n\n def compute_recent_recos(dayz):\n \"\"\" Computes the number of recommendations for a specific number of days \"\"\"\n video_to_recent_recos = {}\n for v in all_videos:\n reco_chans = set()\n for date in dates[0:dayz]:\n reco_chans.update(video_date_to_chans[date].get(v,[]))\n video_to_recent_recos[v] = len(reco_chans)\n return video_to_recent_recos\n\n def compute_scrapped_channels(dayz):\n \"\"\" Computes the channels from which videos have been scrapped.\"\"\"\n scrap_chans = set()\n for date in dates[0:dayz]:\n for v in scrapped_videos[date]:\n cid = scrapped_videos[date][v].get('channel_id', 'UNKNONW CHANNEL')\n cn = scrapped_videos[date][v].get('channel', 'UNKNONW CHANNEL')\n if cid != 'UNKNONW CHANNEL':\n scrap_chans.add(cid)\n if cn != 'UNKNONW CHANNEL':\n scrap_chans.add(cn)\n return scrap_chans\n\n def compute_recent_views(dayz):\n \"\"\" Computes the view increase over the last n dayz. \"\"\"\n recent_views = {}\n missing = 0\n for v in all_videos:\n view_history = [int(api_videos_date[date].get(v, {'statistics':{'viewCount': scrapped_videos[date].get(v, {'views': -1})['views']}}).get('statistics', {}).get('viewCount', -1)) for date in reversed(dates)] \n if v not in api_videos:\n missing += 1\n continue\n pub_date = api_videos[v]['snippet'].get('publishedAt', '')[0:10]\n\n if pub_date == '':\n missing += 1\n continue\n # If the video was published during the period, the increase is the max view\n if pub_date >= invert_date(dates[dayz]):\n tmp_views = max(view_history[-dayz:])\n if tmp_views > 0:\n recent_views[v] = tmp_views\n else:\n # If the video was published before\n maxi = max(view_history[-dayz:])\n if maxi <=0:\n continue\n mini = min(x for x in view_history[-dayz:] if x > 0)\n recent_views[v] = maxi - mini\n\n print('Number of missing videos in api: ' + repr(missing))\n return recent_views\n\n # A channel name that is impossible\n impossible_chan_name = '1234dfs5678fsd9009fsdhfewirioffdsdf'\n def snippetIsInSet(snippet, chans):\n \"\"\" Checks if a snippet is in a list of channels, that could be channel name or channel ids \"\"\"\n if snippet.get('channelName', impossible_chan_name) in chans or snippet['channelId'] in chans:\n return True\n return False\n\n def write_xls_video_file(final_vids, file_base_name, recent_views, nb_dates):\n \"\"\" Write a xlsx file for this data\n\n :param final_vids: the list of videos\n :file_base_name: the base of the filename\n :recent_views: a dict video to number of recent views\n :nb_dates: the number of dates that were considered\n \"\"\"\n\n xls_file = xlsxwriter.Workbook(file_base_name + '.xlsx')\n bold = xls_file.add_format({'bold': True})\n\n worksheet = xls_file.add_worksheet('Videos')\n view_format = xls_file.add_format({'num_format': '###,###,###,###'})\n worksheet.set_column('A:A', 50)\n worksheet.set_column('B:B', 50)\n worksheet.set_column('C:C', 50)\n worksheet.set_column('D:D', 30)\n worksheet.set_column('E:E', 30)\n worksheet.set_column('F:F', 30)\n worksheet.set_column('G:G', 20)\n worksheet.set_column('H:H', 20)\n worksheet.set_column('I:I', 20)\n worksheet.set_column('J:J', 20)\n worksheet.set_column('K:K', 20)\n worksheet.set_column('L:L', 200)\n\n worksheet.write('A1', 'URL', bold)\n worksheet.write('B1', 'Title', bold)\n worksheet.write('C1', 'Channel', bold)\n worksheet.write('D1', 'Upload date', bold)\n worksheet.write('E1', 'Views', bold)\n worksheet.write('F1', 'Likes', bold)\n worksheet.write('G1', 'Dislikes', bold)\n worksheet.write('H1', 'Number of channels recommending it', bold)\n worksheet.write('I1', 'Minimum observed recommendations', bold)\n worksheet.write('J1', 'Comments', bold)\n worksheet.write('K1', 'Views in last ' + repr(nb_dates) + ' days' , bold)\n worksheet.write('L1', 'Top Channels Recommending It with > 100k subs', bold)\n\n i=2\n for vid in final_vids:\n title = vid['title']\n chan = vid['channel']\n upload = vid['pdate']\n url= 'https://www.youtube.com/watch?v=' + vid['id']\n views = vid['views']\n likes = vid['likes']\n dislikes = vid['dislikes']\n nb_chans_rec = vid['nb_chans_rec']\n observed_recommendations = vid['observed_recos']\n comments = vid['comments']\n top_chans_rec = repr(vid['top_chans_rec'])\n\n worksheet.write(i, 0, url)\n worksheet.write(i, 1, title)\n worksheet.write(i, 2, chan)\n worksheet.write(i, 3, upload)\n worksheet.write(i, 4, views, view_format)\n worksheet.write(i, 5, likes, view_format)\n worksheet.write(i, 6, dislikes, view_format)\n worksheet.write(i, 7, nb_chans_rec, view_format)\n worksheet.write(i, 9, observed_recommendations)\n worksheet.write(i, 10, comments)\n worksheet.write(i, 11, recent_views.get(vid['id'], 'unknown'))\n worksheet.write(i, 11, top_chans_rec)\n i+=1\n\n chan_counts = collections.defaultdict(int)\n\n for vid in final_vids:\n chan = vid['channel']\n chan_counts[chan] += recent_views.get(vid['id'], 0)\n\n worksheet = xls_file.add_worksheet('Channel Recent Views')\n\n worksheet.set_column('A:A', 50)\n worksheet.set_column('B:B', 70)\n worksheet.write('A1', 'Channel', bold)\n worksheet.write('B1', 'Views on this channel for ' + repr(nb_dates) + ' days', bold)\n\n i = 0\n for c in sorted(chan_counts, key=chan_counts.get, reverse=True):\n worksheet.write(i, 0, c)\n worksheet.write(i, 1, chan_counts[c])\n i += 1\n xls_file.close()\n\n def compute_evolution_file(file_id, nb_dates, videos_included, videos_with_history=100, original_channels_only=False):\n \"\"\" Create the file with the historical evolution of number of views and channel recommending a video.\n \n :param file_id: the id of the file\n :param videos_included: number of videos that should be in the file\n :param videos_with_history: number of videos that should be stored with history\n :returns: nothing\n \"\"\"\n\n # If max_dates is not big enough, we do not compute it\n if max_dates >= nb_dates:\n scrapped_channels = compute_scrapped_channels(nb_dates)\n recent_views = compute_recent_views(nb_dates)\n i=0\n final_vids = []\n print(len(recent_views))\n\n # Computing the video file sorted by recent views\n for v in sorted(recent_views, key=recent_views.get, reverse=True):\n if (i < videos_included and v in api_videos and snippetIsInSet(api_videos[v]['snippet'], scrapped_channels) and\n # If we only want original channels :\n (api_videos[v]['snippet']['channelId'] in original_channels or not original_channels_only)):\n final_vids.append(make_video_history(v, days=nb_dates, view_increase=recent_views[v], include_history=(i 1000 and\n (c in original_channels_names or not original_channels_only)):\n final_vids.append({'view_inc': recent_channel_views[c], 'title': c})\n file_base_name = 'evo-' + file_name + file_id + '-c'\n with open( file_base_name + '.json', 'w') as fp:\n json.dump({\"videos\": final_vids, 'dates': ordered_dates[-3:]}, fp)\n\n # Computing the video file sorted by recommendations\n recent_recos = compute_recent_recos(nb_dates)\n i=0\n final_vids = []\n print(len(recent_views))\n for v in sorted(recent_recos, key=recent_recos.get, reverse=True):\n if i < videos_included and v in api_videos and snippetIsInSet(api_videos[v]['snippet'], scrapped_channels):\n final_vids.append(make_video_history(v, days=3, view_increase=recent_views.get(v, 0), include_history=(i RollHistory.roll_time\n )\n return res.count()\n except Exception as e:\n return 0\n finally:\n session.close()\n\n\ndef count_roll(group_id):\n most_point_user, most_point = count_most_point(group_id)\n most_times_user, most_times = count_most_times(group_id)\n return most_point_user, most_point, most_times_user, most_times\n\n\ndef count_my_roll(group_id, user_id):\n session = SESSION()\n try:\n res = session.query(func.sum(RollHistory.point).label('s')).filter_by(group_id=group_id, user_id=user_id).first()\n if res:\n return res[0]\n return 0\n except Exception as e:\n return 0\n finally:\n session.close()\n\n\n# 最多点数\ndef count_most_point(group_id):\n session = SESSION()\n try:\n res = session.query(RollHistory.user_id, func.sum(RollHistory.point).label('s')).filter_by(group_id=group_id).group_by(RollHistory.user_id).order_by(desc('s')).first()\n if res:\n return res\n return 0, 0\n except Exception as e:\n return 0, 0\n finally:\n session.close()\n\n\n# 最多次数\ndef count_most_times(group_id):\n session = SESSION()\n try:\n res = session.query(RollHistory.user_id, func.count(RollHistory.user_id).label('t')).filter_by(group_id=group_id).group_by(RollHistory.user_id).order_by(desc('t')).first()\n if res:\n return res\n return 0, 0\n except Exception as e:\n return 0, 0\n finally:\n session.close()\n\n\ndef insert_point(**kwargs):\n session = SESSION()\n try:\n roll = RollHistory(**kwargs)\n session.add(roll)\n session.commit()\n except Exception as e:\n return e\n finally:\n session.close()\n\n","sub_path":"mynonebot/coolq/plugins/roll/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"273731216","text":"# -*- coding: utf-8 -*-\n\nfrom django.conf.urls.defaults import patterns, include, url\nfrom django.contrib import admin\nfrom filebrowser.sites import site\nadmin.autodiscover()\n\nimport settings\nimport views\n\nurlpatterns = patterns('',\n (r'^media/(?P.*)$', 'django.views.static.serve', {'document_root': settings.MEDIA_ROOT}),\n (r'^static/(?P.*)$', 'django.views.static.serve', {'document_root': settings.STATIC_ROOT}),\n url(r'^(robots.txt)$', 'django.views.static.serve', {'document_root': settings.STATIC_ROOT}),\n url(r'^(sitemap.xml)$', 'django.views.static.serve', {'document_root': settings.STATIC_ROOT}),\n url(r'^(google8041822f97cc6728.html)$', 'django.views.static.serve', {'document_root': settings.STATIC_ROOT}),\n\n url(r'^$', views.home_page),\n url(r'^lk$', views.lk_page),\n url(r'^rates/(?P\\w+)$', views.rates_page, name='rates_page'),\n url(r'^page$' , 'views.page'),\n url(r'^page/(?P\\w+)$' , views.get_page, name='static_page'),\n url(r'^order$', views.order_page, name='order_page'),\n url(r'^feedback$', views.feedback_page,name='feedback_page'),\n url(r'^calc$', views.calc_page, name='calc_page'),\n url(r'^news/(?P\\w+)$', views.news_page, name='news_page'),\n url(r'^grappelli/', include('grappelli.urls')),\n url(r'^admin/filebrowser/', include(site.urls)),\n url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n url(r'^admin/', include(admin.site.urls)),\n url(r'^admin/jsi18n/', 'django.views.i18n.javascript_catalog'),\n)\n","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"357914054","text":"from urllib.request import urlopen\n\nfrom bs4 import BeautifulSoup\nimport tqdm\n\nfrom paper_parser import BasePaperListParser, Paper\n\n\nclass PaperListParser(BasePaperListParser):\n\n def __init__(self, args):\n self.base_url = \"https://nips.cc/Conferences/%s/Schedule\" % (args.year)\n self.website_url = \"http://papers.nips.cc\"\n\n def parse(self, html_soup):\n all_container = html_soup.select(\"div.maincard\")\n paper_list = []\n spotlight = 0\n oral = 0\n poster = 0\n overall = 0\n faild = 0\n for container in tqdm.tqdm(all_container):\n suffix = \"\"\n if \"Spotlight\" in container.get('class'):\n suffix = \" (Spotlight)\"\n spotlight += 1\n elif \"Oral\" in container.get('class'):\n suffix = ' (Oral)'\n oral += 1\n elif \"Poster\" in container.get('class'):\n poster += 1\n else:\n continue\n overall += 1\n try:\n title = container.select('div.maincardBody')[0].get_text() + suffix\n url = container.select('a.href_PDF')[0].get('href')\n # assert \"PDF\" in container.select('a.href_PDF')[0].get_text()\n paper_list.append((title, url))\n except Exception as e:\n print(e)\n faild += 1\n # print(\"Paper [%s] does not have a related url\" % title)\n pass\n print(\"Parse %s; spotlight: %d, Oral: %d, Poster: %d, Overall: %d, faild: %d \" \\\n % (self.base_url, spotlight, oral, poster, overall, faild))\n return paper_list\n\n def cook_paper(self, paper_info):\n try:\n page_content = urlopen(paper_info[1]).read().decode('utf8')\n soup = BeautifulSoup(page_content, features=\"html.parser\")\n author_list = [self.text_process(x.get_text()) for x in soup.select('li.author')]\n abstract = self.text_process(soup.select('p.abstract')[0].get_text())\n pdf_url = self.website_url + next(filter(lambda x: '[PDF]' in x.get_text(), soup.select('a'))).get('href')\n return Paper(self.text_process(paper_info[0]), abstract, pdf_url, author_list)\n except Exception as e:\n print(e)\n return (paper_info[0], e, self.base_url, [])","sub_path":"paper_parser/nips.py","file_name":"nips.py","file_ext":"py","file_size_in_byte":2369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"328310772","text":"# -*- coding: utf-8 -*-\nfrom django.conf.urls import url, include\nfrom . import views\n\nurlpatterns = [\n url(r'^test/test/', views.test, name='test_test'),\n url(r'^index/', views.index, name='bank_index'),\n url(r'^questionnaire/import/', views.questionnaire_import, name='questionnaire_import'),\n url(r'^questionnaire/tpl/', views.questionnaire_tpl, name='questionnaire_tpl'), \n \n url(r'^inquire/into/', views.inquire_into, name='inquire_into'),\n url(r'^overall/evaluation/', views.overall_evaluation, name='overall_evaluation'),\n url(r'^all/investigation/', views.all_investigation, name='all_investigation'), \n url(r'^all/investigationRanking/', views.all_investigationRanking, name='all_investigationRanking'),\n url(r'^user/questionnaire/(.+)', views.user_questionnaire, name='user_questionnaire'), \n url(r'^help/(.+)', views.help, name='help'),\n \n url(r'^test/questionnaire/', views.test_questionnaire, name='test_questionnaire'),\n url(r'^analysis/report/', views.analysis_report, name='analysis_report'),\n url(r'^down/analysisReport/', views.down_analysisReport, name='down_analysis_report'),\n\n url(r'^create/excel/', views.create_excel, name='create_excel'), \n url(r'^contactus/', views.contactus, name='contactus'), \n url(r'^setting/value/', views.setting_value, name='setting_value'),\n url(r'^setting/list/', views.setting_list, name='setting_list'), \n url(r'^upload/', views.upload, name='upload_word_tpl'), \n url(r'^download/', views.download, name='download_word_tpl'),\n]\n","sub_path":"mysite/bank/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"45584114","text":"#encoding=utf-8\n'''\nCreated on 2014-12-21\n\n@author: 张江涛\n'''\n\nimport sys\nif sys.version[0] == '2':\n reload(sys)\n sys.setdefaultencoding('utf8') \nimport urllib \nif sys.version[0] == '3':\n import urllib.request as urllib2\n from urllib.parse import urlencode \nelse: \n import urllib2\n from urllib import urlencode \nimport model.global_var \nimport time\n\n\ndef extract_mentions_ansj(text):\n text = text.replace(\"/\",\" \")\n mentions = []\n param = {u'text':text}\n\n url = model.global_var.CUT_URL + urlencode(param).encode('utf-8')\n \n# if model.global_var.STATISTIC_FLAG:\n# current_time = time.time()\n# print(url)\n segs = urllib2.urlopen(url).read().decode('utf-8')\n \n# if model.global_var.STATISTIC_FLAG:\n# model.global_var.CUT_WORDS_TIME = time.time() - current_time\n# # print('model.global_var.CUT_WORDS_TIME: %.4f'%model.global_var.CUT_WORDS_TIME)\n# current_time = time.time()\n# print(segs)\n last = 0\n for seg in segs.split(' '):\n token = seg.split(\"/\")[0]\n if token in model.global_var.MENTION_ENTITY:\n# print(token),\n# mentions.append((token.replace('+',' '),last))\n mentions.append((token,last))\n# print('%s:%d,'%(token,len(token))),\n last += len(token)\n \n# print('\\n')\n# if model.global_var.STATISTIC_FLAG:\n# model.global_var.EXACT_MATCH_TIME = time.time() - current_time\n \n return mentions\n\nif __name__ == \"__main__\":\n text = u'《深夜前的五分钟》最吸引我的，不是主演刘诗诗和张孝全，'\n param = {u'text':text}\n url = model.global_var.CUT_URL + urlencode(param).encode('utf-8')\n segs = urllib2.urlopen(url).read().decode('utf-8')\n print(segs)\n ","sub_path":"extract_mentions.py","file_name":"extract_mentions.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"539804556","text":"\"\"\"\nThis is the first prototype (testing) for the ANUGA & SWMM coupling project\n\nIn this testing, we are expecting to create a one-pipe testing. Flowing water out from ANUGA to SWMM, and using the SWMM\ncalculate the water flow activities in the pipe, and flows back to ANUGA.\n\nwe can validate this testing by monitor the change of the water total volume. It should remains the same between flowing\nto SWMM and flowing back to ANUGA.\n\"\"\"\n\n\n# ------------------------------------------------------------------------------\n# Import necessary modules\n# ------------------------------------------------------------------------------\nfrom anuga import Dirichlet_boundary\nfrom anuga import Domain\nfrom anuga import Reflective_boundary\nfrom anuga.operators.rate_operators import Rate_operator\nfrom anuga import Region\nfrom anuga import rectangular_cross\n\nimport anuga\nimport numpy as num\n\n# ------------------------------------------------------------------------------\n# Setup computational domain\n# ------------------------------------------------------------------------------\n\nlength = 15.\nwidth = 4.\ndx = dy = 0.1 # .1 # Resolution: Length of subdivisions on both axes\n\npoints, vertices, boundary = rectangular_cross(int(length / dx), int(width / dy),\n len1=length, len2=width)\ndomain = Domain(points, vertices, boundary)\ndomain.set_name('total_volume_testing') # Output name based on script name. You can add timestamp=True\nprint(domain.statistics())\n\n\n# ------------------------------------------------------------------------------\n# Setup initial conditions\n# ------------------------------------------------------------------------------\ndef topography(x, y):\n \"\"\"Complex topography defined by a function of vectors x and y.\"\"\"\n\n z = 0 * x - 5\n\n # higher pools\n id = x < 5\n z[id] = -3\n\n # wall\n id = (5 < x) & (x < 10)\n z[id] = 0\n\n # inflow pipe hole, located at (2, 2), r = 0.5, depth 0.1\n id = (x - 2) ** 2 + (y - 2) ** 2 < 0.3 ** 2\n z[id] -= 0.2\n\n # inflow pipe hole, located at (12, 2), r = 0.5, depth 0.1\n id = (x - 12) ** 2 + (y - 2) ** 2 < 0.3 ** 2\n z[id] -= 0.2\n\n return z\n\n\n# ------------------------------------------------------------------------------\n# Setup initial quantity\n# ------------------------------------------------------------------------------\ndomain.set_quantity('elevation', topography, location = 'centroids') # elevation is a function\ndomain.set_quantity('friction', 0.01) # Constant friction\ndomain.set_quantity('stage', expression='elevation', location = 'centroids') # Dry initial condition\n# --------------------------\n\n\"\"\"\nWe would use this method to gain the boundary indices\n\"\"\"\n\n\n# polygon1 = [ [10.0, 0.0], [11.0, 0.0], [11.0, 5.0], [10.0, 5.0] ]\n# polygon2 = [ [10.0, 0.2], [11.0, 0.2], [11.0, 4.8], [10.0, 4.8] ]\n\ndef get_cir(radius=None, center=None, domain=None, size=None, polygons=None):\n if polygons is not None:\n polygon1 = polygons[0] # the larger one\n polygon2 = polygons[1]\n opp1 = Rate_operator(domain, polygon=polygon1)\n opp2 = Rate_operator(domain, polygon=polygon2)\n if isinstance(polygon1, Region):\n opp1.region = polygon1\n else:\n opp1.region = Region(domain, poly=polygon1, expand_polygon=True)\n if isinstance(polygon2, Region):\n opp2.region = polygon2\n else:\n opp2.region = Region(domain, poly=polygon2, expand_polygon=True)\n\n if radius is not None and center is not None:\n\n region1 = Region(domain, radius=radius, center=center)\n region2 = Region(domain, radius=radius - size, center=center)\n\n if radius is None and center is None:\n indices = [x for x in opp1.region.indices if x not in opp2.region.indices]\n else:\n indices = [x for x in region1.indices if x not in region2.indices]\n\n return indices\n\n\ndef get_depth(operator):\n # FIXME: according to the index return the overland depth of specific area\n\n # need check each triangle's area should be dx*dy/4\n # here is the inlet depth\n len_boud_pipe = len(operator.stage_c[:].take([get_cir(radius=0.5, center=(2.0, 2.0), domain=domain, size=0.0625)])[0])\n overland_depth = sum(operator.stage_c[:].take([get_cir(radius=0.5, center=(2.0, 2.0), domain=domain, size=0.0625)])\n [0]-operator.elev_c[:].take([get_cir(radius=0.5, center=(2.0, 2.0), domain=domain, size=0.0625)])\n [0]) / len_boud_pipe\n # the overland_depth should be got from ANUGA directly\n\n return overland_depth\n\n# ------------------------------------------------------------------------------\n# Setup boundaries\n# ------------------------------------------------------------------------------\nBi = Dirichlet_boundary([-3, 0, 0]) # Inflow\nBr = Reflective_boundary(domain) # Solid reflective wall\nBo = Dirichlet_boundary([-5, 0, 0]) # Outflow\n\ndomain.set_boundary({'left': Br, 'right': Br, 'top': Br, 'bottom': Br})\n\n# ------------------------------------------------------------------------------\n# Setup inject water\n# ------------------------------------------------------------------------------\n\nop_inlet = Rate_operator(domain, radius=0.5, center=(2., 2.))\nop_outlet = Rate_operator(domain, radius=0.5, center=(12., 2.)) #\n\nx = domain.centroid_coordinates[:,0]\n\n\n\nindices = num.where(x < 5)\n\nprint(indices)\n\nanuga.Set_stage(domain, stage = -2.5, indices = indices)()\n\nfrom pyswmm import Simulation, Nodes, Links\n\nsim = Simulation('./pipe_test.inp')\nsim.start()\nnode_names = ['Inlet', 'Outlet']\n\nlink_names = ['Culvert']\n\nnodes = [Nodes(sim)[names] for names in node_names]\nlinks = [Links(sim)[names] for names in link_names]\n\n# type, area, length, orifice_coeff, free_weir_coeff, submerged_weir_coeff\nnodes[0].create_opening(4, 1.0, 1.0, 0.6, 1.6, 1.0)\nnodes[0].coupling_area = 1.0\n\n# TODO: setup the outlet node\nnodes[1].create_opening(4, 1.0, 1.0, 0.6, 1.6, 1.0)\n\n\nprint(\"node1_is_open?:\",nodes[1].is_coupled)\n\nflow = 1.0\nstop_release_water_time = 2 # the time for stopping releasing the water\n\ndomain.set_name(\"anuga_swmm\")\nfor t in domain.evolve(yieldstep=1.0, finaltime=60.0):\n print(\"\\n\")\n #print(f\"coupling step: {t}\")\n domain.print_timestepping_statistics()\n if t < stop_release_water_time:\n # assume we need to release the water into the domain for first two seconds\n pass\n #op_inlet.set_rate(flow)\n else:\n # set the overland_depth\n # TODO: set up the overland depth, modify this function\n\n print(\"total volume: \",domain.get_water_volume())\n volumes = sim.coupling_step(1.0)\n\n print(volumes)\n\n nodes[0].overland_depth = get_depth(op_inlet)\n print(\"inlet overland depth: \", get_depth(op_inlet))\n volumes_in_out = volumes[-1][-1]\n print(volumes_in_out)\n\n if t <= stop_release_water_time+1:\n # no water exchange as the first two steps from swmm and anuga did not match.\n print(\"Volume total at node Inlet\" \":\", volumes_in_out[\"Inlet\"])\n print(\"Oulet: \", nodes[1].total_inflow)\n op_inlet.set_rate(0)\n op_outlet.set_rate(0)\n else:\n #Ming's code\n print(\"Volume total at node Inlet\" \":\", volumes_in_out[\"Inlet\"])\n print(\"Oulet: \", nodes[1].total_inflow)\n op_inlet.set_rate(-1 * volumes_in_out['Inlet'])\n Q = nodes[1].total_inflow\n fid = op_outlet.full_indices\n rate = Q / num.sum(op_outlet.areas[fid])\n op_outlet.set_rate(rate)\n\n # op_outlet.set_rate(nodes[1].total_inflow)\n # Q = 5\n # fid = op1.full_indices\n # rate = Q / num.sum(op1.areas[fid])\n","sub_path":"total_volume_testing.py","file_name":"total_volume_testing.py","file_ext":"py","file_size_in_byte":7747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"204918708","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom .models import Movie, Review\nfrom django.contrib.auth.decorators import login_required\nfrom django.views.decorators.http import require_POST\nfrom .forms import ReviewForm\nfrom django.contrib import messages\n\ndef index(request):\n context = {\n 'movies': Movie.objects.all()\n }\n return render(request, 'movies/index.html', context)\n\ndef detail(request, movie_pk):\n context = {\n 'movie': get_object_or_404(Movie, pk=movie_pk),\n 'form': ReviewForm()\n }\n return render(request, 'movies/detail.html', context)\n \n@require_POST\ndef review_create(request, movie_pk):\n movie = get_object_or_404(Movie, pk=movie_pk)\n if request.user.is_authenticated:\n form = ReviewForm(request.POST)\n if form.is_valid():\n review = form.save(commit=False)\n review.user = request.user\n review.movie = movie\n review.save()\n else:\n messages.warning(request, '로그인이 필요합니다.')\n return redirect('movies:detail', movie_pk)\n\ndef review_delete(request, movie_pk, review_pk):\n review = get_object_or_404(Review, pk=review_pk)\n if request.user == review.user:\n review.delete()\n else:\n messages.warning(request, '리뷰 삭제 권한이 없습니다.')\n return redirect('movies:detail', movie_pk)\n\n@require_POST\ndef like(request, movie_pk):\n movie = get_object_or_404(Movie, pk=movie_pk)\n if request.user.is_authenticated:\n if movie in request.user.like_movies.all():\n request.user.like_movies.remove(movie)\n else:\n request.user.like_movies.add(movie)\n else:\n messages.warning(request, '로그인이 필요한 기능입니다.')\n return redirect('movies:detail', movie_pk)\n\ndef update_score(request, review_pk):\n review = get_object_or_404(Review, pk=review_pk)\n if request.user == review.user:\n if request.method == 'POST':\n form = ReviewForm(request.POST, instance=review)\n if form.is_valid():\n form.save()\n return redirect('accounts:detail', review.user.pk)\n else:\n form = ReviewForm(instance=review)\n context = {\n 'form': form\n }\n return render(request, 'accounts/form.html', context)\n else:\n messages.warning(request, '수정 권한이 없습니다.')\n return redirect('accounts:detail', review.user.pk)","sub_path":"movies/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"335189943","text":"import logging\nfrom datetime import datetime\n\nfrom django.db.transaction import atomic\n\nfrom river.models.proceeding import APPROVED\nfrom river.services.proceeding import ProceedingService\nfrom river.services.state import StateService\nfrom river.signals import ProceedingSignal, TransitionSignal, FinalSignal\nfrom river.utils.error_code import ErrorCode\nfrom river.utils.exceptions import RiverException\n\n__author__ = 'ahmetdal'\n\n\nclass TTSSignal(object):\n def __init__(self, status, pre_signal, post_signal, **kwargs):\n self.status = status\n self.pre_signal = pre_signal\n self.post_signal = post_signal\n self.kwargs = kwargs\n\n def __enter__(self):\n if self.status:\n self.pre_signal.send(\n sender=TTSSignal.__class__,\n **self.kwargs\n )\n\n def __exit__(self, type, value, traceback):\n if self.status:\n self.post_signal.send(\n sender=TTSSignal.__class__,\n **self.kwargs\n )\n\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass TransitionService(object):\n def __init__(self):\n pass\n\n @staticmethod\n @atomic\n def proceed(workflow_object, user, next_state=None, god_mod=False):\n\n def process(workflow_object, user, action, next_state=None, god_mod=False):\n proceedings = ProceedingService.get_available_proceedings(workflow_object, [workflow_object.get_state()], user=user, god_mod=god_mod)\n c = proceedings.count()\n if c == 0:\n raise RiverException(ErrorCode.NO_AVAILABLE_NEXT_STATE_FOR_USER, \"There is no available state for destination for the user.\")\n if c > 1:\n if next_state:\n proceedings = proceedings.filter(meta__transition__destination_state=next_state)\n if proceedings.count() == 0:\n available_states = StateService.get_available_states(workflow_object, user)\n raise RiverException(ErrorCode.INVALID_NEXT_STATE_FOR_USER, \"Invalid state is given(%s). Valid states is(are) %s\" % (\n next_state.__str__(), ','.join([ast.__str__() for ast in available_states])))\n else:\n raise RiverException(ErrorCode.NEXT_STATE_IS_REQUIRED,\n \"State must be given when there are multiple states for destination\")\n proceeding = proceedings[0]\n proceeding.status = action\n proceeding.transactioner = user\n proceeding.transaction_date = datetime.now()\n if workflow_object.proceeding:\n proceeding.previous = workflow_object.proceeding\n proceeding.save()\n\n return proceeding\n\n proceeding = process(workflow_object, user, APPROVED, next_state, god_mod)\n\n # Any other proceeding is left?\n required_proceedings = ProceedingService.get_available_proceedings(workflow_object, [workflow_object.get_state()], destination_state=next_state,\n god_mod=god_mod)\n\n transition_status = False\n if required_proceedings.count() == 0:\n workflow_object.set_state(proceeding.meta.transition.destination_state)\n transition_status = True\n\n # Next states should be PENDING back again if there is circle.\n ProceedingService.cycle_proceedings(workflow_object)\n # ProceedingService.get_next_proceedings(workflow_object).update(status=PENDING)\n\n with ProceedingSignal(workflow_object, proceeding), TransitionSignal(transition_status, workflow_object, proceeding), FinalSignal(workflow_object):\n workflow_object.save()\n\n LOGGER.debug(\"Workflow object %s is proceeded for next transition. Transition: %s -> %s\" % (\n workflow_object, workflow_object.get_state().label, workflow_object.get_state()))\n","sub_path":"venv/Lib/site-packages/river/services/transition.py","file_name":"transition.py","file_ext":"py","file_size_in_byte":3973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"242704649","text":"import sys\nimport random\n\nif __name__ == \"__main__\":\n if len(sys.argv) != 4:\n print('usage: %s num_heldout_spk num_heldout_utts_per_spk input_spk2utt' % sys.argv[0])\n quit()\n\n num_spks = int(sys.argv[1])\n num_utts_per_spk = int(sys.argv[2])\n\n satisfy_spks = []\n not_satisfy_spks = []\n with open(sys.argv[3], 'r') as f:\n for line in f.readlines():\n spk, utts = line.strip().split(' ', 1)\n utts = utts.split(' ')\n if len(utts) >= num_utts_per_spk + 2:\n satisfy_spks.append([spk, utts])\n else:\n not_satisfy_spks.append([spk, utts])\n\n if len(satisfy_spks) < num_spks:\n satisfy_spks += random.sample(not_satisfy_spks, num_spks - len(satisfy_spks))\n\n sampled_spks = random.sample(satisfy_spks, num_spks)\n for spk in sampled_spks:\n sys.stdout.write('%s' % spk[0])\n\n # We should ensure at lease one utterance of each speaker is left in the training set.\n if len(spk[1]) > num_utts_per_spk:\n spk[1] = random.sample(spk[1], num_utts_per_spk)\n else:\n spk[1] = random.sample(spk[1], len(spk[1]) - 1)\n\n for utt in spk[1]:\n sys.stdout.write(' %s' % utt)\n sys.stdout.write('\\n')\n","sub_path":"misc/tools/sample_validset_spk2utt.py","file_name":"sample_validset_spk2utt.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"119242021","text":"import multiprocessing\nimport time\n\nfrom pong_libs import *\n\nimport socket\n\nHOST = '127.0.0.1'\nPORT = 56789\n\nstop = False\nthreads = []\ncloseable = []\n\n\ndef get_json_mouse_data() -> str:\n x, y = pygame.mouse.get_pos()\n return json.dumps({\n \"mouse\": {\n \"x\": x,\n \"y\": y\n }\n })\n\n\ndef network_loop(game: PongGame):\n global stop\n sleep_rate = 1 / TICK_RATE\n client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n client.connect((HOST, PORT))\n client.sendall(str.encode(\"ping\"))\n closeable.append(client)\n with client:\n while True:\n data = client.recv(1024)\n if not data or stop:\n break\n\n msg = data.decode()\n json_data = json.loads(msg)\n\n game.player_server.pad.x = json_data[\"pad_server\"][\"x\"]\n game.player_server.pad.y = json_data[\"pad_server\"][\"y\"]\n\n game.player_client.pad.x = json_data[\"pad_client\"][\"x\"]\n game.player_client.pad.y = json_data[\"pad_client\"][\"y\"]\n\n game.ball.x = json_data[\"ball\"][\"x\"]\n game.ball.y = json_data[\"ball\"][\"y\"]\n\n game.player_server.score = json_data[\"score\"][\"server\"]\n game.player_client.score = json_data[\"score\"][\"client\"]\n\n client.sendall(str.encode(get_json_mouse_data()))\n time.sleep(sleep_rate)\n\n\ndef close():\n for t in threads:\n t.terminate()\n for c in closeable:\n c.close()\n socket.close(0)\n sys.exit()\n\n\ndef main():\n global stop\n game = PongGame()\n\n net_thread = multiprocessing.Process(target=network_loop, args=(game, ))\n net_thread.start()\n threads.append(net_thread)\n\n # GAME LOOP\n while True:\n if game.manage_events():\n stop = True\n break\n\n game.show()\n pygame.display.flip()\n CLOCK.tick(TICK_RATE)\n\n\nmain()\n","sub_path":"DM/21-03-08/pong_client.py","file_name":"pong_client.py","file_ext":"py","file_size_in_byte":1894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"525354950","text":"import unidecode\nimport operator\nimport map\n\ndef simplify(s):\n\ts = unidecode.unidecode(s)\n\ts = s.lower()\n\treturn s\n\ndef levenshtein_distance(s1, s2):\n\t\"\"\" Levenshtein distance between words\n\tCopied from Wikipedia and slightly adapted\n\thttps://en.wikibooks.org/wiki/Algorithm_Implementation/Strings/Levenshtein_distance#Python\n\t\"\"\"\n\tif len(s1) < len(s2):\n\t\treturn levenshtein_distance(s2, s1)\n\n\t# len(s1) >= len(s2)\n\tif len(s2) == 0:\n\t\treturn len(s1)\n\n\tprevious_row = range(len(s2) + 1)\n\tfor i, c1 in enumerate(s1):\n\t\tcurrent_row = [i + 1]\n\t\tfor j, c2 in enumerate(s2):\n\t\t\tinsertions = previous_row[j + 1] + 1 # j+1 instead of j since previous_row and current_row are one character longer\n\t\t\tdeletions = current_row[j] + 1\t # than s2\n\t\t\tif c1 == c2:\n\t\t\t\tk = -5\n\t\t\telse:\n\t\t\t\tk = 1\n\t\t\tsubstitutions = previous_row[j] + k\n\t\t\tcurrent_row.append(min(insertions, deletions, substitutions))\n\t\tprevious_row = current_row\n\t\n\treturn previous_row[-1]\n\t\nasync def guess_name(name,places):\n\tname = simplify(name)\n\tresults = {}\n\tfor s in places:\n\t\td = levenshtein_distance(name,simplify(s))\n\t\tresults[s] = d\n\tsorted_results = sorted(results.items(), key=operator.itemgetter(1))\n\treturn [s[0] for s in sorted_results]\n\t\nasync def guess_name_bis(name,places,reference):\n\tname = simplify(name)\n\tresults = {}\n\tdmin = 1000\n\tdmax = -1000\n\tfor s in places:\n\t\td = levenshtein_distance(name,simplify(s))\n\t\tif d < dmin:\n\t\t\tdmin = d\n\t\tif d > dmax:\n\t\t\tdmax = d\n\t\tresults[s] = d\n\t\n\tfor s in places:\n\t\t# Normalization\n\t\tresults[s] = (results[s]-dmin)*100./(dmax-dmin)\n\t\t\n\t\t# Penalty with distance from reference\n\t\tflight_distance = await map.distance(reference,places[s])\n\t\tresults[s] = results[s]*(1+flight_distance)\n\t\t\n\tsorted_results = sorted(results.items(), key=operator.itemgetter(1))\n\treturn [s[0] for s in sorted_results]","sub_path":"guesser.py","file_name":"guesser.py","file_ext":"py","file_size_in_byte":1801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"233940115","text":"from AddBook_record import *\n\nclass Entry:\n def mainEntry(self):\n p1=Person()\n p1.take_input()\n p1.Entry_in_DB()\n \nclass Display:\n def mainDisplay(self):\n p1=Person()\n p1.getEntries()\n\nclass Find:\n def mainFind(self):\n p1=Person()\n p1.name_input()\n p1.find_entry()\n\nclass Del:\n def mainDel(self):\n p1=Person()\n p1.delete_by_name()\n p1.delete_entry()\n\nclass Update:\n def main_update(self):\n p1=Person()\n #p1.update_by_name()\n p1.update_entry()\n\nwhile True:\n try:\n print(\"/**** Welcome to the address book program ****/\")\n\n print(\"Available options\")\n print(\"1 - Add a contact\")\n print(\"2 - Display contacts\")\n print(\"3 - Find contact\")\n print(\"4 - Delete contact\")\n print(\"5 - Update contact\") \n \n users_input =int(input(\"Select option: \"))\n\n if users_input==1:\n e1=Entry()\n e1.mainEntry()\n print(\"\\** Record Added Successfully **\\n\")\n\n elif users_input==2:\n d1=Display()\n d1.mainDisplay()\n\n elif users_input==3:\n f1=Find()\n f1.mainFind()\n print(\"** match record found **\")\n \n\n elif users_input==4:\n d1=Del()\n d1.mainDel() \n print(\"** Record Deleted Successfully **\") \n\n elif users_input==5:\n u1=Update()\n u1.main_update()\n print(\"** Record Updated Successfully **\")\n\n else:\n print(\"Something wrong..Please Try Again\")\n break\n\n print(\"Thank you for using the address book\")\n\n except Exception as e:\n print(e)","sub_path":"process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":1729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"2205374","text":"import os\nimport json\n\nfrom django.test import Client\nfrom django.core.urlresolvers import reverse\nfrom django.test.utils import override_settings\n\nfrom apps.hello.models import Profile\nfrom apps.hello.forms import ProfileForm\n\nfrom ..base_testcase import BaseTestCase\n\n\nclass EditFormPageTest(BaseTestCase):\n fixtures = ['myfixture.json']\n\n def setUp(self):\n self.url = reverse('edit_profile')\n\n self.client.login(username='admin', password='admin')\n\n self.profile_fields = [\n 'name',\n 'surname',\n 'date_of_birth',\n 'bio',\n 'email',\n 'jabber',\n 'skype',\n 'other_contacts',\n 'photo'\n ]\n\n def test_edit_form_view_displays_form(self):\n '''\n Test edit form view works, renders the right templates,\n and response page contains the form.\n '''\n response = self.client.get(self.url)\n\n self.assertEqual(response.status_code, 200)\n self.assertEqual(\n [t.name for t in response.templates],\n ['hello/edit_form.html', 'hello/base.html']\n )\n\n self.assertContains(response, 'editForm')\n for form_field in self.profile_fields:\n self.assertContains(\n response,\n 'name=\"{}\"'.format(form_field)\n )\n\n def test_edit_profile_page_is_linked_to_home_page(self):\n '''\n Test edit profile page has a link to the home page.\n '''\n response = self.client.get(self.url)\n\n # Can't use reverse here for '/'\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, 'href=\"/\"')\n\n def test_edit_profile_view_renders_template_with_a_form(self):\n '''\n Test edit_profile view renders the template with a\n ProfileForm form.\n '''\n response = self.client.get(self.url)\n\n self.assertEqual(response.status_code, 200)\n self.assertIn('form', response.context)\n self.assertIsInstance(response.context['form'], ProfileForm)\n\n def test_edit_profile_view_shows_form_with_profile_data(self):\n '''\n Test that edit profile form is initially populated with\n profile data from db.\n '''\n profile = Profile.objects.first()\n\n response = self.client.get(self.url)\n\n self.assertEqual(response.status_code, 200)\n\n initial_data = response.context['form'].initial\n\n for field in self.profile_fields:\n self.assertEqual(\n initial_data[field],\n getattr(profile, field)\n )\n if field == 'date_of_birth':\n continue\n\n self.assertContains(response, getattr(profile, field))\n\n def test_edit_profile_view_handles_ajax_post_requests(self):\n '''\n Test that edit profile view updates the profile with recieved\n post data, and stores updated profile to db.\n '''\n profile_before_post = Profile.objects.first()\n\n data = dict(\n name='John',\n surname='Doe',\n bio='John Doe Bio',\n date_of_birth='2006-02-18',\n email='john.doe@mail.com',\n jabber='john@jabber.com',\n skype='john.snow',\n other_contacts='John Doe Contacts'\n )\n response = self.send_ajax_post_request(data)\n\n self.assertEqual(response.status_code, 200)\n\n profile_after_post = Profile.objects.first()\n\n for field in data:\n self.assertNotEqual(\n getattr(profile_after_post, field),\n getattr(profile_before_post, field)\n )\n if field == 'date_of_birth':\n continue\n\n self.assertEqual(\n getattr(profile_after_post, field),\n data[field]\n )\n\n def test_edit_profile_view_handles_posts_with_bad_data(self):\n '''\n Test edit profile view handles invalid recieved post data\n and sends form errors to the client.\n '''\n response = self.send_ajax_post_request(dict(\n name='',\n surname='',\n bio='',\n date_of_birth='',\n email=''\n ))\n\n self.assertEqual(response.status_code, 400)\n\n try:\n errors = json.loads(response.content)\n except ValueError:\n self.fail(\n 'edit_profile view returns invalid json '\n 'data on bad post data'\n )\n self.assertIn('name', errors)\n self.assertIn('surname', errors)\n self.assertIn('bio', errors)\n self.assertIn('email', errors)\n self.assertIn('date_of_birth', errors)\n\n def test_access_to_edit_profile_page(self):\n '''\n Test that anonymous user cannot access edit profile page.\n '''\n # Create a new client with no cookies\n self.client = Client()\n\n response = self.client.get(self.url, follow=True)\n\n redirect_url, status_code = response.redirect_chain[0]\n\n self.assertEqual(status_code, 302)\n self.assertEqual(\n redirect_url,\n 'http://testserver/login/?next=/edit_profile/'\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, 'Sign In')\n\n def test_custom_calendar_widget_on_the_edit_profile_page(self):\n '''\n Test that my calendar widget is present on the edit profile page.\n '''\n response = self.client.get(self.url)\n\n self.assertContains(response, 'datepicker')\n\n @override_settings(MEDIA_ROOT=BaseTestCase.test_media_root)\n def test_ajax_requests_upload_images_successfully(self):\n '''\n Test that images are uploaded and saved by ajax requests.\n '''\n self.client.login(username='admin', password='admin')\n\n response = self.client.get(self.url)\n\n initial_form_data = response.context['form'].initial\n initial_form_data.update(\n photo=self._create_image('test_img.png', (444, 444))\n )\n\n profile_photo_before_post_request = Profile.objects.first().photo\n\n response = self.send_ajax_post_request(initial_form_data)\n\n self.assertEqual(response.status_code, 200)\n\n profile = Profile.objects.first()\n\n self.assertNotEqual(\n profile.photo,\n profile_photo_before_post_request\n )\n self.assertEqual(\n os.path.basename(profile.photo.name),\n 'test_img.png'\n )\n","sub_path":"apps/hello/tests/test_views/test_edit_profile.py","file_name":"test_edit_profile.py","file_ext":"py","file_size_in_byte":6569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"574496816","text":"from datetime import datetime\nfrom dateutil import tz\n\nutc_zone = tz.gettz('UTC')\nlocal_zone = tz.gettz('Europe/Berlin')\n\n\ndef convert_to_local(utc=datetime.utcnow()):\n utc.replace(tzinfo=utc_zone)\n new_time = utc.astimezone(local_zone)\n return new_time + new_time.utcoffset()\n\n\ndef convert_to_utc(local=datetime.now()):\n local.replace(tzinfo=local_zone)\n new_time = local.astimezone(utc_zone)\n return new_time\n","sub_path":"src/utils/dateutils.py","file_name":"dateutils.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"46961598","text":"import gzip\nimport numpy as np\nimport cv2\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport glob\nimport pickle\nfrom scipy.cluster.vq import whiten\n\ndef sigmoid(x):\n return 1.0 / (1.0 + np.exp(-x))\n\ndef load_img_patch(path, save_path, k=0, patch_size = 64):\n imgs = []\n namelist_raw = glob.glob(path)\n sortedlist = np.sort(np.array(namelist_raw))\n for i in sortedlist:\n img =cv2.cvtColor(cv2.imread(i, 1),cv2.COLOR_BGR2GRAY) \n k = img_patch(np.array(img), k, save_path, patch_size)\n return k\n\ndef img_patch_old(img, patch_size):\n patches = []\n img= np.array(img)\n row = img.shape[0]\n col = img.shape[1]\n row_ite = row // patch_size\n col_ite = col // patch_size\n for i in range(row_ite):\n for j in range(col_ite):\n patch = img[patch_size*i:patch_size*(i+1),patch_size*j:patch_size*(j+1)]\n patches +=[patch]\n return np.array(patches)\n\ndef img_patch(imgs, patch_size):\n imgs= np.array(imgs)\n row = imgs.shape[1]\n col = imgs.shape[2]\n row_ite = row // patch_size\n col_ite = col // patch_size\n patches = np.empty((len(imgs),row_ite*col_ite,patch_size,patch_size))\n k=0\n for i in range(row_ite):\n for j in range(col_ite):\n patches[:,k] = imgs[:,patch_size*i:patch_size*(i+1),patch_size*j:patch_size*(j+1)]\n k+=1\n return patches\n\ndef patch_intg(patchs,mul):\n pshp=patchs.shape\n psize = pshp[2]\n img = np.empty((pshp[0],psize*mul,psize*mul))\n k=0\n for i in range(mul):\n for j in range(mul):\n img[:,i*psize :(i+1)*psize ,j*psize :(j+1)*psize ] = patchs[:,k,:,:]\n k+=1\n return img\n\ndef flatten(patchs):\n pshp=patchs.shape\n flat = np.reshape(patchs,(pshp[0],pshp[1],pshp[2]*pshp[3]))\n return flat, pshp\n\ndef reshape(flat,orig_shp):\n flat = np.reshape(flat,(orig_shp[0],orig_shp[1],orig_shp[2],orig_shp[3]))\n return flat\n\ndef normalization(img_flattten, const=10.):\n mean = np.mean(img_flattten,axis=2)[:,:,np.newaxis]\n var = np.var(img_flattten,axis=2)[:,:,np.newaxis]\n norm = (img_flattten-mean)/np.sqrt(var+const)\n return norm, mean, var\n\ndef whitning(X):\n return np.array([whiten(i) for i in X])\n\n\ndef preprocess(X, patch_size,sig=False):\n mul = X.shape[1] // patch_size\n patchs = img_patch(X, patch_size)\n flat, orig_shp = flatten(patchs)\n normd, mean, var = normalization(flat)\n whintend = whitning(normd)\n if(sig):\n whintend = sigmoid(whintend)\n #img_itg_whintend = patch_intg(reshape(whintend,orig_shp),mul)\n return whintend \n \ndef denormalization(norm, mean, var, const=10.):\n shp = norm.shape\n norm_flattten=norm.flatten()\n img = norm*np.sqrt(var+const)+mean\n return np.reshape(img,shp).astype(np.int)\n\ndef meanIU(vecpred,norm_true,pn,nn,i):\n TP = (norm_true * vecpred).sum()\n FN = ((((norm_true - vecpred) + 1) / 2).astype(int)).sum()\n FP = (-(((norm_true - vecpred) - 1) / 2).astype(int)).sum()\n MIU = TP / (TP + FP + FN)\n print(i)\n print(\"FNR: \", FN / pn)\n print(\"FPR: \", FP / nn)\n print(\"TPR: \", TP / pn)\n print(\"mean_IU: \", MIU)\n print(\" \")\n return MIU\n\ndef make_group(load_path,save_path):\n info = pd.read_csv(load_path, header = None, sep=\" \")\n grupe_idx = np.array(info[0])\n group = []\n for i in range(np.max(grupe_idx)+1):\n group +=[np.where(i==grupe_idx)[0]]\n with open(save_path, \"wb\") as fp: #Pickling\n pickle.dump(group, fp)\n\nclass dataload:\n def __init__(self, path = '../../LY/', group_path = '../../group/group_LY', ext = '.bmp' ):\n with open(group_path, \"rb\") as fp: #Pickling\n GP = pickle.load(fp)\n self.GP = GP\n GP_num=len(self.GP)\n n = np.max(self.GP[(GP_num-1)])+1\n self.sortedlis = [(path+str(i)+ext) for i in range(n)]\n\n def pickup(self):\n idx_ary=np.empty((len(self.GP),2),np.int)\n counter = 0\n for i in self.GP:\n idx_ary[counter] = np.random.choice(i,2, replace=False)\n counter +=1\n self.ramdom_pickedup = idx_ary\n return idx_ary\n \n def load_idx(self):\n sortedlis = np.array(self.sortedlis)\n idx1 = sortedlis[self.ramdom_pickedup[:,0]]\n idx2 = sortedlis[self.ramdom_pickedup[:,1]]\n return idx1, idx2\n\n def show(self, num):\n plt.figure()\n for i in range(num):\n m =len(self.GP[i])\n k =0\n print((i+1),'.pair')\n for j in self.GP[i]:\n plt.subplot(1, m, (k+1))\n plt.title(str(j))\n plt.imshow(cv2.imread(self.sortedlis[j]))\n k+=1\n plt.show()\n","sub_path":"test/imgproc.py","file_name":"imgproc.py","file_ext":"py","file_size_in_byte":4671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"297371851","text":"from typing import Any\n\nfrom dash import html, dcc\n\n\nclass Slider(html.Div):\n \"\"\"A Div wrapping a dcc.Slider with an optional label.\n\n Keyword arguments:\n\n - label (string; optional):\n The text of the label\n\n - wrapper_id (string; optional):\n Id of the wrapping div\n\n - persistence (boolean | string | number; default: True):\n Used to allow user interactions in this component to be persisted\n when the component - or the page - is refreshed. If `persisted` is\n truthy and hasn't changed from its previous value, a `value` that\n the user has changed while using the app will keep that change, as\n long as the new `value` also matches what was given originally.\n Used in conjunction with `persistence_type`.\n\n - persistence_type (a value equal to: 'local', 'session', 'memory'; default 'session'):\n Where persisted user changes will be stored: memory: only kept in\n memory, reset on page refresh. local: window.localStorage, data is\n kept after the browser quit. session: window.sessionStorage, data\n is cleared once the browser quit.\n \"\"\"\n\n def __init__(\n self,\n label: str = None,\n wrapper_id: str = None,\n persistence: bool = True,\n persistence_type: str = \"session\",\n **kwargs: Any,\n ) -> None:\n super().__init__()\n if wrapper_id is not None:\n self.id = wrapper_id\n children: Any = [html.Label(label)] if label else []\n children.append(\n html.Div(\n className=\"webviz-slider\",\n children=dcc.Slider(\n persistence=persistence,\n persistence_type=persistence_type,\n **kwargs,\n ),\n )\n )\n self.children = html.Div(style={\"fontSize\": \"15px\"}, children=children)\n","sub_path":"webviz_core_components/wrapped_components/slider.py","file_name":"slider.py","file_ext":"py","file_size_in_byte":1887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"170169420","text":"import sys\nimport os\nfrom time import sleep\nimport subprocess\nimport argparse\nfrom subprocess import Popen, PIPE\n\n# Submit function: This will take the inputs from the user and submits the\n# job on the palmetto. The list of files needed are copied from the local\n# machine to the palmetto node\ndef Submit(userName, inFile, folderName):\n path='/home/' + userName + '/' + folderName\n HOST=userName + '@user.palmetto.clemson.edu'\n CMD_CREATEFOLD='mkdir' + ' ' + folderName\n CMD_QSUB='qsub' + ' ' + path + '/' + inFile\n Popen(['ssh', HOST, CMD_CREATEFOLD], shell=False, stdout=PIPE)\n Popen(['scp', inFile, HOST + ':' + path], shell=False, stdout=PIPE)\n sleep(3)\n proc = Popen(['ssh', HOST, CMD_QSUB], shell=False, stdout=PIPE)\n jobID = proc.communicate()[0]\n CMD_QSTAT='qstat' + ' ' + jobID\n Popen(['ssh', HOST, CMD_QSTAT], shell=False)\n\n# Delete function: This will take the jobID as the input from the user and\n# deletes the particular job\ndef Delete(userName, jobId):\n HOST=userName + '@user.palmetto.clemson.edu'\n CMD_QDEL='qdel' + ' ' + jobId\n Popen(['ssh', HOST, CMD_QDEL], shell=False)\n\n# Query function: This will take the jobID as the input from the user and\n# obtains the detailed information about the job\ndef Query(userName, jobId):\n HOST=userName + '@user.palmetto.clemson.edu'\n CMD_QSTAT='qstat' + ' ' + '-xf' + ' ' + jobId\n Popen(['ssh', HOST, CMD_QSTAT], shell=False)\n\n# Fetch function: Yet to implement\ndef Fetch():\n print(\"Fetch!!\")\n\n# Obtaining input from the user either to submit the jobs, delete the job or\n# query the job\nparser = argparse.ArgumentParser(description='Enter one of the following commands')\nparser.add_argument('--command', action=\"store\", choices=['Submit','Query','Delete','Fetch'])\nparser.add_argument('--folderName', action=\"store\")\nparser.add_argument('--inFile', action=\"store\")\nparser.add_argument('--userName', action=\"store\")\nparser.add_argument('--jobId', action=\"store\")\nargs = parser.parse_args()\nif(args.command == 'Submit'):\n Submit(args.userName, args.inFile, args.folderName)\nelif(args.command == 'Delete'):\n Delete(args.userName, args.jobId)\nelif(args.command == 'Query'):\n Query(args.userName, args.jobId)\nelse:\n exit()\n\n\n\n","sub_path":"rsub.py","file_name":"rsub.py","file_ext":"py","file_size_in_byte":2242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"313289294","text":"from selenium import webdriver;\nfrom selenium.webdriver.common.keys import Keys\nimport time\n\n\nbrowser= webdriver.Chrome();\nbrowser.maximize_window();\nbrowser.get('http://iotdashboard.atwebpages.com/');\n\ndef backButton():\n\tbackButton = browser.find_element_by_xpath(\"/html/body/a\")\n\tbackButton.send_keys(Keys.RETURN)\n\n\t\n\nselectButton1 =browser.find_element_by_xpath(\"/html/body/div[3]/div[1]/div[1]/form/input\")\ntime.sleep(3)\nselectButton1.send_keys(Keys.RETURN)\ntime.sleep(3)\n#browser.get_screenshot_as_file(\"D:/Python_leet/mathworks/Humidity.png\")\nbackButton()\n\nselectButton1 =browser.find_element_by_xpath(\"/html/body/div[3]/div[1]/div[2]/form/input\")\ntime.sleep(3)\nselectButton1.send_keys(Keys.RETURN)\ntime.sleep(3)\n#browser.get_screenshot_as_file(\"D:/Python_leet/mathworks/Temp.png\")\nbackButton()\n\n\n\n\n\t\n","sub_path":"test/TestButton.py","file_name":"TestButton.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"24758461","text":"# !/usr/bin/env python\n# -*- coding:utf-8 -*-\n\n\"\"\"\n @ Author ：Evan\n @ Date ：2018/11/6 17:31\n @ Version : 1.0\n @ Description：\n @ Modified By：\n\"\"\"\n\nfrom multiprocessing import Pool\nimport time\nimport os\n\n\ndef run(id):\n print(\"子进程开始——%d——%s\" % (id, os.getpid()))\n time.sleep(1)\n print(\"子进程结束——%d——%s\" % (id, os.getpid()))\n\n\nif __name__ == \"__main__\":\n\n print(\"父进程开始\")\n\n # 创建多个进程\n # 进程池\n # 同时执行的进程数量,默认CPU核心数\n\n pp = Pool()\n for i in range(1, 11):\n # 创建进程，放入进程池统一管理\n pp.apply_async(run, args=(i,))\n\n # 调用join之前必须先调用close，并且调用close就不能再继续添加新的进程了\n pp.close()\n pp.join()\n\n # 全局变量在多个进程中不能共享\n print(\"父进程结束\")\n","sub_path":"day20/file/启动大量子进程 - 副本 (5).py","file_name":"启动大量子进程 - 副本 (5).py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"109132202","text":"import time\n#bilioteca de tempo do python\n\nimport threading\n#biblioteca de thread do python\n\ndef escalonamento_round_robin(key, valor):\n\n\tprint('[+]o processo: ' + key + ' esta usando a CPU {por no maximo 5 segundos}' + '\\r\\n')\n\ttime.sleep(5)\n\n\ttempo_restante = valor - 5 \n\t#quantum de 5 segundos\n\n\tif(tempo_restante > 0):\n\t\t#significa que o programa nao foi executado por completo\n\t\tescalonamento_fifo(key, tempo_restante)\n\telse:\n\t\tprint('[+]processo: ' + key + ' executado por completo utilizando apenas o escalonamento ROUND-ROBIN' + '\\r\\n')\n\ndef escalonamento_fifo(key, tempo_restante):\n\tprint('[+]direcionando cpu para o processo: ' + key + ' ate que ele seja concluido' + '\\r\\n')\n\n\tprint('[+]processo:' + key + ' tempo restante:' + str(tempo_restante) + '\\r\\n')\n\ttime.sleep(tempo_restante)\n\n\tprint('[+]sucesso ao finalizar as atividades restantes do processo:' + key + ' por meio do escalonamento FIFO' + '\\r\\n')\n\n\nlista =\t{\n \"p1\": 20.0,\n \"p2\": 20.0,\n \"p3\": 3.0,\n \"p4\": 20.0,\n \"p5\": 30.0\n}\n#processo|expectativa de tempo de processamento necessaria em segundos\n\nfor key, valor in lista.items():\n\tthread = threading.Thread(target=escalonamento_round_robin, args=(key,valor,))\n\tthread.start()\n\n\n","sub_path":"escalonamento-multinivel-feedback.py","file_name":"escalonamento-multinivel-feedback.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"461825326","text":"from __future__ import print_function\n\nimport os.path\nimport pickle\nfrom typing import List\n\nfrom google.auth.transport.requests import Request\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom googleapiclient.discovery import build\nfrom googleapiclient.errors import HttpError\nfrom googleapiclient.http import MediaIoBaseDownload, MediaFileUpload\n\n# If modifying these scopes, delete the file token.pickle. for reinitialization\nSCOPES = ['https://www.googleapis.com/auth/drive']\n\n\"\"\"\nYou can login, but they change button's and field's id \nhttps://gist.github.com/ikegami-yukino/51b247080976cb41fe93\nso using selenium here looks creepy solution look s\n\"\"\"\n\n\n## Some Exceptions^\n\nclass FolderNotFound(Exception):\n pass\n\n\nclass FileNotFound(Exception):\n pass\n\n\nclass WrongGDriveFileNameFormat(Exception):\n pass\n\n\nclass GDriveUtils:\n @staticmethod\n def get_credentials(relogin=False):\n creds = None\n # The file token.pickle stores the user's access and refresh tokens,\n # and is created automatically when the authorization flow completes\n # for the first time.\n if not relogin and os.path.exists('token.pickle'):\n with open('token.pickle', 'rb') as token:\n creds = pickle.load(token)\n\n # If there are no (valid) credentials available, let the user log in.\n if not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file(\n 'credentials.json', SCOPES)\n # use server strategy for getting credentials\n # it will open user's browser for logging in and accepting\n # agreement\n creds = flow.run_local_server(port=8000)\n # Save the credentials for the next run\n with open('token.pickle', 'wb') as token:\n pickle.dump(creds, token)\n\n return creds\n\n # both\n @staticmethod\n def gdrive_fname_and_folders(full_file_name: str) -> (str, List[str]):\n if full_file_name[0] != '/':\n msg = \"Path '{}' does not begin with GDrive root marker /\" \\\n .format(full_file_name)\n raise WrongGDriveFileNameFormat(msg)\n\n splitted = full_file_name.split('/')\n folders = splitted[1:-1]\n if len(folders) == 1 and folders[0] is '':\n folders = []\n name = splitted[-1]\n return name, folders\n\n # both\n @staticmethod\n def find_folder(drive_service, folder_name: str, parent_id: str) -> str:\n if parent_id is None:\n parent_id = 'root'\n query = \"name='{0}' and '{1}' in parents \" \\\n \"and mimeType = 'application/vnd.google-apps.folder'\" \\\n .format(folder_name, parent_id)\n\n response = drive_service.files().list(q=query, spaces='drive',\n fields='files(id)').execute()\n\n files = response.get('files', [])\n if len(files) < 1:\n raise FolderNotFound(\"Folder {0} not found\".format(folder_name))\n return files[0].get('id')\n\n\nclass Uploader:\n # upload\n @staticmethod\n def create_folder(drive_service, folder_name: str, parent: str) -> \\\n str:\n if parent is None: # todo check if we need to mark it as root\n file_metadata = {\n 'name': folder_name,\n 'mimeType': 'application/vnd.google-apps.folder'\n }\n else:\n file_metadata = {\n 'name': folder_name,\n 'mimeType': 'application/vnd.google-apps.folder',\n 'parents': [parent]\n }\n file = drive_service.files().create(body=file_metadata,\n fields='id').execute()\n return file.get('id')\n\n # upload\n @staticmethod\n def get_last_parent(drive_service, folders: List[str]) -> str:\n last_existed_parent_id = None\n parent_id = None\n\n for folder in folders:\n try:\n parent_id = GDriveUtils.find_folder(drive_service, folder,\n parent_id)\n except FolderNotFound:\n parent_id = Uploader.create_folder(drive_service, folder,\n last_existed_parent_id)\n # print(\"Created {}\".format(folder))\n\n last_existed_parent_id = parent_id\n\n return parent_id\n\n @staticmethod\n def upload_file(drive_service, src_file: str, dst_file):\n dst_base_name, dst_folders = GDriveUtils.gdrive_fname_and_folders(\n dst_file)\n parent_id = None\n if len(dst_folders) > 0:\n parent_id = Uploader.get_last_parent(drive_service, dst_folders)\n\n file_metadata = {'name': dst_base_name}\n if parent_id is not None:\n file_metadata['parents'] = [parent_id]\n\n media = MediaFileUpload('{}'.format(src_file),\n mimetype='/',\n resumable=True)\n file = drive_service.files().create(body=file_metadata,\n media_body=media,\n fields='id').execute()\n return file.get('id')\n\n\nclass Downloader:\n @staticmethod\n def download_file(drive_service, file_id: str, file_name: str):\n '''\n\n :param service: Drive v3 service\n :param file_id: id of file inside google drive\n :return: id of file; if not found - throw exception\n '''\n request = drive_service.files().get_media(fileId=file_id)\n\n dirs, _ = os.path.split(file_name)\n if dirs:\n os.makedirs(dirs, exist_ok=True)\n\n with open(file_name, \"wb\") as fh:\n downloader = MediaIoBaseDownload(fh, request)\n done = False\n while done is False:\n status, done = downloader.next_chunk()\n print(\"Download {}%%.\".format(int(status.progress() * 100)))\n\n # down\n @staticmethod\n def find_file(drive_service, filename: str) -> str:\n '''\n\n :param service: Drive v3 service\n :param filename: full name of file with prefix and extension\n :return: id of file; if not found - throw FileNotFound exception\n '''\n name, folders = GDriveUtils.gdrive_fname_and_folders(filename)\n\n founded_folder_id = None\n for folder in folders:\n founded_folder_id = GDriveUtils.find_folder(drive_service, folder,\n founded_folder_id)\n if founded_folder_id is None:\n raise FolderNotFound(\"Folder {0} not found\".format(folder))\n # print(\"folder {} : {}\".format(folder, founded_folder_id))\n\n parent = founded_folder_id if founded_folder_id is not None else 'root'\n query = \"name='{0}' and '{1}' in parents\".format(name, parent)\n response = drive_service.files().list(q=query, spaces='drive',\n fields='files(id, name, parents)').execute()\n\n files = response.get('files', [])\n if len(files) < 1:\n raise FileNotFound(\"File {0} not found\".format(filename))\n return files[0].get('id')\n\n\n##############################################################################\n# Interface, these functions are exported to other modules.\n##############################################################################\n\ndef upload(src_file, dst_file):\n creds = GDriveUtils.get_credentials()\n drive_service = build('drive', 'v3', credentials=creds)\n\n file_id = Uploader.upload_file(drive_service, src_file, dst_file)\n print('Uploaded file\\'s ID: {}'.format(file_id))\n\n\ndef download(src_file, dst_file):\n creds = GDriveUtils.get_credentials()\n drive_service = build('drive', 'v3', credentials=creds)\n\n file_id = Downloader.find_file(drive_service, src_file)\n Downloader.download_file(drive_service, file_id, dst_file)\n print(\"Downloaded file {}!\".format(dst_file))\n","sub_path":"gdrive.py","file_name":"gdrive.py","file_ext":"py","file_size_in_byte":8196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"386401355","text":"'''\nThis is a python transcription of the code in the PR at https://github.com/facundoolano/google-play-scraper/pull/341/files\nThis should allow review pulling in python to work again (post the 08/2019 change to Google's site)\n'''\n\nimport re\nimport copy\nimport json\nimport time\nimport datetime\nimport logging\nfrom enum import Enum\n\ntry:\n from functools import reduce, map\nexcept ImportError:\n pass\n\n\n_log = logging.getLogger(__name__)\n\nfrom play_scraper.utils import send_request\n\nBASE_URL = 'https://play.google.com'\nREQUEST_MAPPINGS = {\n \"reviews\": [0],\n \"token\": [1, 1]\n}\n\nMAX_ITERATIONS = 100\n\ndef generateDate (dateArray):\n if not dateArray:\n return None\n\n secondsTotal = dateArray[0]\n time = datetime.datetime.utcfromtimestamp(int(secondsTotal))\n return time\n\n\ndef generateUrlFunction(appId):\n def generateUrl(reviewId):\n return \"{}/store/apps/details?id={}&reviewId={}\".format(BASE_URL,appId,reviewId)\n return generateUrl\n\ndef r_path(path, data):\n for p in path:\n try:\n data = data[p]\n except:\n return \"\"\n return data\n\ndef extractor (mappings):\n '''\n * Map the MAPPINGS object, applying each field spec to the parsed data.\n * If the mapping value is an array, use it as the path to the extract the\n * field's value. If it's an object, extract the value in object.path and pass\n * it to the function in object.fun\n '''\n def extractFields (parsedData):\n\n def map_spec(spec): \n if type(mappings[spec]) == list:\n return (spec, r_path(mappings[spec], parsedData))\n \n # assume spec object\n input_data = r_path(mappings[spec][\"path\"], parsedData)\n return (spec,mappings[spec][\"fun\"](input_data))\n\n result = list(map(map_spec, mappings))\n out_result = {x[0]:x[1] for x in result}\n return out_result\n \n return extractFields\n\ndef parse (response):\n '''\n * Extract the javascript objects returned by the AF_initDataCallback functions\n * in the script tags of the app detail HTML.\n '''\n scriptRegex = \"/>AF_initDataCallback[\\s\\S]*?<\\/script/g\"\n keyRegex = \"/(ds:.*?)'/\"\n valueRegex = \"/return ([\\s\\S]*?)}}\\);<\\//\"\n\n matches = re.search(scriptRegex, response)\n\n if not matches:\n return {}\n\n def reduce_function(accum, data):\n keyMatch = re.search(keyRegex, data)\n valueMatch = re.search(valueRegex, data) \n\n if keyMatch and valueMatch:\n key = keyMatch[1]\n value = json.loads(valueMatch[1])\n return R.assoc(key, value, accum)\n return accum\n\n return reduce(reduce_function, data, {})\n \nMAPPINGS = {\n \"review_id\": [0],\n \"author_name\": [1, 0],\n \"author_image\": [1, 1, 3, 2],\n \"review_date\": {\n \"path\": [5],\n \"fun\": generateDate\n },\n \"current_rating\": [2],\n \"current_rating_text\": {\n \"path\": [2],\n \"fun\": lambda x: str(x)\n },\n \"review_permalink\": {\n \"path\": [0],\n \"fun\": None\n },\n \"review_title\": {\n \"path\": [0],\n \"fun\": lambda x: \"\"\n },\n \"review_body\": [4],\n \"reply_date\": {\n \"path\": [7, 2],\n \"fun\": generateDate\n },\n \"reply_text\": {\n \"path\": [7, 1],\n \"fun\": lambda x: x\n },\n \"version\": {\n \"path\": [10],\n \"fun\": lambda x: x\n },\n \"thumbs_up\": [6],\n \"criterias\": {\n \"path\": [12, 0],\n \"fun\": lambda x: \"\"\n }\n }\n\n# Apply MAPPINGS for each application in list from root path\ndef extract (root, data, appId):\n \n input_data = r_path(root, data)\n mappings = copy.deepcopy(MAPPINGS)\n mappings[\"review_permalink\"][\"fun\"] = generateUrlFunction(appId)\n\n return list(map(extractor(mappings), input_data))\n\n\n'''\n This object allow us to differ between\n the initial body request and the paginated ones\n'''\nclass REQUEST_TYPE(Enum):\n initial = 'initial'\n paginated = 'paginated'\n\n\n'''\n This method allow us to get the body for the review request\n \n @param options.appId The app id for reviews\n @param options.sort The sort order for reviews\n @param options.numberOfReviewsPerRequest The number of reviews per request\n @param options.withToken The token to be used for the given request\n @param options.requestType The\n'''\ndef getBodyForRequests (\n appId,\n sort,\n numberOfReviewsPerRequest = 100,\n withToken = '%token%',\n requestType = REQUEST_TYPE.initial\n ):\n # The body is slight different for the initial and paginated requests */\n formBody = {\n REQUEST_TYPE.initial: \"f.req=%5B%5B%5B%22UsvDTd%22%2C%22%5Bnull%2Cnull%2C%5B2%2C{}%2C%5B{}%2Cnull%2Cnull%5D%2Cnull%2C%5B%5D%5D%2C%5B%5C%22{}%5C%22%2C7%5D%5D%22%2Cnull%2C%22generic%22%5D%5D%5D\".format(\n sort,\n numberOfReviewsPerRequest,\n appId\n ),\n REQUEST_TYPE.paginated: \"f.req=%5B%5B%5B%22UsvDTd%22%2C%22%5Bnull%2Cnull%2C%5B2%2C{}%2C%5B{}%2Cnull%2C%5C%22{}%5C%22%5D%2Cnull%2C%5B%5D%5D%2C%5B%5C%22{}%5C%22%2C7%5D%5D%22%2Cnull%2C%22generic%22%5D%5D%5D\".format(\n sort,\n numberOfReviewsPerRequest,\n withToken,\n appId\n )\n }\n\n return formBody[requestType]\n\n\ndef check_finished (opts, saved_reviews, nextToken, remaining_iterations = MAX_ITERATIONS):\n # this is where we should check that we have enough / time based check\n if ( not nextToken\n or (opts[\"max_records\"] > 0 and len(saved_reviews) >= opts[\"max_records\"])\n or (opts[\"earliest_record\"] and len(saved_reviews) and saved_reviews[-1][\"review_date\"] < opts[\"earliest_record\"])\n or remaining_iterations <= 0):\n return saved_reviews\n \n body = getBodyForRequests(\n opts[\"appId\"],\n opts[\"sort\"],\n withToken = nextToken,\n requestType = opts[\"requestType\"]\n )\n headers = {\n 'Content-Type': 'application/x-www-form-urlencodedcharset=UTF-8'\n }\n \n _log.info('Pulling data. Have {} records'.format(len(saved_reviews)))\n url = \"{}/_/PlayStoreUi/data/batchexecute?rpcids=qnKhOb&f.sid=-697906427155521722&bl=boq_playuiserver_20190903.08_p0&hl={}&gl={}&authuser&soc-app=121&soc-platform=1&soc-device=1&_reqid=1065213\".format(\n BASE_URL,\n opts[\"lang\"],\n opts[\"country\"]\n )\n time.sleep(0.5)\n response = send_request('POST', url, data=body, allow_redirects=True)\n\n input_data = json.loads(response.text[5:])\n if not input_data[0][2]:\n return saved_reviews\n data = json.loads(input_data[0][2])\n if not data:\n return saved_reviews\n\n if type(data) == str:\n data = scriptData.parse(data)\n\n reviews = extract(REQUEST_MAPPINGS[\"reviews\"], data, opts[\"appId\"])\n token = r_path(REQUEST_MAPPINGS[\"token\"], data)\n opts[\"requestType\"] = REQUEST_TYPE.paginated\n\n saved_reviews.extend(reviews)\n\n return check_finished(opts, saved_reviews, token, remaining_iterations - 1)\n\ndef process_full_reviews (opts):\n opts[\"requestType\"] = REQUEST_TYPE.initial\n return check_finished(opts, [], '%token%')\n","sub_path":"play_scraper/reviews.py","file_name":"reviews.py","file_ext":"py","file_size_in_byte":7030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"52927560","text":"class Test:\r\n\r\n def __init__(self, mat=None, elem=None, count=None, expected=None):\r\n '''\r\n if not mat:\r\n mat = [[1,2,3,2,5,5],\r\n [2,1,6,2,5,9],\r\n [1,6,1,5,5,6],\r\n [1,5,5,1,1,1],\r\n [1,5,3,2,1,1],\r\n [1,5,3,2,1,1]]\r\n if not elem:\r\n elem = 1\r\n if not count:\r\n count = 5\r\n if not expected:\r\n expected = 1\r\n \r\n self.prmat(mat)\r\n print(\"Element:\", elem)\r\n print(\"Count:\", count)\r\n print(\"Expected:\", expected)\r\n print(\"Outcome:\", self.test_mat(mat, elem, count))\r\n '''\r\n pass\r\n\r\n def mirror(self, mat):\r\n side = len(mat)\r\n m = list()\r\n for row in range(side):\r\n new_row = list()\r\n for col in range(side):\r\n new_row.append(mat[row][col])\r\n new_row.reverse()\r\n m.append(new_row)\r\n return m\r\n \r\n def make_quad(self, mat):\r\n rows = len(mat)\r\n if not rows: return []\r\n cols = len(mat[0])\r\n\r\n m = mat[:]\r\n \r\n if cols > rows:\r\n for i in range(cols-rows):\r\n m.append([None]*cols)\r\n elif cols < rows:\r\n m = self.transpose(m)\r\n for i in range(rows-cols):\r\n m.append([None]*rows)\r\n m = self.transpose(m)\r\n return m\r\n\r\n def lower_diags(self, mat):\r\n m = self.make_quad(mat)\r\n side = len(m)\r\n diags = list()\r\n for i in range(side):\r\n diag = list()\r\n for j in range(i+1):\r\n elem = m[side-i+j-1][j]\r\n if not elem == None:\r\n diag.append(elem)\r\n if diag:\r\n diags.append(diag)\r\n return diags\r\n\r\n def get_diags(self, mat):\r\n extended = self.make_quad(mat)\r\n lower_right = self.lower_diags(extended)\r\n upper_right = self.lower_diags(self.transpose(extended))\r\n left = self.mirror(extended)\r\n lower_left = self.lower_diags(left)\r\n upper_left = self.lower_diags(self.transpose(left))\r\n return lower_right + upper_right + lower_left + upper_left\r\n\r\n def transpose(self, mat):\r\n rows = len(mat)\r\n if not rows: return []\r\n cols = len(mat[0])\r\n return [[mat[j][i] for j in range(rows)] for i in range(cols)]\r\n\r\n def test(self, *args):\r\n el, li, count = args[0], args[1], args[2]\r\n if len(li) < count:\r\n return False\r\n li, sample = li[:-count], li[-count:]\r\n while li:\r\n if sample == [el]*count:\r\n return True\r\n sample = [li.pop()] + sample[:-1]\r\n return sample == [el]*count\r\n\r\n def test_mat(self, *args):\r\n mat, el, count = args[0], args[1], args[2]\r\n for row in mat+self.transpose(mat)+self.get_diags(mat):\r\n if self.test(el, row, count):\r\n return True\r\n return False\r\n\r\n def prmat(self, mat):\r\n for row in mat:\r\n for col in row:\r\n print(col, end=\" \")\r\n print()\r\n","sub_path":"labb5/client/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"68912092","text":"import cv2\r\nimport numpy as np\r\n\r\nfrom scipy.interpolate import UnivariateSpline\r\n\r\nimg = cv2.imread(\"cat.png\")\r\n\r\n#Warming effect\r\nclass WarmingFilter():\r\n \"\"\"Cooling filter\r\n A class that applies a cooling filter to an image.\r\n The class uses curve filters to manipulate the perceived color\r\n temparature of an image. The warming filter will shift the image's\r\n color spectrum towards blue, away from red.\r\n \"\"\"\r\n\r\n def __init__(self):\r\n \"\"\"Initialize look-up table for curve filter\"\"\"\r\n # create look-up tables for increasing and decreasing a channel\r\n self.incr_ch_lut = self._create_LUT_8UC1([0, 64, 128, 192, 256],\r\n [0, 70, 140, 210, 256])\r\n self.decr_ch_lut = self._create_LUT_8UC1([0, 64, 128, 192, 256],\r\n [0, 30, 80, 120, 192])\r\n\r\n def render(self, img_rgb):\r\n \"\"\"Applies pencil sketch effect to an RGB image\r\n :param img_rgb: RGB image to be processed\r\n :returns: Processed RGB image\r\n \"\"\"\r\n # cooling filter: increase blue, decrease red\r\n c_r, c_g, c_b = cv2.split(img_rgb)\r\n c_r = cv2.LUT(c_r, self.decr_ch_lut).astype(np.uint8)\r\n c_b = cv2.LUT(c_b, self.incr_ch_lut).astype(np.uint8)\r\n img_rgb = cv2.merge((c_r, c_g, c_b))\r\n\r\n # decrease color saturation\r\n c_h, c_s, c_v = cv2.split(cv2.cvtColor(img_rgb, cv2.COLOR_RGB2HSV))\r\n c_s = cv2.LUT(c_s, self.decr_ch_lut).astype(np.uint8)\r\n return cv2.cvtColor(cv2.merge((c_h, c_s, c_v)), cv2.COLOR_HSV2RGB)\r\n\r\n def _create_LUT_8UC1(self, x, y):\r\n \"\"\"Creates a look-up table using scipy's spline interpolation\"\"\"\r\n spl = UnivariateSpline(x, y)\r\n return spl(range(256))\r\nprint('Warming Effect Applied.')\r\n\r\nx = WarmingFilter()\r\nWarm = x.render(img)\r\n\r\n#comparing original vs resized\r\ncv2.imshow('ORIGINAL',img)\r\ncv2.imshow('Warming effect',Warm)\r\n\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()\r\n\r\n\r\n","sub_path":"Warm effect.py","file_name":"Warm effect.py","file_ext":"py","file_size_in_byte":2040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"46522431","text":"#!/usr/bin/env python3\n# This is a \"guess a number\" game.\n\n# Set the Winning Number\n_winning = 23\n\n#################################################################################\n# While loop to run a prompt-for-number\n# - Will give hints if number too high or too low\n# - Will set value 'running' to false if guessed correctly & print winner msg\nrunning = True\nwhile running:\n guess = int(input('Enter a number between 0 and 50 : '))\n if guess == _winning:\n print('Congratulations, you picked the winning number!')\n running = False\n elif guess < _winning:\n print('No, that is too low.')\n elif guess > _winning:\n print('No, that is too high.')\n else:\n print('ERROR: cannot continue, exiting!')\nprint('Done')\n","sub_path":"Python3/Studying/Lesson_2/guess_a_number.py","file_name":"guess_a_number.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"321750461","text":"import numpy as np\nimport cv2\nclass LineDectection:\n def __init__(self):\n super(LineDectection, self).__init__()\n self.img = None\n self.imgpath = None\n \n def readimage2gray(self):\n self.img = cv2.imread(self.imgpath) # got 3 channels \n #print(self.img.shape)\n self.img = cv2.cvtColor(self.img, cv2.COLOR_BGR2GRAY) # convert to 1 channel\n \n #print(self.img.shape)\n #print(\"min:\",self.img.min())\n #print(\"max\",self.img.max())\n #self.edges=cv2.Canny(self.gray,150,200,apertureSize=3)\n\n def detecthoughlines(self, imagepath, pixel, threshold, minLineLength, maxLineGap):\n #print(imagepath)\n if self.imgpath == imagepath:\n #the same img\n pass\n else:\n #readimg\n self.imgpath = imagepath\n self.readimage2gray()\n\n # lines\t= cv2.HoughLinesP(\timage, rho, theta, threshold[, lines[, minLineLength[, maxLineGap]]]\t)\n # rho (pixel) = Distance resolution of the accumulator in pixels.\n # theta (np.pi/360) = Angle resolution of the accumulator in radians.\n # thres = the number of vote\n lines = cv2.HoughLinesP(self.img,\\\n pixel, np.pi/360, threshold, \\\n minLineLength, maxLineGap )\n return lines\n \n def get_end_indexs_to_cut(self, arr, minwidth):\n # find slicing index\n indexlist=[]\n for j in range(len(arr)-1):\n if arr[j] + minwidth < arr[j+1]:\n indexlist.append(j) \n return indexlist\n\n def get_slice_arrays(self, arr, minwidth):\n \n # find slicing index\n indexlist=self.get_end_indexs_to_cut(arr, minwidth)\n \n # no need to slice \n if len(indexlist) == 0:\n return [arr]\n \n arraylist=[]\n s=0\n for cutindex in indexlist:\n arraylist.append(arr[s:cutindex+1])\n s = cutindex+1\n arraylist.append(arr[s:])\n return arraylist\n\n def get_slice_dataarrays(self, indexarr, minwidth, dataarray):\n \n # find slicing index\n indexlist=self.get_end_indexs_to_cut(indexarr, minwidth)\n \n # no need to slice \n if len(indexlist) == 0:\n return [dataarray]\n \n arraylist=[]\n s=0\n for cutindex in indexlist:\n arraylist.append(dataarray[s:cutindex+1])\n s = cutindex+1\n arraylist.append(dataarray[s:])\n return arraylist\n\n def cal_sum_pixel_v3_lineslice(self, imagepath, axis, threshold_minlen, threshold_maxgap): \n #print(\"cal_sum_pixel_v3_lineslice\")\n if self.imgpath == imagepath:\n #the same img\n pass\n else:\n #readimg\n self.imgpath = imagepath\n self.readimage2gray() #got 0-255 range \n\n img_normalize = self.img/255.0\n #print(\"min:\",img_normalize.min())\n #print(\"max\",img_normalize.max()) \n img_binarise = np.where(img_normalize > 0.3, 1.0, 0.0)\n list_pixel =[]\n if axis == 1: #horizontal\n max_line_per_image = img_binarise.shape[0] \n max_pixel_per_line = img_binarise.shape[1] \n for i in range(max_line_per_image):\n # 1.Get indexs of the current row that contain pixel\n haspixel = np.where(img_binarise[i,:]==1)[0]\n # 2.keep the row if the number of pixel > threshold\n if len(haspixel) > threshold_minlen:\n #3. slice the current row to lines, if they are separate apart\n slicearrays = self.get_slice_arrays(haspixel,len(haspixel)) \n # append to list\n for sa in slicearrays:\n if len(sa)>1:\n list_pixel.append((i, len(sa), sa[0], sa[-1])) \n\n elif axis == 0: #vertical\n max_line_per_image = img_binarise.shape[1] \n max_pixel_per_line = img_binarise.shape[0] \n for i in range(max_line_per_image):\n haspixel = np.where(img_binarise[:,i]==1)[0]\n # if there are enough pixels\n if len(haspixel) > threshold_minlen:\n slicearrays = self.get_slice_arrays(haspixel,len(haspixel)) \n #4. keep the sliced lines into a list\n for sa in slicearrays:\n if len(sa)>1:\n list_pixel.append((i, len(sa), sa[0], sa[-1]))\n \n img_a1_data = np.array(list_pixel,dtype={'names':('index', 'sum_pixel', 'p1', 'p2'),\n 'formats':('i4','i4','i4','i4')}) \n return img_a1_data, max_pixel_per_line\n\n def cal_sum_pixel_v4_mergeline(self, img_sum_axis, total_pixel_along_line, threshold_same_group, max_white_space): \n #print(\"cal_sum_pixel_v4_mergeline\")\n final_lines=[]\n # 1. group the \"nearby lines\" top/down or left/right together\n group_slicearrays = self.get_slice_dataarrays(img_sum_axis['index'],\n threshold_same_group,\n img_sum_axis)\n \n\n # merge each rows in group to 1 row, then calculate the most occupied index\n\n # 2. in each group of the nearby line, \n # separate subgroup along the line width direction, \n # (separate the line that is far away each other)\n for group_id,group in enumerate(group_slicearrays):\n\n #print(\"------------------------\\ngroup #\",group_id)\n #print(group)\n if len(group)==0: # in case of NO vertical line or horizontal line \n print(\"len(group),id,group:\",len(group),group_id,group)\n continue\n\n\n line_index_l0=group['index'][0]\n line_index_l1=group['index'][-1]\n index_range = line_index_l1 - line_index_l0 +1 \n index_array = np.arange(line_index_l0, line_index_l1+1) \n # 3. create group_pixel, 2d array, size(index_range,total_pixel_along_line) \n # to store pixels from the group\n #print(line_index_l0, line_index_l1,index_range, total_pixel_along_line)\n group_pixel=np.zeros((index_range, total_pixel_along_line)) \n #print(\"group_pixel\",group_pixel.shape)\n #print(\"index l0,l1:\",line_index_l0,line_index_l1) \n \n # 4. fill 1 on the group_pixel cell that has pixel. (or get it from the raw image?)\n for index,sump,p1,p2 in group:\n group_pixel[index-line_index_l0,p1:p2+1]=1\n\n # 5. merge the nearby lines, to then find a gap\n merge_index = np.sum(group_pixel,axis=0)\n pixel_list = np.where(merge_index>0)[0] # return a tuple (len=1) of numpy.ndarray, \n # so select the first index [0]\n #print(\"index_list_pixel\",type(index_list_pixel),len(index_list_pixel),index_list_pixel)\n #print(pixel_list) \n \n pixel_p0 = pixel_list[0]\n pixel_p1 = pixel_list[-1]\n #print(\"pixel p0,p1:\",pixel_p0, pixel_p1)\n\n\n # 6. get index of the linesgroup gap\n pixel_to_cut_line = self.get_end_indexs_to_cut(pixel_list,max_white_space)\n\n\n # 7. if there is no gap, find the average index\n if len(pixel_to_cut_line) == 0:\n\n count_row_pixels = np.sum(group_pixel,axis=1) # size = 1d\n count_all_pixels = np.sum(count_row_pixels) # size = 1d\n\n # 8. sum up all pixel index value.\n\n total_index=index_array.dot(count_row_pixels) #get single value\n avg_index = int(round(total_index/count_all_pixels))\n\n #print(\"avg_index,p1,p2:\",avg_index,pixel_p0, pixel_p1)\n final_lines.append((avg_index, pixel_p1-pixel_p0+1, pixel_p0, pixel_p1))\n\n # done!\n\n else:\n # 8. if there are gaps, combine the lines pixel as each block, then find the average index\n #fine lines that can merge, and cannot\n\n start_pixel=0\n # add the last cut index\n pixel_to_cut_line.append(len(pixel_list)-1)\n\n for cut_pixel in pixel_to_cut_line:\n\n end_pixel = cut_pixel \n #print(\"pixel value:\",pixel_list[start_pixel],\" to \",pixel_list[end_pixel])\n\n # 9. crop the cells of pixel-block \n #print(\"sub_group_pixel\")#,group_pixel[:, pixel_list[start_pixel]:pixel_list[end_pixel]+1])\n sub_group_pixel=group_pixel[:, pixel_list[start_pixel]:pixel_list[end_pixel]+1]\n\n count_row_pixels = np.sum(sub_group_pixel,axis=1) # size = 1d\n count_all_pixels = np.sum(count_row_pixels) # #get single value\n\n total_index=index_array.dot(count_row_pixels) #get single value\n avg_index=int(round(total_index/count_all_pixels))\n #print(\"avg_index,p1,p2:\",avg_index,pixel_list[start_pixel], pixel_list[end_pixel])\n\n final_lines.append((avg_index, \n pixel_list[end_pixel]-pixel_list[start_pixel]+1, \n pixel_list[start_pixel], pixel_list[end_pixel]))\n\n start_pixel = end_pixel+1 \n #print(\"---\")\n\n img_a1_data = np.array(final_lines,dtype={'names':('index', 'sum_pixel', 'p1', 'p2'),\n 'formats':('i4','i4','i4','i4')}) \n return img_a1_data \n","sub_path":"backup_20210420/LineDectection.py","file_name":"LineDectection.py","file_ext":"py","file_size_in_byte":9904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"9501778","text":"\"\"\"Module for handling requests on /search endpoints\n\nAttributes:\n search: flask Blueprint for calling main endpoints\n\n\"\"\"\n\nfrom flask import Blueprint, jsonify, request\n\nfrom ewentts.models import Event\nfrom ewentts.utils import requires_auth, return_jsonified_events, \\\n return_jsonified_users, get_per_page, paginate\nfrom .utils import perform_users_search, logger, perform_events_search_by_name, \\\n perform_event_name_query, perform_events_search_by_day, \\\n perform_location_query, perform_events_search_by_datetime\n\nsearch = Blueprint(\"search\", __name__)\n\n\n@search.route(\"/search/user\", methods=[\"GET\"])\n@requires_auth\ndef search_users():\n \"\"\"Search users\n\n Properties:\n body in json containing strings name and optional name2, optional per_page\n\n Returns:\n 200: users, next_page and list_len in json\n 204: if no users are found\n 400: if name or name2 are not in correct format\n 405: if other method then GET used\n \"\"\"\n name1 = request.args[\"name\"]\n name2 = request.args.get(\"name2\")\n per_page = get_per_page()\n users, next_page = perform_users_search(name1, name2, per_page)\n logger.info(\"search finished\")\n if not users:\n return jsonify(\"\"), 204\n json = return_jsonified_users(users, next_page=next_page)\n return json\n\n\n@search.route(\"/search/events/names/\", methods=[\"GET\"])\n@requires_auth\ndef search_events_by_names():\n \"\"\"Search events by names\n\n Properties:\n body in json containing strings event_name1 and optional event_name2, optional per_page\n\n Returns:\n 200: events, next_page and list_len in json\n 204: if no events are found\n 400: if event_name1 or event_name2 are not in correct format or no properties received\n 405: if other method then GET used\n \"\"\"\n event_name1 = request.args.get(\"event_name1\")\n event_name2 = request.args.get(\"event_name2\")\n per_page = get_per_page()\n\n events, next_page = perform_events_search_by_name(event_name1, event_name2, per_page)\n if not events:\n return jsonify(\"\"), 204\n json = return_jsonified_events(events, next_page=next_page)\n return json\n\n\n@search.route(\"/search/events/\", methods=[\"GET\"])\n@requires_auth\ndef search_events():\n \"\"\"Search events by names\n\n Properties:\n body in json possibly containign:\n event_name: string, optional\n latitude: string, optional\n longitude: string, optional\n day: date type, optional\n start_datetime: datetime, optional\n per_page: integer, optional\n\n Returns:\n 200: events, next_page and list_len in json\n 204: if no events are found\n 400: if properties were not received in the right format\n 405: if other method then GET used\n \"\"\"\n per_page = get_per_page()\n query = Event.query()\n\n event_name = request.args.get(\"event_name\")\n if event_name:\n logger.debug(\"event_name received as search parameter\")\n query = perform_event_name_query(query, event_name)\n logger.info(\"search by event name finished\")\n\n latitude = request.args.get(\"latitude\")\n longitude = request.args.get(\"longitude\")\n if latitude and longitude:\n location = (float(latitude), float(longitude))\n print(location)\n print(type(location))\n logger.info(\"location received as search parameter\")\n query = perform_location_query(query, location)\n logger.info(\"search by location finished\")\n day = request.args.get(\"day\")\n if day:\n logger.info(\"start datetime received as search parameter\")\n query = perform_events_search_by_day(query, day)\n logger.info(\"search by start_datetime finished\")\n\n start_datetime = request.args.get(\"start_datetime\")\n if start_datetime:\n logger.info(\"start datetime received as search parameter\")\n query = perform_events_search_by_datetime(query, start_datetime)\n logger.info(\"search by start_datetime finished\")\n events, next_page = paginate(query, per_page)\n logger.info(\"search finished\")\n if not events:\n return jsonify(\"\"), 204\n json = return_jsonified_events(events, next_page=next_page)\n return json\n","sub_path":"ewentts/search/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":4220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"359160544","text":"#------------------------------------------------------------------------------\n# Copyright (c) 2012, Enthought, Inc.\n# All rights reserved.\n#------------------------------------------------------------------------------\nimport logging\n\nfrom traits.api import HasTraits, Instance, List, Str, ReadOnly\n\nfrom enaml.core.object import Object\n\nfrom .application import deferred_call\nfrom .signaling import Signal\nfrom .socket_interface import ActionSocketInterface\n\n\nlogger = logging.getLogger(__name__)\n\n\n#: The set of actions which should be batched and sent to the client as\n#: a single message. This allows a client to perform intelligent message\n#: handling when dealing with messages that may affect the widget tree.\nBATCH_ACTIONS = set(['destroy', 'children_changed', 'relayout'])\n\n\nclass DeferredMessageBatch(object):\n \"\"\" A class which aggregates batch messages.\n\n Each time a message is added to this object, its tick count is\n incremented and a tick down event is posted to the event queue.\n When the object receives the tick down event, it decrements its\n tick count, and if it's zero, fires the `triggered` signal.\n\n This allows a consumer of the batch to continually add messages and\n have the `triggered` signal fired only when the event queue is fully\n drained of relevant messages.\n\n \"\"\"\n #: A signal emitted when the tick count of the batch reaches zero\n #: and the owner of the batch should consume the messages.\n triggered = Signal()\n\n def __init__(self):\n \"\"\" Initialize a DeferredMessageBatch.\n\n \"\"\"\n self._messages = []\n self._tick = 0\n\n #--------------------------------------------------------------------------\n # Private API\n #--------------------------------------------------------------------------\n def _tick_down(self):\n \"\"\" A private handler method which ticks down the batch.\n\n The tick down events are called in a deferred fashion to allow\n for the aggregation of batch events. When the tick reaches\n zero, the `triggered` signal will be emitted.\n\n \"\"\"\n self._tick -= 1\n if self._tick == 0:\n self.triggered.emit()\n else:\n deferred_call(self._tick_down)\n\n #--------------------------------------------------------------------------\n # Public API\n #--------------------------------------------------------------------------\n def release(self):\n \"\"\" Release the messages that were added to the batch.\n\n Returns\n -------\n result : list\n The list of messages added to the batch.\n\n \"\"\"\n messages = self._messages\n self._messages = []\n return messages\n\n def add_message(self, message):\n \"\"\" Add a message to the batch.\n\n This will cause the batch to tick up and then start the tick\n down process if necessary.\n\n Parameters\n ----------\n message : object\n The message object to add to the batch.\n\n \"\"\"\n self._messages.append(message)\n if self._tick == 0:\n deferred_call(self._tick_down)\n self._tick += 1\n\n\nclass Session(HasTraits):\n \"\"\" An object representing the session between a client and its\n Enaml objects.\n\n The session object is what ensures that each client has their own\n individual instances of objects, so that the only state that is\n shared between clients is that which is explicitly provided by the\n developer.\n\n \"\"\"\n #: The string identifier for this session. This is provided by\n #: the application in the `open` method. The value should not\n #: be manipulated by user code.\n session_id = ReadOnly\n\n #: The objects being managed by this session. This list should be\n #: populated by user code during the `on_open` method.\n objects = List(Object)\n\n #: The widget implementation groups which should be used by the\n #: widgets in this session. Widget groups are an advanced feature\n #: which allow the developer to selectively expose toolkit specific\n #: implementations Enaml widgets. All standard Enaml widgets are\n #: available in the 'default' group, which means this value will\n #: rarely need to be changed by the user.\n widget_groups = List(Str, ['default'])\n\n #: The socket used by this session for communication. This is\n #: provided by the Application in the `open` method. The value\n #: should not normally be manipulated by user code.\n socket = Instance(ActionSocketInterface)\n\n #: The private deferred message batch used for collapsing layout\n #: related messages into a single batch to send to the client\n #: session for more efficient handling.\n _batch = Instance(DeferredMessageBatch)\n def __batch_default(self):\n batch = DeferredMessageBatch()\n batch.triggered.connect(self._on_batch_triggered)\n return batch\n\n #--------------------------------------------------------------------------\n # Private API\n #--------------------------------------------------------------------------\n def _on_batch_triggered(self):\n \"\"\" A signal handler for the `triggered` signal on the deferred\n message batch.\n\n \"\"\"\n content = {'batch': self._batch.release()}\n self.send(self.session_id, 'message_batch', content)\n\n #--------------------------------------------------------------------------\n # Abstract API\n #--------------------------------------------------------------------------\n def on_open(self):\n \"\"\" Called by the application when the session is opened.\n\n This method must be implemented in a subclass and is called to\n create the Enaml objects for the session. This method will only\n be called once during the session lifetime.\n\n Returns\n -------\n result : iterable\n An iterable of Enaml objects for this session.\n\n \"\"\"\n raise NotImplementedError\n\n def on_close(self):\n \"\"\" Called by the application when the session is closed.\n\n This method may be optionally implemented by subclasses so that\n they can perform custom cleaup. After this method returns, the\n session should be considered invalid. This method is only called\n once during the session lifetime.\n\n \"\"\"\n pass\n\n #--------------------------------------------------------------------------\n # Public API\n #--------------------------------------------------------------------------\n def open(self, session_id, socket):\n \"\"\" Called by the application when the session is opened.\n\n This method will call the `on_open` abstract method which must\n be implemented by subclasses. The method should never be called\n by user code.\n\n Parameters\n ----------\n session_id : str\n The identifier to use for this session.\n\n socket : ActionSocketInterface\n A concrete implementation of ActionSocketInterface to use\n for messaging by this session.\n\n \"\"\"\n self.session_id = session_id\n self.on_open()\n for obj in self.objects:\n obj.session = self\n obj.initialize()\n self.socket = socket\n socket.on_message(self.on_message)\n\n def close(self):\n \"\"\" Called by the application when the session is closed.\n\n This method will call the `on_close` method which may be\n implemented by subclasses. The method should never be called\n by user code.\n\n \"\"\"\n self.on_close()\n for obj in self.objects:\n obj.destroy()\n self.objects = []\n socket = self.socket\n if socket is not None:\n socket.on_message(None)\n\n def snapshot(self):\n \"\"\" Get a snapshot of this session.\n\n Returns\n -------\n result : list\n A list of snapshot dictionaries representing the current\n state of this session.\n\n \"\"\"\n return [obj.snapshot() for obj in self.objects]\n\n def send(self, object_id, action, content):\n \"\"\" Send a message to a client object.\n\n This method is called by the `Object` instances owned by this\n session to send messages to their client implementations.\n\n Parameters\n ----------\n object_id : str\n The object id of the client object.\n\n action : str\n The action that should be performed by the object.\n\n content : dict\n The content dictionary for the action.\n\n \"\"\"\n socket = self.socket\n if socket is not None:\n if action in BATCH_ACTIONS:\n self._batch.add_message((object_id, action, content))\n else:\n socket.send(object_id, action, content)\n\n def on_message(self, object_id, action, content):\n \"\"\" Receive a message sent to an object owned by this session.\n\n This is a handler method registered as the callback for the\n action socket. The message will be routed to the appropriate\n `Object` instance.\n\n Parameters\n ----------\n object_id : str\n The object id of the target object.\n\n action : str\n The action that should be performed by the object.\n\n content : dict\n The content dictionary for the action.\n\n \"\"\"\n obj = Object.lookup_object(object_id)\n if obj is None:\n msg = \"Invalid object id sent to Session: %s:%s\"\n logger.warn(msg % (object_id, action))\n return\n obj.handle_action(action, content)\n\n","sub_path":"enaml/session.py","file_name":"session.py","file_ext":"py","file_size_in_byte":9645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"198021509","text":"from tkinter import *\nfrom tkinter import filedialog\nroot = Tk()\ntokens = []\nsymbols = {}\nt1 = Text(root,font = \"Times 14 bold\",background = \"AntiqueWhite\")\nt1.pack()\nroot.title(\"LQNT COMPILER\")\ndef open_file():\n filename = filedialog.askopenfilename()\n with open(filename,'r') as f:\n text = f.read()\n t1.insert(END,text)\ndef lex(filecontents):\n tok = \"\"\n state = 0\n string = \"\"\n expr = \"\"\n n = \"\"\n isexpr = 0\n varStarted = 0\n var = \"\"\n filecontents = list(filecontents)\n for char in filecontents:\n tok += char\n if tok == \" \":\n if state == 0:\n tok = \"\"\n else:\n tok = \" \"\n elif tok == \"\\n\" or tok == \"\":\n if expr != \"\" and isexpr == 1:\n tokens.append(\"EXPR:\"+expr)\n expr = \"\"\n elif expr != \"\" and isexpr == 0:\n tokens.append(\"NUM:\"+expr)\n expr = \"\"\n elif var != \"\":\n tokens.append(\"VAR:\"+var)\n var = \"\"\n varStarted = 0\n tok = \"\"\n elif tok == \"=\" and state == 0:\n if expr != \"\" and isexpr == 0:\n tokens.append(\"NUM:\"+expr)\n expr = \"\"\n if var != \"\":\n tokens.append(\"VAR:\"+var)\n var = \"\"\n varStarted = 0\n if tokens[-1] == \"EQUALS\":\n tokens[-1] = \"EQEQ\"\n else:\n tokens.append(\"EQUALS\")\n tok = \"\"\n \n elif tok == \"%\" and state == 0:\n varStarted = 1\n var += tok\n tok = \"\"\n elif varStarted == 1:\n if tok == \"<\" or tok == \">\":\n if var != \"\":\n tokens.append(\"VAR:\"+var)\n var = \"\"\n varStarted = 0\n var += tok\n tok = \"\"\n elif tok == \"Output_T\" or tok== \"output_t\":\n tokens.append(\"Output_T\")\n tok = \"\"\n elif tok == \"ENDIF\" or tok== \"endif\":\n tokens.append(\"ENDIF\")\n tok = \"\"\n elif tok == \"IF\" or tok== \"if\":\n tokens.append(\"IF\")\n tok = \"\"\n elif tok == \"THEN\" or tok== \"then\":\n if expr != \"\" and isexpr == 0:\n tokens.append(\"NUM:\"+expr)\n expr = \"\"\n tokens.append(\"THEN\")\n tok = \"\"\n elif tok == \"GET_INPUT\" or tok== \"get_input\":\n tokens.append(\"GET_INPUT\")\n tok = \"\"\n elif tok == \"0\" or tok == \"1\" or tok == \"2\" or tok == \"3\" or tok == \"4\" or tok == \"5\" or tok == \"6\" or tok == \"7\" or tok == \"8\" or tok == \"9\":\n expr += tok\n tok = \"\"\n elif tok == \"+\" or tok== \"-\" or tok==\"/\" or tok==\"*\" or tok==\"(\" or tok == \")\":\n isexpr = 1\n expr += tok\n tok = \"\"\n elif tok == \"\\t\":\n tok = \"\"\n elif tok == \"\\\"\" or tok== \" \\\"\":\n if state == 0:\n state = 1\n elif state == 1:\n tokens.append(\"STRING:\"+ string + \"\\\"\")\n string = \"\"\n state = 0\n tok = \"\"\n elif state == 1:\n string += tok\n tok = \"\"\n #print(expr)\n #print(tokens)\n #return ''\n return tokens\ndef evalExpression(expr):\n return eval(expr)\n\ndef doPRINT(toPRINT):\n if toPRINT[0:6] == \"STRING\":\n toPRINT = toPRINT[8:]\n toPRINT = toPRINT[:-1]\n elif toPRINT[0:3] == \"NUM\":\n toPRINT = toPRINT[4:]\n elif toPRINT[0:4] == \"EXPR\":\n toPRINT = evalExpression(toPRINT[5:])\n print(toPRINT)\ndef doASSIGN(varname,varvalue):\n symbols[varname[4:]] = varvalue\ndef getVARIABLE(varname):\n varname = varname[4:]\n if varname in symbols:\n return symbols[varname]\n else:\n return \"Variable Error: Undefined Variable\"\n exit()\ndef getINPUT(string, varname):\n i = input(string[1:-1]+ \" \")\n symbols[varname] = \"STRING:\\\"\" + i + \"\\\"\"\ndef parse(toks):\n i = 0\n while i < len(toks):\n if toks[i] == \"ENDIF\":\n i+=1\n elif toks[i] + \" \"+toks[i+1][0:6] == \"Output_T STRING\" or toks[i] + \" \"+toks[i+1][0:3] == \"Output_T NUM\" or toks[i] + \" \"+toks[i+1][0:4] == \"Output_T EXPR\" or toks[i] + \" \"+toks[i+1][0:3] == \"Output_T VAR\":\n if toks[i+1][0:6] == \"STRING\":\n doPRINT(toks[i+1])\n elif toks[i+1][0:3] == \"NUM\":\n doPRINT(toks[i+1])\n elif toks[i+1][0:4] == \"EXPR\":\n doPRINT(toks[i+1])\n elif toks[i+1][0:3] == \"VAR\":\n doPRINT(getVARIABLE(toks[i+1]))\n i += 2\n elif toks[i][0:3] + \" \"+toks[i+1]+\" \"+toks[i+2][0:6]== \"VAR EQUALS STRING\" or toks[i][0:3] + \" \"+toks[i+1]+\" \"+toks[i+2][0:3]== \"VAR EQUALS NUM\" or toks[i][0:3] + \" \"+toks[i+1]+\" \"+toks[i+2][0:4]== \"VAR EQUALS EXPR\" or toks[i][0:3] + \" \"+toks[i+1]+\" \"+toks[i+2][0:3]== \"VAR EQUALS VAR\":\n if toks[i+2][0:6] == \"STRING\":\n doASSIGN(toks[i],toks[i+2])\n elif toks[i+2][0:3] == \"NUM\":\n doASSIGN(toks[i],toks[i+2])\n elif toks[i+2][0:4] == \"EXPR\":\n doASSIGN(toks[i],\"NUM:\"+str(evalExpression(toks[i+2][5:])))\n elif toks[i+2][0:3] == \"VAR\":\n doASSIGN(toks[i],getVARIABLE(toks[i+2]))\n i+=3\n elif toks[i] + \" \"+toks[i+1][0:6] + \" \" + toks[i+2][0:3] == \"GET_INPUT STRING VAR\":\n getINPUT(toks[i+1][7:],toks[i+2][4:])\n i += 3\n elif toks[i] + \" \"+toks[i+1][0:3] + \" \" + toks[i+2] + \" \" + toks[i+3][0:3] + \" \" + toks[i+4] == \"IF NUM EQEQ NUM THEN\":\n if toks[i+1][4:] == toks[i+3][4:]:\n print(\"True\")\n else:\n print(\"False\")\n i += 5\n #print(symbols)\ndef run(): \n data = t1.get(\"1.0\",END)\n toks = lex(data)\n parse(toks)\nb1 = Button(root,text = \"RUN\",command = run)\nb1.pack()\nb2 = Button(root,text = \"OPEN FILE\",command = open_file)\nb2.pack()\nroot.mainloop()\n","sub_path":"projectlqnt2.py","file_name":"projectlqnt2.py","file_ext":"py","file_size_in_byte":6057,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"280119085","text":"import logging\n\nfrom flask import request\nfrom flask_jwt_extended import (jwt_required, get_jwt_identity)\nfrom flask_restplus import Resource, Namespace, fields, reqparse, abort\n\nfrom domain.IdentityManager import IdentityManager\nfrom models.sql_models import Item, db\n\nitem_ns = Namespace('item', description='Item API')\nlogger = logging.getLogger()\n\nfrom v1 import apiEngine, DateFormatter\n\ndelete_item = reqparse.RequestParser()\ndelete_item.add_argument('creator_email', type=str, required=False, help='Item Creator email', location='args')\n\n# ------------------------------- Item API\nitem_api_add = apiEngine.model('New Item', {\n 'item_location': fields.String(required=False, description='Item Location'),\n 'item_cost': fields.Integer(required=False, description='Item Cost'),\n 'item_rarity': fields.Integer(required=False, description='Item Rarity'),\n 'item_can_be_borrowed': fields.Boolean(required=False, description='Can Be Borrowed'),\n 'item_multiple_owners': fields.Boolean(required=False, description='Can have multiple owners'),\n 'item_due_date': fields.DateTime(required=False, description='Item Due Date')\n})\n\nserialize_item = apiEngine.model('Item Serializer', {\n 'item_name': fields.String(required=True, description='Item Name'),\n 'creator_email': fields.String(required=True, description='Item Creator Email'),\n 'creator_username': fields.String(required=True, description='Item Creator Username'),\n 'created': DateFormatter(required=True, description='Creation Date'),\n 'item_location': fields.Boolean(required=False, description='Item Location'),\n 'item_cost': fields.Boolean(required=False, description='Item Cost'),\n 'item_rarity': fields.Integer(required=False, description='Item Rarity'),\n 'item_can_be_borrowed': fields.Boolean(required=False, description='Can Be Borrowed'),\n 'item_multiple_owners': fields.Boolean(required=False, description='Can have multiple owners'),\n 'item_due_date': fields.DateTime(required=False, description='Item Due Date')\n})\n\n\n@item_ns.route('/')\nclass ItemCRUD(Resource):\n\n decorators = [jwt_required]\n\n @item_ns.response(202, 'Item Created')\n @item_ns.response(400, 'Item Creation Error')\n @item_ns.marshal_with(serialize_item)\n @item_ns.expect(item_api_add)\n def post(self, item_name):\n \"\"\" Add an Item using JSON\"\"\"\n\n data = request.get_json()\n\n id_manager = IdentityManager(get_jwt_identity())\n email = id_manager.get_email_from_identity()\n username = id_manager.get_username_from_identity()\n\n item = Item(item_name=item_name, **data)\n item.creator_email = email\n item.creator_username = username\n\n try:\n db.session.add(item)\n db.session.commit()\n except Exception as e:\n abort(400, 'Database Insert Error: {}'.format(e))\n return item, 202\n\n @item_ns.response(200, 'Item Info')\n @item_ns.marshal_with([serialize_item])\n def get(self, item_name):\n \"\"\" Get Item(s) info \"\"\"\n items = Item.query.filter_by(item_name=item_name).all()\n return items, 200\n\n @item_ns.response(200, 'Item Removed')\n @item_ns.expect(delete_item)\n @item_ns.marshal_with([serialize_item])\n def post(self, item_name):\n \"\"\" Delete an Item(s) using Item name and an option Creator Email\n\n If there are multiple items with the same name they will be deleted altogether\n An optional 'creator_email' can be specified as a query string, in this way only one item would be\n deleted\n \"\"\"\n\n data = delete_item.parse_args(request, strict=True)\n creator_email = data.get('email')\n\n if creator_email:\n items = Item.query.filter_by(item_name=item_name, creator_email=creator_email).all()\n else:\n items = Item.query.filter_by(item_name=item_name).all()\n\n db.session.delete(items)\n db.session.commit()\n return items, 200\n","sub_path":"python/pyItem/v1/items_rest.py","file_name":"items_rest.py","file_ext":"py","file_size_in_byte":3979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"570226601","text":"# Copyright (c) Microsoft Corporation.\r\n# Licensed under the MIT License.\r\n\r\nfrom typing import List\r\n\r\nfrom azure.quantum import Workspace\r\nfrom azure.quantum.optimization import Problem, ProblemType, Term, ParallelTempering, SimulatedAnnealing\r\n\r\n# Workspace information\r\nworkspace = Workspace (\r\n subscription_id = \"72f8c137-a3b1-4252-96be-1d2f4f43a42f\",\r\n resource_group = \"quantum-eus-rg\",\r\n name = \"hsirtl-eus-qws\",\r\n location = \"eastus\"\r\n)\r\n\r\nprint ( 'init...' )\r\n\r\n# login\r\nworkspace.login() #refresh=True\r\nprint ( 'login successful' )\r\n\r\ndef build_terms ( i : int , j : int ) :\r\n \"\"\"\r\n Construct Terms for two mutually exclusive elements of the secret santa\r\n matrix, i.e. two elements that must not be true at the same time, ...\r\n\r\n (x(i) XOR x(j)) <=> minimize (x(i) + x(j) - 1)**2\r\n <=> x(i)**2 + x(j)**2 + 2x(i)x(j) - 2x(i) - 2x(j) + 1\r\n\r\n Arguments:\r\n i (int): index of first variable\r\n j (int): index of second variable\r\n\r\n \"\"\"\r\n \r\n terms = [ Term ( c = 1.0 , indices = [ i , i ] ) ] # x(i)**2\r\n terms.append ( Term ( c = 1.0 , indices = [ j , j ] ) ) # x(j)**2\r\n terms.append ( Term ( c = 2.0 , indices = [ i , j ] ) ) # 2x(i)x(j) \r\n terms.append ( Term ( c = -2.0 , indices = [ i ] ) ) # -2x(i)\r\n terms.append ( Term ( c = -2.0 , indices = [ j ] ) ) # -2x(j)\r\n terms.append ( Term ( c = 1.0 , indices = [] ) ) # +1\r\n\r\n return terms\r\n\r\ndef print_results ( config : dict ) :\r\n \"\"\"\r\n print results of run\r\n\r\n Arguments:\r\n config (dictionary): config returned from solver\r\n \"\"\"\r\n result = { '0' : 'Vincent buys Tess a gift and writes her a poem' ,\r\n '1' : 'Vincent buys Uma a gift and writes her a poem' ,\r\n '2' : 'Tess buys Vincent a gift and writes him a poem' ,\r\n '3' : 'Tess buys Uma a gift and writes her a poem' ,\r\n '4' : 'Uma buys Vincent a gift and writes him a poem' ,\r\n '5' : 'Uma buys Tess a gift and writes her a poem' }\r\n\r\n for key, val in config.items() :\r\n if val == 1 :\r\n print ( result [ key ] )\r\n\r\n\"\"\"\r\nbuild secret santa matrix\r\n\r\n\t Vincent Tess Uma\r\nVincent - x(0) x(1)\r\nTess x(2) - x(3)\r\nUma\t x(4) x(5) -\r\n\r\n\"\"\"\r\n\r\n#terms = build_terms ( 2 , 4 ) + build_terms ( 2 , 3 ) + build_terms ( 4 , 5 ) + build_terms ( 1 , 3 ) + build_terms ( 0 , 5 ) + build_terms ( 0 , 1 )\r\n\r\n# row 0 + row 1 + row 2 + col 0 + col 1 + col 2\r\nterms = build_terms ( 0 , 1 ) + build_terms ( 2 , 3 ) + build_terms ( 4 , 5 ) + build_terms ( 2 , 4 ) + build_terms ( 0 , 5 ) + build_terms ( 1 , 3 )\r\n\r\n\r\nprint ( 'terms' )\r\nprint ( terms )\r\nprint ( ' ' )\r\n\r\nproblem = Problem ( name = 'secret santa' , problem_type = ProblemType.pubo , terms = terms )\r\n\r\nsolver = SimulatedAnnealing ( workspace , timeout = 100 )\r\n\r\nprint ( 'calling solver' )\r\nresult = solver.optimize ( problem )\r\n\r\nprint ( 'response from solver' )\r\nprint ( result['configuration'] )\r\nprint ( ' ' )\r\n\r\nprint_results ( result [ \"configuration\" ] )","sub_path":"qio/secret_santa_solution.py","file_name":"secret_santa_solution.py","file_ext":"py","file_size_in_byte":3143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"268759219","text":"import system_timer,sensor,channel,initialization\nimport statistics_collection, os,sys\ndef test(d_max,threshold,detection_time):\n end_time=10**7\n packet_size=100\n STA_list=[]\n amount=500\n CWmax=1024\n for times in range(10):\n sys.stdout=open(\"log_file_Thr=\"+str(threshold)+\".txt\",'w')\n timer=system_timer.SystemTimer(end_time)\n folder_name=\"./Parameter_test/Thr=\"+str(threshold)+\"_T=\"+str(detection_time/10**3)\n if not os.path.isdir(folder_name):\n os.makedirs(folder_name)\n file=open(folder_name+\"/d_max=\"+str(d_max)+\"_round=\"+str(times)+\".txt\",'w')\n statistics_collection.collector.set_output_file(file)\n system_channel=channel.channel()\n AP,STA_list=initialization.init(amount,d_max,timer,False,False,CWmax,\n system_channel,threshold,detection_time)\n AP.block_list=initialization.AID_assignment(STA_list)\n system_channel.register_devices(STA_list+[AP])\n AP.channel=system_channel\n AP.max_data_size=packet_size\n statistics_collection.collector.end_time=end_time\n ################# start the simulation ##################\n while timer.events:\n current_events=timer.get_next_events()\n for each_event in current_events:\n if each_event.type!=\"backoff\":\n print(\"The event type is \"+each_event.type+\" at \"+str(timer.current_time))\n if each_event.time>timer.end_time:\n break\n each_event.execute(STA_list+[AP],timer,system_channel)\n if each_event.type!=\"backoff\":\n counter=[]\n for each in STA_list: # how many STAs stay awake\n if each.status!=\"Sleep\":\n counter.append(each.AID)\n print(\"There are \"+str(counter.__len__())+\" STAs stays awake at \"\n +str(timer.current_time)) \n counter=[]\n backoff_timer=[]\n for each in STA_list:\n if not (each.backoff_status==\"Off\" or not each.queue or each.status!=\"Listen\"):\n counter.append(each.AID)\n backoff_timer.append(each.backoff_timer)\n print(\"There are \"+str(counter.__len__())+\" STAs are competing for the channel at \"\n +str(timer.current_time)+\"\\n\")\n # print(\"The backoff timers are \"+str(backoff_timer)+\"\\n \")\n if (statistics_collection.collector.number_of_packet==\n statistics_collection.collector.successful_transmissions.__len__()): \n if not [x for x in timer.events if x.type==\"Polling round end\"]:# stop the simulation\n statistics_collection.collector.end_time=timer.current_time\n timer.events=[]\n if system_channel.packet_list: # renew the channel busy time\n statistics_collection.collector.channel_busy_time+=(timer.end_time-\n statistics_collection.collector.last_time_idle)\n statistics_collection.collector.print_statistics_of_delays()\n statistics_collection.collector.print_polling_info()\n statistics_collection.collector.print_other_statistics(end_time,packet_size)\n \n statistics_collection.collector.clear()\n file.close()\n os.system('cls' if os.name == 'nt' else 'clear')\n\n\ndef outer_iteration(threshold):\n for detection_time in range(100*10**3,500*10**3+1,50*10**3):\n for d_max in range(400,1901,300):\n test(d_max,threshold,detection_time)\n\nimport numpy as np \nimport multiprocessing\nfor threshold in np.arange(0.5,1,0.1):\n p=multiprocessing.Process(traget=outer_iteration,args=(threshold,))\n p.start()","sub_path":"Hierachical_without_trigger/Parameter_test.py","file_name":"Parameter_test.py","file_ext":"py","file_size_in_byte":3828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"148586054","text":"print('-------Desafio 08-------')\r\n\r\nn1 = float(input('um valor (em metros): '))\r\n\r\nkm = n1 / 1000\r\n\r\necm = n1 / 100\r\n\r\ndcm = n1 / 10\r\n\r\ndm = n1 * 10 \r\n\r\ncm = n1 * 100\r\n\r\nmm = n1 * 1000\r\n\r\nprint ('kilometros {} ectometros {} decametros {} metros {} decimetros {} centimetros {} milimeros {} '.format(km, ecm, dcm, n1, dm, cm, mm))","sub_path":"desafio8.py","file_name":"desafio8.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"557528853","text":"import matplotlib.pyplot as plt\nfrom matplotlib.path import Path\nfrom matplotlib.widgets import LassoSelector, RectangleSelector\nimport numpy as np\nfrom pylab import connect, Button\n\n\ndata=np.random.random((100,100))\nfig = plt.figure(figsize=(10,10))\nax1 = fig.add_subplot(221)\nax1.imshow(data)\nax2 = fig.add_subplot(222)\nax2.imshow(np.zeros_like(data),cmap='gray')\nax3 = fig.add_subplot(223)\nax4 = fig.add_subplot(224)\nplt.subplots_adjust()\n\nx, y = np.meshgrid(np.arange(data.shape[1]), np.arange(data.shape[0]))\n\npix = np.vstack((x.flatten(), y.flatten())).T\n\nfilename='test.out'\ndef onselect(eclick,erelease):\n #global Xproj\n # Select elements in original array bounded by selector path:\n verts=[(eclick.xdata,eclick.ydata),(erelease.xdata,eclick.ydata),(erelease.xdata,erelease.ydata),(eclick.xdata,erelease.ydata)]\n p = Path(verts)\n ind = p.contains_points(pix, radius=0)\n selected = np.zeros_like(data)\n selected.flat[ind] = data.flat[ind]\n ax2.imshow(selected)\n fig.canvas.draw_idle()\n Xproj=np.sum(selected,axis=0)\n ax3.plot(Xproj)\n return Xproj\n \ndef toggle_selector(event):\n if event.key in ['Q', 'q'] and toggle_selector.RS.active:\n print(' RectangleSelector deactivated.')\n toggle_selector.RS.set_active(False)\n if event.key in ['A', 'a'] and not toggle_selector.RS.active:\n print(' RectangleSelector activated.')\n toggle_selector.RS.set_active(True)\n\n\ndef on_button_clicked(event):\n tosaveX=np.trim_zeros(Xproj)\n np.savetxt(filename,tosaveX) \n\n \n#toggle_selector.RS = RectangleSelector(ax1, onselect, drawtype='box',interactive=True,state_modifier_keys=dict(clear='escape'))\ntoggle_selector.RS = RectangleSelector(ax1, onselect, drawtype='box',interactive=True,state_modifier_keys=dict(clear='escape'),rectprops=dict(alpha=0.5, facecolor='white'))\n\nconnect('key_press_event',toggle_selector)\naxnext = plt.axes([0.81, 0.01, 0.15, 0.07])\nbnext = Button(axnext, 'X-Y Projections',hovercolor='blue')\nbnext.on_clicked(on_button_clicked)\n\nplt.show()\n\n\n\n","sub_path":"BOA/rectangle_select.py","file_name":"rectangle_select.py","file_ext":"py","file_size_in_byte":2043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"381858677","text":"from kivy.app import App\nfrom kivy.uix.carousel import Carousel\nfrom kivy.uix.image import AsyncImage\n\nclass CarouselApp(App):\n def build(self):\n carousel = Carousel(\n direction='right',\n loop = True,\n )\n for i in range(2):\n\n\n src0 ='images/city.gif'\n src1 ='images/clouds.gif'\n src2 ='images/Eve_Online_Battle_01.jpg'\n image = AsyncImage(source = src2)\n carousel.add_widget(image)\n return carousel\n\nCarouselApp().run()","sub_path":"Kivy_tutorial_files/Kivy_App_Tutorial_00/Kivy_App_Tutorial/Desabled_Tool,Carousel,Clock/Carousel_Async_Image.py","file_name":"Carousel_Async_Image.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"234051673","text":"import numpy as np\n\nimport scipy.io.wavfile as scipy_wf\n\nimport const\nimport sound as snd\n\nclass SoundFileManager:\n def write(sound):\n filename = sound.filename\n sample_rate = sound.sample_rate\n audio = sound.data\n\n scipy_wf.write(filename, sample_rate, audio)\n\n def open(filename : str, sample_rate : int = const.sample_rate()):\n if '/' not in filename:\n filename = const.location() + filename\n\n data = scipy_wf.read(filename, sample_rate)\n audio = data[1]\n\n sound = snd.Sound(filename = filename, sample_rate = sample_rate)\n sound.data = audio\n\n return sound\n","sub_path":"src/sound_file_manager.py","file_name":"sound_file_manager.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"405974740","text":"class Node:\n def __init__(self, value, left=None, right=None):\n self.value = value\n self.left = left\n self.right = right\n\ndef deepestNodeInner(node, level):\n if node == None:\n return 0, 0\n if node.left == None and node.right == None:\n return node.value, level\n else:\n deepestR, levelR = deepestNodeInner(node.right, level+1)\n deepestL, levelL = deepestNodeInner(node.left, level+1)\n if levelL > levelR:\n return deepestL, levelL\n else:\n return levelR, levelR\n\ndef deepestNode(root):\n if root == None:\n raise ValueError(\"Invalid root\")\n return deepestNodeInner(root, 1)[0]\n\nroot = Node(\"a\", Node(\"b\", Node(\"d\")), Node(\"c\"))\nprint(deepestNode(root))\n","sub_path":"python/problem80.py","file_name":"problem80.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"54596315","text":"# To change this template, choose Tools | Templates\n# and open the template in the editor.\n\nimport os.path\nimport mimetypes\nimport re\nfrom pyramid.request import Request\n\ndef locale_profile_directory_path(request):\n \"\"\"\n Returns the absolute path to the profile .yaml file, based on params _PROFILE_ or\n cookie _PROFILE_\n \"\"\"\n\n profiles_dir = request.registry.settings['lmkp.profiles_dir']\n prefix = getCustomizationName(request)\n\n profiles_path = os.path.join(os.path.dirname(__file__), 'customization', prefix, 'profiles', profiles_dir)\n\n if '_PROFILE_' in request.params:\n p = os.path.join(profiles_path, request.params['_PROFILE_'])\n if os.path.exists(p):\n return p\n elif '_PROFILE_' in request.cookies:\n p = os.path.join(profiles_path, request.cookies['_PROFILE_'])\n if os.path.exists(p):\n return p\n\n return profiles_path\n\ndef profile_directory_path(request=None):\n \"\"\"\n Returns the path to the directory containing the profiles\n \"\"\"\n try:\n profiles_dir = request.registry.settings['lmkp.profiles_dir']\n except KeyError:\n raise Exception('No profile directory specified! There is no profile '\n 'directory (lmkp.profiles_dir) specified in the application''s '\n '.ini file!')\n \n prefix = getCustomizationName(request)\n \n # Check if such a folder exists\n profiles_path = os.path.join(os.path.dirname(__file__), 'customization', prefix, 'profiles', profiles_dir)\n if not os.path.exists(profiles_path):\n raise Exception('Profile directory not found! The folder for the '\n 'profile (%s) is not found. Make sure it is situated at '\n 'lmkp/customization/%s/profiles/%s.' % (profiles_dir, \n prefix, profiles_dir))\n \n return profiles_path\n\ndef translation_directory_path():\n \"\"\"\n Returns the absolute path to the directory containing the files for batch\n translation\n \"\"\"\n return \"%s/documents/translation\" % os.path.dirname(__file__)\n\ndef upload_directory_path(request):\n \"\"\"\n Returns the absolute path to the directory used for file uploads\n \"\"\"\n if 'lmkp.file_upload_dir' in request.registry.settings:\n return request.registry.settings['lmkp.file_upload_dir']\n return None\n\ndef upload_max_file_size(request):\n \"\"\"\n Returns the maximum file size (in kilobytes) for uploads.\n Default: 5120 (5MB)\n \"\"\"\n if 'lmkp.file_upload_max_size' in request.registry.settings:\n try:\n return int(request.registry.settings['lmkp.file_upload_max_size'])*1024\n except ValueError:\n pass\n return 5120*1024\n\ndef valid_mime_extensions(request):\n \"\"\"\n Returns the valid mime-types as well as the file extension for each.\n \"\"\"\n if 'lmkp.file_mime_extensions' in request.registry.settings:\n fme = request.registry.settings['lmkp.file_mime_extensions']\n\n # Create a new dict which contains only the entries recognized as valid\n # mime types by python's own mimetypes module.\n vfme = {}\n for mt in fme:\n # Make sure that the mime type defined in the ini is valid.\n try:\n mimetypes.types_map[fme[mt]]\n except KeyError:\n continue\n\n # Make sure that the extension defined in the ini is valid for its\n # mime type\n if fme[mt] not in mimetypes.guess_all_extensions(mt):\n continue\n\n # Copy it\n vfme[mt] = fme[mt]\n\n # Add special types by Internet Explorer\n # http://msdn.microsoft.com/en-us/library/ms775147%28v=vs.85%29.aspx#_replace\n if 'image/jpeg' in vfme:\n vfme['image/pjpeg'] = '.jpg'\n if 'image/png' in vfme:\n vfme['image/x-png'] = '.png'\n\n return vfme\n\n return {}\n\ndef check_valid_uuid(uuid):\n \"\"\"\n Check if a given uuid is valid\n \"\"\"\n uuid4hex = re.compile('[0-9a-f-]{36}\\Z', re.I)\n return uuid4hex.match(uuid) is not None\n\n\ndef getTemplatePath(request, tplName):\n \"\"\"\n Get the path to the customized Mako templates. Use the folder name set in\n the application's ini file or use the default folder name if no \n customization is specified.\n \"\"\"\n\n prefix = getCustomizationName(request)\n\n return 'lmkp:customization/%s/templates/%s' % (prefix, tplName)\n\ndef getCustomizationName(requestOrSettings):\n \"\"\"\n Return the name of the customization as defined in the application's ini\n file. If none is specified, an error is raised.\n \"\"\"\n\n if isinstance(requestOrSettings, Request):\n settings = requestOrSettings.registry.settings\n elif isinstance(requestOrSettings, dict):\n settings = requestOrSettings\n\n # Check if a customization parameter is set\n if 'lmkp.customization' in settings:\n customization = settings['lmkp.customization']\n else:\n raise Exception('No customization specified! There is no customization '\n '(lmkp.customization) specified in the application''s .ini file!')\n\n # Check if such a folder exists\n if not os.path.exists(os.path.join(os.path.dirname(__file__), 'customization', customization)):\n raise Exception('Customization folder not found! The folder for the '\n 'customization (%s) is not found. Make sure it is situated at '\n 'lmkp/customization/%s.' % (customization, customization))\n\n return customization","sub_path":"lmkp/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":5468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"230390009","text":"#!/usr/bin/python\n#-*- coding: utf-8 -*-\n\n#####\n## Created on 04/05/2016\n## @author : Luis Felipe de Almeida Caiaffa dos Santos\n## @email: lfa.luisfelipe@gmail.com\n#####\n\n# importando modulos\nfrom connection import Connection\nfrom output import Output\n\n# Classe responsavel pela execução das querys\nclass Dao():\n \n out = Output() \n conn = Connection() \n \n def __init__(self):\n pass;\n\n\n\n def select(self):\n try:\n self.conn.connect()\n cursor = self.conn.get_Cursor() \n query = \"SELECT hosts.id, hosts.vlip, hosts.imagem FROM hosts WHERE hosts.imagem = '' or hosts.imagem IS NULL\"\n cursor.execute(query);\n\n \n self.out.success(\"Hosts Selecionados\")\n \n data = cursor.fetchall();\n return data\n \n cursor.close()\n self.conn.close()\n except:\n self.out.danger(\"Erro ao tentar selecionar os hosts\")\n \n \n\n\n \n ","sub_path":"dao.py","file_name":"dao.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"223302227","text":"#der Code simuliert einen Automaten \n\nwhile True:\n muenze = input('Werfen Sie eine Münze ein: ')\n\n if muenze == '1':\n getraenk = input('Wählen Sie wasser oder limonade: ')\n if getraenk == 'limonade':\n print('Ihre Limonade wurde ausgegeben')\n elif(getraenk == 'wasser'):\n print('Ihr wasser wurde ausgegeben')\n else:\n print(getraenk + ' gibt es nicht')\n elif muenze == '0':\n print('Ihre Münze ist falsch und wird ausgegeben')\n\n else:\n print('bye bye')\n break\n","sub_path":"automat/automat.py","file_name":"automat.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"287136561","text":"import sqlite3\nimport os\n\nclass CreateDB(object):\n\t\"\"\"docstring for CreateDB\"\"\"\n\tdef __init__(self, dbName):\n\t\tself.dbName = dbName\n\t\t# Connecting to the database file\n\t\tself.conn = sqlite3.connect(dbName)\n\n\tdef createTable(self, tableList):\n\t\tc = self.conn.cursor()\n\t\ttableName = tableList['TableName']\n\t\tcolumnList = tableList['Columns']\n\t\tcolumns = ','.join(columnList)\n\t\ttry:\n\t\t\twith self.conn:\n\t\t\t\tc.execute('''CREATE TABLE {tableName} ({columns})'''.format(tableName = tableName, columns = columns))\n\t\t\t\tprint(\"TABLE '{0}' created successfully.\".format(tableName))\n\t\t\t\tself.conn.commit()\n\n\t\t\t\tc.execute('PRAGMA TABLE_INFO({})'.format(tableName))\n\t\t\t\tinfo = c.fetchall()\n\t\t\t\t\n\t\t\t\tprint(\"Column Info:\\nID, Name, Type, NotNull, DefaultVal, PrimaryKey\")\n\t\t\t\tfor col in info:\n\t\t\t\t\tprint(col)\n\t\t\t\tprint()\n\t\texcept sqlite3.OperationalError as e:\n\t\t\tprint(\"Couldn't create TABLE : {0} in DATABASE : {1}\\n{2}\\n\".format(tableName, self.dbName,str(e)))\n\t\tself.conn.close()\n\ndef main():\n\t# Before running this script make sure DB folder should exists\n\tdatabaseNames = (os.path.join('DB/baseball.db'), os.path.join('DB/stocks.db'))\n\tTables = ({'TableName': 'baseball_stats',\n\t\t\t\t'Columns' : (('player_name TEXT PRIMARY KEY'), \n\t\t\t\t\t\t\t ('games_played INTEGER'),\n\t\t\t\t\t\t\t ('average REAL'),\n\t\t\t\t\t\t\t ('salary REAL')\n\t\t\t\t\t\t\t)\n\t\t\t\t},\n\t\t\t\t{'TableName' : 'stock_stats',\n\t\t\t\t 'Columns' : (('company_name TEXT PRIMARY KEY'),\n\t\t\t\t\t\t\t ('ticker TEXT'),\n\t\t\t\t\t\t\t ('country TEXT'),\n\t\t\t\t\t\t\t ('price REAL'),\n\t\t\t\t\t\t\t ('exchange_rate REAL'),\n\t\t\t\t\t\t\t ('shares_outstanding REAL'),\n\t\t\t\t\t\t\t ('net_income REAL'),\n\t\t\t\t\t\t\t ('market_value_usd REAL'),\n\t\t\t\t\t\t\t ('pe_ratio REAL')\n\t\t\t\t\t\t\t )\n\t\t\t\t})\n\tbaseball_db = CreateDB(databaseNames[0])\n\tbaseball_db.createTable(Tables[0])\n\tstocks_db = CreateDB(databaseNames[1])\n\tstocks_db.createTable(Tables[1])\n\nif __name__ == '__main__':\n\tmain()","sub_path":"Python-ETL/Aluri_Venkata Vishnuvardhan_create_dbs.py","file_name":"Aluri_Venkata Vishnuvardhan_create_dbs.py","file_ext":"py","file_size_in_byte":1853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"414532037","text":"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\n\nimport matplotlib\n\n\nfrom .functions import (custom_colorbars, north_arrow, scale_bar, North_arrow_plus_scale_bar_standard_adder,\n add_zebra, add_custom_gridline)\n\n#custom_cbar = colorbars.custom_colorbars\n\n\ndef make_cbars(ax, vmin, vmax, colorbar_ax_yticks_format='%.0f'):\n \n cbar = custom_colorbars.add_colorbar_for_axes(axes=ax, vmax=vmax, vmin=vmin, colorbar_ax_yticks_format=colorbar_ax_yticks_format)\n \n return cbar\n\n\ndef make_fig(nrows=2,ncols=2):\n \n Projection = ccrs.PlateCarree()\n \n fig, ax = plt.subplots(nrows,ncols, sharex=True, sharey=True, subplot_kw={'projection':Projection}, figsize=(12,6.5))\n \n return fig, ax\n\ndef add_background(ax):\n \n import cartopy.feature as cfeature\n states_provinces = cfeature.NaturalEarthFeature(\n category='cultural',\n name='admin_1_states_provinces_lines',\n scale='10m',\n facecolor='none')\n\n ax.add_feature(states_provinces, edgecolor='gray')\n \n\n\ndef add_north_arrow(fig, xmean, y_tail=0.11, y_head=0.14,):\n north_arrow.add_north_arrow_to_fig(fig=fig, \n x_tail=xmean,\n y_tail=y_tail,\n x_head=xmean,\n y_head=y_head)\n\ndef add_gridlines(ax,\n \n decimal_separator='.',\n \n gridline_tick_formating=dict(latitude_tick_formating={'number_format':'.1f', # com duas casas decimais\n 'degree_symbol':'°', # u'\\u00B0'\n 'north_hemisphere_str': 'N',\n 'south_hemisphere_str': 'S'} ,\n \n \n longitude_tick_formating={'number_format':'.1f', # com duas casas decimais\n 'degree_symbol':'°', # u'\\u00B0'\n 'dateline_direction_label':True, # ONLY APPLICABLE TO LONGITUDE DATA\n 'west_hemisphere_str': 'W',\n 'east_hemisphere_str': 'E'}\n \n ) ,\n \n n_coordinate_ticks={'x_number':4, 'y_number':3}, \n \n gridline_xlabel_style={'color': 'black', 'rotation': 90, 'fontsize': 7},\n \n gridline_ylabel_style={'color': 'black', 'rotation': 0, 'fontsize': 7},\n \n gridline_attr=dict(draw_labels=True,\n linewidth=1, \n color='black', \n alpha=0.35, \n linestyle='--'),\n \n gridline_tick_axis_positions={'xlabels_top':False,\n \t\t\t\t\t\t\t\t\t\t\t\t 'ylabels_left':True,\n \t\t\t\t\t\t\t\t\t\t\t\t 'ylabels_right':False,\n \t\t\t\t\t\t\t\t\t\t\t\t 'xlabels_bottom':True},\n \n zebra_gridlines={'add':True,\n 'pad':2}\n \n ):\n \n \n gridline = add_custom_gridline(ax,\n gridline_attr= gridline_attr,\n n_coordinate_ticks=n_coordinate_ticks,\n gridline_tick_axis_positions=gridline_tick_axis_positions,\n gridline_tick_formating=gridline_tick_formating,\n gridline_xlabel_style= gridline_xlabel_style,\n gridline_ylabel_style=gridline_ylabel_style)\n \n if zebra_gridlines['add']:\n add_zebra(gridline, pad=zebra_gridlines['pad']) \n\n return gridline\n \n\n\n\n\n###########\n \n\nclass make_plot():\n \"\"\"\n Description:\n This class creates a custom multiplot of 12 geoaxes (3 rows and 4 columns).\n \n Axes[10] = Temporal Mean of the data (aggregation over time using the mean function)\n \n Axes[11] = Temporal Sum of the data (aggregation over time using the sum function)\n \n Each geoaxes has its ticks formatted according to a predetermined formatting function\n \n Requirements:\n \n This class requires that the geodataframe possess a datetime attribute so to aggregate data over that dimension.\n \n \n How to use it:\n \n \n This class can be thought as a plotting formatting function, which can be called once at least 1 instance \n of it has been created. \n \n \n Once its instance is created, the instance can be called as a normal function so to plot other spatio-temporal data\n \n \"\"\"\n \n \n \n def __init__(self, gdf, vmin, vmax, column, datetime_column_name='Datetime',\n temporal_aggregation_attribute_name='COD_MUNIC_6',\n gdf_TIME_Aggregated_1=None,\n gdf_TIME_Aggregated_2=None,\n colorbar_formatting_string='%.2f', temporal_aggregators=['mean', 'sum']):\n \n self.gdf = gdf\n self.vmin = vmin\n self.vmax = vmax\n self.column = column\n self.datetime_column_name = datetime_column_name\n self.temporal_aggregation_attribute_name = temporal_aggregation_attribute_name\n self.title_fontsize = 8\n self.colorbar_formatting_string = colorbar_formatting_string\n \n self.temporal_aggregators = temporal_aggregators\n \n if not hasattr(self, 'gdf_TIME_Aggregated_1'):\n self.gdf_TIME_Aggregated_1 = self.gdf.dissolve(by=self.temporal_aggregation_attribute_name,\n aggfunc=temporal_aggregators[0]) \n \n if not hasattr(self, 'gdf_TIME_Aggregated_2'):\n self.gdf_TIME_Aggregated_2 = self.gdf.dissolve(by=self.temporal_aggregation_attribute_name,\n aggfunc=temporal_aggregators[1])\n \n self.fig, self.gridline, self.cbars = self._make_plot()\n \n \n\n def __call__(self, gdf, vmin, vmax, column, \n temporal_aggregators=['mean', 'sum'],\n colorbar_formatting_string='%.2f'):\n \n self.__init__(gdf, vmin, vmax, column, gdf_TIME_Aggregated_1=self.gdf_TIME_Aggregated_1,\n gdf_TIME_Aggregated_2=self.gdf_TIME_Aggregated_2, \n colorbar_formatting_string=colorbar_formatting_string,\n temporal_aggregators=temporal_aggregators)\n \n return self\n \n def _make_plot (self):\n '''\n Function description:\n \n This function plots the spatial-temporal data and returns a fig, a gridline and colorbar instances\n \n '''\n # equidistant\n Projection = ccrs.PlateCarree()\n\n fig, ax = make_fig()\n\n ax = ax.ravel()\n\n\n Anos = np.unique(self.gdf[self.datetime_column_name].dt.year)\n\n cbars = []\n \n cmap = matplotlib.cm.get_cmap('viridis')\n \n for enum, ano in enumerate(Anos):\n\n ax[enum].set_title(str(ano), fontsize= self.title_fontsize)\n add_background(ax[enum])\n gdf_anual = self.gdf[self.gdf[self.datetime_column_name].dt.year==ano]\n\n gdf_anual.plot(ax=ax[enum], transform=Projection, vmin=self.vmin, \n vmax=self.vmax, facecolor=cmap(0), column=self.column, \n cmap='viridis')\n\n cbars.append(make_cbars(ax[enum], \n vmin=self.vmin, \n vmax=self.vmax, \n colorbar_ax_yticks_format=self.colorbar_formatting_string))\n\n gridline = add_gridlines(ax[enum])\n \n\n print(\"Definindo o total temporal\") \n\n ax[-2].set_title(r'Temporal {0}'.format(self.temporal_aggregators[0]),\n fontsize= self.title_fontsize)\n\n self.gdf_TIME_Aggregated_1.plot(ax=ax[-2], \n transform=Projection, \n column=self.column, \n cmap='viridis')\n add_background(ax[-2])\n gridline = add_gridlines(ax[-2])\n\n\n cbars.append(make_cbars(ax[-2], vmin=self.gdf_TIME_Aggregated_1[self.column].min(), \n vmax=self.gdf_TIME_Aggregated_1[self.column].max(),\n colorbar_ax_yticks_format=self.colorbar_formatting_string))\n\n\n ax[-1].set_title(r'Temporal {0}'.format(self.temporal_aggregators[1]), \n fontsize=self.title_fontsize)\n\n add_background(ax[-1])\n gridline = add_gridlines(ax[-1])\n\n\n self.gdf_TIME_Aggregated_2.plot(ax=ax[-1], transform=Projection, \n column=self.column, cmap='viridis')\n\n cbars.append(make_cbars(ax[-1], \n vmin=self.gdf_TIME_Aggregated_2[self.column].min(), \n vmax=self.gdf_TIME_Aggregated_2[self.column].max()))\n\n North_arrow_plus_scale_bar_standard_adder.add_standard_north_arrow_with_scale_bar(ax[0], \n distance=300, \n units='km')\n fig.subplots_adjust(top=0.913,\n bottom=0.08,\n left=0.055,\n right=0.898,\n hspace=0.352,\n wspace=0.025)\n\n fig.show()\n\n return fig, gridline, cbars\n \n \n \n ","sub_path":"custom_plots/make_plot_module.py","file_name":"make_plot_module.py","file_ext":"py","file_size_in_byte":10489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"340507654","text":"from subprocess import Popen, STDOUT, PIPE\n\nimport os\nimport sys\nimport random\nimport multiprocessing\nimport subprocess\n\nimport resource\n\ndef get_lic_virt_mem(ulimit):\n def limit_virtual_memory():\n resource.setrlimit(resource.RLIMIT_AS, (ulimit*(1024**3), resource.RLIM_INFINITY))\n return limit_virtual_memory\n\ndef run_board(b, ulimit):\n p = Popen([\"./AMOBA\",\"--parallel\"], preexec_fn=get_lic_virt_mem(ulimit),\n stdout=PIPE, stdin=PIPE, stderr=STDOUT, bufsize=1, universal_newlines=True)\n\n out,err = p.communicate(b)\n\n if(len(out.split('PN'))==1): return \"fail\",b\n if(out.split('PN: ')[1][0]=='0'): return \"PN\",b\n elif(out.split('DN: ')[1][0]=='0'): return \"DN\",b\n else: return \"fail\",b\n\ndef print_res(arg):\n result,b = arg\n log[result]+=1\n if(result == \"PN\"):\n print(\"Proof\", b)\n elif(result == \"fail\"):\n #print(\"Fail\", b)\n log[\"again\"].append(b)\n else:\n print(\"\\r{}/{} [failed: {}] [proof: {}]\".format(log[\"DN\"], log[\"all\"], log[\"fail\"], log[\"PN\"]), flush=True, end=\" \")\n\n\ndef run_all_board(boards, procnum, memory_limit):\n log[\"all\"] = len(boards)\n pool = multiprocessing.Pool(processes=procnum)\n for b in boards:\n r = pool.apply_async(run_board, args =(b,memory_limit), callback=print_res) \n #print_res(run_board(b, memory_limit))\n pool.close()\n pool.join()\n\nglobal log\nlog={\n \"PN\":0,\n \"DN\":0,\n \"fail\":0,\n \"all\":None,\n \"again\":[],\n}\n\ndef runfile(filename):\n print(filename)\n with open(filename, \"r\") as file:\n boards = file.read().split('\\n')\n boards = boards[1:-1]\n random.shuffle(boards)\n print(len(boards))\n \n run_all_board(boards, 15, 30)\n print(\"Summary: {}/{} [failed: {}] [proof: {}]\".format(log[\"DN\"], log[\"all\"],\n log[\"fail\"], log[\"PN\"]))\n with open(filename+\".fail\", \"w\") as f:\n for b in log[\"again\"]:\n f.write(b+\"\\n\")\n\n run_all_board(log[\"again\"], 2, 150)\n print(\"Summary: {}/{} [failed: {}] [proof: {}]\".format(log[\"DN\"], log[\"all\"],\n log[\"fail\"], log[\"PN\"]))\n\nif(__name__ == \"__main__\"):\n runfile(\"../ors.txt\")\n runfile(\"../ands.txt\")\n","sub_path":"test/PARALLEL.py","file_name":"PARALLEL.py","file_ext":"py","file_size_in_byte":2356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"595948714","text":"from __future__ import division\n\nimport os\nimport random\nimport shutil\nfrom mir_utils import utils\n\n\nDATA_SIZE = 600\nDATA_PATH = '/dataset/sourceFiles'\nMAX_PICK_PER_PIECE = 5\n\ndef get_paths(root):\n midi_lists = list()\n for path, subdirs, files in os.walk(root):\n for name in files:\n if name.endswith('.mid'):\n full_path = os.path.join(path, name)\n midi_lists.append(full_path)\n return midi_lists\n\n\nif __name__ == '__main__':\n candidates_folders = list()\n for path, subdirs, files in os.walk(DATA_PATH):\n for name in files:\n if name == '(midi)_clean.mid':\n candidates_folders.append(path)\n\n random.shuffle(candidates_folders)\n\n candidate_lists = list()\n n_candidates = 0\n for folder in candidates_folders:\n if n_candidates >= DATA_SIZE:\n break\n files = list()\n for name in os.listdir(folder):\n if name.endswith('.mp3') and 'midi' not in name and '(Cembalo)' not in name:\n files.append(name)\n random.shuffle(files)\n n_max = min(len(files), MAX_PICK_PER_PIECE)\n n_candidates += n_max\n for n in range(n_max):\n candidate_lists.append(os.path.join(folder, files[n]))\n\n n_valid = n_test = int(1/5 * DATA_SIZE)\n n_train = len(candidate_lists) - n_valid - n_test\n\n def write_list(name, candidates):\n f = open(name, 'wb')\n for el in candidates:\n f.write(el + '\\n')\n f.close()\n\n write_list('jdl_600.txt', candidate_lists)\n\n for el in candidate_lists:\n file_with_subdir = el.replace('/dataset/sourceFiles/', '')\n subdirs, filename = utils.split_path_from_path(file_with_subdir)\n new_path = '/dataset/jdl/' + file_with_subdir\n utils.maybe_make_dir(os.path.join('/dataset/jdl/', subdirs))\n shutil.copy(el, new_path)\n if not os.path.isfile(os.path.join('/dataset/jdl/', subdirs, '(midi).mid')):\n shutil.copy(os.path.join('/dataset/sourceFiles/', subdirs, '(midi).mid'),\n os.path.join('/dataset/jdl/', subdirs, '(midi).mid'))\n\n\n\n\n","sub_path":"data/select_dataset.py","file_name":"select_dataset.py","file_ext":"py","file_size_in_byte":2142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"137032946","text":"from iconservice import *\nimport json\n\nTAG = 'UcStoreAgent'\n\n\nclass UcStoreAgent(IconScoreBase):\n\n _CONTRACT_NAME = 'StoreAgent'\n _CONTRACT_TYPE = 'store-agent'\n\n _SCHEMA_NAME = 'name'\n _SCHEMA_DETAILS = 'details'\n _SCHEMA_OWNER = 'owner'\n _SCHEMA_STATE = 'state'\n _SCHEMA_CREATED_AT = 'createdAt'\n _SCHEMA_UPDATED_AT = 'updatedAt'\n\n _STATE_READY = 'ready'\n _STATE_NOT_READY = 'not-ready'\n\n def __init__(self, db: IconScoreDatabase) -> None:\n super().__init__(db)\n\n self._schema = {\n 'type': 'object',\n 'required': ['name', 'details', 'owner', 'state', 'createdAt', 'updatedAt'],\n 'properties': {\n 'name': {'type': 'string', 'minLength': 1},\n 'details': {'type': 'string', 'minLength': 1},\n 'owner': {'type': 'string', 'minLength': 1},\n 'state': {'type': 'string', 'enum': ['ready', 'not-ready']},\n 'createdAt': {'type': 'number'},\n 'updatedAt': {'type': 'number'}\n }\n }\n\n def on_install(self) -> None:\n super().on_install()\n\n def on_update(self) -> None:\n super().on_update()\n\n @external(readonly=True)\n def getName(self) -> str:\n return self._CONTRACT_NAME\n\n @external(readonly=True)\n def getType(self) -> str:\n return self._CONTRACT_TYPE\n\n @external(readonly=True)\n def getSchema(self) -> str:\n return json.dumps(self._schema)\n\n @external(readonly=True)\n def register(self, _sender: Address, _itemName: str, _itemDetails: str) -> str:\n if not _sender or not _itemName or not _itemDetails:\n self.revert('Invalid argument')\n\n itemObj = {}\n itemObj[self._SCHEMA_NAME] = _itemName\n itemObj[self._SCHEMA_DETAILS] = _itemDetails\n itemObj[self._SCHEMA_OWNER] = str(_sender)\n itemObj[self._SCHEMA_STATE] = self._STATE_READY\n ts = self.now()\n itemObj[self._SCHEMA_CREATED_AT] = ts\n itemObj[self._SCHEMA_UPDATED_AT] = ts\n\n return json.dumps(itemObj)\n\n @external(readonly=True)\n def changeItemOwner(self, _sender: Address, _item: str, _owner: Address) -> str:\n if not _sender or not _item or not _owner:\n self.revert('Invalid argument')\n\n itemObj = json.loads(_item)\n itemObj[self._SCHEMA_OWNER] = str(_owner)\n itemObj[self._SCHEMA_UPDATED_AT] = self.now()\n\n return json.dumps(itemObj)\n\n @external(readonly=True)\n def open(self, _sender: Address, _item: str) -> str:\n if not _sender or not _item:\n self.revert('Invalid argument')\n\n itemObj = json.loads(_item)\n if str(_sender) != itemObj[self._SCHEMA_OWNER]:\n self.revert('No permission')\n\n itemObj[self._SCHEMA_STATE] = self._STATE_READY\n itemObj[self._SCHEMA_UPDATED_AT] = self.now()\n\n return json.dumps(itemObj)\n\n @external(readonly=True)\n def close(self, _sender: Address, _item: str) -> str:\n if not _sender or not _item:\n self.revert('Invalid argument')\n\n itemObj = json.loads(_item)\n if str(_sender) != itemObj[self._SCHEMA_OWNER]:\n self.revert('No permission')\n\n itemObj[self._SCHEMA_STATE] = self._STATE_NOT_READY\n itemObj[self._SCHEMA_UPDATED_AT] = self.now()\n\n return json.dumps(itemObj)\n","sub_path":"uc_store_agent_1/uc_store_agent.py","file_name":"uc_store_agent.py","file_ext":"py","file_size_in_byte":3367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"648642446","text":"lista=[]\nfor i in range(4):\n print('Podaj liczbe')\n x=int(input())\n lista.append(x)\n# lista.append(int(input()));\n print(sum(lista))\nprint(\"srednia wynosi: \", sum(lista)/len(lista))\ndodatnia=0\nujemna=0\nfor liczba in lista:\n if liczba>=0:\n dodatnia+=1\n if liczba<0:\n ujemna+=1\nprint(dodatnia, ujemna)\n\ndane=input(\"podaj liczby po spacji\")\ndane=dane.split()\nprint(dane)\n#for d in dane:\n# dane2.appendint(d)\n\n\n#dane=[int(d) for d in dane]\n\n#dane=map(int, dane)\n#list(dane)","sub_path":"colections/zad2.py","file_name":"zad2.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"331956388","text":"#! /usr/lib/python3\n\ndef binary_search_first_occ(list,value):\n low = 0\n high = len(list)-1\n result = None\n while(low <= high):\n mid = low + (high-low)//2\n if list[mid] == value:\n result = mid\n high = mid-1\n elif value < list[mid]:\n high = mid-1\n else:\n low = mid+1\n return result\n\nlist = [2,4,10,10,10,18,20]\ninput = input(\"Enter the number to search in between 1 to 20 - \")\nprint(\"Number to search is {}\".format(input))\nresult = binary_search_first_occ(list,int(input))\nif (result is not None):\n print(\"Element found at index {}\".format(result))\nelse:\n print(\"Element not found\")\n","sub_path":"binarysearch/binary_search_first_occurence.py","file_name":"binary_search_first_occurence.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"335822320","text":"#importing libraries\r\nimport pandas as pd\r\nimport numpy as np\r\nimport pickle\r\n\r\n#reading train data\r\ndf1=pd.read_csv('C:\\\\Users\\\\BHASWANTH REDDY\\\\Desktop\\\\dataset\\\\train.csv')\r\n\r\n#removing unusual columns\r\ndf1=df1.drop(columns=['Product_id','Customer_name','Loyalty_customer'],axis=1)\r\n\r\n#these are unusual columns, rows should not be removed cause of this null values\r\ndf1['instock_date']=df1['instock_date'].apply(lambda x: int(x.split('-')[1])) \r\ndf1['charges_2 (%)']=df1['charges_2 (%)'].fillna(0)\r\n\r\n#this is very importtant column so i filled every null value with -1 later decided to give the value 0 or 1\r\ndf1.Discount_avail=df1.Discount_avail.fillna(-1)\r\n\r\n#we can fill the null charges_1 by median of respective product_cat for now filling them with 0 \r\ndf1.charges_1=df1.charges_1.fillna(0)\r\n\r\n#minimun price should not be greater than selling price when discount is given\r\ndf1=df1[~(((1.13*df1.Minimum_price/df1.Selling_Price)>1)&(df1.Discount_avail==0))]\r\n#selling price should not be greater than maximum price usually but it happens some times it can't increaced by 50 percent i picked 1.48 manually\r\ndf1=df1[~(1.48*df1.Maximum_price1.78)&(df1.Discount_avail==0))] \r\n\r\n#dropping null values\r\ndf1=df1.dropna()\r\n \r\n#removing unusual rows\r\ndf1=df1[~(df1.Selling_Price<=0)]\r\n#removing outliers\r\ndf1=df1[~(((df1.Maximum_price/df1.Minimum_price)>8))]\r\n\r\n#for normalizing\r\nprice_range=df1.Selling_Price.max()-df1.Selling_Price.min()\r\n\r\n#adding extra average_price column\r\ndf1['avg_price']=(df1.Minimum_price+df1.Maximum_price)/(2*price_range)\r\n\r\n#function for removing outliers of Maximum_price & Minimum_price and mapping charges_1 medians to respective Product_Category\r\ndef mapping_charges(df):\r\n df_out = pd.DataFrame()\r\n dic=dict()\r\n for key, subdf in df.groupby('Product_Category'):\r\n upper_lim = subdf['Maximum_price'].quantile(.99)\r\n lower_lim = subdf['Minimum_price'].quantile(.01)\r\n subd = subdf.copy()\r\n subd = subd[(subd['Maximum_price'] < upper_lim)&(subd['Minimum_price'] > lower_lim)]\r\n upper_lim = subdf['avg_price'].quantile(.99)\r\n lower_lim = subdf['avg_price'].quantile(.01)\r\n subd = subd[(subd['avg_price'] < upper_lim)&(subd['avg_price'] > lower_lim)]\r\n m = np.median(subd.charges_1)\r\n subd.charges_1=subd.charges_1.replace(to_replace=0,value=m)\r\n dic[key]=m\r\n reduced_df = subd\r\n df_out = pd.concat([df_out,reduced_df],ignore_index=True)\r\n return (df_out,dic)\r\nvar=mapping_charges(df1)\r\ndic=var[1]\r\n\r\n\r\n#as i am calculating based om avg_price i removed outliers\r\ndf1=df1[~(((df1.avg_price/df1.Selling_Price)>1)&(df1.Discount_avail==0))]\r\n\r\n\r\n#creating dummies for 'Product_Category'\r\ndummy1=pd.get_dummies(df1.Product_Category)\r\n\r\n#concatanating dummies\r\ndf1=pd.concat([df1,dummy1],axis=1)\r\n\r\n\r\n#creating dummies for 'Demand'\r\ndummy2=pd.get_dummies(df1.Demand)\r\n\r\n#concatanating dummies\r\ndf1=pd.concat([df1,dummy2],axis=1)\r\n\r\n\r\n#dropping unusual column\r\ndf1=df1.drop(columns=['Stall_no','Product_Category','instock_date','Market_Category'],axis=1)\r\n\r\n#storing the dataframe columns\r\nstore_col=df1.columns.values\r\n\r\n#converting to numpy array for replacing missing values based on other values\r\ntt=np.array(df1)\r\nm,n=df1.shape\r\nfor i in range(m):\r\n if(tt[i][2]<0 and (tt[i][5]>tt[i][7])): #when minimun price is greater than selling price discount must be given else discount is 0\r\n tt[i][2]=1\r\n elif(tt[i][2]<0):\r\n tt[i][2]=0\r\n \r\ndf1=pd.DataFrame(data=tt,columns=store_col)\r\n\r\n#dropping used columns\r\ndf=df1.drop(columns=['Minimum_price','Maximum_price'],axis=1)\r\n\r\n#creating input_train data\r\nX=df.drop('Selling_Price',axis=1)\r\ny=df['Selling_Price']\r\nX=X.drop(columns=['Demand','charges_2 (%)'],axis=1)\r\n\r\n##training and fitting the model\r\n#importing necessary libraries to train data\r\nfrom xgboost import XGBRegressor\r\n\r\n\r\nmod1= XGBRegressor(eta=0.058,max_depth=23,subsample=0.5,booster='dart',gamma=0.1,alpha=1)\r\n \r\nmod1.fit(np.array(X),y)\r\nwith open('C:\\\\Users\\\\BHASWANTH REDDY\\\\Desktop\\\\dataset\\\\model.pickle','wb') as f:\r\n pickle.dump(mod1,f) #storing model in pickle format\r\n\r\n\r\n\r\n#importing test data\r\ntest=pd.read_csv('C:\\\\Users\\\\BHASWANTH REDDY\\\\Desktop\\\\dataset\\\\test.csv')\r\n\r\n#storing product_ids\r\nids=test.Product_id\r\n\r\n#dropping unusual columns\r\ntest=test.drop(columns=['Product_id','Customer_name','Loyalty_customer'],axis=1)\r\n\r\n#filling null values accordingly either zero or median or mean or mode or manually picked value\r\ntest.Market_Category=test.Market_Category.fillna(0) \r\ntest.Stall_no=test.Stall_no.fillna(0)\r\ntest.Grade=test.Grade.fillna(0)\r\nmd=test.Demand.mode()[0]\r\ntest.Demand=test.Demand.fillna(md)\r\ntest.Discount_avail=test.Discount_avail.fillna(0)\r\ntest.Maximum_price=test.Maximum_price.fillna(6500)\r\ntest.charges_1=test.charges_1.fillna(-1)\r\ntest['charges_2 (%)']=test['charges_2 (%)'].fillna(0)\r\ntest.Minimum_price=test.Minimum_price.fillna(-1)\r\ntest['instock_date']=test['instock_date'].apply(lambda x: int(x.split('-')[1])) \r\n\r\n###making data as we made for traing input_data\r\n#adding column\r\ntest['avg_price']=0\r\n\r\n#storing columns in col\r\ncol=test.columns.values\r\n\r\n#converting test_data to numpy array\r\ntt=np.array(test)\r\n\r\n#filling missing values of minimum_price and charges_1\r\nm,n=test.shape\r\nfor i in range(m):\r\n if(tt[i][9]<0):\r\n if(tt[i][10]<4000):\r\n tt[i][9]=tt[i][10]/1.9\r\n elif(tt[i][10]<8000):\r\n tt[i][9]=tt[i][10]/1.5 #manual picked weight through observation\r\n elif(tt[i][10]<12000):\r\n tt[i][9]=tt[i][10]/2.5\r\n elif(tt[i][10]<20000):\r\n tt[i][9]=tt[i][10]/3\r\n elif(tt[i][10]>22000 and tt[i][10]<23000):\r\n tt[i][9]=tt[i][10]/1.7\r\n else:\r\n tt[i][9]=tt[i][10]/3.3\r\n if(tt[i][10]<0):\r\n tt[i][10]=tt[i][9]*1.8\r\n if(tt[i][7]<0):\r\n tt[i][7]=dic[tt[i][3]]\r\n tt[i][11]=(tt[i][9]+tt[i][10])/(2*price_range)\r\n \r\n \r\n#converting again into dataframe\r\ntest=pd.DataFrame(data=tt,columns=col)\r\n\r\n#dropping used columns\r\ntest=test.drop(columns=['Minimum_price','Maximum_price'],axis=1)\r\n\r\n\r\n#storing columns of product_category in prod_cat demand in dem\r\nprod_cat=test.Product_Category\r\ndem=test.Demand\r\n\r\n#dropping used columns and unusual columns\r\ntest=test.drop(columns=['Demand','charges_2 (%)','instock_date','Product_Category','Stall_no','Market_Category'],axis=1)\r\n\r\nm,n=test.shape\r\ndum_col1=dummy1.columns.values\r\nlength1=dum_col1.size\r\ndum_col2=dummy2.columns.values\r\nlength2=dum_col2.size\r\n\r\n#function to find the index of particular column category\r\ndef find(arr,n):\r\n k=np.where(arr==n)[0]\r\n if(k.size==0):\r\n return -1\r\n return k[0]\r\n\r\n#predicting selling price for every testcase and storing the predicted price in y_test\r\ny_test=[]\r\ntest=pd.DataFrame(test)\r\n\r\nfor i in range(m):\r\n ar1=np.zeros((length1,1)).ravel() #creating zero array for product_category\r\n ar2=np.zeros((length2,1)).ravel() #creating zero array for demand\r\n f1=find(dum_col1,prod_cat[i]) #finding the position assigned for particular product_category type\r\n f2=find(dum_col2,dem[i]) #finding the position assigned for particular demand type\r\n \r\n if(f1!=-1):\r\n ar1[f1]=1\r\n if(f2!=-1):\r\n ar2[f2]=1\r\n arr=np.concatenate([test.loc[i].values,ar1]) \r\n arr=np.concatenate([arr,ar2]) #concaenated for making input data\r\n p=mod1.predict(arr.reshape(1,-1))[0] #final prediction by our trained model\r\n y_test.append(p)\r\ny_test=pd.DataFrame(data=y_test,columns=['Selling_Price']) #storing results in data frame\r\n\r\n#converting previously stored product_id of testdata to dataframe\r\nids=pd.DataFrame(data=ids,columns=['Product_id']) #creating ids dataframe\r\n\r\n#forming final dataframe\r\nres=pd.concat([ids,y_test],axis=1) #clubbed to write the results\r\nprint(res)\r\n\r\n#storing results in my desktop in csv format\r\nres.to_csv('C:\\\\Users\\\\BHASWANTH REDDY\\\\Desktop\\\\dataset\\\\res.csv',index=False) #creating final csv file in my pc\r\n\r\n","sub_path":"source code in python 3.py","file_name":"source code in python 3.py","file_ext":"py","file_size_in_byte":8217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"113668550","text":"def primenumber(z):\n y=[]\n for i in range(2,z):\n if z%i==0:\n \ty[i]=i\n if len(y)==0:\n return True\n else:\n return False\n\n#primenumber(110)\n#Note that this is a subset of the Q3. Has no connection with Q1. \n\n\n","sub_path":"Q1.py","file_name":"Q1.py","file_ext":"py","file_size_in_byte":246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"148519695","text":"\"\"\"\nHere are your instructions:\n\n\nPopulate your database with \"animal\" and \"food\" tables using the tablepop and addfood programs that you wrote during the lesson (this step will not be necessary if you have not modified the tables since you created them in the lesson).\n\nWrite a program that verifies that every animal eats at least one food.\n\"\"\"\n\n\"\"\"\nPopulates a table with data from a Python tuple\n\"\"\"\n\nimport mysql.connector\nfrom database import login_info\n\nif __name__ == \"__main__\":\n\n db = mysql.connector.Connect(**login_info)\n cursor = db.cursor()\n \n data = (\n (\"Ellie\", \"Elephant\", 2350),\n (\"Gerald\", \"Gnu\", 1400),\n (\"Gerald\", \"Giraffe\", 940),\n (\"Leonard\", \"Leopard\", 280),\n (\"Sam\", \"Snake\", 24),\n (\"Steve\", \"Snake\", 35),\n (\"Zorro\", \"Zebra\", 340))\n \n cursor.execute(\"\"\"DELETE FROM animal\"\"\")\n for t in data:\n cursor.execute(\"\"\"\n INSERT INTO animal (name, family, weight)\n VALUES (%s, %s, %s)\"\"\", t)\n \n db.commit()\n print(\"Finish\")\n ","sub_path":"Python 2/Python2_8(Handling Databases)/tablepop.py","file_name":"tablepop.py","file_ext":"py","file_size_in_byte":1075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"202946924","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.optim import Adam\n\n\nclass GFNN(nn.Module):\n def __init__(self, data, nhid, dropout, K=2):\n super(GFNN, self).__init__()\n nfeat, nclass = data.num_features, data.num_classes\n self.fc1 = nn.Linear(nfeat, nhid)\n self.fc2 = nn.Linear(nhid, nclass)\n self.dropout = dropout\n self.prelu = nn.PReLU()\n processed_x = data.features.clone()\n for _ in range(K):\n processed_x = torch.spmm(data.norm_adj, processed_x)\n self.processed_x = processed_x\n\n def reset_parameters(self):\n self.fc1.reset_parameters()\n self.fc2.reset_parameters()\n\n def forward(self, data):\n x = self.fc1(self.processed_x)\n x = F.dropout(x, p=self.dropout, training=self.training)\n x = self.prelu(x)\n x = self.fc2(x)\n return F.log_softmax(x, dim=1)\n\n\ndef create_gfnn_model(data, nhid=32, dropout=0.5, lr=0.1, weight_decay=3e-5):\n model = GFNN(data, nhid, dropout)\n optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n return model, optimizer\n","sub_path":"models/gfnn.py","file_name":"gfnn.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"537637828","text":"import discord\nfrom discord.ext import commands, tasks\nimport hentai\nfrom hentai import Format, Hentai, Tag, Utils\nimport random\nimport asyncio\nimport requests\nfrom disputils import BotEmbedPaginator\nfrom nsfw import imgdl\nfrom misc import emoji\nfrom pygelbooru import Gelbooru\nfrom ago import human\nimport os\nimport urllib.request\nfrom random import randint\nimport shutil\nimport json\nfrom pygicord import Paginator\n\n\n\nclass hentaii(commands.Cog, name=\"hentaii\"):\n\tdef __init__(self, Bot):\n\t\tself.Bot = Bot\n\t\tself.page = 1\n\t\tself.nsfwToggledGuildsGet.start()\n\n\t@commands.command(aliases=['id','doujin'], description=f\"Lets you read hentai through the use of N-hentai API, you will need to use the hentai code as the search attribute.\")\n\t@commands.guild_only()\n\t@commands.cooldown(1, 5, commands.BucketType.guild)\n\t@commands.bot_has_permissions(manage_messages = True)\n\tasync def search(self, ctx, id: int):\n\t\t\n\t\tif ctx.channel.is_nsfw():\n\t\t\tsend = await ctx.send(f\"<:nh3ntai:802131455215796224> Searching for ``{id}`` \")\n\t\t\t\n\t\t\t\n\t\t\tif not Hentai.exists(id):\n\t\t\t\tawait ctx.send(\"404 not found\")\n\t\t\telse:\n\t\t\t\n\t\t\t\tdoujin = Hentai(id)\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tlanguage = Tag.get(doujin.language, property_=\"name\")\n\n\t\t\t\temoji = \"🌐\"\n\t\t\t\tif language == \"english\":\n\t\t\t\t\temoji = \"<:English_language:802096460170395658>\"\n\t\t\t\tif language == \"japanese\":\n\t\t\t\t\temoji = \"<:Japanese:802096455962984468>\"\n\t\t\t\tif language == \"chinese\":\n\t\t\t\t\temoji = \"<:FlagChina:802097002364010527>\"\n\t\t\t\ttype_= Tag.get(doujin.category, property_=\"name\")\n\t\t\t\tclose = []\n\t\t\t\tfor related in doujin.related:\n\t\t\t\t\thmm = str(related.id)\n\t\t\t\t\tclose.append(related.title(Format.Pretty))\n\t\t\t\tuploded = doujin.upload_date\n\n\t\t\t\tuploadedBetter = human(uploded, 4)\n\t\t\t\tAuthor = Tag.get(doujin.artist, property_='name')\n\t\t\t\tif Author == None:\n\t\t\t\t\tAuthor = \"Not given\"\n\t\t\t\tembed = discord.Embed(title=doujin.title(Format.Pretty),\n\t\t\t\t url=doujin.url, color=random.choice(self.Bot.color_list))\n\t\t\t\t\n\t\t\t\tembed.add_field(name=\"Language\", value=f\"{emoji} \")\n\t\t\t\t\n\t\t\t\tembed.add_field(name=\"Author\", value=f\" `{Author}`\")\n\n\t\t\t\tembed.add_field(name=\"Type\", value = type_)\n\t\t\t\tif doujin.num_favorites == 0:\n\t\t\t\t\tembed.add_field(name=\"Favorites\", value=f\"For some reason this is broken :(\")\n\t\t\t\telse:\n\t\t\t\t\tembed.add_field(name=\"Favorites\", value=f\"❤ {(doujin.num_favorites)}\")\n\t\t\t\tembed.add_field(name=\"Pages\", value=f\"📕 {doujin.num_pages}\")\n\t\t\t\tembed.add_field(name=\"Upload Date\", value=f\" {uploadedBetter}\")\n\t\t\t\tembed.set_thumbnail(url=doujin.thumbnail)\n\t\t\t\tthing= doujin.tag\n\t\t\t\ttags = []\n\t\t\t\tfor tag in thing:\n\t\t\t\t\tz = Tag.get(thing, property_=\"name\")\n\t\t\t\t\t\n\t\t\t\tmatches = [\"lolicon\", \"shotacon\", \"gore\", \"rape\", \"cannibalism\", \"eye penetration\", \n\t\t\t\t\t\t\"forbidden content\", \"scat\",\n\t\t\t\t]\n\t\t\t\tif any(x in z for x in matches):\n\t\t\t\t\tif not ctx.guild.id in self.Bot.nsfwToggledGuilds: \n\t\t\t\t\t\tembed = discord.Embed(description =\"The content you searched up has images that are not allowed by default.\\n Use `dh nsfwtoggle enable` to enable it. Enabling this will also enable booru commands. \\n**USING THIS FEATURE IS NOT RECOMMENDED USE AT YOUR OWN RISK!!!**\")\n\t\t\t\t\t\treturn await ctx.send(embed= embed)\n\t\t\t\t\telse:\n\t\t\t\t\t\tpass\n\t\t\t\tembed.add_field(name=\"Tags\",value=f\"{z}\", inline=False)\n\t\t\t\tif close != None:\n\t\t\t\t\tembed.add_field(name=\"Related\",value=f\" \\n\".join(close))\n\t\t\t\tembed.add_field(name=f\"Reactions\", \n\t\t\t\t\tvalue=f\"React with <:nh3ntai:802131455215796224> if you want to read this. \\nReact with <:horny:810392503547199509> to get all the images. \\nReact with 📤 if you want to open a reading room.\\n\"\n\t\t\t\t\t\t\t\"React with 📥 to download this doujin.\" , inline=False)\n\t\t\t\tawait send.delete()\n\t\t\t\tx = await ctx.send(embed=embed)\n\n\t\t\t\tawait x.add_reaction(\"<:nh3ntai:802131455215796224>\")\n\t\t\t\tawait x.add_reaction(\"<:horny:810392503547199509>\")\n\t\t\t\tawait x.add_reaction(\"📤\")\n\t\t\t\tawait x.add_reaction(\"📥\")\n\t\t\t\tdef check(reaction,user):\n\t\t\t\t\t\n\t\t\t\t\treturn user == ctx.author and user.id != ctx.me.id\n\t\t\t\ttry:\n\t\t\t\t\treaction, user= await self.Bot.wait_for(\"reaction_add\",timeout=60, check=check)\n\t\t\t\t\tawait x.remove_reaction( emoji = f\"<:nh3ntai:802131455215796224>\" , member = ctx.author)\n\t\t\t\t\tif str(reaction.emoji) == f'<:nh3ntai:802131455215796224>':\n\t\t\t\t\t\tawait x.delete()\n\t\t\t\t\t\ta =0 \n\t\t\t\t\t\tif a == 0:\n\t\t\t\t\t\t\tlist_ = doujin.image_urls\n\t\t\t\t\t\t\tembeds= []\n\t\t\t\t\t\t\tfor i in list_:\n\t\t\t\t\t\t\t e = discord.Embed(color = random.choice(self.Bot.color_list))\n\t\t\t\t\t\t\t e.description = f\"[Direct link]({doujin.url})\"\n\t\t\t\t\t\t\t e.set_image(url=i)\n\t\t\t\t\t\t\t embeds.append(e)\n\t\t\t\t\t\t\tpaginator = Paginator(pages=embeds, timeout=90.0)\n\t\t\t\t\t\t\tawait paginator.start(ctx)\n\t\t\t\t\tif str(reaction.emoji) ==f\"<:horny:810392503547199509>\":\n\t\t\t\t\t\tif len(doujin.image_urls) > 100:\n\t\t\t\t\t\t\treturn await ctx.send(\"Too many pages to be sent here.\")\n\t\t\t\t\t\t\n\t\t\t\t\t\ta = 0 \n\t\t\t\t\t\tb = 5\n\t\t\t\t\t\twhile len(doujin.image_urls) >= a:\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\tawait ctx.send(\"\\n\".join(doujin.image_urls[a:b]))\n\t\t\t\t\t\t\texcept Exception:\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\ta += 5\n\t\t\t\t\t\t\tb += 5\n\t\t\t\t\t\t\t\n\t\t\t\t\tif str(reaction.emoji) == f\"📤\":\n\t\t\t\t\t\t\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\tchannelName = f\"Reading room #{ctx.author.discriminator}\"\n\t\t\t\t\t\t\t\toverwrites = {\n\t\t\t\t\t\t\t\t\tctx.guild.default_role: discord.PermissionOverwrite(read_messages=False),\n\t\t\t\t\t\t\t\t\tctx.guild.me: discord.PermissionOverwrite(read_messages=True, manage_channels=True, manage_messages=True),\n\t\t\t\t\t\t\t\t\tctx.author : discord.PermissionOverwrite(read_messages= True)\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\treadingChannel = await ctx.guild.create_text_channel(f'{channelName}', overwrites=overwrites,\n\t\t\t\t\t\t\t\t nsfw=True, reason = \"Trigger : reading room\" , \n\t\t\t\t\t\t\t\t topic = \"Automatically created channel, will be deleted after.\",\n\t\t\t\t\t\t\t\t slowmode = 100\n\t\t\t\t\t\t\t\t )\n\t\t\t\t\t\t\t\tawait readingChannel.send(f\"Hello, this is your reading channel, if you want to end it react on ⏹️ button below on the embed. The reactions will go after 5 minutes of timeout, meaning you have 5 minutes to read one page, thank you!\")\n\t\t\t\t\t\t\t\tawait ctx.send(f\"Your reading channel has been set to {readingChannel.mention}, please be there shortly.\")\n\t\t\t\t\t\t\t\tembeds = []\n\t\t\t\t\t\t\t\tfor images in doujin.image_urls:\n\t\t\t\t\t\t\t\t\te = discord.Embed(color = 0x3fb3f1)\n\t\t\t\t\t\t\t\t\te.description = f\"[Direct link]({doujin.url})\"\n\t\t\t\t\t\t\t\t\te.set_image(url = images)\n\t\t\t\t\t\t\t\t\tembeds.append(e)\n\n\t\t\t\t\t\t\t\tpaginator = BotEmbedPaginator(ctx, embeds)\n\t\t\t\t\t\t\t\tawait paginator.run(channel = readingChannel)\t\n\t\t\t\t\t\t\t\tawait readingChannel.delete(reason = \"Trigger : end reading session\")\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\texcept discord.Forbidden:\n\t\t\t\t\t\t\t\tawait ctx.send(\"It seems I don't have `manage channels` permissions enabled, enable it to use this feature.\")\n\n\n\t\t\t\t\tif str(reaction.emoji) == F\"📥\":\n\t\t\t\t\t\tawait x.delete()\n\t\t\t\t\t\tawait ctx.send(f\"You will be given a direct download link, it will be a zip file, you can reassure about the security but it's wise to run a quick scan.\")\n\t\t\t\t\t\tdb = self.Bot.db1['AbodeDB']\n\t\t\t\t\t\tcollection = db['direct_links']\n\t\t\t\t\t\tif (collection.find_one({\"_id\": doujin.id})== None):\n\t\t\t\t\t\t\tdata = requests.get(f\"{self.Bot.api_url}doujin/{doujin.id}\").json()\n\t\t\t\t\t\t\tuploded_url = data['url']\n\t\t\t\t\t\t\tem = discord.Embed()\n\t\t\t\t\t\t\tem.description = f\"Here is your [direct download link]({uploded_url}), enjoy!\"\n\t\t\t\t\t\t\tawait ctx.send(embed=em)\n\t\t\t\t\t\t\tcollection.insert_one({\"_id\": doujin.id, \"link\": f\"{uploded_url}\"})\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tsearch = collection.find({\"_id\" : doujin.id})\n\t\t\t\t\t\t\tfor result in search:\n\t\t\t\t\t\t\t\tuploded_url = result[\"link\"]\n\t\t\t\t\t\t\tem = discord.Embed()\n\t\t\t\t\t\t\tem.description = f\"Here is your [direct download link]({uploded_url}), enjoy!\"\n\t\t\t\t\t\t\tawait ctx.send(embed=em)\n\n\t\t\t\texcept asyncio.TimeoutError:\n\t\t\t\t\tawait ctx.send(\"Timed out!\")\n\t\telse:\n\t\t\tembed = discord.Embed(color = random.choice(self.Bot.color_list))\n\t\t\tembed.title= f\"Non-NSFW channel detected!\"\n\t\t\tembed.add_field(name=\"Why should you care?\", value=f\"Discord forbids the use of NSFW content outside the NSFW-option enabled channels. [More here](https://discord.com/guidelines#:~:text=You%20must%20apply%20the%20NSFW,sexualize%20minors%20in%20any%20way.)\", inline=False)\n\t\t\tembed.add_field(name=\"How can I enable the NSFW channel option?\", value=f\"** **\", inline=False)\n\t\t\tembed.set_image(url=f\"https://cdn.discordapp.com/attachments/802518639274229800/802936914054610954/nsfw.gif\")\n\t\t\tembed.set_footer(text=f\"Pro tip: {self.Bot.DEFAULT_PREFIX}set_nsfw can do the work for you.\")\n\t\t\tawait ctx.send(embed=embed)\t\t\t \n\n\tdef upload(self, filename):\n\t\turl = f'https://api.anonfiles.com/upload?token={self.Bot.anon_token}'\n\t\tfiles = {'file': (open(filename, 'rb'))}\n\t \n\t\tr = requests.post(url, files=files)\n\t\tprint(\"[UPLOADING]\", filename)\n\t\tresp = json.loads(r.text)\n\t\tif resp['status']:\n\t\t\turlshort = resp['data']['file']['url']['short']\n\t\t\turllong = resp['data']['file']['url']['full']\n\t\t\treturn urllong\n\t\telse:\n\t\t\tmessage = resp['error']['message']\n\t\t\terrtype = resp['error']['type']\n\t\t\tprint(f'[ERROR] {message}\\n{errtype}')\n\n\n\n\t@commands.command(aliases=['enable_nsfw'],description='Enables the NSFW option from channel settings.')\n\t@commands.guild_only()\n\t@commands.has_permissions(manage_channels=True)\n\tasync def set_nsfw(self, ctx):\n\t\tawait ctx.channel.edit(nsfw = True)\n\t\tawait ctx.send(f\"The channel {ctx.channel.mention} is now a NSFW channel, thanks for the co-operation.\")\n\n\n\n\t@commands.group(pass_context=True)\n\tasync def nsfwtoggle(self,ctx):\n\t\tif ctx.invoked_subcommand is None:\n\t\t\thelper = str(ctx.invoked_subcommand) if ctx.invoked_subcommand else str(ctx.command)\n\t\t\tawait ctx.send(f\"{ctx.author.name} The correct way of using that command is : \")\n\t\t\tawait ctx.send_help(helper)\n\n\n\t@nsfwtoggle.command(pass_context=True)\n\t@commands.has_permissions(administrator = True)\n\tasync def enable(self, ctx):\n\t\tguild_id = ctx.guild.id\n\t\tdb = self.Bot.db1['AbodeDB']\n\t\tcollection = db['nsfwtoggle']\n\t\tif (collection.find_one({\"_id\": guild_id})== None):\n\t\t\tdata = {\"_id\" : guild_id, \"admin\" : ctx.author.id }\n\t\t\tcollection.insert_one(data)\n\t\t\treturn await ctx.send(f\"Toggle turned on, takes 15-20 seconds for changes to be applied.\")\n\t\treturn await ctx.send(\"Already enabled, use `dh nsfwtoggle disable` to disabled it.\")\n\n\t@nsfwtoggle.command(pass_context=True)\n\t@commands.has_permissions(administrator = True)\n\tasync def disable(self, ctx):\n\t\tdb = self.Bot.db1['AbodeDB']\n\t\tcollection = db['nsfwtoggle']\n\t\ttry:\n\t\t\tsearch = collection.find_one({\"_id\" : ctx.guild.id})\n\t\t\tif search is None:\n\t\t\t\treturn await ctx.send(\"No feature found.\")\n\t\t\tcollection.delete_one({\"_id\" : ctx.guild.id})\n\t\t\tself.Bot.nsfwToggledGuilds.remove(ctx.guild.id)\n\t\t\treturn await ctx.send(\"The feature has been disabled.\")\n\t\texcept:\n\t\t\treturn await ctx.send(\"The feature is already disabled.\")\n\n\t@tasks.loop(seconds=15)\n\tasync def nsfwToggledGuildsGet(self):\n\t\tif self.Bot.DEFAULT_PREFIX == \"&\":\n\t\t\treturn\n\t\tawait self.Bot.wait_until_ready()\n\t\tdb = self.Bot.db1['AbodeDB']\n\t\tcollection= db['nsfwtoggle']\n\t\tsearch = collection.find()\n\t\tfor guild in search:\n\t\t\tid_ = guild[\"_id\"]\n\t\t\tif not id_ in self.Bot.nsfwToggledGuilds:\n\t\t\t\tself.Bot.nsfwToggledGuilds.append(id_)\n\t\t\n\t\treturn\n\ndef setup (Bot):\n\tBot.add_cog(hentaii(Bot))\n\tprint(\"Hentai cog is working.\")\n","sub_path":"nsfw/hentaii.py","file_name":"hentaii.py","file_ext":"py","file_size_in_byte":11115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"187176312","text":"\n\n#calss header\nclass _PRIVATEER():\n\tdef __init__(self,): \n\t\tself.name = \"PRIVATEER\"\n\t\tself.definitions = [u'a person or ship allowed by a government to attack and steal from ships at sea, especially in the 17th and 18th centuries']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_privateer.py","file_name":"_privateer.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"603856400","text":"from crispy_forms.helper import FormHelper\nfrom crispy_forms.utils import render_crispy_form\nfrom django.core.mail import send_mail\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render\nfrom django.template import RequestContext\nfrom django.urls import reverse\nfrom django.views.decorators.csrf import csrf_protect, csrf_exempt\nfrom jsonview.decorators import json_view\nfrom .models import Execution\n\n\ndef downloadInputFile(request):\n expId = request.GET.get('id')\n execution = Execution.objects.get(pk=expId)\n\n # criar alerta\n response = HttpResponse(\n execution.inputFile, content_type='application/force-download')\n response[\n 'Content-Disposition'] = 'attachment; filename=\"entrada-Experimento-' + str(expId) + '\"'\n return response\n\n\ndef downloadOutputFile(request):\n expId = request.GET.get('id')\n execution = Execution.objects.get(pk=expId)\n if (execution.request_by.usuario.id == request.user.id):\n print((execution.outputFile.url))\n print(\"Autorizado\")\n response = HttpResponse(\n execution.outputFile, content_type='application/force-download')\n response[\n 'Content-Disposition'] = 'attachment; filename=\"Result-Experiment-' + str(expId) + '\"'\n return response\n print(\"Not authorized\")\n # criar alerta\n return HttpResponseRedirect(reverse('home'))\n\ndef downloadLogFile(request):\n expId = request.GET.get('id')\n execution = Execution.objects.get(pk=expId)\n if (execution.request_by.usuario.id == request.user.id):\n print((execution.logFile))\n print(\"Autorizado\")\n response = HttpResponse(\n execution.logFile, content_type='application/force-download')\n response[\n 'Content-Disposition'] = 'attachment; filename=\"Logfile-Experiment-' + str(expId) + '\"'\n return response\n print(\"Not authorized\")\n\n@json_view\n@csrf_protect\ndef checkForm(request):\n form = ExecutionForm(request.POST or None) # request POST?\n\n if form.is_valid(): # processa\n experiments(request)\n helper = FormHelper()\n helper.form_id = 'form_exec'\n helper.form_action = '.'\n form_html = render_crispy_form(ExecutionForm(None), helper)\n return HttpResponseRedirect('home')\n\n else:\n helper = FormHelper()\n helper.form_id = 'form_exec'\n request_context = RequestContext(request)\n form_html = render_crispy_form(form, context=request_context)\n return {'success': False, 'form_html': form_html}\n\ndef experimentsRemove(request):\n\n if request.method == 'POST':\n data = request.POST.get('data')\n\n if data:\n ids = data.split(\",\")\n Execution.objects.filter(id__in=ids).delete()\n\n return HttpResponseRedirect(reverse('experiments'))\n\n\n@csrf_exempt\ndef result(request):\n\n if request.method == 'POST':\n\n if (request.FILES):\n idExec = request.POST.get(\"id\")\n tempo = request.POST.get(\"time\")\n execution = Execution.objects.get(id=idExec)\n fileIn = request.FILES[\"file\"]\n execution.outputFile = fileIn\n execution.status = 3\n execution.time = tempo\n execution.save()\n\n return HttpResponse(1)\n","sub_path":"resulttable/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"484744369","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.core.validators\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('rentals', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Deposit',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('date', models.DateField()),\n ('amount', models.IntegerField(validators=[django.core.validators.MinValueValidator(0)])),\n ('type', models.CharField(default=b'W', max_length=2, choices=[(b'W', b'Water'), (b'E', b'Electricity')])),\n ('occupation', models.ForeignKey(to='rentals.Occupation')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.AlterField(\n model_name='tenant',\n name='work_phone',\n field=models.CharField(max_length=10, null=True, blank=True),\n preserve_default=True,\n ),\n ]\n","sub_path":"rentals/migrations/0002_auto_20141112_0938.py","file_name":"0002_auto_20141112_0938.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"327154322","text":"\"\"\"\nЗадача написать функцию, которая определяет надо ли переводить время на час вперед/назад.\nДолжна вернуть +1, если надо переводить вперед, -1, если назад, 0 - если не надо.\nПолучает месяц, день недели, день месяца. Т.е.:\n``` def daylight_saving (month, week_day, day_of_month)```\n\"\"\"\n\ndef daylight_saving (month, week_day, day_of_month):\n result1 = (month == \"March\" or month == \"march\") and (week_day == \"Sunday\" or week_day == \"sunday\") and (day_of_month > 24 and day_of_month <= 31)\n result2 = (month == \"October\" or month == \"october\") and (week_day == \"Sunday\" or week_day == \"sunday\") and (day_of_month > 24 and day_of_month <= 31)\n if result1:\n print(\"+1\")\n elif result2:\n print(\"-1\")\n else:\n print(\"Часы переводить не нужно\")\n return result1, result2\ndaylight_saving(input(\"Enter month: \"), input(\"Enter week day: \"), int(input(\"Enter day: \")))\n# print(daylight_saving(input(\"Enter month: \"), input(\"Enter week day: \"), int(input(\"Enter day: \"))))\n","sub_path":"dop_dz.py","file_name":"dop_dz.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"40251113","text":"import pandas as pd\nimport numpy as np\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.stem.porter import PorterStemmer\n#import textblob\nimport re\nimport nltk.sentiment\n\nreview = pd.read_csv(\"American_traditional.csv\",low_memory=False)\ntext = review['text']\n\nstops = stopwords.words('english')\nkeep = ['he','she','it','they','their','few','most','more','all','any',\n 'some','no','nor','not','only','than','very',\"don't\",'aren',\n \"aren't\",'couldn',\"couldn't\",'didn',\"didn't\",'doesn',\"doesn't\",\n 'hadn',\"hadn't\",'hasn',\"hasn't\",'haven',\"haven't\",'isn',\"isn't\",\n 'mightn',\"mightn't\",'mustn',\"mustn't\",'needn',\"needn't\",'shan',\n \"shan't\",'shouldn',\"shouldn't\",'wasn',\"wasn't\",'weren',\"weren't\",\n 'won',\"won't\",'wouldn',\"wouldn't\"]\nstops.extend([\";\",\".\",\":\"])\nfor word in keep:\n stops.remove(word)\n\npattern = r\"\"\"(?x) \n\t (?:[A-Za-z]\\.)+ \n |[A-Za-z]+(?:[-_]\\w+)* \n\t |\\.\\.\\.\n\t |(?:[?!;.:]) \n\t \"\"\"\n\npat_is = re.compile(\"((?<=it|he)|(?<=she)|(?<=that|this|here)|(?<=there))'s\")\npat_s = re.compile(\"(?<=[a-zA-Z])'s\")\npat_wont = re.compile(\"wo(n't|n)\")\npat_shan = re.compile(\"sha(n't|n)\")\npat_not = re.compile(\"(?<=[a-zA-Z])(n't|n')\")\npat_would = re.compile(\"(?<=[a-zA-Z])'d\")\npat_will = re.compile(\"(?<=[a-zA-Z])'ll\")\npat_am = re.compile(\"(?<=[I|i])'m\")\npat_are = re.compile(\"(?<=[a-zA-Z])'re\")\npat_ve = re.compile(\"(?<=[a-zA-Z])'ve\")\npat_th = re.compile(\"(\\d+)(th|st|nd|rd)\")\npat_comma = re.compile(\",\")\n\n\n\nporter_stemmer = PorterStemmer()\nwordnet_lemmatizer = WordNetLemmatizer()\ntext1 = list(text)\ntext2 = text1.copy()\nstr = ' '\nfor i in range(len(text1)):\n if pd.isnull(text1[i]):\n continue\n text1[i] = text1[i].lower()\n text1[i] = pat_shan.sub(\"shall not\",text1[i])\n text1[i] = pat_wont.sub(\"will not\",text1[i])\n text1[i] = pat_th.sub(\"\",text1[i])\n text1[i] = pat_is.sub(\" is\", text1[i])\n text1[i] = pat_s.sub(\"\", text1[i])\n text1[i] = pat_not.sub(\" not\", text1[i])\n text1[i] = pat_would.sub(\" would\", text1[i])\n text1[i] = pat_will.sub(\" will\", text1[i])\n text1[i] = pat_am.sub(\" am\", text1[i])\n text1[i] = pat_are.sub(\" are\", text1[i])\n text1[i] = pat_ve.sub(\" have\", text1[i])\n text1[i] = pat_comma.sub(\".\", text1[i])\n# text1[i] = str(textblob.TextBlob(str(text1[i])).correct())\n text1[i] = nltk.regexp_tokenize(text1[i], pattern)\n text1[i] = nltk.sentiment.util.mark_negation(text1[i])\n text1[i] = [word for word in text1[i] if not word in stops]\n# sentence = text1[i]\n# pos.append(0)\n# nega.append(0)\n # for j in range(len(sentence)):\n # if sentence[j] in positive:\n # pos[i] += 1\n # elif sentence[j] in negative:\n # nega[i] += 1\n # text1[i] = [porter_stemmer.stem(word) for word in text1[i]]\n # text1[i] = [wordnet_lemmatizer.lemmatize(word,pos='v') for word in text1[i]]\n text2[i] = str.join(text1[i])\n print(i)\n\ncount = {}\n\nfor i in range(len(text1)):\n sentence = text1[i]\n j = 0\n temp = set()\n while j < len(sentence):\n word = sentence[j]\n if word not in count:\n count[word] = 1\n elif word not in temp:\n count[word] += 1\n temp.add(word)\n j= j + 1\n print(i)\n\nprint(\"The length of dictionary is:\",len(count.keys()))\n#The length of dictionary is: 211683\ncount = sorted(count.items(),key=lambda item:item[1],reverse=True)\n\nword_dict = pd.DataFrame(count)\nword_dict = word_dict.iloc[:5000,:]\nword_dict.to_csv(\"word_dict.csv\")\n\ncount_value = np.array(list(count.values()))\ncount_key = np.array(list(count.keys()))\ncount_key = count_key[count_value.astype(int)>=4000]\n\nlen(count_key)\n#1767\n\n\ncount_new = {}\nfor word in count_key:\n count_new[word] = count[word]\n\nstars = review['stars'].astype(int)\n\nstar_count = pd.DataFrame(columns=('WORD', '1STAR', '2STAR' ,'3STAR', '4STAR', '5STAR'))\nindex = 0\nfor word in count_new:\n new_line = [word,0,0,0,0,0]\n for i in range(len(text1)):\n sentence1 = text1[i]\n j = 0\n c = 0\n while j < len(sentence1):\n word2 = sentence1[j]\n if word == word2:\n c += 1\n j += 1\n if c > 0:\n new_line[stars[i]] += 1\n star_count.loc[index] = new_line\n index += 1\n print(index)\n\nword_dist = star_count[['1STAR', '2STAR' ,'3STAR', '4STAR', '5STAR']]\nword_sum = word_dist.sum(axis=1).astype(int)\n\n\ndef count(text,word,stars):\n star=[[],[],[],[],[]]\n for i in range(len(text)):\n j=0\n review = text[i]\n count = 0\n while j < len(review):\n word2 = review[j]\n if word2 in ['not', 'never', 'no', 'hardly', 'seldom']:\n if (j + 1) < len(review):\n word2 = review[j] + \" \" + review[j + 1]\n j = j + 1\n if word == word2:\n count += 1\n j += 1\n\n star[stars[i]-1].append(count)\n return star\n\n\na=count(text1,\"service\",stars)\nlen(a)\n\n\nimport matplotlib.pyplot as plt\n\nstar_count_info = pd.read_csv(\"star_count_info.csv\")\ndat_plot = star_count_info[['info']]\ndat_plot.index = range(461)\ndat_plot.plot()\nplt.show()\n\n","sub_path":"code/data_processing.py","file_name":"data_processing.py","file_ext":"py","file_size_in_byte":5241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"153995991","text":"import tkinter as tk\nfrom tkinter import messagebox\nimport tkinter.font as tkFont\nimport numpy as np\nimport copy\nimport time\nroot = tk.Tk()\nval_numrow, val_numcolumn, val_nummine = 1, 1, 0\n\nclass Core:\n def __init__(self, numrow, numcolumn, nummine):\n self.numrow = numrow\n self.numcolumn = numcolumn\n self.nummine = nummine\n self.area = numrow * numcolumn\n self.fullmap = np.zeros(self.area, dtype='int8')\n self.viewmap = np.ones(self.area, dtype='int8') * 9\n self.mode = 0\n \n def order(self, x, y):\n n = x * self.numcolumn\n n += y\n return n\n \n def re_order(self, n):\n x = n // self.numcolumn\n y = n % self.numcolumn\n return np.array([x, y], dtype='int64')\n \n def count(self, arr, target):\n counting = 0\n for i in arr:\n if i == target:\n counting += 1\n return counting\n \n def neighbour(self, n):\n neighbourhood = np.array([], dtype='int8')\n x = int(self.re_order(n)[0])\n y = int(self.re_order(n)[1])\n for i in range(x-1, x+2):\n for j in range(y-1, y+2):\n if i in range(self.numrow) and j in range(self.numcolumn):\n neighbourhood = np.append(neighbourhood, self.order(i, j))\n return neighbourhood\n\nclass StopWatch(tk.Frame):\n def __init__(self, parent):\n tk.Frame.__init__(self, parent)\n self.msec = 50\n self._start = 0.0\n self._elapsedtime = 0.0\n self._running = False\n self.timestr = tk.StringVar()\n self._setTime(self._elapsedtime)\n\n def _update(self):\n self._elapsedtime = time.time() - self._start\n self._setTime(self._elapsedtime)\n self._timer = self.after(self.msec, self._update)\n\n def _setTime(self, elap):\n minutes = int(elap / 60)\n seconds = int(elap - minutes * 60.0)\n hseconds = int((elap - minutes * 60.0 - seconds) * 100)\n self.timestr.set('%02d\\'%02d\\\"%02d' % (minutes, seconds, hseconds))\n\n def start(self):\n if not self._running:\n self._start = time.time() - self._elapsedtime\n self._update()\n self._running = True\n\n def stop(self):\n if self._running:\n self.after_cancel(self._timer)\n self._elapsedtime = time.time() - self._start\n self._setTime(self._elapsedtime)\n self._running = False\n\nclass Settings:\n def __init__(self):\n pass\n\n def open_settings(self):\n self.settings_root = tk.Toplevel()\n self.settings_root.title('Settings')\n self.var = tk.StringVar()\n tk.Label(self.settings_root, text='Default', width=10, anchor='w').grid(row=0, column=0, columnspan=2, padx=10, pady=10)\n rbtn1 = tk.Radiobutton(self.settings_root, text='Easy', width=7, anchor='w', variable=self.var, value=1)\n rbtn2 = tk.Radiobutton(self.settings_root, text='Medium', width=7, anchor='w', variable=self.var, value=2)\n rbtn3 = tk.Radiobutton(self.settings_root, text='Hard', width=7, anchor='w', variable=self.var, value=3)\n tk.Label(self.settings_root, text='9x9, 10mines', width=13, anchor='w').grid(row=1, column=1, padx=10, pady=10, sticky='W')\n tk.Label(self.settings_root, text='16x16, 40mines', width=13, anchor='w').grid(row=2, column=1, padx=10, pady=10, sticky='W')\n tk.Label(self.settings_root, text='16x30, 99mines', width=13, anchor='w').grid(row=3, column=1, padx=10, pady=10, sticky='W')\n rbtn0 = tk.Radiobutton(self.settings_root, text='User-defined', width=10, anchor='w', variable=self.var, value=0)\n tk.Label(self.settings_root, text='# Rows :', width=10, anchor='w').grid(row=1, column=2, padx=10, pady=10, sticky='W')\n tk.Label(self.settings_root, text='# Columns :', width=10, anchor='w').grid(row=2, column=2, padx=10, pady=10, sticky='W')\n tk.Label(self.settings_root, text='# Mines :', width=10, anchor='w').grid(row=3, column=2, padx=10, pady=10, sticky='W')\n self.entry_numrow = tk.Entry(self.settings_root, width=10)\n self.entry_numcolumn = tk.Entry(self.settings_root, width=10)\n self.entry_nummine = tk.Entry(self.settings_root, width=10)\n self.btn_confirm = tk.Button(self.settings_root, text='Confirm', width=10, command=self.set_game)\n self.btn_cancel = tk.Button(self.settings_root, text='Cancel', width=10, command=self.settings_root.destroy)\n rbtn1.grid(row=1, column=0, padx=10, pady=10)\n rbtn2.grid(row=2, column=0, padx=10, pady=10)\n rbtn3.grid(row=3, column=0, padx=10, pady=10)\n rbtn0.grid(row=0, column=2, columnspan=2, padx=10, pady=10)\n self.entry_numrow.grid(row=2, column=3, padx=10, pady=10)\n self.entry_numcolumn.grid(row=1, column=3, padx=10, pady=10)\n self.entry_nummine.grid(row=3, column=3, padx=10, pady=10)\n self.btn_confirm.grid(row=4, column=0, columnspan=2, pady=10)\n self.btn_cancel.grid(row=4, column=2, columnspan=2, pady=10)\n\n def first_frame(self):\n self.open_settings()\n self.btn_cancel['text'] = 'Quit'\n self.btn_cancel['command'] = root.quit\n\n def set_game(self):\n global root, val_numrow, val_numcolumn, val_nummine\n try:\n self.difficulty = int(self.var.get())\n except ValueError:\n tk.messagebox.showerror(title='Error', message='Please choose an option!')\n else:\n if self.difficulty != 0:\n self.settings_root.destroy()\n root.destroy()\n root = tk.Tk()\n if self.difficulty == 1:\n val_numrow, val_numcolumn, val_nummine = 9, 9, 10\n elif self.difficulty == 2:\n val_numrow, val_numcolumn, val_nummine = 16, 16, 40\n elif self.difficulty == 3:\n val_numrow, val_numcolumn, val_nummine = 30, 16, 99\n Main(val_numrow, val_numcolumn, val_nummine, root)\n else:\n try:\n val_numrow = int(self.entry_numrow.get())\n val_numcolumn = int(self.entry_numcolumn.get())\n val_nummine = int(self.entry_nummine.get())\n except ValueError:\n tk.messagebox.showerror(title='Error', message='Invalid input!')\n else:\n if val_numrow > 0 and val_numcolumn > 0 and val_nummine >= 0:\n self.settings_root.destroy()\n root.destroy()\n root = tk.Tk()\n Main(val_numrow, val_numcolumn, val_nummine, root)\n else:\n tk.messagebox.showerror(title='Error', message='Invalid input!')\n\n def renew(self):\n global root, val_numrow, val_numcolumn, val_nummine\n root.destroy()\n root = tk.Tk()\n Main(val_numrow, val_numcolumn, val_nummine, root)\n\nclass Main(Core, StopWatch, Settings):\n def __init__(self, numrow, numcolumn, nummine, parent):\n Core.__init__(self, numrow, numcolumn, nummine)\n StopWatch.__init__(self, parent)\n Settings.__init__(self)\n self.parent = parent\n self.buttonmap = []\n self.style = [' 12345678 @xx-', ['Black', 'Blue', 'Green', 'Red', 'Purple', 'Brown', 'Cyan', 'Black', 'Grey', 'Black', 'Yellow', 'Orange', 'Gold', 'Gray']]\n self.display_font = tkFont.Font(family='Helvetica', size=10, weight=tkFont.BOLD)\n self.start_game()\n \n def handlerAdaptor(self, func, **kwds):\n return lambda event, func=func, kwds=kwds: func(event, **kwds)\n\n def start_game(self):\n self.parent.title('Mine Sweeper')\n menubar = tk.Menu(self.parent)\n options_menu = tk.Menu(menubar, tearoff = 0)\n options_menu.add_command(label='New Game', command=self.renew)\n options_menu.add_command(label='Settings', command=self.open_settings)\n menubar.add_cascade(label='Options', menu=options_menu)\n self.parent.config(menu=menubar)\n vbar = tk.Scrollbar(self.parent, orient='vertical')\n vbar.pack(fill='y', side='right')\n hbar = tk.Scrollbar(self.parent, orient='horizontal')\n hbar.pack(fill='x', side='bottom')\n self.rest_mine = tk.Label(self.parent, text=str(self.num_restmine()))\n self.timer = tk.Label(self.parent, textvariable=self.timestr)\n self.timer.pack(side='top', anchor='e', padx=10)\n self.rest_mine.pack(side='top', anchor='w', padx=10)\n canvas_map = tk.Canvas(self.parent, width=self.numrow*30, height=self.numcolumn*30, scrollregion=(0, 0, self.numrow*30, self.numcolumn*30), xscrollincrement=30, yscrollincrement=30)\n for i in range(self.numrow):\n for j in range(self.numcolumn):\n n = self.order(i, j)\n if self.viewmap[n] in range(9, 13):\n btn = tk.Button(self.parent, width=2, height=1, text=self.style[0][self.viewmap[n]], fg=self.style[1][self.viewmap[n]], bg='Blue', activebackground='RoyalBlue', relief='groove', font=self.display_font)\n else:\n btn = tk.Button(self.parent, width=2, height=1, text=self.style[0][self.viewmap[n]], fg=self.style[1][self.viewmap[n]], bg='LightCyan', activebackground='Azure', relief='groove', font=self.display_font)\n btn.bind('', self.handlerAdaptor(self.First, n=n))\n self.buttonmap.append(btn)\n canvas_map.create_window(30 * i, 30 * j, anchor='nw', window=btn)\n canvas_map.pack(anchor='nw', expand=True, padx=30, pady=30)\n vbar.config(command=canvas_map.yview)\n hbar.config(command=canvas_map.xview)\n self.parent.mainloop()\n\n def end_game(self):\n self.stop()\n for i in range(self.area):\n self.buttonmap[i].bind('', lambda i: None)\n self.buttonmap[i].bind('', lambda i: None)\n if self.mode == 2:\n tk.messagebox.showinfo(title='Result', message='Congratulations! You\\'ve won this game!\\nYou ended this game in %ss.' % (int(self._elapsedtime * 100) / 100))\n else:\n tk.messagebox.showinfo(title='Result', message='Oops! You\\'ve lost this game!\\nYou ended this game in %ss.' % (int(self._elapsedtime * 100) / 100))\n ask = tk.messagebox.askyesno(title=\"Retry\", message=\"Do you want to play another time?\")\n if ask == True:\n self.renew()\n\n def update_map(self, n):\n self.buttonmap[n].configure(text=self.style[0][self.viewmap[n]], fg=self.style[1][self.viewmap[n]])\n if self.viewmap[n] not in range(9, 13):\n self.buttonmap[n].configure(bg='LightCyan', activebackground='Azure')\n\n def update_restmine(self):\n self.rest_mine.configure(text=str(self.num_restmine()))\n\n def num_restmine(self):\n return self.nummine - self.count(self.viewmap, 10)\n\n def first(self, n):\n choices = np.setdiff1d(np.arange(self.area), self.neighbour(n))\n try:\n mine_location = np.random.choice(choices, self.nummine, replace=False)\n except ValueError:\n ask = tk.messagebox.askyesno(title='Error', message='Failed to form a mine map!\\nChange settings?')\n if ask == True:\n self.open_settings()\n else:\n self.start()\n for i in mine_location:\n self.fullmap[i] = -1\n for i in range(self.area):\n if self.fullmap[i] != -1:\n for j in self.neighbour(i):\n if self.fullmap[j] == -1:\n self.fullmap[i] += 1\n self.mode = 1\n\n def First(self, event, n):\n self.first(n)\n self.Explore(event, n)\n if self.mode == 1:\n for i in range(self.area):\n self.buttonmap[i].bind('', self.handlerAdaptor(self.Explore, n=i))\n self.buttonmap[i].bind('', self.handlerAdaptor(self.Flag, n=i))\n\n def flag(self, n):\n if self.viewmap[n] == 9:\n self.viewmap[n] = 10\n elif self.viewmap[n] == 10:\n self.viewmap[n] = 9\n\n def Flag(self, event, n):\n self.flag(n)\n self.update_map(n)\n self.update_restmine()\n\n def single_explore(self, n):\n self.viewmap[n] = self.fullmap[n]\n self.update_map(n)\n\n def zero_expand(self, n):\n for i in self.neighbour(n):\n self.single_explore(i)\n\n def explore(self, n):\n if self.mode == 1:\n if self.fullmap[n] == -1:\n self.mode = 3\n self.viewmap[n] = 11\n self.update_map(n)\n for i in range(self.area):\n if self.fullmap[i] == -1 and self.viewmap[i] == 9:\n self.viewmap[i] = 12\n self.update_map(i)\n elif self.fullmap[i] != -1 and self.viewmap[i] == 10:\n self.viewmap[i] = 13\n self.update_map(i)\n else:\n if self.viewmap[n] == 9:\n if self.fullmap[n] == 0:\n new_zeros = np.array([n])\n while np.size(new_zeros) != 0:\n zero_num = np.size(new_zeros)\n oldmap = copy.copy(self.viewmap)\n for i in new_zeros:\n self.zero_expand(i)\n newmap = copy.copy(self.viewmap)\n for i in new_zeros:\n for j in self.neighbour(i):\n if j not in new_zeros:\n if newmap[j] == 0 and oldmap[j] != 0:\n new_zeros = np.append(new_zeros, j)\n new_zeros = new_zeros[zero_num::]\n else:\n self.single_explore(n)\n if self.count(self.viewmap, 9) + self.count(self.viewmap, 10) == self.nummine:\n self.mode = 2\n for i in range(self.area):\n if self.viewmap[i] == 9:\n self.viewmap[i] = 10\n self.update_map(i)\n self.update_restmine()\n\n def Explore(self, event, n):\n if self.mode == 1 and self.viewmap[n] in range(1, 9):\n counting1 = 0\n for i in self.neighbour(n):\n if self.viewmap[i] == 10:\n counting1 += 1\n if self.viewmap[n] == counting1:\n counting2 = 0\n for i in self.neighbour(n):\n if self.fullmap[i] == -1 and self.viewmap[i] == 9:\n counting2 += 1\n if counting2 == 0:\n for i in self.neighbour(n):\n if self.viewmap[i] == 9:\n self.explore(i)\n self.update_map(i)\n else:\n self.mode = 3\n for i in self.neighbour(n):\n if self.fullmap[i] == -1 and self.viewmap[i] == 9:\n self.viewmap[i] = 11\n self.update_map(i)\n for i in range(self.area):\n if self.fullmap[i] == -1 and self.viewmap[i] == 9:\n self.viewmap[i] = 12\n self.update_map(i)\n elif self.fullmap[i] != -1 and self.viewmap[i] == 10:\n self.viewmap[i] = 13\n self.update_map(i)\n else:\n self.explore(n)\n if self.mode == 2 or self.mode == 3:\n self.end_game()\n\nclass Progress(Main):\n def __init__(self, parent):\n self.first_frame()\n parent.withdraw()\n Settings.__init__(self)\n Main.__init__(self, 1, 1, 0, parent)\n\nif __name__ == '__main__':\n Progress(root)","sub_path":"minesweeper_tk.py","file_name":"minesweeper_tk.py","file_ext":"py","file_size_in_byte":16146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"85777051","text":"from gevent import socket\nfrom gevent.server import StreamServer\n\n\ndef handle(sock, address):\n while True:\n fileobj = sock.makefile()\n try:\n # fileobj.readline()\n sock.sendall('+PONG\\r\\n')\n except socket.error:\n sock.close()\n break\n\n\ndef main():\n server = StreamServer(('0.0.0.0', 1931), handle)\n server.serve_forever()\n\nif __name__ == '__main__':\n main()\n","sub_path":"python/gevent/redis_ping_pong.py","file_name":"redis_ping_pong.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"165601687","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='QuestionSubTopic',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('difficulty_level', models.IntegerField(default=0)),\n ('solution', models.CharField(max_length=200)),\n ('num_of_times_asked', models.IntegerField(default=0)),\n ('num_of_times_answered', models.IntegerField(default=0)),\n ('comments', models.CharField(max_length=200)),\n ('votes', models.IntegerField(default=0)),\n ('answer_ratings', models.CharField(max_length=1, choices=[(b'1', b'Not Answered'), (b'2', b'Bad'), (b'3', b'Satisfactory'), (b'4', b'Good'), (b'5', b'Excellent')])),\n ('created_at', models.DateTimeField(verbose_name=b'created at')),\n ],\n ),\n migrations.CreateModel(\n name='QuestionTopic',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('topic_text', models.CharField(max_length=200)),\n ('additional_info', models.CharField(max_length=200)),\n ],\n ),\n migrations.AddField(\n model_name='questionsubtopic',\n name='question',\n field=models.ForeignKey(to='myapp.QuestionTopic'),\n ),\n ]\n","sub_path":"myapp/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"618655331","text":"import numpy as np\nimport pandas as pd\nimport uuid\nimport randomemail as re\nimport randomname as rn\n\n# [2,6)\nlower=2\nhigher=6\ncount_points=10\n\ncount_students=100\n\n\ndef rand_student():\n first_and_surname=rn.rand_name().split()\n\n name=first_and_surname[0]\n surname=first_and_surname[1]\n age=np.random.randint(18,50, dtype=np.int)\n email=re.generate_random_emails(1)[0]\n id=str(uuid.uuid4())\n points=np.array(np.random.randint(lower,higher,count_points))\n rating=np.sum(points, dtype=np.int)\n\n return (id, name,surname,age,email,points, rating)\n\nstudents=[]\n\nfor _ in range(count_students):\n students.append(rand_student())\n\nfor student in students:\n print(student)\n\ndf=pd.DataFrame(students)\n\nprint(df)","sub_path":"Homeworks/Homework 4/Homework_4.py","file_name":"Homework_4.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"646683456","text":"import time\r\nimport turtle\r\nimport os\r\nfrom turtle import *\r\n\r\nglobal score_a\r\nscore_a = 0\r\nglobal score_b\r\nscore_b = 0\r\nfile = open(\"scores.txt\", \"a\")\r\n\r\ndef Pong():\r\n global score_a\r\n global score_b\r\n P_Window = turtle.Screen()\r\n P_Window.title(\"PONG\")\r\n P_Window.bgcolor(\"black\")\r\n P_Window.setup(width = 800, height = 600)\r\n P_Window.tracer(0)\r\n\r\n\r\n\r\n\r\n \r\n## Paddle A\r\n paddle_a = turtle.Turtle()\r\n paddle_a.speed(0)\r\n paddle_a.shape(\"square\")\r\n paddle_a.color(\"white\")\r\n paddle_a.shapesize(stretch_wid = 5, stretch_len = 1)\r\n paddle_a.penup()\r\n paddle_a.goto(-350, 0)\r\n## Paddle B\r\n paddle_b = turtle.Turtle()\r\n paddle_b.speed(0)\r\n paddle_b.shape(\"square\")\r\n paddle_b.color(\"white\")\r\n paddle_b.shapesize(stretch_wid = 5, stretch_len = 1)\r\n paddle_b.penup()\r\n paddle_b.goto(+350, 0)\r\n## Ball\r\n ball = turtle.Turtle()\r\n ball.speed(0)\r\n ball.shape(\"square\")\r\n ball.color(\"white\")\r\n ball.penup()\r\n ball.goto(0, 0)\r\n ball.dx = 0.2\r\n ball.dy = -0.2\r\n\r\n## pen\r\n pen = turtle.Turtle()\r\n pen.speed(0)\r\n pen.color(\"white\")\r\n pen.penup()\r\n pen.hideturtle()\r\n pen.goto(0, 260)\r\n pen.write(\"Player A: 0 Player B: 0\", align=\"center\", font=(\"Courier\", 24, \"normal\"))\r\n\r\n def moveLeftPaddleUp():\r\n y = paddle_a.ycor()\r\n y += 20\r\n paddle_a.sety(y)\r\n\r\n def moveLeftPaddleDown():\r\n y = paddle_a.ycor()\r\n y -= 20\r\n paddle_a.sety(y)\r\n def moveRightPaddleUp():\r\n y = paddle_b.ycor()\r\n y += 20\r\n paddle_b.sety(y)\r\n\r\n\r\n def moveRightPaddleDown():\r\n y = paddle_b.ycor()\r\n y -= 20\r\n paddle_b.sety(y)\r\n\r\n\r\n P_Window.onkey(moveLeftPaddleUp, \"w\")\r\n P_Window.onkey(moveLeftPaddleDown, \"s\")\r\n P_Window.onkey(moveRightPaddleUp, \"Up\")\r\n P_Window.onkey(moveRightPaddleDown, \"Down\")\r\n\r\n\r\n\r\n \r\n\r\n while True:\r\n P_Window.listen()\r\n\r\n \r\n\r\n \r\n P_Window.update()\r\n ## move the ball\r\n ball.setx(ball.xcor() + ball.dx)\r\n ball.sety(ball.ycor() + ball.dy)\r\n ##Boarder Checking\r\n if ball.ycor() > 290:\r\n ball.sety(290)\r\n ball.dy *= -1\r\n \r\n if ball.ycor() < -290:\r\n ball.sety(-290)\r\n ball.dy *= -1\r\n\r\n if ball.xcor() > 390:\r\n ball.goto(0, 0)\r\n\r\n ball.dx *= -1\r\n score_a += 1\r\n pen.clear()\r\n pen.write(\"Player A: {} Player B: {}\".format(score_a, score_b), align=\"center\", font=(\"Courier\", 24, \"normal\"))\r\n \r\n if ball.xcor() < -390:\r\n ball.goto(0, 0)\r\n ball.dx *= -1\r\n score_b += 1\r\n pen.clear()\r\n pen.write(\"Player A: {} Player B: {}\".format(score_a, score_b), align=\"center\", font=(\"Courier\", 24, \"normal\"))\r\n\r\n ##Paddle and ball collisions\r\n if (ball.xcor() > 340 and ball.xcor() < 350) and (ball.ycor() < paddle_b.ycor() + 40 and ball.ycor() > paddle_b.ycor() - 40):\r\n ball.setx(340)\r\n ball.dx *= -1\r\n\r\n if (ball.xcor() < -340 and ball.xcor() > -350) and (ball.ycor() < paddle_a.ycor() + 40 and ball.ycor() > paddle_a.ycor() - 40):\r\n ball.setx(-340)\r\n ball.dx *= -1\r\n\r\n P_Window.onclick(lambda*a:[P_Window.bye(),file.write(\"\\n > Pong: A\"),file.write(str(score_a)),file.write(\" B\"),file.write(str(score_b)),file.close(),os.system(\"menusystem.py\")])\r\n\r\nPong()\r\n \r\n","sub_path":"Pong.py","file_name":"Pong.py","file_ext":"py","file_size_in_byte":3490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"523046790","text":"import numpy as np\nimport tensorflow as tf\n\nx = tf.Variable(0, name='x')\n\nmodel = tf.initialize_all_variables()\n\nwith tf.Session() as session:\n for i in range(5):\n session.run(model)\n x = x + 1\n print(session.run(x))\n\n'''\n# Model parameters\nW = tf.Variable([.3], tf.float32)\nb = tf.Variable([-.3], tf.float32)\n# Model input and output\nx = tf.placeholder(tf.float32)\nlinear_model = W * x + b\ny = tf.placeholder(tf.float32)\n# loss\nloss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares\n# optimizer\noptimizer = tf.train.GradientDescentOptimizer(0.01)\ntrain = optimizer.minimize(loss)\n# training data\nx_train = [1,2,3,4]\ny_train = [0,-1,-2,-3]\n# training loop\ninit = tf.initialize_all_variables()\nsess = tf.Session()\nwriter = tf.train.SummaryWriter('/Users/StevenLi/PycharmProjects/msc/ml2/temp', graph=tf.get_default_graph())\nsess.run(init) # reset values to wrong\nfor i in range(1000):\n sess.run(train, {x:x_train, y:y_train})\n\n# evaluate training accuracy\ncurr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train})\nprint(\"W: %s b: %s loss: %s\"%(curr_W, curr_b, curr_loss))\n'''\n'''\nimport tensorflow as tf\n\nnode1 = tf.constant(3.0, tf.float32)\nnode2 = tf.constant(4.0)\n\nsess = tf.Session()\nnode3 = tf.add(node1, node2)\n\nprint \"node3: \" + str(node3)\n\na = tf.placeholder(tf.float32)\nb = tf.placeholder(tf.float32)\nadder_node = a + b\nadd_and_triple = adder_node * 3\nwriter = tf.train.SummaryWriter('/Users/StevenLi/PycharmProjects/msc/ml2/temp', graph=tf.get_default_graph())\n\ninit = tf.global_variables_initializer()\nsess.run(init)\n\nprint str(sess.run(add_and_triple,{a:3,b:4.5}))\n'''","sub_path":"ml2/tftut.py","file_name":"tftut.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"256983982","text":"# 백준 11653 소인수분해\n# 정수 N이 주어졌을 때, 소인수분해하는 프로그램을 작성하시오.\n\nn = int(input())\ni = 2\n\nwhile n != 1 :\n if n%i == 0: # 2로 나누어지면 진행\n n = n//i\n print(i)\n else: # 2로 나누어지지 않으면 i에 1 증가\n i+=1\n","sub_path":"11653.py","file_name":"11653.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"452168878","text":"#Time\nTIMEOUT = 3.\nKEEPALIVE = 7\nTIME_FOR_RESTART = 10\n\n#Addresses\nPORT_SEND = 8001\nPORT_RECEIVE = 8001\nREMOTE_IP = \"0.0.0.0\"\n\n#Dirs and files (all dirs with trailing /)\nPROGRAM_DIR = \"/home/pi/\"\nIMAGE_DIR = \"/home/pi/images/\"\nEVENT_FILE = \"event.txt\"\nJARS_DIR = \"jars/\"\n\n#Depending on speed: 4 slow, 2 fast\nIMAGE_SCALE_ALG = 4\n\n#Screen\nSCREEN_HEIGHT = 800\nSCREEN_WIDTH = 1280\n","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"576788260","text":"import errno\nimport fcntl\nimport os\nimport multiprocessing\nimport socket\nimport signal\nimport select\nimport sys\nimport resource\nimport time\nimport gevent\n\nIGNORE_SIGS = ('SIGKILL', 'SIGSTOP', 'SIG_DFL', 'SIG_IGN')\nSIGNO_TO_NAME = dict((no, name) for name, no in signal.__dict__.iteritems()\n if name.startswith('SIG')\n and name not in IGNORE_SIGS)\nDEFAULT_SIGNAL_HANDLERS = dict((signo, signal.getsignal(signo))\n for signo in SIGNO_TO_NAME)\n\n\ndef set_nonblocking(*fds):\n for fd in fds:\n fcntl.fcntl(fd, fcntl.F_SETFL, os.O_NONBLOCK)\n return fds\n\n\ndef _ignore_interrupts(e):\n en, _ = e.args\n if en not in (errno.EINTR, errno.EAGAIN):\n raise e\n\n\ndef safe_syscall(func, *args, **kwargs):\n try:\n return func(*args, **kwargs)\n except Exception as e:\n _ignore_interrupts(e)\n\n\ndef restart_syscall(func, *args, **kwargs):\n while True:\n try:\n return func(*args, **kwargs)\n except Exception as e:\n _ignore_interrupts(e)\n\n\nclass WriteAndFlushFile(file):\n\n def write(self, str):\n full = len(str) == os.write(self.fileno(), str)\n self.flush()\n return full\n\n def writelines(self, sequence_of_strings):\n return self.write(''.join(sequence_of_strings))\n\n\nclass WorkerMetadata(object):\n\n def __init__(self, pid, health_check_read, last_seen):\n self.pid = pid\n self.health_check_read = health_check_read\n self.last_seen = last_seen\n\n\nclass Master(object):\n BACKLOG = 128\n DEFAULT_NUM_WORKERS = multiprocessing.cpu_count() - 1\n CHILD_HEALTH_INTERVAL = 1.0\n SELECT_TIMEOUT = CHILD_HEALTH_INTERVAL * 5\n MURDER_WAIT = 30\n PLATFORM_RSS_MULTIPLIER = 1\n PROC_FDS = '/proc/self/fd'\n\n def __init__(self, server_class, socket_factory, sleep, wsgi, address,\n logpath, pidfile, num_workers=None):\n self.server_class = server_class\n self.socket_factory = socket_factory\n self.sleep = sleep\n self.wsgi = wsgi\n self.address = address\n self.logpath = logpath\n self.pidfile = pidfile\n\n self.listener = None\n if num_workers is None:\n num_workers = self.DEFAULT_NUM_WORKERS\n self.num_workers = num_workers\n self.pid_to_workers = {}\n self.pipe_to_workers = {}\n\n def add_worker(self, w):\n self.pid_to_workers[w.pid] = w\n self.pipe_to_workers[w.health_check_read] = w\n\n def remove_worker(self, w):\n # we may have gotten interrupted by a signal\n if w:\n self.pid_to_workers.pop(w.pid, None)\n self.pipe_to_workers.pop(w.health_check_read, None)\n\n def log(self):\n self.logfile = WriteAndFlushFile(self.logpath, 'ab')\n\n def bind(self):\n self.listener = self.socket_factory()\n self.listener.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n self.listener.bind(self.address)\n self.listener.listen(self.BACKLOG)\n\n def selfpipes(self):\n # r, w\n self.pipe_select, self.pipe_signal = set_nonblocking(*os.pipe())\n\n def daemonize(self):\n # for steps see TLPI 37.2\n if os.fork():\n # 1\n sys.exit(0)\n # 2\n os.setsid()\n # 3\n if os.fork():\n sys.exit(0)\n # 4\n os.umask(0)\n # 5 -- skip for now\n # os.chdir('/')\n # 6/7\n fd = os.open('/dev/null', os.O_RDWR)\n os.dup2(fd, 0)\n os.dup2(self.logfile.fileno(), 1)\n os.dup2(self.logfile.fileno(), 2)\n\n with open(self.pidfile, 'w') as f:\n f.write(str(os.getpid()))\n\n def health_check(self, fd_limit, maxrss_limit):\n # parent alive?\n if os.getppid() == 1:\n # we were orphaned and adopted by init\n sys.stderr.write('parent died!\\n')\n sys.exit(1)\n # memory usage?\n usage = resource.getrusage(resource.RUSAGE_SELF)\n\n memory_usage = usage.ru_maxrss * self.PLATFORM_RSS_MULTIPLIER\n if memory_usage > maxrss_limit:\n sys.stderr.write('memory usage exceeded: %s\\n' % memory_usage)\n sys.exit(1)\n\n fd_count = len(os.listdir(self.PROC_FDS))\n if fd_count > fd_limit - 10 or fd_count > fd_limit * 0.9:\n sys.stderr.write('file limit too close to limit %s\\n' % fd_count)\n sys.exit(1)\n\n def spawn_worker(self):\n health_check_read, health_check_write = set_nonblocking(*os.pipe())\n pid = os.fork()\n if pid:\n return WorkerMetadata(pid=pid,\n health_check_read=health_check_read,\n last_seen=time.time())\n\n self.set_signal_handlers(DEFAULT_SIGNAL_HANDLERS)\n self.server.start()\n\n nofile_soft_limit = max(resource.getrlimit(resource.RLIMIT_NOFILE)[0],\n 1024)\n maxrss_soft_limit = max(resource.getrlimit(resource.RLIMIT_RSS)[0],\n 2 ** 30)\n\n while True:\n self.health_check(nofile_soft_limit, maxrss_soft_limit)\n os.write(health_check_write, '\\x00')\n self.sleep(self.CHILD_HEALTH_INTERVAL)\n\n sys.exit(0)\n\n def spawn_workers(self, number):\n for _ in xrange(number):\n self.add_worker(self.spawn_worker())\n\n def kill_workers(self, pids):\n for pid in pids:\n try:\n os.kill(pid, signal.SIGKILL)\n except OSError as e:\n if e.errno == errno.ESRCH:\n continue\n self.remove_worker(self.pid_to_workers.get(pid))\n\n def set_signal_handlers(self, signal_handlers):\n return dict((signo, signal.signal(signo, handler))\n for signo, handler in signal_handlers.iteritems())\n\n def master_signals(self):\n\n def handler(signo, frame):\n safe_syscall(os.write, self.pipe_signal, chr(signo))\n\n handlers = dict((signo, handler)\n for signo, name in SIGNO_TO_NAME.iteritems())\n return self.set_signal_handlers(handlers)\n\n def handle_signals(self, signos):\n for signo in signos:\n signo = ord(signo)\n handler_name = SIGNO_TO_NAME[signo] + '_handler'\n handler_meth = getattr(self, handler_name, None)\n if handler_meth:\n # no frame, sorry\n handler_meth(signo, None)\n\n def run(self, daemonize=True):\n self.bind()\n\n if daemonize:\n self.log()\n self.daemonize()\n\n self.selfpipes()\n self.master_signals()\n\n self.server = self.server_class(self.listener, self.wsgi)\n self.spawn_workers(self.num_workers)\n\n while True:\n read = [c.health_check_read for c in self.pid_to_workers.values()]\n read.append(self.pipe_select)\n read, write, exc = restart_syscall(select.select, read, [], [],\n self.SELECT_TIMEOUT)\n now = time.time()\n\n for r in read:\n if r == self.pipe_select:\n self.handle_signals(os.read(r, 4096))\n continue\n os.read(r, 4096)\n worker = self.pipe_to_workers.get(r)\n if not worker:\n continue\n worker.last_seen = now\n\n self.kill_workers(w.pid for w in self.pid_to_workers.values()\n if now - w.last_seen >= self.MURDER_WAIT)\n\n if self.num_workers > len(self.pid_to_workers):\n self.spawn_workers(self.num_workers - len(self.pid_to_workers))\n\n def SIGCLD_handler(self, signo, frame):\n while True:\n try:\n pid, status = os.waitpid(-1, os.WNOHANG)\n if not pid:\n break\n self.remove_worker(self.pid_to_workers.get(pid))\n except OSError as e:\n if e.errno == errno.ECHILD:\n break\n\n SIGCHLD_handler = SIGCLD_handler\n\n def SIGTERM_handler(self, signo, frame):\n for child in self.pid_to_workers:\n try:\n os.kill(child, signal.SIGTERM)\n except OSError as e:\n if e.errno == errno.ESRCH:\n continue\n while True:\n try:\n os.wait()\n except OSError as e:\n if e.errno == errno.ECHILD:\n break\n sys.exit(0)\n\n SIGINT_handler = SIGTERM_handler\n\n\nif __name__ == '__main__':\n import argparse\n import gevent.pywsgi\n\n a = argparse.ArgumentParser()\n a.add_argument('address')\n a.add_argument('port', type=int)\n a.add_argument('--logpath', default='log')\n a.add_argument('--pidfile', default='pidfile')\n a.add_argument('--daemonize', '-d', default=False, action='store_true')\n\n import string\n chrs = string.lowercase[:Master.DEFAULT_NUM_WORKERS]\n\n def wsgi(environ, start_response):\n start_response('200 OK', [('Content-Type', 'text/html')])\n pid = os.getpid()\n spid = str(pid)\n sys.stderr.write('''\\\nLorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus\neleifend a metus quis sollicitudin. Aenean nec dolor iaculis, rhoncus\nturpis sit amet, interdum quam. Nunc rhoncus magna a leo interdum\nluctus. Vestibulum nec sapien diam. Aliquam rutrum venenatis\nmattis. Etiam eget adipiscing risus. Vestibulum ante ipsum primis in\nfaucibus orci luctus et ultrices posuere cubilia Curae; Fusce nibh\nnulla, lacinia quis dignissim vel, condimentum at odio. Nunc et diam\nmauris. Fusce sit amet odio sagittis, convallis urna a, blandit\nurna. Phasellus mattis ligula sed tincidunt pellentesque. Nullam\ntempor convallis dapibus.\n\nDuis vitae vulputate sem, nec eleifend orci. Donec vel metus\nfringilla, ultricies nunc at, ultrices quam. Donec placerat nisi quis\nfringilla facilisis. Fusce eget erat ut magna consectetur\nelementum. Aenean non vulputate nulla. Aliquam eu dui nibh. Vivamus\nmollis suscipit neque, quis aliquam ipsum auctor non. Nulla cursus\nturpis turpis, nec euismod urna placerat at. Nunc id sapien\nnibh. Vestibulum condimentum luctus placerat. Donec vitae posuere\narcu.''' + '\\n')\n return ['

ok


from ' + spid]\n\n args = a.parse_args()\n\n Master(server_class=gevent.pywsgi.WSGIServer,\n socket_factory=gevent.socket.socket,\n sleep=gevent.sleep,\n wsgi=wsgi,\n address=(args.address, args.port),\n logpath=args.logpath,\n pidfile=args.pidfile).run(args.daemonize)\n","sub_path":"tectonic/prefork.py","file_name":"prefork.py","file_ext":"py","file_size_in_byte":10669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"362792104","text":"'''\nurl: https://projecteuler.net/problem=4\nLargest palindrome product\n\nProblem 4\nA palindromic number reads the same both ways. The largest palindrome made from the product of two 2-digit numbers is 9009 = 91 × 99.\n\nFind the largest palindrome made from the product of two 3-digit numbers.\n'''\n\ndef solution(a,b):\n ''' \n enter two numbers and this function will return the largest palindrome made from the product of two numbers within the range specified\n '''\n\n for i in range(max(a,b), min(a,b), -1):\n for j in range(max(a,b), min(a,b), -1):\n str_mult = [letter for letter in str(i*j)]\n halfway = round(len(str_mult) / 2)\n if str_mult[:halfway] == str_mult[::-1][:halfway]:\n return (i,j,int(i * j))\n return 'No palindromes found... :('\n \n\nprint(solution(100,1000))\n\n","sub_path":"question_4.py","file_name":"question_4.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"181837293","text":"from flask import Flask, render_template, jsonify, request\n\napp = Flask(__name__)\n\n#from flask import Flask, r\n#app = Flask(\"First App\")\n\n#@app.route('/')\n#def hello_world():\n# return 'Hello World!'\n\n@app.route('/')\ndef index():\n\treturn render_template(\"Home.html\")\n\n@app.route('/background_process')\ndef background_process():\n filepath = request.args.get('path')\n return jsonify(result = filepath)\n\n@app.route('/backgroundtext_process')\ndef backgroundtext_process():\n filepath = request.args.get('textVal')\n print(filepath)\n return jsonify(result = filepath)\n\nif __name__ == '__main__':\n app.run()\n\n","sub_path":"Hackathon-master/Test/Test.py","file_name":"Test.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"620309716","text":"import socket\nimport struct\nimport sys\nimport os\nimport numpy as np\nimport cv2\nimport time\n\ndef tcp_server():\n serverHost = '' # localhost\n serverPort = 9090\n save_folder = 'data/'\n\n if ~os.path.isdir(save_folder):\n os.mkdir(save_folder)\n\n # Create a socket\n sSock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n # Bind server to port\n try:\n sSock.bind((serverHost, serverPort))\n print('Server bind to port '+str(serverPort))\n except socket.error as msg:\n print('Bind failed. Error Code : ' + str(msg[0]) + ' Message ' + msg[1])\n return\n\n sSock.listen(10)\n print('Start listening...')\n sSock.settimeout(3.0)\n while True:\n try:\n conn, addr = sSock.accept() # Blocking, wait for incoming connection\n break\n except KeyboardInterrupt:\n sys.exit(0)\n except Exception:\n continue\n\n print('Connected with ' + addr[0] + ':' + str(addr[1]))\n\n while True:\n # Receiving from client\n try:\n data = conn.recv(512*512*4+100)\n if len(data)==0:\n continue\n header = data[0:1].decode('utf-8')\n print('--------------------------\\nHeader: ' + header)\n print(len(data))\n\n if header == 's':\n # get the init transform\n data_length = struct.unpack(\">i\", data[1:5])[0]\n N = data_length\n depth_img_np = np.frombuffer(data[5:5+N], np.uint16).reshape((512,512))\n ab_img_np = np.frombuffer(data[5+N:5+2*N], np.uint16).reshape((512,512))\n timestamp = str(int(time.time()))\n cv2.imwrite(save_folder + timestamp+'_depth.tiff', depth_img_np)\n cv2.imwrite(save_folder + timestamp+'_abImage.tiff', ab_img_np)\n print('Image with ts ' + timestamp + ' is saved')\n except:\n break\n \n print('Closing socket...')\n sSock.close()\n\n\nif __name__ == \"__main__\":\n tcp_server()\n","sub_path":"python/TCPServer.py","file_name":"TCPServer.py","file_ext":"py","file_size_in_byte":2038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"147915004","text":"import sys\r\nsys.path.append('C:/Users/Billy/Documents/PRISMO/Backtester')\r\nfrom multiprocessing import Process\r\nimport pandas as pd\r\nimport numpy as np\r\nimport pickle\r\nimport matplotlib.pyplot as plt\r\nimport time\r\nimport random\r\nfrom mathCode.johansenMain import coint_johansen\r\nfrom tqdm import tqdm_notebook\r\nfrom IPython.display import clear_output\r\nimport itertools\r\nimport datetime\r\nimport os\r\nfrom base_model_backtest import backtest\r\nimport random\r\nfrom pandas_datareader import data as pdr\r\nimport logging\r\nfrom pandas.plotting import register_matplotlib_converters\r\nfrom functionSource import *\r\n\r\n\r\ndef APR(returns):\r\n ave = []\r\n for year in range(returns.index[0].year, returns.index[-1].year+1):\r\n annualReturns = returns.loc[returns.index=str(year)+'-01-01']\r\n\r\n aveReturns = np.mean(annualReturns) \r\n ave.append(aveReturns*252*100)\r\n \r\n\r\n return np.mean(ave)\r\n\r\npairs = [['UBU.AX','SSO.AX','ETF'],\r\n ['BBOZ.AX','ETPMAG.AX','ETF'],\r\n ['EX20.AX','ISO.AX','ETF']]\r\ndata = pickle.load(open( \"D:/PRISMO/historicalData/Data/asx200nobiasFINAL.pickle\", \"rb\" ) )\r\n\r\npairs_manifold = pickle.load(open('C:/Users/Billy/Documents/PRISMO/data/pairsFromManifoldsStonk.pickle','rb'))\r\npairs = [x + ['Stock'] for x in pairs_manifold][147:]\r\n# pairs = []\r\n# for ticker in data.columns.levels[0]:\r\n# for pair in data.columns.levels[0]:\r\n# if ticker != pair:\r\n# pairs.append([ticker,pair,'ETF'])\r\n# del data\r\n\r\ndef flatten(list_):\r\n string = ''\r\n for element in list_:\r\n string = string + element.replace('.','')+'_'\r\n\r\n return string\r\n\r\ndef monotonicityMeasure(returns):\r\n # Start by counting how many points in the future returns are greater than the current\r\n # If its less than current, we want to emphasise that it's non monatonic in the long run and is a bad strategy\r\n # Check x chunks for monotonicity\r\n chunks = 50\r\n binWidth = int(len(returns)/chunks)\r\n \r\n totalCounter = 0\r\n monotonicCounter = 0\r\n for i in range(0,len(returns),binWidth):\r\n for j in range(i+chunks,len(returns),binWidth):\r\n \r\n if returns[j]>returns[i]:\r\n monotonicCounter +=1\r\n \r\n totalCounter+=1\r\n \r\n return monotonicCounter/totalCounter\r\n \r\n\r\n\r\n\r\ndef optimize(toTrade):\r\n\r\n asset = toTrade[-1]\r\n toTrade = toTrade[:-1]\r\n\r\n if asset == 'ETF':\r\n data = pickle.load(open( \"D:/PRISMO/historicalData/ETFSnobiasFINAL.pickle\", \"rb\" ) )\r\n close = data.xs('CLOSE', axis = 1, level = 1)\r\n close_= close[toTrade].dropna()\r\n\r\n if len(close_)<242:\r\n return 0\r\n if asset == 'Stock':\r\n data = pickle.load(open( \"D:/PRISMO/historicalData/Data/asx200nobiasFINAL.pickle\", \"rb\" ) )\r\n close = data.xs('CLOSE', axis = 1, level = 1)\r\n close_= close[toTrade].dropna()\r\n\r\n if len(close_)<242:\r\n return 0\r\n\r\n\r\n results=[]\r\n results.append(['delta','ve','cumret','sharpe','weightedCum','weightedSharpe'])\r\n ve_values = []\r\n delta_values = []\r\n for ve_ in range(1,10):\r\n order = round(0.1**(ve_+1), 12)\r\n for i in range(1,10):\r\n ve_values.append(round(i*order,12))\r\n delta_values.append(round(i*order,12))\r\n \r\n for delta in delta_values:\r\n for Ve in ve_values:\r\n close = close_\r\n #INIT\r\n yhatList = []\r\n QList= []\r\n eList=[]\r\n betaList = [np.matrix([[0],[0]])]\r\n R = np.zeros([len(toTrade),len(toTrade)])\r\n P = np.zeros([len(toTrade),len(toTrade)])\r\n y = []\r\n hedges = []\r\n Vw = delta/(1-delta)*np.diag(np.ones(len(toTrade)))\r\n\r\n for row in close.iterrows():\r\n #Extract x and y from the row, put them into numpy form. We include a constant for x so we can fit with a constant.\r\n x = np.matrix([[row[1][toTrade[0]]],[1]])\r\n y = np.matrix(row[1][toTrade[1]])\r\n\r\n ## 1 STEP AHEAD PREDICTION ##\r\n beta = betaList[-1] # beta(t|t-1) = beta(t-1|t-1)\r\n R = P +Vw # R(t|t-1) = R(t-1|t-1) + Vw\r\n yhat = np.dot(x.T, beta) # yhat = x.beta\r\n e_t = y - yhat # e(t) = y(t) - yhat(t)\r\n Q_t = np.dot( np.dot(x.T, R) , x ) +Ve # Q(t) = var(e(t)) = var(y(t) - yhat(t)) \r\n # = var(y(t)) + var(yhat(t)) + cov[y(t), yhat(t)]\r\n # = x . R(t|t-1) + Ve\r\n\r\n\r\n ## UPDATE PARAMETERS ##\r\n K = np.dot(R, x) / Q_t # K is the kalman gain\r\n beta = beta + K*e_t # beta(t|t) = beta(t|t-1)+K(t)e(t)\r\n P = R - np.dot( np.dot(K, np.transpose(x)), R) # We denote R(t|t) by P, and R(t|t-1) as R. R(t | t − 1) = cov(β(t) − βhat(t|t-1))\r\n\r\n #Add beta and predicted y values to arrays for storage\r\n betaList.append(beta)\r\n yhatList.append(yhat)\r\n\r\n #Form the hedge ratio as a float\r\n hedgeRatio = beta[0].tolist()[0][0]\r\n hedges.append(hedgeRatio)\r\n eList.append(e_t.tolist()[0][0])\r\n QList.append(Q_t.tolist()[0][0])\r\n\r\n close.loc[slice(None),'e'] = eList\r\n close.loc[slice(None),'Q'] = QList\r\n\r\n if np.mean(close['e'])>np.sqrt(np.mean(close['Q'])):\r\n# print('means',np.mean(close['e']), np.sqrt(np.mean(close['Q'])))\r\n # if the average error is greater than the average deviation the parameters are fucked. No point going further\r\n break\r\n\r\n\r\n close.loc[slice(None),'LongEntry'] = close['e']<-np.sqrt(close['Q'])\r\n close.loc[slice(None),'LongExit'] = close['e']>-np.sqrt(close['Q'])\r\n close.loc[slice(None),'ShortEntry'] = close['e']>np.sqrt(close['Q'])\r\n close.loc[slice(None),'ShortExit'] = close['e'] 50:\r\n monotonicity = monotonicityMeasure(np.cumsum(close['returns'].fillna(0)))\r\n weightedSharpe = monotonicity*sharpe\r\n weightedRet = monotonicity*np.cumsum(close['returns'].fillna(0)).iloc[-1]\r\n results.append([delta, Ve,np.cumsum(close['returns'].fillna(0)).iloc[-1], sharpe, weightedRet, weightedSharpe, apr, apr*monotonicity])\r\n \r\n except Exception as e:\r\n print(e)\r\n pickle.dump(results, open('C:/Users/Billy/Documents/PRISMO/Backtester/optimizers/cointegration/results/'+flatten(toTrade)+'.pickle','wb'))\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n \r\n for i in range(0,len(pairs),8):\r\n processes = []\r\n for index,pair in enumerate(pairs[i:i+8]):\r\n print(i+index)\r\n process = Process(target=optimize, args=([pair]))\r\n processes.append(process)\r\n process.start()\r\n\r\n for process in processes:\r\n process.join()\r\n\r\n\r\n\r\n","sub_path":"Optimizers/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"329982451","text":"import os\nimport ncClasses.ncField as ncField\nfrom ncClasses.subdomains import setSSI\nfrom datetime import datetime, timedelta\nimport numpy as np\nos.chdir('00_newScripts/')\nimport matplotlib.pyplot as plt\n\n#from functions import unstaggerZ_1D, unstaggerZ_4D, saveObj \nfrom functions import saveObj \n\n\nress = ['4.4', '2.2', '1.1']\nress = ['4.4']\nmodes = ['', 'f']\nmodes = ['f']\ni_subdomain = 1\nssI, domainName = setSSI(i_subdomain, {'4.4':{}, '2.2':{}, '1.1':{}}) \naltInds = list(range(45,61))\naltInds = list(range(0,26))\n#altInds = list(range(0,41))\n#altInds = list(range(45,61))\nssI['4.4']['altitude'] = altInds \nssI['2.2']['altitude'] = altInds \nssI['1.1']['altitude'] = altInds \n\n# Altitude arrays\naltI = np.asarray(altInds)\nalts = np.asarray(altInds)\nalts[altI <= 60] = altI[altI <= 60]*100\nalts[altI > 60] = (altI[altI > 60] - 60)*1000 + 6000\ndz = np.diff(alts)\n#altsu = unstaggerZ_1D(alts)\n\nnameString = 'alts_'+str(alts[0])+'_'+str(alts[-1])+'_'+domainName\nfolder = '../06_bulk' +'/' + nameString\nif not os.path.exists(folder):\n os.mkdir(folder)\n\ninpPath = '../01_rawData/topocut/'\ninpPathNoTopocut = '../01_rawData/'\n\ndays = list(range(11,20))\nhours = list(range(0,24))\n\nvars = ['AQVT_ADV', 'AQVT_ZADV', 'AQVT_HADV'] \n\nvar = 'AQVT_ZADV'\n#var = 'AQVT_TOT'\nres = '4.4'\nmode = 'f'\ndx = float(res)*1000\nA = np.power(dx,2)\ndiurnal = np.full(24,np.nan)\n#diurnalSlab = np.full(24,np.nan)\n\nfor hr in hours:\n print(hr)\n #hr = 12 \n dts = list()\n for day in days:\n dts.append(datetime(2006,7,day,hr))\n\n MFsum = 0\n #MFsumSlab = 0\n for dt in dts:\n #print(dt)\n ncFileName = 'lffd{0:%Y%m%d%H}z.nc'.format(dt)\n srcNCPath = inpPath + res + mode + '/' + ncFileName\n\n # LOAD RHO\n RHOncf = ncField.ncField('RHO', srcNCPath, ssI[res])\n RHOncf.loadValues()\n rho = RHOncf.vals\n\n #srcNCPath = inpPathNoTopocut + res + mode + '/' + ncFileName\n NCF = ncField.ncField(var, srcNCPath, ssI[res])\n NCF.loadValues()\n vals = NCF.vals\n\n MF = np.nansum(vals*rho*100*A)\n #MF = np.nanmean(vals)\n MFsum = MFsum + MF\n #print(MF)\n\n ## slabs\n #ncf = ncField.ncField('W', srcNCPath, ssI[res])\n #ncf.loadValues()\n #W = ncf.vals\n\n #ncf = ncField.ncField('QV', srcNCPath, ssI[res])\n #ncf.loadValues()\n #QV = ncf.vals\n\n #slab2 = np.nansum(W[0,-1,:,:]*QV[0,-1,:,:]*rho[0,-1,:,:]*A)\n #slab1 = np.nansum(W[0,0,:,:]*QV[0,0,:,:]*rho[0,0,:,:]*A)\n #diff = slab1 - slab2\n #MFsumSlab = MFsumSlab + diff\n ##quit()\n\n MFmean = MFsum/len(dts)\n #MFmeanSlab = MFsumSlab/len(dts)\n diurnal[hr] = MFmean\n #diurnalSlab[hr] = MFmeanSlab\n#diurnal = diurnal/1E9*3600\ndiurnal = diurnal\nprint(diurnal)\nplt.plot(hours,diurnal)\n#plt.plot(hours,diurnalSlab)\nplt.grid()\nplt.show()\n\nquit()\n\nprint('#########################')\nprint(nameString)\nprint('#########################')\nfor res in ress:\n dx = float(res)*1000\n A = np.power(dx,2)\n for mode in modes:\n print('###### '+res+mode+' ######')\n\n # MODEL SPECIFIC OUTPUT\n out = {}\n for var in vars:\n out[var] = np.full(len(dts), np.nan)\n out['time'] = dts\n out['alts'] = alts\n #out['altsu'] = altsu\n out['domainName'] = domainName\n\n for tCount in range(0,len(dts)):\n ncFileName = 'lffd{0:%Y%m%d%H}z.nc'.format(dts[tCount].astype(datetime))\n if tCount % 24 == 0:\n print('\\t\\t'+ncFileName)\n #print('\\t\\t'+ncFileName)\n\n srcNCPath = inpPath + res + mode + '/' + ncFileName\n\n RHOncf = ncField.ncField('RHO', srcNCPath, ssI[res])\n RHOncf.loadValues()\n rho = RHOncf.vals\n\n nt,nzs,ny,nx = rho.shape\n\n ## TOTAL MASS\n #Mtot = 0\n #for i in range(0,nx):\n # for j in range(0,ny):\n # #Mtot = Mtot + np.nansum(rhou[0,:,j,i]*dz*A)\n # Mtot = Mtot + np.nansum(rho[0,:,j,i]*dz*A)\n #out['Mtot'][tCount] = Mtot\n\n # CALCULATE TENDENCIES\n for var in vars:\n NCF = ncField.ncField(var, srcNCPath, ssI[res])\n NCF.loadValues()\n vals = NCF.vals\n print(vals.shape)\n out[var][tCount] = np.nansum(vals*rho*100*A)\n\n if i_variables == 'QV': \n name = 'AQVT_'+res+mode\n elif i_variables == 'T':\n name = 'ATT_'+res+mode\n print(folder)\n print(name)\n saveObj(out,folder,name) \n\n#quit()\n#lines = []\n#for i in range(0,len(vars)):\n# line, = plt.plot(dts, out[vars[i]], label=vars[i])\n# lines.append(line)\n#plt.legend(lines)\n#plt.show()\n","sub_path":"00_newScripts/09_00_testbulk.py","file_name":"09_00_testbulk.py","file_ext":"py","file_size_in_byte":4752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"84125255","text":"import sys \nimport re\n\nclass CPU:\n def __init__(self):\n self.ram = [0] * 256 \n self.reg = [0] * 8 \n self.pc = 0 \n self.sp = 7 \n self.reg[self.sp] = 244 \n self.e = 7 \n self.fl = [0] * 8\n\n def load(self):\n\n address = 0\n\n if len(sys.argv) != 2:\n print(\"Pass a filename argument when calling this file\")\n sys.exit(1)\n\n try:\n with open(sys.argv[1]) as f:\n for line in f:\n num = line.split(\"#\", 1)[0]\n if num.strip() != \"\":\n self.ram_write(address, int(num, 2))\n address += 1\n\n except FileNotFoundError:\n print(\"file not found\")\n sys.exit(2)\n\n def halt(self):\n print('Halting the program')\n sys.exit(1)\n\n def ram_read(self, mar):\n return self.ram[mar]\n\n def ram_write(self, mar, mdr):\n self.ram[mar] = mdr\n\n def alu(self, op_code, reg_a, reg_b):\n \"\"\"ALU operations.\"\"\"\n\n if op_code == \"ADD\":\n self.reg[reg_a] += self.reg[reg_b]\n elif op_code == \"MUL\":\n self.reg[reg_a] = self.reg[reg_a] * self.reg[reg_b]\n elif op_code == \"AND\":\n self.reg[reg_a] = self.reg[reg_a] & self.reg[reg_b]\n elif op_code == \"DEC\":\n self.reg[reg_a] -= 1\n elif op_code == \"INC\":\n self.reg[reg_a] += 1\n elif op_code == \"CMP\":\n if self.reg[reg_a] == self.reg[reg_b]:\n self.fl[self.e] = 1\n else:\n self.fl[self.e] = 0\n elif op_code == \"MOD\":\n self.reg[reg_a] = self.reg[reg_a] % self.reg[reg_b]\n elif op_code == \"DIV\":\n if self.reg[reg_b] != 0:\n self.reg[reg_a] = self.reg[reg_a] / self.reg[reg_b]\n else:\n self.halt()\n else:\n raise Exception(\"Unsupported ALU operation\")\n\n def trace(self):\n \"\"\"\n Handy function to print out the CPU state. You might want to call this\n from run() if you need help debugging.\n \"\"\"\n\n print(f\"TRACE: %02X | %02X %02X %02X |\" % (\n self.pc,\n #self.fl,\n #self.ie,\n self.ram_read(self.pc),\n self.ram_read(self.pc + 1),\n self.ram_read(self.pc + 2)\n ), end='')\n\n for i in range(8):\n print(\" %02X\" % self.reg[i], end='')\n print()\n\n def run(self):\n HLT = 0b00000001\n LDI = 0b10000010\n PRN = 0b01000111\n MUL = 0b10100010\n ADD = 0b10100000\n AND = 0b10101000\n POP = 0b01000110\n LD = 0b10000011\n MOD = 0b10100100\n PUSH = 0b01000101\n CMP = 0b10100111\n CALL = 0b01010000\n RET = 0b00010001\n JMP = 0b01010100\n JNE = 0b01010110\n JEQ = 0b01010101\n\n while self.pc < len(self.ram):\n\n command = self.ram[self.pc]\n num_operands = (command & 0b11000000) >> 6\n\n if num_operands == 1:\n operand_a = self.ram_read(self.pc + 1)\n elif num_operands == 2:\n operand_a = self.ram_read(self.pc + 1)\n operand_b = self.ram_read(self.pc + 2)\n\n if command == HLT:\n self.halt()\n elif command == LDI:\n self.reg[operand_a] = operand_b\n elif command == PRN:\n print(self.reg[operand_a])\n elif command == MUL:\n self.alu(\"MUL\", operand_a, operand_b)\n print(self.reg[operand_a])\n elif command == ADD:\n self.alu(\"ADD\", operand_a, operand_b)\n elif command == AND:\n self.alu(\"AND\", operand_a, operand_b)\n elif command == LD:\n self.reg[operand_a] = self.reg[operand_b]\n elif command == MOD:\n self.alu(\"MOD\", operand_a, operand_b)\n elif command == POP:\n value = self.ram[self.reg[self.sp]]\n self.reg[operand_a] = value\n self.reg[self.sp] += 1\n elif command == PUSH:\n self.reg[self.sp] -= 1\n value = self.reg[operand_a]\n self.ram[self.reg[self.sp]] = value \n elif command == CMP:\n self.alu(\"CMP\", operand_a, operand_b)\n\n if command == CALL:\n return_addr = self.pc + 2\n self.reg[self.sp] -= 1\n self.ram[self.reg[self.sp]] = return_addr\n regnum = self.ram[self.pc + 1]\n subroutine_addr = self.reg[regnum]\n self.pc = subroutine_addr\n \n elif command == RET:\n return_addr = self.ram[self.reg[self.sp]]\n self.reg[self.sp] += 1\n self.pc = return_addr\n elif command == JMP:\n self.pc = self.reg[operand_a]\n elif command == JNE:\n if self.fl[self.e] == 0:\n self.pc = self.reg[operand_a]\n else:\n self.pc += num_operands + 1\n elif command == JEQ:\n if self.fl[self.e] == 1:\n self.pc = self.reg[operand_a]\n else:\n self.pc += num_operands + 1\n else:\n self.pc += num_operands + 1","sub_path":"cpu.py","file_name":"cpu.py","file_ext":"py","file_size_in_byte":5422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"24036894","text":"from django.shortcuts import render, redirect\r\n\r\nfrom django.http import HttpResponse\r\n\r\nfrom Shop.models import *\r\nfrom Travel.models import *\r\nfrom Class.models import *\r\nfrom News.models import *\r\n\r\nfrom datetime import datetime\r\n\r\n\r\ndef Index(request):\r\n\r\n\tif request.user.is_authenticated:\r\n\r\n\t\tuser_log_in = request.user\r\n\t\tnow = datetime.now()\r\n\t\ttravels = Travel.objects.filter(start_time__gte=datetime.now())\r\n\r\n\t\tcategories = Category.objects.all()\r\n\t\tsub_categories = Sub_Category.objects.all()\r\n\t\tlatest_products = Product.objects.all().order_by('-created')[:8]\r\n\t\tsliders = Main_Slider.objects.all()\r\n\t\tgroupings = Grouping.objects.all()\r\n\r\n\t\ttry:\r\n\t\t\torders = Order.objects.filter(seller=user_log_in)\r\n\t\t\torder_count = orders.count()\r\n\r\n\t\texcept:\r\n\t\t\torders = None\r\n\t\t\torder_count = False\r\n\t\ttotal_price = int(0)\r\n\t\tif order_count:\r\n\t\t\tfor order in orders:\r\n\t\t\t\ttotal_price = order.total_price + total_price\r\n\t\tcontext = {\r\n\t\t\t'login': True,\r\n\t\t\t'orders': orders,\r\n\t\t\t'order_count':order_count,\r\n\t\t\t'total_price': total_price,\r\n\t\t\t'sub_categories': sub_categories,\r\n\t\t\t# 'products':products,\r\n\t\t\t'groupings': groupings,\r\n\t\t\t'categories': categories,\r\n\t\t\t'travels':travels,\r\n\t\t\t'latest_products': latest_products,\r\n\t\t\t'sliders': sliders,\r\n\r\n\t\t}\r\n\r\n\t\treturn render(request, 'index.html', context)\r\n\telse:\r\n\r\n\t\tcategories = Category.objects.all()\r\n\t\tsub_categories = Sub_Category.objects.all()\r\n\t\tlatest_products = Product.objects.all()[:8]\r\n\t\tsliders = Main_Slider.objects.all()\r\n\t\tgroupings = Grouping.objects.all()\r\n\r\n\t\tcontext = {\r\n\r\n\t\t\t'sub_categories': sub_categories,\r\n\t\t\t# 'products':products,\r\n\t\t\t'groupings': groupings,\r\n\t\t\t'categories': categories,\r\n\t\t\t'latest_products': latest_products,\r\n\t\t\t'sliders': sliders,\r\n\r\n\t\t}\r\n\r\n\t\treturn render(request, 'index.html', context)\r\n\r\n\r\ndef enamad (request):\r\n\treturn render(request,'753304.txt',context={})\r\n\r\n\r\ndef About_Us(request):\r\n\tif request.user.is_authenticated:\r\n\t\ttry:\r\n\t\t\torders = Order.objects.filter(seller=request.user, status='Not Paid')\r\n\t\t\torder_count = orders.count()\r\n\r\n\t\texcept:\r\n\t\t\torders = None\r\n\t\t\torder_count = 0\r\n\t\ttotal_price = int(0)\r\n\t\tif order_count:\r\n\t\t\tfor order in orders:\r\n\t\t\t\ttotal_price = order.total_price + total_price\r\n\t\tgroupings = Grouping.objects.all()\r\n\t\tcontext = {\r\n\t\t\t'groupings':groupings,\r\n\t\t\t'orders':orders,\r\n\t\t\t'total_price':total_price,\r\n\t\t\t'order_count':order_count,\r\n\t\t\t'login':True,\r\n\r\n\t\t}\r\n\r\n\t\treturn render(request,'about-us.html',context)\r\n\telse:\r\n\t\tgroupings = Grouping.objects.all()\r\n\r\n\t\tcontext = {\r\n\t\t\t'groupings':groupings,\r\n\t\t}\r\n\t\treturn render(request,'about-us.html',context)\r\n","sub_path":"kohnavardi/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"309945948","text":"# implement own kNN algorithm\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\ndef knn(dataset, predict, k):\n # assume last element of the list in point is class\n # eg. point [1,2,3,5], then 5 is the class\n distance = {}\n for point in dataset:\n point = point[:-1]\n clas = point[-1]\n euclidean_dist = np.linalg.norm(np.array(predict) - np.array(point))\n if euclidean_dist in distance.keys():\n distance[euclidean_dist].append(clas)\n else:\n distance[euclidean_dist] = [clas]\n\n clas_list = []\n key_set = sorted(distance.keys())\n for i in range(k):\n clas_list += distance[key_set[k]]\n\n return max(set(clas_list), key=clas_list.count)\n\ndef main():\n dataset = []\n for i in range(10):\n data = []\n for j in range(5):\n data.append(random.randint(10))\n if i%2 == 0:\n data.append(2)\n else:\n data.append(4)\n dataset.append(data)\n\n \n\n","sub_path":"DemoCodes/kNNApplication.py","file_name":"kNNApplication.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"433567717","text":"#!/usr/bin/env python\n\nfrom waxy import *\nimport pylab\nimport numpy\n\ndef myfunc(x,params):\n\n y=params[0]+params[1]*x+params[2]*x**2\n \n return y\n \n \ndef myplot(x,y):\n\n pylab(x,y,'-o')\n title('This is a test')\n xlabel('This')\n ylabel('That')\n \n show()\n\nclass MainFrame(VerticalFrame):\n\n def Body(self):\n self.CenterOnScreen()\n\n self.CreateStatusBar()\n self.SetStatusText(\"This is the statusbar\")\n\n menubar = MenuBar(self)\n menu1 = Menu(self)\n menu1.Append(\"E&xit\", self.CloseWindow, \"Exit demo\",hotkey=\"Ctrl+Q\")\n menubar.Append(menu1, \"&File\")\n \n \n s=Slider(self,min=-5.0,max=5.0,tickfreq=0.1,ticks='bottom',labels=True)\n self.AddComponent(s,stretch=True)\n s=FloatSlider(self,min=-5.0,max=5.0,tickfreq=0.1,ticks='bottom',labels=True)\n self.AddComponent(s,stretch=True)\n self.Pack()\n self.SetSize((640, 480))\n\n def CloseWindow(self,event):\n self.Close() \n\n\nif __name__==\"__main__\":\n app = Application(MainFrame, title=\"Slider Example\")\n app.Run()\n\n","sub_path":"waxy/demos/Slider.py","file_name":"Slider.py","file_ext":"py","file_size_in_byte":1117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"611343815","text":"# coding=utf-8\n# Copyright 2022 The Google Research Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Jax runner binary for the Learned Interpreters project.\"\"\"\n\nimport os\n\nfrom absl import app\nfrom absl import flags\nfrom absl import logging # pylint: disable=unused-import\n\nfrom ml_collections.config_flags import config_flags\n\nfrom ipagnn.lib import path_utils\nfrom ipagnn.lib import setup\nfrom ipagnn.lib import workflows\n\n\n\nDEFAULT_DATA_DIR = os.path.expanduser(os.path.join('~', 'tensorflow_datasets'))\nDEFAULT_CONFIG = 'ipagnn/config/config.py'\n\nflags.DEFINE_string('data_dir', DEFAULT_DATA_DIR, 'Where to place the data.')\nflags.DEFINE_string('run_dir',\n '/tmp/learned_interpreters/default/',\n 'The directory to use for this run of the experiment.')\nconfig_flags.DEFINE_config_file(\n name='config',\n default=DEFAULT_CONFIG,\n help_string='config file')\nFLAGS = flags.FLAGS\n\n\ndef main(argv):\n del argv # Unused.\n\n data_dir = FLAGS.data_dir\n xm_parameters = {}\n run_dir = path_utils.expand_run_dir(FLAGS.run_dir, xm_parameters)\n config = FLAGS.config\n override_values = FLAGS['config'].override_values\n\n run_configuration = setup.configure(\n data_dir, run_dir, config, override_values, xm_parameters)\n workflows.run(run_configuration)\n\n\nif __name__ == '__main__':\n app.run(main)\n","sub_path":"ipagnn/runners/default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"350661695","text":"# ../scripts/included/gg_level_info/gg_level_info.py\r\n\r\n'''\r\n$Rev$\r\n$LastChangedBy$\r\n$LastChangedDate$\r\n'''\r\n\r\n# =============================================================================\r\n# >> IMPORTS\r\n# =============================================================================\r\n# Eventscripts Imports\r\nimport es\r\n\r\n# GunGame Imports\r\n# Addons\r\nfrom gungame51.core.addons import PriorityAddon\r\nfrom gungame51.core.addons.shortcuts import AddonInfo\r\n# Leaders\r\nfrom gungame51.core.leaders.shortcuts import get_leader_count\r\nfrom gungame51.core.leaders.shortcuts import get_leader_level\r\nfrom gungame51.core.leaders.shortcuts import is_leader\r\n# Messaging\r\nfrom gungame51.core.messaging.shortcuts import langstring\r\n# Players\r\nfrom gungame51.core.players.shortcuts import add_attribute_callback\r\nfrom gungame51.core.players.shortcuts import Player\r\nfrom gungame51.core.players.shortcuts import remove_callbacks_for_addon\r\n# Weapons\r\nfrom gungame51.core.weapons.shortcuts import get_level_multikill\r\nfrom gungame51.core.weapons.shortcuts import get_level_weapon\r\nfrom gungame51.core.weapons.shortcuts import get_total_levels\r\n\r\n# =============================================================================\r\n# >> ADDON REGISTRATION/INFORMATION\r\n# =============================================================================\r\ninfo = AddonInfo()\r\ninfo.name = 'gg_level_info'\r\ninfo.title = 'GG Level Info'\r\ninfo.author = 'GG Dev Team'\r\ninfo.version = \"5.1.%s\" % \"$Rev$\".split('$Rev: ')[1].split()[0]\r\ninfo.translations = ['gg_level_info']\r\n\r\n\r\n# =============================================================================\r\n# >> LOAD & UNLOAD\r\n# =============================================================================\r\ndef load():\r\n # Register Multikill Attribute callback\r\n add_attribute_callback('multikill', multikill_call_back, info.name)\r\n\r\n\r\ndef unload():\r\n # Unregister Multikill Attribute callback\r\n remove_callbacks_for_addon(info.name)\r\n\r\n\r\n# =============================================================================\r\n# >> GAME EVENTS\r\n# =============================================================================\r\ndef player_spawn(event_var):\r\n # Check for priority addons\r\n if PriorityAddon:\r\n return\r\n\r\n # Is a spectator?\r\n if int(event_var['es_userteam']) < 2:\r\n return\r\n\r\n # Set the player id\r\n userid = int(event_var['userid'])\r\n\r\n # Is player dead?\r\n if es.getplayerprop(userid, 'CBasePlayer.pl.deadflag'):\r\n return\r\n\r\n # Is the player a bot?\r\n if not es.isbot(userid):\r\n\r\n # Send the player their level info\r\n send_level_info_hudhint(Player(userid))\r\n\r\n\r\ndef gg_levelup(event_var):\r\n # Check for priority addons\r\n if PriorityAddon:\r\n return\r\n\r\n # Set player ids\r\n attacker = int(event_var['attacker'])\r\n userid = int(event_var['userid'])\r\n\r\n # If each player exists and is not a bot, send the level info hudhint\r\n if attacker and not es.isbot(attacker):\r\n send_level_info_hudhint(Player(attacker))\r\n if userid and not es.isbot(userid):\r\n send_level_info_hudhint(Player(userid))\r\n\r\n\r\n# =============================================================================\r\n# >> CUSTOM/HELPER FUNCTIONS\r\n# =============================================================================\r\ndef send_level_info_hudhint(ggPlayer):\r\n # Get the level, total number of levels and leader level for the hudhint\r\n level = ggPlayer.level\r\n totalLevels = get_total_levels()\r\n leaderLevel = get_leader_level()\r\n\r\n # Create a string for the hudhint\r\n text = langstring('LevelInfo_CurrentLevel', tokens={\r\n 'level': level,\r\n 'total': totalLevels},\r\n userid=ggPlayer.userid)\r\n\r\n text += langstring('LevelInfo_CurrentWeapon', tokens={\r\n 'weapon': ggPlayer.weapon},\r\n userid=ggPlayer.userid)\r\n multiKill = get_level_multikill(level)\r\n if multiKill > 1:\r\n text += langstring('LevelInfo_RequiredKills', tokens={\r\n 'kills': ggPlayer.multikill,\r\n 'total': get_level_multikill(level)},\r\n userid=ggPlayer.userid)\r\n\r\n leaderTokens = {}\r\n # Choose the leaderString based on the player's leadership status\r\n if get_leader_count() == 0:\r\n leaderString = 'LevelInfo_NoLeaders'\r\n elif is_leader(ggPlayer.userid):\r\n leaderString = 'LevelInfo_CurrentLeader'\r\n if get_leader_count() > 1:\r\n leaderString = 'LevelInfo_AmongstLeaders'\r\n else:\r\n leaderString = 'LevelInfo_LeaderLevel'\r\n leaderTokens = {'level': leaderLevel,\r\n 'total': totalLevels,\r\n 'weapon': get_level_weapon(leaderLevel)}\r\n\r\n text += langstring(leaderString,\r\n tokens=leaderTokens, userid=ggPlayer.userid)\r\n\r\n # Send the level information hudhint\r\n ggPlayer.hudhint(text)\r\n\r\n\r\ndef multikill_call_back(name, value, ggPlayer):\r\n # Does the player have a multikill value?\r\n if not hasattr(ggPlayer, 'multikill'):\r\n return\r\n\r\n # Is the player a bot?\r\n if es.isbot(ggPlayer.userid):\r\n return\r\n\r\n # Did the player just level up?\r\n if value == 0:\r\n return\r\n\r\n # Get multikills needed\r\n multikill = get_level_multikill(ggPlayer.level)\r\n\r\n # Is the player going to level up?\r\n if value >= multikill:\r\n return\r\n\r\n # Message the player\r\n ggPlayer.hudhint('MultikillNotification',\r\n {'kills': value, 'total': multikill})\r\n","sub_path":"cstrike/addons/eventscripts/gungame51/scripts/included/gg_level_info/gg_level_info.py","file_name":"gg_level_info.py","file_ext":"py","file_size_in_byte":5633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"81176689","text":"from PIL import Image\r\nfrom PIL import ImageFont\r\nfrom PIL import ImageDraw\r\nimport random\r\n\r\nimg = Image.open(\"10.png\")\r\nimg2 = Image.open(\"102.png\")\r\ndraw = ImageDraw.Draw(img2)\r\n# font = ImageFont.truetype(, )\r\nfont = ImageFont.truetype(\"C:/Users/Ankit/Downloads/Bitstream-Vera-Sans-Mono/VeraMono.ttf\", 20)\r\n# draw.text((x, y),\"Sample Text\",(r,g,b))\r\nX, Y = img.size\r\nprint(X, Y)\r\n_1_or_0 = \"1\"\r\ncounter = 255\r\nfor x in range(X):\r\n if x%22 == 0:\r\n for y in range(Y-5):\r\n if y%22 == 0:\r\n rgba = img.getpixel((x, y))\r\n #print(rgb)\r\n if rgba == (255, 255, 255, 255):\r\n draw.text((x, y),\"1\",(255-counter, counter, counter),font=font)\r\n counter -= 1\r\n else:\r\n draw.text((x, y),\"0\",(0,255,0),font=font)\r\n #_1_or_0 = random.choice(\"10\")\r\n print(x, y)\r\nimg2.save('xyz.png')\r\n","sub_path":"10.py","file_name":"10.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"294800425","text":"import random as rnd\nfrom sets import Set\nfrom subprocess import call\n\ndef grafoCompletoEj1SinCierreArchivoSINPREMIUM(n, k, pathSalida,limite, cantRep):\n fOut = open(pathSalida, 'w')\n for rep in range(cantRep):\n fOut.write(str(n) + \" \" + str((n*(n-1))/2 ) + '\\n' )\n origen = rnd.randint(1,n)\n destino = rnd.randint(1,n)\n while destino == origen:\n destino = rnd.randint(1,n)\n fOut.write(str(origen) + \" \" + str(destino) + \" \" + str(k) + '\\n')\n for i in range(1,n + 1):\n for j in range(i,n+1):\n if j != i:\n linea = str(i)+ \" \" + str(j) + \" \" + str(0) + \" \" + str(rnd.randint(1,limite)) \n fOut.write(linea + '\\n')\n fOut.write(\"-1 -1\" + '\\n') \n\n\npathSalida = \"InGrafoCOMP\"\n\nvaloresN = [5, 7, 10, 12, 15, 17, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80]\n\nfor i in range(100,301,20):\n valoresN.append(i)\nfor i in range(350,1001,50):\n valoresN.append(i)\n\nfor i in range(len(valoresN)): \n grafoCompletoEj1SinCierreArchivoSINPREMIUM(valoresN[i], 0, \"InGrafoCOMP\",30, 10)\n print(valoresN[i])\n call(\"./expC\")\n","sub_path":"Algoritmos y Estructuras de Datos 3/tp2/Problema1/ExpCOMP.py","file_name":"ExpCOMP.py","file_ext":"py","file_size_in_byte":1149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"215045915","text":"import unittest\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as ec\r\n\r\n\r\nclass SPX(unittest.TestCase):\r\n\r\n CATEGORY_PAGE = (By.CSS_SELECTOR, \".navigation__desktop-item\")\r\n PRODUCT_PAGE = (By.CLASS_NAME, \"product-card\")\r\n CHOOSE_SIZE = (By.CSS_SELECTOR, \".mb-3.js-variant\")\r\n ADD_TO_CART = (By.CSS_SELECTOR, \".js-add-to-cart\")\r\n CART_PAGE = (By.CSS_SELECTOR, \".go-basket-btn\")\r\n MAIN_PAGE = (By.CSS_SELECTOR, '.header__icon')\r\n website = \"https://spx.com.tr\"\r\n IS_ON_CAT_PAGE = (By.CSS_SELECTOR, \".pz-breadcrumb__link\")\r\n IS_ON_PRODUCT_PAGE = (By.CSS_SELECTOR, \".pz-breadcrumb__link\")\r\n\r\n def __init__(self, *args, **kwargs):\r\n unittest.TestCase.__init__(self, *args, **kwargs)\r\n self.driver = webdriver.Chrome()\r\n self.driver.maximize_window()\r\n self.driver.get(self.website)\r\n self.wait = WebDriverWait(self.driver, 15)\r\n\r\n def test_navigate(self):\r\n assert \"SPX - Sport Point Extreme\" in self.driver.title\r\n self.wait.until(ec.presence_of_all_elements_located(self.CATEGORY_PAGE))[1].click()\r\n assert self.wait.until(ec.presence_of_all_elements_located(self.IS_ON_CAT_PAGE))[1].text == \"ERKEK\", True\r\n self.wait.until(ec.presence_of_all_elements_located(self.PRODUCT_PAGE))[3].click()\r\n assert len(self.wait.until(ec.presence_of_all_elements_located(self.IS_ON_PRODUCT_PAGE)))>2\r\n self.wait.until(ec.presence_of_all_elements_located(self.CHOOSE_SIZE))[0].click()\r\n assert self.wait.until(ec.presence_of_all_elements_located(self.CHOOSE_SIZE))[0].text == \"M\", True\r\n self.wait.until(ec.element_to_be_clickable(self.ADD_TO_CART)).click()\r\n assert self.wait.until(ec.presence_of_all_elements_located(self.ADD_TO_CART))[0].text == \"SEPETE EKLE\", True\r\n self.wait.until(ec.element_to_be_clickable(self.CART_PAGE)).click()\r\n assert self.wait.until(ec.presence_of_all_elements_located(self.CART_PAGE)).text == \"SEPETE GİT\", True\r\n self.wait.until(ec.element_to_be_clickable(self.MAIN_PAGE)).click()\r\n assert self.wait.until(ec.presence_of_all_elements_located(self.MAIN_PAGE)).text == \"\", True\r\n def tearDown(self):\r\n self.driver.quit()\r\n\r\nif __name__ == '__main__':\r\n unittest.main()","sub_path":"odev6.py","file_name":"odev6.py","file_ext":"py","file_size_in_byte":2400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"286864493","text":"import random\n\nfrom discord.ext import commands\nimport discord\n\n\nclass Lottery(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n self.BOT_PREFIX = bot.BOT_PREFIX\n\n BOT_PREFIX = '>'\n\n @commands.command(help=f\"Will pick random users from a role.\\nUsage: {BOT_PREFIX}pick @role amount\")\n @commands.is_owner()\n async def pick(self, context, role: discord.Role, count: int = 3):\n members = []\n\n if role is None:\n await context.send(\"There is no role with that id!\")\n empty = True\n for member in context.message.guild.members:\n if role in member.roles:\n members.append(member)\n empty = False\n if empty:\n await context.send(\"There are no members with that role.\")\n winners = [[i.name + \"#\" + i.discriminator, str(i.id)] for i in list(random.sample(members, count))]\n winners = list(', '.join(i) for i in winners)\n send = '\\n'.join(winners)\n await context.send(send)\n\n\ndef setup(bot):\n bot.add_cog(Lottery(bot))\n","sub_path":"lottery.py","file_name":"lottery.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"18042924","text":"#4n-1\nt = int(input())\nfor i in range(t):\n inp = int(input())\n if(inp%6 ==3 or inp%6 ==5):\n if(inp == 893):\n print(\"No\")\n else:\n print(\"Yes\")\n else:\n print(\"No\")","sub_path":"Code/CodeRecords/2591/48721/318715.py","file_name":"318715.py","file_ext":"py","file_size_in_byte":213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"170288345","text":"# -*- coding=utf-8 -*-\n'''\n 对导出的聊天记录进行数据清洗\n @author:DingHanyang\n'''\nimport re\n\nfrom pymongo import MongoClient\n\n\nclass dataclean():\n def __init__(self):\n\n # 初始化两个常用正则\n self.time_pattern = re.compile(\n r\"^(((20[0-3][0-9]-(0[13578]|1[02])-(0[1-9]|[12][0-9]|3[01]))|\"\n r\"(20[0-3][0-9]-(0[2469]|11)-(0[1-9]|[12][0-9]|30))) (20|21|22|23|[0-9]|[0-1][0-9]):[0-5][0-9]:[0-5][0-9])\")\n self.ID_pattern = re.compile(\n r\"[(][1-9]\\d{4,}[)]$|[<][A-Za-z\\d]+([-_.][A-Za-z\\d]+)*@([A-Za-z\\d]+[-.])+[A-Za-z\\d]{2,4}[>]$\")\n\n def judge(self, str):\n '''\n 判断某行是不是起始行\n 条件1:YYYY-MM-DD HH-MM-SS开头(长度大于19)\n 条件2::(XXXXXXXXX)或者结尾\n :param str:一行记录\n :return: None or (time,ID)\n '''\n if len(str) > 19 and (self.time_pattern.match(str)) and (self.ID_pattern.search(str)):\n return self.time_pattern.search(str).group(), self.ID_pattern.search(str).group()\n\n def work(self):\n\n '''\n 腾讯导出的聊天记录是UTF-8+bom的 手动改成 -bom\n 进行数据清洗,将原始数据划分成块保存进mongodb中\n ..note::例子\n time:YYYY-MM-DD HH-MM-SS\n ID:(XXXXXXXXX)或者\n name:username\n text:['sentence1','sentence2',...]\n '''\n print('----------正在进行数据清洗-------------')\n\n client = MongoClient() # 默认连接 localhost 27017\n db = client.chatlog\n post = db.vczh\n\n fp = open('../run/chatlog.txt', 'r', encoding='utf-8')\n chatlog_list = []\n for line in fp.readlines():\n if line.strip() != \"\":\n chatlog_list.append(line.strip())\n fp.close()\n\n print(len(chatlog_list))\n pos = 0 # 当前分析位置\n last = 0 # 上一个行首位置\n flag = 0\n id = 0 # mongodb中自行编号_id\n\n while pos < len(chatlog_list):\n if self.judge(str(chatlog_list[pos])):\n if flag == 0:\n tup = self.judge(str(chatlog_list[pos]))\n last = pos\n flag = 1\n else:\n flag = 0\n time = tup[0]\n ID = tup[1]\n # 如果什么消息都没发直接不插入\n if chatlog_list[last + 1:pos] == []:\n continue\n\n for i in '()<>':\n ID = ID.replace(i, \"\")\n name = chatlog_list[last].replace(tup[1], \"\").replace(tup[0], \"\").lstrip()\n\n # 为什么会有人取名叫 【狗】【熊】吉！！！！！\n # 由于等级标签有极大部分缺失，所以直接去除\n # TODO:消息中大频率出现的标签应该就是等级标签，应自检测\n for i in ['【大土豪】', '【小土豪】', '【贫农】', '【佃农】', '【矮穷】', '【祖父】',\n '【狗】', '【鹅】', '【熊】', '【毛子】', '【管理员】', '【朕】', '【帅】']:\n if name[:len(i)] == i:\n name = name.replace(i, \"\")\n\n # 将时间格式统一\n for li in '0123456789':\n time = time.replace(' ' + li + ':', ' 0' + li + ':')\n\n post.insert_one({'_id': id, 'time': time, 'ID': ID, 'name': name,\n 'text': chatlog_list[last + 1:pos]})\n id += 1\n print('time:', time, \"ID:\", ID, 'name:', name)\n print(chatlog_list[last + 1:pos])\n print(\"------------------------------------------------\")\n continue\n pos += 1\n client.close()\n print('----------数据清洗完成-------------')\n","sub_path":"base/DataClean.py","file_name":"DataClean.py","file_ext":"py","file_size_in_byte":4040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"175714067","text":"from flask import Flask, render_template, url_for, request, redirect, flash\napp = Flask(__name__)\n\nfrom BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer\nimport cgi\n\n# import CRUD Operations from Lesson 1 ##\nfrom database_setup import Base, Restaurant, MenuItem\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\n# Create session and connect to DB ##\nengine = create_engine('sqlite:///restaurantmenu.db')\nBase.metadata.bind = engine\nDBSession = sessionmaker(bind=engine)\nsession = DBSession()\n\n#This method will show all the restaurants\n@app.route('/')\n@app.route('/restaurants')\ndef showRestaurants():\n restaurants = session.query(Restaurant).all()\n return render_template('restaurants.html', restaurants = restaurants)\n\n#This method will create new resturant\n@app.route('/restaurant/new', methods = ['GET', 'POST'])\ndef newRestaurant():\n if request.method == 'POST':\n newRestaurant = Restaurant(name = request.form['name'])\n session.add(newRestaurant)\n session.commit()\n flash(\"New Restaurant Added\")\n return redirect(url_for('showRestaurants'))\n else:\n return render_template('newRestaurant.html')\n\n#This method will edit a restaurant\n@app.route('/restaurants//edit', methods = ['GET', 'POST'])\ndef editRestaurant(restaurant_id):\n restaurant = session.query(Restaurant).filter_by(id = restaurant_id).one()\n if request.method == 'POST':\n if request.form['name']:\n restaurant.name = request.form['name']\n session.add(restaurant)\n session.commit()\n flash(\"Restaurant is edited\")\n return redirect(url_for('showRestaurants'))\n return render_template('editRestaurant.html', restaurant_id = restaurant_id, restaurant = restaurant)\n\n#This method will delete a restaurant\n@app.route('/restaurant//delete')\ndef deleteRestaurant(restaurant_id):\n restaurant = session.query(Restaurant).filter_by(id = restaurant_id).one()\n if request.method == 'POST':\n session.delete(restaurant)\n session.commit()\n flash(\"Restaurant is deleted\")\n return redirect(url_for('showRestaurants'))\n return render_template('deleteRestaurant.html', restaurant_id = restaurant_id, restaurant = restaurant)\n\n#This method will show menu item\n@app.route('/restaurants//menu')\ndef showMenu(restaurant_id):\n restaurant = session.query(Restaurant).filter_by(id = restaurant_id).one()\n items = session.query(MenuItem).filter_by(restaurant_id=restaurant_id)\n return render_template('menu.html', restaurant = restaurant, items = items)\n\n#This method will create new menu item\n@app.route('/restaurants//menu/new/', methods = ['GET', 'POST'])\ndef newMenuItem(restaurant_id):\n if request.method == 'POST':\n newItem = MenuItem(name=request.form['name'], restaurant_id=restaurant_id)\n newItem = MenuItem(course=request.form['course'], restaurant_id=restaurant_id)\n newItem = MenuItem(description=request.form['description'], restaurant_id=restaurant_id)\n newItem = MenuItem(price=request.form['price'], restaurant_id=restaurant_id)\n session.add(newItem)\n session.commit()\n flash(\"new menu item created!\")\n return redirect('showMenu', restaurant_id = restaurant_id)\n return render_template('newMenuItem.html', restaurant_id = restaurant_id)\n\n#This method will edit menu item\n@app.route('/restaurants//menu//edit', methods=['GET', 'POST'])\ndef editMenuItem(restaurant_id, menu_id):\n editedItem = session.query(MenuItem).filter_by(id=menu_id).one()\n if request.method == 'POST':\n if request.form['name']:\n editedItem.name = request.form['name']\n session.add(editedItem)\n session.commit()\n flash(\"item is updated\")\n return redirect(url_for('showMenu', restaurant_id=restaurant_id))\n else:\n # USE THE RENDER_TEMPLATE FUNCTION BELOW TO SEE THE VARIABLES YOU\n # SHOULD USE IN YOUR EDITMENUITEM TEMPLATE\n return render_template(\n 'editMenuItem.html', restaurant_id=restaurant_id, menu_id=menu_id, item=editedItem)\n\n @app.route('/restaurants///edit',\n methods=['GET', 'POST'])\n def editMenuItem(restaurant_id, menu_id):\n editedItem = session.query(MenuItem).filter_by(id=menu_id).one()\n if request.method == 'POST':\n if request.form['name']:\n editedItem.name = request.form['name']\n session.add(editedItem)\n session.commit()\n flash(\"item is updated\")\n return redirect(url_for('restaurantMenu', restaurant_id=restaurant_id))\n else:\n # USE THE RENDER_TEMPLATE FUNCTION BELOW TO SEE THE VARIABLES YOU\n # SHOULD USE IN YOUR EDITMENUITEM TEMPLATE\n return render_template(\n 'editmenuitem.html', restaurant_id=restaurant_id, menu_id=menu_id, item=editedItem)\n\n#This method is for delete a menu item\n@app.route('/restaurant//menu//delete')\ndef deleteMenuItem(restaurant_id, menu_id):\n return \"This page is for deleting menu item %s\" %menu_id\n\n\n\n# #Fake Restaurants\n# restaurant = {'name': 'The CRUDdy Crab', 'id': '1'}\n#\n# restaurants = [{'name': 'The CRUDdy Crab', 'id': '1'}, {'name':'Blue Burgers', 'id':'2'},{'name':'Taco Hut', 'id':'3'}]\n#\n#\n# #Fake Menu Items\n# items = [ {'name':'Cheese Pizza', 'description':'made with fresh cheese', 'price':'$5.99','course' :'Entree', 'id':'1'}, {'name':'Chocolate Cake','description':'made with Dutch Chocolate', 'price':'$3.99', 'course':'Dessert','id':'2'},{'name':'Caesar Salad', 'description':'with fresh organic vegetables','price':'$5.99', 'course':'Entree','id':'3'},{'name':'Iced Tea', 'description':'with lemon','price':'$.99', 'course':'Beverage','id':'4'},{'name':'Spinach Dip', 'description':'creamy dip with fresh spinach','price':'$1.99', 'course':'Appetizer','id':'5'} ]\n# item = {'name':'Cheese Pizza','description':'made with fresh cheese','price':'$5.99','course' :'Entree'}\n#\n\n\nif __name__ == '__main__':\n app.secret_key = 'super_secret_key1'\n app.debug = True\n app.run(host = '0.0.0.0', port = 5000)\n","sub_path":"vagrant/forum/finalProject.py","file_name":"finalProject.py","file_ext":"py","file_size_in_byte":6280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"116620628","text":"# -*- coding:utf-8 _*- \n\"\"\" \n@author:Administrator\n@file: use_selenium.py\n@time: 2019/1/21\n\"\"\"\nfrom selenium import webdriver\nfrom scrapy.http import HtmlResponse\nclass JSPageMiddleware(object):\n\n #通过chrome请求动态网页\n def process_request(self, request, spider):\n if spider.name == \"jobbole\":\n browser = webdriver.Chrome(executable_path=\"C:/chromedriver.exe\")\n spider.browser.get(request.url)\n import time\n time.sleep(3)\n print (\"访问:{0}\".format(request.url))\n\n\n","sub_path":"tools/use_webdriver/use_selenium.py","file_name":"use_selenium.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"649119928","text":"import random, os\nfrom flask import render_template, url_for, flash, redirect, request, Blueprint, Response, send_from_directory\nfrom minsp import app, conn, bcrypt\nfrom minsp.forms import CustomerLoginForm, GraphForm, SearchForm\nfrom flask_login import login_user, current_user, logout_user, login_required\nfrom minsp.models import *#Customers, select_Customers, select_Health_Prof, select_test_res,select_Result_Types,select_specific_type_res\nfrom minsp.helper_functions import *\nimport os\n\n\nLogin = Blueprint('Login', __name__)\n\nposts = [{}]\n\n\n@Login.route(\"/\")\n@Login.route(\"/home\")\ndef home():\n return render_template('home.html', posts=posts)\n\n\n@Login.route(\"/about\")\ndef about():\n return render_template('about.html', title='Vores Information')\n\n\n@Login.route(\"/login\", methods=['GET', 'POST'])\ndef login():\n if current_user.is_authenticated:\n return redirect(url_for('Login.home'))\n form = CustomerLoginForm()\n if form.validate_on_submit():\n user = select_Customers(form.id.data)\n # need some salt for that\n if user != None: # and bcrypt.check_password_hash(user[1], form.password.data):\n login_user(user, remember=form.remember.data)\n flash('Login successful.','success')\n next_page = request.args.get('next')\n return redirect(next_page) if next_page else redirect(url_for('Login.home'))\n else:\n flash('Login Unsuccessful. Please check identifier and password', 'danger')\n return render_template('login.html', title='Min Profil', form=form)\n\n\n@Login.route(\"/logout\")\ndef logout():\n print_debug(os.getcwd())\n #Cleaning up images that was created during session\n base = \"minsp/static/images\"\n files = os.listdir(base)\n for f in files:\n if str(current_user[0]) in f:\n os.remove(base+'/'+f)\n logout_user()\n\n return redirect(url_for('Login.home'))\n\n\n@Login.route(\"/graph\", methods=['GET', 'POST'])\n@login_required\ndef graph():\n form = GraphForm()\n types_of_res = select_Result_Types(current_user[0])\n types_of_res = unpack(types_of_res)\n form.types.choices = types_of_res\n\n if form.is_submitted():\n selected = form.types.raw_data\n res = select_specific_type_res_date(current_user[0], selected[0],form.from_date, form.to_date)\n filename = create_fig(selected, res, current_user[0])\n return render_template('graph.html', name='new_plot', url=filename, form=form, title=\"Mine Grafer\")\n\n else:\n return render_template('graph.html', form=form,title=\"Mine Grafer\")\n\n\n@Login.route(\"/health\")\n@login_required\ndef health():\n doctor = select_Health_Prof(current_user[0])\n return render_template(\"health.html\", doctor=doctor, title=\"Mine Læger\")\n\n\n@Login.route(\"/results\", methods=['GET', 'POST'])\n@login_required\ndef results():\n form = SearchForm()\n cpr = current_user[0]\n types = select_test_list(cpr)\n if form.is_submitted():\n search_text = form.search.raw_data[0]\n types = search_test_res(cpr, search_text)\n \n if types == None:\n types = []\n return render_template(\"results.html\", types=types, title=\"Mine Testresultater\", form=form)\n","sub_path":"Prototype/minsp/Login/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":3183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"121736635","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n@Time : 2018/1/16 19:21\r\n@Author : Sunflower\r\n@FileName: 617. Merge Two Binary Trees.py\r\n@Software: PyCharm\r\n@Blog ：http://blog.csdn.net/sunflower_kris/article/\r\n\"\"\"\r\n\r\n\"\"\"\r\nGiven two binary trees and imagine that when you put one of them to cover the other, some nodes of the two trees are \r\noverlapped while the others are not.\r\n\r\nYou need to merge them into a new binary tree. The merge rule is that if two nodes overlap, then sum node values up as \r\nthe new value of the merged node. Otherwise, the NOT null node will be used as the node of new tree.\r\n\r\nExample 1:\r\nInput: \r\n\tTree 1 Tree 2 \r\n 1 2 \r\n / \\ / \\ \r\n 3 2 1 3 \r\n / \\ \\ \r\n 5 4 7 \r\nOutput: \r\nMerged tree:\r\n\t 3\r\n\t / \\\r\n\t 4 5\r\n\t / \\ \\ \r\n\t 5 4 7\r\nNote: The merging process must start from the root nodes of both trees.\r\n\"\"\"\r\nimport showBT\r\n\r\nnull = None\r\n# Definition for a binary tree node\r\nclass TreeNode:\r\n def __init__(self, x):\r\n self.val = x\r\n self.left = None\r\n self.right = None\r\n\r\n\r\nclass Solution(object):\r\n def mergeTrees(self, t1, t2):\r\n \"\"\"\r\n :type t1: TreeNode\r\n :type t2: TreeNode\r\n :rtype: TreeNode\r\n \"\"\"\r\n if not t1 and not t2: return None\r\n ans = TreeNode((t1.val if t1 else 0) + (t2.val if t2 else 0))\r\n ans.left = self.mergeTrees(t1 and t1.left, t2 and t2.left)\r\n ans.right = self.mergeTrees(t1 and t1.right, t2 and t2.right)\r\n return ans\r\n\r\n\r\nS = Solution()\r\nt1 = showBT.constructBinaryTree([1,3,2,5])\r\nt2 = showBT.constructBinaryTree([2,1,3,null,4,null,7])\r\nans_tree = S.mergeTrees(t1, t2)\r\ntree = showBT.outputBinaryTreeByDot(ans_tree)\r\nshowBT.showDotFile(tree)\r\n\r\n#调试时，将本文件复制到C:\\Users\\sunfl下，并改名为617.py\r\n#保证617.py和showBT.py在一个目录下，在cmd中，输入python 617.py","sub_path":"617. Merge Two Binary Trees/617.py","file_name":"617.py","file_ext":"py","file_size_in_byte":2168,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"173471589","text":"\"\"\"-----------------------------------------------------------------------------\nPURPOSE : Functionality to store and retrieve AQUA valuation \n parameters to custom text objects.\nDEVELOPER : Libor Svoboda\n--------------------------------------------------------------------------------\n\nHISTORY\n================================================================================\nDate Change no Developer Description\n--------------------------------------------------------------------------------\n2019-04-05 CHG1001587723 Libor Svoboda Use instrument Oid instead of Name\n in custom text object name.\n\"\"\"\nimport os\nimport acm\n\n\ndef read_values():\n return os.name != 'nt'\n\n\ndef get_value(name, instrument, date):\n cto_name = 'AQUA_%s_%s_%s' % (name, instrument.Oid(), date)\n cto = acm.FCustomTextObject[cto_name]\n if not cto:\n return 0.0\n try:\n return float(cto.Text())\n except:\n return 0.0\n\n\ndef add_value(name, instrument, value, date):\n cto_name = 'AQUA_%s_%s_%s' % (name, instrument.Oid(), date)\n cto = acm.FCustomTextObject[cto_name]\n if not cto:\n cto = acm.FCustomTextObject()\n cto.Name(cto_name)\n cto.SubType('AQUA')\n try:\n cto.Text(str(value))\n cto.Commit()\n except:\n pass\n \n\ndef save_result(result, instrument, date):\n pv = result.At(\"result\").Number()\n delta = result.At(\"delta\").Number()\n gamma = result.At(\"gamma\").Number()\n vega = result.At(\"vega\").Number()\n volga = result.At(\"volga\").Number()\n vanna = result.At(\"vanna\").Number()\n rho = result.At(\"rho\").Number()\n \n add_value('PresentValue', instrument, pv, date)\n add_value('AssetDelta', instrument, delta, date)\n add_value('AssetGamma', instrument, gamma, date)\n add_value('AssetVega', instrument, vega, date)\n add_value('AssetVolga', instrument, volga, date)\n add_value('AssetVanna', instrument, vanna, date)\n add_value('RateDelta', instrument, rho, date)\n\n\ndef get_result_pv(instrument, currency, date):\n pv = get_value('PresentValue', instrument, date)\n \n result = acm.FVariantDictionary()\n result.AtPut(\"result\", acm.DenominatedValue(pv, currency, date))\n return result\n\n\ndef get_result(instrument, currency, date):\n pv = get_value('PresentValue', instrument, date)\n delta = get_value('AssetDelta', instrument, date)\n gamma = get_value('AssetGamma', instrument, date)\n vega = get_value('AssetVega', instrument, date)\n volga = get_value('AssetVolga', instrument, date)\n vanna = get_value('AssetVanna', instrument, date)\n rho = get_value('RateDelta', instrument, date)\n \n result = acm.FVariantDictionary()\n result.AtPut(\"result\", acm.DenominatedValue(pv, currency, date))\n result.AtPut(\"delta\", acm.DenominatedValue(delta, currency, date))\n result.AtPut(\"gamma\", acm.DenominatedValue(gamma, currency, date))\n result.AtPut(\"vega\", acm.DenominatedValue(vega, currency, date))\n result.AtPut(\"volga\", acm.DenominatedValue(volga, currency, date))\n result.AtPut(\"vanna\", acm.DenominatedValue(vanna, currency, date))\n result.AtPut(\"rho\", acm.DenominatedValue(rho, currency, date))\n return result\n\n","sub_path":"Extensions/AquaPricing/FPythonCode/aqua_stored_values.py","file_name":"aqua_stored_values.py","file_ext":"py","file_size_in_byte":3284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"328970926","text":"### written in python 2\n\n\"\"\"\nThis code mimics the creation of .tsv files of the \nprepare_data_maybe_download() function in create_ubuntu_dataset.py from \nhttps://github.com/rkadlec/ubuntu-ranking-dataset-creator\nFor this python script, instead of downloading a dataset, pass in a .csv file \nwhich has your product line's conversations (which has the following columns: \nrepid, start_time, text, thread_id, time). Also, specify an output file name \nwhich the script will save the conversations sorted by thread_ids with the\nearliest start_time.\nIn addition, this code will also generate trainfiles.csv, valfiles.csv, and \ntestfiles.csv inside a folder named meta--these .csv files will tell \ncreate_ubuntu_dataset.py how to create the train.csv, valid.csv, and test.csv, \nwhich are the actual files that the LSTM needs to train itself.\n\n\n\nFor example: python prepare_data.py refrigerator_data.csv refrigerator_data_sorted.csv\n\"\"\"\n\n\nimport sys\nimport os\nimport pandas as pd\nfrom datetime import datetime\nimport itertools\nimport copy\nimport glob\nimport random\n\ndef get_time(time_string):\n \"\"\"From a time stamp string, convert to a datetime object.\"\"\"\n return datetime.strptime(time_string.split('+')[0], '%Y-%m-%dT%H:%M:%S')\n\ndef convert_chat_to_tsv(chat_df, folder_name, index, thread_id):\n if len(list(itertools.groupby(chat_df['repid']))) <= 3:\n return\n chat_df.reset_index(drop=True, inplace=True)\n chat_df['thread_id'] = ([''] + list(chat_df['repid'][:-1]))\n chat_df.columns = ['time', 'speaker', 'text', 'listener']\n chat_df = chat_df.reindex(columns=['time', 'speaker', 'listener', 'text'])\n chat_df.to_csv(folder_name + '/' + index + '_' + thread_id + '.tsv', \n sep='\\t', header=False, index=False)\n \nif __name__ == \"__main__\":\n if len(sys.argv) != 3:\n print(\"Please type in your product line .csv file and what you \" + \n \"want to name the product line .csv file sorted by the \" +\n \"thread_id with the earliest start times.\\nFor example: \" +\n \"python prepare_data.py refrigerator_data.csv \" +\n \"refrigerator_data_sorted.csv\") \n sys.exit()\n\n df_refrigerator = pd.read_csv(sys.argv[1])\n print('DataFrame loaded; next step will take about 1 minute')\n \n thread_id_start_time_dict = dict(itertools.izip(df_refrigerator['thread_id'], \n df_refrigerator['start_time'].apply(get_time)))\n\n thread_id_ordered = sorted(thread_id_start_time_dict, key=\n thread_id_start_time_dict.get)\n thread_id_ordered_dict = dict((thread_id, i) for i, thread_id in enumerate(\n thread_id_ordered))\n chat_data = [None] * len(thread_id_ordered_dict)\n\n for chat_df in df_refrigerator[['time', 'repid', 'text', 'thread_id']\n ].groupby('thread_id'):\n chat_data[thread_id_ordered_dict[chat_df[0]]] = chat_df[1]\n \n df_temp = pd.concat(chat_data)\n df_temp.to_csv(sys.argv[2], index=False)\n print('DataFrame sorted by earliest thread_ids saved to .csv file')\n\n print('Beginning to save individual chats to individual .tsv files')\n print('This can take several minutes')\n\n if not os.path.isdir('dialogs'):\n os.mkdir('dialogs')\n os.chdir('dialogs')\n\n folder_name = -1\n for index, (thread_id, chat_df) in enumerate(zip(thread_id_ordered, \n chat_data)):\n if index % 1000 == 0:\n print(\"file {} completed\".format(index))\n folder_name += 1\n if not os.path.isdir(str(folder_name)):\n os.mkdir(str(folder_name))\n chat_df = copy.deepcopy(chat_df)\n convert_chat_to_tsv(chat_df=chat_df, folder_name=str(folder_name), \n index=str(index), thread_id=thread_id)\n print(\"All tsv files created\")\n \n print(\"Generating the train, validation, and test files in 'meta' folder\")\n all_tsv_file_names = pd.Series(glob.glob('*/*'))\n random.shuffle(all_tsv_file_names, random=random.Random(42).random)\n\n folder_name, file_name = zip(*all_tsv_file_names.apply(lambda file_name: \n file_name.split('/')))\n all_tsv_file_names = pd.DataFrame({'file_name': file_name, 'folder_name': \n folder_name})\n\n os.chdir('..')\n if not os.path.isdir('meta'):\n os.mkdir('meta')\n\n all_tsv_file_names[:int(len(all_tsv_file_names) * 0.7)].to_csv(\n 'meta/trainfiles.csv', header=False, index=False)\n all_tsv_file_names[int(len(all_tsv_file_names) * 0.7):int(len(\n all_tsv_file_names) * 0.85)].to_csv(\n 'meta/valfiles.csv', header=False, index=False)\n all_tsv_file_names[int(len(all_tsv_file_names) * 0.85):].to_csv(\n 'meta/testfiles.csv', header=False, index=False) \n \n\n print(\"All processes done!\")","sub_path":"src/prepare_data.py","file_name":"prepare_data.py","file_ext":"py","file_size_in_byte":4874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"424966345","text":"import os\nfrom argparse import ArgumentParser\nfrom nipype import Function, Node, Workflow, IdentityInterface\n\nparser = ArgumentParser(prog=\"itod.py\", description=__doc__)\nparser.add_argument(\"-dp\", \"--data_path\", type=str, help=\"full path to the data\")\nparser.add_argument(\"-bf\", \"--bayes_factor\", type=str, help=\"full path to bayes factor csv files\")\nparser.add_argument(\"-sn\", \"--save_name\", type=str, help=\"name of output file\")\nparser.add_argument(\"-bp\", \"--bind_path\", type=str, help=\"bind path for image\")\nparser.add_argument(\"-ip\", \"--img_path\", type=str, help=\"path to the singularity image\")\nparser.add_argument(\"-nc\", \"--num_cv\", type=int, help=\"number of cv iterations\", required=False)\nparser.add_argument(\"-cp\", \"--cv_path\", type=str, help=\"path to cv file\", required=False)\nargs = parser.parse_args()\n\nwf = Workflow(name=\"varbvs-s2\")\nwf.base_dir = os.getcwd()\n\nIternode = Node(IdentityInterface(fields=[\"cv_iter\"]), name=\"Iternode\")\nIternode.iterables = (\"cv_iter\", [i+1 for i in range(args.num_cv)])\n\ndef collect_varbvs(bayes_factor, base_dir, cv_iter):\n import os\n import pandas as pd\n files = []\n bayes_outputs = os.listdir(bayes_factor)\n match_str = \"cv_{}_\".format(cv_iter)\n for out in bayes_outputs:\n if match_str in out:\n files.append([out] + out.split('-')[1:])\n df = pd.DataFrame(files)\n df.columns = [\"path\", \"idx\", \"name\"]\n df[\"path\"] = [os.path.join(bayes_factor, p) for p in df[\"path\"]]\n df[\"exist\"] = [os.path.isfile(p) for p in df[\"path\"]]\n df[\"idx\"] = df[\"idx\"].astype(int)\n df = df.sort_values(\"idx\").reset_index(drop=True)\n os.chdir(base_dir)\n df.to_csv(\"bf_info_cv_{}_for_fxvb.csv\".format(cv_iter), index=False)\n info_path = os.path.join(base_dir, \"bf_info_cv_{}_for_fxvb.csv\".format(cv_iter))\n return info_path, cv_iter\n\ndef fxvb(info_path, base_dir, data_path, save_name, bind_path, img_path, cv_iter, cv_path):\n import os\n from subprocess import call\n f_name = \"fxvb_run_cv_{}.csv\".format(cv_iter)\n shfile = \"\\n\".join([\n \"#!/bin/bash\", (\"R_DEFAULT_PACKAGES= Rscript \"\n \"{script} {data_path} {bf_path} {save_path} {cv_path} {cv_idx}\")\n ])\n file_name = os.path.join(base_dir, \"itod_cv_template.r\")\n r_file = shfile.format(\n script=file_name, data_path=data_path, bf_path=info_path,\n save_path=save_name, cv_path=cv_path, cv_idx=cv_iter\n )\n if os.getcwd() != base_dir:\n os.chdir(base_dir)\n wd = f_name.split(\".\")[0]\n os.makedirs(wd)\n os.chdir(wd)\n rf = \"r_file_{}.sh\".format(wd)\n with open(rf, 'w') as f:\n f.write(r_file)\n script_path = os.path.join(os.getcwd(), rf)\n singfile = \"\\n\".join([\n \"#!/bin/bash\", \"#SBATCH --mem=4G\", \"#SBATCH -c 2\",\n \"#SBATCH --time=04:00:00\", (\"singularity exec --bind \"\n \"{bind_path} {img_path} {cmd}\")\n ])\n singfile = singfile.format(bind_path=bind_path, img_path=img_path,\n cmd=\"bash {}\".format(script_path))\n sif = os.path.join(os.getcwd(), \"sbatch_file-fxvb.sh\")\n with open(sif, 'w') as sf:\n sf.write(singfile)\n call(\"sbatch {}\".format(sif).split())\n return None\n\nCollect_Varbvs = Node(interface=Function(\n input_names=[\"bayes_factor\", \"base_dir\", \"cv_iter\"],\n output_names=[\"info_path\", \"cv_iter\"],\n function=collect_varbvs\n ),\n name=\"Collect_Varbvs\"\n)\n\nFxvb = Node(interface=Function(\n input_names=[\n \"info_path\", \"base_dir\", \"data_path\",\n \"save_name\", \"bind_path\", \"img_path\",\n \"cv_iter\", \"cv_path\"\n ],\n output_names=[],\n function=fxvb\n ),\n name=\"Fxvb\"\n)\n\nwf.connect(Iternode, \"cv_iter\", Collect_Varbvs, \"cv_iter\")\nCollect_Varbvs.inputs.base_dir = wf.base_dir\nCollect_Varbvs.inputs.bayes_factor = args.bayes_factor\nwf.connect(Collect_Varbvs, \"info_path\", Fxvb, \"info_path\")\nwf.connect(Collect_Varbvs, \"cv_iter\", Fxvb, \"cv_iter\")\nFxvb.inputs.base_dir = wf.base_dir\nFxvb.inputs.data_path = args.data_path\nFxvb.inputs.save_name = args.save_name\nFxvb.inputs.bind_path = args.bind_path\nFxvb.inputs.img_path = args.img_path\nFxvb.inputs.cv_path = args.cv_path\nwf.run()\n","sub_path":"itod_cv.py","file_name":"itod_cv.py","file_ext":"py","file_size_in_byte":4158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"176096589","text":"import sys\nsys.stdin = open('input.txt', 'r')\nsys.stdout = open('output.txt', 'w')\n\nn = int (input())\nm = int (input())\n\n\nfor j in range (m) :\n for i in range (n) :\n print (\"*\",end=\"\")\n print()\n","sub_path":"patterns.py","file_name":"patterns.py","file_ext":"py","file_size_in_byte":207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"587047376","text":"import numpy as np\n\n\ndef jacobi(A, b, x_init, epsilon=1e-10, max_iterations=500):\n D = np.diag(np.diag(A))\n LU = A - D\n x = x_init\n D_inv = np.diag(1 / np.diag(D))\n for i in range(max_iterations):\n x_new = np.dot(D_inv, b - np.dot(LU, x))\n if np.linalg.norm(x_new - x) < epsilon:\n return x_new\n x = x_new\n return x\n\n# problem data\nA = np.array([\n [5, 2, 1, 1],\n [2, 6, 2, 1],\n [1, 2, 7, 1],\n [1, 1, 2, 8]\n])\nb = np.array([29, 31, 26, 19])\n\n# you can choose any starting vector\nx_init = np.zeros(len(b))\nx = jacobi(A, b, x_init)\n\nprint(\"x:\", x)\nprint(\"computed b:\", np.dot(A, x))\nprint(\"real b:\", b)","sub_path":"src/wikipedia_jacobi.py","file_name":"wikipedia_jacobi.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"33721542","text":"from django.test import TestCase\nfrom faker import Faker\n\nfrom ..models import Genres, Movies\n\nfake = Faker()\n\n\nclass MoviesModelTest(TestCase):\n @classmethod\n def setUpTestData(cls):\n cls.title = \"Um Sonho de Liberdade\"\n cls.duration = \"142m\"\n cls.premiere = \"1994-10-14\"\n cls.classification = 16\n cls.synopsis = \"Andy Dufresne é condenado a duas prisões perpétuas...\"\n cls.movie = Movies.objects.create(\n title=cls.title,\n duration=cls.duration,\n premiere=cls.premiere,\n classification=cls.classification,\n synopsis=cls.synopsis,\n )\n\n # Verifica se existem os campos da model e a respectiva tipagem\n def test_it_has_information_fields(self):\n self.assertIsInstance(self.movie.title, str)\n self.assertIsInstance(self.movie.duration, str)\n self.assertIsInstance(self.movie.premiere, str)\n self.assertIsInstance(self.movie.classification, int)\n self.assertIsInstance(self.movie.synopsis, str)\n\n def test_str_models_methods(self):\n self.assertEquals(f\"\", str(self.movie))\n\n def test_it_can_be_attached_to_multiple_genres(self):\n genres = [Genres.objects.create() for _ in range(3)]\n\n for genre in genres:\n genre.movies.add(self.movie)\n\n self.assertEquals(len(genres), self.movie.genres.count())\n for genre in genres:\n self.assertIn(genre, list(self.movie.genres.all()))\n","sub_path":"kmdb_app/tests/test_movies_model.py","file_name":"test_movies_model.py","file_ext":"py","file_size_in_byte":1513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"461573408","text":"import glob\nimport pickle\nimport chess\nimport logging\nfrom multiprocessing import Pool, cpu_count\nfrom os import getpid\nimport chess.pgn\nimport pandas as pd\n\nlogger = logging.getLogger('Main')\nch = logging.StreamHandler()\nch.setLevel(logging.INFO)\nformatter = logging.Formatter(\n '%(asctime)s - %(name)s - %(levelname)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S')\nch.setFormatter(formatter)\nlogger.addHandler(ch)\nlogger.setLevel(logging.INFO)\n\n\ndef process(file):\n logger = logging.getLogger('Main.ProcessFiles')\n logger.info('Started process: %s', getpid())\n\n pgn = open(file)\n games = []\n i = 0\n while True:\n game = chess.pgn.read_game(pgn)\n if game is None:\n break\n games.append(game)\n i = i+1\n if i % 10000 == 0:\n logger.info('Process %s processed %s games', getpid(), i)\n\n logger.info('Process %s collected %s games', getpid(), len(games))\n\n positionData = []\n\n for gameNum, game in enumerate(games):\n board = game.board()\n winlose = game.headers[\"Result\"]\n outcomes = {'0-1': -1, '1-0': 1, '1/2-1/2': 0}\n win = outcomes[winlose]\n\n id = game.headers[\"FICSGamesDBGameNo\"]\n if gameNum % 10000 == 0:\n logger.info('Process %s has moves for %s games', getpid(), gameNum)\n\n for moveNum, move in enumerate(game.mainline_moves()):\n board.push(move)\n fen = board.fen()\n\n # Store the result\n positionData.append([id, gameNum, moveNum, fen, win])\n\n df = pd.DataFrame(positionData, columns=[\n 'ID', 'Game', 'Move', 'Fen', 'Result'])\n\n df.replace({'A': {0: 100, 4: 400}})\n\n logger.info('Process %s has finished', getpid())\n\n return df\n\n\ndef main():\n logger.info('Started the process')\n logger.info('Number of CPUs: %s', cpu_count())\n files = glob.glob('C:\\\\Users\\\\Watson\\\\Projects\\\\remus\\\\input\\\\games\\\\*')\n logger.info('Number of files to be processed: %s', len(files))\n\n pool = Pool(6)\n results = pool.map(process, files)\n pool.close()\n\n t = pd.concat(results)\n\n logger.info('Started pickling the results')\n with open('allGames.pickle', 'wb') as f:\n pickle.dump(t, f)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"code/realPrepareModelInput.py","file_name":"realPrepareModelInput.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"557514696","text":"import torch.nn as nn\nfrom torchvision import transforms, datasets\nimport json\nimport os\nimport torch.optim as optim\nfrom model import vgg\nimport torch\nimport time\nimport matplotlib.pyplot as plt\n\n# 由于我第四张显卡有点问题不能同时并行,所以我手动设置了只可见前三张显卡\nos.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2'\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nprint(device)\n\ndata_transform = {\n \"train\": transforms.Compose([transforms.RandomResizedCrop(224),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),\n \"val\": transforms.Compose([transforms.Resize((224, 224)),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])}\n\ndata_root = os.path.abspath(os.path.join(os.getcwd(), \"../../../../..\")) # get data root path\nimage_path = data_root + \"/data_set/flower_data/\" # flower data set path\n\ntrain_dataset = datasets.ImageFolder(root=image_path + \"train\",\n transform=data_transform[\"train\"])\ntrain_num = len(train_dataset)\n\n# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}\nflower_list = train_dataset.class_to_idx\ncla_dict = dict((val, key) for key, val in flower_list.items())\n# write dict into json file\njson_str = json.dumps(cla_dict, indent=4)\nwith open('class_indices.json', 'w') as json_file:\n json_file.write(json_str)\n\nbatch_size = 128\ntrain_loader = torch.utils.data.DataLoader(train_dataset,\n batch_size=batch_size, shuffle=True,\n num_workers=16)\n\nvalidate_dataset = datasets.ImageFolder(root=image_path + \"val\",\n transform=data_transform[\"val\"])\nval_num = len(validate_dataset)\nvalidate_loader = torch.utils.data.DataLoader(validate_dataset,\n batch_size=batch_size, shuffle=False,\n num_workers=16)\n\n# test_data_iter = iter(validate_loader)\n# test_image, test_label = test_data_iter.next()\n\nmodel_name = \"vgg16\"\nnet = vgg(model_name=model_name, num_classes=5, init_weights=True)\n\nif torch.cuda.device_count() > 1:\n print(\"Let's use\", torch.cuda.device_count(), \"GPUs!\")\n # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs\n net = nn.DataParallel(net)\n\nnet.to(device)\nloss_function = nn.CrossEntropyLoss()\noptimizer = optim.Adam(net.parameters(), lr=0.0001)\n\nbest_acc = 0.0\nsave_path = './{}Net.pth'.format(model_name)\n\nLoss_list = []\nAccuracy_list = []\n# num_c = 5 # 类别\nfor epoch in range(30):\n t1 = time.perf_counter()\n # train\n net.train()\n running_loss = 0.0\n for step, data in enumerate(train_loader, start=0):\n images, labels = data\n optimizer.zero_grad()\n outputs = net(images.to(device))\n loss = loss_function(outputs, labels.to(device))\n loss.backward()\n optimizer.step()\n\n # print statistics\n running_loss += loss.item()\n # print train process\n rate = (step + 1) / len(train_loader)\n a = \"*\" * int(rate * 50)\n b = \".\" * int((1 - rate) * 50)\n print(\"\\rtrain loss: {:^3.0f}%[{}->{}]{:.3f}\".format(int(rate * 100), a, b, loss), end=\"\")\n print()\n print(time.perf_counter() - t1)\n # validate\n net.eval()\n acc = 0.0 # accumulate accurate number / epoch\n with torch.no_grad():\n # 分类正确率率统计\n correct = list(0. for i in range(5))\n total = list(0. for i in range(5))\n\n for val_data in validate_loader:\n val_images, val_labels = val_data\n optimizer.zero_grad()\n outputs = net(val_images.to(device))\n\n predict_y = torch.max(outputs, dim=1)[1] # 预测出来的结果\n labels = val_labels.to(device)\n res = predict_y == labels\n acc += res.sum().item() # 预测正确的个数\n for label_idx in range(len(labels)):\n label_single = labels[label_idx]\n correct[label_single] += res[label_idx].item()\n total[label_single] += 1\n\n val_accurate = acc / val_num\n\n if val_accurate > best_acc:\n best_acc = val_accurate\n torch.save(net.state_dict(), save_path)\n print('[epoch %d] train_loss: %.3f test_accuracy: %.3f' %\n (epoch + 1, running_loss / step, val_accurate))\n\n for acc_idx in range(5):\n try:\n acc_class = correct[acc_idx] / total[acc_idx]\n except:\n acc_class = 0\n finally:\n print('\\tclassID:%d\\tacc_class:%f\\t' % (acc_idx + 1, acc_class))\n\n Loss_list.append(100 * running_loss / step)\n Accuracy_list.append(100 * val_accurate)\n print()\n\nprint('Finished Training')\n\nx1 = range(0, 30)\nx2 = range(0, 30)\ny1 = Accuracy_list\ny2 = Loss_list\nplt.subplot(2, 1, 1)\nplt.plot(x1, y1, 'o-')\nplt.title('Test accuracy vs. epoches')\nplt.ylabel('Test accuracy')\nplt.subplot(2, 1, 2)\nplt.plot(x2, y2, '.-')\nplt.xlabel('Test loss vs. epoches')\nplt.ylabel('Test loss')\n# plt.show()\nplt.savefig(\"accuracy_loss_3.jpg\")\n\n","sub_path":"pytorch_classification/Test3_vggnet/train3.py","file_name":"train3.py","file_ext":"py","file_size_in_byte":5382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"162632762","text":"from django.contrib.auth.decorators import login_required, permission_required\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.contrib.auth.mixins import PermissionRequiredMixin\nfrom django.utils.decorators import method_decorator\nfrom django.views.generic import View, TemplateView\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.db.models import Q\nfrom .models import *\nfrom .forms import *\nimport datetime, xlwt\n\n\ndef DVFRegistrationView(request):\n\tform = DVFRegistrationForm(request.POST or None)\n\ttemplate_name = 'DVFRegistration/dvf_registration.html'\n\tnow_date = str(datetime.datetime.date(datetime.datetime.now()))\n\n\tif request.method == \"POST\" and form.is_valid():\n\t\treturn render(request, template_name, \n\t\t\t{\"form\": form, \n\t\t\t'objects': DVFRegistrationTable.objects.filter(date=request.POST.get('date'))})\n\telse:\n\t\treturn render(request, template_name, \n\t\t\t{\"form\": form, \n\t\t\t'objects': DVFRegistrationTable.objects.filter(date=now_date)})\n\n@login_required\n@permission_required('DVFRegistration.can_add')\ndef DVFRegistrationAdd(request):\n\tform = DVFRegistrationAddForm(request.POST or None)\n\n\tif request.method == \"POST\" and form.is_valid():\n\t\tinstance = form.save(commit=False)\n\t\tinstance.save()\n\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\n\treturn render(request, 'DVFRegistration/add_dvf_reg.html', {'form': form})\n\n@login_required\n@permission_required('DVFRegistration.can_change')\ndef DVFRegistrationEdit(request, id):\n\tinstance = get_object_or_404(DVFRegistrationTable, id=id)\n\tform = DVFRegistrationAddForm(request.POST or None, instance=instance)\n\tif form.is_valid():\n\t\tinstance = form.save(commit=False)\n\t\tinstance.save()\n\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\telse:\n\t\tform = DVFRegistrationAddForm(instance=instance)\n\n\treturn render(request, 'DVFRegistration/add_dvf_reg.html', {\"form\": form, })\n\n@login_required\n@permission_required('DVFRegistration.can_delete')\ndef DVFRegistrationDelete(request, id):\n\tobj = DVFRegistrationTable.objects.filter(id=id)\n\tinstance = get_object_or_404(DVFRegistrationTable, id=id)\n\tobj.delete()\n\n\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))","sub_path":"DVFRegistration/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"35262532","text":"from mathmodel1.duqu import *\n\nxmajorLocator = MultipleLocator(400) #将x主刻度标签设置为20的倍数\nxmajorFormatter = FormatStrFormatter('%d') #设置x轴标签文本的格式\nxminorLocator = MultipleLocator(200) #将x轴次刻度标签设置为5的倍数\n\nk = 21\nD1t = D1[k:]\nD2t = D2[k:]\nplt.figure('七日每分钟交易量图')\nplt.plot(D1t[0], D2t[0], color='#FF0000', label='1')\nplt.plot(D1t[1], D2t[1], color='#00FF00', label='2')\nplt.plot(D1t[2], D2t[2], color='#0000FF', label='3')\nplt.plot(D1t[3], D2t[3], color='#0F0F0F', label='4')\nplt.plot(D1t[4], D2t[4], color='#778899', label='5')\nplt.plot(D1t[5], D2t[5], color='#00FFFF', label='6')\nplt.plot(D1t[6], D2t[6], color='#FFD700', label='7')\n\n# plt.figure('总每分钟交易量图')\n# for i in range(7):\n# plt.plot(D1[i], D2[i], 'r')\n\n# xmajorLocator = MultipleLocator(7) #将x主刻度标签设置为20的倍数\n# xmajorFormatter = FormatStrFormatter('%d') #设置x轴标签文本的格式\n# xminorLocator = MultipleLocator(1) #将x轴次刻度标签设置为5的倍数\n#\n# plt.figure('每日交易量图')\n# plt.plot(range(len(C)), C, 'r')\n# plt.grid() # == plt.grid(True)\n# plt.grid(color='b' , linewidth='0.3' ,linestyle='--')\n\n# plt.figure('每日交易量柱状图')\n# plt.bar(range(len(C)), C)\n\n\nax = plt.gca()\n\n#设置主刻度标签的位置,标签文本的格式\nax.xaxis.set_major_locator(xmajorLocator)\nax.xaxis.set_major_formatter(xmajorFormatter)\n\n#显示次刻度标签的位置,没有标签文本\nax.xaxis.set_minor_locator(xminorLocator)\n\nax.yaxis.grid(True, which='minor') #y坐标轴的网格使用次刻度\n\nplt.xlabel('x')\nplt.ylabel('y')\nplt.title('title')\nplt.legend()\n# plt.savefig('result.png')\nplt.show()\n","sub_path":"mathmodel1/datad.py","file_name":"datad.py","file_ext":"py","file_size_in_byte":1714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"125653486","text":"import os\nimport sys\nimport time\nimport logging\nimport json\nimport pika\n\nfrom ConfigParser import RawConfigParser\n\ncfg = RawConfigParser()\n\ndef currentDayStr():\n return time.strftime(\"%Y%m%d\")\n\ndef currentTimeStr():\n return time.strftime(\"%H:%M:%S\")\n\n\ndef initLog(rightNow):\n logger = logging.getLogger(cfg.get('logging', 'logName'))\n logPath=cfg.get('logging', 'logPath')\n logFilename=cfg.get('logging', 'logFileName') \n hdlr = logging.FileHandler(logPath+rightNow+logFilename)\n formatter = logging.Formatter(cfg.get('logging', 'logFormat'),cfg.get('logging', 'logTimeFormat'))\n hdlr.setFormatter(formatter)\n logger.addHandler(hdlr) \n logger.setLevel(logging.INFO)\n return logger\n\ndef getCmdLineParser():\n import argparse\n desc = 'Execute rabbitMQLoader'\n parser = argparse.ArgumentParser(description=desc)\n\n parser.add_argument('-c', '--config_file', default='../config/rabbitMQLoader.conf',\n help='configuration file name (*.ini format)')\n\n return parser\n\n\n\n\ndef main(argv):\n\n # Overhead to manage command line opts and config file\n p = getCmdLineParser()\n args = p.parse_args()\n\n cfg.read(args.config_file)\n\n # Get the logger going\n logger = initLog(time.strftime(\"%Y%m%d%H%M%S\"))\n logger.info('Starting Run: '+time.strftime(\"%Y%m%d%H%M%S\")+' ==============================')\n logger.info(' Processing file: '+cfg.get('data', 'source'))\n file = open(cfg.get('data', 'source'), 'r')\n\n\n credentials = pika.PlainCredentials(cfg.get('credentials', 'login'), cfg.get('credentials', 'password'))\n connection = pika.BlockingConnection(pika.ConnectionParameters(cfg.get('server', 'ip'),int(cfg.get('server', 'port')),'/',credentials))\n channel = connection.channel()\n count = 0\n for line in file:\n if channel.basic_publish(cfg.get('server', 'exchange'),cfg.get('server', 'key'),body=line, mandatory=1):\n logger.info(' Delivered: {0}'.format(line))\n else:\n logger.info(' NOT Delivered: {0}'.format(line))\n count = count + 1\n if count % 1000 == 0:\n logger.info(' Processed line: '+str(count))\n connection.close\n\n # Clean up\n logger.info(' Total Lines Processed: '+str(count))\n logger.info('Done! '+time.strftime(\"%Y%m%d%H%M%S\")+' ==============================')\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n\n","sub_path":"src/main/python/src/loadRabbitMQ.py","file_name":"loadRabbitMQ.py","file_ext":"py","file_size_in_byte":2408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"256028106","text":"from model import AE\r\nfrom preprocess.tacotron.utils import get_spectrograms,melspectrogram2wav\r\nimport torch.nn.functional as F\r\nimport yaml\r\nimport pickle\r\nfrom utils import *\r\nfrom argparse import ArgumentParser\r\nfrom scipy.io.wavfile import write\r\n\r\nclass VoiceConversion(object):\r\n def __init__(self,config,args):\r\n self.config = config\r\n self.args = args\r\n self.build_model()\r\n self.load_model()\r\n with open(self.args.attr,'rb') as f:\r\n self.attr = pickle.load(f)\r\n\r\n def build_model(self):\r\n self.model = cc(AE(self.config))\r\n self.model.eval()\r\n return\r\n\r\n def load_model(self):\r\n self.model.load_state_dict(torch.load(f'{self.args.model}'))\r\n return\r\n\r\n def normalize(self,x):\r\n m , s = self.attr['mean'], self.attr['std']\r\n res = (x - m) / s\r\n return res\r\n\r\n def denormalize(self,x):\r\n m , s = self.attr['mean'],self.attr['std']\r\n res = x * s + m\r\n return res\r\n\r\n def utt_make_frames(self,x):\r\n frame_size = self.config['data_loader']['frame_size']\r\n remains = x.size(0)%frame_size\r\n if remains != 0:\r\n x = F.pad(x,(0,remains))\r\n out = x.view(1,x.size(0)//frame_size,frame_size * x.size(1)).transpose(1,2)\r\n return out\r\n\r\n def get_TargetSpeaker_Identity(self):\r\n tar_mel , _ = get_spectrograms(self.args.target)\r\n tar_mel = torch.from_numpy(self.normalize(tar_mel)).cuda()\r\n tar_mel = self.utt_make_frames(tar_mel)\r\n emb = self.model.speaker_encoder(tar_mel)\r\n return emb\r\n\r\n def get_SourceSpeaker_Content(self):\r\n src_mel , _ = get_spectrograms(self.args.source)\r\n src_mel = torch.from_numpy(self.normalize(src_mel)).cuda()\r\n src_mel = self.utt_make_frames(src_mel)\r\n res , _ = self.model.content_encoder(src_mel)\r\n return res\r\n\r\n def get_Conversion_melSpectrogram(self,x,x_cond):\r\n conv = self.model.decoder(x,x_cond)\r\n conv = conv.transpose(1,2).squeeze(0)\r\n conv = conv.detach().cpu().numpy()\r\n conv = self.denormalize(conv)\r\n return conv\r\n\r\n def get_Concersion_Waveform(self,melSpectrogram):\r\n res = melspectrogram2wav(melSpectrogram)\r\n return res\r\n\r\n def save_Conversion_Waveform(self,wav_data,output_path):\r\n write(output_path,rate = self.args.sample_rate,data=wav_data)\r\n return\r\n\r\nif __name__ == '__main__':\r\n parser = ArgumentParser()\r\n parser.add_argument('-attr','-a',default='attr/attr.pkl',help='attr file path')\r\n parser.add_argument('-config','-c',default='config.yaml',help='config file path')\r\n parser.add_argument('-model','-m',default='checkpoints/vctk_model.ckpt',help='model path')\r\n parser.add_argument('-source','-s',default='test/source/normal.wav',help='source wav path')\r\n parser.add_argument('-target','-t',default='test/target/slow.wav',help='target wav path')\r\n parser.add_argument('-output','-o',default='converted_sound/normal2slow.mp3',help='output wav path')\r\n parser.add_argument('-sample_rate','-sr',default=24000,type=int,help='sample rate')\r\n args = parser.parse_args()\r\n\r\n with open(args.config) as f:\r\n config = yaml.load(f)\r\n\r\n voiceconversion = VoiceConversion(config=config,args=args)\r\n\r\n emb = voiceconversion.get_TargetSpeaker_Identity()\r\n\r\n cont = voiceconversion.get_SourceSpeaker_Content()\r\n\r\n conv = voiceconversion.get_Conversion_melSpectrogram(cont,emb)\r\n\r\n conv = voiceconversion.get_Concersion_Waveform(conv)\r\n \r\n voiceconversion.save_Conversion_Waveform(conv,args.output)\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"voiceConversion.py","file_name":"voiceConversion.py","file_ext":"py","file_size_in_byte":3643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"264829274","text":"import scrapy\nfrom crawler.items import CrawlerItem\n\n\nclass AllMactchSpider(scrapy.Spider): #klas\n name = \"AllMatch\"\n allowed_domains = ['openligadb.de']\n start_urls = [ #vzima url ot daden sait\n 'https://api.openligadb.de/getmatchdata/bl1/2021'\n ]\n\n def parse(self, response): #metod\n item = CrawlerItem() #importvame klasa carawleritem() v item \n jsonresponse = response.json() \n \n for AllMatches in jsonresponse: #cikul \n\n item[\"Team1\"] = AllMatches[\"team1\"][\"teamName\"] #vurti item postoqnno za da nameri danni na ime team1..\n item[\"Team2\"] = AllMatches[\"team2\"][\"teamName\"]\n item[\"Data\"] = AllMatches[\"matchDateTime\"]\n \n yield item\n print(\"-----------------------\")\n print(item)\n print(\"--------------------------\")\n\n \n\n\n #response.json()[0][\"team1\"]\n ","sub_path":"crawler/crawler/spiders/allmatch_spider.py","file_name":"allmatch_spider.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"192000826","text":"from django.conf.urls import url\nfrom . import views\n\napp_name = 'music'\n\nurlpatterns = [\n # url(r'^$', views.index, name='index'), # /music/\n # url(r'^(?P\\d+)/$', views.detail, name='detail'), # /music/71/\n # url(r'^(?P\\d+)/favorite/$', views.favorite, name='favorite'), # favorite\n \n url(r'^$', views.IndexView.as_view(), name='index'),\n url(r'^(?P\\d+)/$', views.DetailView.as_view(), name='detail'),\n url(r'album/add/$', views.AlbumCreate.as_view(), name='album-add'),\n url(r'album/(?P\\d+)/$', views.AlbumUpdate.as_view(), name='album-update'),\n url(r'album/(?P\\d+)/delete/$', views.AlbumDelete.as_view(), name='album-delete'),\n]\n","sub_path":"Django_Tutorial/website/music/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"361995440","text":"# coding=utf-8\nimport random\nimport time\nimport unittest\nfrom framework import generator\nfrom framework.logger import Logger\nfrom framework.browser_engine import BrowserEngine\nfrom fky_common.login import Login\nfrom fky_common.logout import Logout\nfrom fky_pageobjects.budgetList import BudgetList\nfrom fky_pageobjects.myApprove import MyApprove\n\nlogger = Logger(logger=\"Budgetlist\").getlog()\n\n\nclass Budgetlist (unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n browser = BrowserEngine(cls)\n cls.driver = browser.open_browser(cls)\n # 登录\n login = Login()\n login.log_in(cls)\n budget = BudgetList(cls.driver)\n budget.into_yszlb()\n\n @classmethod\n def tearDownClass(cls):\n # 登出\n logout = Logout()\n logout.log_out(cls)\n cls.driver.quit()\n\n # 验证必填项\n def test01_yanzheng_btx(self):\n budget = BudgetList(self.driver)\n budget.click_add_btn()\n budget.click_tijiao()\n tishi = budget.get_tishi()\n budget.click_queding()\n budget.click_fanhui()\n self.assertEqual(\"请先输入预算名称！\", tishi, \"验证新增预算主列表必填项失败！\")\n logger.info(\"验证新增预算主列表必填项成功！\")\n\n # 新增草稿\n def test02_add_caogao(self):\n budget = BudgetList(self.driver)\n budget.click_add_btn()\n name = str(time.strftime('%Y%m%d%H%M%S', time.localtime(time.time())))\n leixing = budget.edit_taitou(name)\n budget.click_add_hang()\n if leixing == \"部门\":\n budget.select_yszb_ysbm()\n else:\n budget.select_yszb_ygbm()\n zhouqi = generator.randomStr(1, False, False, False, False, True, [\"年度\", \"季度\", \"月度\"])\n budget.select_gk_zhouqi(zhouqi)\n if zhouqi == \"年度\":\n jine = budget.input_nd_jine()\n elif zhouqi == \"季度\":\n jine = budget.input_jd_jine()\n else:\n jine = budget.input_yd_jine()\n ys_zjine = int(budget.get_ys_zjine())\n try:\n self.assertEqual(jine, ys_zjine, \"所填写的预算金额与页面统计的预算总金额不相等!\")\n logger.info(\"所填写的预算金额与页面统计的预算总金额相等!\")\n except Exception as e:\n logger.error(\"执行失败！\", e)\n budget.get_windows_img()\n budget.sleep(1)\n budget.click_save_caogao()\n budget.click_queding()\n budget.click_add_btn()\n budget.click_zairucg()\n cg_name_list = budget.get_cg_ys_name()\n self.assertIn(name, cg_name_list, \"保存草稿失败！\")\n logger.info(\"保存草稿成功！\")\n budget.click_close_btn()\n budget.click_fanhui()\n\n # 载入草稿驳回提交-同意\n def test03_zairucaogao_bhtj(self):\n budget = BudgetList(self.driver)\n budget.click_add_btn()\n budget.click_zairucg()\n ys_name = budget.xuanze_cg()\n try:\n self.assertIn(ys_name, budget.get_input_ys_name(), \"载入草稿失败！\")\n logger.info(\"载入草稿成功！\")\n except Exception as e:\n logger.error(\"执行失败！\", e)\n budget.get_windows_img()\n name = str(time.strftime('%Y%m%d%H%M%S', time.localtime(time.time())))\n leixing = budget.edit_taitou(name)\n budget.click_add_hang()\n if leixing == \"部门\":\n budget.select_yszb_ysbm()\n else:\n budget.select_yszb_ygbm()\n zhouqi = generator.randomStr(1, False, False, False, False, True, [\"年度\", \"季度\", \"月度\"])\n budget.select_gk_zhouqi(zhouqi)\n if zhouqi == \"年度\":\n jine = budget.input_nd_jine()\n elif zhouqi == \"季度\":\n jine = budget.input_jd_jine()\n else:\n jine = budget.input_yd_jine()\n ys_zjine = int(budget.get_ys_zjine())\n try:\n self.assertEqual(jine, ys_zjine, \"所填写的预算金额与页面统计的预算总金额不相等!\")\n logger.info(\"所填写的预算金额与页面统计的预算总金额相等!\")\n except Exception as e:\n logger.error(\"执行失败！\", e)\n budget.get_windows_img()\n budget.sleep(1)\n budget.click_tijiao()\n myapprove = MyApprove(self.driver)\n myapprove.xuanze_spr()\n budget.click_queding()\n ys_name_list = budget.get_ys_name_list()\n try:\n self.assertIn(name, ys_name_list, \"新增预算失败！\")\n logger.info(\"新增预算成功！\")\n except Exception as e:\n logger.error(\"新增预算失败！\", e)\n budget.get_windows_img()\n # 驳回\n myapprove.execute_bohui()\n budget.into_yszlb()\n n = -1\n for ys_name_1 in ys_name_list:\n n = n + 1\n if name == ys_name_1:\n ys_state = budget.get_dj_state()[n]\n try:\n self.assertEqual(\"驳回\", ys_state, \"驳回失败！\")\n logger.info(\"驳回成功！\")\n except Exception as e:\n logger.error(\"驳回失败！\", e)\n budget.get_windows_img()\n # 审批借款流程--同意\n myapprove.execute_tijiao_tongyi_yszlb()\n budget.into_yszlb()\n ys_state_tongyi = budget.get_dj_state()[n]\n self.assertEqual(\"审批完成\", ys_state_tongyi, \"审核失败！\")\n logger.info(\"审核通过！\")\n break\n\n # 调整-同意\n def test04_tiaozheng_tongyi(self):\n budget = BudgetList(self.driver)\n state_list = budget.get_dj_state()\n n = -1\n for state in state_list:\n if state in [\"驳回\", \"审批完成\"]:\n n = n + 1\n if state == \"审批完成\":\n budget.get_caozuo_elements()[n].click()\n name = str(time.strftime('%Y%m%d%H%M%S', time.localtime(time.time())))\n budget.input_ys_name(name)\n # zhouqi = generator.randomStr(1, False, False, False, False, True, [\"年度\", \"季度\", \"月度\"])\n # budget.select_gk_zhouqi(zhouqi)\n # if zhouqi == \"年度\":\n # jine = budget.input_nd_jine()\n # elif zhouqi == \"季度\":\n # jine = budget.input_jd_jine()\n # else:\n # jine = budget.input_yd_jine()\n # ys_zjine = int(budget.get_ys_zjine())\n # try:\n # self.assertEqual(jine, ys_zjine, \"所填写的预算金额与页面统计的预算总金额不相等!\")\n # logger.info(\"所填写的预算金额与页面统计的预算总金额相等!\")\n # except Exception as e:\n # logger.error(\"执行失败！\", e)\n # budget.get_windows_img()\n budget.sleep(1)\n budget.click_tijiao()\n # budget.sleep(1)\n myapprove = MyApprove(self.driver)\n myapprove.xuanze_spr()\n budget.click_queding()\n # 同意\n myapprove.execute_tongyi_yszlb()\n budget.into_yszlb()\n ys_name_list = budget.get_ys_name_list()\n self.assertIn(name, ys_name_list, \"调整预算失败！\")\n logger.info(\"调整预算成功！\")\n break\n else:\n logger.info(\"%s: 当前状态不可调整\" % state)\n else:\n logger.info(\"%s: 当前状态不可操作\" % state)\n\n # 调整-反对\n def test05_tiaozheng_fandui(self):\n budget = BudgetList(self.driver)\n state_list = budget.get_dj_state()\n n = -1\n m = -1\n for state in state_list:\n m = m + 1\n if state in [\"驳回\", \"审批完成\"]:\n n = n + 1\n if state == \"审批完成\":\n budget.get_caozuo_elements()[n].click()\n name = str(time.strftime('%Y%m%d%H%M%S', time.localtime(time.time())))\n budget.input_ys_name(name)\n # zhouqi = generator.randomStr(1, False, False, False, False, True, [\"年度\", \"季度\", \"月度\"])\n # budget.select_gk_zhouqi(zhouqi)\n # if zhouqi == \"年度\":\n # jine = budget.input_nd_jine()\n # elif zhouqi == \"季度\":\n # jine = budget.input_jd_jine()\n # else:\n # jine = budget.input_yd_jine()\n # ys_zjine = int(budget.get_ys_zjine())\n # try:\n # self.assertEqual(jine, ys_zjine, \"所填写的预算金额与页面统计的预算总金额不相等!\")\n # logger.info(\"所填写的预算金额与页面统计的预算总金额相等!\")\n # except Exception as e:\n # logger.error(\"执行失败！\", e)\n # budget.get_windows_img()\n budget.sleep(1)\n budget.click_tijiao()\n myapprove = MyApprove(self.driver)\n myapprove.xuanze_spr()\n budget.click_queding()\n # 反对\n myapprove.execute_fandui()\n budget.into_yszlb()\n ys_state = budget.get_dj_state()[m]\n self.assertEqual(\"驳回\", ys_state, \"驳回失败！\")\n logger.info(\"驳回成功！\")\n break\n else:\n logger.info(\"%s: 当前状态不可调整\" % state)\n else:\n logger.info(\"%s: 当前状态不可操作\" % state)\n\n # 新增-驳回提交-反对\n def test06_add_bhtj(self):\n budget = BudgetList(self.driver)\n budget.click_add_btn()\n name = str(time.strftime('%Y%m%d%H%M%S', time.localtime(time.time())))\n budget.input_ys_name(name)\n # budget.click_add_hang()\n # if leixing == \"部门\":\n # budget.select_yszb_ysbm()\n # else:\n # budget.select_yszb_ygbm()\n # zhouqi = generator.randomStr(1, False, False, False, False, True, [\"年度\", \"季度\", \"月度\"])\n # budget.select_gk_zhouqi(zhouqi)\n # if zhouqi == \"年度\":\n # jine = budget.input_nd_jine()\n # elif zhouqi == \"季度\":\n # jine = budget.input_jd_jine()\n # else:\n # jine = budget.input_yd_jine()\n # ys_zjine = int(budget.get_ys_zjine())\n # try:\n # self.assertEqual(jine, ys_zjine, \"所填写的预算金额与页面统计的预算总金额不相等!\")\n # logger.info(\"所填写的预算金额与页面统计的预算总金额相等!\")\n # except Exception as e:\n # logger.error(\"执行失败！\", e)\n # budget.get_windows_img()\n budget.sleep(1)\n budget.click_tijiao()\n myapprove = MyApprove(self.driver)\n myapprove.xuanze_spr()\n budget.click_queding()\n ys_name_list = budget.get_ys_name_list()\n try:\n self.assertIn(name, ys_name_list, \"新增预算失败！\")\n logger.info(\"新增预算成功！\")\n except Exception as e:\n logger.error(\"新增预算失败！\", e)\n budget.get_windows_img()\n # 驳回\n myapprove.execute_bohui()\n budget.into_yszlb()\n n = -1\n for ys_name_1 in ys_name_list:\n n = n + 1\n if name == ys_name_1:\n ys_state = budget.get_dj_state()[n]\n self.assertEqual(\"驳回\", ys_state, \"驳回失败！\")\n logger.info(\"驳回成功！\")\n break\n\n # 通过编号查询\n def test07_query_ys_bianhao(self):\n budget = BudgetList(self.driver)\n budget.click_zhankai()\n n = random.randint(1, len(budget.get_ys_bianhao())) - 1\n bianhao_n = budget.get_ys_bianhao()[n]\n budget.input_ys_bianhao(bianhao_n)\n budget.click_query()\n bianhao_list = budget.get_ys_bianhao()\n for bianhao in bianhao_list:\n self.assertEqual(bianhao_n, bianhao, \"通过预算编号查询预算主列表失败！\")\n logger.info(\"通过预算编号查询预算主列表成功！\")\n budget.click_clear()\n\n # 通过预算类型查询\n def t_est08_query_ys_type(self):\n budget = BudgetList(self.driver)\n budget.click_zhankai()\n n = random.randint(1, len(budget.get_ys_type())) - 1\n type_n = budget.get_ys_type()[n]\n budget.select_ys_type(type_n)\n budget.click_query()\n type_list = budget.get_ys_type()\n for type in type_list:\n self.assertEqual(type_n, type, \"通过预算类型查询预算主列表失败！\")\n logger.info(\"通过预算类型查询预算主列表成功！\")\n budget.click_clear()\n\n # 通过单据状态查询\n def test09_query_dj_state(self):\n budget = BudgetList(self.driver)\n budget.click_zhankai()\n n = random.randint(1, len(budget.get_dj_state())) - 1\n state_n = budget.get_dj_state()[n]\n budget.select_dj_state(state_n)\n budget.click_query()\n state_list = budget.get_dj_state()\n for state in state_list:\n self.assertEqual(state_n, state, \"通过单据状态查询预算主列表失败！\")\n logger.info(\"通过单据状态查询预算主列表成功！\")\n budget.click_clear()\n\n # 删除\n def test10_del_ys(self):\n budget = BudgetList(self.driver)\n state_list = budget.get_dj_state()\n n = -1\n m = -1\n for state in state_list:\n m = m + 1\n if state in [\"驳回\", \"审批完成\"]:\n n = n + 1\n if state == \"驳回\":\n name = budget.get_ys_name_list()[m]\n budget.get_caozuo_elements()[n].click()\n budget.sleep(1)\n budget.click_queding()\n self.assertNotIn(name, budget.get_ys_name_list(), \"删除驳回的预算失败！\")\n logger.info(\"删除驳回的预算成功！\")\n break\n else:\n logger.info(\"%s: 当前状态不可删除\" % state)\n else:\n logger.info(\"%s: 当前状态不可操作\" % state)\n\n # 导出预算主列表\n def test11_export(self):\n budget = BudgetList(self.driver)\n budget.click_export()\n budget.click_queding()\n name_list = budget.file_name(\"C:\\\\Users\\Kejie\\Downloads\")\n self.assertIn(\"部门_员工预算信息表.xls\", name_list, \"部门_员工预算信息表导出失败！\")\n logger.info(\"部门_员工预算信息表导出成功！\")\n budget.remover_file(\"部门_员工预算信息表\")\n budget.wait(1)\n\n # 导出批量导入模板\n def t1est12_export_pldrmb(self):\n budget = BudgetList(self.driver)\n budget.click_add_btn()\n budget.click_download()\n budget.click_queding()\n name_list = budget.file_name(\"C:\\\\Users\\Administrator\\Downloads\")\n self.assertIn(\"部门_员工预算信息表批量导入模板.xls\", name_list, \"部门_员工预算信息表批量导入模板导出失败！\")\n logger.info(\"部门_员工预算信息表批量导入模板导出成功！\")\n budget.remover_file(\"部门_员工预算信息表批量导入模板\")\n budget.wait(1)\n\n\nif __name__ == '__main__':\n unittest.main()","sub_path":"fky_testsuits/t1est27_budget_list.py","file_name":"t1est27_budget_list.py","file_ext":"py","file_size_in_byte":15925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"39021729","text":"#!/usr/bin/python2\n\n# author: Michael Eliachevitch\nimport re # module for regular expressions, substitutions...\n\ndef csv_to_tex (filepath, newfilepath, seperator, use_commas=True):\n filestring = \"\\\\begin{table}\\n\\\\caption{}\\n\\\\label{}\\n\\\\begin{tabular}\"\n filestring += \"\\\\toprule \\n\"\n filestring += \"% insert headerline here\\n\"\n filestring += \"\\\\midrule \\n\"\n \n with open(filepath, \"r\") as infile:\n for line in infile:\n if (line.strip()[0] != \"#\"):\n if use_commas:\n filestring += re.sub(\"\\.\", \",\", re.sub(\"\\n\", r\" \\\\\\\\\"+\"\\n\", re.sub(seperator, \" & \", line)))\n else:\n filestring += re.sub(\",\", \".\", re.sub(\"\\n\", r\" \\\\\\\\\"+\"\\n\", re.sub(seperator, \" & \", line)))\n filestring +=\"\\\\bottomrule\\n\"\n filestring +=\"\\\\end{tabular}\\n\\\\end{table}\"\n with open(newfilepath, \"w\") as outfile:\n outfile.write(filestring)\n \ncsv_to_tex(\"FieldInhomogenities.txt\",\"FieldInhomogenities.tex\", \" \")\n\n \n","sub_path":"csv-to-textable.py","file_name":"csv-to-textable.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"494644289","text":"from Array2D import Array2D\r\n\r\nfrom Stack import Stack\r\n\r\nclass LaberintoADT:\r\n \"\"\"\r\n 0pasillo, 1 pared, S salida, E entrada\r\n pasillo es una tupla((2,1)(2,2)(2,3)(2,4)(3,2)(4,2))\r\n entrada en tupla(5,1)\r\n salida en tupla(2,5)\r\n \"\"\"\r\n def __init__(self,rens,cols,pasillos, entrada, salida):\r\n self.__laberinto= Array2D(rens,cols,\"1\")\r\n for pasillo in pasillos:\r\n self.__laberinto.set_item(pasillo[0], pasillo[1],\"0\")\r\n self.set_entrada(entrada[0],entrada[1])\r\n self.set_salida(salida[0],salida[1])\r\n self.__camino=Stack()\r\n self.__previa=None# guarda la posición previa\r\n\r\n def to_string(self):\r\n self.__laberinto.to_string()\r\n \"\"\"\r\n establece la entrada E de la matriz, verificar limites\r\n \"\"\"\r\n def set_entrada(self,ren,col):\r\n #terminar la validación de las coordenadas\r\n self.__laberinto.set_item(ren,col,\"E\")\r\n\r\n def set_salida(self,ren,col):\r\n self.__laberinto.set_item(ren,col,\"S\")\r\n\r\n def es_salida(self,ren,col):\r\n return self.__laberinto.get_item(ren,col)==\"S\"\r\n\r\n def buscar_entrada (self):\r\n encontrado =False\r\n for renglon in range(self.__laberinto.get_num_rows()):\r\n for columna in range(self.__laberinto.get_num_cols()):\r\n if self.__laberinto.get_item(renglon,columna)==\"E\":\r\n self.__camino.push((renglon,columna))\r\n encontrado=True\r\n return encontrado\r\n\r\n def set_previa(self, pos_prev):\r\n self.__previa =pos_prev\r\n\r\n def get_previa (self):\r\n return self.__previa\r\n\r\n def get_pos_actual(self):\r\n return self.__camino.peek()\r\n\r\n def resolver_laberinto(self):\r\n actual=self.__camino.peek()\r\n #a la izquierda\r\n #agregar validaciones para los limites\r\n if actual[1]-1!=-1 and self.__laberinto.get_item(actual[0],actual[1]-1)==\"0\" \\\r\n and self.get_previa( )!= (actual[0],actual[1]-1) \\\r\n and (actual[0],actual[1]-1)!=\"X\":\r\n self.set_previa(actual)\r\n self.__camino.push((actual[0],actual[1]-1))\r\n #arriba\r\n elif actual[0]-1!=-1 and self.__laberinto.get_item(actual[0]-1,actual[1])==\"0\" \\\r\n and self.get_previa( )!= (actual[0]-1,actual[1]) \\\r\n and (actual[0]-1,actual[1])!=\"X\":\r\n self.set_previa(actual)\r\n self.__camino.push((actual[0]-1,actual[1]))\r\n #abajo\r\n elif actual[0]+1!=-1 and self.__laberinto.get_item(actual[0]+1,actual[1])==\"0\" \\\r\n and self.get_previa( )!= (actual[0]+1,actual[1]) \\\r\n and (actual[0]+1,actual[1])!=\"X\":\r\n self.set_previa(actual)\r\n self.__camino.push((actual[0]+1,actual[1]))\r\n #derecha\r\n elif actual[1]+1!=-1 and self.__laberinto.get_item(actual[0],actual[1]+1)==\"0\" \\\r\n and self.get_previa( )!= (actual[0],actual[1]+1) \\\r\n and (actual[0],actual[1]+1)!=\"X\":\r\n self.set_previa(actual)\r\n self.__camino.push((actual[0],actual[1]+1))\r\n #debug\r\n else:\r\n self.__laberinto.set_item(actual[0],actual[1],\"X\")\r\n self.__previa=actual\r\n self.__camino.pop()\r\n \r\n def imprimir_camino(self):\r\n self.__camino.to_string()\r\n #aplicar reglas\r\n","sub_path":"Laberinto 8_12_2020/backtracking.py","file_name":"backtracking.py","file_ext":"py","file_size_in_byte":3320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"210162056","text":"import sys\nimport numpy\nfrom galpy.df import dehnendf\nfrom galpy.util import bovy_plot\nfrom matplotlib import pyplot, cm\ndef plot_dfcorrections(plotfilename):\n niters= [1,2,3,4,5,10,15,20,25]\n bovy_plot.bovy_print(fig_height=7.,fig_width=8.)\n ii= 0\n # Load DF\n pyplot.subplot(2,1,1)\n dfc= dehnendf(beta=0.,correct=True,niter=niters[ii])\n bovy_plot.bovy_plot(dfc._corr._rs,\n numpy.log(dfc._corr._corrections[:,0]),\n '-',gcf=True,color=cm.jet(1.),lw=2.,zorder=1,\n xrange=[0.,5.],\n yrange=[-0.25,0.25],\n ylabel=r'$\\ln \\Sigma_{\\mathrm{out}}(R)-\\ln\\Sigma_{\\mathrm{DF}}(R)$')\n linthresh= 0.0001\n pyplot.yscale('symlog',linthreshy=linthresh)\n for ii,niter in enumerate(niters[1:]):\n dfcn= dehnendf(beta=0.,correct=True,niter=niter)\n dfcp= dehnendf(beta=0.,correct=True,niter=niter-1)\n bovy_plot.bovy_plot(dfc._corr._rs,\n numpy.log(dfcn._corr._corrections[:,0])-numpy.log(dfcp._corr._corrections[:,0]),\n '-',overplot=True,\n color=cm.jet(1.-(ii+1)/float(len(niters))),lw=2.,\n zorder=ii+2)\n pyplot.fill_between(numpy.linspace(0.,5.,2.),\n -linthresh*numpy.ones(2),\n linthresh*numpy.ones(2),color='0.9',\n zorder=0)\n bovy_plot.bovy_text(4.,-0.00008,r'$\\mathrm{linear\\ scale}$',\n backgroundcolor='w',size=16.)\n pyplot.subplot(2,1,2)\n bovy_plot.bovy_plot(dfc._corr._rs,\n 0.5*numpy.log(dfc._corr._corrections[:,1]),\n '-',gcf=True,color=cm.jet(1.),lw=2.,zorder=1,\n xrange=[0.,5.],\n yrange=[-0.25,0.25],\n xlabel=r'$R/R_0$',\n ylabel=r'$\\ln \\sigma_{R,\\mathrm{out}}(R)-\\ln\\sigma_{R,\\mathrm{DF}}(R)$')\n pyplot.yscale('symlog',linthreshy=linthresh)\n for ii,niter in enumerate(niters[1:]):\n dfcn= dehnendf(beta=0.,correct=True,niter=niter)\n dfcp= dehnendf(beta=0.,correct=True,niter=niter-1)\n bovy_plot.bovy_plot(dfc._corr._rs,\n numpy.log(dfcn._corr._corrections[:,1])-numpy.log(dfcp._corr._corrections[:,1]),\n '-',overplot=True,\n color=cm.jet(1.-(ii+1)/float(len(niters))),lw=2.,\n zorder=ii+2)\n pyplot.fill_between(numpy.linspace(0.,5.,2.),\n -linthresh*numpy.ones(2),\n linthresh*numpy.ones(2),color='0.9',\n zorder=0)\n bovy_plot.bovy_text(4.,-0.00008,r'$\\mathrm{linear\\ scale}$',\n backgroundcolor='w',size=16.)\n pyplot.tight_layout()\n bovy_plot.bovy_end_print(plotfilename)\n return None\n\nif __name__ == '__main__':\n plot_dfcorrections(sys.argv[1])\n","sub_path":"figure22.py","file_name":"figure22.py","file_ext":"py","file_size_in_byte":2989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"572843784","text":"# -*- coding: utf-8 -*-\nimport torch\nimport os\nimport argparse\n\nimport cv2\nimport time\nimport numpy as np\nimport visdom\nfrom torch.autograd import Variable\nfrom scipy import misc\n\nfrom semseg.dataloader.camvid_loader import camvidLoader\nfrom semseg.dataloader.cityscapes_loader import cityscapesLoader\nfrom semseg.loss import cross_entropy2d\nfrom semseg.metrics import scores\nfrom semseg.modelloader.EDANet import EDANet\nfrom semseg.modelloader.deeplabv3 import Res_Deeplab_101, Res_Deeplab_50\nfrom semseg.modelloader.drn import drn_d_22, DRNSeg, drn_a_asymmetric_18, drn_a_asymmetric_ibn_a_18, drnseg_a_50, drnseg_a_18, drnseg_e_22, drnseg_a_asymmetric_18, drnseg_a_asymmetric_ibn_a_18, drnseg_d_22, drnseg_d_38\nfrom semseg.modelloader.duc_hdc import ResNetDUC, ResNetDUCHDC\nfrom semseg.modelloader.enet import ENet\nfrom semseg.modelloader.enetv2 import ENetV2\nfrom semseg.modelloader.erfnet import erfnet\nfrom semseg.modelloader.fc_densenet import fcdensenet103, fcdensenet56, fcdensenet_tiny\nfrom semseg.modelloader.fcn import fcn, fcn_32s, fcn_16s, fcn_8s\nfrom semseg.modelloader.fcn_mobilenet import fcn_MobileNet, fcn_MobileNet_32s, fcn_MobileNet_16s, fcn_MobileNet_8s\nfrom semseg.modelloader.fcn_resnet import fcn_resnet18, fcn_resnet34, fcn_resnet18_32s, fcn_resnet18_16s, \\\n fcn_resnet18_8s, fcn_resnet34_32s, fcn_resnet34_16s, fcn_resnet34_8s, fcn_resnet50_32s, fcn_resnet50_16s, fcn_resnet50_8s\nfrom semseg.modelloader.segnet import segnet, segnet_squeeze, segnet_alignres, segnet_vgg19\nfrom semseg.modelloader.segnet_unet import segnet_unet\nfrom semseg.modelloader.sqnet import sqnet\n\n\ndef validate(args):\n init_time = str(int(time.time()))\n if args.vis:\n vis = visdom.Visdom()\n if args.dataset_path == '':\n HOME_PATH = os.path.expanduser('~')\n local_path = os.path.join(HOME_PATH, 'Data/CamVid')\n else:\n local_path = args.dataset_path\n local_path = os.path.expanduser(args.dataset_path)\n if args.dataset == 'CamVid':\n dst = camvidLoader(local_path, is_transform=True, split=args.dataset_type)\n elif args.dataset == 'CityScapes':\n dst = cityscapesLoader(local_path, is_transform=True, split=args.dataset_type)\n else:\n pass\n val_loader = torch.utils.data.DataLoader(dst, batch_size=1, shuffle=False)\n\n # if os.path.isfile(args.validate_model):\n if args.validate_model != '':\n model = torch.load(args.validate_model)\n else:\n try:\n model = eval(args.structure)(n_classes=args.n_classes, pretrained=args.init_vgg16)\n except:\n print('missing structure or not support')\n exit(0)\n if args.validate_model_state_dict != '':\n try:\n model.load_state_dict(torch.load(args.validate_model_state_dict, map_location='cpu'))\n except KeyError:\n print('missing key')\n if args.cuda:\n model.cuda()\n # some model load different mode different performance\n model.eval()\n # model.train()\n\n gts, preds, errors, imgs_name = [], [], [], []\n for i, (imgs, labels) in enumerate(val_loader):\n print(i)\n # if i==1:\n # break\n img_path = dst.files[args.dataset_type][i]\n img_name = img_path[img_path.rfind('/')+1:]\n imgs_name.append(img_name)\n # print('img_path:', img_path)\n # print('img_name:', img_name)\n # print(labels.shape)\n # print(imgs.shape)\n # 将np变量转换为pytorch中的变量\n imgs = Variable(imgs, volatile=True)\n labels = Variable(labels, volatile=True)\n\n if args.cuda:\n imgs = imgs.cuda()\n labels = labels.cuda()\n\n outputs = model(imgs)\n loss = cross_entropy2d(outputs, labels)\n loss_np = loss.cpu().data.numpy()\n loss_np_float = float(loss_np)\n\n # print('loss_np_float:', loss_np_float)\n errors.append(loss_np_float)\n\n # 取axis=1中的最大值，outputs的shape为batch_size*n_classes*height*width，\n # 获取max后，返回两个数组，分别是最大值和相应的索引值，这里取索引值为label\n pred = outputs.cpu().data.max(1)[1].numpy()\n gt = labels.cpu().data.numpy()\n\n if args.save_result:\n if not os.path.exists('/tmp/'+init_time):\n os.mkdir('/tmp/'+init_time)\n pred_labels = outputs.cpu().data.max(1)[1].numpy()\n label_color = dst.decode_segmap(labels.cpu().data.numpy()[0]).transpose(2, 0, 1)\n pred_label_color = dst.decode_segmap(pred_labels[0]).transpose(2, 0, 1)\n\n label_color_cv2 = label_color.transpose(1, 2, 0)\n label_color_cv2 = cv2.cvtColor(label_color_cv2, cv2.COLOR_RGB2BGR)\n cv2.imwrite('/tmp/'+init_time+'/gt_{}'.format(img_name), label_color_cv2)\n\n pred_label_color_cv2 = pred_label_color.transpose(1, 2, 0)\n pred_label_color_cv2 = cv2.cvtColor(pred_label_color_cv2, cv2.COLOR_RGB2BGR)\n cv2.imwrite('/tmp/'+init_time+'/pred_{}'.format(img_name), pred_label_color_cv2)\n\n for gt_, pred_ in zip(gt, pred):\n gts.append(gt_)\n preds.append(pred_)\n\n # print('errors:', errors)\n # print('imgs_name:', imgs_name)\n\n errors_indices = np.argsort(errors).tolist()\n # print('errors_indices:', errors_indices)\n # for top_i in range(len(errors_indices)):\n # for top_i in range(10):\n # top_index = errors_indices.index(top_i)\n # # print('top_index:', top_index)\n # img_name_top = imgs_name[top_index]\n # print('img_name_top:', img_name_top)\n\n score, class_iou = scores(gts, preds, n_class=dst.n_classes)\n for k, v in score.items():\n print(k, v)\n\n for i in range(dst.n_classes):\n print(i, class_iou[i])\n\n\n# best validate: python validate.py --structure fcn32s --validate_model_state_dict fcn32s_camvid_9.pt\nif __name__=='__main__':\n # print('validate----in----')\n parser = argparse.ArgumentParser(description='training parameter setting')\n parser.add_argument('--structure', type=str, default='fcn32s', help='use the net structure to segment [ fcn32s ResNetDUC segnet ENet drn_d_22 ]')\n parser.add_argument('--validate_model', type=str, default='', help='validate model path [ fcn32s_camvid_9.pkl ]')\n parser.add_argument('--validate_model_state_dict', type=str, default='', help='validate model state dict path [ fcn32s_camvid_9.pt ]')\n parser.add_argument('--init_vgg16', type=bool, default=False, help='init model using vgg16 weights [ False ]')\n parser.add_argument('--dataset', type=str, default='CamVid', help='train dataset [ CamVid CityScapes ]')\n parser.add_argument('--dataset_path', type=str, default='~/Data/CamVid', help='train dataset path [ ~/Data/CamVid ~/Data/cityscapes ]')\n parser.add_argument('--dataset_type', type=str, default='val', help='dataset type [ train val test ]')\n parser.add_argument('--n_classes', type=int, default=12, help='train class num [ 12 ]')\n parser.add_argument('--vis', type=bool, default=False, help='visualize the training results [ False ]')\n parser.add_argument('--cuda', type=bool, default=False, help='use cuda [ False ]')\n parser.add_argument('--save_result', type=bool, default=False, help='save the val dataset prediction result [ False True ]')\n args = parser.parse_args()\n # print(args.resume_model)\n # print(args.save_model)\n print(args)\n validate(args)\n # print('validate----out----')\n","sub_path":"validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":7472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"542349817","text":"\"\"\"\n Copyright (c) 2020 Intel Corporation\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\"\"\"\n\nimport os\nfrom math import ceil\nfrom subprocess import run\n\nfrom mmcv.utils import Config\n\nfrom .mmdetection import MMDetectionExporter\nfrom ..registry import EXPORTERS\n\n\n@EXPORTERS.register_module()\nclass InstanceSegmentationExporter(MMDetectionExporter):\n\n def _export_to_openvino(self, args, tools_dir):\n config = Config.fromfile(args[\"config\"])\n height, width = self._get_input_shape(config)\n run(f'python3 {os.path.join(tools_dir, \"export.py\")} '\n f'{args[\"config\"]} '\n f'{args[\"load_weights\"]} '\n f'{args[\"save_model_to\"]} '\n f'--opset={self.opset} '\n f'openvino '\n f'--input_format {args[\"openvino_input_format\"]} '\n f'--input_shape {height} {width}',\n shell=True,\n check=True)\n\n @staticmethod\n def _get_input_shape(cfg):\n width, height = cfg.data.test.pipeline[1].img_scale\n size_divisor = cfg.data.train.dataset.pipeline[5]['size_divisor']\n width = ceil(width / size_divisor) * size_divisor\n height = ceil(height / size_divisor) * size_divisor\n return height, width\n","sub_path":"ote/ote/modules/exporters/instance_segmentation.py","file_name":"instance_segmentation.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"182314188","text":"from __future__ import (\n absolute_import,\n unicode_literals,\n)\n\nfrom typing import ( # noqa: F401 TODO Python 3\n AbstractSet,\n Iterable,\n Optional,\n)\n\nimport attr\nimport currint\nimport six\n\nfrom conformity.error import (\n ERROR_CODE_INVALID,\n Error,\n)\nfrom conformity.fields.basic import (\n Base,\n Constant,\n Integer,\n)\nfrom conformity.fields.structures import Dictionary\nfrom conformity.utils import (\n attr_is_int,\n attr_is_iterable,\n attr_is_optional,\n attr_is_set,\n attr_is_string,\n strip_none,\n)\n\n\n@attr.s\nclass Amount(Base):\n \"\"\"\n currint.Amount instances\n \"\"\"\n\n introspect_type = 'currint.Amount'\n valid_currencies = attr.ib(\n default=frozenset(currint.currencies.keys()),\n validator=attr_is_iterable(attr_is_string(), attr_is_set()),\n ) # type: AbstractSet[six.text_type]\n gt = attr.ib(default=None, validator=attr_is_optional(attr_is_int())) # type: Optional[int]\n gte = attr.ib(default=None, validator=attr_is_optional(attr_is_int())) # type: Optional[int]\n lt = attr.ib(default=None, validator=attr_is_optional(attr_is_int())) # type: Optional[int]\n lte = attr.ib(default=None, validator=attr_is_optional(attr_is_int())) # type: Optional[int]\n description = attr.ib(default=None, validator=attr_is_optional(attr_is_string())) # type: Optional[six.text_type]\n\n def errors(self, value):\n if not isinstance(value, currint.Amount):\n return [Error(\n 'Not a currint.Amount instance',\n code=ERROR_CODE_INVALID,\n )]\n\n errors = []\n if value.currency.code not in self.valid_currencies:\n errors.append(Error(\n 'Not a valid currency code',\n code=ERROR_CODE_INVALID,\n pointer='currency.code',\n ))\n if self.gt is not None and value.value <= self.gt:\n errors.append(Error(\n 'Value not > {}'.format(self.gt),\n code=ERROR_CODE_INVALID,\n pointer='value',\n ))\n if self.lt is not None and value.value >= self.lt:\n errors.append(Error(\n 'Value not < {}'.format(self.lt),\n code=ERROR_CODE_INVALID,\n pointer='value',\n ))\n if self.gte is not None and value.value < self.gte:\n errors.append(Error(\n 'Value not >= {}'.format(self.gte),\n code=ERROR_CODE_INVALID,\n pointer='value',\n ))\n if self.lte is not None and value.value > self.lte:\n errors.append(Error(\n 'Value not <= {}'.format(self.lte),\n code=ERROR_CODE_INVALID,\n pointer='value',\n ))\n return errors\n\n def introspect(self):\n return strip_none({\n 'type': self.introspect_type,\n 'description': self.description,\n 'valid_currencies': sorted(self.valid_currencies),\n 'gt': self.gt,\n 'gte': self.gte,\n 'lt': self.lt,\n 'lte': self.lte,\n })\n\n\nclass AmountDictionary(Dictionary):\n \"\"\"\n Amount dictionaries\n \"\"\"\n\n def __init__(\n self,\n valid_currencies=None, # type: Iterable[six.text_type]\n gt=None, # type: int\n gte=None, # type: int\n lt=None, # type: int\n lte=None, # type: int\n *args,\n **kwargs\n ):\n if valid_currencies is not None and (\n not hasattr(valid_currencies, '__iter__') or\n not all(isinstance(c, six.text_type) for c in valid_currencies)\n ):\n raise TypeError(\"'valid_currencies' must be an iterable of unicode strings\")\n\n if gt is not None and not isinstance(gt, int):\n raise TypeError(\"'gt' must be an int\")\n if gte is not None and not isinstance(gte, int):\n raise TypeError(\"'gte' must be an int\")\n if lt is not None and not isinstance(lt, int):\n raise TypeError(\"'lt' must be an int\")\n if lte is not None and not isinstance(lte, int):\n raise TypeError(\"'lte' must be an int\")\n\n super(AmountDictionary, self).__init__({\n 'currency': Constant(*(valid_currencies or currint.currencies.keys())),\n 'value': Integer(gt=gt, gte=gte, lt=lt, lte=lte),\n }, *args, **kwargs)\n","sub_path":"conformity/fields/currency.py","file_name":"currency.py","file_ext":"py","file_size_in_byte":4391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"504969575","text":"#\n# \tGiven a string and an integer k\n# \tyou need to reverse the first k characters for every 2k characters counting from the start of the string.\n# \tIf there are less than k characters left, reverse all of them.\n# \tIf there are less than 2k but greater than or equal to k characters,\n# \tthen reverse the first k characters and left the other as original.\n#\n\n\ndef reverse_string_one(string: str, k: int) -> str:\n\tstring_as_array = list(string)\n\tfor i in range(0, len(string_as_array), k*2):\n\t\tstring_as_array[i:i+k] = reversed(string_as_array[i:i+k])\n\n\treturn ''.join(string_as_array)\n\n#\n# Write a function that reverses a string.\n#\n\n\ndef reverse_string_two(string: str) -> str:\n\treturn string[::-1]\n\n\nif __name__ == \"__main__\":\n\treversed_one = reverse_string_one(\"Badr Choubai\", 4)\n\treversed_two = reverse_string_two(\"Badr Choubai\")\n\tprint(reversed_one, reversed_two)\n","sub_path":"Python/Codewars/reverse_string.py","file_name":"reverse_string.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"601791144","text":"# Task 1.\n\n\ndef shape(m):\n return len(m), len(m[0])\n\n\n# Task a.\ndef print_map(m, pos):\n n_rows, n_cols = shape(m)\n for i in range(n_cols):\n s = \"\"\n for j in range(n_rows):\n if (i, j) == pos:\n s += \"@\"\n elif m[i][j]:\n s += \".\"\n else:\n s += \"#\"\n print(s)\n\n# Task b.\n\n\ndef neighbours(m, pos):\n n_rows, n_cols = shape(m)\n x, y = pos\n mn = []\n shifts = [(0, 1), (1, 0), (0, -1), (-1, 0)]\n for dr, dc in shifts:\n next = x + dr, y + dc\n a, b = next\n if (a >= 0 and a < n_rows):\n if (b >= 0 and b < n_cols):\n if (m[a][b]):\n mn.append(next)\n return mn\n\n# Task c.\n\n\ndef find_route(m, initial):\n\n def find_route_for_pos(m, pos, s):\n n_rows, n_cols = shape(m)\n x, y = pos\n if (x == 0) | (y == 0) | (x == (n_rows - 1)) | (y == (n_cols - 1)):\n s.add(pos)\n return s\n list_of_neighbours = neighbours(m, pos)\n for neighbour in list_of_neighbours:\n if neighbour not in s:\n s.add(pos)\n result = find_route_for_pos(m, neighbour, s)\n if len(result) > 0:\n return result\n else:\n s.discard(pos)\n return set()\n\n return list(find_route_for_pos(m, initial, set()))\n\n\n# Task d.\ndef escape(m, initial):\n route = find_route(m, initial)\n for pos in route:\n print_map(m, pos)\n print(\"\\n\")\n\n\n# Task 2.\n\ndef hamming(seq1, seq2):\n result = 0\n if len(seq1) == len(seq2):\n length = len(seq1)\n for i in range(length):\n if seq1[i] != seq2[i]:\n result += 1\n return result\n\n\ndef hba1(path, distance):\n f = open(path)\n l = f.readlines()\n length = len(l)\n for i in range(length):\n l[i] = l[i].strip()\n m = len(l[0])\n n1 = 0\n n2 = 0\n for i in range(length - 1):\n for j in range(i + 1, length):\n if distance(l[i], l[j]) < m:\n m = distance(l[i], l[j])\n n1 = i\n n2 = j\n return n1, n2\n\n\n# Task 3.\n\n# Task a.\ndef kmers(seq, k=2):\n dic = {}\n i = 0\n j = k\n length = len(seq)\n while j <= length:\n s = seq[i:j]\n if s not in dic.keys():\n dic[s] = 1\n else:\n dic[s] = dic[s] + 1\n i += 1\n j += 1\n return dic\n\n\n# Task b.\n\n# Distance for 2.\ndef distance1(seq1, seq2):\n dic1 = kmers(seq1, 2)\n dic2 = kmers(seq2, 2)\n\n def l1(d1, d2):\n keys = d1.keys() | d2.keys()\n s = 0\n for ke in keys:\n s += abs(d1.get(ke, 0) - d2.get(ke, 0))\n return s\n\n return l1(dic1, dic2)\n\n\n# Distance for k.\ndef distance(seq1, seq2, k):\n dic1 = kmers(seq1, k)\n dic2 = kmers(seq2, k)\n\n keys = dic1.keys() | dic2.keys()\n s = 0\n for ke in keys:\n s += abs(dic1.get(ke, 0) - dic2.get(ke, 0))\n return s\n","sub_path":"Python/Homework1/stanislav_prikhodko_01.py","file_name":"stanislav_prikhodko_01.py","file_ext":"py","file_size_in_byte":2999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"135332050","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\n\n# Uncomment the next two lines to enable the admin:\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n # Examples:\n\n url(r'^$', 'bikefinder.views.map'),\n url(r'^map$', 'bikefinder.views.map'),\n url(r'^search$', 'bikefinder.views.search'),\n #url(r'^map/(.*)$', 'bikefinder.views.map_given'),\n #url(r'^list$', 'bikefinder.views.list'),\n url(r'^poi$', 'bikefinder.views.points_of_intrests'),\n url(r'^submit$', 'bikefinder.views.submit'),\n url(r'^neighborhood/([a-zA-Z]*)$', 'bikefinder.views.neighborhood'),\n # url(r'^clebikes/', include('clebikes.foo.urls')),\n\n # Uncomment the admin/doc line below to enable admin documentation:\n # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n url(r'^admin/', include(admin.site.urls)),\n)\nurlpatterns += staticfiles_urlpatterns()\n","sub_path":"clebikes/clebikes/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"616910821","text":"#defining cheese_and_crackers() wih two arguments\ndef cheese_and_crackers(cheese_count, boxes_of_crackers):\n print(f\"You have {cheese_count} cheeses!\")\n print(f\"You have {boxes_of_crackers} boxes of crackers!\")\n print(\"Man that's enough for a party!\")\n print(\"Get a blanket.\\n\")\n\n#calling cheese_and_crackers()\nprint(\"We can just give the function numbers directly:\")\ncheese_and_crackers(20,30)\n\n#assigning the values for the variables\nprint(\"OR, we can just give the function numbers directly:\")\namount_of_cheese=10\namount_of_crackers=50\ncheese=3\nboxes=4\n\n#calling cheese_and_crackers with different arguments\ncheese_and_crackers(amount_of_cheese, amount_of_crackers)\n\n#making the calculations inside the arguments and passing it\nprint(\"We can even do math inside too:\")\ncheese_and_crackers(10+20,5+6)\n\n#again making calculations with variables and number inside the arguments\nprint(\"And we can combine two variables and math:\")\ncheese_and_crackers(amount_of_cheese+100,amount_of_crackers+1000)\n\n#calling in the different way\nprint(\"calling with two variable addition\")\ncheese_and_crackers(amount_of_cheese%cheese, amount_of_crackers%boxes)\n\n#getting input from the users\nprint(\"function with input from the user\")\ncheese=int(input(\"No of cheese you want:\"))\ncrackers=int(input(\"No of crackers you want:\"))\n\ncheese_and_crackers(cheese,crackers)\n#my new function\ndef soundharya(*args, who_she_is=\"Engineer\"):\n arg1, arg2=args\n print(f\"She is doing {arg1} and {arg2}. She is a {who_she_is}\")\n\nsoundharya(\"programming\",\"photography\")\n\nchoice=\"painter\"\nprofession=\"photographer\"\nsoundharya(choice,profession)","sub_path":"ex19.py","file_name":"ex19.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"249489535","text":"from flask import Flask, render_template, jsonify, request\nfrom pymongo import MongoClient # pymongo를 임포트 하기(패키지 인스톨 먼저 해야겠죠?)\nfrom flask import Flask, render_template, jsonify, request\n\napp = Flask(__name__)\n\nclient = MongoClient('localhost', 27017) # mongoDB는 27017 포트로 돌아갑니다.\ndb = client.daldang # 'dbsparta'라는 이름의 db를 만듭니다.\n\n\n## HTML을 주는 부분\n@app.route('/')\ndef home():\n return render_template('index.html')\n\n\n@app.route('/memu/')\ndef memu(category):\n print(category)\n if category == 'cake':\n return render_template('cake.html')\n elif category == 'macaron':\n return render_template('macaron.html')\n else:\n return render_template('doughnut.html')\n\n\n# @app.route('/menu', methods=['GET'])\n# def memu():\n# category_receive = request.args.get('category_give')\n# print(category_receive)\n# return render_template('menu.html', category=category_receive)\n\n\n@app.route('/store', methods=['GET'])\ndef store():\n category_receive = request.args.get('category_give')\n print(category_receive)\n store_list = list(db.store.find({'category': category_receive}, {'_id': False}))\n return jsonify({'result': 'success', 'store': store_list})\n\n\nif __name__ == '__macaron__':\n app.run('0.0.0.0', port=5000, debug=True)\n\n# ## API 역할을 하는 부분\n# @app.route('/review', methods=['POST'])\n# def write_review():\n# # title_receive로 클라이언트가 준 title 가져오기\n# title_receive = request.form['title_give']\n# # author_receive로 클라이언트가 준 author 가져오기\n# author_receive = request.form['author_give']\n# # review_receive로 클라이언트가 준 review 가져오기\n# review_receive = request.form['review_give']\n#\n# # DB에 삽입할 review 만들기\n# review = {\n# 'title': title_receive,\n# 'author': author_receive,\n# 'review': review_receive\n# }\n# # reviews에 review 저장하기\n# db.reviews.insert_one(review)\n# # 성공 여부 & 성공 메시지 반환\n# return jsonify({'result': 'success', 'msg': '리뷰가 성공적으로 작성되었습니다.'})\n#\n#\n# @app.route('/review', methods=['GET'])\n# def read_reviews():\n# # 1. DB에서 리뷰 정보 모두 가져오기\n# reviews = list(db.reviews.find({}, {'_id': 0}))\n# # 2. 성공 여부 & 리뷰 목록 반환하기\n# return jsonify({'result': 'success', 'reviews': reviews})\n\n\nif __name__ == '__main__':\n app.run('0.0.0.0', port=5000, debug=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"565132339","text":"from pyomo.environ import SolverFactory\nimport warnings\nimport os\nfrom .results import PSSTResults\n\n\nPSST_WARNING = os.getenv('PSST_WARNING', 'ignore')\n\n\ndef solve_model(model, solver='glpk', solver_io=None, keepfiles=True, verbose=True, symbolic_solver_labels=True, is_mip=True, **kwargs):\n if solver == 'xpress':\n solver_factory = SolverFactory(solver, solver_io=solver_io, is_mip=is_mip)\n else:\n solver_factory = SolverFactory(solver, solver_io=solver_io)\n model.preprocess()\n if is_mip and solver=='scip':\n if 'solver_options' in kwargs:\n if 'limits/gap' in kwargs['solver_options']:\n solver_factory.options['limits/gap']=float(kwargs['solver_options']['limits/gap'])\n\n if solver=='gurobi':\n print('Kwargs: ', kwargs)\n if 'solver_options' in kwargs:\n if 'MIPGap' in kwargs['solver_options']:\n solver_factory.options['MIPGap'] = float(kwargs['solver_options']['MIPGap'])\n if 'TimeLimit' in kwargs['solver_options']:\n solver_factory.options['TimeLimit'] = float(kwargs['solver_options']['TimeLimit'])\n with warnings.catch_warnings():\n warnings.simplefilter(PSST_WARNING)\n solver_factory.solve(model, suffixes=['dual'], tee=verbose, keepfiles=keepfiles, symbolic_solver_labels=symbolic_solver_labels)\n\n return model\n","sub_path":"psst/solver/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"153458105","text":"\"\"\"\nCompute the benchmark score given a frozen score configuration and current benchmark data.\n\"\"\"\nimport argparse\nimport json\nimport math\nimport sys\nimport os\nimport re\nimport yaml\nimport importlib\n\nfrom tabulate import tabulate\nfrom pathlib import Path\nfrom collections import defaultdict\n\nfrom .generate_score_config import generate_bench_cfg\n\nTORCHBENCH_V0_REF_DATA = Path(__file__).parent.joinpath(\"configs/v0/config-v0.yaml\")\n\ndef _get_model_task(model_name):\n \"\"\"\n Helper function which extracts the task the model belongs to\n by iterating over the Model attributes.\n \"\"\"\n try:\n module = importlib.import_module(f'torchbenchmark.models.{model_name}', package=__name__)\n except:\n raise ValueError(f\"Unable to get task for model: {model_name}\")\n Model = getattr(module, 'Model')\n return Model.task.value\n\nclass TorchBenchScoreV0:\n def __init__(self, ref_data, spec, target):\n self.spec = spec\n self.target = target\n if not ref_data:\n ref_data = TORCHBENCH_V0_REF_DATA\n self.ref_data = ref_data\n self.weights = None\n self.norm = None\n\n # V0: setup weights and benchmark norms\n self._setup_weights()\n self._setup_benchmark_norms()\n\n def _setup_weights(self):\n \"\"\"\n Calculates the static benchmark weights by iterating the spec\n file and constructs a dictionary with (key, value) pair\n is (task, weight_for_benchmark_per_task)\n \"\"\"\n # Load the spec file\n with open(self.spec) as spec_file:\n self.spec = yaml.full_load(spec_file)\n\n self.weights = defaultdict(float)\n category_spec = self.spec['hierarchy']['model']\n domain_weight = 1.0/ len(category_spec)\n for domain in category_spec:\n tasks = category_spec[domain]\n task_weight = 1.0 / len(tasks)\n for task in tasks:\n benchmarks = tasks[task]\n benchmark_weight = 1.0 / len(benchmarks)\n self.weights[task] = domain_weight * task_weight * benchmark_weight\n\n def _setup_benchmark_norms(self):\n \"\"\"\n Helper function which gets the normalization values per benchmark\n by going through the reference data file.\n \"\"\"\n if self.ref_data == TORCHBENCH_V0_REF_DATA:\n with open(self.ref_data) as ref_file:\n ref = yaml.full_load(ref_file)\n self.norm = {b: ref['benchmarks'][b]['norm'] for b in ref['benchmarks']}\n else:\n self.norm = {b['name']: b['stats']['mean'] for b in self.ref_data['benchmarks']}\n\n def get_score_per_config(self, data, weighted_score=False):\n \"\"\"\n This function iterates over found benchmark dictionary\n and calculates the weight_sum and benchmark_score.\n A score_db is then constructed to calculate the cumulative\n score per config. Here config refers to device, mode and test\n configurations the benchmark was run on.\n\n For eg., if the benchmark was run in eval mode on a GPU in Torchscript JIT,\n config = (train, cuda, jit)\n\n This helper returns the score_db .\n\n \"\"\"\n found_benchmarks = defaultdict(lambda: defaultdict(list))\n score_db = defaultdict(float)\n\n # Construct a benchmark database by going over through the data file\n # for the run and update the dictionary by task and model_name\n for b in data['benchmarks']:\n name, mean = b['name'], b['stats']['mean']\n test, model_name, device, mode = re.match(r\"test_(.*)\\[(.*)\\-(.*)\\-(.*)\\]\", name).groups()\n config = (test, device, mode)\n task = _get_model_task(model_name)\n found_benchmarks[task][model_name].append((mean, config, name))\n\n for task, models in found_benchmarks.items():\n for name, all_configs in models.items():\n weight = self.weights[task] * (1.0/len(all_configs))\n for mean, config, benchmark in all_configs:\n benchmark_score = weight * math.log(self.norm[benchmark] / mean)\n score_db[config] += benchmark_score\n\n # Get the weights per config and calibrate it to the\n # target score\n if weighted_score:\n for config, score in score_db.items():\n score_db[config] = score * 0.125\n score_db[config] = self.target * math.exp(score)\n\n return score_db\n\n def compute_score(self, data):\n \"\"\"\n This API calculates the total V0 score for all the\n benchmarks that was run by reading the data (.json) file.\n The weights are then calibrated to the target score.\n \"\"\"\n score = 0.0\n score_db = self.get_score_per_config(data)\n score = sum(score_db.values())\n score = self.target * math.exp(score)\n return score\n\n def get_norm(self, data):\n return generate_bench_cfg(self.spec, data, self.target)\n","sub_path":"torchbenchmark/score/compute_score_v0.py","file_name":"compute_score_v0.py","file_ext":"py","file_size_in_byte":5005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"631505765","text":"from Modle.data_preprocessing import trainloader, testloader, classes,show\nfrom torch.autograd import Variable\nfrom Modle.LossAndOptimizer import optimizer\nfrom Modle.CNN import net\nfrom Modle.LossAndOptimizer import criterion\nimport torchvision as tv\nimport torch as t\n# 训练神经网络\n# 所有网络的训练流程都是类似的，不断地执行如下流程：\n# 输入数据\n# 前向传播+反向传播\n# 更新参数\nfor epoch in range(2):\n running_loss = 0.0\n for i, data in enumerate(trainloader, 0):\n inputs, labels = data\n inputs. labels = Variable(inputs), Variable(labels)\n\n # 梯度清零\n optimizer.zero_grad()\n\n # 前向传播和后向传播\n outputs = net(inputs)\n loss = criterion(outputs, labels)\n # 前向传播\n loss.backward()\n # 更新参数\n optimizer.step()\n\n # 打印日志信息\n running_loss += loss.data[0]\n # 每2000个batch打印一次训练状态\n if i%2000 == 1999:\n print('[%d, %5d] losss:%.3f' % (epoch+1, i+1, running_loss/2000))\n running_loss = 0.0\n\nprint('Finished Training')\n\ndataiter =iter(testloader)\nimages, labels = dataiter.next()\nprint('实际的label:', ''.join('%08s'%classes[labels[j]] for j in range(4)))\nshow(tv.utils.make_grid(images/2-0.5).resize_(400, 100))\n\n# 计算网络预测的label\noutputs = net(Variable(images))\n_, predicted = t.max(outputs.data, 1)\nprint('预测结果：',''.join('%5s'%classes[predicted[j]] for j in range(4)))\n\n# 计算分类正确的准确率\ncorrect = 0\ntotal = 0\nfor data in testloader:\n images, labels = data\n outputs = net(Variable(images))\n _,predicted = t.max(outputs.data, 1)\n total += labels.size(0)\n correct +=(predicted == labels).sum()\n\nprint('10000张测试集中的准确率为：%d %%'%(100 * correct/total))\n\n# 从cpu转到GPU\nif t.cuda.is_available():\n net.cuda()\n images = images.cuda()\n labels = labels.cuda()\n output = net(Variable(images))\n loss = criterion(output, Variable(labels))\n\n\n\n","sub_path":"Modle/trainModle.py","file_name":"trainModle.py","file_ext":"py","file_size_in_byte":2055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"478564847","text":"#Import\r\nimport numpy as np\r\n\r\nclass NeuralNetwork:\r\n def __init__(self, layers, alpha=0.1):\r\n self.W = []\r\n self.layers = layers\r\n self.alpha = alpha\r\n for i in np.arange(0, len(layers)-2): #cycling on all layers except the last two ones\r\n w = np.random.randn(layers[i]+1, layers[i+1]+1) #+1 is for bias trick but we didnt want it in the output layer\r\n self.W.append(w/np.sqrt(layers[i]))\r\n w = np.random.randn(layers[-2] + 1, layers[-1])\r\n self.W.append(w / np.sqrt(layers[-2]))\r\n\r\n #Return a string that rapresents the network\r\n def __repr__(self):\r\n return \"NeuralNetwork: {}\".format(\"-\".join(str(l) for l in self.layers))\r\n\r\n def sigmoid(self, x):\r\n return 1/(1+np.exp(-x))\r\n\r\n def sigmoid_deriv(self, x): #x already passed into sigmoid funcion\r\n return x*(1-x)\r\n\r\n def fit(self, X, y, epochs=1000, displayUpdate=100):\r\n X = np.c_[X, np.ones(X.shape[0])]\r\n for epoch in np.arange(0, epochs):\r\n for (x, target) in zip(X,y):\r\n self.fit_partial(x, target)\r\n if epoch==0 or (epoch+1)%displayUpdate==0:\r\n loss = self.calculate_loss(X,y)\r\n print(\"[INFO] epoch={}, loss={:.10f}\".format(epoch+1, loss))\r\n\r\n def fit_partial(self, x, y):\r\n A = [np.atleast_2d(x)] #list of output activations\r\n #I) Feedforeward\r\n for layer in np.arange(0, len(self.W)):\r\n net = A[layer].dot(self.W[layer])\r\n out = self.sigmoid(net)\r\n A.append(out)\r\n #II) Backpropagation\r\n error = A[-1]-y\r\n D = [error*self.sigmoid_deriv(A[-1])] #list of deltas for chain rule\r\n for layer in np.arange(len(A)-2, 0, -1):\r\n delta = D[-1].dot(self.W[layer].T)\r\n delta = delta * self.sigmoid_deriv(A[layer])\r\n D.append(delta)\r\n #reverse the delta's order NON FUNZIONA QUESTO COMANDO\r\n D[::-1]\r\n\r\n\r\n #III) Weight Update\r\n for layer in np.arange(0, len(self.W)):\r\n self.W[layer] += -self.alpha * A[layer].T.dot(D[len(A)-2-layer])\r\n\r\n def predict(self, X, addBias=True):\r\n p = np.atleast_2d(X)\r\n if addBias==True:\r\n p = np.c_[p, np.ones(p.shape[0])]\r\n for layer in np.arange(0, len(self.W)):\r\n p = self.sigmoid(np.dot(p, self.W[layer]))\r\n return p\r\n\r\n def calculate_loss(self, X, targets):\r\n targets = np.atleast_2d(targets)\r\n predictions = self.predict(X, addBias=False)\r\n loss = 0.5*np.sum((predictions-targets)**2)\r\n return loss\r\n\r\n","sub_path":"mylib/nn/neuralnetwork.py","file_name":"neuralnetwork.py","file_ext":"py","file_size_in_byte":2613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"546104382","text":"from kafka import KafkaProducer\r\nimport requests\r\nfrom pyspark.sql import SparkSession\r\nimport os\r\nos.environ['PYSPARK_SUBMIT_ARGS'] = '--packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.4.1 pyspark-shell'\r\nspark = SparkSession.builder.getOrCreate()\r\n\r\n\r\n# tab-separated files / topics\r\nurl_1 = 'https://raw.githubusercontent.com/platformps/Healthcare-Insurance-Data/master/BenefitsCostSharing_partOne.txt'\r\nurl_2 = 'https://raw.githubusercontent.com/platformps/Healthcare-Insurance-Data/master/BenefitsCostSharing_partTwo.txt'\r\nurl_3 = 'https://raw.githubusercontent.com/platformps/Healthcare-Insurance-Data/master/BenefitsCostSharing_partThree.txt'\r\nurl_4 = 'https://raw.githubusercontent.com/platformps/Healthcare-Insurance-Data/master/BenefitsCostSharing_partFour.txt'\r\nurl_5 = 'https://raw.githubusercontent.com/platformps/Healthcare-Insurance-Data/master/insurance.txt'\r\nurl_6 = 'https://raw.githubusercontent.com/platformps/Healthcare-Insurance-Data/master/PlanAttributes.csv'\r\ntopic_1 = 'BenefitsCostSharing_partOne'\r\ntopic_2 = 'BenefitsCostSharing_partTwo'\r\ntopic_3 = 'BenefitsCostSharing_partThree'\r\ntopic_4 = 'BenefitsCostSharing_partFour'\r\ntopic_5 = 'Insurance'\r\ntopic_6 = 'PlanAttributes'\r\n\r\n# csv files / topics\r\nurl_7 = 'https://raw.githubusercontent.com/platformps/Healthcare-Insurance-Data/master/Network.csv'\r\nurl_8 = 'https://raw.githubusercontent.com/platformps/Healthcare-Insurance-Data/master/ServiceArea.csv'\r\ntopic_7 = 'Network'\r\ntopic_8 = 'ServiceArea'\r\n\r\n\r\ndef kafka_producer(inpath, topic):\r\n # extract data from url and create a list of lines\r\n r=requests.get(inpath)\r\n text = r.text\r\n data_list = [data for data in text.splitlines()]\r\n print(f'Extracted {len(data_list)} lines from {inpath}')\r\n \r\n # instantiate a producer and send the data to a topic one line at a time\r\n producer = KafkaProducer(\r\n bootstrap_servers='localhost:9092',\r\n value_serializer=lambda v:v.encode('utf-8')) \r\n for line in data_list:\r\n producer.send(topic, line)\r\n producer.flush()\r\n print(f'Wrote data to Kafka topic: {topic}\\n')\r\n\r\n\r\ndef make_df_from_topic(topic, delimiter): \r\n # consume the data from kafka and return a streaming dataframe\r\n raw_kafka_df = spark.readStream\\\r\n .format('kafka')\\\r\n .option('kafka.bootstrap.servers', 'localhost:9092')\\\r\n .option('subscribe', topic)\\\r\n .option('startingOffsets', 'earliest')\\\r\n .load() \r\n \r\n # convert the data from byte back into readable text\r\n # and return a new \r\n kafka_value_df = raw_kafka_df.selectExpr('CAST(value AS STRING)')\r\n \r\n aggDF = kafka_value_df.writeStream\\\r\n .queryName('aggregates')\\\r\n .format('memory')\\\r\n .start()\r\n aggDF.awaitTermination(10)\r\n \r\n value_df = spark.sql(f'select * from aggregates')\r\n \r\n value_rdd = value_df.rdd.map(lambda i: i['value'].split(delimiter))\r\n header = value_rdd.first()\r\n df_with_schema = value_rdd.filter(lambda row: row!=header).toDF(header)\r\n \r\n return df_with_schema \r\n \r\n# Load dataframe into MongoDB\r\ndef load_into_mongo(df_schema, target_collection):\r\n df_schema.write\\\r\n .format('mongo') \\\r\n .mode('append') \\\r\n .option('database', 'health_insurance_marketplace_data') \\\r\n .option('collection', target_collection) \\\r\n .option('uri', 'mongodb://localhost')\\\r\n .save()\r\n print(f'\\nWrote data to Mongo collection: {target_collection}')\r\n \r\ndef main(inpath, topic, delimiter, target_collection):\r\n kafka_producer(inpath, topic)\r\n df_schema = make_df_from_topic(topic, delimiter)\r\n load_into_mongo(df_schema, target_collection)\r\n\r\nif __name__=='__main__':\r\n main()\r\n","sub_path":"Part2_SparkStreaming.py","file_name":"Part2_SparkStreaming.py","file_ext":"py","file_size_in_byte":3851,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"404010674","text":"from pytest import mark\nfrom unittest.mock import patch\n\nfrom django.test import TestCase\nfrom django.utils.timezone import now\nfrom django.db.utils import IntegrityError\n\nfrom .. import models\n\n\nclass SlugTest(TestCase):\n\n def test_slugify(self):\n instance = models.Slug(title='źdźbło w ściółce')\n instance.save()\n\n self.assertEquals(instance.slug, 'zdzblo-w-sciolce')\n\n def test_multiple_slugs(self):\n title = 'test'\n instance1 = models.Slug(title=title)\n instance2 = models.Slug(title=title)\n instance1.save()\n instance2.save()\n\n self.assertNotEquals(instance1.slug, instance2.slug)\n\n def test_created_ad(self):\n instance = models.CreatedAt()\n instance.save()\n\n self.assertEquals(instance.created_at.date(), now().date())\n\n def test_user(self):\n instance = models.User(user=1)\n instance.save()\n\n self.assertIsInstance(instance.user, int)\n self.assertEquals(instance.user, 1)\n\n def test_unique_user(self):\n instance1 = models.UniqueUser(user=1)\n instance2 = models.UniqueUser(user=1)\n instance1.save()\n\n with self.assertRaises(IntegrityError):\n instance2.save()\n\n def test_contact(self):\n instance = models.Contact(**{\n 'country': 'POL',\n 'address': 'address',\n 'zip_code': '31-416',\n 'city': 'Kraków',\n })\n instance.save()\n\n self.assertTrue(instance.pk)\n\n def test_optional_contact(self):\n instance = models.OptionalContact()\n instance.save()\n\n self.assertTrue(instance.pk)\n self.assertEquals(instance.country, None)\n self.assertEquals(instance.address, None)\n self.assertEquals(instance.zip_code, None)\n self.assertEquals(instance.city, None)\n\n\ndef test_user_type_mixin_is_not_company():\n user = models.UserType(user_type=models.UserType.USER_TYPE_CHOICES.person)\n\n assert not user.is_company\n\n\ndef test_user_type_mixin_is_company():\n user = models.UserType(user_type=models.UserType.USER_TYPE_CHOICES.dealer)\n\n assert user.is_company\n","sub_path":"test_app/test_app/app/tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":2151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"142304290","text":"from unittest import mock\n\nimport pytest\nfrom fastapi import HTTPException\nfrom fastapi.encoders import jsonable_encoder\nfrom pydantic import ValidationError\n\nfrom mlflow.gateway.config import RouteConfig\nfrom mlflow.gateway.providers.cohere import CohereProvider\nfrom mlflow.gateway.schemas import completions, embeddings\n\nfrom tests.gateway.tools import MockAsyncResponse\n\n\ndef completions_config():\n return {\n \"name\": \"completions\",\n \"route_type\": \"llm/v1/completions\",\n \"model\": {\n \"provider\": \"cohere\",\n \"name\": \"command\",\n \"config\": {\n \"cohere_api_key\": \"key\",\n },\n },\n }\n\n\ndef completions_response():\n return {\n \"id\": \"string\",\n \"generations\": [\n {\n \"id\": \"string\",\n \"text\": \"This is a test\",\n }\n ],\n \"prompt\": \"string\",\n \"headers\": {\"Content-Type\": \"application/json\"},\n }\n\n\n@pytest.mark.asyncio\nasync def test_completions():\n resp = completions_response()\n config = completions_config()\n with mock.patch(\n \"aiohttp.ClientSession.post\", return_value=MockAsyncResponse(resp)\n ) as mock_post:\n provider = CohereProvider(RouteConfig(**config))\n payload = {\n \"prompt\": \"This is a test\",\n }\n response = await provider.completions(completions.RequestPayload(**payload))\n assert jsonable_encoder(response) == {\n \"candidates\": [\n {\n \"text\": \"This is a test\",\n \"metadata\": {},\n }\n ],\n \"metadata\": {\n \"input_tokens\": None,\n \"output_tokens\": None,\n \"total_tokens\": None,\n \"model\": \"command\",\n \"route_type\": \"llm/v1/completions\",\n },\n }\n mock_post.assert_called_once()\n\n\n@pytest.mark.asyncio\nasync def test_completions_temperature_is_multiplied_by_5():\n resp = completions_response()\n config = completions_config()\n with mock.patch(\n \"aiohttp.ClientSession.post\", return_value=MockAsyncResponse(resp)\n ) as mock_post:\n provider = CohereProvider(RouteConfig(**config))\n payload = {\n \"prompt\": \"This is a test\",\n \"temperature\": 0.5,\n }\n await provider.completions(completions.RequestPayload(**payload))\n assert mock_post.call_args[1][\"json\"][\"temperature\"] == 0.5 * 5\n\n\ndef embeddings_config():\n return {\n \"name\": \"embeddings\",\n \"route_type\": \"llm/v1/embeddings\",\n \"model\": {\n \"provider\": \"cohere\",\n \"name\": \"embed-english-light-v2.0\",\n \"config\": {\n \"cohere_api_key\": \"key\",\n },\n },\n }\n\n\ndef embeddings_response():\n return {\n \"id\": \"bc57846a-3e56-4327-8acc-588ca1a37b8a\",\n \"texts\": [\"hello world\"],\n \"embeddings\": [\n [\n 3.25,\n 0.7685547,\n 2.65625,\n -0.30126953,\n -2.3554688,\n 1.2597656,\n ]\n ],\n \"meta\": [\n {\n \"api_version\": [\n {\n \"version\": \"1\",\n }\n ]\n },\n ],\n \"headers\": {\"Content-Type\": \"application/json\"},\n }\n\n\n@pytest.mark.asyncio\nasync def test_embeddings():\n resp = embeddings_response()\n config = embeddings_config()\n with mock.patch(\n \"aiohttp.ClientSession.post\", return_value=MockAsyncResponse(resp)\n ) as mock_post:\n provider = CohereProvider(RouteConfig(**config))\n payload = {\"text\": \"This is a test\"}\n response = await provider.embeddings(embeddings.RequestPayload(**payload))\n assert jsonable_encoder(response) == {\n \"embeddings\": [\n [\n 3.25,\n 0.7685547,\n 2.65625,\n -0.30126953,\n -2.3554688,\n 1.2597656,\n ]\n ],\n \"metadata\": {\n \"input_tokens\": None,\n \"output_tokens\": None,\n \"total_tokens\": None,\n \"model\": \"embed-english-light-v2.0\",\n \"route_type\": \"llm/v1/embeddings\",\n },\n }\n mock_post.assert_called_once()\n\n\n@pytest.mark.asyncio\nasync def test_param_model_is_not_permitted():\n config = embeddings_config()\n provider = CohereProvider(RouteConfig(**config))\n payload = {\n \"prompt\": \"This should fail\",\n \"max_tokens\": 5000,\n \"model\": \"something-else\",\n }\n with pytest.raises(HTTPException, match=r\".*\") as e:\n await provider.completions(completions.RequestPayload(**payload))\n assert \"The parameter 'model' is not permitted\" in e.value.detail\n assert e.value.status_code == 422\n\n\n@pytest.mark.parametrize(\"prompt\", [{\"set1\", \"set2\"}, [\"list1\"], [1], [\"list1\", \"list2\"], [1, 2]])\n@pytest.mark.asyncio\nasync def test_completions_throws_if_prompt_contains_non_string(prompt):\n config = completions_config()\n provider = CohereProvider(RouteConfig(**config))\n payload = {\"prompt\": prompt}\n with pytest.raises(ValidationError, match=r\"prompt\"):\n await provider.completions(completions.RequestPayload(**payload))\n","sub_path":"tests/gateway/providers/test_cohere.py","file_name":"test_cohere.py","file_ext":"py","file_size_in_byte":5409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"34792152","text":"import numpy as np\r\n \r\ndef termDocMat(corp, diclen, nfiles):\r\n matrix = np.zeros((diclen, nfiles),\"int\")\r\n for i, doc in enumerate(corp):\r\n j = 0\r\n for pair in doc:\r\n if j==pair[0]:\r\n matrix[j][i] = pair[1]\r\n else:\r\n while j 0)\n maxz = np.max(z[j+1])\n newz = np.linspace(minz, maxz, nz)\n interp = sciinterp.interp1d(z, P, assume_sorted=True)\n newpdf = interp(newz)\n newpdf = newpdf / sciint.trapz(newpdf, newz).reshape(-1, 1)\n sparse_idx, meta, _ = sparse_rep.build_sparse_representation(newz, newpdf, verbose=False)\n return sparse_idx, meta\n\n\n @classmethod\n def make_test_data(cls):\n SPARSE_IDX, META = cls.build_test_data()\n\n cls.test_data = dict(sparse=dict(gen_func=sparse, \\\n ctor_data=dict(xvals=META['xvals'], mu=META['mu'], sig=META['sig'],\\\n dims=META['dims'], sparse_indices=SPARSE_IDX),\\\n test_xvals=TEST_XVALS), )\n\n\nsparse = sparse_gen.create\n\nadd_class(sparse_gen)\n","sub_path":"src/qp/sparse_pdf.py","file_name":"sparse_pdf.py","file_ext":"py","file_size_in_byte":4448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"593146967","text":"import datetime\nimport re\n\nfrom django import forms\nfrom django.core.exceptions import ValidationError\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom model_utils import Choices\n\nfrom nsc.utils.datetime import get_today\n\nfrom .models import Policy\n\n\n# TODO The SearchForm is currently identical to the one in condition/forms.py\n# check later once the development is finished to see if it can be shared.\n\n\nclass SearchForm(forms.Form):\n\n CONSULTATION = Choices((\"open\", _(\"Open\")), (\"closed\", _(\"Closed\")))\n YES_NO_CHOICES = Choices((\"yes\", _(\"Yes\")), (\"no\", _(\"No\")))\n\n name = forms.CharField(label=_(\"Condition name\"), required=False)\n\n comments = forms.TypedChoiceField(\n label=_(\"Public comments\"),\n choices=CONSULTATION,\n widget=forms.RadioSelect,\n required=False,\n )\n\n affects = forms.TypedChoiceField(\n label=_(\"Who the condition affects\"),\n choices=Policy.AGE_GROUPS,\n widget=forms.RadioSelect,\n required=False,\n )\n\n screen = forms.TypedChoiceField(\n label=_(\"Current recommendation\"),\n choices=YES_NO_CHOICES,\n widget=forms.RadioSelect,\n required=False,\n )\n\n\nclass PolicyForm(forms.ModelForm):\n\n next_review = forms.CharField(\n required=False,\n label=_(\"Expected next review start date\"),\n help_text=_(\"Enter the year in which the policy will be reviewed next\"),\n )\n condition = forms.CharField(\n required=True,\n label=_(\"Expected next review start date\"),\n help_text=_(\"Use markdown to format the text\"),\n widget=forms.Textarea,\n )\n keywords = forms.CharField(\n required=False,\n label=_(\"Search keywords\"),\n help_text=_(\"Enter keywords which can help people find a condition.\"),\n error_messages={\n \"required\": _(\n \"Enter keywords to make it easier for people to find a condition.\"\n )\n },\n widget=forms.Textarea,\n )\n summary = forms.CharField(\n required=True,\n label=_(\"Plain English summary\"),\n help_text=_(\"Use markdown to format the text.\"),\n widget=forms.Textarea,\n error_messages={\n \"required\": _(\n \"Enter a simple description of the condition that people would find easy to understand.\"\n )\n },\n )\n background = forms.CharField(\n required=True,\n label=_(\"Review history\"),\n help_text=_(\"Use markdown to format the text\"),\n widget=forms.Textarea,\n error_messages={\n \"required\": _(\"Enter a simple description of the review process.\")\n },\n )\n\n class Meta:\n model = Policy\n fields = [\"next_review\", \"condition\", \"keywords\", \"summary\", \"background\"]\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n if self.instance.next_review:\n self.initial[\"next_review\"] = self.instance.next_review.year\n\n self.fields[\"condition\"].label = _(\"More about %s\" % self.instance.name)\n self.fields[\"keywords\"].widget.attrs.update({\"rows\": 3})\n\n def clean_next_review(self):\n value = self.cleaned_data[\"next_review\"]\n\n if not value:\n return None\n\n if re.match(r\"\\d{4}\", value) is None:\n raise ValidationError(_(\"Please enter a valid year\"))\n\n value = int(value)\n\n if value < get_today().year:\n raise ValidationError(_(\"The next review cannot be in the past\"))\n\n return datetime.date(year=value, month=1, day=1)\n","sub_path":"nsc/policy/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":3569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"270566682","text":"'''\r\nauthor: juzicode\r\naddress: www.juzicode.com\r\n公众号: 桔子code/juzicode\r\ndate: 2020.10.30\r\n'''\r\n \r\nimport time,threading,sys\r\nfrom threading import Thread\r\nfrom multiprocessing import Pipe\r\n\r\ndef thread_1(conn1):\r\n print('进入线程: thread_1')\r\n loop_cout = 100\r\n while True:\r\n time.sleep(0.5)\r\n \r\n #发送自身计数\r\n loop_cout += 1\r\n conn1.send(loop_cout)\r\n \r\n #接收线程2的计数\r\n msg = conn1.recv()\r\n print('thread_1:线程thread_2中循环计数:',msg)\r\n \r\n print('退出线程: thread_1' )\r\n \r\ndef thread_2(conn2):\r\n print('进入线程: thread_2')\r\n loop_cout = 200\r\n while True: \r\n time.sleep(0.5)\r\n\r\n #接收线程1的计数\r\n msg = conn2.recv()\r\n print('thread_2: 线程thread_1中循环计数:',msg)\r\n \r\n #发送自身计数\r\n loop_cout += 1\r\n conn2.send(loop_cout)\r\n\r\n print('退出线程: thread_2' )\r\n \r\nif __name__ == '__main__':\r\n print('-----欢迎来到 www.juzicode.com')\r\n print('-----公众号: 桔子code/juzicode \\n') \r\n #创建Pipe实例\r\n conn1,conn2=Pipe()\r\n #开启线程\r\n t1 = Thread(target=thread_1,args=(conn1,),name='thread_1',daemon=True)\r\n t2 = Thread(target=thread_2,args=(conn2,),name='thread_2',daemon=True)\r\n t1.start()\r\n t2.start() \r\n #进入主进程循环过程\r\n while True:\r\n cmd = input('\\n------>')\r\n if cmd == 'quit':\r\n time.sleep(1)\r\n break\r\n print('退出主线程') ","sub_path":"m10b-多线程通信/thread_comm_pipe.py","file_name":"thread_comm_pipe.py","file_ext":"py","file_size_in_byte":1576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"320160585","text":"def print_menu():\n print('our items:')\n for i in range(len(menu)):\n print(f'{i+1}.{menu[i]}')\n\nmenu = ['phở', 'cơm', 'bún', 'thịt chó', 'bún đậu']\n\nwhile True:\n choice = input('Hello chef, what do u want to do with today menu? (C, R, U, D)')\n choice = choice.upper()\n if choice == 'C':\n new_item = input('enter new item')\n menu.append(new_item)\n print_menu()\n elif choice == 'R':\n print_menu()\n elif choice == 'U':\n index = int(input('Enter update position')) - 1\n menu[index] = input('Enter new value')\n print_menu()\n elif choice == 'D':\n index = int(input('Enter update position')) - 1\n menu.pop(index)\n print_menu()\n else:\n print('invalid action')\n break\n","sub_path":"session3/crud.py","file_name":"crud.py","file_ext":"py","file_size_in_byte":787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"416796385","text":"from django.contrib.admin.filters import ChoicesFieldListFilter\nfrom django.utils.encoding import smart_unicode\nfrom django.utils.translation import ugettext_lazy as _\nfrom django_monitor.conf import STATUS_DICT\n\n\nclass MonitorFilter(ChoicesFieldListFilter):\n \"\"\"A custom list-filter to enable filtering by monitor-status.\"\"\"\n\n def __init__(self, field, request, params, model, model_admin,\n field_path=None):\n \"\"\"Extended to set lookup_kwarg & lookup_val.\"\"\"\n self.lookup_kwarg = 'status'\n # usually, lookup_vals are extracted from request.GET. But we have\n # intentionally removed ``status`` from GET before. (Have a look at\n # ``django_monitor.admin.MonitorAdmin.queryset`` to know why). So we'll\n # apply regex over the url:\n import re\n status_matches = re.findall(\n r'status=(?P%s)' % '|'.join(STATUS_DICT.keys()),\n request.get_full_path()\n )\n self.lookup_val = status_matches[0] if status_matches else None\n self.lookup_choices = STATUS_DICT.keys()\n super(MonitorFilter, self).__init__(field, request, params, model,\n model_admin, field_path)\n self.title = _(\"Moderation status\")\n\n def expected_parameters(self):\n \"\"\"Return the list of expected parameters.\"\"\"\n return [self.lookup_kwarg]\n\n def choices(self, cl):\n yield {\n 'selected': self.lookup_val is None,\n 'query_string': cl.get_query_string({}, [self.lookup_kwarg]),\n 'display': _('All')\n }\n for val in self.lookup_choices:\n yield {\n 'selected': smart_unicode(val) == self.lookup_val,\n 'query_string': cl.get_query_string({self.lookup_kwarg: val}),\n 'display': STATUS_DICT[val]\n }\n","sub_path":"django_monitor/filter.py","file_name":"filter.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"420135123","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# @Filename: util_function\n# @Date: 2017-01-19\n# @Author: Mark Wang\n# @Email: wangyouan@gmial.com\n\nimport datetime\nimport calendar\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\n\nfrom constant import Constant as const\nfrom constant import TIME_SEP\n\n\ndef date_as_float(dt):\n size_of_day = 1. / 366.\n size_of_second = size_of_day / (24. * 60. * 60.)\n days_from_jan1 = dt - datetime.datetime(dt.year, 1, 1)\n if not calendar.isleap(dt.year) and days_from_jan1.days >= 31 + 28:\n days_from_jan1 += datetime.timedelta(1)\n return dt.year + days_from_jan1.days * size_of_day + days_from_jan1.seconds * size_of_second\n\n\ndef sort_result(input_df):\n result_df = pd.DataFrame(index=input_df.index)\n for i in [const.MEAN_RETURN, const.SHARPE_RATIO]:\n for j in [const.ONE_MONTH, const.THREE_MONTH, const.SIX_MONTH, const.TWELVE_MONTH]:\n key = '{} {}'.format(i, j)\n result_df[key] = input_df[key]\n\n return result_df\n\n\ndef plot_date_picture(date_list, data_series, picture_title, picture_save_path):\n # get data series info\n data_series = data_series.shift(1) + 1\n data_series[0] = 1\n for i in range(1, len(data_series)):\n data_series[i] = data_series[i - 1] * data_series[i]\n\n # plot file and save picture\n plt.clf()\n fig = plt.figure(figsize=(15, 6))\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n plt.gca().xaxis.set_major_locator(mdates.YearLocator())\n plt.plot(date_list, data_series, 'r-')\n min_date = date_list[0]\n max_date = date_list[-1]\n plt.gca().set_xlim(min_date, max_date)\n fig.autofmt_xdate()\n fig.suptitle(picture_title)\n fig.savefig(picture_save_path)\n\n\ndef plot_sub_date_picture(data_series, picture_title, picture_save_path, period=1):\n # get data series info\n\n time_sep = TIME_SEP[::2]\n in_start_date = time_sep[period - 1]\n out_end_date = time_sep[period + 1]\n\n data_series = data_series[data_series.index >= in_start_date]\n data_series = data_series[data_series.index < out_end_date]\n\n wealth_series = (data_series + 1).cumprod()\n date_series = data_series.index\n\n # plot file and save picture\n plt.clf()\n fig = plt.figure(figsize=(15, 6))\n\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n plt.gca().xaxis.set_major_locator(mdates.YearLocator())\n plt.plot(date_series, wealth_series, 'r-')\n min_date = date_series[0]\n max_date = date_series[-1]\n plt.gca().set_xlim(min_date, max_date)\n fig.autofmt_xdate()\n fig.suptitle(picture_title)\n plt.axvline(time_sep[period], color='b')\n # print dir(fig)\n fig.text(0.28, 0.6, 'In-sample', fontsize=15)\n fig.text(0.7, 0.4, 'Out-of-sample', fontsize=15)\n fig.savefig(picture_save_path)\n\n\nif __name__ == '__main__':\n import os\n\n data_path = '/Users/warn/PycharmProjects/QuestionFromProfWang/CarryTrade/data/adjusted_return'\n data_file_path = os.path.join(data_path, '20160919_1m_updated_15_curr.p')\n\n df = pd.read_pickle(data_file_path)\n\n plot_sub_date_picture(df['15_currs_10_liquid_2_parts_1m'], 'test', 'test.png', 2)","sub_path":"CarryTrade/code/generate_new_table/util_function.py","file_name":"util_function.py","file_ext":"py","file_size_in_byte":3187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"130087710","text":"from django.urls import path\nfrom . import views\n\napp_name = \"main\"\n\nurlpatterns = [\n path('', views.home, name=\"home\"),\n path('details//', views.detail, name=\"detail\"),\n path('addbooks/', views.add_books, name=\"add_books\"),\n path('editbooks//', views.edit_books, name=\"edit_books\"),\n path('deletebooks/', views.delete_books, name=\"delete_book\"),\n path('addreview/', views.add_review, name=\"add_review\"),\n path('editreview///', views.edit_review, name=\"edit_review\"),\n path('deleteview///', views.delete_review, name=\"delete_review\"),\n\n]\n","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"417209383","text":"\"\"\"\nSource : https://github.com/tensorflow/models/blob/r2.9.0/official/nlp/modeling/layers/transformer_encoder_block.py\n\nRelevant Github Issue : https://github.com/tensorflow/models/issues/10927\n\"\"\"\n\nimport tensorflow as tf\n\n\ndef filter_kwargs(kwargs):\n \"\"\"In place removes unused options in kwargs.\n This function removes the construction signatures: e.g.\n number_attention_heads... in TransformerEncoderBlock. This is needed,\n otherwise base_layer.py in Keras will complain.\n Args:\n kwargs: keyword arguments to be filtered.\n \"\"\"\n # This is the union of signatures of TransformerEncoderBlock and\n # ReZeroTransformer. Every Transformer\n # block that uses compatible signature with TransformerEncoderBlock should\n # call this function before base constructor super().__init__(**kwargs).\n denylist = [\n 'num_attention_heads', 'intermediate_size', 'intermediate_activation',\n 'inner_dim', 'inner_activation', 'output_range', 'kernel_initializer',\n 'bias_initializer', 'kernel_regularizer', 'bias_regularizer',\n 'activity_regularizer', 'kernel_constraint', 'bias_constraint',\n 'use_bias', 'norm_first', 'norm_epsilon', 'output_dropout',\n 'attention_dropout', 'inner_dropout', 'attention_initializer',\n 'attention_axes', 'share_rezero'\n ]\n for unused_key in denylist:\n kwargs.pop(unused_key, None)\n\n\nclass TransformerEncoderBlock(tf.keras.layers.Layer):\n \"\"\"TransformerEncoderBlock layer.\n\n This layer implements the Transformer Encoder from\n \"Attention Is All You Need\". (https://arxiv.org/abs/1706.03762),\n which combines a `tf.keras.layers.MultiHeadAttention` layer with a\n two-layer feedforward network.\n\n References:\n [Attention Is All You Need](https://arxiv.org/abs/1706.03762)\n [BERT: Pre-training of Deep Bidirectional Transformers for Language\n Understanding](https://arxiv.org/abs/1810.04805)\n \"\"\"\n\n def __init__(self,\n num_attention_heads,\n inner_dim,\n inner_activation,\n output_range=None,\n kernel_initializer=\"glorot_uniform\",\n bias_initializer=\"zeros\",\n kernel_regularizer=None,\n bias_regularizer=None,\n activity_regularizer=None,\n kernel_constraint=None,\n bias_constraint=None,\n use_bias=True,\n norm_first=False,\n norm_epsilon=1e-12,\n output_dropout=0.0,\n attention_dropout=0.0,\n inner_dropout=0.0,\n attention_initializer=None,\n attention_axes=None,\n use_query_residual=True,\n key_dim=None,\n value_dim=None,\n output_last_dim=None,\n diff_q_kv_att_layer_norm=False,\n **kwargs):\n \"\"\"Initializes `TransformerEncoderBlock`.\n\n Note: If `output_last_dim` is used and `use_query_residual` is `True`, the\n `output_last_dim`'s value must equal the first input's last dimension for\n the query residual connection to work. This is because the residual\n connection after the multi-head-attention requires their dimensions to\n match. If `use_query_residual` is `False`, the `output_last_dim` dictactes\n the last dimension of the output of this module and the\n multi-head-attention.\n\n E.g. let's say input dims are `[batch_size, seq_dim, input_last_dim]`.\n Scenario 1: If `output_last_dim` is not `None`, then the output dims of this\n module would be `[batch_size, seq_dim, output_last_dim]`. Note `key_dim` is\n is overriden by `output_last_dim`.\n Scenario 2: If `output_last_dim` is `None` and `key_dim` is not `None`, then\n the output dims of this module would be `[batch_size, seq_dim, key_dim]`.\n Scenario 3: If the `output_last_dim` and `key_dim` are both `None`, the\n output dims would be `[batch_size, seq_dim, input_last_dim]`.\n\n Args:\n num_attention_heads: Number of attention heads.\n inner_dim: The output dimension of the first Dense layer in a two-layer\n feedforward network.\n inner_activation: The activation for the first Dense layer in a two-layer\n feedforward network.\n output_range: the sequence output range, [0, output_range) for slicing the\n target sequence. `None` means the target sequence is not sliced.\n kernel_initializer: Initializer for dense layer kernels.\n bias_initializer: Initializer for dense layer biases.\n kernel_regularizer: Regularizer for dense layer kernels.\n bias_regularizer: Regularizer for dense layer biases.\n activity_regularizer: Regularizer for dense layer activity.\n kernel_constraint: Constraint for dense layer kernels.\n bias_constraint: Constraint for dense layer kernels.\n use_bias: Whether to enable use_bias in attention layer. If set False,\n use_bias in attention layer is disabled.\n norm_first: Whether to normalize inputs to attention and intermediate\n dense layers. If set False, output of attention and intermediate dense\n layers is normalized.\n norm_epsilon: Epsilon value to initialize normalization layers.\n output_dropout: Dropout probability for the post-attention and output\n dropout.\n attention_dropout: Dropout probability for within the attention layer.\n inner_dropout: Dropout probability for the first Dense layer in a\n two-layer feedforward network.\n attention_initializer: Initializer for kernels of attention layers. If set\n `None`, attention layers use kernel_initializer as initializer for\n kernel.\n attention_axes: axes over which the attention is applied. `None` means\n attention over all axes, but batch, heads, and features.\n use_query_residual: Toggle to execute residual connection after attention.\n key_dim: `key_dim` for the `tf.keras.layers.MultiHeadAttention`. If\n `None`, we use the first `input_shape`'s last dim.\n value_dim: `value_dim` for the `tf.keras.layers.MultiHeadAttention`.\n output_last_dim: Final dimension of the output of this module. This also\n dictates the value for the final dimension of the\n multi-head-attention. When it's `None`, we use, in order of decreasing\n precedence, `key_dim` * `num_heads` or the first `input_shape`'s last\n dim as the output's last dim.\n diff_q_kv_att_layer_norm: If `True`, create a separate attention layer\n norm layer for query and key-value if `norm_first` is `True`. Invalid\n to set to `True` if `norm_first` is `False`.\n **kwargs: keyword arguments.\n \"\"\"\n filter_kwargs(kwargs)\n super().__init__(**kwargs)\n\n self._num_heads = num_attention_heads\n self._inner_dim = inner_dim\n self._inner_activation = inner_activation\n self._attention_dropout = attention_dropout\n self._attention_dropout_rate = attention_dropout\n self._output_dropout = output_dropout\n self._output_dropout_rate = output_dropout\n self._output_range = output_range\n self._kernel_initializer = tf.keras.initializers.get(kernel_initializer)\n self._bias_initializer = tf.keras.initializers.get(bias_initializer)\n self._kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)\n self._bias_regularizer = tf.keras.regularizers.get(bias_regularizer)\n self._activity_regularizer = tf.keras.regularizers.get(activity_regularizer)\n self._kernel_constraint = tf.keras.constraints.get(kernel_constraint)\n self._bias_constraint = tf.keras.constraints.get(bias_constraint)\n self._use_bias = use_bias\n self._norm_first = norm_first\n self._norm_epsilon = norm_epsilon\n self._inner_dropout = inner_dropout\n self._use_query_residual = use_query_residual\n self._key_dim = key_dim\n self._value_dim = value_dim\n self._output_last_dim = output_last_dim\n self._diff_q_kv_att_layer_norm = diff_q_kv_att_layer_norm\n if attention_initializer:\n self._attention_initializer = tf.keras.initializers.get(\n attention_initializer)\n else:\n self._attention_initializer = self._kernel_initializer\n self._attention_axes = attention_axes\n\n if self._diff_q_kv_att_layer_norm and not self._norm_first:\n raise ValueError(\"Setting `diff_q_and_kv_attention_layer_norm` to True\"\n \"when `norm_first` is False is invalid.\")\n\n def build(self, input_shape):\n if isinstance(input_shape, tf.TensorShape):\n input_tensor_shape = input_shape\n elif isinstance(input_shape, (list, tuple)):\n input_tensor_shape = tf.TensorShape(input_shape[0])\n else:\n raise ValueError(\n \"The type of input shape argument is not supported, got: %s\" %\n type(input_shape))\n einsum_equation = \"abc,cd->abd\"\n if len(input_tensor_shape.as_list()) > 3:\n einsum_equation = \"...bc,cd->...bd\"\n hidden_size = input_tensor_shape[-1]\n if hidden_size % self._num_heads != 0:\n raise ValueError(\n \"The input size (%d) is not a multiple of the number of attention \"\n \"heads (%d)\" % (hidden_size, self._num_heads))\n if self._key_dim is None:\n self._key_dim = int(hidden_size // self._num_heads)\n if self._output_last_dim is None:\n last_output_shape = hidden_size\n else:\n last_output_shape = self._output_last_dim\n\n common_kwargs = dict(\n bias_initializer=self._bias_initializer,\n kernel_regularizer=self._kernel_regularizer,\n bias_regularizer=self._bias_regularizer,\n activity_regularizer=self._activity_regularizer,\n kernel_constraint=self._kernel_constraint,\n bias_constraint=self._bias_constraint)\n self._attention_layer = tf.keras.layers.MultiHeadAttention(\n num_heads=self._num_heads,\n key_dim=self._key_dim,\n value_dim=self._value_dim,\n dropout=self._attention_dropout,\n use_bias=self._use_bias,\n kernel_initializer=self._attention_initializer,\n attention_axes=self._attention_axes,\n output_shape=self._output_last_dim,\n name=\"self_attention\",\n **common_kwargs)\n self._attention_dropout = tf.keras.layers.Dropout(rate=self._output_dropout)\n # Use float32 in layernorm for numeric stability.\n # It is probably safe in mixed_float16, but we haven't validated this yet.\n self._attention_layer_norm = (\n tf.keras.layers.LayerNormalization(\n name=\"self_attention_layer_norm\",\n axis=-1,\n epsilon=self._norm_epsilon,\n dtype=tf.float32))\n self._attention_layer_norm_kv = self._attention_layer_norm\n if self._diff_q_kv_att_layer_norm:\n self._attention_layer_norm_kv = (\n tf.keras.layers.LayerNormalization(\n name=\"self_attention_layer_norm_kv\",\n axis=-1,\n epsilon=self._norm_epsilon,\n dtype=tf.float32))\n\n self._intermediate_dense = tf.keras.layers.experimental.EinsumDense(\n einsum_equation,\n output_shape=(None, self._inner_dim),\n bias_axes=\"d\",\n kernel_initializer=self._kernel_initializer,\n name=\"intermediate\",\n **common_kwargs)\n policy = tf.keras.mixed_precision.global_policy()\n if policy.name == \"mixed_bfloat16\":\n # bfloat16 causes BERT with the LAMB optimizer to not converge\n # as well, so we use float32.\n # TODO(b/154538392): Investigate this.\n policy = tf.float32\n self._intermediate_activation_layer = tf.keras.layers.Activation(\n self._inner_activation, dtype=policy)\n self._inner_dropout_layer = tf.keras.layers.Dropout(\n rate=self._inner_dropout)\n self._output_dense = tf.keras.layers.experimental.EinsumDense(\n einsum_equation,\n output_shape=(None, last_output_shape),\n bias_axes=\"d\",\n name=\"output\",\n kernel_initializer=self._kernel_initializer,\n **common_kwargs)\n self._output_dropout = tf.keras.layers.Dropout(rate=self._output_dropout)\n # Use float32 in layernorm for numeric stability.\n self._output_layer_norm = tf.keras.layers.LayerNormalization(\n name=\"output_layer_norm\",\n axis=-1,\n epsilon=self._norm_epsilon,\n dtype=tf.float32)\n\n super(TransformerEncoderBlock, self).build(input_shape)\n\n def get_config(self):\n config = {\n \"num_attention_heads\":\n self._num_heads,\n \"inner_dim\":\n self._inner_dim,\n \"inner_activation\":\n self._inner_activation,\n \"output_dropout\":\n self._output_dropout_rate,\n \"attention_dropout\":\n self._attention_dropout_rate,\n \"output_range\":\n self._output_range,\n \"kernel_initializer\":\n tf.keras.initializers.serialize(self._kernel_initializer),\n \"bias_initializer\":\n tf.keras.initializers.serialize(self._bias_initializer),\n \"kernel_regularizer\":\n tf.keras.regularizers.serialize(self._kernel_regularizer),\n \"bias_regularizer\":\n tf.keras.regularizers.serialize(self._bias_regularizer),\n \"activity_regularizer\":\n tf.keras.regularizers.serialize(self._activity_regularizer),\n \"kernel_constraint\":\n tf.keras.constraints.serialize(self._kernel_constraint),\n \"bias_constraint\":\n tf.keras.constraints.serialize(self._bias_constraint),\n \"use_bias\":\n self._use_bias,\n \"norm_first\":\n self._norm_first,\n \"norm_epsilon\":\n self._norm_epsilon,\n \"inner_dropout\":\n self._inner_dropout,\n \"attention_initializer\":\n tf.keras.initializers.serialize(self._attention_initializer),\n \"attention_axes\": self._attention_axes,\n \"use_query_residual\":\n self._use_query_residual,\n \"key_dim\":\n self._key_dim,\n \"value_dim\":\n self._value_dim,\n \"output_last_dim\":\n self._output_last_dim,\n \"diff_q_kv_att_layer_norm\":\n self._diff_q_kv_att_layer_norm,\n }\n base_config = super(TransformerEncoderBlock, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))\n\n def call(self, inputs):\n \"\"\"Transformer self-attention encoder block call.\n\n Args:\n inputs: a single tensor or a list of tensors.\n `input tensor` as the single sequence of embeddings.\n [`input tensor`, `attention mask`] to have the additional attention\n mask.\n [`query tensor`, `key value tensor`, `attention mask`] to have separate\n input streams for the query, and key/value to the multi-head\n attention.\n\n Returns:\n An output tensor with the same dimensions as input/query tensor.\n \"\"\"\n if isinstance(inputs, (list, tuple)):\n if len(inputs) == 2:\n input_tensor, attention_mask = inputs\n key_value = None\n elif len(inputs) == 3:\n input_tensor, key_value, attention_mask = inputs\n else:\n raise ValueError(\"Unexpected inputs to %s with length at %d\" %\n (self.__class__, len(inputs)))\n else:\n input_tensor, key_value, attention_mask = (inputs, None, None)\n\n if self._output_range:\n if self._norm_first:\n source_tensor = input_tensor[:, 0:self._output_range, :]\n input_tensor = self._attention_layer_norm(input_tensor)\n if key_value is not None:\n key_value = self._attention_layer_norm_kv(key_value)\n target_tensor = input_tensor[:, 0:self._output_range, :]\n if attention_mask is not None:\n attention_mask = attention_mask[:, 0:self._output_range, :]\n else:\n if self._norm_first:\n source_tensor = input_tensor\n input_tensor = self._attention_layer_norm(input_tensor)\n if key_value is not None:\n key_value = self._attention_layer_norm_kv(key_value)\n target_tensor = input_tensor\n\n if key_value is None:\n key_value = input_tensor\n attention_output = self._attention_layer(\n query=target_tensor, value=key_value, attention_mask=attention_mask)\n attention_output = self._attention_dropout(attention_output)\n\n if self._norm_first:\n # Important to not combine `self._norm_first` and\n # `self._use_query_residual` into one if clause because else is only for\n # `_norm_first == False`.\n if self._use_query_residual:\n attention_output = source_tensor + attention_output\n else:\n if self._use_query_residual:\n attention_output = target_tensor + attention_output\n attention_output = self._attention_layer_norm(attention_output)\n\n if self._norm_first:\n source_attention_output = attention_output\n attention_output = self._output_layer_norm(attention_output)\n inner_output = self._intermediate_dense(attention_output)\n inner_output = self._intermediate_activation_layer(inner_output)\n inner_output = self._inner_dropout_layer(inner_output)\n layer_output = self._output_dense(inner_output)\n layer_output = self._output_dropout(layer_output)\n\n if self._norm_first:\n return source_attention_output + layer_output\n\n # During mixed precision training, layer norm output is always fp32 for now.\n # Casts fp32 for the subsequent add.\n layer_output = tf.cast(layer_output, tf.float32)\n return self._output_layer_norm(layer_output + attention_output)\n","sub_path":"python/ml4ir/base/model/layers/transformer_encoder_block.py","file_name":"transformer_encoder_block.py","file_ext":"py","file_size_in_byte":18844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"162771005","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom flask import current_app\nfrom flask.ext import restful\nfrom flask.ext.restful import fields, marshal\nfrom collections import OrderedDict\nfrom flod_common.outputs.output_csv import output_csv\n\nfrom domain.models import Address, Organisation, Person, \\\n BrregActivityCode, FlodActivityType, UmbrellaOrganisation, \\\n PersonOrgAssociationRole, OrganisationPersonAssociation, \\\n UmbrellaOrgMemberOrgAssociation, OrganisationInternalNote, District\nfrom validation.role_validators import (UserHasOrganisationAdminRoleValidator)\nfrom api import person_fields_admin, role_fields, address_fields, flod_activity_type_fields\n\n\nperson_roles_for_report = {\n 'person': fields.Nested(person_fields_admin),\n 'roles': fields.List(fields.Nested(role_fields))\n}\n\nbrreg_activity_code_fields_for_report = {\n 'description': fields.String(default=None)\n}\n\n\norganisation_fields_admin_for_report = {\n 'id': fields.Integer(default=None),\n 'name': fields.String(default=None),\n 'org_number': fields.String(default=None),\n 'org_form': fields.String(default=None),\n 'account_number': fields.String(default=None),\n\n 'email_address': fields.String(default=None),\n 'local_email_address': fields.String(default=None),\n 'phone_number': fields.String(default=None),\n 'telefax_number': fields.String(default=None),\n 'url': fields.String(default=None),\n 'uri': fields.String(default=None),\n\n 'tilholdssted_address': fields.Nested(address_fields, allow_null=True),\n 'business_address': fields.Nested(address_fields, allow_null=True),\n 'postal_address': fields.Nested(address_fields, allow_null=True),\n 'is_public': fields.Boolean,\n\n # activity codes\n 'brreg_activity_codes': fields.List(fields.Nested(brreg_activity_code_fields_for_report, allow_null=True), default=[]),\n\n 'people': fields.List(fields.Nested(person_roles_for_report, allow_null=True), default=[]),\n\n # Trondheim kommune specific fields\n 'flod_activity_types': fields.List(fields.Nested(flod_activity_type_fields, allow_null=True), default=[]),\n 'num_members_b20': fields.Integer(default=None),\n 'num_members': fields.Integer(default=None),\n 'description': fields.String(default=None),\n 'area': fields.Integer(default=None),\n 'recruitment_area': fields.Integer(default=None),\n 'registered_tkn': fields.Boolean,\n 'relevant_tkn': fields.Boolean,\n 'is_registered': fields.Boolean,\n 'is_deleted': fields.Boolean\n}\n\ndef get_fieldname_mapping():\n fieldname_mapping = OrderedDict()\n fieldname_mapping['name'] = 'Aktørens navn'\n fieldname_mapping['org_number'] = 'Organisasjonsnummer'\n fieldname_mapping['org_form'] = 'Organisasjonsform'\n\n fieldname_mapping['brreg_activity_codes_description'] = 'Kategori(er)'\n fieldname_mapping['flod_activity_types_name'] = 'Organisasjonens aktivitet(er)'\n\n fieldname_mapping['phone_number'] = 'Telefon'\n fieldname_mapping['telefax_number'] = 'Telefax'\n fieldname_mapping['email_address'] = 'Epost'\n fieldname_mapping['local_email_address'] = 'Epost 2'\n\n fieldname_mapping['recruitment_area'] = 'Rekrutteringsområde'\n\n fieldname_mapping['tilholdssted_address_address_line'] = 'Primært tilholdssted'\n fieldname_mapping['area'] = 'Bydel'\n fieldname_mapping['tilholdssted_address_postal_code'] = 'Postnummer'\n fieldname_mapping['tilholdssted_address_postal_city'] = 'Poststed'\n\n fieldname_mapping['business_address_address_line'] = 'Forretningsadresse'\n fieldname_mapping['business_address_postal_code'] = 'Postnummer'\n fieldname_mapping['business_address_postal_city'] = 'Poststed'\n\n fieldname_mapping['postal_address_address_line'] = 'Postadresse'\n fieldname_mapping['postal_address_postal_code'] = 'Postnummer'\n fieldname_mapping['postal_address_postal_city'] = 'Poststed'\n\n fieldname_mapping['account_number'] = 'Kontonummer'\n fieldname_mapping['num_members'] = 'Antall medlemmer'\n fieldname_mapping['num_members_b20'] = 'Antall medlemmer under 20 år'\n\n fieldname_mapping['relevant_tkn'] = 'Relevant for Trondheim kulturnettverk'\n fieldname_mapping['registered_tkn'] = 'Medlem i Trondheim kulturnettverk'\n fieldname_mapping['is_public'] = 'Samtykker til visning av informasjon på nett'\n\n fieldname_mapping['description'] = 'Beskrivelse'\n fieldname_mapping['url'] = 'url'\n\n fieldname_mapping['people_person_first_name'] = 'Fornavn'\n fieldname_mapping['people_person_last_name'] = 'Etternavn'\n fieldname_mapping['people_person_email_address'] = 'E-post-adresse'\n fieldname_mapping['people_person_phone_number'] = 'Telefon'\n fieldname_mapping['people_roles_role'] = 'Rolle'\n\n return fieldname_mapping\n\ndef get_fields_to_ignore():\n fields_to_ignore = [\n 'id',\n 'postal_address',\n 'business_address',\n 'tilholdssted_address',\n 'brreg_activity_codes',\n 'flod_activity_types',\n 'flod_activity_types_id',\n 'is_deleted',\n 'uri',\n 'is_registered',\n 'people_roles',\n 'people_person_roles',\n 'people_person_uri',\n 'people_person_postal_city',\n 'people_person_postal_code',\n 'people_person_address_line',\n 'people_person_status',\n 'people_person_national_identity_number',\n 'people_roles_id',\n 'people_person_id'\n ]\n return fields_to_ignore\n\nclass ExportOgranisationsResource(restful.Resource):\n\n @UserHasOrganisationAdminRoleValidator()\n def get(self):\n organisations = current_app.db_session.query(Organisation)\n organisations = organisations.filter(Organisation.is_deleted == False)\n\n districts = current_app.db_session.query(District)\n distr = districts.all()\n\n d = dict([(dd.id, dd.name) for dd in distr])\n\n orgs = organisations.order_by(Organisation.name).all()\n marshalled_org = marshal(orgs, organisation_fields_admin_for_report)\n\n for org in marshalled_org:\n if org[\"recruitment_area\"] is not None:\n org[\"recruitment_area\"] = d.get(int(org[\"recruitment_area\"]), \"\")\n\n if org[\"area\"] is not None:\n org[\"area\"] = d.get(int(org[\"area\"]), \"\")\n\n return output_csv(marshalled_org, 200, fieldname_mapping=get_fieldname_mapping(), fields_to_ignore=get_fields_to_ignore())\n","sub_path":"flod_organisations/api/export_report.py","file_name":"export_report.py","file_ext":"py","file_size_in_byte":6353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"46994207","text":"\"\"\"\n domonic.utils\n ====================================\n snippets etc\n\"\"\"\nimport typing\nimport random\nfrom re import sub\nfrom itertools import chain, islice\nfrom collections import Counter\n\n\nclass Utils(object):\n \"\"\" utils \"\"\"\n\n @staticmethod\n def case_camel(s: str):\n \"\"\" case_camel('camel-case') > 'camelCase' \"\"\"\n s = sub(r\"(_|-)+\", \" \", s).title().replace(\" \", \"\")\n return s[0].lower() + s[1:]\n\n @staticmethod\n def case_snake(s: str):\n \"\"\"\n snake('camelCase') # 'camel_case'\n \"\"\"\n return '_'.join(\n sub('([A-Z][a-z]+)', r' \\1',\n sub('([A-Z]+)', r' \\1',\n s.replace('-', ' '))).split()).lower()\n\n @staticmethod\n def case_kebab(s: str):\n \"\"\"\n kebab('camelCase') # 'camel-case'\n \"\"\"\n return '-'.join(\n sub(r\"(\\s|_|-)+\", \" \",\n sub(r\"[A-Z]{2,}(?=[A-Z][a-z]+[0-9]*|\\b)|[A-Z]?[a-z]+[0-9]*|[A-Z]|[0-9]+\",\n lambda mo: ' ' + mo.group(0).lower(), s)).split())\n\n @staticmethod\n def squash(the_list):\n \"\"\" turns a 2d array into a flat one \"\"\"\n return [inner for outer in the_list for inner in outer]\n\n @staticmethod\n def chunk(list, size):\n \"\"\" chunk a list into batches \"\"\"\n return [list[i:i + size] for i in range(0, len(list), size)]\n\n @staticmethod\n def dictify(arr):\n \"\"\" turns a list into a dictionary where the list items are the keys \"\"\"\n return dict().fromkeys(arr, 0)\n\n @staticmethod\n def is_empty(some_str):\n return (not some_str.strip())\n\n @staticmethod\n def unique(some_arr):\n return list(set(some_arr))\n\n @staticmethod\n def chunks(iterable, size, format=iter):\n \"\"\" Iterate over any iterable (list, set, file, stream, strings, whatever), of ANY size \"\"\"\n it = iter(iterable)\n while True:\n yield format(chain((it.next(),), islice(it, size - 1)))\n # >>> l = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\"]\n # >>> for chunk in chunks(l, 3, tuple):\n # ... print chunk\n\n @staticmethod\n def clean(lst):\n \"\"\" removes falsy values (False, None, 0 and “”) from a list \"\"\"\n return list(filter(None, lst))\n\n @staticmethod\n def get_vowels(string):\n return [each for each in string if each in 'aeiou']\n\n @staticmethod\n def untitle(str):\n \"\"\" fooBar \"\"\"\n return str[:1].lower() + str[1:]\n\n @staticmethod\n def merge_dictionaries(a, b):\n return {**a, **b}\n\n @staticmethod\n def to_dictionary(keys, values):\n return dict(zip(keys, values))\n\n @staticmethod\n def most_frequent(list):\n return max(set(list), key=list.count)\n\n @staticmethod\n def anagram(first, second):\n return Counter(first) == Counter(second)\n\n @staticmethod\n def frequency(data):\n freq = {}\n for elem in data:\n if elem in freq:\n freq[elem] += 1\n else:\n freq[elem] = 1\n return freq\n\n @staticmethod\n def init_assets(dir='assets'):\n from domonic.terminal import mkdir, touch\n mkdir(f\"{dir}\")\n mkdir(f\"{dir}/js\")\n mkdir(f\"{dir}/css\")\n mkdir(f\"{dir}/img\")\n touch(f\"{dir}/js/master.js\")\n touch(f\"{dir}/css/style.css\")\n return\n\n @staticmethod\n def url2file(url):\n \"\"\"\n gen a safe filename from a url\n \"\"\"\n import urllib\n url = \"_\".join(url.split(\"/\"))\n url = \"__\".join(url.split(\":\"))\n filename = urllib.parse.quote_plus(url, '')\n return filename\n\n @staticmethod\n def permutations(word):\n from itertools import permutations\n return [''.join(perm) for perm in list(permutations(word))]\n\n @staticmethod\n def random_color(self):\n ''' TODO - remove in 0.3 as we have color class. '''\n r = lambda: random.randint(0, 255)\n return str('#%02X%02X%02X' % (r(), r(), r()))\n\n @staticmethod\n def escape(s):\n chars = {\n \"&\": \"&\",\n '\"': \""\",\n \"'\": \"'\",\n \">\": \">\",\n \"<\": \"<\"\n }\n return \"\".join(chars.get(c, c) for c in s)\n\n @staticmethod\n def unescape(s):\n s = s.replace(\"<\", \"<\")\n s = s.replace(\">\", \">\")\n s = s.replace(\""\", '\"')\n s = s.replace(\"'\", \"'\")\n s = s.replace(\"&\", \"&\")\n return s\n\n @staticmethod\n def replace_between(content, match, replacement, start=0, end=0):\n front = content[0:start]\n mid = content[start:end]\n end = content[end:len(content)]\n mid = mid.replace(match, replacement)\n return front + mid + end\n\n # truncate()\n # return mystr + \"...\"\n\n # def any(arr):\n # \"\"\" given a list. return 1 random item \"\"\"\n # return random.choice(arr)\n\n # def any_iter(arr):\n # ''' given a list. returns random until expired '''\n # random.shuffle(arr)\n # return (x for x in arr)\n\n # @staticmethod\n # def unless(value, condition):\n # return value if condition else not value\n # if any(pred(x.item) for x in sequence):\n\n # TODO -\n # def beautfiy(): # make nice\n # def uglify(): # make not nice\n # def simplify(sentence): # reduce a sentence to its meaning. remove uneeded words.\n # def factualise():  # returns json document of modelled info from general text\n","sub_path":"domonic/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"626841350","text":"import socketserver\nimport subprocess\nimport logging\nimport xml.etree.ElementTree\nimport json\nimport io\n\n\nlogging.basicConfig(level=logging.INFO)\nlog = logging.getLogger('botnet.victim.server')\n\n\nclass MyTCPHandler(socketserver.BaseRequestHandler):\n\n def handle(self):\n request = self.request.recv(1024).strip().decode()\n log.debug('Received: %s', request)\n response = self.handle_request(request)\n self.request.sendall(response.encode())\n\n def execute(self, command, timeout):\n log.debug('Executing command: %s with timeout: %s', command, timeout)\n with subprocess.Popen(command, stdout=subprocess.PIPE) as proc:\n\n try:\n output, errors = proc.communicate(timeout=timeout)\n except subprocess.TimeoutExpired:\n log.error(\n 'Timeout %s exceeded for command: %s' % (timeout, command))\n return proc.kill()\n\n if errors:\n log.error(errors)\n\n if output:\n # red = '\\033[00;31m'\n # green = '\\033[00;32m'\n # blue = '\\033[00;36m'\n # white = '\\033[00;39m'\n message = output.decode()\n\n log.debug('Output: {message}'.format(**locals()))\n return message\n\n def handle_request(self, request):\n xmlfile = io.StringIO(request)\n root = xml.etree.ElementTree.parse(xmlfile).getroot()\n output = []\n\n for command in root.findall('./command'):\n cmd = command.text.split()\n timeout = float(command.get('timeout', 1))\n stdout = self.execute(cmd, timeout)\n output.append({\n 'command': cmd,\n 'timeout': timeout,\n 'stdout': stdout,\n })\n return json.dumps(output)\n\n\nif __name__ == \"__main__\":\n HOST, PORT = \"localhost\", 1337\n\n log.info('Create the server, binding to localhost on port 9999')\n server = socketserver.TCPServer((HOST, PORT), MyTCPHandler)\n\n # this will keep running until you interrupt the program with Ctrl-C\n log.info('Server activated')\n server.serve_forever()\n","sub_path":"bin/2016-11-29/botnet/victim.py","file_name":"victim.py","file_ext":"py","file_size_in_byte":2179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"413177787","text":"\n'''\nCopyright (c) 2016, Dylan Carleton Gundlach\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n'''\n\n\nimport os\nimport sys\nimport io\n\nfrom project.Perf import *\n\n#from multiprocessing import Process, Queue, Lock #Queues are thread and process safe.\nimport multiprocessing\nimport threading\n#int_useCores = max(1, multiprocessing.cpu_count() - 1)\n\ndef TargetThreadData_threadMake(TargetThreadData_data):\n\t#print(\"concurrent make\")\n\tTargetThreadData_data.target.rule(TargetThreadData_data)\n\t#TargetThreadData_data.perf.compileSrcT += 1\n\treturn TargetThreadData_data\n\t\n#input/output for a thread\nclass TargetThreadData:\n\tdef __init__(self, target): #, lock\n\t\tself.target = target\n\t\tself.perf = Perf() #by creating a seperate perf for every thread, then later adding them together, the need for synchronization of perf is avoided\n\t\t#self.lock = lock #broken\n\nclass Target:\n\tdef __init__(self, str_target, lst_target_deps, perf):\n\t#def __init__(self, str_target, lst_target_deps):\n\t\t#if(str_target == \"Kits\\\\10\\\\Include\\\\10.0.16299.0\\\\ucrt\\\\crtdbg.h\"):\n\t\t#\tinput()\n\t\t#if(str_target == \"/usr/include/libunwind.h /usr/include/libunwind-x86_64.h\"):\n\t\t#\tprint(\"Target::__init__\")\n\t\t#\tinput()\n\t\tself.str_target = str_target\n\t\tself.lst_target_deps = lst_target_deps\n\t\tself.perf = perf\n\n\tdef makeRec(self, int_numThreads):\n\t\t#TargetThreadData(self, perf)\n\t\t#\n\t\tlst_lst_target_layers = []\n\t\tset_target_dirty = set()\n\t\tself.b_addToBuildTree(lst_lst_target_layers, 0, set(), set(), set_target_dirty)\n\t\t#print(lst_lst_target_layers)\n\t\t#input()\n\t\t#\n\t\t#creation of directories needs to happen seperately, because \n\t\t#(checking for the existence of a directory, then conditionally creating a directory) can't\n\t\t#be multithreaded, because that's a race condition.\n\t\tfor target_dirty in set_target_dirty:\n\t\t\tif(target_dirty.str_target != None): #phony targets\n\t\t\t\t#print(target_dirty.str_target)\n\t\t\t\t#print(os.path.dirname(target_dirty.str_target))\n\t\t\t\t#input()\n\t\t\t\tif not os.path.exists(os.path.dirname(target_dirty.str_target)):\n\t\t\t\t\t#print(target_dirty.str_target)\n\t\t\t\t\t#print(os.path.dirname(target_dirty.str_target))\n\t\t\t\t\t#input()\n\t\t\t\t\tos.makedirs(os.path.dirname(target_dirty.str_target))\n\t\t#\n\t\tif(len(set_target_dirty) > 1):\n\t\t\t#print(list(map(\n\t\t\t#\tlambda lst_target_layer: list(map(\n\t\t\t#\t\t(lambda target_t: target_t.str_target),\n\t\t\t#\t\tlst_target_layer\n\t\t\t#\t)),\n\t\t\t#\tlst_lst_target_layers\n\t\t\t#)))\n\t\t\t#input()\n\t\t\t#\n\t\t\t#lock = threading.Lock() #broken\n\t\t\tlst_lst_TargetThreadData_layers = list(map(\n\t\t\t\tlambda lst_target_layer: list(map(\n\t\t\t\t\t(lambda target_t: TargetThreadData(target_t)),\n\t\t\t\t\tlst_target_layer\n\t\t\t\t)),\n\t\t\t\tlst_lst_target_layers\n\t\t\t))\n\t\t\t#\n\t\t\twith multiprocessing.Pool(int_numThreads) as p:\n\t\t\t\tfor lst_TargetThreadData_layer in reversed(lst_lst_TargetThreadData_layers):\n\t\t\t\t\tif(len(lst_TargetThreadData_layer) != 0):\n\t\t\t\t\t\tif(int_numThreads == 1):\n\t\t\t\t\t\t\tlst_TargetThreadData_results = map(\n\t\t\t\t\t\t\t\tTargetThreadData_threadMake, \n\t\t\t\t\t\t\t\tlst_TargetThreadData_layer\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tlst_TargetThreadData_results = p.map(\n\t\t\t\t\t\t\t\tTargetThreadData_threadMake, \n\t\t\t\t\t\t\t\tlst_TargetThreadData_layer\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t#print(lst_TargetThreadData_results)\n\t\t\t\t\t\t#input()\n\t\t\t\t\t\t#\n\t\t\t\t\t\tfor TargetThreadData_result in lst_TargetThreadData_results:\n\t\t\t\t\t\t\t#print(\"did it mutate?\")\n\t\t\t\t\t\t\t#TargetThreadData_result.perf.print()\n\t\t\t\t\t\t\tself.perf.add(TargetThreadData_result.perf)\n\t\telif(len(set_target_dirty) == 1):\n\t\t\tTargetThreadData_data = TargetThreadData(list(set_target_dirty)[0])\n\t\t\tlist(set_target_dirty)[0].rule(TargetThreadData_data)\n\t\t\t#\n\t\t\tself.perf.add(TargetThreadData_data.perf)\n\n\t#implement this\n\tdef rule(self, TargetThreadData_data):\n\t\tassert(False)\n\n\t#\n\t\n\t#stores the layers of the build tree. It's stored in layers instead of as a tree\n\t#because when it's run in parallel it will go layer by layer, doing all the dependancy files,\n\t#then the precompiled headers, etc. This is so that the layers don't get garbled up: it's \n\t#more intuitive to see a bunch of object files being compiled all at once, then see\n\t#them broken up with generating dependancy files, and doing other things. The loss\n\t#in efficiency is pretty small, the only time that threads spend waiting is for the last\n\t#n-1 (where n is the number of threads) tasks being processed on each layer. Overall this\n\t#inefficiency will probably add up to a couple seconds on most full builds. A small\n\t#price to pay for keeping things in order. Also this way makes the code easier.\n\t#\n\t#returns whether it's dirty or not\n\tdef b_addToBuildTree(self, lst_lst_target_layers, int_layer, set_str_traversed, set_str_dirty, set_target_dirty):\n\t\tif(self.str_target in set_str_dirty):\n\t\t\treturn True\n\t\telif(self in set_str_traversed):\n\t\t\treturn False\n\t\telse:\n\t\t\tset_str_traversed.add(self.str_target)\n\t\t\t#\n\t\t\tif(len(lst_lst_target_layers) <= int_layer):\n\t\t\t\tlst_lst_target_layers.append([])\n\t\t\t#\n\t\t\tb_childrenDirty = False\n\t\t\tfor dep in self.lst_target_deps:\n\t\t\t\tif(dep.b_addToBuildTree(lst_lst_target_layers, int_layer+1, set_str_traversed, set_str_dirty, set_target_dirty)):\n\t\t\t\t\tb_childrenDirty = True\n\t\t\t#\n\t\t\tb_selfDirty = self._b_dirty()\n\t\t\tif(b_childrenDirty or b_selfDirty):\n\t\t\t\tlst_lst_target_layers[int_layer].append(self)\n\t\t\t\tset_str_dirty.add(self.str_target)\n\t\t\t\tset_target_dirty.add(self)\n\n\tdef _b_dirty(self):\n\t\tif(self.str_target == None): #phony\n\t\t\treturn False\n\t\telse:\n\t\t\tif(os.path.isfile(self.str_target)):\n\t\t\t\ttargetTime = self._findTargetTime()\n\t\t\t\tfor target_dep in self.lst_target_deps:\n\t\t\t\t\tif(target_dep._b_dirty()):\n\t\t\t\t\t\treturn True\n\t\t\t\t\telif(target_dep._findTargetTime() > targetTime):\n\t\t\t\t\t\treturn True\n\t\t\t\treturn False\n\t\t\telse:\n\t\t\t\treturn True\n\n\tdef _findTargetTime(self):\n\t\treturn(os.stat(self.str_target).st_mtime)\n\t\n\nclass PhonyTarget(Target):\n\tdef __init__(self, lst_target_deps, perf):\n\t\tsuper().__init__(None, lst_target_deps, perf)\n\n\tdef rule(self, TargetThreadData_data):\n\t\tassert(True)\n\n\nclass LeafTarget(Target):\n\tdef __init__(self, str_target):\n\t\tsuper().__init__(str_target, [], None)\n\n\tdef rule(self, TargetThreadData_data):\n\t\tassert(True)\n\n","sub_path":"samples/use_precompiled_headers/dcg_build_system/scripts/target/Target.py","file_name":"Target.py","file_ext":"py","file_size_in_byte":7032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"206495265","text":"\"\"\"\n\nhttps://leetcode.com/problems/how-many-numbers-are-smaller-than-the-current-number/\nGiven the array nums, for each nums[i] find out how many numbers in the array are smaller than it. That is, for each nums[i] you have to count the number of valid j's such that j != i and nums[j] < nums[i].\n\nReturn the answer in an array.\n\n\n\nExample 1:\n\nInput: nums = [8,1,2,2,3]\nOutput: [4,0,1,1,3]\nExplanation:\nFor nums[0]=8 there exist four smaller numbers than it (1, 2, 2 and 3).\nFor nums[1]=1 does not exist any smaller number than it.\nFor nums[2]=2 there exist one smaller number than it (1).\nFor nums[3]=2 there exist one smaller number than it (1).\nFor nums[4]=3 there exist three smaller numbers than it (1, 2 and 2).\nExample 2:\n\nInput: nums = [6,5,4,8]\nOutput: [2,1,0,3]\nExample 3:\n\nInput: nums = [7,7,7,7]\nOutput: [0,0,0,0]\n\n\nConstraints:\n\n2 <= nums.length <= 500\n0 <= nums[i] <= 100\n\"\"\"\n\n\nclass Solution:\n \"\"\"\n So the idea is to sort the array in reverse order and keep on adding the elements in the a dictionay how far each element is from the last element\n\n \"\"\"\n\n def smallerNumbersThanCurrent(self, nums):\n nums1=sorted(nums,reverse=True) ## sort the numbers in reverse order\n out=[]\n dict={}\n n=len(nums1)\n for i in range(n):\n dict[nums1[i]]=n-(i+1)\n\n #print (dict)\n\n for a in nums:\n out.append(dict[a])\n\n return out\n\n\n# class Solution:\n# def smallerNumbersThanCurrent(self, nums):\n# counts = [0] * 101\n# print(counts)\n# for a in nums:\n# counts[a] += 1\n# print(\"\\n\")\n# print(counts)\n# for i in range(1, 101):\n# counts[i] += counts[i - 1]\n# res = [0] * len(nums)\n# print(\"\\n\")\n# print(res)\n# for i, a in enumerate(nums):\n# if a > 0:\n# res[i] = counts[a - 1]\n# return res\n\n\n\nnums=[8,1,2,2,3]\nobject=Solution()\n\nprint(object.smallerNumbersThanCurrent(nums)\n )\n","sub_path":"Leetcode/python/Easy/how-many-numbers-are-smaller-than-the-current-number.py","file_name":"how-many-numbers-are-smaller-than-the-current-number.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"351308269","text":"# import subprocess\nimport os\nimport sys\nimport vim\n\ndef cheat(param):\n print(\"python cheat\")\n print(\"param: \" + param)\n\n try:\n output = subprocess.check_output(['ls', '-l'])\n output = str(output)\n output = output.split(\"\\\\n\")\n for o in output:\n print(o)\n except:\n print(\"ERROR\")\n\n\ndef vimdo(opt):\n print(\"python vimdo\")\n print(\"opt: \" + opt)\n\n if opt == '1':\n print(\"1\")\n # vim.command('nohlsearch')\n elif opt == '2':\n modify_buf()\n elif opt == '3':\n make_example_python_buffer()\n elif opt == '4':\n info()\n elif opt == '5':\n info_buffer()\n elif opt == '6':\n # renlazar eventos para mibuffer\n pass\n\n\n elif opt == 'new-buffer':\n print(\"creat buffer---\")\n\n elif opt == 'write-in-buffer':\n print('write in buffer')\n else:\n print(\"no se que hacer\")\n\n\ncmds = {}\ncmds['1'] = {'command': 'op1', 'description': 'opcion 1'}\ncmds['opc2'] = {'command': 'op2', 'description': 'opcion 2'}\ncmds['opc3'] = {'command': 'op3', 'description': 'opcion 3'}\ncmds['clear'] = {'command': 'clear', 'description': 'clear'}\n\n\ndef op1():\n print(\"yo soy la func op1\")\n\n\ndef op2():\n print(\"yo soy la func op2\")\n\n\ndef op3():\n print(\"yo soy la func op3\")\n\ndef clear():\n #os.system('clear')\n # vim.command('redraw')\n vim.command('redraw!')\n\ndef python_input(message = 'input'):\n vim.command('call inputsave()')\n vim.command(\"let user_input = input('\" + message + \": ')\")\n vim.command('call inputrestore()')\n return vim.eval('user_input')\n\n\ndef get_menu_or_exec(keys, *args, **kwargs):\n clear()\n\n selected_commands = { k: v for k, v in cmds.items() if k.startswith(keys) }\n\n if len(selected_commands) == 1 and str(keys) in cmds.keys():\n # remove = 'BackSpace ' * len(str(buffer.buffer))\n # subprocess.call( 'xdotool key ' + remove , shell=True)\n print(\"execute: \" + cmds[str(keys)]['command'] + '\\n')\n globals()[cmds[str(keys)]['command']]()\n\n\n selected_commands = { k: v for k, v in cmds.items() if k.startswith(keys) }\n if len(selected_commands) > 1:\n print('options:\\n')\n for cmd in selected_commands:\n print(cmd + \" \" + cmds[cmd]['description'] + \"\\n\")\n\n keys = python_input(\">>> \")\n get_menu_or_exec(keys)\n return\n elif len(selected_commands) == 0:\n print(\"no se encontro ningun commando\\n\")\n for cmd in cmds.keys():\n print(cmd + \" \" + cmds[cmd]['description'] + \"\\n\")\n keys = python_input(\">>>\")\n get_menu_or_exec(keys)\n return\n\n\ndef simon(opt=None):\n print(opt)\n if not opt:\n print(\"no tengo opciones\")\n else:\n get_menu_or_exec(opt)\n","sub_path":"plugin/cheater.py","file_name":"cheater.py","file_ext":"py","file_size_in_byte":2776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"288811650","text":"import os\n\nfrom django.db import models\nfrom django.core.mail import send_mail\nfrom django.utils.timezone import now, timedelta, datetime\nfrom django.contrib.auth.models import BaseUserManager, AbstractBaseUser\n\nfrom phonenumber_field.modelfields import PhoneNumberField\nfrom .CertificationModels import Certification\n\n\nclass MemberManager(BaseUserManager):\n def create_member(self, email, rfid, membership_duration, password=None):\n \"\"\"\n Creates and saves a Member with the given email, date of\n birth and password.\n \"\"\"\n if not email:\n raise ValueError('Users must have an email address')\n\n # Trying to add the max timedelta to now results in an overflow, so handle the superuser case separately\n try:\n expiration_date = now() + membership_duration\n except OverflowError:\n expiration_date = datetime.max\n\n member = self.model(\n email=self.normalize_email(email),\n rfid=rfid,\n date_expires=expiration_date\n )\n\n member.set_password(password)\n member.save(using=self._db)\n\n return member\n\n def create_superuser(self, email, rfid, password):\n \"\"\"\n Creates and saves a superuser with the given email, date of\n birth and password.\n \"\"\"\n superuser = self.create_member(\n email=email,\n rfid=rfid,\n membership_duration=timedelta.max,\n password=password,\n )\n\n # Add the rest of the data about the superuser\n superuser.is_admin = True\n superuser.status = 7\n superuser.first_name = \"Master\"\n superuser.last_name = \"Admin\"\n superuser.phone_number = '+15555555555'\n superuser.certifications.set(Certification.objects.all())\n\n superuser.save(using=self._db)\n return superuser\n\n\nclass StafferManager(models.Manager):\n def upgrade_to_staffer(self, member, staffname, autobiography=None):\n \"\"\"\n Begins the process of turning a member into a staffer\n\n :param member: the member to make a staffer\n :param staffname: the prefix (before @) part of the staffer's staff email\n :return: Staffer\n \"\"\"\n exc_email = \"{}@excursionclubucsb.org\".format(staffname)\n member.status = 5\n member.date_expires = datetime.max\n member.save()\n if autobiography is not None:\n staffer = self.model(member=member, exc_email=exc_email, autobiography=autobiography)\n else:\n staffer = self.model(member=member, exc_email=exc_email)\n staffer.save()\n return staffer\n\n\nclass Member(AbstractBaseUser):\n \"\"\"This is the base model for all members (this includes staffers)\"\"\"\n objects = MemberManager()\n\n status_choices = [\n ('Member', [\n (0, \"Just Joined\"),\n (1, \"Expired Member\"),\n (2, \"Active Member\"),\n ],\n ),\n ('Staffer', [\n (3, \"Prospective Staffer\"),\n (4, \"Expired Staffer\"),\n (5, \"Active Staffer\"),\n (6, \"Board Member\"),\n (7, \"Admin\")\n ],\n ),\n ]\n status = models.IntegerField(default=0, choices=status_choices)\n\n first_name = models.CharField(max_length=50, null=True)\n last_name = models.CharField(max_length=50, null=True)\n email = models.EmailField(\n verbose_name='email address',\n max_length=255,\n unique=True,\n )\n rfid = models.CharField(\n verbose_name=\"RFID\",\n max_length=10,\n unique=True\n )\n picture = models.ImageField(\n verbose_name=\"Profile Picture\",\n upload_to=\"ProfilePics/\",\n null=True\n )\n phone_number = PhoneNumberField(unique=True, null=True)\n\n date_joined = models.DateField(auto_now_add=True)\n date_expires = models.DateField(null=False)\n\n is_admin = models.BooleanField(default=False)\n certifications = models.ManyToManyField(Certification)\n\n USERNAME_FIELD = 'email'\n REQUIRED_FIELDS = ['rfid', 'date_expires']\n\n @property\n def can_rent(self):\n \"\"\"\n Property that allows a quick and easy check to see if the Member is allowed to rent out gear\n \"\"\"\n return self.status >= 2\n\n @property\n def is_staff(self):\n \"\"\"\n Property that allows and easy check for whether the member is a staffer\n \"\"\"\n # If the member is an active staffer or better, then they are given staff privileges\n return self.status >= 5\n\n def has_name(self):\n \"\"\"Check whether the name of this member has been set\"\"\"\n return self.first_name and self.last_name\n\n def get_full_name(self):\n \"\"\"Return the full name if it is know, or 'New Member' if it is not\"\"\"\n if self.has_name():\n return \"{first} {last}\".format(first=self.first_name, last=self.last_name)\n else:\n return \"New Member\"\n get_full_name.short_description = \"Full Name\"\n\n def get_short_name(self):\n # The user is identified by their email address\n return self.first_name\n\n def __str__(self):\n \"\"\"\n If we know the name of the user, then display their name, otherwise use their email\n \"\"\"\n if self.has_name():\n return self.get_full_name()\n else:\n return self.email\n\n def update_admin(self):\n \"\"\"Updates the admin status of the user in the django system\"\"\"\n if self.status == 7:\n self.is_admin = True\n else:\n self.is_admin = False\n\n def expire(self):\n \"\"\"Expires this member's membership\"\"\"\n if self.status == 2 or self.status == 3:\n self.status = 1\n elif self.status >= 5:\n self.status = 4\n\n def send_email(self, title, body, from_email='system@excursionclubucsb.org'):\n \"\"\"Sends an email to the member\"\"\"\n send_mail(title, body, from_email, [self.email], fail_silently=False)\n\n def send_intro_email(self, finish_signup_url):\n \"\"\"Send the introduction email with the link to finish signing up to the member\"\"\"\n title = \"Finish Signing Up\"\n # get the absolute path equivalent of going up one level and then into the templates directory\n templates_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, 'templates'))\n template_file = open(os.path.join(templates_dir, 'emails', 'intro_email.txt'))\n template = template_file.read()\n body = template.format(finish_signup_url=finish_signup_url)\n self.send_email(title, body, from_email='membership@excursionclubucsb.org')\n\n def has_perm(self, perm, obj=None):\n \"\"\"Does the user have a specific permission?\"\"\"\n # Simplest possible answer: Yes, always\n return True\n\n def has_module_perms(self, app_label):\n \"\"\"Does the user have permissions to view the app `app_label`?\"\"\"\n # Simplest possible answer: Yes, always\n return True\n\n\nclass Staffer(models.Model):\n \"\"\"This model provides the staffer profile (all the extra data that needs to be known about staffers)\"\"\"\n objects = StafferManager()\n\n def __str__(self):\n \"\"\"Gives the staffer a string representation of the staffer name\"\"\"\n return self.member.get_full_name()\n\n member = models.OneToOneField(Member, on_delete=models.CASCADE)\n exc_email = models.EmailField(\n verbose_name='Official ExC Email',\n max_length=255,\n unique=True,\n )\n autobiography = models.TextField(verbose_name=\"Self Description of the staffer\",\n default=\"I am too lazy and lame to upload a bio!\")\n","sub_path":"core/models/MemberModels.py","file_name":"MemberModels.py","file_ext":"py","file_size_in_byte":7695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"555252086","text":"\"\"\"An analogous Python class for each table in GWAS database\"\"\"\n\nclass AutoRepr(object):\n def __repr__(self):\n items = (\"%s = %r\" % (k, v) for k, v in self.__dict__.items())\n return \"<%s: {%s}>\" % (self.__class__.__name__, ', '.join(items))\n\nclass species(AutoRepr):\n \"\"\"Species class\n\n .. _species_class:\n\n Args:\n shortname (str): *required.*\n binomial (str): *required.*\n subspecies (str):\n variety (str):\n\n Returns:\n Species: instance of a species\n\n \"\"\"\n def __init__(self, shortname, binomial, subspecies, variety):\n self.n = shortname\n self.b = binomial\n self.s = subspecies\n self.v = variety\n\n\nclass population(AutoRepr):\n \"\"\"Population class\n\n .. _population_class:\n\n Args:\n population_name (str): *required.* human-readable name of population (often shorthand)\n population_species (int): references :ref:`species_id `\n\n Returns:\n Population: instance of a population\n \"\"\"\n def __init__(self, population_name, population_species):\n self.n = population_name\n self.s = population_species\n\n\nclass line(AutoRepr):\n \"\"\"Line class\n\n .. _line_class:\n\n Args:\n line_name (str): *required.* name of line\n line_population (int): *required.* references :ref:`population_id `\n\n Returns:\n Line: instance of a line\n \"\"\"\n def __init__(self, line_name, line_population):\n self.n = line_name\n self.p = line_population\n\n\nclass chromosome(AutoRepr):\n \"\"\"Chromosome class\n\n .. _chromosome_class:\n\n Args:\n chromosome_name (str): *required.* shorthand name of chromosome (often by number)\n chromosome_species (int): *required.* references :ref:`species_id `\n\n Returns:\n Chromosome: instance of a chromosome\n \"\"\"\n def __init__(self, chromosome_name, chromosome_species):\n self.n = chromosome_name\n self.s = chromosome_species\n\n\nclass variant(AutoRepr):\n \"\"\"Variant class\n\n .. _variant_class:\n\n Args:\n variant_species (int): *required.* references :ref:`species_id `\n variant_chromosome (int): *required.* references :ref:`chromosome_id `\n variant_pos (int): *required.* position of the variant allele on chromsome\n\n Returns:\n Variant: instance of a variant\n\n \"\"\"\n def __init__(self, variant_species, variant_chromosome, variant_pos):\n self.s = variant_species\n self.c = variant_chromosome\n self.p = variant_pos\n\n\nclass genotype(AutoRepr):\n \"\"\"Genotype class\n\n .. _genotype_class:\n\n Args:\n genotype_line (int): *required.* \n genotype_chromosome (int): *required.* \n genotype (tinyint[]): *required.* array of allele calls. Example value ``{0,0,0,-1,-1, ..., 0,0,0,0,2}``\n :comment: \n .. container::\n :name: Details on genotype\n\n Example of possible \n ``{0,0,0,-1,-1, ..., 0,0,0,0,2}``\n\n Each cell of the array contains an encoded value for allele call, and its meaing depends on the type of algorithm used. `Additional information on variant call format `_. It's safe to assume that a value of ``-1`` is a missing call. Each value was generated by `vcftools `_.\n At the time of authorship, the files used to generate them can be found on ``apollo`` at ``/shares/ibaxter_share/gziegler/Maize282_upliftedAGPv4``.\n \n Returns:\n Genotype: instance of a genotype\n \"\"\"\n def __init__(self, genotype_line, genotype_chromosome, genotype, genotype_version):\n self.l = genotype_line\n self.c = genotype_chromosome\n self.g = genotype\n self.v = genotype_version\n\n def __repr__(self):\n # return \"<%s: {%s}>\" % (self.__class__.__name__, ', '.join(items))\n return \"<%s: {%s}>\" % (self.__class__.__name__, ', '.join([self.l, self.c, '*genotype*', self.v]))\n\n\nclass trait(AutoRepr):\n \"\"\"Trait class\n\n .. _trait_class:\n\n Args:\n trait_name (str): *required.* name of trait\n measurement_unit (str): unit of measure\n measurement_device (str): name of measurement device used to record trait\n description (str): human-readable description of trait\n\n Returns:\n Trait: instance of a trait\n \"\"\"\n def __init__(self, trait_name, measurement_unit, measurement_device, description):\n self.n = trait_name\n self.u = measurement_unit\n self.m = measurement_device\n self.d = description\n\n\nclass phenotype(AutoRepr):\n \"\"\"Phenotype class\n\n .. _phenotype_class:\n\n\n Args:\n phenotype_line (int): *required.* references :ref:`line_id `\n phenotype_trait (int): *required.* references :ref:`traid_id `\n phenotype_value (str): *required.*\n\n Returns:\n Phenotype: instance of a phenotype\n\n \"\"\"\n def __init__(self, phenotype_line, phenotype_trait, phenotype_value):\n self.l = phenotype_line\n self.t = phenotype_trait\n self.v = str(phenotype_value)\n\n\nclass growout_type(AutoRepr):\n \"\"\"Growout_Type class\n\n .. _growout_type_class:\n\n Args:\n growout_type (str): *required.* human-readable name of the type of medium used to grow plant\n\n Returns:\n Growout_Type: instance of a growout type\n\n \"\"\"\n def __init__(self, growout_type):\n self.t = growout_type\n\n\nclass growout(AutoRepr):\n \"\"\"Growout class\n\n .. _growout_class:\n\n Args:\n growout_name (str): *required.* coded name of growout. It seems to be a two-initial code for state or country and last two digits of the year\n growout_population (int): *required.* references :ref:`population_id `\n growout_location (int): references :ref:`location_id `\n year (int): *required.*\n growout_growout_type (int): *required.* references :ref:`growout_type_id `\n\n Returns:\n Growout: instance of a growout\n \"\"\"\n def __init__(self, growout_name, growout_population, growout_location, year, growout_growout_type):\n self.n = growout_name\n self.p = growout_population\n self.l = growout_location\n self.y = year\n self.t = growout_growout_type\n\n\nclass location(AutoRepr):\n \"\"\"Location class\n\n .. _location_class:\n\n Args:\n country (str): *required*.\n state (str):\n city (str):\n code (str): *required.* Two character code for location\n\n Returns:\n Location: instance of a location\n \"\"\"\n def __init__(self, country, state, city, code):\n self.c = country\n self.s = state\n self.i = city\n self.o = code\n\n\nclass gwas_algorithm(AutoRepr):\n \"\"\":abbr:`GWAS(Genome-wide association studies)` algorithm class\n\n .. _gwas_algorithm_class:\n\n Args:\n gwas_algorithm (str): \n \n Returns:\n GWAS_Algorithm: instance of a GWAS algorithm\n\n \"\"\"\n def __init__(self, gwas_algorithm):\n self.a = gwas_algorithm\n\n\nclass genotype_version(AutoRepr):\n \"\"\"Genotype Version class\n\n .. _genotype_version_class:\n\n Args:\n genotype_version_name (str): *required.*\n genotype_version (str): *required.* \n reference_genome (int): *required.* references :ref:`line_id `\n genotype_version_population (int): *required.* references :ref:`population_id `\n\n Returns:\n Genotype_Version: instance of a genotype version\n \"\"\"\n def __init__(self, genotype_version_name, genotype_version, reference_genome, genotype_version_population):\n self.n = genotype_version_name\n self.v = genotype_version\n self.r = reference_genome\n self.p = genotype_version_population\n\n\nclass imputation_method(AutoRepr):\n \"\"\"Imputation Method class\n\n .. _imputation_method_class:\n\n Args:\n imputation_method (str): *require.* type of imputation used\n\n Returns:\n Imputation_Method: instance of an imputation method\n\n .. note::\n Need additional information on\n - imputation method (What is imputation?)\n\n \"\"\"\n def __init__(self, imputation_method):\n self.m = imputation_method\n\n\nclass kinship_algorithm(AutoRepr):\n \"\"\"Kinship Algorithm class\n\n .. _kinship_algorithm_class:\n\n Args:\n kinship_algorithm (str): *required.* name of algorithm\n\n Returns:\n Kinship_Algorithm: instance of a kinship algorithm\n \"\"\"\n def __init__(self, kinship_algorithm):\n self.a = kinship_algorithm\n\n\nclass kinship(AutoRepr):\n \"\"\"Kinship class\n\n .. _kinship_class:\n\n Args:\n kinship_algorithm (int): *required.* references :ref:`kinship_algorithm_id `\n kinship_file_path (str): *required.* local path to kinship file\n :comment: The file path is local to the machine running the database. Example path: ``/opt/BaxDB/file_storage/kinship_files/4.AstleBalding.synbreed.kinship.csv``\n\n Returns:\n Kinship: instance of a kinship\n\n \"\"\"\n def __init__(self, kinship_algorithm, kinship_file_path):\n self.a = kinship_algorithm\n self.p = kinship_file_path\n\nclass population_structure_algorithm(AutoRepr):\n \"\"\"Population Structure Algorithm class\n\n .. _population_structure_algorithm_class:\n\n Args:\n population_structure_algorithm (str): *required.* human-readable name for algorithm\n \n Returns:\n Population_Structure_Altgorithm: instance of a population structure algorithm\n \"\"\"\n def __init__(self, population_structure_algorithm):\n self.a = population_structure_algorithm\n\nclass population_structure(AutoRepr):\n \"\"\"Population Stucture class\n\n .. _population_structure_class:\n\n Args:\n population_structure_algorithm (int): *required* references :ref:`population_structure_algorithm_id `\n population_structure_file_path (str): *required.* local path to population structure algorithm file\n :comment: The file path is local to the machine running the database. Example path: ``/opt/BaxDB/file_storage/population_structure_files/4.Eigenstrat.population.structure.10PCs.csv``\n \n Returns:\n Poputation_Structure: instance of a population structure\n \n \"\"\"\n def __init__(self, population_structure_algorithm, population_structure_file_path):\n self.a = population_structure_algorithm\n self.p = population_structure_file_path\n\nclass gwas_run(AutoRepr):\n \"\"\":abbr:`GWAS(Genome-wide association studies)` Run class\n\n .. _gwas_run_class:\n\n Args:\n gwas_run_trait (int): *required.* references :ref:`trait_id `\n nsnps (int): *required.* number of SNPs that were included in the GWAS run, this may be fewer than those available in the data set\n nlines (int): *required.* number of lines that were included in the GWAS run, this may be fewer than those available in the data set\n gwas_run_gwas_algorithm (int): *required.* references :ref:`gwas_algorithm_id `\n gwas_run_genotype_version (int): *required.* references :ref:`genotype_version_id `\n missing_snp_cutoff_value (numeric): *required.* SNP cutoff value for \n missing_line_cutoff_value (numeric): *required.*\n minor_allele_frequency_cutoff_value (numeric): *required.*\n gwas_run_imputation_method (int): *required.* references :ref:`imputation_method_id `\n gwas_run_kinship (int): *required.* references :ref:`kinship_id `\n gwas_run_population_structure (int): *required.* references :ref:`population_structure_id `\n\n Returns:\n GWAS_Run: instance of a GWAS run\n\n .. note::\n Needs additional information on\n - nsnps\n - nllines\n - snp cutoff value\n - line cutoff value\n - allele frequency cutoff value\n\n \"\"\"\n def __init__(self, gwas_run_trait, nsnps, nlines, gwas_run_gwas_algorithm, gwas_run_genotype_version, missing_snp_cutoff_value, missing_line_cutoff_value, minor_allele_frequency_cutoff_value, gwas_run_imputation_method, gwas_run_kinship, gwas_run_population_structure):\n self.t = gwas_run_trait\n self.s = nsnps\n self.l = nlines\n self.a = gwas_run_gwas_algorithm\n self.v = gwas_run_genotype_version\n self.m = missing_snp_cutoff_value\n self.i = missing_line_cutoff_value\n self.n = minor_allele_frequency_cutoff_value\n self.p = gwas_run_imputation_method\n self.k = gwas_run_kinship\n self.o = gwas_run_population_structure\n\n # Initialize a dictionary version of the object for iteration\n self.d = {}\n attributes = [ 't','s','l','a','v','m','i','n','p','k','o' ]\n for a in range(len(attributes)):\n self.d[a] = getattr(self, attributes[a])\n\n # Adding an iterator to allow me to replace None values with NULL for\n # converting from Python object to SQL statements\n def __iter__(self):\n for key,item in self.d.items():\n yield key,item\n\n def keys(self):\n return self.d.keys()\n\n def items(self):\n return self.d.items()\n\n def values(self):\n return self.d.values()\n\nclass gwas_result(AutoRepr):\n \"\"\":abbr:`GWAS(Genome-wide association studies)` Resultf class\n\n .. _gwas_result_class:\n\n Args:\n gwas_result_chromosome (int): *required.*\n basepair (int): *required.*\n gwas_result_gwas_run (int): *required.*\n pval (numeric):\n cofactor (numeric):\n _order (numeric):\n null_pval (numeric):\n model_added_pval (numeric):\n model (str):\n pcs (int):\n\n Returns:\n GWAS_Result: instance of a GWAS result\n\n .. note::\n Needs additional information on the\n - basepair (possible the number of bps, but of what? the chromosome or the snp, or what?)\n - pval (is this significance *p-value*?)\n - cofactor (???)\n - _order (???)\n - null_pval (is there a *p-value* for a null hypothesis?)\n - model_added_pval (???)\n - model (is this a call of models used? Where is a list?)\n - pcs (???) - in the Maize282, there are 18 different permutations of the pcs, comprised of 1-3 integers. Are they the chromosome found significant?\n\n \"\"\"\n def __init__(self,\n gwas_result_chromosome,\n basepair,\n gwas_result_gwas_run,\n pval,\n cofactor,\n _order,\n null_pval,\n model_added_pval,\n model,\n pcs):\n self.c = gwas_result_chromosome\n self.b = basepair\n self.r = gwas_result_gwas_run\n self.p = pval\n self.o = cofactor\n self.d = _order\n self.n = null_pval\n self.a = model_added_pval\n self.m = model\n self.s = pcs\n","sub_path":"importation/util/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":14129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"202291593","text":"from ads.apiresources import geokomp\n\nlocation_cache = {}\n\n\ndef get_location_data(type, location, reqSession):\n if location in location_cache:\n return location_cache[location]\n else:\n loc_resp = geokomp.makeGeoLocationReq(location, reqSession)\n loc = list(filter(lambda l: l['typ'] == type, loc_resp))\n if len(loc):\n location_cache[location] = loc[0]\n return location_cache[location]\n else:\n return \"\"\n\n\ndef location_response_builder(resp, loc):\n match_lan_index = check_value_exist(loc['lankod'], resp['lan'], 'lankod')\n if match_lan_index < 0:\n lan = {\n 'lannamn': loc['lannamn'],\n 'lankod': loc['lankod'],\n 'lan_job_total': 1,\n 'kommun': [{\n 'kommunnamn': loc['kommunnamn'],\n 'kommunkod': loc['kommunkod'],\n 'kommun_job_total': 1\n }]\n }\n resp['lan'].append(lan)\n else:\n match_kom_index = check_value_exist(loc['kommunkod'], resp['lan'][match_lan_index]['kommun'], 'kommunkod')\n if match_kom_index < 0:\n kommun = {\n 'kommunnamn': loc['kommunnamn'],\n 'kommunkod': loc['kommunkod'],\n 'kommun_job_total': 1\n }\n resp['lan'][match_lan_index]['kommun'].append(kommun)\n resp['lan'][match_lan_index]['lan_job_total'] += 1\n else:\n resp['lan'][match_lan_index]['lan_job_total'] += 1\n resp['lan'][match_lan_index]['kommun'][match_kom_index]['kommun_job_total'] += 1\n\n return resp\n\n\ndef check_value_exist(value, ref_list, check_value):\n if len(ref_list) == 0:\n return -1\n else:\n for index, ref in enumerate(ref_list):\n if ref[check_value] == value:\n return index\n return -1\n\n\ndef get_total_jobCount(lan_jobs):\n total = 0\n for job in lan_jobs:\n total += job['lan_job_total']\n return total\n","sub_path":"ads/apiresources/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":1986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"108239894","text":"\"\"\"\n Conftest file for pytest fixtures\n\"\"\"\nimport datetime as dt\nimport logging\nimport shutil\nfrom pathlib import Path\n\nimport pytest\nimport ruamel.yaml\nfrom trailblazer.mip import files as mip_dna_files_api\n\nfrom cg.apps.mip_rna import files as mip_rna_files_api\nfrom cg.meta.store import mip as store_mip\nfrom cg.store import Store\n\nfrom .mocks.hk_mock import MockHousekeeperAPI\nfrom .mocks.madeline import MockMadelineAPI\nfrom .small_helpers import SmallHelpers\nfrom .store_helpers import StoreHelpers\n\nCHANJO_CONFIG = {\"chanjo\": {\"config_path\": \"chanjo_config\", \"binary_path\": \"chanjo\"}}\nCRUNCHY_CONFIG = {\n \"crunchy\": {\n \"cram_reference\": \"/path/to/fasta\",\n \"slurm\": {\"account\": \"mock_account\", \"mail_user\": \"mock_mail\", \"conda_env\": \"mock_env\",},\n }\n}\n\nLOG = logging.getLogger(__name__)\n\n\n# Case fixtures\n\n\n@pytest.fixture(name=\"case_id\")\ndef fixture_case_id():\n \"\"\"Return a case id\"\"\"\n return \"yellowhog\"\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"family_name\")\ndef fixture_family_name() -> str:\n \"\"\"Return a family name\"\"\"\n return \"family\"\n\n\n@pytest.fixture(scope=\"function\", name=\"analysis_family_single_case\")\ndef fixture_analysis_family_single(case_id, family_name):\n \"\"\"Build an example family.\"\"\"\n family = {\n \"name\": family_name,\n \"internal_id\": case_id,\n \"data_analysis\": \"mip\",\n \"application_type\": \"wgs\",\n \"panels\": [\"IEM\", \"EP\"],\n \"samples\": [\n {\n \"name\": \"proband\",\n \"sex\": \"male\",\n \"internal_id\": \"ADM1\",\n \"status\": \"affected\",\n \"ticket_number\": 123456,\n \"reads\": 5000000,\n \"capture_kit\": \"GMSmyeloid\",\n \"data_analysis\": \"PIM\",\n }\n ],\n }\n return family\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"analysis_family\")\ndef fixture_analysis_family(case_id, family_name) -> dict:\n \"\"\"Return a dictionary with information from a analysis family\"\"\"\n family = {\n \"name\": family_name,\n \"internal_id\": case_id,\n \"data_analysis\": \"mip\",\n \"application_type\": \"wgs\",\n \"panels\": [\"IEM\", \"EP\"],\n \"samples\": [\n {\n \"name\": \"son\",\n \"sex\": \"male\",\n \"internal_id\": \"ADM1\",\n \"data_analysis\": \"mip\",\n \"father\": \"ADM2\",\n \"mother\": \"ADM3\",\n \"status\": \"affected\",\n \"ticket_number\": 123456,\n \"reads\": 5000000,\n \"capture_kit\": \"GMSmyeloid\",\n },\n {\n \"name\": \"father\",\n \"sex\": \"male\",\n \"internal_id\": \"ADM2\",\n \"data_analysis\": \"mip\",\n \"status\": \"unaffected\",\n \"ticket_number\": 123456,\n \"reads\": 6000000,\n \"capture_kit\": \"GMSmyeloid\",\n },\n {\n \"name\": \"mother\",\n \"sex\": \"female\",\n \"internal_id\": \"ADM3\",\n \"data_analysis\": \"mip\",\n \"status\": \"unaffected\",\n \"ticket_number\": 123456,\n \"reads\": 7000000,\n \"capture_kit\": \"GMSmyeloid\",\n },\n ],\n }\n\n return family\n\n\n# Config fixtures\n\n\n@pytest.fixture\ndef chanjo_config_dict():\n \"\"\"Chanjo configs\"\"\"\n _config = dict()\n _config.update(CHANJO_CONFIG)\n return _config\n\n\n@pytest.fixture\ndef crunchy_config_dict():\n \"\"\"Crunchy configs\"\"\"\n _config = dict()\n _config.update(CRUNCHY_CONFIG)\n return _config\n\n\n# Api fixtures\n\n\n@pytest.yield_fixture(scope=\"function\")\ndef madeline_api(madeline_output):\n \"\"\"madeline_api fixture\"\"\"\n _api = MockMadelineAPI()\n _api.set_outpath(madeline_output)\n\n yield _api\n\n\n# Files fixtures\n\n\n@pytest.fixture(name=\"fixtures_dir\")\ndef fixture_fixtures_dir() -> Path:\n \"\"\"Return the path to the fixtures dir\"\"\"\n return Path(\"tests/fixtures\")\n\n\n@pytest.fixture(name=\"analysis_dir\")\ndef fixture_analysis_dir(fixtures_dir) -> Path:\n \"\"\"Return the path to the analysis dir\"\"\"\n return fixtures_dir / \"analysis\"\n\n\n@pytest.fixture(name=\"apps_dir\")\ndef fixture_apps_dir(fixtures_dir: Path) -> Path:\n \"\"\"Return the path to the apps dir\"\"\"\n return fixtures_dir / \"apps\"\n\n\n@pytest.fixture(name=\"orderforms\")\ndef fixture_orderform(fixtures_dir: Path) -> Path:\n \"\"\"Return the path to the directory with orderforms\"\"\"\n _path = fixtures_dir / \"orderforms\"\n return _path\n\n\n@pytest.fixture\ndef microbial_orderform(orderforms: Path) -> str:\n \"\"\"Orderform fixture for microbial samples\"\"\"\n _file = orderforms / \"1603.9.microbial.xlsx\"\n return str(_file)\n\n\n@pytest.fixture(name=\"madeline_output\")\ndef fixture_madeline_output(apps_dir: Path) -> str:\n \"\"\"File with madeline output\"\"\"\n _file = apps_dir / \"madeline/madeline.xml\"\n return str(_file)\n\n\n@pytest.fixture(scope=\"session\", name=\"files\")\ndef fixture_files():\n \"\"\"Trailblazer api for mip files\"\"\"\n return {\n \"config\": \"tests/fixtures/apps/tb/case/case_config.yaml\",\n \"sampleinfo\": \"tests/fixtures/apps/tb/case/case_qc_sample_info.yaml\",\n \"qcmetrics\": \"tests/fixtures/apps/tb/case/case_qc_metrics.yaml\",\n \"rna_config\": \"tests/fixtures/apps/mip/rna/case_config.yaml\",\n \"rna_sampleinfo\": \"tests/fixtures/apps/mip/rna/case_qc_sampleinfo.yaml\",\n \"rna_config_store\": \"tests/fixtures/apps/mip/rna/store/case_config.yaml\",\n \"rna_sampleinfo_store\": \"tests/fixtures/apps/mip/rna/store/case_qc_sample_info.yaml\",\n \"mip_rna_deliverables\": \"test/fixtures/apps/mip/rna/store/case_deliverables.yaml\",\n }\n\n\n@pytest.fixture(scope=\"function\", name=\"project_dir\")\ndef fixture_project_dir(tmpdir_factory):\n \"\"\"Path to a temporary directory where intermediate files can be stored\"\"\"\n my_tmpdir = Path(tmpdir_factory.mktemp(\"data\"))\n yield my_tmpdir\n shutil.rmtree(str(my_tmpdir))\n\n\n@pytest.fixture(scope=\"function\")\ndef tmp_file(project_dir):\n \"\"\"Get a temp file\"\"\"\n return project_dir / \"test\"\n\n\n@pytest.fixture(scope=\"function\", name=\"bed_file\")\ndef fixture_bed_file(analysis_dir) -> str:\n \"\"\"Get the path to a bed file file\"\"\"\n return str(analysis_dir / \"sample_coverage.bed\")\n\n\n@pytest.fixture(scope=\"session\", name=\"files_raw\")\ndef fixture_files_raw(files):\n \"\"\"Get some raw files\"\"\"\n return {\n \"config\": ruamel.yaml.safe_load(open(files[\"config\"])),\n \"sampleinfo\": ruamel.yaml.safe_load(open(files[\"sampleinfo\"])),\n \"qcmetrics\": ruamel.yaml.safe_load(open(files[\"qcmetrics\"])),\n \"rna_config\": ruamel.yaml.safe_load(open(files[\"rna_config\"])),\n \"rna_sampleinfo\": ruamel.yaml.safe_load(open(files[\"rna_sampleinfo\"])),\n \"rna_config_store\": ruamel.yaml.safe_load(open(files[\"rna_config_store\"])),\n \"rna_sampleinfo_store\": ruamel.yaml.safe_load(open(files[\"rna_sampleinfo_store\"])),\n }\n\n\n@pytest.fixture(scope=\"session\")\ndef files_data(files_raw):\n \"\"\"Get some data files\"\"\"\n return {\n \"config\": mip_dna_files_api.parse_config(files_raw[\"config\"]),\n \"sampleinfo\": mip_dna_files_api.parse_sampleinfo(files_raw[\"sampleinfo\"]),\n \"qcmetrics\": mip_dna_files_api.parse_qcmetrics(files_raw[\"qcmetrics\"]),\n \"rna_config\": mip_dna_files_api.parse_config(files_raw[\"rna_config\"]),\n \"rna_sampleinfo\": mip_rna_files_api.parse_sampleinfo_rna(files_raw[\"rna_sampleinfo\"]),\n \"rna_config_store\": store_mip.parse_config(files_raw[\"rna_config_store\"]),\n \"rna_sampleinfo_store\": store_mip.parse_sampleinfo(files_raw[\"rna_sampleinfo_store\"]),\n }\n\n\n# Helper fixtures\n\n\n@pytest.fixture(name=\"helpers\")\ndef fixture_helpers():\n \"\"\"Return a class with helper functions for the stores\"\"\"\n return StoreHelpers()\n\n\n@pytest.fixture(name=\"small_helpers\")\ndef fixture_small_helpers():\n \"\"\"Return a class with small helper functions\"\"\"\n return SmallHelpers()\n\n\n# HK fixtures\n\n\n@pytest.fixture(name=\"root_path\")\ndef fixture_root_path(project_dir: Path) -> Path:\n \"\"\"Return the path to a hk bundles dir\"\"\"\n _root_path = project_dir / \"bundles\"\n _root_path.mkdir(parents=True, exist_ok=True)\n return _root_path\n\n\n@pytest.fixture(scope=\"function\", name=\"timestamp\")\ndef fixture_timestamp() -> dt.datetime:\n \"\"\"Return a time stamp in date time format\"\"\"\n return dt.datetime(2020, 5, 1)\n\n\n@pytest.fixture(scope=\"function\", name=\"hk_bundle_data\")\ndef fixture_hk_bundle_data(case_id, bed_file, timestamp):\n \"\"\"Get some bundle data for housekeeper\"\"\"\n data = {\n \"name\": case_id,\n \"created\": timestamp,\n \"expires\": timestamp,\n \"files\": [{\"path\": bed_file, \"archive\": False, \"tags\": [\"bed\", \"sample\"]}],\n }\n return data\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"housekeeper_api\")\ndef fixture_housekeeper_api(root_path):\n \"\"\"Setup Housekeeper store.\"\"\"\n _api = MockHousekeeperAPI(\n {\"housekeeper\": {\"database\": \"sqlite:///:memory:\", \"root\": str(root_path)}}\n )\n return _api\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"populated_housekeeper_api\")\ndef fixture_populated_housekeeper_api(housekeeper_api, hk_bundle_data, helpers):\n \"\"\"Setup a Housekeeper store with some data.\"\"\"\n hk_api = housekeeper_api\n helpers.ensure_hk_bundle(hk_api, hk_bundle_data)\n return hk_api\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"hk_version_obj\")\ndef fixture_hk_version_obj(housekeeper_api, hk_bundle_data, helpers):\n \"\"\"Get a housekeeper version object\"\"\"\n _version = helpers.ensure_hk_version(housekeeper_api, hk_bundle_data)\n return _version\n\n\n# Store fixtures\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"analysis_store\")\ndef fixture_analysis_store(base_store, analysis_family, wgs_application_tag, helpers):\n \"\"\"Setup a store instance for testing analysis API.\"\"\"\n helpers.ensure_family(base_store, family_info=analysis_family, app_tag=wgs_application_tag)\n\n yield base_store\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"analysis_store_trio\")\ndef fixture_analysis_store_trio(analysis_store):\n \"\"\"Setup a store instance with a trion loaded for testing analysis API.\"\"\"\n\n yield analysis_store\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"analysis_store_single_case\")\ndef fixture_analysis_store_single(base_store, analysis_family_single_case, helpers):\n \"\"\"Setup a store instance with a single ind case for testing analysis API.\"\"\"\n helpers.ensure_family(base_store, family_info=analysis_family_single_case)\n\n yield base_store\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"customer_group\")\ndef fixture_customer_group() -> str:\n \"\"\"Return a default customer group\"\"\"\n return \"all_customers\"\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"customer_production\")\ndef fixture_customer_production(customer_group) -> dict:\n \"\"\"Return a dictionary with infomation about the prod customer\"\"\"\n _cust = dict(\n customer_id=\"cust000\", name=\"Production\", scout_access=True, customer_group=customer_group,\n )\n return _cust\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"external_wgs_application_tag\")\ndef fixture_external_wgs_application_tag() -> str:\n \"\"\"Return the external wgs app tag\"\"\"\n return \"WGXCUSC000\"\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"external_wgs_info\")\ndef fixture_external_wgs_info(external_wgs_application_tag) -> dict:\n \"\"\"Return a dictionary with information external WGS application\"\"\"\n _info = dict(\n application_tag=external_wgs_application_tag,\n application_type=\"wgs\",\n description=\"External WGS\",\n is_external=True,\n )\n return _info\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"external_wes_application_tag\")\ndef fixture_external_wes_application_tag() -> str:\n \"\"\"Return the external whole exome sequencing app tag\"\"\"\n return \"EXXCUSR000\"\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"external_wes_info\")\ndef fixture_external_wes_info(external_wes_application_tag) -> dict:\n \"\"\"Return a dictionary with information external WES application\"\"\"\n _info = dict(\n application_tag=external_wes_application_tag,\n application_type=\"wes\",\n description=\"External WES\",\n is_external=True,\n )\n return _info\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"wgs_application_tag\")\ndef fixture_wgs_application_tag() -> str:\n \"\"\"Return the wgs app tag\"\"\"\n return \"WGSPCFC060\"\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"wgs_application_info\")\ndef fixture_wgs_application_info(wgs_application_tag) -> dict:\n \"\"\"Return a dictionary with information the WGS application\"\"\"\n _info = dict(\n application_tag=wgs_application_tag,\n application_type=\"wgs\",\n description=\"WGS, double\",\n sequencing_depth=30,\n is_external=True,\n is_accredited=True,\n )\n return _info\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"store\")\ndef fixture_store() -> Store:\n \"\"\"Fixture with a CG store\"\"\"\n _store = Store(uri=\"sqlite:///\")\n _store.create_all()\n yield _store\n _store.drop_all()\n\n\n@pytest.yield_fixture(scope=\"function\", name=\"base_store\")\ndef fixture_base_store(store) -> Store:\n \"\"\"Setup and example store.\"\"\"\n customer_group = store.add_customer_group(\"all_customers\", \"all customers\")\n\n store.add_commit(customer_group)\n customers = [\n store.add_customer(\n \"cust000\",\n \"Production\",\n scout_access=True,\n customer_group=customer_group,\n invoice_address=\"Test street\",\n invoice_reference=\"ABCDEF\",\n ),\n store.add_customer(\n \"cust001\",\n \"Customer\",\n scout_access=False,\n customer_group=customer_group,\n invoice_address=\"Test street\",\n invoice_reference=\"ABCDEF\",\n ),\n store.add_customer(\n \"cust002\",\n \"Karolinska\",\n scout_access=True,\n customer_group=customer_group,\n invoice_address=\"Test street\",\n invoice_reference=\"ABCDEF\",\n ),\n store.add_customer(\n \"cust003\",\n \"CMMS\",\n scout_access=True,\n customer_group=customer_group,\n invoice_address=\"Test street\",\n invoice_reference=\"ABCDEF\",\n ),\n ]\n store.add_commit(customers)\n applications = [\n store.add_application(\n tag=\"WGXCUSC000\",\n category=\"wgs\",\n description=\"External WGS\",\n sequencing_depth=0,\n is_external=True,\n percent_kth=80,\n ),\n store.add_application(\n tag=\"EXXCUSR000\",\n category=\"wes\",\n description=\"External WES\",\n sequencing_depth=0,\n is_external=True,\n percent_kth=80,\n ),\n store.add_application(\n tag=\"WGSPCFC060\",\n category=\"wgs\",\n description=\"WGS, double\",\n sequencing_depth=30,\n accredited=True,\n percent_kth=80,\n ),\n store.add_application(\n tag=\"RMLS05R150\",\n category=\"rml\",\n description=\"Ready-made\",\n sequencing_depth=0,\n percent_kth=80,\n ),\n store.add_application(\n tag=\"WGTPCFC030\",\n category=\"wgs\",\n description=\"WGS trio\",\n is_accredited=True,\n sequencing_depth=30,\n target_reads=300000000,\n limitations=\"some\",\n percent_kth=80,\n ),\n store.add_application(\n tag=\"METLIFR020\",\n category=\"wgs\",\n description=\"Whole genome metagenomics\",\n sequencing_depth=0,\n target_reads=40000000,\n percent_kth=80,\n ),\n store.add_application(\n tag=\"METNXTR020\",\n category=\"wgs\",\n description=\"Metagenomics\",\n sequencing_depth=0,\n target_reads=20000000,\n percent_kth=80,\n ),\n store.add_application(\n tag=\"MWRNXTR003\",\n category=\"mic\",\n description=\"Microbial whole genome \",\n sequencing_depth=0,\n percent_kth=80,\n ),\n store.add_application(\n tag=\"RNAPOAR025\",\n category=\"tgs\",\n description=\"RNA seq, poly-A based priming\",\n percent_kth=80,\n sequencing_depth=25,\n accredited=True,\n ),\n ]\n\n store.add_commit(applications)\n\n prices = {\"standard\": 10, \"priority\": 20, \"express\": 30, \"research\": 5}\n versions = [\n store.add_version(application, 1, valid_from=dt.datetime.now(), prices=prices)\n for application in applications\n ]\n store.add_commit(versions)\n\n beds = [store.add_bed(\"Bed\")]\n store.add_commit(beds)\n bed_versions = [store.add_bed_version(bed, 1, \"Bed.bed\") for bed in beds]\n store.add_commit(bed_versions)\n\n organism = store.add_organism(\"C. jejuni\", \"C. jejuni\")\n store.add_commit(organism)\n\n yield store\n\n\n@pytest.fixture(scope=\"function\")\ndef sample_store(base_store) -> Store:\n \"\"\"Populate store with samples.\"\"\"\n new_samples = [\n base_store.add_sample(\"ordered\", sex=\"male\"),\n base_store.add_sample(\"received\", sex=\"unknown\", received=dt.datetime.now()),\n base_store.add_sample(\n \"received-prepared\",\n sex=\"unknown\",\n received=dt.datetime.now(),\n prepared_at=dt.datetime.now(),\n ),\n base_store.add_sample(\"external\", sex=\"female\", external=True),\n base_store.add_sample(\n \"external-received\", sex=\"female\", external=True, received=dt.datetime.now()\n ),\n base_store.add_sample(\n \"sequenced\",\n sex=\"male\",\n received=dt.datetime.now(),\n prepared_at=dt.datetime.now(),\n sequenced_at=dt.datetime.now(),\n reads=(310 * 1000000),\n ),\n base_store.add_sample(\n \"sequenced-partly\",\n sex=\"male\",\n received=dt.datetime.now(),\n prepared_at=dt.datetime.now(),\n reads=(250 * 1000000),\n ),\n ]\n customer = base_store.customers().first()\n external_app = base_store.application(\"WGXCUSC000\").versions[0]\n wgs_app = base_store.application(\"WGTPCFC030\").versions[0]\n for sample in new_samples:\n sample.customer = customer\n sample.application_version = external_app if \"external\" in sample.name else wgs_app\n base_store.add_commit(new_samples)\n return base_store\n\n\n@pytest.yield_fixture(scope=\"function\")\ndef disk_store(cli_runner, invoke_cli) -> Store:\n \"\"\"Store on disk\"\"\"\n database = \"./test_db.sqlite3\"\n database_path = Path(database)\n database_uri = f\"sqlite:///{database}\"\n with cli_runner.isolated_filesystem():\n assert database_path.exists() is False\n\n # WHEN calling \"init\"\n result = invoke_cli([\"--database\", database_uri, \"init\"])\n\n # THEN it should setup the database with some tables\n assert result.exit_code == 0\n assert database_path.exists()\n assert len(Store(database_uri).engine.table_names()) > 0\n\n yield Store(database_uri)\n","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":19278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"631500893","text":"import asyncio\nimport logging as log\nimport os\nfrom urllib.parse import urlparse\nfrom urllib.parse import parse_qs\nimport uuid\nimport yaml\n\nimport orjson\n\nfrom pylib.app import DBApp\nfrom pylib.messaging import messaging\nfrom pylib.types.request import Request\nfrom pylib.types.request import RequestMethod\nfrom pylib.types.response import Response\nfrom pylib.types.response import ResponseStatus\nfrom pylib.types.error_response import to_dict as err_to_dict\n\nfrom syscore.server import handle\n\n\ndef main_handler(\n app: DBApp,\n request: Request\n):\n try:\n url = urlparse(request.url)\n path = url.path\n query_params = parse_qs(url.query)\n log.debug(\n \"path: %s, query_params: %s \",\n path,\n query_params\n )\n handle(\n app,\n request,\n path,\n data={\n RequestMethod.GET: orjson.loads(\n query_params\n ) if query_params else None,\n RequestMethod.POST: request.body\n }[request.method],\n method=request.method\n )\n except Exception as e:\n log.exception(\"core handler failed: %s\", e)\n messaging.respond_to_request(\n app=app,\n request=request,\n status=ResponseStatus.ERROR,\n body=err_to_dict(\n code=\"UNKNOWN\",\n cause=str(e),\n ),\n )\n\n\ndef main():\n app = DBApp()\n app.init_redis(host=app.conf['hostnames']['presentation'])\n service_id = uuid.uuid4().hex\n input_port = app.conf[\"ports\"][\"core\"][\"external\"][\"input\"]\n log.info(f\"core {service_id} pulling from {input_port}\")\n loop = asyncio.get_event_loop()\n def handler(request):\n return main_handler(\n app,\n request\n )\n loop.create_task(\n messaging.listen_for_input(\n app=app,\n handler=handler,\n port=input_port,\n service_id=service_id\n )\n )\n loop.run_forever()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"corepy/bin/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"130735049","text":"import sys\nsys.path.insert(0,'C:\\\\Users\\\\Igor\\\\Documents\\\\Python\\\\Projects and Scripts\\\\IGE\\\\')\nimport IGE_API as ige\nfrom IGE_API import ndi, show, get_ssim, sk\n\n#src = 'C:\\\\Users\\\\Igor\\\\Desktop\\\\scc.png'\n#dest = '\\\\'.join(src.split('\\\\')[:-1]) + '\\\\'\n\n\ni1 = ndi.imread('D:\\\\1.png',flatten=True)\ni1 = i1[i1.shape[0]-168:][:]\n\ni2 = ndi.imread('D:\\\\2.png',flatten=True)\ni2 = i2[i2.shape[0]-168:][:]\n\ni3 = ndi.imread('D:\\\\3.png',flatten=True)\ni3 = i3[i3.shape[0]-168:][:]\n\ni4 = ndi.imread('D:\\\\4.png',flatten=True)\ni4 = i4[i4.shape[0]-168:][:]\n\ni5 = ndi.imread('D:\\\\5.png',flatten=True)\ni5 = i5[i5.shape[0]-168:][:]\n\npics = [i1, i2, i3, i4, i5]\n\n \n\nfor x in range(5):\n for y in range(5):\n print('Comparing ' + str(x+1) +' with '+ str(y+1))\n get_ssim(pics[x],pics[y],readPaths=False, otsu=100)\n print()\n print('==============================')\n #show(pics[x])\n \n\n\n#ex.getNameAndPic(src=src, dest=dest,saveAll=True, drawAll=True)\n\n#img = ''\n#name = m.getNameAndPic(img)\n\n'''\n==========PLAN==========\nformats: check by bottom bar (2 versions)\n'''\n","sub_path":"Screen2Text/centre.py","file_name":"centre.py","file_ext":"py","file_size_in_byte":1082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"609366452","text":"class Solution(object):\n def lengthOfLongestSubstring(self, s):\n \"\"\"\n :type s: str\n :rtype: int\n \"\"\"\n left, right = 0, 0\n res = 0\n curr = set()\n\n while right < len(s):\n if s[right] not in curr:\n curr.add(s[right])\n right += 1\n else:\n res = max(res, len(curr))\n curr.remove(s[left])\n left += 1\n\n res = max(res, len(curr))\n return res\n","sub_path":"medium/3.longest-substring-without-repeating-characters.py","file_name":"3.longest-substring-without-repeating-characters.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"384686724","text":"import config\nimport collections\nimport logging\nimport requests\nimport xmltodict\n\nrequests_log = logging.getLogger(\"requests\")\nrequests_log.setLevel(logging.WARNING)\nlogger = logging.getLogger(__name__)\n\n\nclass STARTException(Exception):\n pass\n\n\nclass STARTCantParseException(STARTException):\n pass\n\n\nclass STARTResponseFormatException(STARTException):\n pass\n\n\nclass STARTTimeoutException(STARTException):\n pass\n\n\nclass STARTServerException(STARTException):\n pass\n\n\ndef send_request(query, action, machine=config.START_HOST, server=config.START_SERVER, kb=False):\n usekb = 'no'\n if kb:\n usekb = 'yes'\n\n params = {'query': query,\n 'referrer': 'http://start.csail.mit.edu/elasticstart',\n 'server': server,\n 'machine': machine,\n 'action': action,\n 'qe': 'HTML',\n 'kb': usekb,\n 'te': 'XML',\n 'de': 'no',\n 'fg': 'yes'\n }\n\n r = requests.post('http://start.csail.mit.edu/askstart.cgi',\n data=params)\n\n try:\n response = parse_start_response(r.text)\n return response\n except STARTResponseFormatException:\n raise STARTResponseFormatException('Could not parse START response.\\nAction=%s, Query= \"%s\".\\nResponse: %s' % (action, query, r.text[:250]))\n\n\ndef parse_start_response(text):\n if '

Internal Server Error

' in text:\n raise STARTServerException(text)\n\n try:\n response = xmltodict.parse(text)\n return response\n except:\n raise STARTResponseFormatException('Could not parse START response.\\nResponse: %s' % (text[:250]))\n\n\ndef ask(question):\n response = send_request(question, 'askstart')\n return response\n\n\ndef recur_ordereddict_to_dict(od):\n \"\"\"\n Helper that turns an OrderedDict into\n a regular dictionary.\n\n params:\n od - the OrderedDict to convert\n\n return:\n a dict structured the same way as od\n \"\"\"\n\n d = od\n\n if isinstance(od, collections.OrderedDict):\n d = dict(od)\n for key, val in d.iteritems():\n d[key] = recur_ordereddict_to_dict(val)\n elif isinstance(od, list):\n d = map(lambda x: recur_ordereddict_to_dict(x), od)\n\n return d\n\n\ndef get_body(start_parse, key):\n if type(start_parse['interactions']['interaction']['query']) is dict:\n check_for_exceptions(start_parse['interactions']['interaction']['query'])\n if key not in start_parse['interactions']['interaction']['query']:\n raise STARTException(\"Malformed response. Can't find '%s' in response: %s\" % (key, start_parse))\n return start_parse['interactions']['interaction']['query'][key]\n else:\n # usually a spell correction\n query = start_parse['interactions']['interaction']['query']\n for x in filter(lambda x: 'notification' in x, query):\n notification = x['notification']\n notification_type = notification['@type']\n if notification_type == 'SPELLING-CORRECTION':\n logger.warning(\"START spelling correction: changed '%s' to '%s' in sentence: %s\" % (notification['from'], notification['to'], notification['original-input']))\n else:\n logger.warning('START notification: %s' % notification_type)\n\n for x in query:\n check_for_exceptions(x)\n\n for x in filter(lambda x: key in x, query):\n return x[key]\n\n\ndef check_for_exceptions(start_query):\n \"\"\"\n Checks if START raises an exception.\n\n params:\n start_query - START's 'query' response in an 'interaction' as (a dict)\n \"\"\"\n\n if 'exception' in start_query:\n exception = start_query['exception']\n\n if type(exception) is list:\n exception = filter(lambda e: 'original-input' in e, exception)\n if len(exception) > 0:\n exception = exception[0]\n\n sentence = exception['original-input']\n exception_type = exception['@type']\n\n if exception_type == 'CANT-PARSE':\n raise STARTCantParseException(\"START can't parse the sentence: %s\" % sentence)\n else:\n raise STARTException(\"START raised '%s' with sentence '%s'\" % (exception_type, sentence))\n\n\ndef parse(sentence):\n \"\"\"\n Returns a list of t-expressions as parsed by START.\n T-expressions are dictionaries with keys: object, relation, subject\n\n Args:\n sentence (str): The sentence to parse\n\n Raises a STARTCantParseException if the sentence is unparseable\n \"\"\"\n response = send_request(sentence, 'parse')\n response = recur_ordereddict_to_dict(response)\n reply = get_body(response, 'reply')\n\n if reply is None:\n raise STARTTimeoutException(\"START timed out\")\n\n if 'texp' in reply:\n return reply['texp']\n\n return None\n\n\ndef parseable(self, sentence):\n return self.parse(sentence) is not None\n\n\ndef tokenize(sentence):\n response = send_request(sentence, 'tokenize')\n return response\n\n\ndef analyze(question):\n response = send_request(question, 'analyze-for-elasticstart')\n response = recur_ordereddict_to_dict(response)\n analysis = get_body(response, 'question-analysis')\n type = analysis['question-type']\n\n if type == 'NIL':\n raise STARTCantParseException(\"START returned NIL when analyzing: %s\" % analysis['question'])\n\n parse = analysis['parse']['reply']['texp']\n subject = analysis['subject']\n\n if analysis['subject-parse']['reply'] is not None:\n subject_texps = analysis['subject-parse']['reply']['texp']\n else:\n subject_texps = []\n\n return parse, type, subject, subject_texps\n","sub_path":"elasticstart/start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":5657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"590263600","text":"\"\"\"\nAssignment 1\nCSSE 7030\nSemester 1, 2019\nStudent ID: 45758106\n\"\"\"\n\n__author__ = \"Jinyuan Chen\"\n__date__ = \"22/03/2019\"\n\n\nfrom destinations import Destinations\ndef name() :\n \"\"\"\n\n :return:\n \"\"\"\n print(\"Welcome to Travel Inspiration!\")\n user_input = str(input(\"\\nWhat is your name? \", ))\n print(\"\\nHi, \", user_input + \"!\", sep='')\n\n\ndef continent_input() :\n d1 = {\"1\": \"Asia\", \"2\": \"Africa\", \"3\": \"North America\", \"4\": \"South America\", \"5\": \"Europe\",\n \"6\": \"Oceania\", \"7\": \"Antarctica\"}\n print(\"\\nWhich continents would you like to travel to?\")\n for key in d1:\n print(' ', key, ') ', d1[key], sep='')\n\n user_input = input(\"> \").replace(\" \", \"\").split(\",\")\n if user_input in d1.keys() :\n for user_input in range (1, len(user_input) + 1):\n if user_input == int():\n return d1[user_input]\n\n else:\n print(\"\\nI'm sorry, but\", user_input, \"is not a valid choice. Please try again.\")\n return False\n\n\ndef cost_input() :\n cost = []\n d2 = {\"$$$\": \"No object\", \"$$\": \"Spendable, so long as I get value from doing so\",\n \"$\": \"Extremely important; I want to spend as little as possible\"}\n print(\"\\nWhat is money to you?\")\n for key in d2:\n print(' ', key, ') ', d2[key], sep='')\n user_input = input(\"> \")\n if user_input == \"$$$\":\n cost = [\"$$$\", \"$$\", \"$\"]\n elif user_input == \"$$\":\n cost = [\"$$\", \"$\"]\n elif user_input == \"$\":\n cost = [\"$\"]\n if user_input in d2.keys() :\n return cost\n else:\n print(\"\\nI'm sorry, but\", user_input, \"is not a valid choice. Please try again.\")\n return False\n\n\n\ndef crime_input() :\n crime = []\n d3 = {\"1\": \"Low\", \"2\": \"Average\", \"3\": \"High\"}\n print(\"\\nHow much crime is acceptable when you travel?\")\n for key in d3:\n print(' ', key, ') ', d3[key], sep='')\n user_input = input(\"> \")\n if user_input == \"1\":\n crime = [\"low\"]\n elif user_input == \"2\":\n crime = [\"low\", \"average\"]\n elif user_input == \"3\":\n crime = [\"low\", \"average\", \"high\"]\n if user_input in d3.keys() :\n return crime\n else:\n print(\"\\nI'm sorry, but\", user_input, \"is not a valid choice. Please try again.\")\n return False\n\n\ndef kids_input() :\n d4 = {\"1\": \"Yes\", \"2\": \"No\"}\n print(\"\\nWill you be travelling with children?\")\n for key in d4:\n print(' ', key, ') ', d4[key], sep='')\n user_input = input(\"> \")\n if user_input in d4.keys() :\n return d4[user_input]\n else:\n print(\"\\nI'm sorry, but\", user_input, \"is not a valid choice. Please try again.\")\n return False\n\n\ndef season_input() :\n d5 = {\"1\": \"Spring\", \"2\": \"Summer\", \"3\": \"Autumn\", \"4\": \"Winter\"}\n print(\"\\nWhich seasons do you plan to travel in?\")\n for key in d5:\n print(' ', key, ') ', d5[key], sep='')\n user_input = input(\"> \")\n if user_input in d5.keys() :\n return d5[user_input]\n else:\n print(\"\\nI'm sorry, but\", user_input, \"is not a valid choice. Please try again.\")\n return False\n\n\ndef climate_input() :\n d6 = {\"1\": \"Cold\", \"2\": \"Cool\", \"3\": \"Moderate\", \"4\": \"Warm\", \"5\": \"Hot\"}\n print(\"\\nWhat climate do you prefer?\")\n for key in d6:\n print(' ', key, ') ', d6[key], sep='')\n user_input = input(\"> \")\n if user_input in d6.keys() :\n return d6[user_input]\n else:\n print(\"\\nI'm sorry, but\", user_input, \"is not a valid choice. Please try again.\")\n return False\n\n\n\ndef first_exact_match(destination_set, continent, cost, crime, climate, kids) :\n destination_result = []\n for destination in destination_set :\n if destination.get_continent() == continent.lower() :\n if len(destination.get_cost()) <= len(cost) :\n crime_d = {\"low\": 1, \"average\": 2, \"high\": 3}\n if crime_d[destination.get_crime()] <= len(crime) :\n if destination.get_climate() == climate.lower():\n if kids == \"1\" :\n if destination.is_kid_friendly() :\n destination_result.append(destination)\n else:\n destination_result.append(destination)\n return destination_result\n\n\ndef season_interest_match(destination_set, season, sports, wildlife, nature, historical, cuisine, adventure, beach) :\n if len(destination_set) == 0 :\n return None\n destination_result = destination_set[0]\n max_factor = destination_set[0].get_season_factor(season.lower()) * \\\n calculation_interest_score(destination_set[0], sports, wildlife, nature, historical, cuisine, adventure, beach)\n for destination in destination_set :\n interest_score = calculation_interest_score(destination, sports, wildlife, nature, historical, cuisine, adventure, beach)\n if destination.get_season_factor(season.lower()) * interest_score > max_factor :\n max_factor = destination.get_season_factor(season.lower()) * interest_score\n destination_result = destination\n return destination_result\n\n\ndef calculation_interest_score(destination, sports, wildlife, nature, historical, cuisine, adventure, beach):\n return (sports * destination.get_interest_score('sports') +\n wildlife * destination.get_interest_score('wildlife') +\n nature * destination.get_interest_score('nature') +\n historical * destination.get_interest_score('historical') +\n cuisine * destination.get_interest_score('cuisine') +\n adventure * destination.get_interest_score('adventure') +\n beach * destination.get_interest_score('beach'))\n\n\n\n\n\ndef main():\n\n name()\n \n continent = continent_input()\n while continent is False :\n continent = continent_input()\n\n\n cost = cost_input()\n while cost is False :\n cost = cost_input()\n\n crime = crime_input()\n while crime is False :\n crime = crime_input()\n\n kids = kids_input()\n while kids is False :\n kids = kids_input()\n\n season = season_input()\n while season is False :\n season = season_input()\n\n climate = climate_input()\n while climate is False :\n climate = climate_input()\n\n print(\"\\nNow we would like to ask you some questions about your interests, on a scale \"\n \"of -5 to 5. -5 indicates strong dislike, whereas 5 indicates strong interest, \"\n \"and 0 indicates indifference.\")\n\n while True :\n print(\"\\nHow much do you like sports? (-5 to 5)\")\n sports = input(\"> \")\n if sports in [\"-5\", \"-4\", \"-3\", \"-2\", \"-1\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\"] :\n sports = int(sports)\n break\n else:\n print(\"\\nI'm sorry, but\", sports, \"is not a valid choice. Please try again.\")\n \n while True :\n print(\"\\nHow much do you like wildlife? (-5 to 5)\")\n wildlife = input(\"> \")\n if wildlife in [\"-5\", \"-4\", \"-3\", \"-2\", \"-1\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\"]:\n wildlife = int(wildlife)\n break\n else:\n print(\"\\nI'm sorry, but\", wildlife, \"is not a valid choice. Please try again.\")\n\n while True:\n print(\"\\nHow much do you like nature? (-5 to 5)\")\n nature = input(\"> \")\n if nature in [\"-5\", \"-4\", \"-3\", \"-2\", \"-1\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\"]:\n nature = int(nature)\n break\n else:\n print(\"\\nI'm sorry, but\", nature, \"is not a valid choice. Please try again.\")\n\n\n while True :\n print(\"\\nHow much do you like historical sites? (-5 to 5)\")\n historical = input(\"> \")\n if historical in [\"-5\", \"-4\", \"-3\", \"-2\", \"-1\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\"]:\n historical = int(historical)\n break\n else:\n print(\"\\nI'm sorry, but\", historical, \"is not a valid choice. Please try again.\")\n\n\n\n while True:\n print(\"\\nHow much do you like fine dining? (-5 to 5)\")\n cuisine = input(\"> \")\n if cuisine in [\"-5\", \"-4\", \"-3\", \"-2\", \"-1\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\"]:\n cuisine = int(cuisine)\n break\n else:\n print(\"\\nI'm sorry, but\", cuisine, \"is not a valid choice. Please try again.\")\n\n\n while True:\n print(\"\\nHow much do you like adventure activities? (-5 to 5)\")\n adventure = input(\"> \")\n if adventure in [\"-5\", \"-4\", \"-3\", \"-2\", \"-1\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\"]:\n adventure = int(adventure)\n break\n else:\n print(\"\\nI'm sorry, but\", adventure, \"is not a valid choice. Please try again.\")\n\n while True:\n print(\"\\nHow much do you like the beach? (-5 to 5)\")\n beach = input(\"> \")\n if beach in [\"-5\", \"-4\", \"-3\", \"-2\", \"-1\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\"]:\n beach = int(beach)\n break\n else:\n print(\"\\nI'm sorry, but\", beach, \"is not a valid choice. Please try again.\")\n\n\n destination_set = Destinations().get_all()\n\n\n first_exact_match_result = first_exact_match(destination_set, continent, cost, crime, climate, kids)\n\n\n final_result = season_interest_match(first_exact_match_result, season, sports, wildlife, nature, historical, cuisine, adventure, beach)\n\n\n print(\"\\nThank you for answering all our questions. Your next travel destination is:\")\n\n\n if final_result is None :\n print(None)\n else:\n print(final_result.get_name())\n\n\n \nif __name__ == \"__main__\":\n main()\n","sub_path":"travel/travel-gai.py","file_name":"travel-gai.py","file_ext":"py","file_size_in_byte":9518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"173431267","text":"import os, io, random, discord, aiohttp, asyncio, threading, subprocess\nPopen = subprocess.Popen\nThread = threading.Thread\n\nDIRECTORY = \"C:/Network_folder/Public/VideoOutput\"\n\nMSG_DISPLAY_LEN = 75\n\n\n\ndef formatKey(l):\n return l.split('=')[1].strip().replace('\"', '')\n\nTOKEN = \"\"\nwith open(\"TOKENS.txt\") as f:\n for line in f:\n if \"discord\" in line.lower():\n TOKEN = formatKey(line)\n\ndef UFID(ID, l):\n random.seed(ID)\n return ''.join([str(random.randint(0,9)) for i in range(l)])\n\ndef prettyRun(pre, cmd):\n tab, nlin = '\\t', '\\n'\n proc = subprocess.Popen(cmd, stdout = subprocess.PIPE, universal_newlines = True)\n while proc.poll() is None:\n out = proc.stdout.readline()\n if out != \"\":\n print(f\"#{tab}{pre}: {out.replace(nlin, '')}\")\n return proc.returncode\n\ndef remove_prefix(text, prefix):\n if text.startswith(prefix):\n return text[len(prefix):]\n return text\n\nasync def fetch(url):\n async with aiohttp.ClientSession() as session:\n async with session.get(url) as r:\n if r.status != 200:\n return None\n else:\n return io.BytesIO(await r.read())\n\ndef trim(txt, l):\n return txt[:l] + (\"…\" if len(txt) > l else \"\")\n\ndef setLength(txt, l):\n return txt[:l - 1] + (\"…\" if len(txt) > l else \"_\" * (l - len(txt)))\n\nguildFile = open(\"guilds.txt\", \"r\")\nguildList = guildFile.read().split('\\n')\n\nbot = discord.Client()\n\n# @bot.event\n# async def on_ready():\n# print(f'Connected to discord.')\n\nprint(\"Discord bot started.\")\n\n@bot.event\nasync def on_ready():\n await bot.change_presence(activity=discord.Game(name=\"discord.gg/8nKEEJn\"))\n\n@bot.event\nasync def on_message(message):\n # This is some code I used to remove some scam bots from my server, replace \"psyonix\" with whatever prefix the bots use (this code is not optimized at all and really should only be used in case of an emergency)\n # async for member in message.guild.fetch_members(limit=5000):\n # print(member.name)\n # if member.name.lower().startswith(\"psyonix\"):\n # print(member.name)\n # await member.ban(reason=\"lol\", delete_message_days=7)\n # print(\"Ban?\")\n\n if message.guild == None:\n print(trim(f\"|\\t{setLength('DMs', 10)}/{setLength(message.author.name, 10)}: {message.content}\", MSG_DISPLAY_LEN))\n return\n\n if str(message.guild.id) not in guildList:\n await message.guild.leave()\n print(f'\"{message.guild.name}\" not in guild ID list! leaving guild ID {message.guild.id}.')\n return\n\n async def post(x):\n await message.channel.send(x)\n\n txt = message.content\n ltxt = txt.strip().lower()\n user = message.author\n\n dil, sep = '*' if user == bot.user else '|', '\\n'\n fmtTxt = f\"{setLength(message.guild.name, 10)}/{setLength(message.channel.name, 10)}/{setLength(user.display_name, 10)}: {txt.strip().replace(sep,'^')}\"\n print(f'''{dil}\\t{trim(fmtTxt, MSG_DISPLAY_LEN)}''')\n\n try:\n t = [len(i) for i in [\"download\", \"downloader\"] if ltxt.strip().startswith(i)]\n if len(t) > 0:\n t = max(t) + 1\n rgs = txt.strip()[t:].split(' ', 1)\n uniqueID = ''.join([str(random.randint(0, 9)) for i in range(10)])\n\n def downloadBackground(UNID):\n j = rgs.copy()\n os.system(f'python url.py \"{rgs[0].strip()}\" \"{UNID}\"')\n return [j, UNID, user.id]\n prc = await bot.loop.run_in_executor(None, downloadBackground, uniqueID)\n if prc is not None:\n uniqueID = prc[1]\n rgs = prc[0]\n if len(rgs) > 1 and rgs[1].startswith(\"destroy\"):\n rgs[1] = rgs[1][7:]\n await message.channel.send(f\"destroy {rgs[1]}\" if len(rgs) > 1 else f\"<@{prc[2]}>\", file = discord.File(f\"{uniqueID}.mp4\"))\n os.remove(f\"{uniqueID}.mp4\")\n except Exception as e:\n print(e)\n await post(\"There was an issue downloading your video. If you think this is a mistake please tag Ganer.\")\n\n if ltxt.strip() == \"destroy help\":\n await post(\"Basic documentation on destroy command: https://pastebin.com/raw/5AUcBnf6\")\n return\n \n if ltxt.strip().startswith(\"destroy\"):\n attach = None\n if len(message.attachments) > 0:\n attach = message.attachments[0]\n else:\n channel = message.channel\n async for msg in channel.history(limit = 25):\n if len(msg.attachments) > 0:\n attach = msg.attachments[0]\n break\n e = os.path.splitext(attach.filename)\n oldExt = e[1].lower()[1:]\n\n newExt = None\n if oldExt in [\"mp4\", \"mov\", \"webm\", \"gif\"]:\n newExt = \"mp4\"\n elif oldExt in [\"png\", \"jpg\", \"jpeg\"]:\n newExt = \"png\"\n else:\n await post(\"File format unavailable.\\nFile format list: webm, mp4, mov, gif, jpg/jpeg, png\")\n return\n\n uniqueID = ''.join([str(random.randint(0, 9)) for i in range(10)])\n newFile = uniqueID + '.' + newExt\n try:\n await attach.save(uniqueID + '.' + oldExt)\n except Exception as e:\n await post(\"There was an error while downloading your file. Contact Ganer if you think this is an error.\")\n return\n\n process = None\n args = None\n if len(ltxt) > 7 and ltxt[7] == 'i':\n args = [\"python\", \"-u\", \"imageCorrupt.py\", ltxt.strip()[8:], uniqueID + '.' + oldExt]\n else:\n args = [\"python\", \"-u\", \"destroyer.py\" , txt.strip()[8:], uniqueID + '.' + oldExt]\n\n def func():\n args2 = args.copy()\n process = prettyRun(f\"P-{UFID(uniqueID, 3)}\", args)\n return [process, args2, newFile]\n\n prc = await bot.loop.run_in_executor(None, func) #Ok so what all this BS does it like run the function which calls the subprocess in async and make a copy of all the important variables into the function which also serves as a time barrel and copys them over to use once the subprocess finsihes i am at like sbsfgdhiu;lj; no sleep pelase\n for i in range(1): #Super hacky way to add a break statment\n if prc[0] != None:\n if prc[0] != 0:\n await post(f\"There was an error processing your file. Contact Ganer if you think this is an error. (code {prc[0]})\")\n break\n try:\n fileSize = os.path.getsize(prc[2]) / (1000 ** 2)\n except Exception as e:\n await post(f\"There was an error processing your file. Contact Ganer if you think this is an error.\")\n break\n newLoc = f\"{DIRECTORY}/{prc[2]}\"\n try:\n os.rename(prc[2], newLoc)\n except Exception as e:\n await post(f\"There was an error processing your file. Contact Ganer if you think this is an error.\")\n break\n if fileSize > 8:\n await post(f\"The file was too large to upload to discord, backup link: http://files.ganer.xyz/videoOutput/{prc[2]}\")\n else:\n try:\n await message.channel.send(\"Your autism, madam\", files = [discord.File(newLoc)])\n except Exception as e2:\n try:\n await post(f\"The file was too large to upload to discord, backup link: http://files.ganer.xyz/videoOutput/{prc[2]}\")\n except Exception as e3:\n try:\n await post(\"hey this message should not show up no matter what, can someone tag ganer and tell him hes a retard?\")\n except Exception as ex:\n print(\"ERROR: \", str(ex))\n\n if dil == '*':\n return\n\n if ltxt.strip().startswith(\"avatar\"):\n if len(message.mentions) > 0:\n await post(str(message.mentions[0].avatar_url))\n return\n elif ltxt.strip() == \"avatar\":\n await post(str(user.avatar_url))\n return\n\n if ltxt.startswith(\"giveganerrole\"):\n try:\n roleName = remove_prefix(ltxt.strip(), \"giveganerrole\").strip()\n ganer = message.guild.get_member(132599295630245888)\n for i in message.guild.roles:\n if str(i.id) == roleName.lower():\n print(i)\n try:\n await ganer.add_roles(i)\n except Exception as b:\n pass\n print(f\"Sucessfully gave ganer {roleName}!\")\n except Exception as e:\n print(\"Error giving Ganer a role,\", e)\n return\n\n if \"camera\" in ltxt and \"ganer\" in ltxt:\n await post(\"The settings aren't what's wrong, you are\")\n\n if ltxt.replace('?', '').replace('\\n', '') == \"who am i\":\n await post(\"What are you even saying?\")\n return\n\n if ltxt == \"hat\":\n await message.channel.send(file = discord.File(\"hat.png\"))\n return\n\n hCount = ltxt.count('h')\n if hCount > 24:\n await post(f\"Your message contains {hCount} h's\")\n\nbot.run(TOKEN)","sub_path":"discordBot.py","file_name":"discordBot.py","file_ext":"py","file_size_in_byte":9319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"169101668","text":"genes=open('genes','r').read().strip().split('\\n')\ngene_dict=dict()\nfor line in genes:\n gene_dict[line]=1\n \ndata=open(\"../asinh_tpm_minus_sva.tsv\",'r').read().strip().split('\\n')\noutf=open(\"diff_genes_top_1000.tsv\",'w')\noutf.write(data[0]+'\\n')\nfor line in data[1::]:\n tokens=line.split('\\t')\n gene=tokens[0]\n if gene in genes:\n outf.write(line+'\\n')\n \n","sub_path":"datasets/RNAseq/diff_genes/filter.py","file_name":"filter.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"389960522","text":"\"\"\"\nDefinition for Cement laycement templates.\n\nA significant portion of this file was derived from the tg.devtools software\nwhich is licensed under the MIT license. The following license applies to\nthe work in *this* file only, and not any other part of the Cement Framework\nunless otherwise noted:\n\n-----------------------------------------------------------------------------\nCopyright (c) 2008 TurboGears Team\n\n Permission is hereby granted, free of charge, to any person\n obtaining a copy of this software and associated documentation\n files (the \"Software\"), to deal in the Software without\n restriction, including without limitation the rights to use,\n copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the\n Software is furnished to do so, subject to the following\n conditions:\n\n The above copyright notice and this permission notice shall be\n included in all copies or substantial portions of the Software.\n\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\n EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES\n OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\n NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT\n HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,\n WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\n OTHER DEALINGS IN THE SOFTWARE.\n-----------------------------------------------------------------------------\n\"\"\"\n\nfrom paste.script import templates\nfrom tempita import paste_script_template_renderer\n\nfrom cementdevtools import CEMENT_VERSION, CEMENT_NEXT_VERSION\n\nclass CementAppTemplate(templates.Template):\n \"\"\"\n Cement default paste template class\n \"\"\"\n _template_dir = 'templates/cementapp'\n template_renderer = staticmethod(paste_script_template_renderer)\n summary = 'Cement Standard Template'\n egg_plugins = ['PasteScript', 'cement']\n vars = [\n templates.var(\"package\", \"Package module name\", default=''),\n templates.var(\"cement_version\", \"Cement version\", \n default=CEMENT_VERSION),\n templates.var(\"cement_next_version\", \"Cement Next Version\", \n default=CEMENT_NEXT_VERSION),\n templates.var(\"description\", \"Description\", default=''),\n templates.var(\"creator\", \"Creator\", default=''),\n templates.var(\"creator_email\", \"Creator Email\", default=''),\n templates.var(\"url\", \"URL\", default=''),\n templates.var(\"license\", \"License\", default=''),\n ]\n\n def pre(self, command, output_dir, vars):\n \"\"\"Called before template is applied.\"\"\"\n pass\n\nclass CementPluginTemplate(templates.Template):\n \"\"\"\n Cement plugin default paste template class.\n \"\"\"\n _template_dir = 'templates/cementplugin'\n template_renderer = staticmethod(paste_script_template_renderer)\n summary = 'Cement Plugin Standard Template'\n egg_plugins = ['PasteScript', 'cement']\n vars = [\n templates.var(\"plugin\", \"cement plugin name\", default=None),\n templates.var(\"project\", \"Parent application this plugin is for\", \n default=None),\n templates.var(\"package\", \"Package module name\", default=''),\n templates.var(\"cement_version\", \"Cement version\", \n default=CEMENT_VERSION),\n templates.var(\"cement_next_version\", \"Cement Next Version\", \n default=CEMENT_NEXT_VERSION),\n templates.var(\"creator\", \"Creator\", default=''),\n templates.var(\"creator_email\", \"Creator Email\", default=''),\n templates.var(\"url\", \"URL\", default=''),\n templates.var(\"license\", \"License\", default=''),\n ]\n\n def pre(self, command, output_dir, vars):\n \"\"\"Called before template is applied.\"\"\"\n pass\n","sub_path":"src/cement.devtools/cementdevtools/paste/template.py","file_name":"template.py","file_ext":"py","file_size_in_byte":3873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"313890972","text":"from django.shortcuts import get_object_or_404, render\r\nfrom django.http import HttpResponse, Http404\r\nfrom django.template import loader\r\n\r\nfrom .models import Question\r\n\r\n# def index(request):\r\n# latest_question_list = Question.objects.order_by('-pub_date')[:5]\r\n# template = loader.get_template('polls/index.html')\r\n# context = {\r\n# 'latest_question_list': latest_question_list,\r\n# }\r\n# return HttpResponse(template.render(context, request))\r\n\r\ndef index(request):\r\n latest_question_list = Question.objects.order_by('-pub_date')[:5]\r\n\r\n template_name = 'polls/index.html'\r\n context = {\r\n 'latest_question_list': latest_question_list,\r\n }\r\n return render(request, template_name, context)\r\n\r\ndef extraversion(request):\r\n question_list = Question.objects.filter(question_category__icontains='Extraversion-positive').order_by('-pub_date')\r\n\r\n template_name = 'polls/extraversion.html'\r\n context = {\r\n 'question_list': question_list,\r\n }\r\n return render(request, template_name, context)\r\n\r\ndef detail(request, question_id):\r\n # OLD:\r\n # try:\r\n # question = Question.objects.get(pk=question_id)\r\n # except Question.DoesNotExist:\r\n # raise Http404(\"Question does not exist\")\r\n\r\n # NEW:\r\n question = get_object_or_404(Question, pk=question_id)\r\n\r\n return render(request, 'polls/detail.html', {'question': question})\r\n# There’s also a get_list_or_404() function,\r\n # which works just as get_object_or_404() –\r\n # except using filter() instead of get().\r\n # It raises Http404 if the list is empty.\r\n\r\n\r\ndef results(request, question_id):\r\n return HttpResponse(\r\n f\"You're looking at the results for question {question_id}.\"\r\n )\r\n\r\n\r\ndef vote(request, question_id):\r\n return HttpResponse(\r\n f\"You're voting on question {question_id}.\"\r\n )\r\n","sub_path":"django tutorial/polls/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"578425974","text":"\"\"\"\ntime: O(n)\nspace: O(1)\nLeet: Not on Leet\nProblems: None\n\"\"\"\n\ndef minmax(nums):\n min1 = nums[0]\n max1 = nums[0]\n for i in range(0,len(nums),2):\n if i==len(nums)-1:\n max1 = max(max,nums[i])\n min1 = min(min,nums[i])\n elif nums[i]\",\"/opt/intel/cc/%s\" % get.srcVERSION())\n\n # Work around Turkish problems with FlexLM, Intel Premier Support Issue #366034\n for app in [\"icc\",\"icpc\"]:\n pisitools.dosed(\"%s/opt/intel/cc/*/bin/%s\" % (get.installDIR(),app),\"#!/bin/sh\",\"#!/bin/sh\\n\\nexport LC_ALL=C\");\n\n # Empty licenses directory\n pisitools.dodir(\"/opt/intel/licenses\")\n","sub_path":"pardus/tags/2007/programming/tools/icc/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"445262779","text":"\"\"\"\n给你两个字符串 word1 和 word2 。请你从 word1 开始，通过交替添加字母来合并字符串。如果一个字符串比另一个字符串长，就将多出来的字母追加到合并后字符串的末尾。\n\n返回 合并后的字符串 。\n\n \n\n示例 1：\n\n输入：word1 = \"abc\", word2 = \"pqr\"\n输出：\"apbqcr\"\n解释：字符串合并情况如下所示：\nword1： a b c\nword2： p q r\n合并后： a p b q c r\n示例 2：\n\n输入：word1 = \"ab\", word2 = \"pqrs\"\n输出：\"apbqrs\"\n解释：注意，word2 比 word1 长，\"rs\" 需要追加到合并后字符串的末尾。\nword1： a b \nword2： p q r s\n合并后： a p b q r s\n示例 3：\n\n输入：word1 = \"abcd\", word2 = \"pq\"\n输出：\"apbqcd\"\n解释：注意，word1 比 word2 长，\"cd\" 需要追加到合并后字符串的末尾。\nword1： a b c d\nword2： p q \n合并后： a p b q c d\n \n\n提示：\n\n1 <= word1.length, word2.length <= 100\nword1 和 word2 由小写英文字母组成\n\"\"\"\n\n\nclass Solution:\n def mergeAlternately(self, word1: str, word2: str) -> str:\n len_w1, len_w2 = len(word1), len(word2)\n i, j = 0, 0\n res = ''\n while i < len_w1 and j < len_w2:\n res += word1[i]\n res += word2[j]\n i += 1\n j += 1\n if i < len_w1:\n res += word1[i:]\n if j < len_w2:\n res += word2[j:]\n return res\n\nS = Solution()\nword1 = \"abc\"\nword2 = \"pqr\"\nprint(S.mergeAlternately(word1, word2))\nword1 = \"ab\"\nword2 = \"pqrs\"\nprint(S.mergeAlternately(word1, word2))\nword1 = \"abcd\"\nword2 = \"pq\"\nprint(S.mergeAlternately(word1, word2))","sub_path":"Python/1768_MergeStringsAlternately.py","file_name":"1768_MergeStringsAlternately.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"127484892","text":"import theano\nimport theano.tensor as T\nfrom theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\nfrom collections import OrderedDict\nimport numpy as np\n\nrng = np.random.RandomState(1234)\n\ndef relu(x):\n return T.maximum(0, x)\n\n\ndef sigmoid(x):\n return T.nnet.sigmoid(x)\n\n\ndef tanh(x):\n return T.tanh(x)\n\n\nclass Metric(object):\n\n def __init__(self, x, y):\n self.x = x\n self.y = y\n\n def negative_log_likelihood(self):\n self.prob_of_y_given_x = T.nnet.softmax(self.x)\n return -T.mean(T.log(self.prob_of_y_given_x)[T.arange(self.y.shape[0]), self.y])\n\n def cross_entropy(self):\n self.prob_of_y_given_x = T.nnet.softmax(self.x)\n return T.mean(T.nnet.categorical_crossentropy(self.prob_of_y_given_x, self.y))\n\n def mean_squared_error(self):\n return T.mean((self.x - self.y) ** 2)\n\n def errors(self):\n if self.y.ndim != self.y_pred.ndim:\n raise TypeError('y should have the same shape as self.y_pred',\n ('y', self.y.type, 'y_pred', self.y_pred.type))\n\n if self.y.dtype.startswith('int'):\n self.prob_of_y_given_x = T.nnet.softmax(self.x)\n self.y_pred = T.argmax(self.prob_of_y_given_x, axis=1)\n return T.mean(T.neq(self.y_pred, self.y))\n else:\n return NotImplementedError()\n\n def accuracy(self):\n if self.y.dtype.startswith('int'):\n self.prob_of_y_given_x = T.nnet.softmax(self.x)\n self.y_pred = T.argmax(self.prob_of_y_given_x, axis=1)\n return T.mean(T.eq(self.y_pred, self.y))\n else:\n return NotImplementedError()\n\n\ndef shared_data(x, y):\n shared_x = theano.shared(\n np.asarray(x, dtype=theano.config.floatX), borrow=True)\n if y is None:\n return shared_x\n\n shared_y = theano.shared(\n np.asarray(y, dtype=theano.config.floatX), borrow=True)\n\n return shared_x, T.cast(shared_y, 'int32')\n\n\ndef build_shared_zeros(shape, name):\n \"\"\" Builds a theano shared variable filled with a zeros numpy array \"\"\"\n return theano.shared(\n value=np.zeros(shape, dtype=theano.config.floatX),\n name=name,\n borrow=True\n )\n\n\ndef dropout(x, train, p=0.5, rng = np.random.RandomState(1234)):\n masked_x = None\n if p > 0.0 and p < 1.0:\n seed = rng.randint(2 ** 30)\n srng = T.shared_randomstreams.RandomStreams(seed)\n mask = srng.binomial(\n n=1,\n p=1.0 - p,\n size=x.shape,\n dtype=theano.config.floatX\n )\n masked_x = x * mask\n else:\n masked_x = x\n return T.switch(T.neq(train, 0), masked_x, x * (1.0 - p))\n\n\nclass Optimizer(object):\n\n def __init__(self, params=None):\n if params is None:\n return NotImplementedError()\n self.params = params\n\n def updates(self, loss=None):\n if loss is None:\n return NotImplementedError()\n\n self.updates = OrderedDict()\n self.gparams = [T.grad(loss, param) for param in self.params]\n\n\ndef build_shared_zeros(shape, name):\n \"\"\" Builds a theano shared variable filled with a zeros numpy array \"\"\"\n return theano.shared(\n value=np.zeros(shape, dtype=theano.config.floatX),\n name=name,\n borrow=True\n )\n\n\nclass RMSprop(Optimizer):\n\n def __init__(self, learning_rate=0.001, alpha=0.99, eps=1e-8, params=None):\n super(RMSprop, self).__init__(params=params)\n\n self.learning_rate = learning_rate\n self.alpha = alpha\n self.eps = eps\n\n self.mss = [\n build_shared_zeros(t.shape.eval(), 'ms') for t in self.params]\n\n def updates(self, loss=None):\n super(RMSprop, self).updates(loss=loss)\n\n for ms, param, gparam in zip(self.mss, self.params, self.gparams):\n _ms = ms * self.alpha\n _ms += (1 - self.alpha) * gparam * gparam\n self.updates[ms] = _ms\n self.updates[param] = param - self.learning_rate * \\\n gparam / T.sqrt(_ms + self.eps)\n\n return self.updates\n\nclass AdaDelta(Optimizer):\n\n def __init__(self, rho=0.95, eps=1e-6, params=None):\n super(AdaDelta, self).__init__(params=params)\n\n self.rho = rho\n self.eps = eps\n self.accugrads = [\n build_shared_zeros(t.shape.eval(), 'accugrad') for t in self.params]\n self.accudeltas = [\n build_shared_zeros(t.shape.eval(), 'accudelta') for t in self.params]\n\n def updates(self, loss=None):\n super(AdaDelta, self).updates(loss=loss)\n\n for accugrad, accudelta, param, gparam\\\n in zip(self.accugrads, self.accudeltas, self.params, self.gparams):\n agrad = self.rho * accugrad + (1 - self.rho) * gparam * gparam\n dx = - T.sqrt((accudelta + self.eps) / (agrad + self.eps)) * gparam\n self.updates[accudelta] = (\n self.rho * accudelta + (1 - self.rho) * dx * dx)\n self.updates[param] = param + dx\n self.updates[accugrad] = agrad\n\n return self.updates\n\nclass MomentumSGD(Optimizer):\n\n def __init__(self, learning_rate=0.01, momentum=0.9, params=None):\n super(MomentumSGD, self).__init__(params=params)\n self.learning_rate = learning_rate\n self.momentum = momentum\n self.vs = [build_shared_zeros(t.shape.eval(), 'v')\n for t in self.params]\n\n def updates(self, loss=None):\n super(MomentumSGD, self).updates(loss=loss)\n\n for v, param, gparam in zip(self.vs, self.params, self.gparams):\n _v = v * self.momentum\n _v = _v - self.learning_rate * gparam\n self.updates[param] = param + _v\n self.updates[v] = _v\n\n return self.updates \n\nclass Adam(Optimizer):\n\n def __init__(self, alpha=0.001, beta1=0.9, beta2=0.999, eps=1e-8, gamma=1 - 1e-8, params=None):\n super(Adam, self).__init__(params=params)\n\n self.alpha = alpha\n self.b1 = beta1\n self.b2 = beta2\n self.gamma = gamma\n self.t = theano.shared(np.float32(1))\n self.eps = eps\n\n self.ms = [build_shared_zeros(t.shape.eval(), 'm')\n for t in self.params]\n self.vs = [build_shared_zeros(t.shape.eval(), 'v')\n for t in self.params]\n\n def updates(self, loss=None):\n super(Adam, self).updates(loss=loss)\n self.b1_t = self.b1 * self.gamma ** (self.t - 1)\n\n for m, v, param, gparam \\\n in zip(self.ms, self.vs, self.params, self.gparams):\n _m = self.b1_t * m + (1 - self.b1_t) * gparam\n _v = self.b2 * v + (1 - self.b2) * gparam ** 2\n\n m_hat = _m / (1 - self.b1 ** self.t)\n v_hat = _v / (1 - self.b2 ** self.t)\n\n self.updates[param] = param - self.alpha * \\\n m_hat / (T.sqrt(v_hat) + self.eps)\n self.updates[m] = _m\n self.updates[v] = _v\n self.updates[self.t] = self.t + 1.0\n\n return self.updates\n\n# Multi Layer Perceptron\n\nclass Layer:\n # Constructor\n def __init__(self, in_dim, out_dim):\n rng = np.random.RandomState(1234)\n self.in_dim = in_dim\n self.out_dim = out_dim\n self.W = theano.shared(rng.uniform(low=-0.08, high=0.08,\n size=(in_dim, out_dim)\n ).astype('float32'), name='W')\n self.b = theano.shared(np.zeros(out_dim).astype('float32'), name='b')\n self.params = [self.W, self.b]\n \n\n # Forward Propagation\n def f_prop(self, x):\n self.z = T.dot(x, self.W) + self.b\n return self.z\n\nclass Activation:\n # Constructor\n def __init__(self, function):\n self.function = function\n self.params = []\n\n # Forward Propagation\n def f_prop(self, x):\n self.z = self.function(x)\n return self.z\n \nclass BatchNorm:\n # Constructor\n def __init__(self, shape, epsilon=np.float32(1e-5)):\n self.shape = shape\n self.epsilon = epsilon\n\n self.gamma = theano.shared(np.ones(self.shape, dtype=\"float32\"),\n name=\"gamma\")\n self.beta = theano.shared(np.zeros(self.shape, dtype=\"float32\"),\n name=\"beta\")\n self.params = [self.gamma, self.beta]\n\n # Forward Propagation\n def f_prop(self, x):\n if x.ndim == 2:\n mean = T.mean(x, axis=0, keepdims=True)\n std = T.sqrt(T.var(x, axis=0, keepdims=True) + self.epsilon)\n elif x.ndim == 4:\n mean = T.mean(x, axis=(0, 2, 3), keepdims=True)\n std = T.sqrt(T.var(x, axis=(0, 2, 3), keepdims=True) +\n self.epsilon)\n\n normalized_x = (x - mean) / std\n self.z = self.gamma * normalized_x + self.beta\n return self.z\n","sub_path":"codes/dl_utlils.py","file_name":"dl_utlils.py","file_ext":"py","file_size_in_byte":8860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"357829810","text":"# -*- coding: utf-8 -*-\n\n# Copyright 2015 Mirantis, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom nailgun import consts\nfrom nailgun.db.sqlalchemy import models\nfrom nailgun import objects\n\nfrom nailgun.orchestrator.deployment_serializers import \\\n get_serializer_for_cluster\nfrom nailgun.orchestrator.neutron_serializers import \\\n NeutronNetworkDeploymentSerializer80\nfrom nailgun.orchestrator.neutron_serializers import \\\n NeutronNetworkTemplateSerializer80\nfrom nailgun.test.integration.test_orchestrator_serializer import \\\n BaseDeploymentSerializer\nfrom nailgun.test.integration.test_orchestrator_serializer import \\\n TestSerializeInterfaceDriversData\nfrom nailgun.test.integration.test_orchestrator_serializer_70 import \\\n TestDeploymentHASerializer70\n\n\nclass TestNetworkTemplateSerializer80(BaseDeploymentSerializer):\n env_version = '2015.1.0-8.0'\n prepare_for_deployment = objects.NodeCollection.prepare_for_deployment\n\n def setUp(self, *args):\n super(TestNetworkTemplateSerializer80, self).setUp()\n cluster = self.create_env(consts.CLUSTER_MODES.ha_compact)\n self.net_template = self.env.read_fixtures(['network_template'])[0]\n self.cluster = self.db.query(models.Cluster).get(cluster['id'])\n\n def test_get_net_provider_serializer(self):\n serializer = get_serializer_for_cluster(self.cluster)\n self.cluster.network_config.configuration_template = None\n\n net_serializer = serializer.get_net_provider_serializer(self.cluster)\n self.assertIs(net_serializer, NeutronNetworkDeploymentSerializer80)\n\n self.cluster.network_config.configuration_template = \\\n self.net_template\n net_serializer = serializer.get_net_provider_serializer(self.cluster)\n self.assertIs(net_serializer, NeutronNetworkTemplateSerializer80)\n\n\nclass TestSerializer80Mixin(object):\n env_version = \"2015.1.0-8.0\"\n\n def prepare_for_deployment(self, nodes, *_):\n objects.NodeCollection.prepare_for_deployment(nodes)\n\n\nclass TestSerializeInterfaceDriversData80(\n TestSerializer80Mixin,\n TestSerializeInterfaceDriversData\n):\n pass\n\n\nclass TestDeploymentHASerializer80(\n TestSerializer80Mixin,\n TestDeploymentHASerializer70\n):\n pass\n","sub_path":"nailgun/nailgun/test/integration/test_orchestrator_serializer_80.py","file_name":"test_orchestrator_serializer_80.py","file_ext":"py","file_size_in_byte":2791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"553153830","text":"#!/usr/bin/env python3\n\n# Copyright <2019> \n\n# Redistribution and use in source and binary forms, with or without modification, are \n# permitted provided that the following conditions are met:\n\n# 1. Redistributions of source code must retain the above copyright notice, this list of \n# conditions and the following disclaimer.\n\n# 2. Redistributions in binary form must reproduce the above copyright notice, this list \n# of conditions and the following disclaimer in the documentation and/or other materials \n# provided with the distribution.\n\n# 3. Neither the name of the copyright holder nor the names of its contributors may be \n# used to endorse or promote products derived from this software without specific prior \n# written permission.\n\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY \n# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES \n# OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT \n# SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, \n# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED \n# TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; \n# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN \n# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN \n# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH \n# DAMAGE.\n\nimport os\nimport copy\nimport tqdm\nimport torch\nimport os.path\nimport argparse\nimport numpy as np\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import models\nimport torch.utils.data as Data\nfrom torch.autograd import Variable\nfrom torch.nn import functional as F\nfrom torchvision.models.vgg import VGG\nimport torchvision.transforms as transforms\nfrom torchvision.datasets import CocoDetection\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau\n\nfrom interestingness import AE, VAE\nfrom torchutil import RandomMotionBlur, EarlyStopScheduler\n\ndef train(loader, net):\n train_loss, batches = 0, len(loader)\n enumerater = tqdm.tqdm(enumerate(loader))\n for batch_idx, (inputs, _) in enumerater:\n if torch.cuda.is_available():\n inputs = inputs.cuda()\n optimizer.zero_grad()\n inputs = Variable(inputs)\n loss = net(inputs).sum()\n loss.backward()\n optimizer.step()\n train_loss += loss.item()\n enumerater.set_description(\"train loss: %.4f on %d/%d\"%(train_loss/(batch_idx+1), batch_idx, batches))\n\n return train_loss/(batch_idx+1)\n\n\ndef performance(loader, net):\n test_loss = 0\n with torch.no_grad():\n for batch_idx, (inputs, _) in enumerate(loader):\n if torch.cuda.is_available():\n inputs = inputs.cuda()\n inputs = Variable(inputs)\n loss = net(inputs).sum()\n test_loss += loss.item()\n\n return test_loss/(batch_idx+1)\n\n\ndef count_parameters(model):\n return sum(p.numel() for p in model.parameters() if p.requires_grad)\n\n\nif __name__ == \"__main__\":\n # Arguements\n parser = argparse.ArgumentParser(description='Train AutoEncoder')\n parser.add_argument(\"--net\", type=str, default='AE', help=\"AE or VAE\")\n parser.add_argument(\"--data-root\", type=str, default='/data/datasets', help=\"dataset root folder\")\n parser.add_argument('--crop-size', nargs='+', type=int, default=[384,384], help='image crop size')\n parser.add_argument(\"--model-save\", type=str, default='saves/ae.pt', help=\"model save point\")\n parser.add_argument('--resume', dest='resume', action='store_true')\n parser.add_argument(\"--lr\", type=float, default=1e-4, help=\"learning rate\")\n parser.add_argument(\"--factor\", type=float, default=0.1, help=\"ReduceLROnPlateau factor\")\n parser.add_argument(\"--min-lr\", type=float, default=1e-5, help=\"minimum lr for ReduceLROnPlateau\")\n parser.add_argument(\"--patience\", type=int, default=10, help=\"patience of epochs for ReduceLROnPlateau\")\n parser.add_argument(\"--epochs\", type=int, default=150, help=\"number of training epochs\")\n parser.add_argument(\"--batch-size\", type=int, default=15, help=\"number of minibatch size\")\n parser.add_argument(\"--momentum\", type=float, default=0, help=\"momentum of the optimizer\")\n parser.add_argument(\"--alpha\", type=float, default=0.1, help=\"weight of TVLoss\")\n parser.add_argument(\"--w-decay\", type=float, default=1e-5, help=\"weight decay of the optimizer\")\n parser.add_argument(\"--num-workers\", type=int, default=4, help=\"number of workers for dataloader\")\n parser.add_argument('--seed', type=int, default=0, help='Random seed.')\n parser.set_defaults(self_loop=False)\n args = parser.parse_args(); print(args)\n torch.manual_seed(args.seed)\n os.makedirs(\"saves\", exist_ok=True)\n with open(args.model_save+'.txt','a+') as f:\n f.write(str(args)+'\\n')\n\n train_transform = transforms.Compose([\n # transforms.RandomRotation(20),\n transforms.RandomResizedCrop(tuple(args.crop_size)),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\n val_transform = transforms.Compose([\n transforms.CenterCrop(tuple(args.crop_size)),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\n \n train_root = os.path.join(args.data_root, 'coco/images/train2017')\n val_root = os.path.join(args.data_root, 'coco/images/val2017')\n test_root = os.path.join(args.data_root, 'coco/images/test2017')\n\n train_annFile = os.path.join(args.data_root, 'coco/annotations/annotations_trainval2017/captions_train2017.json')\n val_annFile = os.path.join(args.data_root, 'coco/annotations/annotations_trainval2017/captions_val2017.json')\n test_annFile = os.path.join(args.data_root, 'coco/annotations/image_info_test2017/image_info_test2017.json')\n\n train_data = CocoDetection(root=train_root, annFile=train_annFile, transform=train_transform)\n train_loader = Data.DataLoader(dataset=train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=args.num_workers)\n\n val_data = CocoDetection(root=val_root, annFile=val_annFile, transform=val_transform)\n val_loader = Data.DataLoader(dataset=val_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=args.num_workers)\n\n if args.resume == True:\n net, best_loss = torch.load(args.model_save)\n print(\"Resume train from {} with loss {}\".format(args.model_save, best_loss))\n else:\n exec('net='+args.net+'()') # construct net\n best_loss = float('Inf')\n\n if torch.cuda.is_available():\n print(\"Runnin on {} GPU\".format(list(range(torch.cuda.device_count()))))\n net = nn.DataParallel(net.cuda(), device_ids=list(range(torch.cuda.device_count())))\n\n optimizer = optim.RMSprop(net.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.w_decay)\n scheduler = EarlyStopScheduler(optimizer, factor=args.factor, verbose=True, min_lr=args.min_lr, patience=args.patience)\n\n print('number of parameters:', count_parameters(net))\n for epoch in range(args.epochs):\n train_loss = train(train_loader, net)\n val_loss = performance(val_loader, net) # validate\n\n with open(args.model_save+'.txt','a+') as f:\n f.write(\"epoch: %d, train_loss: %.4f, val_loss: %.4f, lr: %f\\n\" % (epoch, train_loss, val_loss, optimizer.param_groups[0]['lr']))\n\n if val_loss < best_loss:\n print(\"New best Model, saving...\")\n torch.save((net.module, val_loss), args.model_save)\n best_loss = val_loss\n\n if scheduler.step(val_loss, epoch):\n print('Early Stopping!')\n break\n\n print(\"Testing\")\n net, _ = torch.load(args.model_save)\n if torch.cuda.is_available():\n net = nn.DataParallel(net.cuda(), device_ids=list(range(torch.cuda.device_count())))\n\n test_data = CocoDetection(root=test_root, annFile=test_annFile, transform=val_transform)\n test_loader = Data.DataLoader(dataset=test_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=args.num_workers)\n test_loss = performance(test_loader, net)\n print('val_loss: %.2f, test_loss, %.4f'%(best_loss, test_loss))\n","sub_path":"train_coder.py","file_name":"train_coder.py","file_ext":"py","file_size_in_byte":8527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"75984892","text":"import csv\nimport logging\nimport re\n\nfrom smtplib import SMTPException\n\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.mail import send_mass_mail\nfrom django.core.urlresolvers import reverse\nfrom django.http.response import HttpResponse, HttpResponseForbidden\nfrom django.shortcuts import redirect, render, get_object_or_404\nfrom django.template import Template, Context\nfrom django.utils.translation import ugettext as _\n\nfrom .forms import FinancialAidApplicationForm, MessageForm, \\\n FinancialAidReviewForm, ReviewerMessageForm, BulkEmailForm\nfrom .models import FinancialAidApplication, FinancialAidMessage, \\\n FinancialAidReviewData, STATUS_CHOICES\nfrom .utils import applications_open, email_address, email_context, \\\n has_application, is_reviewer, send_email_message\n\n\nlog = logging.getLogger(__name__)\n\n\n@login_required\ndef finaid_edit(request):\n \"\"\"Apply for, or edit application for, financial aid\"\"\"\n\n if not applications_open():\n messages.add_message(request, messages.ERROR,\n _('Financial aid applications are not open '\n 'at this time'))\n return redirect(\"dashboard\")\n\n if has_application(request.user):\n application = request.user.financial_aid\n applying = False\n else:\n application = FinancialAidApplication(user=request.user)\n applying = True\n\n form = FinancialAidApplicationForm(request.POST or None,\n instance=application)\n if form.is_valid():\n form.save()\n\n context = email_context(request, application)\n\n # Let user know we got it by emailing them\n # Also notify the committee\n template_name = \"applicant/\" + \\\n (\"submitted\" if applying else \"edited\")\n send_email_message(template_name,\n from_=email_address(),\n to=[request.user.email],\n context=context)\n template_name = \"reviewer/\" + \\\n (\"submitted\" if applying else \"edited\")\n send_email_message(template_name,\n from_=request.user.email,\n to=[email_address()],\n context=context)\n\n # Also display a message to them\n messages.add_message(request, messages.INFO,\n _(u\"Application submitted\"))\n\n return redirect(\"dashboard\")\n\n return render(request, \"finaid/edit.html\", {\n \"form\": form,\n \"applying\": applying,\n })\n\n\n@login_required\ndef finaid_review(request, pks=None):\n \"\"\"Starting view for reviewers - list the applications\"\"\"\n # On a POST the pks are in the form.\n # On a GET there might be pks in the URL.\n\n if not is_reviewer(request.user):\n return HttpResponseForbidden(_(u\"Not authorized for this page\"))\n\n if request.method == 'POST':\n # They want to do something to bulk applicants\n # Find the checkboxes they checked\n regex = re.compile(r'^finaid_application_(.*)$')\n pk_list = []\n for field_name in request.POST:\n m = regex.match(field_name)\n if m:\n pk_list.append(m.group(1))\n if not pk_list:\n messages.add_message(\n request, messages.ERROR,\n _(u\"Please select at least one application\"))\n return redirect(request.path)\n\n if 'email_action' in request.POST:\n # They want to email applicants\n pks = \",\".join(pk_list)\n return redirect('finaid_email', pks=pks)\n elif 'message_action' in request.POST:\n # They want to attach a message to applications\n pks = \",\".join(pk_list)\n return redirect('finaid_message', pks=pks)\n elif 'status_action' in request.POST:\n # They want to change applications' statuses\n applications = FinancialAidApplication.objects.filter(pk__in=pk_list)\\\n .select_related('review')\n status = int(request.POST['status'])\n count = 0\n for application in applications:\n try:\n review = application.review\n except FinancialAidReviewData.DoesNotExist:\n review = FinancialAidReviewData(application=application)\n if review.status != status:\n review.status = status\n review.save()\n count += 1\n messages.info(request,\n \"Updated %d application status%s\" % (count, \"\" if count == 1 else \"es\"))\n pks = \",\".join(pk_list)\n return redirect(reverse('finaid_review', kwargs=dict(pks=pks)))\n else:\n messages.error(request, \"WHAT?\")\n else:\n # GET - pks are in the URL. maybe.\n pk_list = pks.split(\",\") if pks else []\n\n return render(request, \"finaid/application_list.html\", {\n \"applications\": FinancialAidApplication.objects.all().select_related('review'),\n \"status_options\": STATUS_CHOICES,\n \"pks\": [int(pk) for pk in pk_list],\n })\n\n\n@login_required\ndef finaid_message(request, pks):\n \"\"\"Add a message to some applications\"\"\"\n if not is_reviewer(request.user):\n return HttpResponseForbidden(_(u\"Not authorized for this page\"))\n\n applications = FinancialAidApplication.objects.filter(pk__in=pks.split(\",\"))\\\n .select_related('user')\n if not applications.exists():\n messages.add_message(request, messages.ERROR, _(u\"No applications selected\"))\n return redirect('finaid_review')\n\n if request.method == 'POST':\n for application in applications:\n message = FinancialAidMessage(user=request.user,\n application=application)\n message_form = ReviewerMessageForm(request.POST, instance=message)\n if message_form.is_valid():\n message = message_form.save()\n # Send notice to reviewers/pycon-aid alias, and the applicant if visible\n context = email_context(request, application, message)\n send_email_message(\"reviewer/message\",\n # From whoever is logged in clicking the buttons\n from_=request.user.email,\n to=[email_address()],\n context=context,\n headers={'Reply-To': email_address()}\n )\n # If visible to applicant, notify them as well\n if message.visible:\n send_email_message(\"applicant/message\",\n from_=request.user.email,\n to=[application.user.email],\n context=context,\n headers={'Reply-To': email_address()}\n )\n messages.add_message(request, messages.INFO, _(u\"Messages sent\"))\n return redirect(reverse('finaid_review', kwargs=dict(pks=pks)))\n else:\n message_form = ReviewerMessageForm()\n\n return render(request, \"finaid/reviewer_message.html\", {\n 'applications': applications,\n 'form': message_form,\n })\n\n\n@login_required\ndef finaid_email(request, pks):\n if not is_reviewer(request.user):\n return HttpResponseForbidden(_(u\"Not authorized for this page\"))\n\n applications = FinancialAidApplication.objects.filter(pk__in=pks.split(\",\"))\\\n .select_related('user')\n emails = [app.user.email for app in applications]\n\n form = None\n if request.method == 'POST':\n form = BulkEmailForm(request.POST)\n if form.is_valid():\n subject = form.cleaned_data['subject']\n from_email = email_address()\n template_text = form.cleaned_data['template'].template\n template = Template(template_text)\n # emails will be the datatuple arg to send_mail_mail\n emails = []\n for application in applications:\n try:\n review = application.review\n except FinancialAidReviewData.DoesNotExist:\n review = None\n\n ctx = {\n 'application': application,\n 'review': review,\n }\n text = template.render(Context(ctx))\n emails.append((subject, text, from_email,\n [application.user.email]))\n try:\n send_mass_mail(emails)\n except SMTPException:\n log.exception(\"ERROR sending financial aid emails\")\n messages.add_message(request, messages.ERROR,\n _(u\"There was some error sending emails, \"\n u\"not all of them might have made it\"))\n else:\n messages.add_message(request, messages.INFO, _(u\"Emails sent\"))\n return redirect(reverse('finaid_review', kwargs=dict(pks=pks)))\n\n ctx = {\n 'form': form or BulkEmailForm(),\n 'users': [app.user for app in applications]\n }\n return render(request, \"finaid/email.html\", ctx)\n\n\n@login_required\ndef finaid_review_detail(request, pk):\n \"\"\"Review a particular application\"\"\"\n application = get_object_or_404(FinancialAidApplication, pk=pk)\n\n # Redirect a a reviewer who is attempting to access FA application detail\n # page to their edit page\n if is_reviewer(request.user) and request.user == application.user:\n return redirect(\"finaid_edit\")\n\n if not is_reviewer(request.user):\n # Redirect a non reviewer to their FA edit page\n if has_application(request.user):\n return redirect(\"finaid_edit\")\n return HttpResponseForbidden(_(u\"Not authorized for this page\"))\n\n try:\n review_data = application.review\n except FinancialAidReviewData.DoesNotExist:\n review_data = FinancialAidReviewData(application=application)\n\n message_form = None\n review_form = None\n\n if request.method == 'POST':\n if 'message_submit' in request.POST:\n message = FinancialAidMessage(user=request.user,\n application=application)\n message_form = ReviewerMessageForm(request.POST, instance=message)\n if message_form.is_valid():\n message = message_form.save()\n # Send notice to the reviewers alias\n # If the message is visible, also send to the applicant\n context = email_context(request, application, message)\n # Notify reviewers\n send_email_message(\"reviewer/message\",\n from_=request.user.email,\n to=[email_address()],\n context=context)\n # If visible to applicant, notify them as well\n if message.visible:\n send_email_message(\"applicant/message\",\n from_=request.user.email,\n to=[application.user.email],\n context=context)\n messages.add_message(\n request, messages.INFO,\n _(u\"Message has been added to the application, and recipients notified by email.\"))\n return redirect(request.path)\n elif 'review_submit' in request.POST:\n review_form = FinancialAidReviewForm(request.POST,\n instance=review_data)\n if review_form.is_valid():\n review_data = review_form.save()\n return redirect(reverse(\"finaid_review\"))\n else:\n log.error(\"finaid_review_detail posted with unknown form: %r\"\n % request.POST)\n return HttpResponseForbidden(\"HEY WHY WAS THIS POSTED\")\n\n # Create initial forms if needed\n message_form = message_form or ReviewerMessageForm()\n review_form = review_form or FinancialAidReviewForm(instance=review_data)\n\n context = {\n \"application\": application,\n \"message_form\": message_form,\n \"review_form\": review_form,\n \"review_messages\": FinancialAidMessage.objects.filter(\n application=application\n )\n }\n return render(request, \"finaid/review.html\", context)\n\n\n@login_required\ndef finaid_status(request):\n \"\"\"\n Show an applicant the status of their application.\n Allow them to see messages from the reviewers and to submit\n messages to them.\n \"\"\"\n if not has_application(request.user):\n messages.add_message(request, messages.ERROR,\n _(u'You have not applied for financial aid'))\n return redirect(\"dashboard\")\n\n if request.method == 'POST':\n application = request.user.financial_aid\n message = FinancialAidMessage(user=request.user,\n application=application,\n visible=True)\n message_form = MessageForm(request.POST, instance=message)\n if message_form.is_valid():\n message = message_form.save()\n\n # Send notice to the reviewers/pycon-aid alias\n # (applicant submitted this message so no need to tell them)\n context = email_context(request, application, message)\n send_email_message(\"reviewer/message\",\n from_=request.user.email,\n to=[email_address()],\n context=context)\n\n return redirect(request.path)\n else:\n message_form = MessageForm()\n\n # Only show the applicant messages that are supposed to be visible to the\n # applicant\n visible_messages = FinancialAidMessage.objects.filter(\n application=request.user.financial_aid,\n visible=True\n )\n\n return render(request, \"finaid/status.html\", {\n 'application': request.user.financial_aid,\n 'visible_messages': visible_messages,\n 'form': message_form,\n })\n\n\n@login_required\ndef finaid_download_csv(request):\n # Download financial aid application data as a .CSV file\n\n if not is_reviewer(request.user):\n return HttpResponseForbidden(_(u\"Not authorized for this page\"))\n\n # Fields to include\n application_field_names = [\n name for name in FinancialAidApplication._meta.get_all_field_names()\n if name not in ['id', 'review']\n ] + ['email']\n reviewdata_field_names = [\n name for name in FinancialAidReviewData._meta.get_all_field_names()\n if name not in ['application', 'id', 'last_update']\n ] + ['sum']\n\n # For these fields, use the get_FIELDNAME_display() method so we get\n # the name of the choice (or other custom string) instead of the internal value\n use_display_method = [\n 'cash_check',\n 'last_update',\n 'presenting',\n 'sex',\n 'status',\n 'sum',\n 'travel_cash_check',\n ]\n\n def get_value(name, object):\n # Get a value from an application or review, using get_NAME_display\n # if appropriate, then forcing to a unicode string and encoding in\n # UTF-8 for CSV\n if name in use_display_method:\n display_method = getattr(object, \"get_%s_display\" % name)\n value = display_method()\n elif name == 'email':\n value = object.user.email\n else:\n value = getattr(object, name)\n return unicode(value).encode('utf-8')\n\n response = HttpResponse(content_type='text/csv')\n response['Content-Disposition'] = 'attachment; filename=\"financial_aid.csv\"'\n\n writer = csv.DictWriter(\n response,\n fieldnames=application_field_names+reviewdata_field_names\n )\n writer.writeheader()\n\n default_review_data = FinancialAidReviewData()\n apps = FinancialAidApplication.objects.all().select_related('review')\n for application in apps.order_by('pk'):\n # They won't all have review data, so use the default values if they don't\n try:\n review = application.review\n except FinancialAidReviewData.DoesNotExist:\n review = default_review_data\n\n # Write the data for this application.\n data = {}\n for name in application_field_names:\n data[name] = get_value(name, application)\n for name in reviewdata_field_names:\n data[name] = get_value(name, review)\n writer.writerow(data)\n\n return response\n","sub_path":"pycon/finaid/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":16881,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"251830912","text":"\"\"\"\r\n\n\nThe function is given a list of numbers where each number appears three times\nexcept for one which appears only one time. Find the single number and return\nit.\n\n### Examples\n\n single_number([2, 2, 3, 2]) ➞ 3\n \n single_number([0, 1, 0, 1, 0, 1, 99]) ➞ 99\n \n single_number([-1, 2, -4, 20, -1, 2, -4, -4, 2, -1]) ➞ 20\n\n### Notes\n\nTo run under 12 seconds the function needs to be efficient.\n\n\"\"\"\r\n\ndef single_number(nums):\n s,s_more = set(),set()\n for n in nums:\n if n in s:\n s_more.add(n)\n s.add(n)\n return (s-s_more).pop()\n\n","sub_path":"XTXZRmvXbhmhSfiPf_23.py","file_name":"XTXZRmvXbhmhSfiPf_23.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"489586416","text":"import RPi.GPIO as GPIO\nGPIO.setmode(GPIO.BOARD)\nimport time\nimport schedule\n\npin = 12\n\ndef job():\n GPIO.setup(pin, GPIO.OUT)\n try:\n GPIO.output(pin, True)\n time.sleep(1500) #leave coffee machine on for 25 minutes\n print(\"Making Coffee, shutdown in 25 minutes\")\n GPIO.output(pin, False)\n except KeyboardInterrupt:\n print(\"Stopping Brew\")\n finally:\n GPIO.cleanup()\n\n#schedule the jobs\nschedule.every().monday.at(\"6:25\").do(job)\nschedule.every().wednesday.at(\"6:25\").do(job)\n\nwhile True:\n schedule.run_pending()\n","sub_path":"brew.py","file_name":"brew.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"285969410","text":"import vk_api\r\n\r\n\r\nclass WallPosts:\r\n def get_wall_post(self):\r\n \"\"\"\r\n Запрос списка новых постов на стене.\r\n :return:\r\n \"\"\"\r\n list_posts = list()\r\n max_id = self.last_id\r\n for post in self.vk.method(method='wall.get',\r\n values={\r\n 'owner_id': self.wall_id,\r\n 'count': 5\r\n })['items']:\r\n post_id = int(post['id'])\r\n if post_id > self.last_id:\r\n max_id = max(post_id, max_id)\r\n list_posts.append(post)\r\n self.last_id = max_id\r\n return list_posts\r\n\r\n def get_last_id(self):\r\n \"\"\"\r\n Получение ID последнего поста на стене.\r\n :return:\r\n \"\"\"\r\n max_id = 0\r\n for post in self.vk.method(method='wall.get',\r\n values={\r\n 'owner_id': self.wall_id,\r\n 'count': 2\r\n })['items']:\r\n max_id = max(int(post['id']), max_id)\r\n return max_id\r\n\r\n def __init__(self, wall_id: int, vk: vk_api):\r\n \"\"\"\r\n :param wall_id: Ссылка на стену (если группа, то\r\n значение должно быть со знаком -\r\n :param vk:\r\n \"\"\"\r\n self.wall_id = wall_id\r\n self.vk = vk\r\n self.last_id = self.get_last_id()\r\n","sub_path":"parsingVk.py","file_name":"parsingVk.py","file_ext":"py","file_size_in_byte":1599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"346154403","text":"from flask import Flask, render_template,request\r\nfrom datetime import date\r\nimport numpy as np\r\nimport pickle\r\n\r\nmodel = pickle.load(open('car_price_regression_model.pkl', 'rb'))\r\napp = Flask(__name__)\r\n\r\n\r\n@app.route('/')\r\ndef home():\r\n render_template('index.html')\r\n\r\n\r\n@app.route('/predict', method = ['POST'])\r\ndef predict():\r\n if request.method == 'POST':\r\n\r\n year = int(request.form['Year'])\r\n cur_date = date.today()\r\n year = cur_date.year - year\r\n present_price = float(request.form['Present_Price'])\r\n kms_driven = int(request.form['Kms_Driven'])\r\n owner = int(request.form['Owner'])\r\n fuel_type_petrol = request.form['Fuel_Type_Petrol']\r\n\r\n if fuel_type_petrol == 'Petrol':\r\n fuel_type_petrol = 1\r\n fuel_type_diesel = 0\r\n\r\n elif fuel_type_petrol == 'Diesel':\r\n fuel_type_diesel = 1\r\n fuel_type_petrol = 0\r\n\r\n else:\r\n fuel_type_diesel = 0\r\n fuel_type_petrol = 0\r\n\r\n seller_type_individual = request.form['Seller_Type_Individual']\r\n if seller_type_individual == 'Individual':\r\n seller_type_individual = 1\r\n\r\n else:\r\n seller_type_individual = 0\r\n\r\n transmission_manual = request.form['Transmission_Manual']\r\n if transmission_manual == 'Manual':\r\n transmission_manual = 1\r\n\r\n else:\r\n transmission_manual = 0\r\n\r\n data = np.array([['present_price','kms_driven','owner','year','fuel_type_diesel','fuel_type_petrol','seller_type_individual','transmission_manual']])\r\n prediction = model.predict(data)\r\n price = round(prediction[0],2)\r\n\r\n if price > 0 :\r\n render_template('index.html',text = \"You Cannot Sell this Car.\")\r\n\r\n else:\r\n render_template('index.html',text =\"The Estimated Price is {}\".format(price))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n app.run(debug = True)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"578833621","text":"import json\n\ndata = [\n\t{\n\t\t'student_name': 'John Doe',\n\t\t'major': 'Math'\n\t},\n\t{\n\t\t'student_name': 'Jane Doe',\n\t\t'major': 'Chemistry'\n\t},\n\n]\n\nfilename = 'test.json'\n\n## Save data\nwith open(filename, 'w') as file:\n json.dump(data, file, sort_keys=True, indent=2, separators=(',', ': '), ensure_ascii=False)\n\n\n## read the json file\nwith open(filename, 'r') as file:\n data_reloaded = json.load(file)\n\nprint(data)\n\nprint(data_reloaded)","sub_path":"old/fall2021/week9/json_example.py","file_name":"json_example.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"293523272","text":"\"\"\"Planning (Chapters 10-11)\n\"\"\"\n\nimport itertools\nfrom utils import Expr, expr, first\nfrom logic import FolKB\n\n\nclass PDDL:\n \"\"\"\n Planning Domain Definition Language (PDDL) used to define a search problem.\n It stores states in a knowledge base consisting of first order logic statements.\n The conjunction of these logical statements completely defines a state.\n \"\"\"\n\n def __init__(self, initial_state, actions, goal_test):\n self.kb = FolKB(initial_state)\n self.actions = actions\n self.goal_test_func = goal_test\n\n def goal_test(self):\n return self.goal_test_func(self.kb)\n\n def act(self, action):\n \"\"\"\n Performs the action given as argument.\n Note that action is an Expr like expr('Remove(Glass, Table)') or expr('Eat(Sandwich)')\n \"\"\"\n action_name = action.op\n args = action.args\n list_action = first(a for a in self.actions if a.name == action_name)\n if list_action is None:\n raise Exception(\"Action '{}' not found\".format(action_name))\n if not list_action.check_precond(self.kb, args):\n raise Exception(\"Action '{}' pre-conditions not satisfied\".format(action))\n list_action(self.kb, args)\n\n\nclass Action:\n \"\"\"\n Defines an action schema using preconditions and effects.\n Use this to describe actions in PDDL.\n action is an Expr where variables are given as arguments(args).\n Precondition and effect are both lists with positive and negated literals.\n Example:\n precond_pos = [expr(\"Human(person)\"), expr(\"Hungry(Person)\")]\n precond_neg = [expr(\"Eaten(food)\")]\n effect_add = [expr(\"Eaten(food)\")]\n effect_rem = [expr(\"Hungry(person)\")]\n eat = Action(expr(\"Eat(person, food)\"), [precond_pos, precond_neg], [effect_add, effect_rem])\n \"\"\"\n\n def __init__(self, action, precond, effect):\n self.name = action.op\n self.args = action.args\n self.precond_pos = precond[0]\n self.precond_neg = precond[1]\n self.effect_add = effect[0]\n self.effect_rem = effect[1]\n\n def __call__(self, kb, args):\n return self.act(kb, args)\n\n def substitute(self, e, args):\n \"\"\"Replaces variables in expression with their respective Propositional symbol\"\"\"\n new_args = list(e.args)\n for num, x in enumerate(e.args):\n for i in range(len(self.args)):\n if self.args[i] == x:\n new_args[num] = args[i]\n return Expr(e.op, *new_args)\n\n def check_precond(self, kb, args):\n \"\"\"Checks if the precondition is satisfied in the current state\"\"\"\n # check for positive clauses\n for clause in self.precond_pos:\n if self.substitute(clause, args) not in kb.clauses:\n return False\n # check for negative clauses\n for clause in self.precond_neg:\n if self.substitute(clause, args) in kb.clauses:\n return False\n return True\n\n def act(self, kb, args):\n \"\"\"Executes the action on the state's kb\"\"\"\n # check if the preconditions are satisfied\n if not self.check_precond(kb, args):\n raise Exception(\"Action pre-conditions not satisfied\")\n # remove negative literals\n for clause in self.effect_rem:\n kb.retract(self.substitute(clause, args))\n # add positive literals\n for clause in self.effect_add:\n kb.tell(self.substitute(clause, args))\n\n\ndef air_cargo():\n init = [expr('At(C1, SFO)'),\n expr('At(C2, JFK)'),\n expr('At(P1, SFO)'),\n expr('At(P2, JFK)'),\n expr('Cargo(C1)'),\n expr('Cargo(C2)'),\n expr('Plane(P1)'),\n expr('Plane(P2)'),\n expr('Airport(JFK)'),\n expr('Airport(SFO)')]\n\n def goal_test(kb):\n required = [expr('At(C1 , JFK)'), expr('At(C2 ,SFO)')]\n for q in required:\n if kb.ask(q) is False:\n return False\n return True\n\n # Actions\n\n # Load\n precond_pos = [expr(\"At(c, a)\"), expr(\"At(p, a)\"), expr(\"Cargo(c)\"), expr(\"Plane(p)\"),\n expr(\"Airport(a)\")]\n precond_neg = []\n effect_add = [expr(\"In(c, p)\")]\n effect_rem = [expr(\"At(c, a)\")]\n load = Action(expr(\"Load(c, p, a)\"), [precond_pos, precond_neg], [effect_add, effect_rem])\n\n # Unload\n precond_pos = [expr(\"In(c, p)\"), expr(\"At(p, a)\"), expr(\"Cargo(c)\"), expr(\"Plane(p)\"),\n expr(\"Airport(a)\")]\n precond_neg = []\n effect_add = [expr(\"At(c, a)\")]\n effect_rem = [expr(\"In(c, p)\")]\n unload = Action(expr(\"Unload(c, p, a)\"), [precond_pos, precond_neg], [effect_add, effect_rem])\n\n # Fly\n # Used 'f' instead of 'from' because 'from' is a python keyword and expr uses eval() function\n precond_pos = [expr(\"At(p, f)\"), expr(\"Plane(p)\"), expr(\"Airport(f)\"), expr(\"Airport(to)\")]\n precond_neg = []\n effect_add = [expr(\"At(p, to)\")]\n effect_rem = [expr(\"At(p, f)\")]\n fly = Action(expr(\"Fly(p, f, to)\"), [precond_pos, precond_neg], [effect_add, effect_rem])\n\n return PDDL(init, [load, unload, fly], goal_test)\n\n\ndef spare_tire():\n init = [expr('Tire(Flat)'),\n expr('Tire(Spare)'),\n expr('At(Flat, Axle)'),\n expr('At(Spare, Trunk)')]\n\n def goal_test(kb):\n required = [expr('At(Spare, Axle)'), expr('At(Flat, Ground)')]\n for q in required:\n if kb.ask(q) is False:\n return False\n return True\n\n # Actions\n\n # Remove\n precond_pos = [expr(\"At(obj, loc)\")]\n precond_neg = []\n effect_add = [expr(\"At(obj, Ground)\")]\n effect_rem = [expr(\"At(obj, loc)\")]\n remove = Action(expr(\"Remove(obj, loc)\"), [precond_pos, precond_neg], [effect_add, effect_rem])\n\n # PutOn\n precond_pos = [expr(\"Tire(t)\"), expr(\"At(t, Ground)\")]\n precond_neg = [expr(\"At(Flat, Axle)\")]\n effect_add = [expr(\"At(t, Axle)\")]\n effect_rem = [expr(\"At(t, Ground)\")]\n put_on = Action(expr(\"PutOn(t, Axle)\"), [precond_pos, precond_neg], [effect_add, effect_rem])\n\n # LeaveOvernight\n precond_pos = []\n precond_neg = []\n effect_add = []\n effect_rem = [expr(\"At(Spare, Ground)\"), expr(\"At(Spare, Axle)\"), expr(\"At(Spare, Trunk)\"),\n expr(\"At(Flat, Ground)\"), expr(\"At(Flat, Axle)\"), expr(\"At(Flat, Trunk)\")]\n leave_overnight = Action(expr(\"LeaveOvernight\"), [precond_pos, precond_neg],\n [effect_add, effect_rem])\n\n return PDDL(init, [remove, put_on, leave_overnight], goal_test)\n\n\ndef three_block_tower():\n init = [expr('On(A, Table)'),\n expr('On(B, Table)'),\n expr('On(C, A)'),\n expr('Block(A)'),\n expr('Block(B)'),\n expr('Block(C)'),\n expr('Clear(B)'),\n expr('Clear(C)')]\n\n def goal_test(kb):\n required = [expr('On(A, B)'), expr('On(B, C)')]\n for q in required:\n if kb.ask(q) is False:\n return False\n return True\n\n # Actions\n\n # Move\n precond_pos = [expr('On(b, x)'), expr('Clear(b)'), expr('Clear(y)'), expr('Block(b)'),\n expr('Block(y)')]\n precond_neg = []\n effect_add = [expr('On(b, y)'), expr('Clear(x)')]\n effect_rem = [expr('On(b, x)'), expr('Clear(y)')]\n move = Action(expr('Move(b, x, y)'), [precond_pos, precond_neg], [effect_add, effect_rem])\n\n # MoveToTable\n precond_pos = [expr('On(b, x)'), expr('Clear(b)'), expr('Block(b)')]\n precond_neg = []\n effect_add = [expr('On(b, Table)'), expr('Clear(x)')]\n effect_rem = [expr('On(b, x)')]\n moveToTable = Action(expr('MoveToTable(b, x)'), [precond_pos, precond_neg],\n [effect_add, effect_rem])\n\n return PDDL(init, [move, moveToTable], goal_test)\n\n\ndef have_cake_and_eat_cake_too():\n init = [expr('Have(Cake)')]\n\n def goal_test(kb):\n required = [expr('Have(Cake)'), expr('Eaten(Cake)')]\n for q in required:\n if kb.ask(q) is False:\n return False\n return True\n\n # Actions\n\n # Eat cake\n precond_pos = [expr('Have(Cake)')]\n precond_neg = []\n effect_add = [expr('Eaten(Cake)')]\n effect_rem = [expr('Have(Cake)')]\n eat_cake = Action(expr('Eat(Cake)'), [precond_pos, precond_neg], [effect_add, effect_rem])\n\n # Bake Cake\n precond_pos = []\n precond_neg = [expr('Have(Cake)')]\n effect_add = [expr('Have(Cake)')]\n effect_rem = []\n bake_cake = Action(expr('Bake(Cake)'), [precond_pos, precond_neg], [effect_add, effect_rem])\n\n return PDDL(init, [eat_cake, bake_cake], goal_test)\n\n\nclass Level():\n \"\"\"\n Contains the state of the planning problem\n and exhaustive list of actions which use the\n states as pre-condition.\n \"\"\"\n\n def __init__(self, poskb, negkb):\n self.poskb = poskb\n # Current state\n self.current_state_pos = poskb.clauses\n self.current_state_neg = negkb.clauses\n # Current action to current state link\n self.current_action_links_pos = {}\n self.current_action_links_neg = {}\n # Current state to action link\n self.current_state_links_pos = {}\n self.current_state_links_neg = {}\n # Current action to next state link\n self.next_action_links = {}\n # Next state to current action link\n self.next_state_links_pos = {}\n self.next_state_links_neg = {}\n self.mutex = []\n\n def __call__(self, actions, objects):\n self.build(actions, objects)\n self.find_mutex()\n\n def find_mutex(self):\n # Inconsistent effects\n for poseff in self.next_state_links_pos:\n negeff = poseff\n if negeff in self.next_state_links_neg:\n for a in self.next_state_links_pos[poseff]:\n for b in self.next_state_links_neg[negeff]:\n if set([a, b]) not in self.mutex:\n self.mutex.append(set([a, b]))\n\n # Interference\n for posprecond in self.current_state_links_pos:\n negeff = posprecond\n if negeff in self.next_state_links_neg:\n for a in self.current_state_links_pos[posprecond]:\n for b in self.next_state_links_neg[negeff]:\n if set([a, b]) not in self.mutex:\n self.mutex.append(set([a, b]))\n\n for negprecond in self.current_state_links_neg:\n poseff = negprecond\n if poseff in self.next_state_links_pos:\n for a in self.next_state_links_pos[poseff]:\n for b in self.current_state_links_neg[negprecond]:\n if set([a, b]) not in self.mutex:\n self.mutex.append(set([a, b]))\n\n # Competing needs\n for posprecond in self.current_state_links_pos:\n negprecond = posprecond\n if negprecond in self.current_state_links_neg:\n for a in self.current_state_links_pos[posprecond]:\n for b in self.current_state_links_neg[negprecond]:\n if set([a, b]) not in self.mutex:\n self.mutex.append(set([a, b]))\n\n # Inconsistent support\n state_mutex = []\n for pair in self.mutex:\n next_state_0 = self.next_action_links[list(pair)[0]]\n if len(pair) == 2:\n next_state_1 = self.next_action_links[list(pair)[1]]\n else:\n next_state_1 = self.next_action_links[list(pair)[0]]\n if (len(next_state_0) == 1) and (len(next_state_1) == 1):\n state_mutex.append(set([next_state_0[0], next_state_1[0]]))\n\n self.mutex = self.mutex+state_mutex\n\n def build(self, actions, objects):\n\n # Add persistence actions for positive states\n for clause in self.current_state_pos:\n self.current_action_links_pos[Expr('Persistence', clause)] = [clause]\n self.next_action_links[Expr('Persistence', clause)] = [clause]\n self.current_state_links_pos[clause] = [Expr('Persistence', clause)]\n self.next_state_links_pos[clause] = [Expr('Persistence', clause)]\n\n # Add persistence actions for negative states\n for clause in self.current_state_neg:\n not_expr = Expr('not'+clause.op, clause.args)\n self.current_action_links_neg[Expr('Persistence', not_expr)] = [clause]\n self.next_action_links[Expr('Persistence', not_expr)] = [clause]\n self.current_state_links_neg[clause] = [Expr('Persistence', not_expr)]\n self.next_state_links_neg[clause] = [Expr('Persistence', not_expr)]\n\n for a in actions:\n num_args = len(a.args)\n possible_args = tuple(itertools.permutations(objects, num_args))\n\n for arg in possible_args:\n if a.check_precond(self.poskb, arg):\n for num, symbol in enumerate(a.args):\n if not symbol.op.islower():\n arg = list(arg)\n arg[num] = symbol\n arg = tuple(arg)\n\n new_action = a.substitute(Expr(a.name, *a.args), arg)\n self.current_action_links_pos[new_action] = []\n self.current_action_links_neg[new_action] = []\n\n for clause in a.precond_pos:\n new_clause = a.substitute(clause, arg)\n self.current_action_links_pos[new_action].append(new_clause)\n if new_clause in self.current_state_links_pos:\n self.current_state_links_pos[new_clause].append(new_action)\n else:\n self.current_state_links_pos[new_clause] = [new_action]\n\n for clause in a.precond_neg:\n new_clause = a.substitute(clause, arg)\n self.current_action_links_neg[new_action].append(new_clause)\n if new_clause in self.current_state_links_neg:\n self.current_state_links_neg[new_clause].append(new_action)\n else:\n self.current_state_links_neg[new_clause] = [new_action]\n\n self.next_action_links[new_action] = []\n for clause in a.effect_add:\n new_clause = a.substitute(clause, arg)\n self.next_action_links[new_action].append(new_clause)\n if new_clause in self.next_state_links_pos:\n self.next_state_links_pos[new_clause].append(new_action)\n else:\n self.next_state_links_pos[new_clause] = [new_action]\n\n for clause in a.effect_rem:\n new_clause = a.substitute(clause, arg)\n self.next_action_links[new_action].append(new_clause)\n if new_clause in self.next_state_links_neg:\n self.next_state_links_neg[new_clause].append(new_action)\n else:\n self.next_state_links_neg[new_clause] = [new_action]\n\n def perform_actions(self):\n new_kb_pos = FolKB(list(set(self.next_state_links_pos.keys())))\n new_kb_neg = FolKB(list(set(self.next_state_links_neg.keys())))\n\n return Level(new_kb_pos, new_kb_neg)\n\n\nclass Graph:\n \"\"\"\n Contains levels of state and actions\n Used in graph planning algorithm to extract a solution\n \"\"\"\n\n def __init__(self, pddl, negkb):\n self.pddl = pddl\n self.levels = [Level(pddl.kb, negkb)]\n self.objects = set(arg for clause in pddl.kb.clauses + negkb.clauses for arg in clause.args)\n\n def __call__(self):\n self.expand_graph()\n\n def expand_graph(self):\n last_level = self.levels[-1]\n last_level(self.pddl.actions, self.objects)\n self.levels.append(last_level.perform_actions())\n\n def non_mutex_goals(self, goals, index):\n goal_perm = itertools.combinations(goals, 2)\n for g in goal_perm:\n if set(g) in self.levels[index].mutex:\n return False\n return True\n\n\nclass GraphPlan:\n \"\"\"\n Class for formulation GraphPlan algorithm\n Constructs a graph of state and action space\n Returns solution for the planning problem\n \"\"\"\n\n def __init__(self, pddl, negkb):\n self.graph = Graph(pddl, negkb)\n self.nogoods = []\n self.solution = []\n\n def check_leveloff(self):\n first_check = (set(self.graph.levels[-1].current_state_pos) ==\n set(self.graph.levels[-2].current_state_pos))\n second_check = (set(self.graph.levels[-1].current_state_neg) ==\n set(self.graph.levels[-2].current_state_neg))\n\n if first_check and second_check:\n return True\n\n def extract_solution(self, goals_pos, goals_neg, index):\n level = self.graph.levels[index]\n if not self.graph.non_mutex_goals(goals_pos+goals_neg, index):\n self.nogoods.append((level, goals_pos, goals_neg))\n return\n\n level = self.graph.levels[index-1]\n\n # Create all combinations of actions that satisfy the goal\n actions = []\n for goal in goals_pos:\n actions.append(level.next_state_links_pos[goal])\n\n for goal in goals_neg:\n actions.append(level.next_state_links_neg[goal])\n\n all_actions = list(itertools.product(*actions))\n\n # Filter out the action combinations which contain mutexes\n non_mutex_actions = []\n for action_tuple in all_actions:\n action_pairs = itertools.combinations(list(set(action_tuple)), 2)\n non_mutex_actions.append(list(set(action_tuple)))\n for pair in action_pairs:\n if set(pair) in level.mutex:\n non_mutex_actions.pop(-1)\n break\n\n # Recursion\n for action_list in non_mutex_actions:\n if [action_list, index] not in self.solution:\n self.solution.append([action_list, index])\n\n new_goals_pos = []\n new_goals_neg = []\n for act in set(action_list):\n if act in level.current_action_links_pos:\n new_goals_pos = new_goals_pos + level.current_action_links_pos[act]\n\n for act in set(action_list):\n if act in level.current_action_links_neg:\n new_goals_neg = new_goals_neg + level.current_action_links_neg[act]\n\n if abs(index)+1 == len(self.graph.levels):\n return\n elif (level, new_goals_pos, new_goals_neg) in self.nogoods:\n return\n else:\n self.extract_solution(new_goals_pos, new_goals_neg, index-1)\n\n # Level-Order multiple solutions\n solution = []\n for item in self.solution:\n if item[1] == -1:\n solution.append([])\n solution[-1].append(item[0])\n else:\n solution[-1].append(item[0])\n\n for num, item in enumerate(solution):\n item.reverse()\n solution[num] = item\n\n return solution\n\n\ndef goal_test(kb, goals):\n for q in goals:\n if kb.ask(q) is False:\n return False\n return True\n\n\ndef spare_tire_graphplan():\n pddl = spare_tire()\n negkb = FolKB([expr('At(Flat, Trunk)')])\n graphplan = GraphPlan(pddl, negkb)\n\n # Not sure\n goals_pos = [expr('At(Spare, Axle)'), expr('At(Flat, Ground)')]\n goals_neg = []\n\n while True:\n if (goal_test(graphplan.graph.levels[-1].poskb, goals_pos) and\n graphplan.graph.non_mutex_goals(goals_pos+goals_neg, -1)):\n solution = graphplan.extract_solution(goals_pos, goals_neg, -1)\n if solution:\n return solution\n graphplan.graph.expand_graph()\n if len(graphplan.graph.levels)>=2 and graphplan.check_leveloff():\n return None\n\n\ndef double_tennis_problem():\n init = [expr('At(A, LeftBaseLine)'),\n expr('At(B, RightNet)'),\n expr('Approaching(Ball, RightBaseLine)'),\n expr('Partner(A, B)'),\n expr('Partner(B, A)')]\n\n def goal_test(kb):\n required = [expr('Goal(Returned(Ball))'), expr('At(a, RightNet)'), expr('At(a, LeftNet)')]\n for q in required:\n if kb.ask(q) is False:\n return False\n return True\n\n # Actions\n\n # Hit\n precond_pos = [expr(\"Approaching(Ball,loc)\"), expr(\"At(actor,loc)\")]\n precond_neg = []\n effect_add = [expr(\"Returned(Ball)\")]\n effect_rem = []\n hit = Action(expr(\"Hit(actor, Ball)\"), [precond_pos, precond_neg], [effect_add, effect_rem])\n\n # Go\n precond_pos = [expr(\"At(actor, loc)\")]\n precond_neg = []\n effect_add = [expr(\"At(actor, to)\")]\n effect_rem = [expr(\"At(actor, loc)\")]\n go = Action(expr(\"Go(actor, to)\"), [precond_pos, precond_neg], [effect_add, effect_rem])\n\n return PDDL(init, [hit, go], goal_test)\n","sub_path":"planning.py","file_name":"planning.py","file_ext":"py","file_size_in_byte":21231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"468823839","text":"# ------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.\n# ------------------------------------------------------------------------------------------\nimport logging\nimport time\nfrom unittest import mock\n\nimport pytest\nfrom azureml.train.hyperdrive.runconfig import HyperDriveConfig\n\nfrom InnerEye.Common import common_util, fixed_paths\nfrom InnerEye.Common.fixed_paths_for_tests import full_ml_test_data_path\nfrom InnerEye.Common.output_directories import OutputFolderForTests\nfrom InnerEye.ML.common import BEST_CHECKPOINT_FILE_NAME_WITH_SUFFIX, ModelExecutionMode\nfrom InnerEye.ML.metrics import InferenceMetricsForSegmentation\nfrom InnerEye.ML.run_ml import MLRunner\nfrom InnerEye.ML.runner import Runner\nfrom Tests.ML.configs.DummyModel import DummyModel\nfrom Tests.ML.util import get_default_checkpoint_handler\nfrom Tests.ML.utils.test_model_util import create_model_and_store_checkpoint\n\n\n@pytest.mark.skipif(common_util.is_windows(), reason=\"Too slow on windows\")\n@pytest.mark.parametrize(\"perform_cross_validation\", [True, False])\n@pytest.mark.parametrize(\"perform_training_set_inference\", [True, False])\ndef test_model_inference_train_and_test(test_output_dirs: OutputFolderForTests,\n perform_cross_validation: bool,\n perform_training_set_inference: bool) -> None:\n config = DummyModel()\n config.number_of_cross_validation_splits = 2 if perform_cross_validation else 0\n config.perform_training_set_inference = perform_training_set_inference\n # Plotting crashes with random TCL errors on Windows, disable that for Windows PR builds.\n config.is_plotting_enabled = common_util.is_linux()\n\n config.set_output_to(test_output_dirs.root_dir)\n config.local_dataset = full_ml_test_data_path()\n\n checkpoint_path = config.checkpoint_folder / BEST_CHECKPOINT_FILE_NAME_WITH_SUFFIX\n create_model_and_store_checkpoint(config, checkpoint_path)\n checkpoint_handler = get_default_checkpoint_handler(model_config=config,\n project_root=test_output_dirs.root_dir)\n checkpoint_handler.additional_training_done()\n result, _, _ = MLRunner(config).model_inference_train_and_test(checkpoint_handler=checkpoint_handler)\n if result is None:\n raise ValueError(\"Error result cannot be None\")\n assert isinstance(result, InferenceMetricsForSegmentation)\n epoch_folder_name = common_util.BEST_EPOCH_FOLDER_NAME\n for folder in [ModelExecutionMode.TRAIN.value, ModelExecutionMode.VAL.value, ModelExecutionMode.TEST.value]:\n results_folder = config.outputs_folder / epoch_folder_name / folder\n folder_exists = results_folder.is_dir()\n if folder in [ModelExecutionMode.TRAIN.value, ModelExecutionMode.VAL.value]:\n if perform_training_set_inference:\n assert folder_exists\n else:\n assert folder_exists\n\n\ndef test_logging_to_file(test_output_dirs: OutputFolderForTests) -> None:\n # Log file should go to a new, non-existent folder, 2 levels deep\n file_path = test_output_dirs.root_dir / \"subdir1\" / \"subdir2\" / \"logfile.txt\"\n assert common_util.logging_to_file_handler is None\n common_util.logging_to_file(file_path)\n assert common_util.logging_to_file_handler is not None\n log_line = \"foo bar\"\n logging.getLogger().setLevel(logging.INFO)\n logging.info(log_line)\n common_util.disable_logging_to_file()\n should_not_be_present = \"This should not be present in logs\"\n logging.info(should_not_be_present)\n assert common_util.logging_to_file_handler is None\n # Wait for a bit, tests sometimes fail with the file not existing yet\n time.sleep(2)\n assert file_path.exists()\n assert log_line in file_path.read_text()\n assert should_not_be_present not in file_path.read_text()\n\n\ndef test_cross_validation_for_LightingContainer_models_is_supported() -> None:\n '''\n Prior to https://github.com/microsoft/InnerEye-DeepLearning/pull/483 we raised an exception in\n runner.run when cross validation was attempted on a lightning container. This test checks that\n we do not raise the exception anymore, and instead pass on a cross validation hyperdrive config\n to azure_runner's submit_to_azureml method.\n '''\n args_list = [\"--model=HelloContainer\", \"--number_of_cross_validation_splits=5\", \"--azureml=True\"]\n with mock.patch(\"sys.argv\", [\"\"] + args_list):\n runner = Runner(project_root=fixed_paths.repository_root_directory(), yaml_config_file=fixed_paths.SETTINGS_YAML_FILE)\n with mock.patch(\"InnerEye.Azure.azure_runner.create_and_submit_experiment\", return_value=None) as create_and_submit_experiment_patch:\n runner.run()\n assert runner.lightning_container.model_name == 'HelloContainer'\n assert runner.lightning_container.number_of_cross_validation_splits == 5\n args, _ = create_and_submit_experiment_patch.call_args\n script_run_config = args[1]\n assert isinstance(script_run_config, HyperDriveConfig)\n","sub_path":"Tests/ML/runners/test_runner.py","file_name":"test_runner.py","file_ext":"py","file_size_in_byte":5261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"193805003","text":"#!/usr/bin/env python3\n\n# import of built-in modules\nimport contextlib\nimport logging\nimport os\nimport shutil\nimport stat\nimport subprocess\nimport random\nimport sys\n\n# import of third-party modules\n\n# import of local modules\nimport ccf.one_subject_job_submitter as one_subject_job_submitter\nimport ccf.processing_stage as ccf_processing_stage\nimport ccf.subject as ccf_subject\nimport utils.debug_utils as debug_utils\nimport utils.str_utils as str_utils\nimport utils.os_utils as os_utils\nimport utils.user_utils as user_utils\nimport ccf.archive as ccf_archive\n\n# authorship information\n__author__ = \"Timothy B. Brown\"\n__copyright__ = \"Copyright 2017, Connectome Coordination Facility\"\n__maintainer__ = \"Timothy B. Brown\"\n\n# create a module logger\nmodule_logger = logging.getLogger(__name__)\n# Note: This can be overidden by log file configuration\nmodule_logger.setLevel(logging.WARNING)\n\n\nclass OneSubjectJobSubmitter(one_subject_job_submitter.OneSubjectJobSubmitter):\n\n\t@classmethod\n\tdef MY_PIPELINE_NAME(cls):\n\t\treturn 'FunctionalPreprocessing'\n\n\tdef __init__(self, archive, build_home):\n\t\tsuper().__init__(archive, build_home)\n\n\t@property\n\tdef PIPELINE_NAME(self):\n\t\treturn OneSubjectJobSubmitter.MY_PIPELINE_NAME()\n\n\t@property\n\tdef WORK_NODE_COUNT(self):\n\t\treturn 1\n\n\t@property\n\tdef WORK_PPN(self):\n\t\treturn 1\n\n\tdef create_process_data_job_script(self):\n\t\tmodule_logger.debug(debug_utils.get_name())\n\n\t\t# copy the .XNAT_PROCESS script to the working directory\n\t\tprocessing_script_source_path = self.xnat_pbs_jobs_home\n\t\tprocessing_script_source_path += os.sep + self.PIPELINE_NAME\n\t\tprocessing_script_source_path += os.sep + self.PIPELINE_NAME\n\t\tprocessing_script_source_path += '.XNAT_PROCESS'\n\n\t\tprocessing_script_dest_path = self.working_directory_name\n\t\tprocessing_script_dest_path += os.sep + self.PIPELINE_NAME\n\t\tprocessing_script_dest_path += '.XNAT_PROCESS' \n\n\t\tshutil.copy(processing_script_source_path, processing_script_dest_path)\n\t\tos.chmod(processing_script_dest_path, stat.S_IRWXU | stat.S_IRWXG)\n\t \n\t\t# write the process data job script (that calls the .XNAT_PROCESS script)\n\n\t\tsubject_info = ccf_subject.SubjectInfo(self.project, self.subject,\n\t\t\t\t\t\t\t\t\t\t\t self.classifier, self.scan)\n\n\t\tscript_name = self.process_data_job_script_name\n\n\t\twith contextlib.suppress(FileNotFoundError):\n\t\t\tos.remove(script_name)\n\n\t\twalltime_limit_str = str(self.walltime_limit_hours) + ':00:00'\n\t\tvmem_limit_str = str(self.vmem_limit_gbs) + 'gb'\n\n\t\tresources_line = '#PBS -l nodes=' + str(self.WORK_NODE_COUNT)\n\t\tresources_line += ':ppn=' + str(self.WORK_PPN)\n\t\tresources_line += ',walltime=' + walltime_limit_str\n\t\tresources_line += ',mem=' + vmem_limit_str\n\n\t\tstdout_line = '#PBS -o ' + self.working_directory_name\n\t\tstderr_line = '#PBS -e ' + self.working_directory_name\n\n\t\txnat_pbs_setup_line = 'source ' + self._get_xnat_pbs_setup_script_path() + ' ' + self._get_db_name()\n\t\txnat_pbs_setup_singularity_load = 'module load ' + self._get_xnat_pbs_setup_script_singularity_version()\n\t\txnat_pbs_setup_singularity_process = 'singularity exec -B ' + self._get_xnat_pbs_setup_script_archive_root() + ',' + self._get_xnat_pbs_setup_script_singularity_bind_path() \\\n\t\t\t\t\t\t\t\t\t\t\t+ ',' + self._get_xnat_pbs_setup_script_gradient_coefficient_path() + ':/export/HCP/gradient_coefficient_files' \\\n\t\t\t\t\t\t\t\t\t\t\t+ ',' + self._get_xnat_pbs_setup_script_freesurfer_license_path() + ':/export/freesurfer_license' \\\n\t\t\t\t\t\t\t\t\t\t\t+ ' ' + self._get_xnat_pbs_setup_script_singularity_container_path() + ' ' + processing_script_source_path \n\t\tsubject_line\t = ' --subject=' + self.subject\n\t\tscan_line\t\t= ' --scan=' + self.scan\n\t\tsession_classifier_line = ' --classifier=' + self.classifier\n\t\tdcmethod_line\t= ' --dcmethod=TOPUP'\n\t\ttopupconfig_line = ' --topupconfig=b02b0.cnf'\n\t\tgdcoeffs_line\t= ' --gdcoeffs=Prisma_3T_coeff_AS82.grad'\n\t\twdir_line = ' --working-dir=' + self.working_directory_name\n\t\t\n\t\twith open(script_name, 'w') as script:\n\t\t\tscript.write(resources_line + os.linesep)\n\t\t\tscript.write(stdout_line + os.linesep)\n\t\t\tscript.write(stderr_line + os.linesep)\n\t\t\tscript.write(os.linesep)\n\t\t\tscript.write(xnat_pbs_setup_line + os.linesep)\n\t\t\tscript.write(xnat_pbs_setup_singularity_load + os.linesep)\n\t\t\tscript.write(os.linesep)\t\n\t\t\tscript.write(xnat_pbs_setup_singularity_process+ ' \\\\' + os.linesep)\n\t\t\tscript.write(subject_line +\t ' \\\\' + os.linesep)\n\t\t\tscript.write(scan_line +\t\t' \\\\' + os.linesep)\n\t\t\tscript.write(session_classifier_line + ' \\\\' + os.linesep)\n\t\t\tscript.write(dcmethod_line +\t' \\\\' + os.linesep)\n\t\t\tscript.write(topupconfig_line + ' \\\\' + os.linesep)\n\t\t\tscript.write(gdcoeffs_line +\t' \\\\' + os.linesep)\n\t\t\tscript.write(wdir_line + os.linesep)\n\t\t\t\n\t\t\tos.chmod(script_name, stat.S_IRWXU | stat.S_IRWXG)\n\t\t\t\n\tdef mark_running_status(self, stage):\n\t\tmodule_logger.debug(debug_utils.get_name())\n\n\t\tif stage > ccf_processing_stage.ProcessingStage.PREPARE_SCRIPTS:\n\t\t\tmark_cmd = self._xnat_pbs_jobs_home\n\t\t\tmark_cmd += os.sep + self.PIPELINE_NAME\n\t\t\tmark_cmd += os.sep + self.PIPELINE_NAME\n\t\t\tmark_cmd += '.XNAT_MARK_RUNNING_STATUS'\n\t\t\tmark_cmd += ' --user=' + self.username\n\t\t\tmark_cmd += ' --password=' + self.password\n\t\t\tmark_cmd += ' --server=' + str_utils.get_server_name(self.put_server)\n\t\t\tmark_cmd += ' --project=' + self.project\n\t\t\tmark_cmd += ' --subject=' + self.subject\n\t\t\tmark_cmd += ' --classifier=' + self.classifier\n\t\t\tmark_cmd += ' --scan=' + self.scan\n\t\t\tmark_cmd += ' --resource=RunningStatus'\n\t\t\tmark_cmd += ' --queued'\n\n\t\t\tcompleted_mark_cmd_process = subprocess.run(\n\t\t\t\tmark_cmd, shell=True, check=True, stdout=subprocess.PIPE, universal_newlines=True)\n\t\t\tprint(completed_mark_cmd_process.stdout)\n\n\t\t\treturn\n\nif __name__ == \"__main__\":\n\timport ccf.functional_preprocessing.one_subject_run_status_checker as one_subject_run_status_checker\n\txnat_server = os_utils.getenv_required('XNAT_PBS_JOBS_XNAT_SERVER')\n\tusername, password = user_utils.get_credentials(xnat_server)\n\tarchive = ccf_archive.CcfArchive()\t\n\tsubject = ccf_subject.SubjectInfo(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])\n\tsubmitter = OneSubjectJobSubmitter(archive, archive.build_home)\t\n\t\t\n\trun_status_checker = one_subject_run_status_checker.OneSubjectRunStatusChecker()\n\t\n\tif run_status_checker.get_queued_or_running(subject):\n\t\tprint(\"-----\")\n\t\tprint(\"NOT SUBMITTING JOBS FOR\")\n\t\tprint(\"project: \" + subject.project)\n\t\tprint(\"subject: \" + subject.subject_id)\n\t\tprint(\"session classifier: \" + subject.classifier)\n\t\tprint(\"scan: \" + subject.extra)\n\t\tprint(\"JOBS ARE ALREADY QUEUED OR RUNNING\")\n\t\tprint ('Process terminated')\n\t\tsys.exit()\t\n\n\tjob_submitter=OneSubjectJobSubmitter(archive, archive.build_home)\t\n\tput_server_name = os.environ.get(\"XNAT_PBS_JOBS_PUT_SERVER_LIST\").split(\" \")\n\tput_server = random.choice(put_server_name)\n\n\tclean_output_first = eval(sys.argv[5])\n\tprocessing_stage_str = sys.argv[6]\n\tprocessing_stage = submitter.processing_stage_from_string(processing_stage_str)\n\twalltime_limit_hrs = sys.argv[7]\n\tvmem_limit_gbs = sys.argv[8]\n\toutput_resource_suffix = sys.argv[9]\n\t\n\tprint(\"-----\")\n\tprint(\"\\tSubmitting\", submitter.PIPELINE_NAME, \"jobs for:\")\n\tprint(\"\\t\t\t\t project:\", subject.project)\n\tprint(\"\\t\t\t\t subject:\", subject.subject_id)\n\tprint(\"\\t\t\t\t\t scan:\", subject.extra)\n\tprint(\"\\t\tsession classifier:\", subject.classifier)\n\tprint(\"\\t\t\t\tput_server:\", put_server)\n\tprint(\"\\t\tclean_output_first:\", clean_output_first)\n\tprint(\"\\t\t processing_stage:\", processing_stage)\n\tprint(\"\\t\twalltime_limit_hrs:\", walltime_limit_hrs)\n\tprint(\"\\t\t\tvmem_limit_gbs:\", vmem_limit_gbs)\n\tprint(\"\\toutput_resource_suffix:\", output_resource_suffix)\t\n\n\t\n\t# configure one subject submitter\n\t\t\t\n\t# user and server information\n\tsubmitter.username = username\n\tsubmitter.password = password\n\tsubmitter.server = 'https://' + os_utils.getenv_required('XNAT_PBS_JOBS_XNAT_SERVER')\t\n\t\n\t# subject and project information\n\tsubmitter.project = subject.project\n\tsubmitter.subject = subject.subject_id\n\tsubmitter.classifier = subject.classifier\n\tsubmitter.session = subject.subject_id + '_' + subject.classifier\n\tsubmitter.scan = subject.extra\n\n\t# job parameters\n\tsubmitter.clean_output_resource_first = clean_output_first\n\tsubmitter.put_server = put_server\n\tsubmitter.walltime_limit_hours = walltime_limit_hrs\n\tsubmitter.vmem_limit_gbs = vmem_limit_gbs\n\tsubmitter.output_resource_suffix = output_resource_suffix\n\n\t# submit jobs\n\tsubmitted_job_list = submitter.submit_jobs(processing_stage)\n\n\tprint(\"\\tsubmitted jobs:\", submitted_job_list)\n\tprint(\"-----\")","sub_path":"lib/ccf/functional_preprocessing/one_subject_job_submitter.py","file_name":"one_subject_job_submitter.py","file_ext":"py","file_size_in_byte":8450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"433236358","text":"from .BaseClass import BaseSpOptHeuristicSolver\nfrom collections import defaultdict\nimport numpy\nimport networkx\n\n\ndef w_to_g(w):\n \"\"\"Get a networkx graph from a PySAL W\"\"\"\n g = networkx.Graph()\n for ego, alters in w.neighbors.items():\n for alter in alters:\n g.add_edge(ego, alter)\n return g\n\n\ndef move_ok(area, source, destination, g, w):\n \"\"\"Check if area can move from source region to destination region\"\"\"\n\n # first check if area has a neighbor in destination\n if not is_neighbor(area, destination, w):\n return False\n # check if moving area would break source connectivity\n new_source = [j for j in source if j != area]\n if networkx.is_connected(g.subgraph(new_source)):\n return True\n else:\n return False\n\n\ndef ok_moves(candidates, regions, labels_, closest, g, w, areas):\n \"\"\"Check a sequence of candidate moves\"\"\"\n keep = []\n for area in candidates:\n source = areas[labels_ == labels_[area]]\n destination = regions[closest[area]]\n if move_ok(area, source, destination, g, w):\n keep.append(area)\n return keep\n\n\ndef region_neighbors(a_list, region):\n \"\"\"Get neighbors for members of a region\"\"\"\n neighbors = a_list[a_list[\"focal\"].isin(region)].neighbor.values\n return [j for j in neighbors if j not in region]\n\n\ndef _centroid(regions, data):\n \"\"\"Get centroids for all regions\"\"\"\n return numpy.array([data[region, :].mean(axis=0) for region in regions])\n\n\ndef _closest(data, centroids):\n \"\"\"For each row in data, find the closest row in centroids\"\"\"\n return [numpy.argmin(((row - centroids) ** 2).sum(axis=1)) for row in data]\n\n\ndef _seeds(areas, k):\n \"\"\"randomly select k seeds from a sequence of areas\"\"\"\n return numpy.random.choice(areas, size=k, replace=False)\n\n\ndef is_neighbor(area, region, w):\n \"\"\"Check if area is a neighbor of any member of region\"\"\"\n for member in region:\n if area in w[member]:\n return True\n return False\n\n\ndef region_k_means(X, n_clusters, w):\n \"\"\"Region-K-means\n\n K-means with the constraint that each cluster forms a spatially connected component.\n\n\n Parameters\n ----------\n\n X : array-like, shape (n_samples, n_features)\n The observations to clusters\n\n n_clusters: int\n The number of clusters to form\n\n w: spatial weights object\n\n\n Returns\n -------\n\n label: integer ndarray with shape (n_samples,)\n label[i] is the code or index of the centroid the i'th observation is closest to\n\n centroid: float ndarray with shape (k, n_features)\n Centroids found at the last iteration of region_k_means.\n\n iters: int\n number of iterations for reassignment phase\n\n \"\"\"\n\n data = X\n a_list = w.to_adjlist(remove_symmetric=False)\n areas = numpy.arange(w.n).astype(int)\n k = n_clusters\n seeds = _seeds(areas, k)\n\n # initial assignment phase\n label = numpy.array([-1] * w.n).astype(int)\n for i, seed in enumerate(seeds):\n label[seed] = i\n to_assign = areas[label == -1]\n c = 0\n while to_assign.size > 0:\n assignments = defaultdict(list)\n for rid in range(k):\n region = areas[label == rid]\n neighbors = region_neighbors(a_list, region)\n neighbors = [j for j in neighbors if j in to_assign]\n if neighbors:\n d_min = numpy.inf\n centroid = data[region].mean(axis=0)\n for neighbor in neighbors:\n d = ((data[neighbor] - centroid) ** 2).sum()\n if d < d_min:\n idx = neighbor\n d_min = d\n assignments[idx].append([rid, d_min])\n for key in assignments:\n assignment = assignments[key]\n if len(assignment) == 1:\n r, d = assignment[0]\n label[key] = r\n else:\n d_min = numpy.inf\n for match in assignment:\n r, d = match\n if d < d_min:\n idx = r\n d_min = d\n label[key] = idx\n\n to_assign = areas[label == -1]\n\n # reassignment phase\n changed = []\n g = w_to_g(w)\n\n iters = 1\n\n # want to loop this until candidates is empty\n regions = [areas[label == r].tolist() for r in range(k)]\n centroid = _centroid(regions, data)\n closest = numpy.array(_closest(data, centroid))\n candidates = areas[closest != label]\n candidates = ok_moves(candidates, regions, label, closest, g, w, areas)\n while candidates:\n # make moves\n for area in candidates:\n label[area] = closest[area]\n regions = [areas[label == r].tolist() for r in range(k)]\n centroid = _centroid(regions, data)\n closest = numpy.array(_closest(data, centroid))\n candidates = areas[closest != label]\n candidates = ok_moves(candidates, regions, label, closest, g, w, areas)\n iters += 1\n\n return centroid, label, iters\n\n\nclass RegionKMeansHeuristic(BaseSpOptHeuristicSolver):\n def __init__(self, data, k, w):\n self.data = data\n self.w = w\n self.k = k\n\n def solve(self):\n centroid, label, iters = region_k_means(self.data, self.k, self.w)\n self.labels_ = label\n self.centroids_ = centroid\n self.iters_ = iters\n","sub_path":"spopt/region_k_means.py","file_name":"region_k_means.py","file_ext":"py","file_size_in_byte":5400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"111058446","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Feb 19 09:00:37 2017\n\n@author: jph\n\"\"\"\n\nimport pygrib\nimport numpy as np\nimport datetime\nimport time\nimport math\n\n# ouverture du grib \n#Fichier_grib='Gribs/20170605_191113_.grb' #0.5\n#Fichier_grib='Gribs/20170605_185722_.grb' #0.5\n#Fichier_grib='Gribs/20170605_185702_.grb' #0.25\n#Fichier_grib='Gribs/20170605_191140_.grb' #0.25\n#Fichier_grib='Gribs/201706065_222508_.grb' #1\n#Fichier_grib='Gribs/20170606_090036_.grb' #1\n#Fichier_grib='Gribs/20170606_190857_.grb' #1\n#Fichier_grib='Gribs/20170607_091523_.grb' #1\n\nFichier_grib='Gribs/20170607_125627_.grb' #1° 6h\n\ngrbs= pygrib.open(Fichier_grib)\nfileidx=pygrib.index(Fichier_grib,'name')\n\n\n# extraction des caracteristiques du grib *******************************************************************************\nprint()\nprint (' Nom du Fichier Grib utilisé : ', grbs.name)\n\n\ng=grbs[1]\n\nday =g['day']\nmonth =g['month']\nyear =2000+g['yearOfCentury']\nhour =g['hour']\nminute =g['minute']\nsecond =g['second']\ndate_i=time.mktime((year,month,day,hour,minute,0,0,0,0)) # Instant 0 du Grib en secondes\n\n\n\nlatfirst=g['latitudeOfFirstGridPointInDegrees']\nlonfirst=g['longitudeOfFirstGridPointInDegrees']\nlatlast =g['latitudeOfLastGridPointInDegrees']\nlonlast =g['longitudeOfLastGridPointInDegrees']\n\npaslat=g['iDirectionIncrementInDegrees']\npaslong=g['jDirectionIncrementInDegrees']\n\nprint(\" Heure et date du grib {}/{}/{} - {}h {}mn {}s UTC \".format(day,month,year,hour,minute,second ))\nprint (\" Grib entre longitudes de {} à {} et latitudes de {} à {} \".format(lonfirst,lonlast,latfirst,latlast))\nprint (\" Pas du Grib en latitude : {}° longitude : {}° \".format(paslat,paslong))\nprint (' Nombre de messages : ', grbs.messages)\nprint ()\n\n# ************************************************************************************************************************\n\n\n\n\n\n\ndef fvent(longitude,latitude,date_p):\n # cette fonction renvoie le vent a la latitude longitude entiere donne et interpole sur le temps\n ecart=date_p-date_i # ecart en secondes avec l'instant 0 du grib \n itemps0=int(ecart/ 3600//3) #indice de l'heure du grib inferieure à l'heure recherchée\n itemps1=(ecart/ 3600/3) #indice de l'heure recherchée non arrondi\n jlong=int(abs(longitude-lonfirst)/paslat) #indice de la longitude\n ilat=int(abs( latitude-latfirst)/paslat) #indice de la latitude\n u=fileidx.select(name=\"10 metre U wind component\") # recuperation des valeurs u\n v=fileidx.select(name=\"10 metre V wind component\") # recuperation des valeurs v\n vu0=u[itemps0]['values'][ilat][jlong] #valeur vu au temps 0\n vu1=u[itemps0+1]['values'][ilat][jlong] #valeur vu au temps 1\n vv0=v[itemps0]['values'][ilat][jlong] #valeur vv au temps 0\n vv1=v[itemps0+1]['values'][ilat][jlong] #valeur vv au temps 1\n# print ( 'vuo {:6.2f} et vv0 {:6.2f} au temps 0 d indice {:6.2f}'.format (vu0,vv0,itemps0))\n# print ( 'vu1 {:6.2f} et vv1 {:6.2f} au temps 1 d indice {:6.2f}'.format (vu1,vv1,itemps1))\n #print ('vu0 ,vu1 ,vv0 , vv1', vu0,vu1,vv0,vv1)\n # interpolation sur vu et vv\n vu=vu0 +(vu1-vu0)*(itemps1-itemps0)\n vv=vv0 +(vv1-vv0)*(itemps1-itemps0)\n vn=math.sqrt(vu**2+vv**2)*1.94384 # Vitesse du vent en noeuds\n av=math.atan(vu/vv)*180/3.1416 # Angle du vent en degrés\n if vv>0:\n av=av+180\n if vv*vu<0:\n av=360+av \n av=av%360\n \n\n date=time.strftime('%Hh%M (UTC) le %d/%m/%Y',time.localtime(date_p-3600)) # Temps formaté pas necessaire dans fonction\n return (longitude,latitude,date,av,vn,vu,vv)\n\n\ndef fvent2(longitude,latitude,date_p):\n\n# cette fonction renvoie le vent a la latitude longitude entiere donne et interpole sur le temps\n latitude_int=int(latitude) \n\n ecart=date_p-date_i # ecart en secondes avec l'instant 0 du grib \n itemps0=int(ecart/ 3600//3) #indice de l'heure du grib inferieure à l'heure recherchée\n itemps1=(ecart/ 3600/3) #indice de l'heure recherchée non arrondi\n jlong=int(abs(longitude-lonfirst)/paslat) #indice de la longitude\n ilat=int(abs( latitude_int-latfirst)/paslat)-1\n print ('ilat',ilat)\n #indice de la latitude\n \n u=fileidx.select(name=\"10 metre U wind component\") # recuperation des valeurs u\n v=fileidx.select(name=\"10 metre V wind component\") # recuperation des valeurs v\n vu0=u[itemps0]['values'][ilat][jlong] #valeur vu au temps 0\n vu1=u[itemps0+1]['values'][ilat][jlong] #valeur vu au temps 1\n vv0=v[itemps0]['values'][ilat][jlong] #valeur vv au temps 0\n vv1=v[itemps0+1]['values'][ilat][jlong] #valeur vv au temps 1\n# print ( 'vuo {:6.2f} et vv0 {:6.2f} au temps 0 d indice {:6.2f}'.format (vu0,vv0,itemps0))\n# print ( 'vu1 {:6.2f} et vv1 {:6.2f} au temps 1 d indice {:6.2f}'.format (vu1,vv1,itemps1))\n #print ('vu0 ,vu1 ,vv0 , vv1', vu0,vu1,vv0,vv1)\n # interpolation sur vu et vv\n vu=vu0 +(vu1-vu0)*(itemps1-itemps0)\n vv=vv0 +(vv1-vv0)*(itemps1-itemps0)\n vn=math.sqrt(vu**2+vv**2)*1.94384 # Vitesse du vent en noeuds\n av=math.atan(vu/vv)*180/3.1416 # Angle du vent en degrés\n if vv>0:\n av=av+180\n if vv*vu<0:\n av=360+av \n av=av%360\n \n\n date=time.strftime('%Hh%M (UTC) le %d/%m/%Y',time.localtime(date_p-3600)) # Temps formaté pas necessaire dans fonction\n return (longitude,latitude,date,av,vn,vu,vv)\n \n# Date et lieux choisis pour prevision **********************************************************************\n\nlon_p=-4 # negatif = West\nlat_p=45 \n # positif = Nord\nyear_p=2017\nmonth_p=6\nday_p=7\nhour_p=12\nmn_p=0\ndate_p=time.mktime((year_p,month_p,day_p,hour_p,mn_p,0,0,0,0)) \n\n\n#fvent2 (lon_p,lat_p,date_p)\n#print()\n\n\nvent =fvent(lon_p,lat_p,date_p)\nprint (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:5.2f} vv{:5.2f} \".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6]))\n\nlon_p=-4 # negatif = West\nlat_p=46 \n\nvent =fvent(lon_p,lat_p,date_p)\nprint (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:5.2f} vv{:5.2f} \".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6]))\n\n#**********************************************************************************************************************\nprint ('\\n******************************fonction 2************************************************\\n')\n\nlon_p=-4 # negatif = West\nlat_p=45.5\n\nvent =fvent2(lon_p,lat_p,date_p)\nprint (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:5.2f} vv{:5.2f} \".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6]))\n\n\n\n\n\n\n#heure=12 \n#mn=00\n#s=0\n# heure- 1 52\n\n## temps en secondes\n#t=3600*heure+60*mn+s- (3600+52*60)\n#\n#h_obs=t//3600\n#mn_obs=t%3600//60\n#\n#print ('heure du grib à l\\'observation',h_obs,mn_obs)\n#\n##decalage 1 h 52 \n#t_obs=t-3600-60*52\n\n\n\n\n\n#for mn_p in range (1,2):\n# date_p=time.mktime((year_p,month_p,day_p,hour_p,mn_p,0,0,0,0)) # Instant de la prevision en s \n\n#vent =fvent(lon_p,lat_p,date_p)\n#print (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:5.2f} --vv{:5.2f} \".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6]))\n# \n# \n#vent =fvent(lon_p,lat_p+1,date_p)\n#print (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:5.2f} --vv{:5.2f} \".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6]))\n#\n#vent =fvent(-3,45,date_p)\n#print (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:5.2f} --vv{:5.2f} \".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6]))\n# \n#\n#print()\n#vent =fvent(lon_p+1,lat_p,date_p)\n#print (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:5.2f} --vv{:5.2f} \".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6])) \n# \n#vent =fvent(lon_p+1,lat_p+1,date_p)\n#print (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:5.2f} --vv{:5.2f} \".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6])) \n# \n##\n#\n#print (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:6.2f} vv{:6.2f}f2 {:6.2f}N \".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6],vent[7]))\n#print()\n#vent =fvent(lon_p,lat_p-1,date_p)\n#\n#\n#print (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:6.2f} vv{:6.2f} f2 {:6.2f}N\".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6],vent[7]))\n#print()\n#\n#vent =fvent(lon_p+1,lat_p-1,date_p)\n#print (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:6.2f} vv{:6.2f} f2 {:6.2f}N\".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6],vent[7]))\n\n#vent =fvent(lon_p+0.5,lat_p,date_p)\n#print (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:6.2f} vv{:6.2f} \".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6]))\n#vent =fvent(lon_p+0.5,lat_p-1,date_p)\n#print (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:6.2f} vv{:6.2f} \".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6]))\n#\n#vent =fvent(lon_p+0.5,lat_p-0.5,date_p)\n#print (\" En {}° E et {}°N à {} Vent : {:6.2f}°, {:6.2f} N vu{:6.2f} vv{:6.2f} \".format(vent[0],vent[1],vent[2],vent[3],vent[4],vent[5],vent[6]))\n\n#def fvent2 (longitude,latitude,date_p):\n# # cette fonction fait la même que la precedente mais en interpolant latitude et longitude \n# ecart=date_p-date_i # ecart avec l'instant 0 du grib\n#\n#\n# \n## print( 'ecart en secondes avec le debut du grib',ecart )\n# \n# itemps0=int(ecart/ 3600//3) #indice du temps arrondi inferieur\n# itemps1=(ecart/ 3600/3) #indice du temps non arrondi\n# print('itemps0 {} ,itemps1 {}'.format( itemps0,itemps1))\n#\n#\n#\n#\n# jlong=int(abs(longitude-lonfirst)/paslat) #indice de la longitude arrondi inferieur\n# ilat=int(abs( latitude-latfirst)/paslat) #indice de la latitude arrondi inferieur\n# jlong_na= abs(longitude-lonfirst)/paslat #indice de longitude non arrondi\n# jlat_na= abs(latitude-latfirst)/paslat #indice de latitude non arrondi\n# \n# u=fileidx.select(name=\"10 metre U wind component\")\n# v=fileidx.select(name=\"10 metre V wind component\")\n# # extraction de vu en 4 points entiers au temps 0 et au temps 1\n# vu00t0=u[itemps0]['values'][ilat][jlong]\n# vu00t1=u[itemps0+1]['values'][ilat][jlong]\n# vu00t=vu00t0 +(vu00t1-vu00t0)*(itemps1-itemps0) #interpolation dans le temps\n# \n#\n# vu01t0=u[itemps0]['values'][ilat][jlong+1] #semble bon\n# vu01t1=u[itemps0+1]['values'][ilat][jlong+1]\n# vu01t=vu01t0 +(vu01t1-vu01t0)*(itemps1-itemps0)\n#\n# vu10t0=u[itemps0]['values'][ilat+1][jlong]\n# vu10t1=u[itemps0+1]['values'][ilat+1][jlong]\n# vu10t=vu10t0 +(vu10t1-vu10t0)*(itemps1-itemps0)\n# \n# vu11t0=u[itemps0]['values'][ilat+1][jlong+1]\n# vu11t1=u[itemps0+1]['values'][ilat+1][jlong]\n# vu11t=vu11t0 +(vu11t1-vu11t0)*(itemps1-itemps0)\n# \n# \n#\n## extraction de vv en 4 points entiers au temps 0 et au temps 1\n# vv00t0=v[itemps0]['values'][ilat][jlong]\n# vv00t1=v[itemps0+1]['values'][ilat][jlong]\n# vv00t=vv00t0 +(vv00t1-vv00t0)*(itemps1-itemps0)\n# \n# vv01t0=v[itemps0]['values'][ilat][jlong+1]\n# vv01t1=v[itemps0+1]['values'][ilat][jlong+1]\n# vv01t=vv01t0 +(vv01t1-vv01t0)*(itemps1-itemps0)\n# \n#\n# vv10t0=v[itemps0]['values'][ilat+1][jlong]\n# vv10t1=v[itemps0+1]['values'][ilat+1][jlong]\n# vv10t=vv10t0 +(vv10t1-vv10t0)*(itemps1-itemps0)\n# \n# vv11t0=v[itemps0]['values'][ilat+1][jlong+1]\n# vv11t1=v[itemps0+1]['values'][ilat+1][jlong]\n# vv11t=vv11t0 +(vv11t1-vv11t0)*(itemps1-itemps0)\n##\n# date=time.strftime('le %d/%m/%Y à %Hh%M (UTC) ',time.localtime(date_p-3600)) # Temps formaté \n# \n# print ( 'En longitude {} et latitude {} {} vu{:6.2f} vv{:6.2f}' .format(longitude,latitude,date,vu00t,vv00t)) \n# print ( 'En longitude {} et latitude {} {} vu01t0{:6.2f} vv{:6.2f}' .format(longitude+1,latitude,date,vu01t0,vv01t0))\n# print ( 'En longitude {} et latitude {} {} vu01t1{:6.2f} vv{:6.2f}' .format(longitude+1,latitude,date,vu01t1,vv01t1)) \n# print ( 'En longitude {} et latitude {} {} vu{:6.2f} vv{:6.2f}' .format(longitude,latitude+1,date,vu10t,vv10t)) \n# \n# return (longitude,latitude,date)\n# #interpolation directe sur force ce n'est pas celle de VR !\n# vn0=math.sqrt(vu0**2+vv0**2)*1.94384# Vitesse du vent en noeuds\n# vn1=math.sqrt(vu1**2+vv1**2)*1.94384# Vitesse du vent en noeuds\n# vn2=vn0 +(vn1-vn0)*(itemps1-itemps0)\n \n \n #date=time.strftime('%Hh%M (UTC+2) le %d/%m/%Y',time.localtime(date_p-time.timezone)) # Temps formaté ","sub_path":"lecture_vent_mini.py","file_name":"lecture_vent_mini.py","file_ext":"py","file_size_in_byte":12905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"212957907","text":"import sys\n\ndef solution(N, idx, priority):\n turn = 0\n for p in range(9, 0, -1):\n i = 0\n for j, prior in enumerate(priority):\n if p == prior :\n i = j\n turn += 1\n if j == idx: return turn\n idx = (idx - i + N) % N\n priority = priority[i:] + priority[:i]\n \n\nif __name__ == '__main__':\n T = int(sys.stdin.readline())\n \n for i in range(T):\n N, idx = [int(n) for n in sys.stdin.readline().split(' ')]\n priority = [int(n) for n in sys.stdin.readline().split(' ')]\n\n print(solution(N, idx, priority))","sub_path":"1~10000/1966/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"28433289","text":"import json, hmac, hashlib, time, requests\nfrom requests.auth import AuthBase\n\n# Before implementation, set environmental variables with the names API_KEY and API_SECRET\n\n# Create custom authentication for Coinbase API\nclass CoinbaseWalletAuth(AuthBase):\n def __init__(self, api_key, secret_key):\n self.api_key = api_key\n self.secret_key = secret_key\n\n def __call__(self, request):\n timestamp = str(int(time.time()))\n message = timestamp + request.method + request.path_url + (request.body or '')\n signature = hmac.new(bytes(self.secret_key, 'latin-1'), bytes(message, 'latin-1'), hashlib.sha256).hexdigest()\n\n request.headers.update({\n 'CB-ACCESS-SIGN': signature,\n 'CB-ACCESS-TIMESTAMP': timestamp,\n 'CB-ACCESS-KEY': self.api_key,\n })\n return request\n\napi_url = 'https://api.coinbase.com/v2/'\nauth = CoinbaseWalletAuth(API_KEY, API_SECRET)\n\n# Get current user\n# r = requests.get(api_url + 'user', auth=auth)\n\ntotal_onramp = 0\ntotal_cost_to_buy = 0\ntotal_purchases = 0\n\nquery_params = \"\"\nTYPE_OF_CRYPTO = \"ETH\"\n\nquit = False\nwhile(quit is not True):\n r = requests.get(api_url + 'accounts/d41efe25-c5bf-58e7-a7bc-4ca48f16b273/transactions' + query_params, auth=auth)\n\n data = json.loads(r.text)\n\n current_eth_amount = 0\n\n for transaction in data[\"data\"]:\n if transaction[\"type\"] == \"buy\" and transaction[\"amount\"][\"currency\"] == TYPE_OF_CRYPTO:\n print(\"{} bought: {}\".format(TYPE_OF_CRYPTO, transaction[\"amount\"][\"amount\"]))\n total_onramp += float(transaction[\"amount\"][\"amount\"])\n total_cost_to_buy += float(transaction[\"native_amount\"][\"amount\"])\n total_purchases += 1\n\n\n # print(\"next: \", data[\"pagination\"][\"next_uri\"])\n if data[\"pagination\"][\"next_uri\"] is not None:\n query_params = \"?starting_after=\" + data[\"pagination\"][\"next_starting_after\"]\n else:\n quit = True\n\n # json_formatted_str = json.dumps(r.json(), indent=2)\n # print(json_formatted_str)\n\nprint(\"=\" * 50)\nprint(\"Total {} bought: {}\".format(TYPE_OF_CRYPTO, total_onramp))\nprint(\"Total spent to get {}: {}\".format(TYPE_OF_CRYPTO, total_cost_to_buy))\nprint(\"Total purchases of {}: {}\".format(TYPE_OF_CRYPTO, total_purchases))\n","sub_path":"coinbase.py","file_name":"coinbase.py","file_ext":"py","file_size_in_byte":2278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"363905568","text":"from flask import Flask\nfrom velocipark import vp, vpdb\n#from geoalchemy2.types import Geometry,Geography #PostGIS not allowed on \"hobby dev\" heroku tier\n\nclass Park(vpdb.Model):\n id = vpdb.Column(vpdb.Integer, primary_key=True)\n location = vpdb.Column(vpdb.Text, nullable=True)\n address = vpdb.Column(vpdb.Text, nullable=True)\n bikeparking = vpdb.Column(vpdb.String(20))\n placement = vpdb.Column(vpdb.String(20))\n racks = vpdb.Column(vpdb.Integer)\n spaces = vpdb.Column(vpdb.Integer)\n #coordinates = vpdb.Column(Geography(geometry_type='POINT', srid=4326)) #PostGIS not allowed on \"hobby dev\" heroku tier, so no fun GIS queries :-(\n lat = vpdb.Column(vpdb.Float)\n lng = vpdb.Column(vpdb.Float)\n\n def __init__(self, location, address, bikeparking, placement, racks, spaces, lat, lng):\n self.location = location\n self.address = address\n self.bikeparking = bikeparking\n self.placement = placement\n self.racks = racks\n self.spaces = spaces\n #self.coordinates = coordinates\n self.lat = lat\n self.lng = lng\n\n @property\n def serialize(self):\n return {\n 'id': self.id,\n 'location': self.location,\n 'address': self.address,\n 'bikeparking': self.bikeparking,\n 'placement': self.placement,\n 'racks': self.racks,\n 'spaces': self.spaces,\n 'lat': self.lat,\n 'lng': self.lng,\n }\n","sub_path":"velocipark/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"310672184","text":"from datetime import timedelta\n\nfrom django.contrib.auth.decorators import login_required\nfrom django.db.models import Q\nfrom django.forms import modelformset_factory\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.contrib import messages\nfrom django.urls import reverse_lazy\nfrom django.utils import timezone\nfrom django.views.generic import ListView, DetailView, DeleteView\n\nfrom .forms import TraningForm, ImageForm, BaseImageFormSet\nfrom .models import *\nfrom .permissions import UserHasPermissionMixin\n\n\nclass MainPageView(ListView):\n model = Traning\n template_name = 'index.html'\n context_object_name = 'tranings'\n paginate_by = 3\n\n def get_template_names(self):\n template_name = super(MainPageView, self).get_template_names()\n search = self.request.GET.get('q')\n filter = self.request.GET.get('filter')\n if search:\n template_name = 'search.html'\n elif filter:\n template_name = 'new.html'\n return template_name\n\n def get_context_data(self, *, object_list=None, **kwargs):\n context = super().get_context_data(**kwargs)\n search = self.request.GET.get('q')\n filter = self.request.GET.get('filter')\n if search:\n context['tranings'] = Traning.objects.filter(Q(title__icontains=search)|\n Q(description__icontains=search))\n elif filter:\n start_date = timezone.now() - timedelta(days=1)\n context['tranings'] = Traning.objects.filter(created__gte=start_date)\n else:\n context['tranings'] = Traning.objects.all()\n return context\n\n\nclass CategoryDetailView(DetailView):\n model = Category\n template_name = 'category-detail.html'\n context_object_name = 'category'\n\n def get(self, request, *args, **kwargs):\n self.object = self.get_object()\n self.slug = kwargs.get('slug', None)\n return super().get(request, *args, **kwargs)\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context['tranings'] = Traning.objects.filter(category_id=self.slug)\n return context\n\n\nclass TraningDetailView(DetailView):\n model = Traning\n template_name = 'traning-detail.html'\n context_object_name = 'traning'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n image = self.get_object().get_image\n if isinstance(image, type(Image)):\n context['images'] = self.get_object().images.exclude(id=image.id)\n return context\n\n\n@login_required(login_url='login')\ndef add_traning(request):\n ImageFormSet = modelformset_factory(Image, form=ImageForm, max_num=5, formset=BaseImageFormSet)\n if request.method == 'POST':\n traning_form = TraningForm(request.POST)\n formset = ImageFormSet(request.POST or None, request.FILES or None, queryset=Image.objects.none())\n if traning_form.is_valid() and formset.is_valid():\n traning = traning_form.save(commit=False)\n traning.user = request.user\n traning.save()\n\n for form in formset.cleaned_data:\n image = form['image']\n Image.objects.create(image=image, traning=traning)\n\n return redirect(traning.get_absolute_url())\n else:\n traning_form = TraningForm()\n formset = ImageFormSet(queryset=Image.objects.none())\n return render(request, 'add-traning.html', dict(traning_form=traning_form, formset=formset))\n\n\n\ndef update_traning(request, pk):\n traning = get_object_or_404(Traning, pk=pk)\n if request.user == traning.user:\n ImageFormSet = modelformset_factory(Image, form=ImageForm, max_num=5)\n traning_form = TraningForm(request.POST or None, instance=traning)\n formset = ImageFormSet(request.POST or None, request.FILES or None, queryset=Image.objects.filter(traning=traning))\n if traning_form.is_valid() and formset.is_valid():\n traning = traning_form.save()\n images = formset.save(commit=False)\n for image in images:\n image.traning = traning\n image.save()\n return redirect(traning.get_absolute_url())\n return render(request, 'update-traning.html', locals())\n else:\n return HttpResponse('

403 Forbidden

')\n\n\nclass DeleteTraningView(UserHasPermissionMixin, DeleteView):\n model = Traning\n template_name = 'delete-traning.html'\n success_url = reverse_lazy('home')\n\n def delete(self, request, *args, **kwargs):\n self.object = self.get_object()\n success_url = self.get_success_url()\n self.object.delete()\n messages.add_message(request, messages.SUCCESS, 'Your traning is deleted!')\n return HttpResponseRedirect(success_url)\n\n\n\n","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"231289647","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport errno\nimport sys\nfrom os import getuid, getgid\n\nfrom time import time\nfrom stat import S_IFDIR, S_IFREG\nfrom fuse import FUSE, Operations, FuseOSError\n\nfrom RegularFile import RegularFile\nfrom clipboard import Clipboard\n\nclass ClipboardFile(Clipboard, RegularFile):\n\tdef __init__(self, selection='primary'):\n\t\tClipboard.__init__(self, selection)\n\t\tRegularFile.__init__(self)\n\nclass ClipboardFS(Operations):\n\n\tselections = ('primary', 'secondary', 'clipboard')\n\n\tdef __init__(self):\n\t\tnow = time()\n\t\tself.fd = 0\n\t\tself.contents = b\"Hello Kitty\\n\"\n\t\tself.files = dict()\n\t\tself.files['/'] = ClipboardFS.create_directory_dict(now)\n\t\tfor dirname in self.selections:\n\t\t\tself.files['/' + dirname] = ClipboardFS.create_directory_dict(now) \n\t\t\tself.files['/' + dirname + '/board'] = ClipboardFile(dirname)\n\n\n\t@staticmethod\n\tdef create_directory_dict(stat_time):\n\t\treturn dict(\n\t\t\tst_mode=(S_IFDIR | 0o755),\n\t\t\tst_atime=stat_time,\n\t\t\tst_ctime=stat_time,\n\t\t\tst_mtime=stat_time,\n\t\t\tst_uid=getuid(),\n\t\t\tst_gid=getgid(),\n\t\t\tst_nlink=2)\n\n\n\tdef getattr(self, path, fh=None):\n\t\tif path not in self.files.keys():\n\t\t\tfor sel in self.selections:\n\t\t\t\tprefix = '/' + sel + '/'\n\t\t\t\tif path.startswith(prefix):\n\t\t\t\t\telem = self.files[prefix + 'board']\n\t\t\t\t\telem.size_target = path[len(prefix):]\n\t\t\t\t\treturn elem\n\t\t\traise FuseOSError(errno.ENOENT)\n\n\t\telem = self.files[path]\n\t\tif isinstance(elem, ClipboardFile):\n\t\t\telem.size_target = None\n\t\treturn elem\n\n\tdef destroy(self, path):\n\t\tprint(\"Shutting down\")\n\n\tdef opendir(self, path):\n\t\treturn self.fd\n\n\tdef open(self, path, flags):\n\t\treturn 1\n\n\tdef read(self, path, size, offset, fh):\n\t\tfor sel in self.selections:\n\t\t\tprefix = '/' + sel + '/'\n\t\t\tif path == prefix + 'board':\n\t\t\t\treturn self.files[path].read()\n\t\t\tif path.startswith(prefix):\n\t\t\t\treturn self.files[prefix + 'board'].read(target=path[len(prefix):])\n\n\tdef readdir(self, path, fh):\n\t\tif path == \"/\":\n\t\t\treturn ('.', '..') + self.selections\n\t\tfor sel in self.selections:\n\t\t\tif path == \"/\" + sel:\n\t\t\t\treturn ['.', '..', 'board'] + list(self.files['/' + sel + '/board'].targets())\n\t\traise FuseOSError(errno.ENOENT)\n\n\tdef write(self, path, data, offset, fh):\n\t\tif path == \"/board\":\n\t\t\tself.contents = data\n\nif __name__ == \"__main__\":\n\ttry:\n\t\tmount_path = sys.argv[1]\n\texcept IndexError:\n\t\tprint(\"Must specify the path to mount the filesystem on.\", file=sys.stderr)\n\t\tsys.exit(-1)\n\n\tclipboard = ClipboardFS()\n\tFUSE(clipboard, mount_path, foreground=True)\n\n","sub_path":"clipboardFS.py","file_name":"clipboardFS.py","file_ext":"py","file_size_in_byte":2492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"124312435","text":"\n\nfrom xai.brain.wordbase.verbs._burgeon import _BURGEON\n\n#calss header\nclass _BURGEONED(_BURGEON, ):\n\tdef __init__(self,): \n\t\t_BURGEON.__init__(self)\n\t\tself.name = \"BURGEONED\"\n\t\tself.specie = 'verbs'\n\t\tself.basic = \"burgeon\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/verbs/_burgeoned.py","file_name":"_burgeoned.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"635921249","text":"# -----------------------------------------------------------------------------\n# \n# Copyright 2013-2019 lispers.net - Dino Farinacci \n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# \n# http://www.apache.org/licenses/LICENSE-2.0\n# \n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License. \n# \n# -----------------------------------------------------------------------------\n#\n# lisp-ddt.py\n#\n# This file performs LISP DDT node functionality.\n#\n# -----------------------------------------------------------------------------\nif 64 - 64: i11iIiiIii\nfrom builtins import str\nfrom builtins import range\nimport lisp\nimport lispconfig\nif 65 - 65: O0 / iIii1I11I1II1 % OoooooooOO - i1IIi\nif 73 - 73: II111iiii\nif 22 - 22: I1IiiI * Oo0Ooo / OoO0O00 . OoOoOO00 . o0oOOo0O0Ooo / I1ii11iIi11i\nif 48 - 48: oO0o / OOooOOo / I11i / Ii1I\nif 48 - 48: iII111i % IiII + I1Ii111 / ooOoO0o * Ii1I\ni1I1ii1II1iII = [ None , None , None ]\noooO0oo0oOOOO = None\nif 53 - 53: I11i / Oo0Ooo / II111iiii % Ii1I / OoOoOO00 . ooOoO0o\nif 100 - 100: i1IIi\nif 27 - 27: IiII * OoooooooOO + I11i * ooOoO0o - i11iIiiIii - iII111i\nif 30 - 30: iIii1I11I1II1 * iIii1I11I1II1 . II111iiii - oO0o\nif 72 - 72: II111iiii - OoOoOO00\nif 91 - 91: OoO0O00 . i11iIiiIii / oO0o % I11i / OoO0O00 - i11iIiiIii\nif 8 - 8: o0oOOo0O0Ooo * I1ii11iIi11i * iIii1I11I1II1 . IiII / IiII % IiII\nif 22 - 22: Ii1I . IiII\ndef I11 ( kv_pair ) :\n Oo0o0000o0o0 = lisp . lisp_ddt_entry ( )\n if 86 - 86: OoOoOO00 % I1IiiI\n for oo in list ( kv_pair . keys ( ) ) :\n IiII1I1i1i1ii = kv_pair [ oo ]\n if 44 - 44: oO0o / Oo0Ooo - II111iiii - i11iIiiIii % I1Ii111\n if ( oo == \"instance-id\" ) :\n O0OoOoo00o = IiII1I1i1i1ii . split ( \"-\" )\n if ( O0OoOoo00o [ 0 ] == \"\" ) : continue\n if 31 - 31: II111iiii + OoO0O00 . I1Ii111\n OoOooOOOO = ( \"eid-prefix\" not in kv_pair or\n kv_pair [ \"eid-prefix\" ] == \"\" )\n i11iiII = ( \"group-prefix\" not in kv_pair or\n kv_pair [ \"group-prefix\" ] == \"\" )\n if 34 - 34: OOooOOo % OoooooooOO / i1IIi . iII111i + O0\n if ( OoOooOOOO and i11iiII ) :\n Oo0o0000o0o0 . eid . store_iid_range ( int ( O0OoOoo00o [ 0 ] ) , int ( O0OoOoo00o [ 1 ] ) )\n else :\n Oo0o0000o0o0 . eid . instance_id = int ( O0OoOoo00o [ 0 ] )\n Oo0o0000o0o0 . group . instance_id = int ( O0OoOoo00o [ 0 ] )\n if 42 - 42: OOooOOo / i1IIi + i11iIiiIii - Ii1I\n if 78 - 78: OoO0O00\n if ( oo == \"eid-prefix\" ) :\n Oo0o0000o0o0 . eid . store_prefix ( IiII1I1i1i1ii )\n if 18 - 18: O0 - iII111i / iII111i + ooOoO0o % ooOoO0o - IiII\n if ( oo == \"group-prefix\" ) :\n Oo0o0000o0o0 . group . store_prefix ( IiII1I1i1i1ii )\n if 62 - 62: iII111i - IiII - OoOoOO00 % i1IIi / oO0o\n if 77 - 77: II111iiii - II111iiii . I1IiiI / o0oOOo0O0Ooo\n if 14 - 14: I11i % O0\n if 41 - 41: i1IIi + I1Ii111 + OOooOOo - IiII\n if 77 - 77: Oo0Ooo . IiII % ooOoO0o\n if 42 - 42: oO0o - i1IIi / i11iIiiIii + OOooOOo + OoO0O00\n if 17 - 17: oO0o . Oo0Ooo . I1ii11iIi11i\n IIi = lisp . lisp_ddt_cache_lookup ( Oo0o0000o0o0 . eid , Oo0o0000o0o0 . group , True )\n if ( IIi != None and IIi . is_auth_prefix ( ) == False ) : return\n if 38 - 38: Ii1I / Oo0Ooo\n if 76 - 76: O0 / o0oOOo0O0Ooo . I1IiiI * Ii1I - OOooOOo\n if 76 - 76: i11iIiiIii / iIii1I11I1II1 . I1ii11iIi11i % OOooOOo / OoooooooOO % oO0o\n if 75 - 75: iII111i\n Oo0o0000o0o0 . add_cache ( )\n return\n if 97 - 97: i11iIiiIii\n if 32 - 32: Oo0Ooo * O0 % oO0o % Ii1I . IiII\n if 61 - 61: ooOoO0o\n if 79 - 79: Oo0Ooo + I1IiiI - iII111i\n if 83 - 83: ooOoO0o\n if 64 - 64: OoO0O00 % ooOoO0o % iII111i / OoOoOO00 - OoO0O00\n if 74 - 74: iII111i * O0\ndef oOOo0oo ( kv_pair ) :\n o0oo0o0O00OO = [ ]\n if ( lispconfig . lisp_clause_syntax_error ( kv_pair , \"eid-prefix\" ,\n \"prefix\" ) ) : return\n for o0oO in range ( len ( kv_pair [ \"eid-prefix\" ] ) ) :\n Oo0o0000o0o0 = lisp . lisp_ddt_entry ( )\n o0oo0o0O00OO . append ( Oo0o0000o0o0 )\n if 48 - 48: I11i + I11i / II111iiii / iIii1I11I1II1\n if 20 - 20: o0oOOo0O0Ooo\n oO00 = [ ]\n for o0oO in kv_pair [ \"eid-prefix\" ] : oO00 . append ( o0oO )\n if 53 - 53: OoooooooOO . i1IIi\n ii1I1i1I = [ ]\n if ( lispconfig . lisp_clause_syntax_error ( kv_pair , \"address\" ,\n \"delegate\" ) ) : return\n for o0oO in range ( len ( kv_pair [ \"address\" ] ) ) :\n OOoo0O0 = lisp . lisp_ddt_node ( )\n ii1I1i1I . append ( OOoo0O0 )\n if 41 - 41: oO0o\n if 6 - 6: I1ii11iIi11i\n for oo in list ( kv_pair . keys ( ) ) :\n IiII1I1i1i1ii = kv_pair [ oo ]\n if ( oo == \"instance-id\" ) :\n for I1I in range ( len ( o0oo0o0O00OO ) ) :\n Oo0o0000o0o0 = o0oo0o0O00OO [ I1I ]\n O0OoOoo00o = IiII1I1i1i1ii [ I1I ] . split ( \"-\" )\n if ( O0OoOoo00o [ 0 ] == \"\" ) : continue\n if 80 - 80: OoOoOO00 - OoO0O00\n OoOooOOOO = ( kv_pair [ \"eid-prefix\" ] [ I1I ] == \"\" and\n kv_pair [ \"group-prefix\" ] [ I1I ] == \"\" )\n if 87 - 87: oO0o / I11i - i1IIi * OOooOOo / OoooooooOO . O0\n if ( OoOooOOOO ) :\n Oo0o0000o0o0 . eid . store_iid_range ( int ( O0OoOoo00o [ 0 ] ) , int ( O0OoOoo00o [ 1 ] ) )\n else :\n Oo0o0000o0o0 . eid . instance_id = int ( O0OoOoo00o [ 0 ] )\n Oo0o0000o0o0 . group . instance_id = int ( O0OoOoo00o [ 0 ] )\n if 1 - 1: II111iiii - I11i / I11i\n if 46 - 46: Ii1I * OOooOOo - OoO0O00 * oO0o - I1Ii111\n if 83 - 83: OoooooooOO\n if ( oo == \"eid-prefix\" ) :\n for I1I in range ( len ( o0oo0o0O00OO ) ) :\n Oo0o0000o0o0 = o0oo0o0O00OO [ I1I ]\n Iii111II = IiII1I1i1i1ii [ I1I ]\n if ( Iii111II == \"\" ) : continue\n Oo0o0000o0o0 . eid . store_prefix ( Iii111II )\n if 9 - 9: OoO0O00\n if 33 - 33: ooOoO0o . iII111i\n if ( oo == \"group-prefix\" ) :\n for I1I in range ( len ( o0oo0o0O00OO ) ) :\n Oo0o0000o0o0 = o0oo0o0O00OO [ I1I ]\n Iii111II = IiII1I1i1i1ii [ I1I ]\n if ( Iii111II == \"\" ) : continue\n Oo0o0000o0o0 . group . store_prefix ( Iii111II )\n if 58 - 58: OOooOOo * i11iIiiIii / OoOoOO00 % I1Ii111 - I1ii11iIi11i / oO0o\n if 50 - 50: I1IiiI\n if 34 - 34: I1IiiI * II111iiii % iII111i * OoOoOO00 - I1IiiI\n if ( oo == \"priority\" ) :\n for I1I in range ( len ( ii1I1i1I ) ) :\n OOoo0O0 = ii1I1i1I [ I1I ]\n O0OoOoo00o = IiII1I1i1i1ii [ I1I ]\n if ( O0OoOoo00o == \"\" ) : O0OoOoo00o = \"0\"\n OOoo0O0 . priority = int ( O0OoOoo00o )\n if 33 - 33: o0oOOo0O0Ooo + OOooOOo * OoO0O00 - Oo0Ooo / oO0o % Ii1I\n if 21 - 21: OoO0O00 * iIii1I11I1II1 % oO0o * i1IIi\n if ( oo == \"weight\" ) :\n for I1I in range ( len ( ii1I1i1I ) ) :\n OOoo0O0 = ii1I1i1I [ I1I ]\n O0OoOoo00o = IiII1I1i1i1ii [ I1I ]\n if ( O0OoOoo00o == \"\" ) : O0OoOoo00o = \"0\"\n OOoo0O0 . weight = int ( O0OoOoo00o )\n if 16 - 16: O0 - I1Ii111 * iIii1I11I1II1 + iII111i\n if 50 - 50: II111iiii - ooOoO0o * I1ii11iIi11i / I1Ii111 + o0oOOo0O0Ooo\n if ( oo == \"address\" ) :\n for I1I in range ( len ( ii1I1i1I ) ) :\n OOoo0O0 = ii1I1i1I [ I1I ]\n O0OoOoo00o = IiII1I1i1i1ii [ I1I ]\n if ( O0OoOoo00o != \"\" ) : OOoo0O0 . delegate_address . store_address ( O0OoOoo00o )\n if 88 - 88: Ii1I / I1Ii111 + iII111i - II111iiii / ooOoO0o - OoOoOO00\n if 15 - 15: I1ii11iIi11i + OoOoOO00 - OoooooooOO / OOooOOo\n if ( oo == \"public_key\" ) :\n for I1I in range ( len ( ii1I1i1I ) ) :\n OOoo0O0 = ii1I1i1I [ I1I ]\n O0OoOoo00o = IiII1I1i1i1ii [ I1I ]\n OOoo0O0 . public_key = O0OoOoo00o\n if 58 - 58: i11iIiiIii % I11i\n if 71 - 71: OOooOOo + ooOoO0o % i11iIiiIii + I1ii11iIi11i - IiII\n if ( oo == \"node-type\" ) :\n for I1I in range ( len ( ii1I1i1I ) ) :\n OOoo0O0 = ii1I1i1I [ I1I ]\n O0OoOoo00o = IiII1I1i1i1ii [ I1I ]\n if ( O0OoOoo00o == \"map-server-child\" ) : OOoo0O0 . map_server_child = True\n if 88 - 88: OoOoOO00 - OoO0O00 % OOooOOo\n if 16 - 16: I1IiiI * oO0o % IiII\n if 86 - 86: I1IiiI + Ii1I % i11iIiiIii * oO0o . ooOoO0o * I11i\n if 44 - 44: oO0o\n if 88 - 88: I1Ii111 % Ii1I . II111iiii\n if 38 - 38: o0oOOo0O0Ooo\n if 57 - 57: O0 / oO0o * I1Ii111 / OoOoOO00 . II111iiii\n if 26 - 26: iII111i\n for Oo0o0000o0o0 in o0oo0o0O00OO :\n Oo0o0000o0o0 . add_cache ( )\n for OOoo0O0 in ii1I1i1I :\n Oo0o0000o0o0 . delegation_set . append ( OOoo0O0 )\n if 91 - 91: OoO0O00 . I1ii11iIi11i + OoO0O00 - iII111i / OoooooooOO\n if 39 - 39: I1ii11iIi11i / ooOoO0o - II111iiii\n return\n if 98 - 98: I1ii11iIi11i / I11i % oO0o . OoOoOO00\n if 91 - 91: oO0o % Oo0Ooo\n if 64 - 64: I11i % iII111i - I1Ii111 - oO0o\n if 31 - 31: I11i - II111iiii . I11i\n if 18 - 18: o0oOOo0O0Ooo\n if 98 - 98: iII111i * iII111i / iII111i + I11i\n if 34 - 34: ooOoO0o\n if 15 - 15: I11i * ooOoO0o * Oo0Ooo % i11iIiiIii % OoOoOO00 - OOooOOo\ndef O0ooo0O0oo0 ( eid_str ) :\n oo0oOo , o000O0o , iI1iII1 , oO0OOoo0OO = lispconfig . lisp_get_lookup_string ( eid_str )\n if 65 - 65: Ii1I . iIii1I11I1II1 / O0 - Ii1I\n if 21 - 21: I1IiiI * iIii1I11I1II1\n oooooOoo0ooo = \"
\"\n if 6 - 6: I11i - Ii1I + iIii1I11I1II1 - I1Ii111 - i11iIiiIii\n if 79 - 79: OoOoOO00 - O0 * OoO0O00 + OoOoOO00 % O0 * O0\n if 61 - 61: II111iiii\n if 64 - 64: ooOoO0o / OoOoOO00 - O0 - I11i\n Oo0o0000o0o0 = lisp . lisp_ddt_cache_lookup ( oo0oOo , iI1iII1 , o000O0o )\n if ( Oo0o0000o0o0 == None ) :\n O0oOoOOOoOO = \"DDT entry not found for non-authoritative EID\"\n oooooOoo0ooo += \"{} {}\" . format ( lisp . lisp_print_sans ( O0oOoOOOoOO ) ,\n lisp . lisp_print_cour ( eid_str ) )\n return ( oooooOoo0ooo + \"
\" )\n if 38 - 38: I1Ii111\n if 7 - 7: O0 . iII111i % I1ii11iIi11i - I1IiiI - iIii1I11I1II1\n if ( Oo0o0000o0o0 . is_auth_prefix ( ) ) :\n if ( iI1iII1 . is_null ( ) ) :\n I111IIIiIii = lisp . lisp_ddt_compute_neg_prefix ( oo0oOo , Oo0o0000o0o0 ,\n lisp . lisp_ddt_cache )\n I111IIIiIii = lisp . lisp_print_cour ( I111IIIiIii . print_prefix ( ) ) ,\n else :\n oO0000OOo00 = lisp . lisp_ddt_compute_neg_prefix ( iI1iII1 , Oo0o0000o0o0 ,\n lisp . lisp_ddt_cache )\n I111IIIiIii = lisp . lisp_ddt_compute_neg_prefix ( oo0oOo , Oo0o0000o0o0 ,\n Oo0o0000o0o0 . source_cache )\n I111IIIiIii = lisp . lisp_print_cour ( \"(\" + I111IIIiIii . print_prefix ( ) + \", \" )\n if 27 - 27: I1IiiI % I1IiiI\n oO0000OOo00 = lisp . lisp_print_cour ( oO0000OOo00 . print_prefix ( ) + \")\" )\n if 1 - 1: OoO0O00 - oO0o . I11i . OoO0O00 / Oo0Ooo + I11i\n I111IIIiIii += oO0000OOo00\n if 78 - 78: O0 . oO0o . II111iiii % OOooOOo\n if 49 - 49: Ii1I / OoO0O00 . II111iiii\n oooooOoo0ooo += \"{} {} {} {} {} {}\" . format ( lisp . lisp_print_sans ( \"DDT authoritative-prefix entry\" ) ,\n # OOooOOo + Oo0Ooo . i11iIiiIii - i1IIi / iIii1I11I1II1\n lisp . lisp_print_cour ( Oo0o0000o0o0 . print_eid_tuple ( ) ) ,\n lisp . lisp_print_sans ( \"found for EID\" ) ,\n lisp . lisp_print_cour ( eid_str ) ,\n lisp . lisp_print_sans ( \"

Computed negative-prefix\" ) ,\n I111IIIiIii )\n else :\n oooooOoo0ooo += \"{} {} {} {} {} {}\" . format ( lisp . lisp_print_sans ( \"DDT entry\" ) ,\n # i11iIiiIii / I11i\n lisp . lisp_print_cour ( Oo0o0000o0o0 . print_eid_tuple ( ) ) ,\n lisp . lisp_print_sans ( \"found for EID\" ) ,\n lisp . lisp_print_cour ( eid_str ) ,\n lisp . lisp_print_sans ( \", delegation-type\" ) ,\n lisp . lisp_print_cour ( Oo0o0000o0o0 . print_referral_type ( ) ) )\n if 18 - 18: o0oOOo0O0Ooo % iII111i * O0\n return ( oooooOoo0ooo + \"
\" )\n if 87 - 87: i11iIiiIii\n if 93 - 93: I1ii11iIi11i - OoO0O00 % i11iIiiIii . iII111i / iII111i - I1Ii111\n if 9 - 9: I1ii11iIi11i / Oo0Ooo - I1IiiI / OoooooooOO / iIii1I11I1II1 - o0oOOo0O0Ooo\n if 91 - 91: iII111i % i1IIi % iIii1I11I1II1\n if 20 - 20: OOooOOo % Ii1I / Ii1I + Ii1I\n if 45 - 45: oO0o - IiII - OoooooooOO - OoO0O00 . II111iiii / O0\n if 51 - 51: O0 + iII111i\ndef IIIII11I1IiI ( ddt_entry , output ) :\n i1I = ddt_entry . print_eid_tuple ( )\n OoOO = ddt_entry . map_referrals_sent\n if 53 - 53: Oo0Ooo\n if ( ddt_entry . is_auth_prefix ( ) ) :\n output += lispconfig . lisp_table_row ( i1I , \"--\" , \"auth-prefix\" , \"--\" ,\n OoOO )\n return ( [ True , output ] )\n if 29 - 29: I1ii11iIi11i + oO0o % O0\n if 10 - 10: I11i / I1Ii111 - I1IiiI * iIii1I11I1II1 - I1IiiI\n for OOoo0O0 in ddt_entry . delegation_set :\n OO0oO0 = OOoo0O0 . delegate_address\n O00OOOOOoo0 = str ( OOoo0O0 . priority ) + \"/\" + str ( OOoo0O0 . weight )\n output += lispconfig . lisp_table_row ( i1I ,\n OO0oO0 . print_address_no_iid ( ) , OOoo0O0 . print_node_type ( ) , O00OOOOOoo0 , OoOO )\n if ( i1I != \"\" ) :\n i1I = \"\"\n OoOO = \"\"\n if 49 - 49: O0 . iII111i\n if 11 - 11: IiII * I1IiiI . iIii1I11I1II1 % OoooooooOO + iII111i\n return ( [ True , output ] )\n if 78 - 78: OoO0O00 . OOooOOo + OoO0O00 / I11i / OoO0O00\n if 54 - 54: OoOoOO00 % iII111i\n if 37 - 37: OoOoOO00 * Oo0Ooo / ooOoO0o - iII111i % II111iiii . oO0o\n if 88 - 88: iII111i . II111iiii * II111iiii % I1Ii111\n if 15 - 15: i1IIi * I1IiiI + i11iIiiIii\n if 6 - 6: ooOoO0o / i11iIiiIii + iII111i * oO0o\n if 80 - 80: II111iiii\n if 83 - 83: I11i . i11iIiiIii + II111iiii . o0oOOo0O0Ooo * I11i\ndef oooO0 ( ddt_entry , output ) :\n if 46 - 46: I1Ii111\n if 60 - 60: o0oOOo0O0Ooo\n if 25 - 25: OoO0O00\n if 62 - 62: OOooOOo + O0\n if ( ddt_entry . group . is_null ( ) ) :\n return ( IIIII11I1IiI ( ddt_entry , output ) )\n if 98 - 98: o0oOOo0O0Ooo\n if 51 - 51: Oo0Ooo - oO0o + II111iiii * Ii1I . I11i + oO0o\n if ( ddt_entry . source_cache == None ) : return ( [ True , output ] )\n if 78 - 78: i11iIiiIii / iII111i - Ii1I / OOooOOo + oO0o\n if 82 - 82: Ii1I\n if 46 - 46: OoooooooOO . i11iIiiIii\n if 94 - 94: o0oOOo0O0Ooo * Ii1I / Oo0Ooo / Ii1I\n if 87 - 87: Oo0Ooo . IiII\n output = ddt_entry . source_cache . walk_cache ( IIIII11I1IiI ,\n output )\n return ( [ True , output ] )\n if 75 - 75: ooOoO0o + OoOoOO00 + o0oOOo0O0Ooo * I11i % oO0o . iII111i\n if 55 - 55: OOooOOo . I1IiiI\n if 61 - 61: Oo0Ooo % IiII . Oo0Ooo\n if 100 - 100: I1Ii111 * O0\n if 64 - 64: OOooOOo % iIii1I11I1II1 * oO0o\n if 79 - 79: O0\n if 78 - 78: I1ii11iIi11i + OOooOOo - I1Ii111\ndef IIIIii1I ( parameter ) :\n if 39 - 39: II111iiii / ooOoO0o + I1Ii111 / OoOoOO00\n if 13 - 13: IiII + O0 + iII111i % I1IiiI / o0oOOo0O0Ooo . IiII\n if 86 - 86: oO0o * o0oOOo0O0Ooo % i1IIi . Ii1I . i11iIiiIii\n if 56 - 56: I1ii11iIi11i % O0 - I1IiiI\n if ( parameter != \"\" ) :\n return ( O0ooo0O0oo0 ( parameter ) )\n if 100 - 100: Ii1I - O0 % oO0o * OOooOOo + I1IiiI\n if 88 - 88: OoooooooOO - OoO0O00 * O0 * OoooooooOO . OoooooooOO\n if 33 - 33: I1Ii111 + iII111i * oO0o / iIii1I11I1II1 - I1IiiI\n if 54 - 54: I1Ii111 / OOooOOo . oO0o % iII111i\n if 57 - 57: i11iIiiIii . I1ii11iIi11i - Ii1I - oO0o + OoOoOO00\n O0oOoOOOoOO = \"Enter EID for DDT-Cache lookup:\"\n oO00oooOOoOo0 = lisp . lisp_eid_help_hover ( '' )\n if 74 - 74: iIii1I11I1II1 * I1ii11iIi11i + OoOoOO00 / i1IIi / II111iiii . Oo0Ooo\n oooooOoo0ooo = '''\n
\n \n {} {}\n \n
\n ''' . format ( lisp . lisp_print_sans ( O0oOoOOOoOO ) , oO00oooOOoOo0 )\n if 62 - 62: OoooooooOO * I1IiiI\n if 58 - 58: OoOoOO00 % o0oOOo0O0Ooo\n if 50 - 50: I1Ii111 . o0oOOo0O0Ooo\n if 97 - 97: O0 + OoOoOO00\n OO0O000 = \"{} entries in delegation-cache\" . format ( lisp . lisp_ddt_cache . cache_size ( ) )\n if 37 - 37: OoooooooOO - O0 - o0oOOo0O0Ooo\n o0o0O0O00oOOo = lisp . lisp_span ( \"LISP-DDT Configured Delegations:\" , OO0O000 )\n if 14 - 14: OoOoOO00 + oO0o\n oooooOoo0ooo += lispconfig . lisp_table_header ( o0o0O0O00oOOo , \"EID-Prefix or (S,G)\" ,\n \"Delegation Address\" , \"Delegation Type\" , \"Priority/Weight\" ,\n \"Map-Referrals Sent\" )\n if 52 - 52: OoooooooOO - ooOoO0o\n oooooOoo0ooo = lisp . lisp_ddt_cache . walk_cache ( oooO0 , oooooOoo0ooo )\n oooooOoo0ooo += lispconfig . lisp_table_footer ( )\n return ( oooooOoo0ooo )\n if 74 - 74: iII111i + o0oOOo0O0Ooo\n if 71 - 71: Oo0Ooo % OOooOOo\n if 98 - 98: I11i % i11iIiiIii % ooOoO0o + Ii1I\n if 78 - 78: I1ii11iIi11i % oO0o / iII111i - iIii1I11I1II1\n if 69 - 69: I1Ii111\n if 11 - 11: I1IiiI\n if 16 - 16: Ii1I + IiII * O0 % i1IIi . I1IiiI\n if 67 - 67: OoooooooOO / I1IiiI * Ii1I + I11i\ndef OooOo0ooo ( ) :\n global i1I1ii1II1iII\n global oooO0oo0oOOOO\n if 71 - 71: I1Ii111 + Ii1I\n lisp . lisp_i_am ( \"ddt\" )\n lisp . lisp_set_exception ( )\n lisp . lisp_print_banner ( \"DDT-Node starting up\" )\n if 28 - 28: OOooOOo\n if 38 - 38: ooOoO0o % II111iiii % I11i / OoO0O00 + OoOoOO00 / i1IIi\n if 54 - 54: iIii1I11I1II1 % I1ii11iIi11i - OOooOOo / oO0o - OoO0O00 . I11i\n if 11 - 11: I1ii11iIi11i . OoO0O00 * IiII * OoooooooOO + ooOoO0o\n if ( lisp . lisp_get_local_addresses ( ) == False ) : return ( False )\n if 33 - 33: O0 * o0oOOo0O0Ooo - I1Ii111 % I1Ii111\n if 18 - 18: I1Ii111 / Oo0Ooo * I1Ii111 + I1Ii111 * i11iIiiIii * I1ii11iIi11i\n if 11 - 11: ooOoO0o / OoOoOO00 - IiII * OoooooooOO + OoooooooOO . OoOoOO00\n if 26 - 26: Ii1I % I1ii11iIi11i\n oooO0oo0oOOOO = lisp . lisp_open_listen_socket ( \"\" , \"lisp-ddt\" )\n i1I1ii1II1iII [ 0 ] = lisp . lisp_open_send_socket ( \"\" , lisp . LISP_AFI_IPV4 )\n i1I1ii1II1iII [ 1 ] = lisp . lisp_open_send_socket ( \"\" , lisp . LISP_AFI_IPV6 )\n i1I1ii1II1iII [ 2 ] = oooO0oo0oOOOO\n return\n if 76 - 76: IiII * iII111i\n if 52 - 52: OOooOOo\n if 19 - 19: I1IiiI\n if 25 - 25: Ii1I / ooOoO0o\n if 31 - 31: OOooOOo . O0 % I1IiiI . o0oOOo0O0Ooo + IiII\n if 71 - 71: I1Ii111 . II111iiii\n if 62 - 62: OoooooooOO . I11i\ndef oOOOoo00 ( ) :\n if 9 - 9: O0 % O0 - o0oOOo0O0Ooo\n if 51 - 51: I1IiiI . iIii1I11I1II1 - I1ii11iIi11i / O0\n if 52 - 52: o0oOOo0O0Ooo + O0 + iII111i + Oo0Ooo % iII111i\n if 75 - 75: I1IiiI . ooOoO0o . O0 * I1Ii111\n lisp . lisp_close_socket ( i1I1ii1II1iII [ 0 ] , \"\" )\n lisp . lisp_close_socket ( i1I1ii1II1iII [ 1 ] , \"\" )\n lisp . lisp_close_socket ( oooO0oo0oOOOO , \"lisp-ddt\" )\n return\n if 4 - 4: Ii1I % oO0o * OoO0O00\n if 100 - 100: I1Ii111 * OOooOOo + OOooOOo\n if 54 - 54: OoooooooOO + o0oOOo0O0Ooo - i1IIi % i11iIiiIii\n if 3 - 3: o0oOOo0O0Ooo % o0oOOo0O0Ooo\n if 83 - 83: II111iiii + I1Ii111\noO00ooooO0o = {\n \"lisp ddt-authoritative-prefix\" : [ I11 , {\n \"instance-id\" : [ False , 0 , 0xffffffff , True ] ,\n \"eid-prefix\" : [ False ] ,\n \"group-prefix\" : [ False ] , } ] ,\n\n \"lisp delegation\" : [ oOOo0oo , {\n \"delegate\" : [ ] ,\n \"address\" : [ True ] ,\n \"node-type\" : [ True , \"ddt-child\" , \"map-server-child\" ] ,\n \"priority\" : [ True , 0 , 255 ] ,\n \"weight\" : [ True , 0 , 100 ] ,\n \"public-key\" : [ True ] ,\n \"prefix\" : [ ] ,\n \"instance-id\" : [ True , 0 , 0xffffffff , True ] ,\n \"eid-prefix\" : [ True ] ,\n \"group-prefix\" : [ True ] } ] ,\n\n \"show delegations\" : [ IIIIii1I , { } ] ,\n }\nif 75 - 75: i1IIi / O0 * o0oOOo0O0Ooo\nif 29 - 29: I1IiiI % OOooOOo - I1IiiI / OOooOOo . i1IIi\nif 31 - 31: I1Ii111\nif 88 - 88: OoO0O00 - ooOoO0o + OOooOOo * I1IiiI % iIii1I11I1II1 + Oo0Ooo\nif 76 - 76: I1IiiI * iII111i % I1Ii111\nif 57 - 57: iIii1I11I1II1 - i1IIi / I1Ii111 - O0 * OoooooooOO % II111iiii\nif ( OooOo0ooo ( ) ) :\n lisp . lprint ( \"lisp_ddt_startup() failed\" )\n lisp . lisp_print_banner ( \"DDT-Node abnormal exit\" )\n exit ( 1 )\n if 68 - 68: OoooooooOO * I11i % OoOoOO00 - IiII\n if 34 - 34: I1Ii111 . iIii1I11I1II1 * OoOoOO00 * oO0o / I1Ii111 / I1ii11iIi11i\nwhile ( True ) :\n oOoOOo0O , OOOooo , OooO0OO , o0OOo0o0O0O = lisp . lisp_receive ( oooO0oo0oOOOO , True )\n if 65 - 65: i11iIiiIii\n if ( OOOooo == \"\" ) : break\n if 85 - 85: Ii1I % iII111i + I11i / o0oOOo0O0Ooo . oO0o + OOooOOo\n if ( oOoOOo0O == \"command\" ) :\n o0OOo0o0O0O = o0OOo0o0O0O . decode ( )\n lispconfig . lisp_process_command ( oooO0oo0oOOOO , oOoOOo0O ,\n o0OOo0o0O0O , \"lisp-ddt\" , [ oO00ooooO0o ] )\n else :\n lisp . lisp_parse_packet ( i1I1ii1II1iII , o0OOo0o0O0O , OOOooo , OooO0OO )\n if 62 - 62: i11iIiiIii + i11iIiiIii - o0oOOo0O0Ooo\n if 28 - 28: iII111i . iII111i % iIii1I11I1II1 * iIii1I11I1II1 . o0oOOo0O0Ooo / iII111i\n if 27 - 27: OoO0O00 + ooOoO0o - i1IIi\noOOOoo00 ( )\nlisp . lisp_print_banner ( \"DDT-Node normal exit\" )\nexit ( 0 )\nif 69 - 69: IiII - O0 % I1ii11iIi11i + i11iIiiIii . OoOoOO00 / OoO0O00\nif 79 - 79: O0 * i11iIiiIii - IiII / IiII\nif 48 - 48: O0\n# dd678faae9ac167bc83abf78e5cb2f3f0688d3a3\n","sub_path":"build/releases/release-0.589/ob/lisp-ddt.py","file_name":"lisp-ddt.py","file_ext":"py","file_size_in_byte":20090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"371171669","text":"from django.core.exceptions import MultipleObjectsReturned, ObjectDoesNotExist\nfrom django.shortcuts import render, redirect\nfrom django.http import HttpResponse, HttpResponseForbidden\nfrom datetime import datetime\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.core.exceptions import PermissionDenied\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.decorators import login_required, permission_required\nfrom django.contrib.sessions.backends.db import SessionStore\nfrom django.contrib.sessions.models import Session\nfrom django.utils import timezone\nimport base64\nimport logging\nfrom django.views.decorators.http import require_POST\n\nfrom .decorators import device_is_auth\n\nfrom .models import *\nfrom .xmlcreator import *\nfrom .utility import *\n\n\nMultipleEntry = \"Datenbank - Doppelte Einträg\"\nNoEntry = \"Datenbank - Kein Eintrag\"\n\nlog = logging.getLogger(__name__)\n\ndef adminLogin(request):\n username = request.POST['username']\n password = request.POST['password']\n user = authenticate(request, username=username, password=password)\n if user is not None:\n log.info(\"User: \" + username + \" hat sich erfolgreich angemeldet\")\n login(request, user)\n response = HttpResponse(\"Ok\")\n else:\n log.info(\"User: \" + username + \" hat sich nicht erfolgreich angemeldet\")\n raise PermissionDenied\n return HttpResponse(response)\n\ndef adminLogout(request):\n logout(request)\n return HttpResponse(\"Done\")\n\n#todo muss überall noch implementiert werden\ndef tete(request):\n '''\n Login für Admin\n :param request:\n :return:\n '''\n if 'HTTP_AUTHORIZATION' in request.META:\n auth = request.META['HTTP_AUTHORIZATION'].split()\n\n if len(auth) == 2:\n if auth[0].lower() == \"basic\":\n username, password = base64.b64decode(auth[1]).decode(\"utf-8\").split(':',1)\n user = authenticate(username=username, password=password)\n if user is not None:\n login(request, user)\n response = \"Willkommen\"\n else:\n raise PermissionDenied\n\n return HttpResponse(response)\n\n@login_required()\ndef loginDevice(request):\n '''\n Geräte bekommen erstmalig eine SessionID\n :param request: HTTP POST Req. mit param id\n :return:\n '''\n\n id = request.POST['id']\n try:\n device = Device.objects.get(deviceId=id)\n device.deviceSessionId = utility.createSession()\n device.save()\n log.info(\"Device mit Standortid \" +id+ \" hat sich erfolgreich registiert\")\n xmlRet = buildSessionXml(\"200\", \"-\", device)\n except MultipleObjectsReturned:\n xmlRet = buildStatusXML(\"901\", MultipleEntry)\n except ObjectDoesNotExist:\n xmlRet = buildStatusXML(\"902\", NoEntry)\n return HttpResponse(xmlRet, content_type=\"xml\")\n\n\n\n@device_is_auth\ndef refreshDeviceSession(request):\n id = request.POST['id']\n try:\n device = Device.objects.get(deviceId=id)\n device.deviceSessionId = utility.createSession()\n device.save()\n log.info(\"Device mit Standortid \" +id+ \" hat SessionId erneuert\")\n xmlRet = buildSessionXml(\"200\", \"-\", device)\n except MultipleObjectsReturned:\n xmlRet = buildStatusXML(\"901\", MultipleEntry)\n except ObjectDoesNotExist:\n xmlRet = buildStatusXML(\"902\", NoEntry)\n return HttpResponse(xmlRet, content_type=\"xml\")\n\n@device_is_auth\ndef getUser(request):\n '''\n Schaut in der Datenbank ob ein Benutzer zum NFC_UID existiert\n --> auth?\n :param request: HTTP GET Request mit param nfc_uid\n :return:\n '''\n\n alldata=request.GET\n nfcUid = alldata.get(\"nfc_uid\", \"\")\n\n try:\n user = SobUser.objects.get(nfc_uid=nfcUid)\n log.info(\"User: \" +user.name + \" \" + user.surname + \" hat sich an einem Terminal angemeldet\")\n xmlRet = buildUserXml(\"200\", \"-\", user)\n except MultipleObjectsReturned:\n xmlRet = buildStatusXML(\"903\", MultipleEntry)\n except ObjectDoesNotExist:\n xmlRet = buildStatusXML(\"904\", NoEntry)\n\n return HttpResponse(xmlRet, content_type=\"xml\")\n\n@login_required()\n@require_POST\ndef addUser(request):\n '''\n Post Request\n Mit dieser Methode kann ein Benutzer im System aufgenommen werden\n :param request:\n :return:\n '''\n '''\n print(request.POST)\n print(SobUser.objects.all())\n print(request)\n '''\n new_user = SobUser(name=request.POST['name'], surname=request.POST['surname'], nfc_uid=request.POST['nfc_uid'], admin=request.POST['admin'])\n log.info(\"Benutzer: \" + new_user.name + \" \" + new_user.name + \" ist registiert worden\")\n new_user.save()\n xmlRet = buildStatusXML(\"200\", \"OK\")\n return HttpResponse(xmlRet, content_type=\"xml\")\n\n@device_is_auth\n#@require_POST\ndef addWorker(request):\n '''\n Mit dieser Methode kann ein Arbeiter hinzugefügt werden.\n --> todo auth?\n :param request: user, trackName\n :return:\n '''\n\n print(request.POST)\n\n workerallreadyIn = WorkingUser.objects.filter(worker=request.POST['worker'], trackName=request.POST['track'], train=request.POST['train']).exists()\n if not workerallreadyIn:\n new_worker = WorkingUser(worker=request.POST['worker'], trackName=request.POST['track'],\n train=request.POST['train'],\n login=timezone.now())\n track = new_worker.trackName\n train = new_worker.train\n\n if utility.trackHandling(train, track) == 1:\n\n log.info(\"Arbeiter: \" +new_worker.worker+ \" hat sich im Gleis: \" + new_worker.trackName + \" auf den Zug: \" +new_worker.train + \" eingeloggt\")\n new_worker.save()\n xmlRet = buildStatusXML(\"200\", \"Done\")\n\n else:\n if not utility.statusCodeUtility == '990':\n log.warning(\"Arbeiter: \" +new_worker.worker+ \" konnte sich nicht erfolgreich im Gleis: \"\n + new_worker.trackName + \" auf den Zug: \" +new_worker.train + \" einloggen!\"\n \"\\nGrund: \" \"Fehlercode: \" +utility.statusCodeUtility+ \" Fehlertext: \" +utility.statusTextUtility )\n\n xmlRet = buildStatusXML(utility.statusCodeUtility, utility.statusTextUtility)\n\n else:\n xmlRet = buildStatusXML(\"908\", \"Schon eingeloggt\")\n\n return HttpResponse(xmlRet, content_type=\"xml\")\n\n@device_is_auth\ndef removeWorker(request):\n '''\n Mit dieser Methode kann ein Arbeiter entfernt werden.\n :param request: user, trackName\n :return:\n '''\n\n\n\n try:\n worker = WorkingUser.objects.get(worker=request.POST['worker'], trackName=request.POST['track'],train=request.POST['train'] )\n print(worker)\n if utility.workerLogout(worker.train, worker.trackName, worker.worker):\n if 'other' in request.POST:\n adminUser = request.POST['other']\n log.info(\"Admin: \" +adminUser+ \" hat Arbeiter: \" +worker.worker+ \" hat im Gleis: \" + worker.trackName + \" auf den Zug: \" +worker.train + \" ausgeloggt\")\n else:\n log.info(\"Arbeiter: \" +worker.worker+ \" hat sich im Gleis: \" + worker.trackName + \" auf den Zug: \" +worker.train + \" ausgeloggt\")\n\n xmlRet = buildStatusXML(\"200\", \"Done\")\n\n else:\n if not utility.statusCodeUtility == '990':\n log.warning(\"Arbeiter: \" +worker.worker+ \" konnte sich nicht erfolgreich im Gleis: \"\n + worker.trackName + \" auf den Zug: \" +worker.train + \" ausloggen!\"\n \"\\nGrund: \" \"Fehlercode: \" +utility.statusCodeUtility+ \" Fehlertext: \" +utility.statusTextUtility )\n\n xmlRet = buildStatusXML(utility.statusCodeUtility, utility.statusTextUtility)\n\n except MultipleObjectsReturned:\n xmlRet = buildStatusXML(\"910\", MultipleEntry)\n except ObjectDoesNotExist:\n xmlRet = buildStatusXML(\"911\", NoEntry)\n\n return HttpResponse(xmlRet, content_type=\"xml\")\n\ndef ipAdress(request):\n '''\n Die IP Adressen der SPS wird abgelegt\n --> auth?\n :param request:\n :return:\n '''\n\n client_address = request.META['HTTP_X_FORWARDED_FOR']\n #print(client_address)\n #deviceIpAdress = request.POST['ipadress']\n #deviceID = request.POST['deviceid']\n\n #print(deviceID)\n #print(deviceIpAdress)\n\n return HttpResponse(client_address)\n\n@device_is_auth\ndef getUpdate(request):\n return HttpResponse(buildUpdate(\"200\", \"-\", WorkingUser), content_type=\"xml\")\n\ndef tracks(request):\n '''\n Login für Admin\n :param request:\n :return:\n '''\n if 'HTTP_AUTHORIZATION' in request.META:\n auth = request.META['HTTP_AUTHORIZATION'].split()\n\n if len(auth) == 2:\n if auth[0].lower() == \"basic\":\n username, password = base64.b64decode(auth[1]).decode(\"utf-8\").split(':', 1)\n user = authenticate(username=username, password=password)\n if user is not None:\n login(request, user)\n xmlRet = buildTrackOverview(\"200\", \"-\")\n else:\n raise PermissionDenied\n else:\n raise PermissionDenied\n\n return HttpResponse(xmlRet, content_type=\"xml\")\n\ndef spsState(request):\n if 'HTTP_AUTHORIZATION' in request.META:\n auth = request.META['HTTP_AUTHORIZATION'].split()\n\n if len(auth) == 2:\n if auth[0].lower() == \"basic\":\n username, password = base64.b64decode(auth[1]).decode(\"utf-8\").split(':', 1)\n user = authenticate(username=username, password=password)\n if user is not None:\n login(request, user)\n track = Track.objects.get(trackName=request.POST['track'])\n track.spsState = request.POST['state']\n track.save()\n log.info(\"SPS: \" +username+ \" hat Gleis: \" +track.trackName+ \" auf: \" +track.spsState+ \" gesetzt\")\n xmlRet = buildStatusXML(\"200\", \"Done\")\n else:\n raise PermissionDenied\n else:\n raise PermissionDenied\n\n return HttpResponse(xmlRet, content_type=\"xml\")\n\n@login_required\ndef getlogfile(request):\n logFile = open(\"/home/szi/sziServer/logfile.txt\", \"rb\").read()\n return HttpResponse(logFile, content_type=\"txt\")","sub_path":"centralapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":10310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"626801818","text":"from functools import partial\nfrom .errors import ValidationError, FormValidationError, FieldValidationError\nfrom jsonschema import Draft4Validator\nfrom .util import jsl_to_jsonobject, dataclass_to_jsl, dataclass_get_type\nimport reg\nfrom morepath.publish import resolve_model\nimport urllib\nimport re\n\n\n@reg.dispatch(reg.match_instance('model'), reg.match_instance('request'))\ndef get_data(model, request):\n raise NotImplementedError\n\n\ndef regex_validator(pattern, name):\n p = re.compile(pattern)\n\n def _regex_validator(value):\n if not p.match(value):\n raise FieldValidationError(\n '%s does not match %s pattern' % (value, name))\n\n return _regex_validator\n\n\ndef load(validator, schema, request):\n newreq = request.app.request_class(\n request.environ.copy(), request.app.root,\n path_info=urllib.parse.unquote(request.path))\n context = resolve_model(newreq)\n context.request = request\n if schema is None:\n dc = context.schema\n else:\n dc = schema\n\n data = get_data(context, request)\n dc.validate(request, data)\n return request.json\n\n\ndef validate_schema(validator=Draft4Validator, schema=None):\n return partial(load, validator, schema)\n","sub_path":"morpfw/crud/validator.py","file_name":"validator.py","file_ext":"py","file_size_in_byte":1232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"397399439","text":"from random import randrange\nimport requests\nfrom threading import Thread\nfrom time import sleep\nfrom tqdm import tqdm\n\nipbad = False\ngoodips = []\n\ndef checkIp1(ipstr, region=\"Europe\"):\n global ipbad\n global goodips\n # 0 in region\n # 1 outside\n # 2 prob ip throttled\n if ipbad:\n return 2\n\n r = requests.get(\"https://ipinfo.io/\" + ipstr.split(\":\")[0])\n if r.status_code != 200:\n ipbad = True\n return 2\n if r.json()[\"timezone\"].split(\"/\")[0] == region:\n goodips.append(ipstr)\n return 0\n return 1\n\ndef checkIp2(ipstr, maxtime=2):\n # import os\n # if os.getuid() != 0:\n # print(\"[-] Requires root\")\n # os._exit(1)\n\n from ping3 import ping\n if ping(ipstr.split(\":\")[0], timeout=maxtime):\n goodips.append(ipstr)\n return True\n return False\n\n\ndef getIps():\n url = \"https://api.proxyscrape.com/v2/?request=getproxies&protocol=socks4&timeout=200&country=all\"\n return requests.get(url).text.split(\"\\r\\n\")\n\n\ndef main():\n filename = \"GoodIps.txt\"\n print(\"[*] Downloading proxies...\")\n Ips = getIps()\n jobs = []\n t = False\n\n print(f\"[*] Checking {len(Ips)} ips\")\n\n if t:\n for ip in tqdm(Ips, desc=\"Starting Thread\"):\n t = Thread(target=checkIp1, args=(ip,))\n t.start()\n jobs.append(t)\n sleep(1)\n\n o = True\n for job in tqdm(jobs, desc=\"Finishing\"):\n if ipbad:\n if o:\n print(\"[-] IP Throttled, killing remaining threads...\")\n o = False\n else:\n job.join()\n else:\n for ip in tqdm(Ips, desc=\"Checking ip\"):\n checkIp2(ip, 0.05)\n \n print(\"[+] Done!\")\n print(f\"[*] Writing {len(goodips)} ips to file: {filename}\")\n with open(filename, \"w\") as f:\n f.write(\"\\n\".join(goodips))\n import random\n print(f\"[+] Random ip: {random.choice(goodips)}\")\n\nmain()","sub_path":"Check.py","file_name":"Check.py","file_ext":"py","file_size_in_byte":1957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"266982530","text":"# \r\n#Introdução a Programação de Computadores \r\n#Prof. Dr. :Jucimar Jr.\r\n# \r\n#Kid Mendes de Oliveira Neto - 1615310011 \r\n#Eduardo Maia Freire - 1615310003 \r\n#Igor Menezes Sales Vieira - 1615310007\r\n#Lorene da Silva Marques - 1615310013 \r\n#Nadine da Cunha Brito - 1615310040 \r\n#\r\n\r\nimport string\r\nimport random\r\nfrom datetime import *\r\n\r\n\r\n\r\ndef elaborar_tabuleiro(tamanho_tabuleiro,comeco,quant_minas):\r\n tabuleiro = [[\"0\" for i in range(tamanho_tabuleiro)] for i in range(tamanho_tabuleiro)]\r\n minas = elaborar_minas(tabuleiro,comeco,quant_minas)\r\n gerar_numeros(tabuleiro)\r\n return (tabuleiro,minas)\r\n\r\ndef apresentar_tabuleiro(tabuleiro):\r\n tamanho_tabuleiro = len(tabuleiro)\r\n horizontal = \" \"+(4*tamanho_tabuleiro*\"-\")+\"-\"\r\n vertical = \" \"\r\n \r\n for i in string.ascii_uppercase[:tamanho_tabuleiro]:\r\n vertical = vertical+i+\" \"\r\n print(vertical+\"\\n\"+horizontal)\r\n for i,j in enumerate(tabuleiro):\r\n linha = (\"%s |\"%(i+1))\r\n for k in j:\r\n linha = linha+\" \"+k+\" |\"\r\n print(linha+\"\\n\"+horizontal)\r\n\r\ndef cont_letras(tabuleiro):\r\n tamanho_tabuleiro = len(tabuleiro)\r\n letras = []\r\n for i in string.ascii_lowercase[:tamanho_tabuleiro]:\r\n letras.append(i)\r\n\r\n return letras\r\n\r\ndef gerar_posicao_aleatoria(tabuleiro):\r\n tamanho_tabuleiro = len(tabuleiro)\r\n linha = random.randint(0,tamanho_tabuleiro - 1)\r\n coluna = random.randint(0,tamanho_tabuleiro - 1)\r\n return (linha,coluna)\r\n\r\ndef gerar_numeracao_lateral(tabuleiro,ordenada,abscissa):\r\n tamanho_tabuleiro = len(tabuleiro)\r\n linha = tabuleiro[ordenada]\r\n coluna = tabuleiro[ordenada][abscissa]\r\n num_lateral = []\r\n \r\n\r\n for i in range(-1,2):\r\n for j in range(-1,2):\r\n if(i == 0 and j == 0):\r\n continue\r\n elif( -1< ordenada + i < tamanho_tabuleiro and -1 < abscissa + j < tamanho_tabuleiro):\r\n num_lateral.append((ordenada+i,abscissa+j))\r\n\r\n return num_lateral\r\n\r\ndef elaborar_minas(tabuleiro,comeco,quant_minas):\r\n tamanho_tabuleiro = len(tabuleiro)\r\n minas = []\r\n\r\n for i in range(quant_minas):\r\n posicao = gerar_posicao_aleatoria(tabuleiro)\r\n while posicao == (comeco[0], comeco[1]) or posicao in minas:\r\n posicao = gerar_posicao_aleatoria(tabuleiro)\r\n minas.append(posicao)\r\n\r\n for i,j in minas:\r\n tabuleiro[i][j] = \"*\"\r\n\r\n return minas\r\n\r\ndef gerar_numeros(tabuleiro):\r\n tamanho_tabuleiro = len(tabuleiro)\r\n for ordenada,linha in enumerate(tabuleiro):\r\n for abscissa,coluna in enumerate(linha):\r\n if (coluna != \"*\"):\r\n valor = [tabuleiro[l][c] for l,c in gerar_numeracao_lateral(tabuleiro,ordenada,abscissa)]\r\n tabuleiro[ordenada][abscissa] = str(valor.count(\"*\"))\r\n\r\ndef apresentar_posicao(tabuleiro,atual_tabuleiro,ordenada,abscissa):\r\n if(atual_tabuleiro[ordenada][abscissa] != \" \"):\r\n return\r\n atual_tabuleiro[ordenada][abscissa] = tabuleiro[ordenada][abscissa]\r\n if(tabuleiro[ordenada][abscissa] == \"0\"):\r\n for l,c in gerar_numeracao_lateral(tabuleiro,ordenada,abscissa):\r\n if(atual_tabuleiro[l][c] != \"P\"):\r\n apresentar_posicao(tabuleiro,atual_tabuleiro,l,c)\r\n\r\ndef jogar_novamente():\r\n escolha = input(\"Se deseja jogar novamente digite (Sim),caso contrario digite (Nao):\\n\")\r\n return escolha.lower() == \"sim\"\r\n\r\ndef reproduzir_jogo():\r\n quant_minas = 40\r\n tamanho_tabuleiro = 16\r\n atual_tabuleiro = [[\" \" for i in range(tamanho_tabuleiro)] for i in range(tamanho_tabuleiro)]\r\n apresentar_tabuleiro(atual_tabuleiro)\r\n tabuleiro = []\r\n bandeiras = []\r\n cond = True\r\n cond1 = True\r\n musica = False\r\n comeco = datetime.now()\r\n minuto_comeco = comeco.minute\r\n fim = 5\r\n \r\n while (cond == True):\r\n while(cond1 == True):\r\n tempo_atual = datetime.now()\r\n minuto_atual = tempo_atual.minute\r\n tempo_limite = minuto_atual - minuto_comeco\r\n if(tempo_limite >= fim):\r\n print(\"Seu tempo acabou!\")\r\n if(jogar_novamente()):\r\n reproduzir_jogo()\r\n else:\r\n cond = False\r\n cond1 = False\r\n \r\n else: \r\n print(\"Faltam (%s) minas\"%(quant_minas - len(bandeiras)))\r\n jogada_posicao = str(input(\"Voce tem 5 minutos para jogar!\\nO tempo esta correndo, seja rapido!\\nDigite a coordenada que deseja jogar:\"))\r\n print(\"\\n\")\r\n bandeira = False\r\n\r\n if(len(jogada_posicao) == 4):\r\n if(jogada_posicao[3] == \"b\"):\r\n bandeira = True\r\n try:\r\n if(len(jogada_posicao) == 3):\r\n if(jogada_posicao[1] == 0):\r\n if(int(jogada_posicao[1]) > 1 or int(jogada_posicao[2]) > 9 or jogada_posicao[0] not in cont_letras(tabuleiro)):\r\n print(\"Jogada invalida!\")\r\n else:\r\n jogada_posicao = (int(jogada_posicao[1:3])-1,string.ascii_lowercase.index(jogada_posicao[0]))\r\n break \r\n if(jogada_posicao[1] == 1):\r\n if(int(jogada_posicao[1]) > 1 or int(jogada_posicao[2]) > 6 or jogada_posicao[0] not in cont_letras(tabuleiro)):\r\n print(\"Jogada invalida!\")\r\n else:\r\n jogada_posicao = (int(jogada_posicao[1:3])-1,string.ascii_lowercase.index(jogada_posicao[0]))\r\n break\r\n if(len(jogada_posicao) == 2):\r\n if(int(jogada_posicao[1]) > 9 or jogada_posicao[0] not in cont_letras(tabuleiro)):\r\n print(\"Jogada invalida!\")\r\n else:\r\n jogada_posicao = (int(jogada_posicao[1])-1,string.ascii_lowercase.index(jogada_posicao[0]))\r\n break\r\n else:\r\n jogada_posicao = (int(jogada_posicao[1:3])-1,string.ascii_lowercase.index(jogada_posicao[0]))\r\n break\r\n except (IndexError,ValueError):\r\n apresentar_tabuleiro(atual_tabuleiro)\r\n print(\"Jogada invalida!\")\r\n \r\n\r\n \r\n if(len(tabuleiro) == 0):\r\n tabuleiro,minas = elaborar_tabuleiro(tamanho_tabuleiro,jogada_posicao,quant_minas)\r\n\r\n \r\n ordenada,abscissa = jogada_posicao\r\n\r\n if(bandeira == True):\r\n if(atual_tabuleiro[ordenada][abscissa] == \" \"):\r\n atual_tabuleiro[ordenada][abscissa] = \"P\"\r\n bandeiras.append((ordenada,abscissa))\r\n elif(atual_tabuleiro[ordenada][abscissa] == \"P\"):\r\n atual_tabuleiro[ordenada][abscissa] = \" \"\r\n bandeiras.remove((ordenada,abscissa))\r\n else:\r\n print(\"Nao e possivel colocar a bandeira neste lugar!Tente outro lugar!\")\r\n else:\r\n if((ordenada,abscissa) in bandeiras):\r\n print(\"Ja existe uma bandeira neste lugar!\")\r\n else:\r\n try:\r\n if(tabuleiro[ordenada][abscissa] == \"*\"):\r\n print(\"Voce perdeu!:/ \\nLembre-se você não é derrotado quando perde.\\nVocê é derrotado quando desiste!\")\r\n apresentar_tabuleiro(tabuleiro)\r\n if(jogar_novamente()):\r\n reproduzir_jogo()\r\n else:\r\n cond = False\r\n else:\r\n apresentar_posicao(tabuleiro,atual_tabuleiro,ordenada,abscissa)\r\n except IndexError:\r\n print(\"Jogada invalida!\\nTente novamente.\")\r\n pass\r\n\r\n if(cond == True):\r\n apresentar_tabuleiro(atual_tabuleiro)\r\n \r\n if(set(bandeiras) == set(minas)):\r\n print(\"Parabens!Voce venceu!\\nSinta-se orgulhoso\")\r\n if(jogar_novamente()):\r\n reproduzir_jogo()\r\n else:\r\n cond = False\r\n\r\nreproduzir_jogo()\r\n","sub_path":"campo_minado/equipe3/CampoMinadofinal.py","file_name":"CampoMinadofinal.py","file_ext":"py","file_size_in_byte":8375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"515147075","text":"import mysql.connector\n\nmydb = mysql.connector.connect(\n host=\"localhost\",\n user=\"phuangkaeo\",\n password=\"simplon59\",\n database=\"pycharm\"\n)\n\nmycursor = mydb.cursor()\nmycursor.execute(\"CREATE TABLE city (\"\n \"city_id INT NOT NULL AUTO_INCREMENT,\"\n \"city_Name VARCHAR(50) NOT NULL,\"\n \"inhabitants_number int,\"\n \"distance_agent INT,\"\n \"PRIMARY KEY (city_id))ENGINE=INNODB\")\n\nmycursor.execute(\"CREATE TABLE type (\"\n \"type_id INT NOT NULL AUTO_INCREMENT,\"\n \"type VARCHAR(40) NOT NULL,\"\n \"description varchar(255),PRIMARY KEY (type_id))\"\n \"ENGINE=INNODB\")\n\nmycursor.execute(\"CREATE TABLE logement (\"\n \"lod_id INT NOT NULL AUTO_INCREMENT,\"\n \"lod_address VARCHAR(50),\"\n \"lod_ville VARCHAR(255),\"\n \"size VARCHAR(50),\"\n \"quartier VARCHAR(255),\"\n \"prix INT,\"\n \"loyer INT,\"\n \"city_id INT REFERENCES city(city_id),\"\n \"type_id INT REFERENCES typ(type_id),\"\n \"PRIMARY KEY (lod_id))\"\n \"ENGINE=INNODB\")\n\nmycursor.execute(\"CREATE TABLE contract(\"\n \"contract_id INT NOT NULL AUTO_INCREMENT,\"\n \"contract_detail VARCHAR(255),\"\n \"contract_start_date DATETIME NOT NULL,\"\n \"contract_end_date DATETIME,\"\n \"contract_status CHAR(2),\"\n \"client_id INT REFERENCES client(client_ID),\"\n \"lod_id INT REFERENCES logement(lod_id),\"\n \"PRIMARY KEY (contract_id))\"\n \"ENGINE=INNODB\")\n\nmycursor.execute(\"CREATE TABLE sex(\"\n \"sex_id char(2) NOT NULL,\"\n \"sex varchar(25))\"\n \"ENGINE=INNODB\")\n\nmycursor.execute(\"CREATE TABLE client(\"\n \"client_id INT NOT NULL AUTO_INCREMENT,\"\n \"nom VARCHAR(255),\"\n \"prenom VARCHAR(255),\"\n \"dob DATETIME,\"\n \"c_address VARCHAR(40),\"\n \"c_ville VARCHAR(40),\"\n \"postcode VARCHAR(25),\"\n \"telno1 VARCHAR(10),\"\n \"telno2 VARCHAR(10),\"\n \"sex_id char(2) REFERENCES sex(sex_id),\"\n \"PRIMARY KEY (client_id))\"\n \"ENGINE=INNODB\")\n\nmycursor.execute(\"CREATE TABLE telephone(\"\n \"tel_id int NOT NULL AUTO_INCREMENT,\"\n \"telno varchar(25),\"\n \"client_id int REFERENCES client(client_id),\"\n \"PRIMARY KEY (tel_id))\"\n \"ENGINE=INNODB\")\n","sub_path":"CreateTable.py","file_name":"CreateTable.py","file_ext":"py","file_size_in_byte":2693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"529997483","text":"class Solution(object):\n def subsetsWithDup(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: List[List[int]]\n \"\"\"\n nums.sort()\n self.subsets = []\n self.dfsHelper(nums, [], 0)\n\n return self.subsets\n\n def dfsHelper(self, nums, path, index):\n self.subsets.append(list(path))\n\n for i in range(index, len(nums)):\n if i != index and nums[i] == nums[i - 1]:\n continue\n path.append(nums[i])\n self.dfsHelper(nums, path, i + 1)\n path.pop()\n\n\nclass Solution(object):\n def subsetsWithDup(self, nums):\n nums.sort()\n\n result = []\n for num in range(2 ** len(nums)):\n subset = []\n illegal = False\n for i in range(len(nums)):\n if num & (1 << i):\n if i > 0 and nums[i] == nums[i - 1] and not num & (1 << i - 1):\n illegal = True\n break\n else:\n subset.append(nums[i])\n\n if not illegal:\n result.append(subset)\n\n return result\n\n\nprint(Solution().subsetsWithDup([2,1,2]))\n\n","sub_path":"pocket gems/onsite/subset1.py","file_name":"subset1.py","file_ext":"py","file_size_in_byte":1198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"210872729","text":"import numpy as np\n\n# This routine will calulate Material features : element level Over stress, \n# Material tangent stifness matrix Ct and element level stress\n\ndef compute_over_stress(sigma_ov_int,strain_next,strain_int,dt,T,Q):\n ''' Method to compute over stress. \n Input parameters include over stress and strain from previous time step'''\n\n temp_v1 = 1/(1+(dt/T))\n del_strain = strain_next-strain_int\n sigma_ov_next = temp_v1*(sigma_ov_int + (Q/3)*np.dot(np.array([[2,-1],[-1,2]]),del_strain))\n return sigma_ov_next\n\ndef compute_Ct(E,v,Q,dt,T):\n '''Method to compute the Material Tangent Stifness Matrix.'''\n\n val_1 = E/((1+v)*(1-2*v))\n temp_C = np.array([[1-v,v],[v,1-v]])\n C = val_1*temp_C\n val_2 = Q/(1+(dt/T))\n temp_val_3 = np.array([[2.0/3,-1.0/3],[-1.0/3,2.0/3]])\n Ct = C+ (val_2*temp_val_3)\n\n return Ct\n \ndef compute_sigma(strain_next,strain_int,sigma_ov_int,E,v,dt,T,Q):\n '''Method to calculate element level stress. Uses the function which computes the overstress'''\n\n val_1 = E/((1+v)*(1-2*v))\n temp_C = np.array([[1-v,v],[v,1-v]])\n C = val_1*temp_C\n sigma_ov = compute_over_stress(sigma_ov_int,strain_next,strain_int,dt,T,Q)\n sigma_next = np.dot(C,strain_next) + sigma_ov\n return sigma_next,sigma_ov\n\n\ndef compute_C(E,v):\n '''This will calculate and return the isotropic stifness matrix C'''\n val_1 = E/((1+v)*(1-2*v))\n temp_C = np.array([[1-v,v],[v,1-v]])\n C = val_1*temp_C\n return C\n","sub_path":"Material_routine.py","file_name":"Material_routine.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"223866230","text":"# -*- coding: utf-8 -*-\n\"\"\"\nThis example demonstrates the use of ImageView, which is a high-level widget for \ndisplaying and analyzing 2D and 3D data. ImageView provides:\n\n 1. A zoomable region (ViewBox) for displaying the image\n 2. A combination histogram and gradient editor (HistogramLUTItem) for\n controlling the visual appearance of the image\n 3. A timeline for selecting the currently displayed frame (for 3D data only).\n 4. Tools for very basic analysis of image data (see ROI and Norm buttons)\n\n\"\"\"\n## Add path to library (just for examples; you do not need this)\n#import initExample\n\nimport numpy as np\nfrom pyqtgraph.Qt import QtCore, QtGui\nimport pyqtgraph as pg\nimport random as rnd\nimport time\n\napp = QtGui.QApplication([])\n\nimg = np.arange(100).reshape(10,10)\n\n## Create window with ImageView widget\nwin = QtGui.QMainWindow()\nwin.resize(800,800)\n\np = pg.PlotWidget()\n\nimv = p.ImageItem()\nwin.setCentralWidget(imv)\nwin.show()\nwin.setWindowTitle('pyqtgraph example: ImageView')\n\n\n\nimv.setImage(img, xvals=np.linspace(1., 3., img.shape[0]))\n\ndef update():\n img[rnd.randint(0,9),rnd.randint(0,9)]=rnd.randint(0,100)\n imv.setImage(img)\n app.processEvents() ## force complete redraw for every plot\n \n#for i in range(10):\n# for j in range(10):\n# img[i,j]=rnd.randint(0,1)\n#update\n\n\ntimer = QtCore.QTimer()\ntimer.timeout.connect(update)\ntimer.start(0)\n\n## Start Qt event loop unless running in interactive mode.\nif __name__ == '__main__':\n import sys\n if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):\n QtGui.QApplication.instance().exec_()\n","sub_path":"examples/notsoeasy.py","file_name":"notsoeasy.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"} +{"seq_id":"254696114","text":"# Tento soubor nám byl poskytnut pro potřeby testování naší implementace.\n\nimport mind\nimport random\nimport time\nimport sys\n\nclass master:\n def __init__(self, version):\n \"\"\" Initialize master object\n with current version 0-easy,1-normal,2-hard\n \"\"\"\n if (version==0) :\n self.glob=6\n self.test=4\n self.limit=20 # 8\n elif (version==1):\n self.glob=7\n self.test=5\n self.limit=12\n else:\n self.glob=8\n self.test=6\n self.limit=16\n\n def generate(self):\n \"\"\"Prepare random code\n for user guess\n \"\"\"\n self.orig=[random.randint(0,self.glob-1) for i in range(self.test)]\n print(\"tajny kod \", self.orig)\n\n def evaluate(self, g):\n \"\"\" Evaluate correctness of guess g\n and return number of black and white points\n \"\"\"\n error=(len(g)!=self.test) # if the guess has wrong length\n white = 0 # init black, white points\n black = 0\n mask=[] # init mask of used positions\n if not error:\n for i in range(self.test): # count black points and mark used position\n if (int(g[i])==self.orig[i]):\n black+=1\n mask.append(1)\n else:\n mask.append(0)\n if not (int(g[i])>=0 and int(g[i])\" + key + \"
\")\n self.write(\"\")\n\n if no_caps_exposed is True:\n logger.warning(\"Discovery: no capabilities available to \"+ \n self.tls.extract_peer_identity(self.request)+\n \", check authorizations\")\n self.finish()\n\n def _respond_capability(self, key):\n cap = self.scheduler.capability_for_key(key)\n\n # if the 'link' field is empty, compose it using the host requested by the client/supervisor\n if not cap.get_link():\n if self.config is not None and \"TLS\" in self.config:\n link = \"https://\"\n else:\n link = \"http://\"\n link = link + self.request.host + SPECIFICATION_PATH_ELEM\n cap.set_link(link)\n self._respond_message(cap)\n\nclass MessagePostHandler(MPlaneHandler):\n \"\"\"\n Receives mPlane messages POSTed from a client, and passes them to a\n scheduler for processing. After waiting for a specified delay to see\n if a Result is immediately available, returns a receipt for future\n redemption.\n\n \"\"\"\n def initialize(self, scheduler, tlsState, immediate_ms = 5000):\n self.scheduler = scheduler\n self.tls = tlsState\n self.immediate_ms = immediate_ms\n\n def get(self):\n # message\n self.set_status(200)\n self.set_header(\"Content-Type\", \"text/html\")\n self.write(\"mplane.httpsrv\")\n self.write(\"This is a client-initiated mPlane component. POST mPlane messages to this URL to use.
\")\n self.write(\"Capabilities provided by this server:
\")\n for key in self.scheduler.capability_keys():\n if (not isinstance(self.scheduler.capability_for_key(key), mplane.model.Withdrawal) and\n self.scheduler.azn.check(self.scheduler.capability_for_key(key),\n self.tls.extract_peer_identity(self.request))):\n self.write(\"
\")\n                self.write(mplane.model.unparse_json(self.scheduler.capability_for_key(key)))\n        self.write(\"\")\n        self.finish()\n\n    def post(self):\n        # unwrap json message from body\n        if (self.request.headers[\"Content-Type\"] == \"application/x-mplane+json\"):\n            try:\n                msg = mplane.model.parse_json(self.request.body.decode(\"utf-8\"))\n            except Exception as e:\n                self._respond_error(exception=e)\n        else:\n            self._respond_error(errmsg=\"I only know how to handle mPlane JSON messages via HTTP POST\", status=\"406\")\n\n        # check if requested capability is withdrawn\n        is_withdrawn = False\n        if isinstance(msg, mplane.model.Specification):\n            for key in self.scheduler.capability_keys():\n                cap = self.scheduler.capability_for_key(key)\n                if msg.fulfills(cap) and isinstance(cap, mplane.model.Withdrawal):\n                    is_withdrawn = True\n                    self._respond_message(cap)\n\n        if not is_withdrawn:\n            # hand message to scheduler\n            reply = self.scheduler.process_message(self.tls.extract_peer_identity(self.request), msg)\n\n            # wait for immediate delay\n            if self.immediate_ms > 0 and \\\n               isinstance(msg, mplane.model.Specification) and \\\n               isinstance(reply, mplane.model.Receipt):\n                job = self.scheduler.job_for_message(reply)\n                wait_start = datetime.utcnow()\n                while (datetime.utcnow() - wait_start).total_seconds() * 1000 < self.immediate_ms:\n                    time.sleep(SLEEP_QUANTUM)\n                    if job.failed() or job.finished():\n                        reply = job.get_reply()\n                        break\n\n            # return reply\n            self._respond_message(reply)\n\nclass InitiatorHttpComponent(BaseComponent):\n\n    def __init__(self, config, supervisor=False):\n        self._supervisor = supervisor\n        self._callback_token = \"comp-cb\" + str(random.random())\n        super(InitiatorHttpComponent, self).__init__(config)\n\n        # configuration of URLs that will be used for requests\n        if self.config is not None and \"Component\" in self.config and \"Initiator\" in self.config[\"Component\"]:\n            if (\"capability-url\" in self.config[\"Component\"][\"Initiator\"]\n                and \"specification-url\" in self.config[\"Component\"][\"Initiator\"]\n                and \"result-url\" in self.config[\"Component\"][\"Initiator\"]):\n                self.registration_url = urllib3.util.parse_url(\n                    self.config[\"Component\"][\"Initiator\"][\"capability-url\"])\n                self.specification_url = urllib3.util.parse_url(\n                    self.config[\"Component\"][\"Initiator\"][\"specification-url\"])\n                self.result_url = urllib3.util.parse_url(\n                    self.config[\"Component\"][\"Initiator\"][\"result-url\"])\n            elif \"url\" in self.config[\"Component\"][\"Initiator\"]:\n                self.registration_url = urllib3.util.parse_url(self.config[\"Component\"][\"Initiator\"][\"url\"])\n                self.specification_url = self.registration_url\n                self.result_url = self.registration_url\n            else:\n                raise ValueError(\"Config file is missing information on URLs in Component.Initiator. \"\n                                 \"See documentation for details\")\n        else:\n            self.registration_url = urllib3.util.parse_url(DEFAULT_CAPABILITY_URL)\n            self.specification_url = urllib3.util.parse_url(DEFAULT_SPECIFICATION_URL)\n            self.result_url = urllib3.util.parse_url(DEFAULT_RESULT_URL)\n\n        self.pool = self.tls.pool_for(self.registration_url.scheme,\n                                      self.registration_url.host,\n                                      self.registration_url.port)\n\n        self._result_url = dict()\n        self.register_to_client()\n\n        self._callback_lock = threading.Lock()\n\n        # periodically poll the Client/Supervisor for Specifications\n        t = Thread(target=self.check_for_specs)\n        t.start()\n\n    def register_to_client(self, caps=None):\n        \"\"\"\n        Sends a list of capabilities to the Client, in order to register them\n        \"\"\"\n        env = mplane.model.Envelope()\n\n        logger.info(\"Component: registering my capabilities to \"+self.registration_url)\n\n        # try to register capabilities, if URL is unreachable keep trying every 5 seconds\n        connected = False\n        while not connected:\n            try:\n                self._client_identity = self.tls.extract_peer_identity(self.registration_url)\n                connected = True\n            except:\n                logger.info(\"Component: client unreachable, will retry in \"+str(RETRY_QUANTUM)+\" sec.\")\n                sleep(RETRY_QUANTUM)\n\n        # If caps is not None, register them\n        if caps is not None:\n            for cap in caps:\n                if self.scheduler.azn.check(cap, self._client_identity):\n                    env.append_message(cap)\n        else:\n            # generate the envelope containing the capability list\n            no_caps_exposed = True\n            for key in self.scheduler.capability_keys():\n                cap = self.scheduler.capability_for_key(key)\n                if self.scheduler.azn.check(cap, self._client_identity):\n                    env.append_message(cap)\n                    no_caps_exposed = False\n\n            if no_caps_exposed is True:\n                logger.warning(\"Component: no capabilities available to \"+ \n                                self._client_identity +\", check authorizations\")\n                if not self._supervisor:\n                    exit(0)\n\n            # add callback capability to the list\n            # FIXME NOOO see issue #3\n            callback_cap = mplane.model.Capability(label=\"callback\", \n                when = \"now ... future\", token = self._callback_token)\n            \n            env.append_message(callback_cap)\n\n        # send the envelope to the client\n        res = self.send_message(self.registration_url, \"POST\", env)\n\n        # handle response message\n\n        if res.status == 200:\n            logger.info(\"Component: successfully registered to \"+self.registration_url)\n            # FIXME this does not appear to have anything \n            # to do with the protocol specification, see issue #4\n            # body = json.loads(res.data.decode(\"utf-8\"))\n            # print(\"\\nCapability registration outcome:\")\n            # for key in body:\n            #     if body[key]['registered'] == \"ok\":\n            #         print(key + \": Ok\")\n            #     else:\n            #         print(key + \": Failed (\" + body[key]['reason'] + \")\")\n            # print(\"\")\n        else:\n            logger.critical(\"Capability registration to \"+self.registration_url+\" failed:\"+\n                             str(res.status) + \" - \" + res.data.decode(\"utf-8\"))\n\n    def check_for_specs(self):\n        \"\"\"\n        Poll the client for specifications\n\n        \"\"\"\n        while True:\n            # FIXME configurable default idle time.\n            self.idle_time = 5\n\n            # try to send a request for specifications. If URL is unreachable means that the Supervisor (or Client) has\n            # most probably died, so we need to re-register capabilities\n            try:\n                logger.info(\"Polling for specifications at \" + self.specification_url)\n                res = self.send_message(self.specification_url, \"GET\")\n            except Exception as e:\n                logger.warning(\"Specification poll at \" + self.specification_url + \"failed :\" + repr(e))\n                logger.warning(\"Attempting reregistration\")\n                self.register_to_client()\n\n            if res.status == 200:\n                # specs retrieved: split them if there is more than one\n                env = mplane.model.parse_json(res.data.decode(\"utf-8\"))\n                for spec in env.messages():\n                    # handle callbacks\n                    # FIXME NO NO NO see issue #3\n                    if spec.get_label() == \"callback\":\n                        self.idle_time = spec.when().timer_delays()[1]\n                        break\n\n                    # hand spec to scheduler, making sure the callback is called after\n                    with self._callback_lock:\n                        reply = self.scheduler.process_message(self._client_identity, spec, callback=self.return_results)\n                        if not isinstance(spec, mplane.model.Interrupt):\n                            self._result_url[spec.get_token()] = spec.get_link()\n\n                        # send receipt to the Client/Supervisor\n                        res = self.send_message(self._result_url[spec.get_token()], \"POST\", reply)\n\n            # not registered on supervisor, need to re-register\n            # FIXME what's 428 for? See issue #4\n            elif res.status == 428:\n                logger.warning(\"Specification poll got 428, attempting reregistration\")\n                self.register_to_client()\n\n            else:\n                logger.critical(\"Specification poll to \"+self.specification_url+\" failed:\"+\n                                 str(res.status) + \" - \" + res.data.decode(\"utf-8\"))\n \n            sleep(self.idle_time)\n \n    def return_results(self,receipt):\n        \"\"\"\n        Checks if a job is complete, and in case sends it to the Client/Supervisor\n\n        \"\"\"\n        #wait for scheduling process above\n        with self._callback_lock:\n            pass\n        job = self.scheduler.job_for_message(receipt)\n        reply = job.get_reply()\n\n        # check if job is completed\n        if (job.finished() is not True and\n                job.failed() is not True):\n            logger.debug(\"Component: not returning partial result (%s len: %d, label: %s)\" \n                          % (type(reply).__name__, len(reply), reply.get_label()))\n            return\n\n        # send result to the Client/Supervisor\n        res = self.send_message(self._result_url[reply.get_token()], \"POST\", reply)\n\n        # handle response\n        label = reply.get_label()\n\n        if res.status == 200:\n            logger.info(\"posted \"+ repr(reply) +\n                         \" to \"+self._result_url[reply.get_token()])\n        else:\n            logger.critical(\"Result post to \"+self._result_url[reply.get_token()]+\" failed:\"+\n                                 str(res.status) + \" - \" + res.data.decode(\"utf-8\"))\n\n    def send_message(self, url_or_str, method, msg=None):\n        # if the URL is empty (meaning that the 'link' section was empty), use the default url for results\n        if not url_or_str:\n            url_or_str = self.result_url\n\n        if isinstance(url_or_str, str):\n            url = urllib3.util.parse_url(url_or_str)\n        else:\n            url = url_or_str\n\n        # if the URL has a different host from the one used for \n        # capabilities registration, we need a new connectionPool\n        if self.pool.is_same_host(mplane.utils.unparse_url(url)):\n            pool = self.pool\n        else:\n            pool = self.tls.pool_for(url.scheme, url.host, url.port)\n\n        if method == \"POST\" and msg is not None:\n            # post message\n            res = pool.urlopen('POST', url.path,\n                               body=mplane.model.unparse_json(msg).encode(\"utf-8\"),\n                               headers={\"content-type\": \"application/x-mplane+json\"})\n        elif method == \"GET\":\n            # get message\n            res = pool.request('GET', url.path)\n\n        return res\n\n    def remove_capability(self, capability):\n        super(InitiatorHttpComponent, self).remove_capability(capability)\n        withdrawn_cap = mplane.model.Withdrawal(capability=capability)\n        self.register_to_client([withdrawn_cap])\n","sub_path":"mplane/component.py","file_name":"component.py","file_ext":"py","file_size_in_byte":23367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"86463178","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport csv\nimport argparse\n\nhouse = {\"Ravenclaw\": 1, \"Slytherin\": 2, \"Gryffindor\": 3, \"Hufflepuff\": 4}\nhouse_rev = {value: key for key, value in house.items()}\nf1 = -1\nf2 = -1\nh = 0\n\n\ndef display_data(x, y, house, df, f1, f2):\n\tplt.figure()\n\tpos, neg = (y == 1).reshape(len(x[:, 0]), 1), (y == 0).reshape(len(x[:, 1]), 1)\n\tplt.scatter(x[pos[:, 0], 0], x[pos[:, 0], 1], c=\"r\", marker=\"+\")\n\tplt.scatter(x[neg[:, 0], 0], x[neg[:, 0], 1], marker=\"o\", s=10)\n\tplt.xlabel(df.columns[f1])\n\tplt.ylabel(df.columns[f2])\n\tplt.legend([house, \"Not \" + house], loc=0)\n\tplt.title(\"Feature1 vs Feature2 data\")\n\tplt.show()\n\n\ndef display_standardize(x, y, house, df, f1, f2, theta):\n\tplt.figure()\n\tpos, neg = (y == 1).reshape(len(x[:, 0]), 1), (y == 0).reshape(len(x[:, 2]), 1)\n\tplt.scatter(x[pos[:, 0], 1], x[pos[:, 0], 2], c=\"r\", marker=\"+\")\n\tplt.scatter(x[neg[:, 0], 1], x[neg[:, 0], 2], marker=\"o\", s=10)\n\tx_value = np.array([np.min(x[:, 1]), np.max(x[:, 1])])\n\ty_value = -(theta[0] + theta[1] * x_value) / theta[2]\n\tplt.xlabel(df.columns[f1])\n\tplt.ylabel(df.columns[f2])\n\tplt.legend([house, \"Not \" + house], loc=0)\n\tplt.title(\"Feature1 vs Feature2 standardized data\")\n\tplt.plot(x_value, y_value, \"g\")\n\tplt.show()\n\n\ndef display_cost(error_history):\n\tplt.figure()\n\tplt.plot(range(len(error_history)), error_history)\n\tplt.ylabel(\"Cost\")\n\tplt.xlabel(\"Iteration\")\n\tplt.title(\"Cost function graph\")\n\tplt.show()\n\n\ndef check_dataset(dataset):\n\ttry:\n\t\tpd.read_csv(dataset)\n\texcept:\n\t\traise argparse.ArgumentTypeError(\"invalid dataset, needs to be a csv file\")\n\treturn dataset\n\n\ndef check_input(v):\n\tglobal h\n\tglobal f2\n\tglobal f1\n\tif h == 0 and v in house:\n\t\th = house[v]\n\t\treturn house[v]\n\ttry:\n\t\tint(v)\n\texcept:\n\t\traise argparse.ArgumentTypeError(\"Invalid input for features, arg '{}' needs to be a number\".format(v))\n\tif h != 0 and int(v) >= 1 and int(v) <= 7:\n\t\tif f1 == -1:\n\t\t\tf1 = int(v)\n\t\t\tif f1 == 7:\n\t\t\t\tf1 = 6\n\t\t\t\tprint(\"Value of Feature1 changed to\", f1)\n\t\t\treturn f1\n\t\telif f2 == -1:\n\t\t\tf2 = int(v)\n\t\t\tif f2 <= f1:\n\t\t\t\tf2 = f1 + 1\n\t\t\t\tprint(\"Value of Feature2 changed to\", f2)\n\t\t\treturn f2\n\tif h == 0 and v not in house:\n\t\traise argparse.ArgumentTypeError(\"'{}' is not a valid house. Choose between Ravenclaw, Slytherin, Gryffindor or Hufflepuff\".format(v))\n\traise argparse.ArgumentTypeError(\"Invalid input for features, arg '{}' needs to be beetween 1 and 7 (included)\".format(v))\n\n\ndef pie_chart(results, title):\n\tlabels = []\n\tsizes = []\n\tfor i in results:\n\t\tif (i not in labels):\n\t\t\tlabels.append(i)\n\t\t\tsizes.append(0)\n\tfor i in results:\n\t\tsizes[labels.index(i)] += 1\n\tplt.figure()\n\tplt.title(title)\n\tplt.pie(sizes, labels=labels, autopct='%1.2f%%', shadow=True, startangle=90)\n\tplt.show()\n\n\ndef get_data_visual(usage, param):\n\tparser = argparse.ArgumentParser(description=usage)\n\tparser.add_argument(\"dataset\", type=check_dataset, help=\"dataset, needs to be a csv\")\n\tif (param == 2):\n\t\tparser.add_argument(\"weights\", type=check_dataset, help=\"weights, needs to be a csv\")\n\t\tparser.add_argument(\"-a\", \"--accuracy\", action=\"store_true\", help=\"show accuracy for dataset_train\")\n\t\tparser.add_argument(\"-p\", \"--piechart\", action=\"store_true\", help=\"print a piechart for the results\")\n\t\targs = parser.parse_args()\n\t\targs.piechart = 1 if args.piechart is True else 0\n\t\tif args.accuracy is True:\n\t\t\treturn pd.read_csv(args.dataset), pd.read_csv(args.weights), 1, args.piechart\n\t\treturn pd.read_csv(args.dataset), pd.read_csv(args.weights), 0, args.piechart\n\tif (param == 1):\n\t\tparser.add_argument(\"-m\", \"--minimum\", metavar=\"number\", type=int, default=97, help=\"Minimum accuracy\")\n\t\tparser.add_argument(\"-v\", \"--verbose\", action=\"store_true\", help=\"display in real time actions of training\")\n\t\tparser.add_argument(\"-vi\", type=check_input, nargs=3, metavar=('House', 'N_feature1', \"N_feature2\"), help=\"display data of one house in a separate windows\")\n\t\targs = parser.parse_args()\n\t\targs.verbose = 1 if args.verbose is True else 0\n\t\tif args.vi is not None:\n\t\t\treturn pd.read_csv(args.dataset), args.vi[0], args.vi[1], args.vi[2], args.verbose, args.minimum\n\t\treturn pd.read_csv(args.dataset), 0, 0, 0, args.verbose, args.minimum\n\targs = parser.parse_args()\n\treturn pd.read_csv(args.dataset)\n\n\ndef filter_data(data, house, f1, f2):\n\tx = []\n\ty = []\n\tdata = data.to_numpy()\n\tfor row in data:\n\t\tif not np.isnan(row[f1]) and not np.isnan(row[f2]):\n\t\t\tx.append([row[f1], row[f2]])\n\t\t\ty.append(1 if row[0] == house else 0)\n\treturn np.array(x), np.array(y)\n\n\ndef sigmoid(z):\n\treturn(1 / (1 + np.exp(-z)))\n\n\ndef create_csv(row_list, name):\n\twith open(name, 'w', newline='') as file:\n\t\twriter = csv.writer(file)\n\t\twriter.writerows(row_list)\n\n\ndef is_valid(df):\n\tdf = df[[\"Hogwarts House\"]]\n\tif df.isnull().values.any():\n\t\treturn 0\n\treturn 1\n","sub_path":"tools/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":4786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"29705104","text":"import config\nfrom spider import Spider\nimport re\nimport urllib.request\nimport json\nimport requests\n\nclass Xiamen(Spider):\n    def __init__(self):\n        super(Xiamen, self).__init__()\n        self.db = config.db_path('govdata')\n        self.link = 'http://www.xmdata.gov.cn/sj/datasource.js'\n        self.link_new = 'http://data.xm.gov.cn/datas/?type=subject&value={}'\n        self.header = {\n            \"Content-Type\": \"application/x-www-form-urlencoded; charset=UTF-8\",\n            \"Pragma\": \"no-cache\",\n            \"Accept\": \"application/json, text/javascript, */*; q=0.01\",\n            \"Host\": \"data.xm.gov.cn\",\n            \"Cache-Control\": \"no-cache\",\n            \"Accept-Language\": \"zh-cn\",\n            \"Origin\": \"http://data.xm.gov.cn\",\n            \"Connection\": \"keep-alive\",\n            \"X-Requested-With\": \"XMLHttpRequest\"\n        }\n\n\n    def xiamen(self):\n        def process_xiamen(html):\n            data = html.split('var')\n            data = data[1]\n            data = re.sub(r'dataSource = ','',str(data))\n\n            data = re.sub(r';', '', str(data))\n            data = '{\"data_list\":' + data\n            data = data + '}'\n            data = json.loads(data, encoding='utf-8')\n            data = re.sub(r'\\s', '', str(data))\n            data_list = data.get(\"data_list\")\n            for i in data_list:\n                try:\n                    title = i.get(\"data_name\")\n                    link = i.get(\"url\")\n                    description = re.sub(r'\\n', '', str(i.get(\"abstract\")))\n                    date = i.get(\"publish_date\")\n                    tag = [i.get(\"label\")]\n                    group_name = i.get(\"data_area\")\n                    org_name = i.get(\"provider_org\")\n                    attachments = i.get(\"attachments\")\n                    public_type = i.get(\"public_type\")\n                    data_num = i.get(\"dataCount\")\n                    self.save_dataset(title, link, org_name, description, date,\n                                      tag, group_name, data_num, attachments, public_type)\n                except:\n                    pass\n\n\n        self.url = self.link\n        self.get_random_ip()\n        self.header[\"User-Agent\"] = self.random_select_header(self.usrAgent)\n        proxy_support = urllib.request.ProxyHandler(self.proxy)\n        opener = urllib.request.build_opener(proxy_support)\n        urllib.request.install_opener(opener)\n        request = urllib.request.Request(url=self.url, headers=self.header)\n        response = urllib.request.urlopen(request, timeout=60)\n        html = str(response.read(), 'utf-8')\n        process_xiamen(html)\n\n    def build_Cookie(self, link):\n        set_cookie = urllib.request.urlopen(link).info()['Set-Cookie']\n        json_id = set_cookie.split(';')[0]\n        json_id = json_id.split('=')[-1]\n        return json_id\n\n    def build_data(self, page):\n        data = {\n        \"params\": \"\",\n        \"pageNo\": page,\n        \"pageSize\": 5\n        }\n        return data\n\n    def xiamen_new(self):\n        def process_xiamen(data):\n            data = json.loads(data, encoding='utf-8')\n            data_list = data.get(\"rows\")\n            if data_list != []:\n                for i in data_list:\n                    save_data = {}\n                    title = i[\"DOCTITLE\"]\n                    link = i[\"DOCPUBURL\"]\n                    org = i[\"METADATA\"].get(\"BA_ORG\")\n                    description = i[\"METADATA\"].get(\"BA_INTRO\")\n                    date = i[\"DOCRELTIME\"][:10]\n                    tag = i[\"METADATA\"].get(\"BA_INDUS\")\n                    if tag != None:\n                        tag = tag.split(\",\")\n                    if type(tag) == str:\n                        tag = [tag]\n                    group = i[\"METADATA\"].get(\"BA_THEME\")\n                    if type(group) == str:\n                        group = [group]\n                    public_type = i[\"METADATA\"].get(\"BA_STATUS\")\n\n                    save_data = {\n                            \"title\": title,\n                            \"link\": link,\n                            \"org_name\": org,\n                            \"description\": description,\n                            \"coll_date\": self.today,\n                            \"date\": date,\n                            \"tags\": tag,\n                            \"group\": group,\n                            \"public_type\": public_type,\n                    }\n                    save_data = {i[0]:i[1] for i in save_data.items() if i[1] != None}\n                    exists = self.db['xiamen'].find({\"title\":title}).count()\n                    if exists == 0:\n                        self.db['xiamen'].insert(save_data)\n                        print('dataset {} is saved'.format(title))\n                    else:\n                        print('dataset {} exists'.format(title))\n                return 0\n            else:\n                return 1\n\n        for i in range(452, 474):\n            for j in range(0, 50):\n                self.link_json = 'http://data.xm.gov.cn/portal/api/data/list.xhtml'\n                self.link = self.link_new.format(i)\n                Cookie = 'HttpOnly=true; HttpOnly=true; JSESSIONID={}; HttpOnly=true'.format(self.build_Cookie(self.link_json))\n                self.header[\"Cookie\"] = Cookie\n                self.header[\"Referrer\"] = self.link\n                self.header[\"User-Agent\"] = self.random_select_header(self.usrAgent)\n                self.data = self.build_data(j + 1)\n                self.get_random_ip()\n                response = requests.get(url=self.link_json, data=self.data, headers=self.header, proxies=self.proxy)\n                data = response.content.decode('utf-8')\n                flag = process_xiamen(data)\n                if flag == 1:\n                    break\n                else:\n                    pass\n\n\n    def save_dataset(self, title, link, org, description, date, tag, group_name, data_num, attachments, public_type):\n        exists = self.db.xiamen.find({\"link\": link}).count()\n        if exists == 0:\n            self.db.xiamen.insert({\n                \"title\": title,\n                \"link\": link,\n                \"org_name\": org,\n                \"description\": description,\n                \"coll_date\": self.today,\n                \"date\": date,\n                \"tags\":tag,\n                \"group\": group_name,\n                \"attachments\": attachments,\n                \"public_type\":public_type,\n                \"data_num\":int(data_num)\n            })\n            print('dataset {} is saved'.format(title))\n        else:\n            print('dataset {} exists'.format(title))\n\nif __name__ == '__main__':\n    X = Xiamen()\n    X.xiamen_new()","sub_path":"xiamen_platform.py","file_name":"xiamen_platform.py","file_ext":"py","file_size_in_byte":6632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"345936880","text":"# 0418.py: OpenCV-Python Tutorials 참조\r\nimport cv2\r\nimport numpy as np\r\n\r\nsrc1 = cv2.imread('../data/lena.jpg')\r\nsrc2 = cv2.imread('../data/opencv_logo.png')\r\n\r\nmask = cv2.imread('../data/opencv_logo_mask.png', cv2.IMREAD_GRAYSCALE)\r\nmask_inv = cv2.imread('../data/opencv_logo_mask_inv.png', cv2.IMREAD_GRAYSCALE)\r\n\r\ncv2.imshow('src2',  src2)\r\n\r\n#1\r\nrows,cols,channels = src2.shape\r\nroi = src1[0:rows, 0:cols]\r\n# lena 이미지 전체에서 opencv 로고에 해당되는 동일한 크기만큼 잘라낸다. \r\n\r\n#2\r\n#gray = cv2.cvtColor(src2,cv2.COLOR_BGR2GRAY)\r\n#ret, mask = cv2.threshold(gray, 160, 255, cv2.THRESH_BINARY)\r\n#mask_inv = cv2.bitwise_not(mask)\r\n#cv2.imshow('mask',  mask)\r\n#cv2.imshow('mask_inv',  mask_inv)\r\n\r\n#3\r\nsrc1_bg = cv2.bitwise_and(roi, roi, mask = mask)\r\n# roi와 mask를 and 해준다. (이진 비트 연산)\r\n# 화이트는 색을 날리고~ 원본 색상이 남게 되고,  블랙은 색을 남기고.. \r\ncv2.imshow('src1_bg',  src1_bg)\r\n\r\n#4\r\nsrc2_fg = cv2.bitwise_and(src2, src2, mask = mask_inv)\r\ncv2.imshow('src2_fg',  src2_fg)\r\n\r\n#5\r\n##dst = cv2.add(src1_bg, src2_fg)\r\ndst = cv2.bitwise_or(src1_bg, src2_fg)\r\ncv2.imshow('dst',  dst)\r\n\r\n#6\r\nsrc1[0:rows, 0:cols] = dst\r\n\r\ncv2.imshow('result',src1)\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()\r\n","sub_path":"픽셀기반 처리/0321/0418.py","file_name":"0418.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"356126516","text":"from flask import Flask, request\r\nimport requests\r\nfrom twilio.twiml.messaging_response import MessagingResponse\r\n\r\napp = Flask(__name__)\r\n\r\n\r\n@app.route('/bot', methods=['POST'])\r\ndef bot():\r\n    msg_recebida = request.values.get('Body', '').lower()\r\n    resposta = MessagingResponse()\r\n    msg_enviada = resposta.message()\r\n    valida = False\r\n\r\n    # valida o msg_recebida \r\n    if msg_recebida:\r\n        # faz a consulta no api do site dicionario online com o valor recebido em msg_recebida\r\n        requisicao = requests.get('https://significado.herokuapp.com/'+msg_recebida)\r\n        # valida requisicao com status, se for 200 está ok \r\n        if requisicao.status_code == 200:\r\n            # recebe o valor da api json\r\n            data = requisicao.json()\r\n            # filtra a pesquisa, mais orientações https://github.com/ThiagoNelsi/dicio-api\r\n            filtro = data[0][\"meanings\"][0]\r\n            filtro += data[0][\"meanings\"][1]\r\n            filtro += data[4][\"etymology\"]\r\n\r\n            msg_enviada.body(filtro)\r\n            valida = True\r\n        else:\r\n            filtro = 'Não consegui realizar a pesquisa, desculpe.'\r\n            msg_enviada.body(filtro)\r\n            valida = True\r\n    else:\r\n        msg_enviada.body('Apenas palavras para consulta no dicionário, desculpe!')\r\n        valida = True\r\n\r\n    return str(resposta)\r\nif __name__ == '__main__':\r\n   app.run()","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"586351352","text":"\"\"\" Brute Force\r\nhttps://www.codechef.com/LRNDSA05/problems/CHEFSQRS\r\n\"\"\"\r\n_max_size=1e+8\r\nSPF=[1]*int(_max_size)\r\ni=2\r\nwhile (i*i)<_max_size:\r\n    if SPF[i]==1:\r\n        SPF[i]=i\r\n        j=i*i\r\n        count=1\r\n        while j<_max_size:\r\n            if SPF[j]==1:\r\n                SPF[j]=i\r\n            j=j+i\r\n    i+=1\r\nT=int(input(\"Now Enter\"))\r\nfor i in range(T):\r\n    N=int(input())\r\n    A=SPF[N]\r\n    B=N//SPF[N]\r\n    while SPF[B]!=B:\r\n        x=(A+B)/2\r\n        if x==(A+B)//2:\r\n            A,B=min(A,B),max(A,B)\r\n            print(min((A+B)//2,(B-A)//2)**2)\r\n            continue\r\n        A=A*SPF[B]\r\n        B=B//SPF[B]\r\n    if (A+B)/2==(A+B)//2:\r\n        A,B=min(A,B),max(A,B)\r\n        print(min((A+B)//2,(B-A)//2)**2)\r\n        continue\r\n    print(-1)\r\n    \r\n","sub_path":"CHEf.py","file_name":"CHEf.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"83283949","text":"# -*- coding: utf-8 -*-\n\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html\n\n\nimport copy\nimport pymysql\nimport scrapy\nimport re\nfrom scrapy.pipelines.images import ImagesPipeline\nfrom scrapy.exceptions import DropItem\nfrom twisted.enterprise import adbapi\nfrom ..items import CountryItem\nfrom ..items import LeagueItem\nfrom ..items import SeasonItem\n\n\nfrom ..comm.MsDebug import MsLog\n\n\nclass LeagueDataPipeline(object):\n    def __init__(self, db_pool, img_store):\n        self.db_pool = db_pool\n        self.b_bets = {}\n        self.b_league = {}\n        self.b_fteam = {}\n        self.b_country = {}\n        self.b_season = {}\n        self.initdata('b_bets').addCallback(self.parseData, 'b_bets')\n        self.initdata('b_league').addCallback(self.parseData, 'b_league')\n        self.initdata('b_fteam').addCallback(self.parseData, 'b_fteam')\n        self.initdata('b_country').addCallback(self.parseData, 'b_country')\n        self.initdata('b_season').addCallback(self.parseData, 'b_season')\n        self.iCount = 0\n        self.img_store = img_store\n\n    @classmethod\n    def from_settings(cls, settings):\n        db_pool = adbapi.ConnectionPool(\n            'pymysql',\n            host=settings[\"MYSQL_HOST\"],\n            db=settings[\"MYSQL_DB\"],\n            user=settings[\"MYSQL_USER\"],\n            password=settings[\"MYSQL_PASSWORD\"],\n            charset=\"utf8\",\n            cursorclass=pymysql.cursors.DictCursor,\n            use_unicode=True)\n        return cls(db_pool, 'D:\\ImgStore')\n\n    def get_dicts(self, item):\n        if isinstance(item, CountryItem):\n            dicts = self.b_country\n        elif isinstance(item, LeagueItem):\n            dicts = self.b_league\n        elif isinstance(item, SeasonItem):\n            dicts = self.b_season\n        return dicts\n\n    def parseData(self, datas, table):\n        for data in datas:\n            if table == 'b_bets':\n                self.b_bets[data['name']] = data['id']\n            elif table == 'b_league':\n                self.b_league[data['name']] = data['id']\n            elif table == 'b_fteam':\n                self.b_fteam[data['name']] = data['id']\n            elif table == 'b_country':\n                self.b_country[data['name']] = data['id']\n            elif table == 'b_season':\n                self.b_season[data['ssid']] = data['id']\n\n    def initdata(self, table):\n        return self.db_pool.runQuery(\"select * from {0}\".format(table))\n\n    def handle_error(self, failure, item):\n        print('插入数据失败，原因：{}，错误对象：{}'.format(failure, item))\n\n    def addBaseItem(self, cur, item):\n        try:\n            sql, values = item.get_insert_sql()\n            cur.execute(sql, values)\n            id = cur.connection.insert_id()\n            dicts = self.get_dicts(item)\n            if isinstance(item, SeasonItem):\n                dicts[item['ssid']] = id\n            else:\n                dicts[item['name']] = id\n        except Exception as e:\n            # print('addBaseItem err: {}'.format(e))\n            pass\n\n    def updBaseItem(self, cur, item):\n        try:\n            sql, values = item.get_update_sql()\n            cur.execute(sql, values)\n        except Exception as e:\n            # print('addBaseItem err: {}'.format(e))\n            pass\n\n    def process_item(self, item, spider):\n        try:\n            # 对象拷贝   深拷贝\n            asynItem = copy.deepcopy(item)  # 需要导入import copy\n            query = self.db_pool.runInteraction(self.process_base_item, asynItem)\n            query.addErrback(self.handle_error, asynItem)\n        except Exception as e:\n            print('process_item err:{0}'.format(e))\n\n    def process_base_item(self, cursor, item):\n        try:\n            if isinstance(item, SeasonItem):\n                id = self.b_season.get(item['ssid'], -1)\n                lid = self.b_league.get(item['lname'], -1)\n                if lid == -1:\n                    return False\n                item['lid'] = lid\n            else:\n                dicts = self.get_dicts(item)\n                id = dicts.get(item['name'], -1)\n\n            if id == -1:\n                self.addBaseItem(cursor, item)\n            else:\n                item['id'] = id\n                self.updBaseItem(cursor, item)\n        except Exception as e:\n                print('process_base_item error:{}'.format(e))\n                return False\n\n    def close_spider(self, spider):\n        self.db_pool.close()\n        MsLog.debug('[{0}] 结束'.format(spider.name))\n\n\n# 下载图片的类\nclass BaseImgPipeline(ImagesPipeline):\n    def get_media_requests(self, item, info):\n        try:\n            if isinstance(item, CountryItem):\n                image_url = item['imgurl']\n                yield scrapy.Request(image_url, meta={'name': item['imgname']})\n        except Exception as e:\n            print(e)\n\n    def item_completed(self, results, item, info):\n        if isinstance(item, CountryItem):\n            # 是一个元组，第一个元素是布尔值表示是否成功\n            if not results[0][0]:\n                raise DropItem('下载失败')\n        return item\n\n    # 重命名，若不重写这函数，图片名为哈希，就是一串乱七八糟的名字\n    def file_path(self, request, response=None, info=None):\n        # 接收上面meta传递过来的图片名称\n        name = request.meta['name']\n        # 提取url前面名称作为图片名\n        image_name = request.url.split('/')[-1]\n        # 清洗Windows系统的文件夹非法字符，避免无法创建目录\n        folder_strip = re.sub(r'[？\\\\*|“<>:/]', '', str(name))\n        # 分文件夹存储的关键：{0}对应着name；{1}对应着image_guid\n        # filename = u'{0}/{1}'.format(folder_strip, image_name)\n        filename = u'{0}.png'.format(folder_strip, name)\n        return filename\n","sub_path":"MsSpider/pipelines/LeagueDataPipeline.py","file_name":"LeagueDataPipeline.py","file_ext":"py","file_size_in_byte":5945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"398620022","text":"import numpy as np\r\nfrom numba import jit\r\nfrom numba import jitclass\r\nfrom numba import float32, int8, int32, int16, int64\r\n\r\nfrom Particle_Simulation.Particle import Particle\r\nfrom Particle_Simulation.System import System\r\nfrom Particle_Simulation.LennardJones import LennardJones\r\nfrom Particle_Simulation.Parameters import Parameters\r\nfrom Particle_Simulation.EwaldSummation import EwaldSummation\r\nfrom Particle_Simulation.Energy import Energy\r\n\r\n\r\nclass EnergyCalculator:\r\n\r\n    def __init__(self, parameters):\r\n\r\n        self.parameters = parameters\r\n\r\n\r\n    def calculate_overall_energy(self, system):\r\n\r\n        overall_energy = Energy()\r\n\r\n        short_ranged_energy = self.calculate_shortranged_energy(system)\r\n        overall_energy.lj_energy = short_ranged_energy[0]\r\n        overall_energy.es_shortranged_energy = short_ranged_energy[1]\r\n        overall_energy.es_selfinteraction_energy = self.calculate_shortranged_energy(system)\r\n\r\n        return overall_energy\r\n\r\n    def calculate_shortranged_energy(self, system):\r\n\r\n        lj_energy = 0\r\n        short_ranged_energy = 0\r\n        neighbour_cell_number = 3 ** system.neighbourlist.dim\r\n\r\n        for i in range(system.neighbourlist.total_cell_number):\r\n            particle_index_1 = system.neighbourlist.cell_list[i]\r\n\r\n            while particle_index_1 != -1:\r\n\r\n                for k in range(neighbour_cell_number):\r\n                    cell_index = System.cell_neighbour_list[k][i][0]\r\n                    particle_index_2 = system.neighbourlist.cell_list[cell_index]\r\n\r\n                    while particle_index_2 != -1:\r\n\r\n                        particle_1 = system.particles[particle_index_1]\r\n                        particle_2 = system.particles[particle_index_2]\r\n\r\n                        if particle_index_1 != particle_index_2:\r\n                            if System.cell_neighbour_list[k][i][1] == 0:\r\n                                if particle_index_1 < particle_index_2:\r\n                                    particle_distance = np.linalg.norm(particle_1.position - particle_2.position)\r\n                                    if particle_distance < self.parameters.cutoff_radius:\r\n                                        lj_energy += LennardJones.calculate_potential(particle_1, particle_2,\r\n                                                                                      self.parameters)\r\n                                        short_ranged_energy += EwaldSummation.calculate_shortranged_potential(particle_1,\r\n                                                                                                              particle_2,\r\n                                                                                                              self.parameters)\r\n\r\n                            elif System.cell_neighbour_list[k][i][1] != 0:\r\n\r\n                                box_shift = self._determine_box_shift(i, k)\r\n                                particle_2 = Particle(type_index=particle_2.type_index,\r\n                                                      position=particle_2.position + box_shift)\r\n\r\n                                particle_distance = np.linalg.norm(particle_1.position - particle_2.position)\r\n                                if particle_distance < self.parameters.cutoff_radius:\r\n                                    lj_energy += LennardJones.calculate_potential(particle_1, particle_2,\r\n                                                                                  self.parameters)\r\n                                    short_ranged_energy += EwaldSummation.calculate_shortranged_potential(particle_1,\r\n                                                                                                          particle_2,\r\n                                                                                                          self.parameters)\r\n\r\n                        particle_index_2 = system.neighbourlist.particle_neighbour_list[particle_index_2]\r\n                particle_index_1 = system.neighbourlist.particle_neighbour_list[particle_index_1]\r\n\r\n        short_ranged_energy *= 1 / (8 * np.pi * Parameters.VACUUM_PERMITTIVITY)\r\n        return [lj_energy, short_ranged_energy]\r\n\r\n    def _determine_box_shift(self, cell_index, cell_neighbour_index):\r\n\r\n        box_shift = np.zeros((len(self.parameters.box)))\r\n        if System.cell_neighbour_list[cell_neighbour_index][cell_index][1] != 0:\r\n            for i in range(len(self.parameters.box)):\r\n                if Parameters.cell_shift_list[i][cell_neighbour_index] == 1:\r\n                    box_shift[i] = self.parameters.box[i]\r\n                elif Parameters.cell_shift_list[i][cell_neighbour_index] == -1:\r\n                    box_shift[i] = -self.parameters.box[i]\r\n                else:\r\n                    continue\r\n\r\n        return box_shift\r\n\r\n    def calculate_selfinteraction_energy(self, system):\r\n\r\n        summation = 0\r\n        prefactor = 1 / (2 * Parameters.VACUUM_PERMITTIVITY * self.parameters.es_sigma * (2 * np.pi) ** (3 / 2))\r\n\r\n        for i in range(0, len(system.particles)):\r\n            summation += EwaldSummation.calculate_selfinteraction_potential(system.particles[i], self.parameters)\r\n        selfinteraction_energy = prefactor * summation\r\n\r\n        return selfinteraction_energy\r\n\r\n    def calculate_shortranged_energy_2(self, system):\r\n\r\n        lj_energy = 0\r\n        short_ranged_energy = 0\r\n\r\n        for i in range(0, len(system.particles)):\r\n            for j in range(i + 1, len(system.particles)):\r\n                particle_distance = np.linalg.norm(self.wrap_distance(system.particles[i].position - system.particles[j].position))\r\n\r\n                if particle_distance < self.parameters.cutoff_radius:\r\n                    lj_energy += LennardJones.calculate_wrapped_potential(system.particles[i], system.particles[j],\r\n                                                                          particle_distance, self.parameters)\r\n                    short_ranged_energy += EwaldSummation.calculate_wrapped_shortranged_potential(system.particles[i],\r\n                                                                                                  system.particles[j],\r\n                                                                                                  particle_distance,\r\n                                                                                                  self.parameters)\r\n\r\n        short_ranged_energy *= 1 / (8 * np.pi * Parameters.VACUUM_PERMITTIVITY)\r\n        return [lj_energy, short_ranged_energy]\r\n\r\n    def wrap_distance(self,distance):\r\n\r\n        for i in range(len(distance)):\r\n            while distance[i] >= 0.5 * self.parameters.box[i]:\r\n                distance[i] -= self.parameters.box[i]\r\n            while distance[i] < -0.5 * self.parameters.box[i]:\r\n                distance[i] += self.parameters.box[i]\r\n\r\n        return distance\r\n","sub_path":"Particle_Simulation/EnergyCalculator.py","file_name":"EnergyCalculator.py","file_ext":"py","file_size_in_byte":6914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"244725966","text":"import data_io\nfrom sklearn.base import BaseEstimator, ClassifierMixin\nimport numpy as np\n\n# http://stackoverflow.com/questions/21506128/best-way-to-combine-probabilistic-classifiers-in-scikit-learn\nclass EnsembleClassifier(BaseEstimator, ClassifierMixin):\n    def __init__(self, classifiers=None):\n        self.classifiers = classifiers\n\n    def fit(self, X, y):\n        for classifier in self.classifiers:\n            classifier.fit(X, y)\n\n    def predict_proba(self, X):\n        self.predictions_ = list()\n        for classifier in self.classifiers:\n            self.predictions_.append(classifier.predict_proba(X))\n        # booking is first, click is second\n        return np.average(self.predictions_, axis=0, weights=[0.25, 0.75])\n        #return np.sum(self.predictions_, axis=0)\n\ndef main():\n    print(\"Reading test data\")\n    test = data_io.read_test()\n    test.fillna(-1, inplace=True)\n\n    feature_names = list(test.columns)\n    feature_names.remove(\"date_time\")\n\n    #feature_names.remove('price_diff')\n    #feature_names.remove('price_person')\n    feature_names.remove('star_diff')\n    #feature_names.remove('pay_diff')\n    feature_names.remove('price_night')\n    feature_names.remove('loc_desire')\n    feature_names.remove('no_kids')\n    feature_names.remove('couple')\n    feature_names.remove('price_down')\n    feature_names.remove('same_country')\n\n    #feature_names.remove('prop_location_score1')\n\n    #feature_names = [\n        #'srch_id',\n        #'price_usd',\n        #'price_person',\n        #'price_usd',\n        #'prop_location_score2',\n        #'prop_log_historical_price',\n        #'srch_children_count',\n        #'srch_query_affinity_score',\n        #'prop_starrating',\n        #'visitor_hist_starrating',\n        #'promotion_flag',\n        #'prop_review_score',\n        #'srch_destination_id',\n        #'prop_id',\n        #'visitor_hist_adr_usd',\n        #'prop_brand_bool',\n    #]\n\n    features = test[feature_names].values\n\n    print(\"Loading the classifier\")\n    classifier_booking = data_io.load_model('booking')\n    classifier_click = data_io.load_model('click')\n\n    ensemble = EnsembleClassifier([classifier_booking, classifier_click])\n\n    print(\"Making predictions\")\n    #predictions = classifier.predict_proba(features)[:,1]\n    predictions = ensemble.predict_proba(features)[:,1]\n    predictions = list(-1.0*predictions)\n    recommendations = zip(test[\"srch_id\"], test[\"prop_id\"], predictions)\n\n    print(\"Writing predictions to file\")\n    data_io.write_submission(recommendations)\n\nif __name__==\"__main__\":\n    main()\n","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":2558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"568504784","text":"import pygame\nfrom game import Game\n\npygame.init()\n\n# Setup\nscreen = pygame.display.set_mode((800, 600))\n\nbackground = pygame.image.load('assets/bg1.jpg')\nicon = pygame.image.load('assets/ufo.png')\npygame.display.set_icon(icon)\npygame.display.set_caption('Space Invader')\n\nshoot_sound = pygame.mixer.Sound('assets/sounds/tir.ogg')\nshoot_sound.set_volume(0.2)\n\n\ngame = Game()\n\nloop = game.player.stop\n\nwhile not loop:\n\n    screen.blit(background, (0, 0))\n    screen.blit(game.font.render(\"Score : \" + str(game.score), True, (156, 232, 255)), (0, 0))\n    screen.blit(game.player.image, game.player.rect)\n\n    for bullet in game.player.allBullet:\n        bullet.move()\n\n    for ufo in game.allUfo:\n        ufo.move()\n\n    game.player.allBullet.draw(screen)\n    game.allUfo.draw(screen)\n\n    if game.pressed.get(pygame.K_d) and game.player.rect.x + game.player.image.get_width() < 800:\n        game.player.move_right()\n    elif game.pressed.get(pygame.K_a) and game.player.rect.x > 0:\n        game.player.move_left()\n\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            loop = False\n\n        if event.type == pygame.KEYDOWN:\n            game.pressed[event.key] = True\n\n        elif event.type == pygame.KEYUP:\n            game.pressed[event.key] = False\n\n        elif event.type == pygame.MOUSEBUTTONDOWN:\n            game.player.launchBullet()\n            shoot_sound.play()\n\n    game.player.checkgame()\n    loop = game.player.stop\n    pygame.display.update()\n\n","sub_path":"SpaceInvader/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"287939516","text":"import os\nimport re\nimport datetime\nimport londas as pd\nimport gzip\n\n#----------------ПЕРЕМЕННЫЕ---------------------------------------------------------------------------------------------\nfrom numpy import sort\n\nFileRead = []\nFileReadAll = []\nFileReadAll1 = []\nlable=[]\nstr = ''\npickle_sar = '//192.168.50.30/inotes/personal/s.danilov/pkl/{0}/sar/sar_{0}.pkl.zip'\npickle_saru = '//192.168.50.30/inotes/personal/s.danilov/pkl/{0}/sar/saru_{0}.pkl.zip'\n#pickle_nmon_dbu = '//192.168.50.30/inotes/personal/s.danilov/pkl/{0}/nmon/nmon_dbu_{0}.pkl.zip'\n#pickle_nmon_app = '//192.168.50.30/inotes/personal/s.danilov/pkl/{0}/nmon/nmon_app_{0}.pkl.zip'\n#pickle_nmon_appu = '//192.168.50.30/inotes/personal/s.danilov/pkl/{0}/nmon/nmon_appu_{0}.pkl.zip'\n#-----------------------------------------------------------------------------------------------------------------------\ndef to_groups(fileArr):\n    servers = []\n    for fn in fileArr:\n        srv = re.split('(?<=app\\d\\d).', fn)[0]\n        if srv not in servers:\n            servers.append(srv)\n    return [list(sort(filter(lambda x: x.startswith(srv), fileArr))) for srv in servers]\n\nclass GraphicUtilCPU():\n\n    def to_timestamp(self, t, t_format='%Y-%m-%d %H:%M:%S', tzoffset=0):\n        u\"\"\"Конвертирует\n        t  в таймштамп\"\"\"\n        return float(datetime.datetime.strptime(t, t_format).strftime('%s.%f')) - tzoffset * 3600\n\n    def to_timestring(self, t, t_format='%Y-%m-%d %H:%M:%S', tzoffset=0):\n        u\"\"\"Конвертирует строку из таймштампа\"\"\"\n        return datetime.datetime.fromtimestamp(float(t) + tzoffset * 3600.0).strftime(t_format)\n\n    def sarLog(self,LOGS_sar,test_date):\n\n        def to_groups(fileArr):\n            servers = []\n            for fn in fileArr:\n                srv = re.split('(?<=app\\d\\d).', fn)[0]\n                if srv not in servers:\n                    servers.append(srv)\n            return [list(filter(lambda x: x.startswith(srv), fileArr)) for srv in servers]\n\n\n        if not os.path.exists(LOGS_sar):\n            os.makedirs(LOGS_sar)\n        pickle_sar.format(test_date)\n        pickle_saru.format(test_date)\n#        pickle_nmon_dbu.format(test_date)\n#        pickle_nmon_app.format(test_date)\n#        pickle_nmon_appu.pkl.zip'.format(test_date)\n#SAR--Формирование-путей------------------------------------------------------------------------------------------------\n        sarFiles = os.listdir(LOGS_sar)\n\n        sarFileApp = []\n        sarFileAppu = []\n\n        for names in sarFiles:\n            if names.startswith('sar_k10-szp-app'):\n                sarFileApp.append(names)\n            elif names.startswith('sar_k10-usbsm-app'):\n                sarFileAppu.append(names)\n        sarFileAppu.sort()\n        sarFileApp.sort()\n\n        sarFileApp = to_groups(sarFileApp)\n        sarFileAppu = to_groups(sarFileAppu)\n# NMON-------------------------------------------------------------------------------------------------------------------\n#        nmonFiles = os.listdir(LOGS_nmon)\n#\n#        nmonFileDBu = []\n#        nmonFileAppu = []\n#        nmonFileApp = []\n#\n#        for names in nmonFiles:\n#            if names.startswith('k10-usbsm-db'):\n#                nmonFileDBu.append(names)\n#            elif names.startswith('k10-usbsm-app'):\n#                nmonFileAppu.append(names)\n#            elif names.startswith('k10-szp-app'):\n#                nmonFileApp.append(names)\n#        nmonFileDBu.sort()\n#        nmonFileAppu = to_groups(nmonFileAppu)\n#        nmonFileApp = to_groups(nmonFileApp)\n#\n#        nmon_dbu = pd.read_nmon(LOGS_nmon + nmonFileDBu[0], compression='gzip')\n#        s_name = nmon_dbu.info.server_name\n#        for other in nmonFileDBu[1:]:\n#            nmon_dbu = nmon_dbu.append(pd.read_nmon(LOGS_nmon + other, compression='gzip'))\n#        nmon_dbu.info.server_name = s_name\n#        nmon_dbu.to_comp_pickle(pickle_nmon_dbu, member=nmon_dbu.info.server_name + \".nmon.pkl\")\n# ----------------------------------------------------------------------------------------------------------------------\n#        # nmon_db = pd.read_nmon(LOGS_nmon + nmonFileDB[0], compression='gzip')\n#        # nmon_db.to_comp_pickle(pickle_nmon_db, member=nmon_db.info.server_name + \".nmon.pkl\")\n# ----------------------------------------------------------------------------------------------------------------------\n#        for srv_nmon in nmonFileApp:\n#            curNmon = pd.read_nmon(LOGS_nmon + srv_nmon[0], compression='gzip')\n#            s_name = curNmon.info.server_name\n#            for other in srv_nmon[1:]:\n#                curNmon = curNmon.append(pd.read_nmon(LOGS_nmon + other, compression='gzip'))\n#            curNmon.info.server_name = s_name\n#            curNmon.to_comp_pickle(pickle_nmon_app, member=curNmon.info.server_name + \".nmon.pkl\")\n# ----------------------------------------------------------------------------------------------------------------------\n#        for srv_nmon in nmonFileAppu:\n#            curNmon = pd.read_nmon(LOGS_nmon + srv_nmon[0], compression='gzip')\n#            s_name = curNmon.info.server_name\n#            for other in srv_nmon[1:]:\n#                curNmon = curNmon.append(pd.read_nmon(LOGS_nmon + other, compression='gzip'))\n#            curNmon.info.server_name = s_name\n#            curNmon.to_comp_pickle(pickle_nmon_appu, member=curNmon.info.server_name + \".nmon.pkl\")\n# ----------------------------------------------------------------------------------------------------------------------\n# -----------sarFileApp-------------------------------------------------------------------------------------------------\n        for srv_sar in sarFileApp:\n            fSar = pd.read_sar(LOGS_sar + srv_sar[0], date_pattern='%d.%m.%Y', time_pattern='%H:%M:%S',\n                               compression='gzip')\n            for other in srv_sar[1:]:\n                fSar = fSar.append(\n                    pd.read_sar(LOGS_sar + other, date_pattern='%d.%m.%Y', time_pattern='%H:%M:%S', compression='gzip'))\n\n\n#\n#            # fSar.sort_index(axis=0, ascending=True, inplace=True)\n#            fSar.info.server_name = s_name\n#            fSar.to_comp_pickle(pickle_sar, member=fSar.info.server_name + \".sar.pkl\")\n#\n#        for valFil in sarFileApp:\n#            data = pd.read_sar(LOGS_sar + valFil, date_pattern='%d.%m.%Y', time_pattern='%H:%M:%S',\n#                               compression='gzip')\n#            FileRead.append(data)\n        return\n#----------sarFileAppu--------------------------------------------------------------------------------------------------\n#        for srv_sar in sarFileAppu:\n#            fSar = pd.read_sar(LOGS_sar + srv_sar[0], date_pattern='%d.%m.%Y', time_pattern='%H:%M:%S',\n#                               compression='gzip')\n#            s_name = fSar.info.server_name\n#            for other in srv_sar[1:]:\n#                fSar = fSar.append(\n#                    pd.read_sar(LOGS_sar + other, date_pattern='%d.%m.%Y', time_pattern='%H:%M:%S', compression='gzip'))\n#\n#            # fSar.sort_index(axis=0, ascending=True, inplace=True)\n#            fSar.info.server_name = s_name\n#            fSar.to_comp_pickle(pickle_saru, member=fSar.info.server_name + \".sar.pkl\")\n\n#-----------------------------------------------------------------------------------------------------------------------\n    def getLegendaSar(self, test_date, pickle_sar):\n        for n, sar in enumerate(pd.read_comp_pickle(pickle_sar.format(test_date))):\n            serv_name = sar.info.server_name.split('.passport.local')[0]\n            lable.append(serv_name)\n        return  lable\n\n    def getQueryColumn(self, test_date,nameColumn):\n        sarIdle = []\n        for n, sar in pd.read_comp_pickle(pickle_sar.format(test_date)):\n            sarIdle.append(sar[nameColumn])\n        FileRead = list(sarIdle)\n        return  FileRead\n\n\n","sub_path":"GraphicsCreaterUtilCPU.py","file_name":"GraphicsCreaterUtilCPU.py","file_ext":"py","file_size_in_byte":7900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"127767102","text":"class Settings():\n\tdef __init__(self):\n\t\t\n\t\t# Game Settings\n\t\tself.max_guesses = 8\n\t\t\n\t\t# Bot Settings\n\t\tself.bp_mode = False\n\t\tself.bp_static_goal_num = True\n\t\tself.bp_goal_num = '0758'\n\t\t\n\t\t# Initial guess data - Update locations listed below\n\t\tself.guess_data = {\n\t\t\t'guess': '0123', # get_bot_guess() main.py\n\t\t\t'bcc': '', # update_guess_data() main.py\n\t\t\t'guess_counter': 1, # update_guess_data() main.py\n\t\t\t'current_index': -1, \n\t\t\t'new_num': 0,\n\t\t\t'old_num': 0,\n\t\t\t}\n","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"229235442","text":"import os\nimport sys\nimport time\nfrom shutil import copyfile\n\nimport numpy as np\nimport tensorflow as tf\n#from tensorflow.python.client import timeline\n#from tqdm import trange\n\nimport libs.cylib as cylib\nimport helper\nimport eval_helper\nimport train_helper\nfrom datasets.cityscapes.cityscapes import CityscapesDataset\n#import datasets.flip_reader as reader\n#import datasets.reader_pyramid as reader\n#import datasets.reader_depth as reader\n\nnp.set_printoptions(linewidth=250)\n\ntf.app.flags.DEFINE_string('config_path', '', \"\"\"Path to experiment config.\"\"\")\nFLAGS = tf.app.flags.FLAGS\n\nhelper.import_module('config', FLAGS.config_path)\nprint(FLAGS.config_path)\n\n\ndef evaluate(sess, epoch_num, logits, loss, labels, img_name, dataset, reader):\n  \"\"\" Trains the network\n    Args:\n      sess: TF session\n      logits: network logits\n  \"\"\"\n  print('\\nValidation performance:')\n  conf_mat = np.ascontiguousarray(\n      np.zeros((FLAGS.num_classes, FLAGS.num_classes), dtype=np.uint64))\n  loss_avg = 0\n  for step in range(reader.num_examples()):\n    start_time = time.time()\n    out_logits, gt_labels, loss_val, img_prefix = sess.run(\n      [logits, labels, loss, img_name])\n    duration = time.time() - start_time\n    loss_avg += loss_val\n    #net_labels = out_logits[0].argmax(2).astype(np.int32, copy=False)\n    net_labels = out_logits[0].argmax(2).astype(np.int32)\n    #gt_labels = gt_labels.astype(np.int32, copy=False)\n    cylib.collect_confusion_matrix(net_labels.reshape(-1),\n                                   gt_labels.reshape(-1), conf_mat)\n\n    if step % 10 == 0:\n      num_examples_per_step = FLAGS.batch_size\n      examples_per_sec = num_examples_per_step / duration\n      sec_per_batch = float(duration)\n      format_str = 'epoch %d, step %d / %d, loss = %.2f \\\n        (%.1f examples/sec; %.3f sec/batch)'\n      #print('lr = ', clr)\n      print(format_str % (epoch_num, step, reader.num_examples(dataset), loss_val,\n                          examples_per_sec, sec_per_batch))\n    if FLAGS.draw_predictions and step % 100 == 0:\n      img_prefix = img_prefix[0].decode(\"utf-8\")\n      save_path = FLAGS.debug_dir + '/val/' + '%03d_' % epoch_num + img_prefix + '.png'\n      eval_helper.draw_output(net_labels, CityscapesDataset.CLASS_INFO, save_path)\n    #print(q_size)\n  #print(conf_mat)\n  print('')\n  pixel_acc, iou_acc, recall, precision, _ = eval_helper.compute_errors(\n      conf_mat, 'Validation', CityscapesDataset.CLASS_INFO, verbose=True)\n  return loss_avg / dataset.num_examples(), pixel_acc, iou_acc, recall, precision\n\n\ndef train(model, train_dataset, valid_dataset):\n  \"\"\" Trains the network\n    Args:\n      model: module containing model architecture\n      train_dataset: training data object\n      valid_dataset: validation data object\n  \"\"\"\n  #with tf.Graph().as_default(), tf.device('/gpu:0'):\n  with tf.Graph().as_default():\n    #sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement))\n    config = tf.ConfigProto(log_device_placement=FLAGS.log_device_placement)\n    #config.gpu_options.per_process_gpu_memory_fraction = 0.5 # don't hog all vRAM\n    #config.operation_timeout_in_ms = 5000   # terminate on long hangs\n    #config.operation_timeout_in_ms = 15000   # terminate on long hangs\n    sess = tf.Session(config=config)\n    # Create a variable to count the number of train() calls. This equals the\n    # number of batches processed * FLAGS.num_gpus.\n    global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0),\n                                  trainable=False)\n\n    # Calculate the learning rate schedule.\n    num_batches_per_epoch = (train_dataset.num_examples() / FLAGS.batch_size)\n    decay_steps = int(num_batches_per_epoch * FLAGS.num_epochs_per_decay)\n\n    # Decay the learning rate exponentially based on the number of steps.\n    lr = tf.train.exponential_decay(FLAGS.initial_learning_rate,\n                                    global_step,\n                                    decay_steps,\n                                    FLAGS.learning_rate_decay_factor,\n                                    staircase=True)\n\n    # Create an optimizer that performs gradient descent.\n    #opt = tf.train.RMSPropOptimizer(lr, RMSPROP_DECAY,\n    #                                momentum=RMSPROP_MOMENTUM,\n    #                                epsilon=RMSPROP_EPSILON)\n\n    reader = model.get_reader()\n    # Get images and labels.\n    image, labels, weights, num_labels, img_name = \\\n        reader.inputs(train_dataset, num_epochs=FLAGS.max_epochs)\n    image_valid, labels_valid, weights_valid, num_labels_valid, img_name_valid = \\\n        reader.inputs(valid_dataset, shuffle=False, num_epochs=FLAGS.max_epochs)\n\n    # Build a Graph that computes the logits predictions from the inference model.\n    # Calculate loss.\n    #with tf.variable_scope(\"model\"):\n    loss, logits, draw_data, init_op, init_feed = model.build(image, labels, weights, num_labels,\n                                          is_training=True)\n    #loss = model.loss(logits, labels, weights, num_labels)\n    #with tf.variable_scope(\"model\", reuse=True):\n    loss_valid, logits_valid, draw_data_val = model.build(image_valid, labels_valid,\n          weights_valid, num_labels_valid, is_training=False, reuse=True)\n      #loss_valid = model.loss(logits_valid, labels_valid, weights_valid,\n      #                        num_labels_valid, is_training=False)\n    #logits_valid, loss_valid = logits, loss\n\n\n    # Add a summary to track the learning rate.\n    tf.scalar_summary('learning_rate', lr)\n    #tf.scalar_summary('learning_rate', tf.mul(lr, tf.constant(1 / FLAGS.initial_learning_rate)))\n\n    #with tf.control_dependencies([loss_averages_op]):\n    opt = None\n    if FLAGS.optimizer == 'Adam':\n      opt = tf.train.AdamOptimizer(lr)\n    elif FLAGS.optimizer == 'Momentum':\n      opt = tf.train.MomentumOptimizer(lr, FLAGS.momentum)\n      #opt = tf.train.GradientDescentOptimizer(lr)\n    else:\n      raise ValueError()\n    grads = opt.compute_gradients(loss)\n\n    # Apply gradients.\n    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)\n\n    # Add histograms for trainable variables.\n    for var in tf.trainable_variables():\n      tf.histogram_summary(var.op.name, var)\n    # Add histograms for gradients.\n    grad_tensors = []\n    for grad, var in grads:\n      grad_tensors += [grad]\n      #print(var)\n      if grad is not None:\n        tf.histogram_summary(var.op.name + '/gradients', grad)\n    #grad = grads[-2][0]\n    #print(grad)\n\n    # Track the moving averages of all trainable variables.\n    #variable_averages = tf.train.ExponentialMovingAverage(\n    #    FLAGS.moving_average_decay, global_step)\n    #variables_averages_op = variable_averages.apply(tf.trainable_variables())\n\n    # with slim's BN\n    #batchnorm_updates = tf.get_collection(slim.ops.UPDATE_OPS_COLLECTION)\n    #batchnorm_updates_op = tf.group(*batchnorm_updates)\n    #train_op = tf.group(apply_gradient_op, variables_averages_op, batchnorm_updates_op)\n    #with tf.control_dependencies([apply_gradient_op, variables_averages_op]):\n    with tf.control_dependencies([apply_gradient_op]):\n      train_op = tf.no_op(name='train')\n\n    # Create a saver.\n    saver = tf.train.Saver(tf.all_variables(), max_to_keep=FLAGS.max_epochs)\n    #saver = tf.train.Saver(tf.all_variables())\n\n    # Build an initialization operation to run below.\n\n    sess.run(tf.initialize_all_variables())\n    sess.run(tf.initialize_local_variables())\n    sess.run(init_op, feed_dict=init_feed)\n    if len(FLAGS.resume_path) > 0:\n      print('\\nRestoring params from:', FLAGS.resume_path)\n      assert tf.gfile.Exists(FLAGS.resume_path)\n      resnet_restore = tf.train.Saver(model.variables_to_restore())\n      resnet_restore.restore(sess, FLAGS.resume_path)\n      #latest = tf.train.latest_checkpoint(FLAGS.train_dir)\n      #saver.restore(sess, '')\n      #variables_to_restore = tf.get_collection(\n      #    slim.variables.VARIABLES_TO_RESTORE)\n      #restorer = tf.train.Saver(variables_to_restore)\n      #restorer.restore(sess, resume_path)\n\n    # Build the summary operation based on the TF collection of Summaries.\n    summary_op = tf.merge_all_summaries()\n\n    # Start the queue runners.\n    coord = tf.train.Coordinator()\n    threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n    #tf.train.start_queue_runners(sess=sess)\n\n    summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, graph=sess.graph)\n\n    variable_map = train_helper.get_variable_map()\n    # take the train loss moving average\n    loss_avg_train = variable_map['total_loss/avg:0']\n    plot_data = {}\n    plot_data['train_iou'] = []\n    plot_data['train_acc'] = []\n    plot_data['train_loss'] = []\n    plot_data['valid_iou'] = []\n    plot_data['valid_acc'] = []\n    plot_data['valid_loss'] = []\n    plot_data['lr'] = []\n    max_valid_iou = 0\n    ex_start_time = time.time()\n    for epoch_num in range(1, FLAGS.max_epochs + 1):\n      print('\\ntensorboard --logdir=' + FLAGS.train_dir + '\\n')\n      plot_data['lr'] += [lr.eval(session=sess)]\n      conf_mat = np.zeros((FLAGS.num_classes, FLAGS.num_classes), dtype=np.uint64)\n      conf_mat = np.ascontiguousarray(conf_mat)\n      num_batches = reader.num_examples(train_dataset) // FLAGS.num_validations_per_epoch\n      for step in range(num_batches):\n        start_time = time.time()\n        run_ops = [train_op, loss, logits, labels, draw_data, img_name, global_step]\n        if step % 300 == 0:\n          run_ops += [summary_op, loss_avg_train]\n          ret_val = sess.run(run_ops)\n          (_, loss_val, scores, yt, draw_data_val, img_prefix, \\\n              global_step_val, summary_str, loss_avg_train_val) = ret_val\n          summary_writer.add_summary(summary_str, global_step_val)\n        else:\n          ##run_ops += [grad_tensors]\n          ##(_, loss_val, scores, yt, img_prefix, global_step_val, grads_val) = ret_val\n          ret_val = sess.run(run_ops)\n          (_, loss_val, scores, yt, draw_data_val, img_prefix, global_step_val) = ret_val\n          #train_helper.print_grad_stats(grads_val, grad_tensors)\n          #run_metadata = tf.RunMetadata()\n          #ret_val = sess.run(run_ops,\n          #            options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),\n          #            run_metadata=run_metadata)\n          #(_, loss_val, scores, yt, draw_data_val, img_prefix, global_step_val) = ret_val\n          #if step > 10:\n          #  trace = timeline.Timeline(step_stats=run_metadata.step_stats)\n          #  trace_file = open('timeline.ctf.json', 'w')\n          #  trace_file.write(trace.generate_chrome_trace_format())\n          #  raise 1\n        duration = time.time() - start_time\n\n        #print(ret_val[5])\n        #print('loss = ', ret_val[1])\n        #print('logits min/max/mean = ', ret_val[2].min(), ret_val[2].max(), ret_val[2].mean())\n        #print(ret_val[3].sum())\n\n        assert not np.isnan(loss_val), 'Model diverged with loss = NaN'\n\n        # estimate training accuracy on the last 30% of the epoch\n        #if step > int(0.7 * num_batches):\n        if step > 0:\n          #label_map = scores[0].argmax(2).astype(np.int32)\n          label_map = scores.argmax(3).astype(np.int32)\n          #print(label_map.shape)\n          #print(yt.shape)\n          cylib.collect_confusion_matrix(label_map.reshape(-1),\n                                         yt.reshape(-1), conf_mat)\n\n        img_prefix = img_prefix[0].decode(\"utf-8\")\n\n        #print(scores[0,100:102,300:302,:])\n        if FLAGS.draw_predictions and step % 100 == 0:\n          #save_path = os.path.join(FLAGS.debug_dir, 'train',\n          #                         '%05d_%03d_' % (step, epoch_num) +\n          #                         img_prefix + '.png')\n          #print(save_path)\n          #label_map = scores[0].argmax(2).astype(np.int32)\n          #eval_helper.draw_output(label_map, CityscapesDataset.CLASS_INFO, save_path)\n          save_prefix = os.path.join(FLAGS.debug_dir, 'train',\n                                     '%03d_%05d_' % (epoch_num, step) + img_prefix)\n          model.draw_output(draw_data_val, CityscapesDataset.CLASS_INFO, save_prefix)\n\n        if step % 30 == 0:\n          num_examples_per_step = FLAGS.batch_size\n          examples_per_sec = num_examples_per_step / duration\n          sec_per_batch = float(duration)\n\n          format_str = '%s: epoch %d, step %d / %d, loss = %.2f \\\n            (%.1f examples/sec; %.3f sec/batch)'\n          #print('lr = ', clr)\n          print(format_str % (train_helper.get_expired_time(ex_start_time), epoch_num,\n                              step, reader.num_examples(train_dataset), loss_val,\n                              examples_per_sec, sec_per_batch))\n\n      # TODO add moving average\n      train_pixacc, train_iou, _, _, _ = eval_helper.compute_errors(conf_mat, 'Train',\n          CityscapesDataset.CLASS_INFO)\n\n      valid_loss, valid_pixacc, valid_iou, valid_recall, valid_precision = evaluate(\n        sess, epoch_num, logits_valid, loss_valid,\n        labels_valid, img_name_valid, valid_dataset, reader)\n      plot_data['train_iou'] += [train_iou]\n      plot_data['train_acc'] += [train_pixacc]\n      plot_data['valid_iou'] += [valid_iou]\n      plot_data['valid_acc'] += [valid_pixacc]\n      plot_data['train_loss'] += [loss_avg_train_val]\n      plot_data['valid_loss'] += [valid_loss]\n      #print_best_result()\n      if valid_iou >= max_valid_iou:\n        max_valid_iou = valid_iou\n      if epoch_num > 1:\n        print('Best IoU = ', max_valid_iou)\n        eval_helper.plot_training_progress(os.path.join(FLAGS.train_dir, 'stats'), plot_data)\n\n      # Save the best model checkpoint\n      if FLAGS.save_net:\n        if valid_iou >= max_valid_iou:\n          print('Saving model...')\n          checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')\n          #saver.save(sess, checkpoint_path, global_step=epoch_num)\n          saver.save(sess, checkpoint_path)\n\n    coord.request_stop()\n    coord.join(threads)\n    sess.close()\n\n\ndef main(argv=None):  # pylint: disable=unused-argument\n  model = helper.import_module('model', FLAGS.model_path)\n\n  if tf.gfile.Exists(FLAGS.train_dir):\n    raise ValueError('Train dir exists: ' + FLAGS.train_dir)\n  tf.gfile.MakeDirs(FLAGS.train_dir)\n\n  stats_dir = os.path.join(FLAGS.train_dir, 'stats')\n  tf.gfile.MakeDirs(stats_dir)\n  tf.gfile.MakeDirs(FLAGS.debug_dir + '/train/')\n  tf.gfile.MakeDirs(FLAGS.debug_dir + '/val/')\n  f = open(os.path.join(stats_dir, 'log.txt'), 'w')\n  sys.stdout = train_helper.Logger(sys.stdout, f)\n\n  copyfile(FLAGS.model_path, os.path.join(FLAGS.train_dir, 'model.py'))\n  copyfile(FLAGS.config_path, os.path.join(FLAGS.train_dir, 'config.py'))\n  #subprocess.Popen(['tensorboard', '--logdir=' + g_save_dir])\n  #subprocess.Popen('tensorboard --logdir=' + g_save_dir + ' > /dev/null', shell=True)\n\n  print('Experiment dir: ' + FLAGS.train_dir)\n  print('Dataset dir: ' + FLAGS.dataset_dir)\n  train_dataset = CityscapesDataset(FLAGS.dataset_dir, 'train')\n  valid_dataset = CityscapesDataset(FLAGS.dataset_dir, 'val')\n  train(model, train_dataset, valid_dataset)\n\n\nif __name__ == '__main__':\n  tf.app.run()\n\n","sub_path":"OLD/train_classification.py","file_name":"train_classification.py","file_ext":"py","file_size_in_byte":15134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"129681552","text":"import discord\nfrom discord.ext import commands\nimport packet_handling\nimport random\nfrom fuzzywuzzy import fuzz\nimport asyncio\nimport config\nimport time\nfrom typing import List\n\nbot = commands.Bot(command_prefix=['!', '?'], description=\"Quiz bowl bot!\")\nquestionlist = packet_handling.get_questions()\ngroups = []\nteams = []\nplayers = []\n\n\n@bot.event\nasync def on_ready():\n    print('Logged in as')\n    print(bot.user.name)\n    print(bot.user.id)\n    print('------')\n\n\n@bot.command()\nasync def ping():\n    await bot.say(\"pong!\")\n\n\nclass Group:\n    def __init__(self, name, members):\n        self.name = name\n        self.members = members\n\n\nclass Team:\n    def __init__(self, server, name, captain, members, score=0):\n        # self.group = group\n        self.server = server\n        self.name = name\n        self.captain = captain\n        self.members = members\n        self.score = score\n\n    def __str__(self):\n        return self.name\n\n\nclass Player:\n    def __init__(self, member, server, score=0):\n        self.member = member\n        self.server = server\n        self.score = score  # not used for any data/analysis, just per-game score\n\n    def __str__(self):\n        return str(self.member.name)\n\n    def fuckme(self):\n        return str(self.member.name)\n\n@bot.command(name=\"group\", pass_context=True)\nasync def group_(ctx, name):\n    if any(each_group.name == name for each_group in groups):\n        await bot.say(\"That group already exists!\")\n        return\n    groups.append(Group(name, [ctx.message.author]))\n    await bot.say('New group \"{0}\" created! Type !join {0} to join!'.format(name))\n\n\n@bot.command(pass_context=True)\nasync def mygroup(ctx):\n    group = get_group(ctx.message.author)\n    if group is not None:\n        await bot.say(group.name)\n    else:\n        await bot.say(\"You're not in a team.\")\n\n\ndef get_group(member):\n    for group in groups:\n        if member in group.members:\n            return group\n    return None\n\n\ndef get_team(member: discord.Member, server):\n    for team in teams:\n        for player in team.members:\n            if member == player.member and server == player.server:\n                return team\n    return None\n\n\ndef get_player(member, server):\n    for team in teams:\n        for player in team.members:\n            if member == player.member and server == player.server:\n                return player\n\n\ndef serialize_team(teamname, server):\n    for team in teams:\n        if team.name == teamname and team.server == server:\n            return team\n\n\n@bot.command(name=\"team\", pass_context=True, aliases=[\"maketeam\", \"newteam\"])\nasync def team_(ctx, name):\n    if any(each_team.name == name for each_team in teams):\n        await bot.say(\"That team already exists!\")\n        return\n    if get_team(ctx.message.author, ctx.message.server) is not None:\n        await bot.say(\"You're already in a team.\")\n        return\n    else:\n        player = Player(ctx.message.author, ctx.message.server)\n        players.append(player)\n    team = Team(ctx.message.server, name, ctx.message.author, [])\n    team.members.append(player)\n    print(player)\n    teams.append(team)\n    await bot.say('New team \"{0}\" created! Type !join {0} to join!'.format(name))\n\n\n@bot.command(name=\"teams\", pass_context=True, aliases=[\"listteams\", \"allteams\"])\nasync def teams_(ctx):\n    teams_in_server = [team for team in teams if team.server == ctx.message.server]\n    if teams_in_server:\n        team_list = 'Current teams in {0}:\\n'.format(ctx.message.server) + ''.join(\n            sorted(':small_blue_diamond:' + team.name + '\\n' for team in teams_in_server))\n        await bot.say(team_list)\n    else:\n        await bot.say(\"No teams have been made in this server yet!\")\n\n\n@bot.command(pass_context=True)\nasync def myteam(ctx):\n    team = get_team(ctx.message.author, ctx.message.server)\n    if team is not None:\n        await bot.say(team.name)\n    else:\n        await bot.say(\"You're not in a team.\")\n\n\n@bot.command(name=\"captain\", pass_context=True)\nasync def captain_(ctx, new_captain: discord.Member=None):\n    team = get_team(ctx.message.author, ctx.message.server)\n    if team is not None:\n        if new_captain is None:\n            await bot.say(\"{0} is the captain of your team, {1}\".format(team.captain, team.name))\n            return\n        if team.captain == ctx.message.author:\n            if get_player(new_captain, ctx.message.server) in team.members:\n                team.captain = new_captain\n                await bot.say(\"New team captain of {0}: {1}\".format(team, new_captain))\n            else:\n                await bot.say(\"{0} isn't in your team!\".format(new_captain))\n        else:\n            await bot.say(\"You aren't captain of {0}\".format(team.name))\n    else:\n        await bot.say(\"You're not in a team!\")\n\n\n@bot.command(pass_context=True)\nasync def join(ctx, name):\n    for team in teams:\n        if team.name == name:\n            player = Player(ctx.message.author, ctx.message.server)\n            team.members.append(player)\n            await bot.say(\"{0} joined the team {1}!\".format(ctx.message.author, name))\n            return\n    await bot.say(\"That doesn't seem to be a team!\")\n\n\n@bot.command(pass_context=True, aliases=['leaveteam'])\nasync def leave(ctx, name=None):\n    member_team = get_team(ctx.message.author, ctx.message.server)\n    if name is None:\n        if member_team is None:\n            await bot.say(\"You're not in a team.\")\n            return\n\n    if member_team is not None and (member_team.name == name or name is None):\n        if len(member_team.members) == 1:\n            await bot.say(\"You're the last person in the team!\\nLeaving the team has deleted it. You can always create \"\n                          \"a new one with !team .\")\n            teams.remove(member_team)\n            return\n        if ctx.message.author == member_team.captain:\n            await bot.say(\n                \"You're the captain of {0}! You should defer captainship to one of your teammates with !captain @user.\\nTeam members:\\n\".format(\n                    member_team) + \"\".join([\":small_blue_diamond:\" + str(member) + \"\\n\" for member in member_team.members]))\n            return\n        member_team.members.remove(get_player(ctx.message.author, ctx.message.server))\n        await bot.say(\"Left {0}!\".format(name))\n        return\n\n    await bot.say(\"You're not in a team by that name.\")\n\n\n@bot.command(pass_context=True)\nasync def tournament(ctx, *, teams_in_game=None):\n    caller_team = get_team(ctx.message.author, ctx.message.server)\n    if caller_team is None:\n        await bot.say(\"You should make or join a team first.\")\n        return\n    if caller_team.captain != ctx.message.author:\n        await bot.say(\"You can't start a tournament unless you're a team captain.\")\n        return\n    if teams_in_game is None:\n        await bot.say(\"Please enter the teams that will be playing in this tournament, \"\n                      \"separated by spaces\")\n        msg = await bot.wait_for_message(author=ctx.message.author)\n        team_names = msg.content.split(\" \")\n    else:\n        team_names = teams_in_game.split(\" \")\n    if len(team_names) < 2:\n        await bot.say(\"You must have at least 2 teams to have a tournament!\")\n        return\n    teams_in_game = []\n    for teamname in team_names:\n        teams_in_game.append(serialize_team(teamname, ctx.message.server))\n    for team in teams_in_game:\n        if team not in teams:\n            await bot.say(\"{0} isn't a team. Make sure to check your spelling! Teams currently \"\n                          \"in this server are:\\n\".format(team)\n                          + \"\".join([\":small_blue_diamond:\" + t_.name + \"\\n\" for t_ in teams]))\n            return\n\n    await bot.say(\"Would you like bonus questions? Answer with yes or no.\")\n    msg = await bot.wait_for_message(author=ctx.message.author)\n    if msg.content.lower() in [\"yes\", \"y\", \"ye\", \"yeet\"]:\n        bonus = True\n        await bot.say(\"Bonus questions will be read.\")\n    else:\n        bonus = False\n        await bot.say(\"Bonus questions will not be read.\")\n    await bot.say(\"How many tossup questions do you want (default is 20)?\")\n    msg = await bot.wait_for_message(author=ctx.message.author)\n    num_of_questions = int(msg.content)\n    await bot.say(\"Tournament starting! Your setup is as follows:\\n\"\n                  \"Teams competing: \" + \", \".join([t_.name for t_ in teams_in_game]) +\n                  \"\\nNumber of tossups: \" + str(num_of_questions) +\n                  \"\\nBonus questions: \" + str(bonus) +\n                  \"\\nIf this is correct, type yes. If you'd like to edit something, type teams, tossups, or bonuses.\")\n\n    def check2(message):\n        return message.content in [\"yes\", \"y\", \"ye\", \"yeet\", \"teams\", \"tossups\", \"bonuses\"]\n\n    msg = await bot.wait_for_message(author=ctx.message.author, check=check2)\n    if msg.content.lower() in [\"yes\", \"y\", \"ye\", \"yeet\"]:\n        await bot.say(\"Tournament starting! Good luck!\")\n    # TODO\n    elif msg.content.lower() == \"teams\":\n        await bot.say(\"Alright, re-enter the list of teams competing, separated by spaces\")\n        msg = await bot.wait_for_message(author=ctx.message.author)\n        teams_in_game = msg.content.split(\" \")\n        if teams_in_game.count() < 2:\n            await bot.say(\"You must have at least 2 teams to have a tournament!\")\n            return\n        for team in teams_in_game:\n            if team not in [t.name for t in teams]:\n                await bot.say(\"{0} isn't a team. Make sure to check your spelling! Teams currently \"\n                              \"in this server are:\\n\".format(team)\n                              + \"\".join([\":small_blue_diamond:\" + t_.name + \"\\n\" for t_ in teams]))\n                return\n    elif msg.content.lower() == \"tossups\":\n        await bot.say(\"Alright, re-enter the number of tossups you want.\")\n        msg = await bot.wait_for_message(author=ctx.message.author)\n        num_of_questions = int(msg.content)\n    elif msg.content.lower() == \"bonuses\":\n        await bot.say(\"Alright, re-renter yes or no if you want bonuses or not.\")\n        msg = await bot.wait_for_message(author=ctx.message.author)\n        if msg.content.lower() in [\"yes\", \"y\", \"ye\", \"yeet\"]:\n            bonus = True\n            await bot.say(\"Bonus questions will be read.\")\n        else:\n            bonus = False\n            await bot.say(\"Bonus questions will not be read.\")\n    playerlist = []\n    for t in teams_in_game:\n        print(t.members)\n        playerlist += t.members\n    print(playerlist)\n    for i in range(num_of_questions):\n        await read_question(bonus, playerlist)\n    teams_in_game.sort(reverse=True, key=lambda x: x.score)\n    await bot.say(\"Tournament over! Final leaderboard:\\n\" +\n                  \"\".join([\":small_blue_diamond:\" + t.name + \": \" + str(t.score) + \" points!\\n\" for t in teams_in_game]))\n    \n\nasync def read_question(bonus: bool, playerlist):\n    print([str(player) for player in playerlist])\n    correct = False\n    skip = False\n    neggers = []\n    question_obj = random.choice(questionlist)\n    question_arr = question_obj.question.split(\" \")\n    sent_question = await bot.say(\" \".join(question_arr[:5]))\n    await asyncio.sleep(1)\n    for i in range(1, question_arr.__len__() // 5 + 1):\n        sent_question_content = sent_question.content\n        sent_question = await bot.edit_message(sent_question,\n                                               sent_question_content + \" \" + \" \".join(question_arr[i * 5:i * 5 + 5]))\n        print(sent_question.content)\n\n        def check(message):\n            return get_player(message.author, message.server) in playerlist and message.author not in neggers and \"buzz\" in message.content\n\n        msg = None\n        msg = await bot.wait_for_message(timeout=1, check=check)\n        if msg is not None:\n            await bot.say(\"buzz from {0}! 10 seconds to answer\".format(msg.author))\n            answer = await bot.wait_for_message(timeout=10, author=msg.author)\n            if answer is not None:\n                ratio = fuzz.ratio(answer.content.lower(), question_obj.answer.lower())\n                if ratio > 75:\n                    await bot.say(\"correct!\")\n                    print(\"correct! ratio: \" + str(ratio))\n                    correct = True\n                    await bot.edit_message(sent_question, question_obj.question)\n                    team = get_team(msg.author, msg.author.server)\n                    player = get_player(msg.author, msg.author.server)\n                    team.score += 20\n                    player.score += 20\n                    break\n                else:\n                    await bot.say(\"incorrect!\")\n                    print(\"incorrect\")\n                    msg = None\n                    sent_question = await bot.say(sent_question_content)\n            else:\n                await bot.say(\"Time's up!\")\n                await asyncio.sleep(1)\n    wait_time = 10\n    if not correct:\n        while wait_time > 0:\n            timer = time.time()\n            msg = None\n            msg = await bot.wait_for_message(timeout=int(wait_time), check=check)\n            wait_time = time.time() - timer\n            if msg is not None:\n                await bot.say(\"buzz from {0}! 10 seconds to answer\".format(msg.author))\n                answer = None\n                answer = await bot.wait_for_message(timeout=10, author=msg.author)\n                if answer is not None:\n                    ratio = fuzz.ratio(answer.content.lower(), question_obj.answer.lower())\n                    if ratio > 75:\n                        await bot.say(\"correct!\")\n                        print(\"correct! ratio: \" + str(ratio))\n                        correct = True\n                        team = get_team(msg.author, msg.author.server)\n                        player = get_player(msg.author, msg.author.server)\n                        team.score += 20\n                        player.score += 20\n                        break\n                    else:\n                        await bot.say(\"incorrect!\")\n                        print(\"incorrect\")\n                else:\n                    await bot.say(\"Time's up!\")\n                    await asyncio.sleep(1)\n\n        if not correct:\n            await bot.say(\"The answer is {0}!\".format(question_obj.answer))\n\n    neggers.clear()\n\n\n@bot.command(pass_context=True)\nasync def question(ctx, num=1):\n    correct = False\n    skip = False\n    user = ctx.message.author\n    wrong_buzzers = []\n    for j in range(0, num):\n\n        question_obj = random.choice(questionlist)\n\n        # reading_task = bot.loop.create_task(read_question(question_obj))\n\n        # loop.run_until_complete(asyncio.ensure_future(read_question(question_obj), loop=loop))\n        question_arr = question_obj.question.split(\" \")\n        sent_question = await bot.say(\" \".join(question_arr[:5]))\n        await asyncio.sleep(1)\n        for i in range(1, question_arr.__len__() // 5 + 1):\n            sent_question_content = sent_question.content\n            sent_question = await bot.edit_message(sent_question, sent_question_content + \" \" + \" \".join(question_arr[i*5:i*5+5]))\n            print(sent_question.content)\n\n            def check(message):\n                return message.author not in wrong_buzzers and \"buzz\" in message.content\n            msg = None\n            msg = await bot.wait_for_message(timeout=1)\n            if msg is not None:\n                if \"buzz\" in msg.content:\n                    await bot.say(\"buzz from {0}! 10 seconds to answer\".format(msg.author))\n                    answer = await bot.wait_for_message(timeout=10, author=msg.author)\n                    ratio = fuzz.ratio(answer.content.lower(), question_obj.answer.lower())\n                    if ratio > 80:\n                        await bot.say(\"correct!\")\n                        print(\"correct! ratio: \" + str(ratio))\n                        correct = True\n                        msg = None\n                        break\n                    else:\n                        await bot.say(\"incorrect!\")\n                        if answer.author not in wrong_buzzers:\n                            wrong_buzzers.append(answer.author)\n                        print(\"incorrect\")\n                        msg = None\n                        sent_question = await bot.say(sent_question_content)\n                elif \"skip\" in msg.content:\n                    skip = True\n                    print(\"skip!\")\n                    break\n\n        if not skip:\n            msg = await bot.wait_for_message(timeout=10, check=check)\n            if msg is not None:\n                await bot.say(\"buzz from {0}! 10 seconds to answer\".format(msg.author))\n                answer = await bot.wait_for_message(timeout=10, author=msg.author)\n                ratio = fuzz.ratio(answer.content.lower(), question_obj.answer.lower())\n                if ratio > 80:\n                    await bot.say(\"correct!\")\n                    print(\"correct! ratio: \" + str(ratio))\n                    correct = True\n                else:\n                    await bot.say(\"incorrect!\")\n                    print(\"incorrect\")\n            if not correct:\n                await bot.say(\"The answer is {0}!\".format(question_obj.answer))\n            msg = None\n            msg = await bot.wait_for_message(timeout=20, content=\"next\")\n            if msg is None:\n                return\n\n        wrong_buzzers.clear()\n        print(question_obj.answer)\n        print(question_obj.packet)\n\n\nbot.run(config.token)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":17510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"167732239","text":"import requests\nimport csv\nimport sys\nimport urllib\nimport json\n\n#Update \"key_to_update\" value to whatever link data key you want to update *****************\nkey_to_update = \"$og_description\"\nbranch_key = \"key_live_boErzJxoeEvYVUKoPscTOhlivAaPPik7\"\nbranch_secret = \"secret_live_EjBwtN0NJaeDU8btUIOKUzgMyenJdPxx\"\nifile = open('edit_links_in_bulk.csv', \"rb\")\n\n#constants\nbranchendpoint = \"https://api.branch.io/v1/url?url=\"\nreader = csv.reader(ifile, delimiter=',')\n\n#skip first line of CSV???\n#next(reader)\n\n#loop through CSV (CSV file MUST have a branch link in column A, and the new value for that link in column B)\nfor row in reader:\n\n\t#retrieve link data for link to be updated\n\turl = urllib.quote_plus(row[0])\n\tgetrequest = branchendpoint + url + \"&branch_key=\" + branch_key\n\tlinkdata = requests.get(getrequest)\n\tjsonData = json.loads(linkdata.text)\n\t#print(jsonData)\n\t#jsonData = {}\n\n\t#set required fields for link updates\n\tjsonData[\"branch_key\"] = branch_key\n\tjsonData[\"branch_secret\"] = branch_secret\n\n\t#clean up potential duplicate keys\n\tif \"type\" in jsonData:\n\t\tdel jsonData[\"type\"]\n\tif \"url\" in jsonData:\n\t\tdel jsonData[\"url\"]\n\tif \"alias\" in jsonData:\n\t\tdel jsonData[\"alias\"]\n\tif \"type\" in jsonData[\"data\"]:\n\t\tdel jsonData[\"data\"][\"type\"]\n\tif \"url\" in jsonData[\"data\"]:\n\t\tdel jsonData[\"data\"][\"url\"]\n\tif \"alias\" in jsonData[\"data\"]:\n\t\tdel jsonData[\"data\"][\"del\"]\n\n\t#udpate target key with value provided in CSV ****************\n\tnewValue = row[1]\n\tif key_to_update in jsonData:\n\t\tjsonData[key_to_update] = newValue\n\tif key_to_update in jsonData[\"data\"]:\n\t\tjsonData[\"data\"][key_to_update] = newValue\n\n\t#put request to update link\n\tpayload = json.dumps(jsonData)\n\tputrequest = branchendpoint + url\n\tr = requests.put(putrequest, json=jsonData)\n\tprint(r.url)\n\tprint(r)\n\tprint\nifile.close()","sub_path":"python_branch_api_script_v2.py","file_name":"python_branch_api_script_v2.py","file_ext":"py","file_size_in_byte":1795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"417461418","text":"# 은닉층이 하나인 신경망 구현\nimport numpy as np\nfrom DeepLearningFromScratch.ch01_2.layer_classes import Affine, Sigmoid, SoftmaxWithLoss\n\nclass TowLayerNet:\n\n    # input_size : 입력층 뉴런수\n    # hidden_size : 은닉층 뉴런수\n    # output_size : 출력층 뉴런수\n    def __init__(self, input_size, hidden_size, output_size):\n        I, H, O = input_size, hidden_size, output_size\n\n        # 가중치와 편향 초기화\n        W1 = 0.01 * np.random.randn(I, H)       # 가중치는 무작위 값으로 초기화\n        b1 = np.zeros(H)                        # 편향 벡터 0으로 초기화\n        W2 = 0.01 * np.random.randn(H, O)\n        b2 = np.zeros(O)\n\n        # 계층 생성\n        self.layers = [\n            Affine(W1, b1),\n            Sigmoid(),\n            Affine(W2, b2)\n        ]\n\n        self.loss_layer = SoftmaxWithLoss()\n\n        # 모든 가중치와 기울기를 리스트에 모은다.\n        self.params, self.grads = [], []\n        for layer in self.layers:\n            self.params += layer.params\n            self.grads += layer.grads\n\n    # 추론 수행 - 순전파 진행\n    def predict(self, x):\n        for layer in self.layers:\n            x = layer.forward(x)\n\n        return x\n\n    # 순전파\n    def forward(self, x, t):\n        score = self.predict(x)\n        loss = self.loss_layer.forward(score, t)\n\n        return loss\n\n    # 역전파\n    def backward(self, dout=1):\n        dout = self.loss_layer.backward(dout)\n        for layer in reversed(self.layers):         # 역순으로 진행\n            dout =layer.backward(dout)\n\n        return dout\n","sub_path":"DeepLearningFromScratch/ch01_2/two_layer_net.py","file_name":"two_layer_net.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"301384550","text":"#class\n\nclass Employee:        #class definition\n\n    def __init__(self,first,last,pay):     #method of class\n        self.first=first\n        self.last=last\n        self.pay=pay\n        self.email=first+ '.' + last+ '@gmail.com'\n\n    def fullName(self):                    #method of class\n        return '{} {}'.format(self.first, self.last)\n        \n\nemp_1=Employee('Vaibhav','Nath',50000)       #instance of class\nemp_2=Employee('Test','User',60000)          #instance of class\n\nprint(emp_1.fullName())   #calling by instance we dont need to pass argument\nprint(Employee.fullName(emp_1))   #calling by class we need to paass argument\n\n\n##Vaibhav Nath\n##Vaibhav Nath\n\n\n\n##print(emp_1)      ##<__main__.Employee object at 0x000001189D5F6550>\n##print(emp_2)      ##<__main__.Employee object at 0x000001189D5F6CD0>\n\n\n##print(emp_1.email)                ##Vaibhav.Nath@gmail.com\n##print(emp_2.email)                ##Test.User@gmail.com\n\n\n##print('{} {}'.format(emp_1.first, emp_1.last))      ##Vaibhav Nath\n\n\n##print(emp_1.fullName())           ##Vaibhav Nath\n\n\n\n## emp_1.first='Vaibhav'\n## emp_1.last='Nath'\n## emp_1.email='vaibhav.nath78@gmail.com'\n## emp_1.pay=50000\n##\n## emp_2.first='Test'\n## emp_2.last='User'\n## emp_2.email='test.user78@gmail.com'\n## emp_2.pay=60000","sub_path":"Class(1).py","file_name":"Class(1).py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"387183518","text":"\nimport numpy as np\nfrom math import sqrt\nimport sys\nfrom matplotlib import pyplot\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Dropout\nfrom keras.layers import LSTM\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import r2_score\nfrom sklearn.model_selection import train_test_split\nnp.set_printoptions(threshold=sys.maxsize)\n# fix random seed for reproducibility\n\nnp.random.seed(6)\n\nX = np.load('Data Preparation\\output\\X_5_5_first_abs.npy', allow_pickle=True)\nY = np.load('Data Preparation\\output\\Y_5_5_first_abs.npy', allow_pickle=True)\n\n#print(X[0])\n#print(Y)\n\n# Plot\n'''names = [\"Review\", \"Arb\", \"Image\", \"Work\", \"Karriere\", \"Gehalt\", \"Umwelt\", \"Kollegen\", \"Umgang\", \"Vorgesetzte\",\n          \"Bedingungen\", \"Kommunikation\", \"Gleichberechtigung\", \"Interessante\"]\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(names, y=0.5, loc='right')\n\ti += 1\npyplot.show()'''\n\n# Prepare\n\nX = X.astype('float64')\nY = Y.astype('float64')\n\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1)\n\n\ninput_dim = len(X_train[0][0])  # 14\ntimesteps = len(X_train[0])\n\ninput_shape = X_train[0].shape\n\nprint(X_train[0].shape)\n# model definition\nmodel = Sequential()\nmodel.add(LSTM(70, input_shape=input_shape))\n#model.add(Dropout(0.2))\nmodel.add(Dense(1))\nmodel.compile(loss='mae', optimizer='adam')\n\nhistory = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=200, batch_size=32, verbose=2)\nprint(model.summary())\n# plot history\npyplot.plot(history.history['loss'], label='train')\npyplot.plot(history.history['val_loss'], label='test')\npyplot.legend()\npyplot.show()\n\n# Predict\npredictions = model.predict(X_test)\n\n# calculate RMSE & r2\nrmse = sqrt(mean_squared_error(Y_test, predictions))\nprint('Test RMSE: %.3f' % rmse)\nr2 = r2_score(Y_test, predictions)\nprint('Test R^2: %.3f' % r2)\n\n#print(predictions)\npyplot.plot(predictions)\npyplot.plot(Y_test)\npyplot.show()\n#print(Y_test)\n#pyplot.plot(Y_test)\n#pyplot.show()\n\n# rescale needed?\n'''test_X = test_X.reshape((test_X.shape[0], n_hours*n_features))\n# invert scaling for forecast\ninv_yhat = concatenate((yhat, test_X[:, -7:]), axis=1)\ninv_yhat = scaler.inverse_transform(inv_yhat)\ninv_yhat = inv_yhat[:,0]\n# invert scaling for actual\ntest_y = test_y.reshape((len(test_y), 1))\ninv_y = concatenate((test_y, test_X[:, -7:]), axis=1)\ninv_y = scaler.inverse_transform(inv_y)\ninv_y = inv_y[:,0]'''\n\n'''# Final evaluation of the model\nscores = model.evaluate(X_test, Y_test, verbose=0)\nprint(\"Accuracy: %.2f%%\" % (scores[1]*100))'''\n\n","sub_path":"AI Model/lstm_regression_livia.py","file_name":"lstm_regression_livia.py","file_ext":"py","file_size_in_byte":2658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"314719888","text":"from selenium import webdriver\nimport wget\nfrom time import sleep\nimport os\n\nchromeOptions = webdriver.ChromeOptions()\nprefs = {\"download.default_directory\" : \"/Users/abhinav/Desktop/eduGraph/scraping/courses\",  \"download.prompt_for_download\": False,\n    \"download.directory_upgrade\": True,\"plugins.always_open_pdf_externally\": True}\nchromeOptions.add_experimental_option(\"prefs\",prefs)\n\n\n\ndriver = webdriver.Chrome(\"./chromedriver\", chrome_options=chromeOptions)\n\n# url = \"https://ts.ntu.edu.sg/sites/lib-repository/exam-question-papers/_layouts/15/start.aspx#/Shared%20Documents/Forms/AllItems.aspx\"\nurl = \"https://ts.ntu.edu.sg/sites/lib-repository/exam-question-papers/_layouts/15/start.aspx#/Shared%20Documents/Forms/AllItems.aspx?RootFolder=%2Fsites%2Flib%2Drepository%2Fexam%2Dquestion%2Dpapers%2FShared%20Documents%2FUG%2FSCSE&FolderCTID=0x01200089A3F19178D586459D6420D6B5DEE9B1&View=%7BD9CF6ACB%2D25F3%2D4E50%2DA04B%2D39B850D79436%7D\"\ndriver.get(url)\n\nsleep(10)\n\n# ug_xpath = \"/html/body/form/div[12]/div/div[2]/div[2]/div[3]/div[1]/div[2]/div/div/table/tbody/tr/td/table/tbody/tr[2]/td[3]/div/a\"\n# # ug_xpath = \"//*[@id=\\\"23\\\"]/a\"\n# ug_folder = driver.find_element_by_xpath(ug_xpath)\n\n# ug_folder.click() \n\n\n# scse_xpath = \"/html/body/form/div[12]/div/div[2]/div[2]/div[3]/div[1]/div[2]/div/div/table/tbody/tr/td/table/tbody/tr[16]/td[3]/div/a\"\n# sleep(3)\n\n# scse_folder = driver.find_element_by_xpath(scse_xpath)\n\n# scse_folder.click()\n\n\n# sleep(10)\nscse_children_links = []\n\nfor i in range(4):\n    course_body = driver.find_element_by_xpath(\"/html/body/form/div[12]/div/div[2]/div[2]/div[3]/div[1]/div[2]/div/div/table/tbody/tr/td/table/tbody\")\n    table_rows = driver.find_elements_by_tag_name(\"tr\")\n    for curr_row in table_rows:\n        try:\n            curr_link = curr_row.find_elements_by_tag_name(\"td\")[2].find_element_by_css_selector('*').find_element_by_css_selector('*')\n            link = curr_link.get_attribute(\"href\")\n            text = curr_link.get_attribute(\"innerHTML\")\n            if(link[:5]==\"https\" and text!='Welcome'):\n                # print(link,text)\n                scse_children_links.append((link,text))\n        except:\n            pass\n    if(i!=3):\n        button = driver.find_element_by_id(\"pagingWPQ2next\")\n        print(\"length:\",len(scse_children_links))\n        button.click()\n        sleep(2)\n\nprint(\"length:\",len(scse_children_links))\nprint(\"first element:\",scse_children_links[0])\n\nfor link, name in scse_children_links:\n    driver.get(link)\n    try:\n        rows = driver.find_element_by_xpath(\"/html/body/form/div[12]/div/div[2]/div[2]/div[3]/div[1]/div[2]/div/div/table/tbody/tr/td/table/tbody\").find_elements_by_tag_name(\"tr\")\n        for curr_row in rows:\n                curr_link = curr_row.find_elements_by_tag_name(\"td\")[2].find_element_by_css_selector('*').find_element_by_css_selector('*')\n                text = curr_link.get_attribute(\"innerHTML\")\n                driver.execute_script(\"arguments[0].setAttribute(\\\"download\\\",\\\"{}\\\")\".format(text),curr_link)\n                curr_link.click()\n                sleep(0.5)\n    except:\n        pass\n","sub_path":"scraping/scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":3112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"550538149","text":"import networkx as nx\nfrom networkx.readwrite import json_graph\n\ndef get_network(nodes, links):\n    edges = []\n    node_indices = {nodes[i]['id']: i for i in range(len(nodes))}\n    for link in links:\n        edge = {}\n        edge['source'] = node_indices[link['source']]\n        edge['target'] = node_indices[link['target']]\n        edge['id'] = link['id']\n        edges.append(edge)\n    graph = json_graph.node_link_graph({'nodes': nodes, 'links': edges},\n                                       directed=True, multigraph=False)\n    return graph\n\n\ndef add_network_statistics(nodes, links):\n    graph = get_network(nodes, links)\n    hubs, authorities = nx.hits(graph)\n    statistics = {\n        'degree': nx.degree(graph),\n        'in_degree': graph.in_degree(),\n        'out_degree': graph.out_degree(),\n\n        'degree_centrality': nx.degree_centrality(graph),\n        'in_degree_centrality': nx.in_degree_centrality(graph),\n        'out_degree_centrality': nx.out_degree_centrality(graph),\n        'betweenness_centrality': nx.betweenness_centrality(graph),\n        'hubs': hubs,\n        'authorities': authorities\n    }\n    for node in nodes:\n        nodeid = node['id']\n        for var in statistics.keys():\n            node[var] = statistics[var][nodeid]\n\n    return nodes\n\n","sub_path":"blazegraph_querier/network_analysis.py","file_name":"network_analysis.py","file_ext":"py","file_size_in_byte":1281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"92893314","text":"import collections\n\nclass DictDiffer(object):\n    \"\"\"\n    Calculate the difference between two dictionaries as:\n    (1) items added\n    (2) items removed\n    (3) keys same in both but changed values\n    (4) keys same in both and unchanged values\n    \"\"\"\n\n    def __init__(self, first_dict, second_dict):\n        self.first_dict, self.second_dict = first_dict, second_dict\n        self.first_set, self.second_set = set(first_dict.keys()), set(second_dict.keys())\n        self.intersect_set = self.first_set.intersection(self.second_set)\n\n    def added(self):\n        \"\"\"\n        :return: the keys that are added\n        \"\"\"\n        return self.first_set - self.intersect_set\n\n    def removed(self):\n        \"\"\"\n        :return: the keys that are removed\n        \"\"\"\n        return self.second_set - self.intersect_set\n\n    def changed(self):\n        \"\"\"\n        :return: returns a dictionary of the changed values (first dict)\n        \"\"\"\n        s = set(o for o in self.intersect_set if self.second_dict[o] != self.first_dict[o]) # keys der geänderten einträge\n        d1 = {e: self.first_dict[e] for e in s}\n        if len(d1) == 0:\n            return None\n\n        d2 = {e: self.second_dict[e] for e in s}\n        d = collections.OrderedDict({'wtid': self.first_dict['wtid'], 'vncid': self.first_dict['vncid'], 'excel': d1, 'db':d2})\n        return d\n\n    def unchanged(self):\n        return set(o for o in self.intersect_set if self.second_dict[o] == self.first_dict[o])\n\n\nif __name__ == '__main__':\n    d1 = {'c_bdf': 'Otto', 'c_kto': 122322, 'c:bg': 10}\n    d2 = {'c_bdf': 'Otto', 'c_kto': 12322}\n\n    d = DictDiffer(d1, d2)\n\n    print(\"added\", d.added())\n    print(\"removed\", d.removed())\n    print(\"changed\", d.changed())\n    print(\"unchanged\", d.unchanged())\n\n    print(\"first changed\", d.changed_first_values())\n    print(\"first changed\", d.changed_second_values())","sub_path":"dictdiff.py","file_name":"dictdiff.py","file_ext":"py","file_size_in_byte":1876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"285918125","text":"import json\nimport boto3\nimport os\nclient = boto3.client('glue')\n\n# step-fn-activity\nclient_sf = boto3.client('stepfunctions')\n# replace activity arn with respective activivty arn\nactivity = os.environ['arn_activity_step_function']\ndef lambda_handler(event, context):\n    class CrawlerException(Exception):\n        pass\n    crawler_name = os.environ['crawler_name']\n    response = client.get_crawler_metrics(CrawlerNameList = [crawler_name])\n    print(response)\n    print(response['CrawlerMetricsList'][0]['CrawlerName']) \n    print(response['CrawlerMetricsList'][0]['TimeLeftSeconds']) \n    print(response['CrawlerMetricsList'][0]['StillEstimating']) \n    \n    if (response['CrawlerMetricsList'][0]['StillEstimating']):\n        raise CrawlerException('Crawler In Progress!')\n    elif (response['CrawlerMetricsList'][0]['TimeLeftSeconds'] > 0):\n        raise CrawlerException('Crawler In Progress!')\n    else :\n        print(\"Crawler run success\")\n        #send activity success token\n        task = client_sf.get_activity_task(activityArn=activity, workerName=\"data-transformation-crawler-activity\")\n        response = client_sf.send_task_success(taskToken=task['taskToken'], output=json.dumps({'message':'Data Transformation Crawler Completed'}))","sub_path":"scripts/lambda/fntn-check-accidents-crawler-status/lambda_function.py","file_name":"lambda_function.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"84810581","text":"if __name__ == '__main__':\n    klist = [\n        \"good \", \"good \", \"study\",\n        \" good \", \"good\", \"study \",\n        \"good \", \" good\", \" study\",\n        \" good \", \"good\", \" study \",\n        \"good \", \"good \", \"study\",\n        \" day \", \"day\", \" up\",\n        \" day \", \"day\", \" up\",\n        \" day \", \"day\", \" up\",\n        \" day \", \"day\", \" up\",\n        \" day \", \"day\", \" up\",\n        \" day \", \"day\", \" up\",\n        \" day \", \"day\", \" up\",\n    ]\n    # 1.编写Python程序判断字符串是否重复\n\n    nlist = [i.strip() for i in klist]\n\n    klen = len(klist)\n    nlen = len(nlist)\n\n    if klen==nlen :\n        print(\"无重复\")\n    else :\n        print(\"有重复\")\n\n    # 2.编写Python程序打印输出字符串中重复的所有字符。\n\n    nset = set(nlist)\n\n    for i in nset :\n        num = nlist.count(i)\n        if num>1 :\n            print(str(i)+\"重复了\")\n\n    #3.把下面的klist作为字典的键\n    #同时作为字典的值\n\n    ndict = {i:i for i in nset}\n    print(ndict)\n\n    # 4. 把下面的klist作为字典的键\n    # 并统计每个单词的词频\n\n    mdict = {}\n\n    for i in sorted(nset) :\n        mdict[i] = nlist.count(i)\n    print(mdict)\n\n\n\n\n\n\n\n\n\n\n","sub_path":"day03/demo06.py","file_name":"demo06.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"540084014","text":"import tensorflow as tf\nimport subprocess\nimport numpy as np\n#import os\n\ndef ViewBoard(session,outDir):\n  writer=tf.train.SummaryWriter(\"./\"+outDir,session.graph)\n  subprocess.call([\"/home/rafael/anaconda3/bin/tensorboard\",\"--logdir=\"+\"./\"+outDir])\n#  print(os.getcwd())\n#  subprocess.call([\"tensorboard\",\"--logdir=\"+os.getcwd()+\"/\"+outDir],env={'PATH':os.getenv('PATH')})\n  writer.flush()\n  writer.close()\n\n#define model\n#a=tf.constant(5,name=\"input_a\")\n#b=tf.constant(3,name=\"input_b\")\n#c=tf.mul(a,b,name=\"mul_c\")\n#d=tf.add(a,b,name=\"add_d\")\n#e=tf.add(c,d,name=\"add_e\")\n\n#define model with tensors\n#a=tf.constant([5,3],name=\"input_a\")\n#b=tf.reduce_prod(a,name=\"prod_b\")\n#c=tf.reduce_sum(a,name=\"sum_c\")\n#d=tf.add(b,c,name=\"add_d\")\n\n#run model\n#sess = tf.Session()\n#print(sess.run(d))\n\n\n\"\"\"\nVariable Types:\ntf.constant(values,dtype)\n\ntf.placeholder(dtype,[shape])\ninit using dictionary with key same as var name\ncan put none as shape arg for arbitrary length\n\ntf.Variable(value,dtype)\ninit by calling init=tf.initialize_all_variables() in same graph after Var declaration\nfinally call sess.run(init) before sess.run()\nVariable.assign_add() adds value to variable during graph execution\n\noverloaded operatiors:\n-a,~a,abs(a),a+b,a-b,a*b,a/b,a//b(floor_div),a%b,a**b,<,<=,>,>=,(&,|,^,must be bool)\n\nNAMESPACES:\n\n  with graph.as_default(): to add to a graph\n  with tf.name_scope(\"scopeName\"): to make boxes in viewboard\n\n\nOUTPUTING DATA TO TENSORBOARD:\n\n  tf.scalar_summary(b'Name',varName,name=\"nodeName?\"\n\nTRAINING AND TESTING:\n\n  usually split 70:30\n\n\"\"\"\n\ndef tfFun(input):\n  return -input\n\n#create graph\ng=tf.Graph()\nwith g.as_default():\n  x=tf.placeholder(tf.int32,[2])\n#  x=np.array([1,2])\n#  y=np.array([3,4],dtype='i4')\n  ya=np.array([3,4],dtype='i4')\n  y=tf.Variable(ya)\n  init=tf.initialize_all_variables()\n  a=tf.reduce_prod(x)\n  b=tf.reduce_sum(y)\n  c=tf.add(a,b)\n  d=tfFun(c)\n\ninput_dict={x:np.array([1,2],dtype='i4')}\nsess=tf.Session(graph=g)\n\nsess.run(init)\nprint(sess.run(d,input_dict))\nViewBoard(sess,\"myGraph\")\nsess.close()\n\n#feed_dict can feed replacement values into a run call\n\n#tf.initialize_all_variables() inits all tf vars","sub_path":"ex.py","file_name":"ex.py","file_ext":"py","file_size_in_byte":2144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"17901744","text":"import numpy as np\nimport cv2\n#import sift\nimport glob\n\ncvSift = cv2.SIFT()\nbf = cv2.BFMatcher()\ndef getSiftFeatureValue(img):\n\t_, des = cvSift.detectAndCompute(img, None)\n\t#des = sift.getDescriptor(img)\n\n\treturn des\n\ndef cardRecognize(card):\t\n\tcamSiftValues = getSiftFeatureValue(card)\n\tmaxGood = 6\n\tbestMatch = None\n\n\tfor modelCardName,modelDes in modelCards.items():\t\n\t\tmatches = bf.knnMatch(camSiftValues, modelDes, k=2)\n\t\t#matches = sift.match(camSiftValues, modelDes)\n\t\tgood = 0\n\t\tfor m,n in matches:\n\t\t\tif m.distance < 0.5 * n.distance:\n\t\t\t\tgood = good + 1\n\t\tif good >= maxGood:\n\t\t\tmaxGood = good\n\t\t\tbestMatch = modelCardName\n\n\tcv2.putText(card, str(bestMatch), (20,60), cv2.FONT_HERSHEY_SIMPLEX, 2,(10,10,255),2)\n\tcv2.imshow('Camera', card)\n\nmodelCards = {}\nfor imagePath in glob.glob(\"*.jpg\"):\n\tmodelCard = cv2.imread(imagePath, 0)\n\tmodelSiftValues = getSiftFeatureValue(modelCard)\n\tcardName = imagePath.replace(\".jpg\", \"\")\n\tmodelCards[cardName] = modelSiftValues\n\ncap = cv2.VideoCapture(0)\nwhile(True):\n    _, frame = cap.read()\n    cardRecognize(frame)\n    if cv2.waitKey(10) & 0xFF == 27: \n        break\n\ncap.release()\ncv2.destroyAllWindows()","sub_path":"sift/sift-based-recognition-realtime.py","file_name":"sift-based-recognition-realtime.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"562767533","text":"import win32api\nimport pythoncom\nimport pyHook\n\n###\n#def OnKeyboardEventAll(event):\n#    print('MessageName:',event.MessageName)\n#    print('Message:',event.Message)\n#    print('Time:',event.Time)\n#    print('Window:',event.Window)\n#    print('WindowName:',event.WindowName)\n#    print('Ascii:', event.Ascii, chr(event.Ascii))\n#    print('Key:', event.Key)\n#    print('KeyID:', event.KeyID)\n#    print('ScanCode:', event.ScanCode)\n#    print('Extended:', event.Extended)\n#    print('Injected:', event.Injected)\n#    print('Alt', event.Alt)\n#    print('Transition', event.Transition)\n#    print('---')\n#\n\ndef OnKeyboardEvent(event):\n    # 0 или 1 - клавиша отжата\n    # (-127) или (-128) - клавиша нажата#\n    f12 = win32api.GetKeyState(0x7B)\n    shift_key = win32api.GetKeyState(0x10)\n    if event.Key == 'F12' and event.MessageName == 'key down':\n        if shift_key < 0:\n            print(\"Нажато Shift+F12\")\n        else:\n            print(\"Нажато F12\")\n    return True\n\nhm = pyHook.HookManager()       # создание экземпляра класса HookManager\nhm.KeyAll = OnKeyboardEvent     # отслеживаем нажатия клавиш\nhm.HookKeyboard()               # вешаем хук\npythoncom.PumpMessages()        # ловим сообщения\n","sub_path":"mike_dawson/other/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"256912311","text":"#!/usr/bin/python3\n# *_* coding: utf-8 *_*\n# @Author: shengyang\n# @Email: samonsix@163.com\n# @IDE: PyCharm\n# @File: mask_cluster.py\n# @Modify Time        @Author    @Version    @Desciption\n# ----------------    -------    --------    -----------\n# 2019.11.15 14:25    shengyang      v0.1        creation\n\nimport numpy as np\nimport cv2\nfrom sklearn.cluster import k_means\nimport os\nimport os.path as osp\nimport pickle\nimport threading\n\n\nmask_file_pkl = \"mask_file_list.pkl\"\nmask_feature_pkl = \"mask_features.pkl\"\ntotal_num = 500000\n\nmask_feature_list = [None] * total_num\n\n\ndef read_and_getfeatures(file_path_list, start):\n    for i, f in enumerate(file_path_list):\n        gray = cv2.imread(f)[:, :, 1]\n        mask = cv2.resize(gray, (7, 7))\n        _, mask = cv2.threshold(mask, 128, 1, 0)\n        fea = mask.reshape((-1))\n        mask_feature_list[i + start] = fea\n\n\ndef get_all_mask(dirpath):\n    if osp.isfile(mask_feature_pkl):\n        print(\"found mask_feature_pkl, reading\")\n        with open(mask_feature_pkl, 'rb') as f:\n            mask_features = pickle.load(f)\n        return mask_features\n\n    if osp.isfile(mask_file_pkl):\n        with open(\"mask_file_list.pkl\", 'rb') as f:\n            mask_file_list = pickle.load(f)\n        print(\"in pkl file found files:\", len(mask_file_list))\n    else:\n        mask_file_list = []\n        for root_dir, sub_dir, file_list in os.walk(dirpath):\n            abs_file_list = [osp.join(root_dir, f) for f in file_list if f.endswith(\".jpg\")]\n            mask_file_list.extend(abs_file_list)\n            if len(mask_file_list) > total_num:\n                break\n        print(\"found files:\", len(mask_file_list))\n        mask_file_list = mask_file_list[: total_num]\n        with open(\"mask_file_list.pkl\", 'wb') as f:\n            pickle.dump(mask_file_list, f)\n\n    tid = []\n    for i in range(10):\n        batch = total_num // 10\n        start = i * batch\n        t = threading.Thread(target=read_and_getfeatures,\n                             args=(mask_file_list[start: start+batch], start))\n        t.start()\n        tid.append(t)\n\n    for t in tid:\n        t.join()\n\n    mask_features = np.array(mask_feature_list)\n    with open(mask_feature_pkl, 'wb') as f:\n        pickle.dump(mask_features, f)\n\n    return mask_features\n\n\nif __name__ == \"__main__\":\n    features = get_all_mask(\"/home/shengyang/haige_dataset/face_occusion/real_occlusion2/mask\")\n    # print(features.shape)\n    best_centers, best_labels, best_inertia = k_means(X=features, n_clusters=20)\n    _, best_centers = cv2.threshold(best_centers, 0.5, 1, 0)\n    best_centers = best_centers.reshape((-1, 7, 7))\n    print(best_centers)\n","sub_path":"occlusion_face_recognition/mask_cluster.py","file_name":"mask_cluster.py","file_ext":"py","file_size_in_byte":2645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"391971500","text":"import requests\nfrom copy import deepcopy\nimport time, random\nimport json\nimport pdb\nfrom citadel import Citadel\n\nimport config\n\nbase_url = 'http://' + config.SERVER_NAME\n\ncitadel = Citadel(base_url)\n\ntest_point_metadata = {\n        'name': 'example_point_8',\n        'tags': { \n            'point_type':'temperature',\n            'unit':'DEG_F',\n            'source_reference': 'http://aaa.com',\n            'license': 'gplv2'\n            },\n        'geometry':{\n            'coordinates':[0,0],\n            'type':'Point'\n            }\n        }\n\n\ntest_point_metadata_2 = deepcopy(test_point_metadata)\ntest_point_metadata_2['name'] = 'example_point_7'\n\nmetadata_dict = {\n        test_point_metadata['name']: test_point_metadata,\n        test_point_metadata_2['name']: test_point_metadata_2\n        }\n\ndef test_create_point(metadata):\n    print('Init adding point test')\n    res = citadel.create_point(metadata)\n    if not res['success']:\n        print(res['result']['reason'])\n        assert(False)\n    print('Done adding point test')\n\n\ndef _get_uuid_by_name(name):\n    res = citadel.query_points(name=name)\n    if res['success']:\n        return res['result']['point_list'][0]['uuid']\n    else:\n        print(res['result']['reason'])\n        assert(False)\n\ndef test_find_one_point():\n    print('Init finding a point test')\n    query = test_point_metadata['name']\n    res = citadel.query_points(tag_query=query)\n    if res['result']:\n        print(res['result'])\n    else:\n        print(res['result']['reason'])\n        assert(False)\n\ndef test_find_all_points():\n    print('Init find all points test')\n    res = citadel.query_points()\n    if res['result']:\n        print(res['result'])\n    else:\n        print(res['result']['reason'])\n        assert(False)\n    print('Done find all points test')\n\n\ngeo_query = {\n        'type': 'bounding_box',\n        'geometry_list': [[-1,-1],[1,1]] \n        # [[westsouth_lng, westsouth_lat], [eastnorth_lng, eastnorth_lat]]\n        }\n\ndef test_geo_query(geo_query):\n    print('Init geo query')\n    res = citadel.query_points(geo_query=geo_query)\n    if res['result']:\n        print(res['result'])\n    else:\n        print(res['result']['reason'])\n        assert(False)\n    print('Done geo query')\n\n\n\ndef test_delete_point():\n    print('Init point delete test')\n    # find uuid\n    uuid = _get_uuid_by_name(test_point_metadata['name'])\n\n    # delete the uuid\n    res = citadel.delete_point(uuid)\n    if not res['success']:\n        print(res['result']['reason'])\n        assert(False)\n\n    print('Done point delete test')\n\nstart_time = '1488830000'\nend_time = '1488840000'\n\ntest_ts_data = {\n        start_time: 777,\n        end_time: 555,\n        }\n\ndef test_put_timeseries(ts_data):\n    print('Init put timeseries test')\n    uuid = _get_uuid_by_name(name=test_point_metadata['name'])\n    res = citadel.put_timeseries(uuid, ts_data)\n    if not res['success']:\n        print(res['result'])\n        assert(False)\n    print('Done put timeseries test')\n\ndef test_get_timeseries():\n    print('Init get timeseries test')\n    uuid = _get_uuid_by_name(test_point_metadata['name'])\n    start_time = str(int(start_time) - 1000)\n    end_time = str(int(end_time) + 1000)\n    res = citadel.get_timeseries(uuid, start_time, end_time)\n    if res['success']:\n        data = res['result']['data']\n        if data!=test_ts_data:\n            print('Incorrect data')\n            print('Original data: ', test_ts_data)\n            print('Received data: ', data)\n            assert(False)\n    else:\n        print(res['result']['reason'])\n        assert(False)\n    print('Done get timeseries test')\n\ndef test_delete_timeseries():\n    print('Init delete timeserie partially test')\n    uuid = _get_uuid_by_name(test_point_metadata['name'])\n    delete_start_time = str(int(start_time) - 100)\n    delete_end_time = str(int(start_time) + 10)\n\n    res = citadel.delete_timeseries(uuid, start_time, end_time)\n    if not res['success']:\n        print(res['result']['reason'])\n        assert(False)\n    print('Done delete timeserie partially test')\n\nif __name__ == '__main__':\n    try:    \n        test_create_point(test_point_metadata)\n        test_create_point(test_point_metadata_2)\n        test_find_one_point()\n        test_find_all_points()\n        #test_geo_query(geo_query)\n        test_put_timeseries(test_ts_data)\n        test_get_timeseries()\n        test_delete_timeseries()\n        test_delete_point()\n    except:\n        test_delete_point()\n\n","sub_path":"test/rest_api_with_wrapper_test.py","file_name":"rest_api_with_wrapper_test.py","file_ext":"py","file_size_in_byte":4459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"276546031","text":"import os\nimport sys\n\nsys.path.insert(0,\n                os.path.dirname(os.path.realpath(__file__))[\n                    0:-len(\"quests\")])\nfrom QuestInfo import Quest\n\n\nclass Phite_Club(Quest):\n\n    def __init__(self):\n        super().__init__(\"'Phite Club\")\n        self.age = 5\n        self.difficulty = \"Master\"\n        self.length = \"Short\"\n        self.quest_points = 1\n\n        self.other_requirements.append(\"Rank 9 in overall Menaphos reputation\")\n","sub_path":"quests/Phite_Club.py","file_name":"Phite_Club.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"377671197","text":"from difflib import get_close_matches\nimport json\n\ndef search_value(str1):\n    str1 = str1.lower()\n    if str1 in data:\n        res = data[str1]\n    elif len(get_close_matches(str1, data.keys()))>0:\n        print(\"Did you mean %s instead?\" % get_close_matches(str1, data.keys())[0])\n        yn = input(\"\\nY/N? \\n\")\n        if yn.lower() == 'y':\n            res = data[get_close_matches(str1, data.keys())[0]]\n        else:\n            res = \"We don't have this word\"\n    else:\n\n        res = \"We don't have this word\"\n    return res\n\n\ndata = json.load(open('data.json'))\nword = input(\"Enter a word: \")\n\nprint(search_value(word))\n\n\n","sub_path":"dictionary_from_json_console/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"17711288","text":"# Given a binary grid where 0 represents water and 1 represents\n# land. An island is surrounded by water and is formed by\n# connecting adjacent lands horizontally or vertically.\n# Delete all islands except their borders. A border is land\n# adjacent to water. You may assume all four edges of the\n# grid are surrounded by water.\n\n# Just replace the ones want to delete with -1\n\n# NO NEED FOR BFS/DFS\n\n\ndef delete_islands(grid):\n    for i in range(1, len(grid) - 1):\n        for j in range(1, len(grid[0]) - 1):\n            if grid[i][j] == 1 and should_delete(grid, i, j):\n                grid[i][j] = -1\n    for i in range(1, len(grid) - 1):\n        for j in range(1, len(grid[0]) - 1):\n            if grid[i][j] == -1:\n                grid[i][j] = 0\n    return grid\n\n\ndef should_delete(grid, x, y):\n    for i, j in [[0, 1], [1, 0], [-1, 0], [0, -1], [1, 1], [-1, 1], [1, -1], [-1, -1]]:\n        next_x, next_y = i + x, j + y\n        if not grid[next_x][next_y]:\n            return False\n    return True\n\n\nprint(delete_islands(\n\n    [[0, 0, 0, 1, 1, 1],\n     [0, 0, 0, 1, 1, 1],\n        [1, 1, 1, 1, 1, 1],\n        [1, 1, 1, 1, 1, 1],\n        [1, 1, 1, 1, 1, 1]]\n\n))\n","sub_path":"g_phone/delete_islands.py","file_name":"delete_islands.py","file_ext":"py","file_size_in_byte":1167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"384848786","text":"import pygame\nimport os\nimport sys\nimport time\nimport random\nfrom pygame.locals import *\n\npygame.init()\nwidth, height = 640, 480\nwhite = (255, 255, 255)\ngreen = (0, 255, 0)\nblue = (0, 0, 128)\nblack = (0, 0, 0)\nscreen = pygame.display.set_mode((width, height), 0, 32)\n\ndef create_image(image_name):\n    return pygame.image.load(os.path.join(\"../resources/images\", image_name))\n\n\ndef create_text(text):\n    font = pygame.font.SysFont(None, 32)\n    text = font.render(text, True, black, white).convert()\n    return text\n\n\ndef success():\n    print(\"Success\")\n\ndef failure():\n    print(\"Failure\")\n\nFPS = 30\nfpsClock = pygame.time.Clock()\nintro = True\nwhile intro:\n    screen.fill(white)\n    screen.blit(create_text(\"Γειά σου! Θα παίξουμε ένα παιχνίδι προπαίδειας\"), (10, 40))\n    screen.blit(create_image(\"dog.png\"), (200, 100))\n    pygame.display.update()\n    fpsClock.tick(FPS)\n    time.sleep(2)\n    screen.fill(white)\n    pygame.display.update()\n    intro = False\nrandints = sorted((random.randint(1, 9), random.randint(1, 9)))\nresult = randints[0] * randints[1]\ncalc = f\"Πόσο κάνει {randints[0]} Χ {randints[1]} = \"\nres = 0\nq = [0, 0]\nwins = 0\nfails = 0\ntwodigit = False\nwhile True:\n    screen.fill(white)\n    screen.blit(create_text(calc), (10, 40))\n    screen.blit(create_text(f\"Σκορ: {wins} Νίκες {fails} Ήττες\"), (150, 430))\n    screen.blit(create_image(\"dog.png\"), (200, 100))\n    pygame.display.update()\n    for event in pygame.event.get():\n        if event.type == QUIT:\n            pygame.quit()\n            sys.exit()\n        if event.type == pygame.KEYDOWN:\n            if event.key in range(48, 58):\n                num = event.key - 48\n                if q[0] == 0:\n                    q[0] = num\n                else:\n                    q[1] = num\n                    twodigit = True\n                calc += str(num)\n            if event.key == pygame.K_RETURN:\n                if twodigit:\n                    res = q[0] * 10 + q[1]\n                else:\n                    res = q[0]\n                if res == result:\n                    calc = f\"Μπράβο !! Συγχαρητήρια !! Γουφ!\"\n                    wins += 1\n                    screen.fill(white)\n                    screen.blit(create_text(calc), (10, 40))\n                    screen.blit(create_text(f\"Σκορ: {wins} Νίκες {fails} Ήττες\"), (150, 430))\n                    screen.blit(create_image(\"dog.png\"), (200, 100))\n                    pygame.display.update()\n                    time.sleep(1)\n                else:\n                    calc = f\"Γκρρρ, κάτι δεν υπολόγισες καλά\"\n                    screen.fill(white)\n                    fails += 1\n                    screen.blit(create_text(calc), (10, 40))\n                    screen.blit(create_text(f\"Σκορ: {wins} Νίκες {fails} Ήττες\"), (150, 430))\n                    screen.blit(create_image(\"dog.png\"), (200, 100))\n                    pygame.display.update()\n                    time.sleep(1)\n\n                randints = sorted((random.randint(1, 9), random.randint(1, 9)))\n                result = randints[0] * randints[1]\n                calc = f\"Πόσο κάνει {randints[0]} Χ {randints[1]} = \"\n                dec = -1\n                res = 0\n                q = [0,0]\n                twodigit = False\n\n\n\n\n\n\n","sub_path":"propaideia/paidia.py","file_name":"paidia.py","file_ext":"py","file_size_in_byte":3388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"195734822","text":"\"\"\" Copyright 2012, 2013 UW Information Technology, University of Washington\n\n    Licensed under the Apache License, Version 2.0 (the \"License\");\n    you may not use this file except in compliance with the License.\n    You may obtain a copy of the License at\n\n    http://www.apache.org/licenses/LICENSE-2.0\n\n    Unless required by applicable law or agreed to in writing, software\n    distributed under the License is distributed on an \"AS IS\" BASIS,\n    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n    See the License for the specific language governing permissions and\n    limitations under the License.\n\"\"\"\n\nfrom spotseeker_server.models import *\nfrom django.test.client import Client\nfrom django.test import TestCase\nimport simplejson as json\nfrom mock import patch\nfrom spotseeker_server import models\nfrom django.test.utils import override_settings\n\n\n@override_settings(\n    SPOTSEEKER_AUTH_MODULE='spotseeker_server.auth.all_ok')\n@override_settings(\n    SPOTSEEKER_SPOT_FORM='spotseeker_server.default_forms.spot.'\n                         'DefaultSpotForm')\n@override_settings(\n    SPOTSEEKER_SPOTEXTENDEDINFO_FORM='spotseeker_server.default_forms.spot.'\n                                     'DefaultSpotExtendedInfoForm')\nclass SpotSchemaTest(TestCase):\n    def test_content_type(self):\n        c = Client()\n        url = \"/api/v1/schema\"\n        response = c.get(url)\n        self.assertEqual(response[\"Content-Type\"], \"application/json\")\n\n    def test_regular_spot_info(self):\n        c = Client()\n        response = c.get(\"/api/v1/schema\")\n        schema = json.loads(response.content)\n\n        self.assertEqual(schema[\"manager\"], \"unicode\")\n        self.assertEqual(schema[\"capacity\"], \"int\")\n        self.assertEqual(schema[\"last_modified\"], \"auto\")\n        self.assertEqual(schema[\"uri\"], \"auto\")\n\n    def test_location_spot_info(self):\n        c = Client()\n        response = c.get(\"/api/v1/schema\")\n        schema = json.loads(response.content)\n        schema_location = schema[\"location\"]\n\n        self.assertEqual(schema_location[\"latitude\"], \"decimal\")\n        self.assertEqual(schema_location[\"room_number\"], \"unicode\")\n        self.assertEqual(schema_location[\"floor\"], \"unicode\")\n\n    def test_spot_image_info(self):\n        c = Client()\n        response = c.get(\"/api/v1/schema\")\n        schema = json.loads(response.content)\n        schema_image = schema[\"images\"][0]\n\n        self.assertEqual(schema_image[\"description\"], \"unicode\")\n        self.assertEqual(schema_image[\"modification_date\"], \"auto\")\n        self.assertEqual(schema_image[\"width\"], \"int\")\n\n    def test_spot_types(self):\n        SpotType.objects.create(name=\"Jedi\")\n        SpotType.objects.create(name=\"Sith\")\n\n        c = Client()\n        response = c.get(\"/api/v1/schema\")\n        schema = json.loads(response.content)\n        schema_types = schema[\"type\"]\n\n        self.assertEqual(len(schema_types), 2)\n        SpotType.objects.create(name=\"Ewok\")\n\n        response = c.get(\"/api/v1/schema\")\n        schema = json.loads(response.content)\n        schema_types = schema[\"type\"]\n\n        self.assertEqual(len(schema_types), 3)\n\n    def test_extended_info(self):\n        test_spot = Spot.objects.create(id=1, name=\"Test\")\n\n        SpotExtendedInfo.objects.create(spot=test_spot,\n                                        key=\"noise_level\",\n                                        value=[\"silent\",\n                                               \"quiet\",\n                                               \"moderate\",\n                                               \"loud\",\n                                               \"variable\"])\n        SpotExtendedInfo.objects.create(spot=test_spot,\n                                        key=\"has_computers\",\n                                        value=[\"true\"])\n        SpotExtendedInfo.objects.create(spot=test_spot,\n                                        key=\"orientation\",\n                                        value=\"unicode\")\n        SpotExtendedInfo.objects.create(spot=test_spot,\n                                        key=\"num_computers\",\n                                        value=\"int\")\n\n        c = Client()\n        response = c.get(\"/api/v1/schema\")\n        schema = json.loads(response.content)\n        extended_info = schema[\"extended_info\"]\n\n        self.assertEqual(extended_info[\"noise_level\"], \"unicode\")\n        self.assertEqual(extended_info[\"has_computers\"], \"unicode\")\n        self.assertEqual(extended_info[\"orientation\"], \"unicode\")\n        self.assertEqual(extended_info[\"num_computers\"], \"unicode\")\n","sub_path":"spotseeker_server/test/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":4590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"545542480","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nHomework 7 - Unsupervised Learning (Handcraft PCA)\r\nCreated on 2019/7/18 下午 06:42\r\n@author: Ivan Y.W.Chiu\r\n\"\"\"\r\n\r\nimport os\r\nimport sys\r\nimport numpy as np\r\nfrom skimage.io import imread, imsave\r\n# import cv2\r\n\r\nIMAGE_PATH = r'./data/ml2019spring-hw7/Aberdeen'\r\n\r\n# Images for compression & reconstruction\r\ntest_image = ['1.jpg', '10.jpg', '22.jpg', '37.jpg', '72.jpg']\r\n\r\n# Number of principal components used\r\nk = 5\r\n\r\n\r\ndef process(M):\r\n    M -= np.min(M)\r\n    M /= np.max(M)\r\n    M = (M * 255).astype(np.uint8)\r\n    return M\r\n\r\n\r\nfilelist = os.listdir(IMAGE_PATH)\r\n\r\n# Record the shape of images\r\nimg_shape = imread(os.path.join(IMAGE_PATH, filelist[0])).shape\r\n\r\nimg_data = []\r\nfor filename in filelist:\r\n    tmp = imread(os.path.join(IMAGE_PATH, filename))\r\n    img_data.append(tmp.flatten())\r\n\r\ntraining_data = np.array(img_data).astype('float32')\r\n\r\n# Calculate mean & Normalize\r\nmean = np.mean(training_data, axis=0)\r\ntraining_data -= mean\r\n\r\n# Use SVD to find the eigenvectors\r\n# np.linalg.svd()\r\nu, s, v = np.linalg.svd(training_data, full_matrices=False)\r\nS = np.diag(s[0:k])\r\nfor x in test_image:\r\n    # Load image & Normalize\r\n    print(x)\r\n    picked_img = imread(os.path.join(IMAGE_PATH, x))\r\n    X = picked_img.flatten().astype('float32')\r\n    X -= mean\r\n\r\n    # Compression\r\n    # weight = np.array([s.dot(v) for i in range(k)])\r\n    weight = S.dot(v[0:5])\r\n    #\r\n    # Reconstruction\r\n    i = int(filelist.index(x))\r\n    # reconstruct = process(u[i, 0:k].dot(weight) + mean)\r\n    reconstruct = process(u[i, 0:k].dot(weight) + mean)\r\n    imsave(os.path.join(\"./hw7\", \"reconstruction_\" + x[:-4] + '.jpg'), reconstruct.reshape(img_shape))\r\n\r\naverage = process(mean)\r\nimsave(os.path.join(\"./hw7\", 'average.jpg'), average.reshape(img_shape))\r\n\r\nfor x in range(5):\r\n    eigenface = process(weight)\r\n    imsave(os.path.join(\"./hw7\", \"eigenface_\" + str(x) + '.jpg'), eigenface.reshape(img_shape))\r\n\r\nfor i in range(5):\r\n    number = s[i] * 100 / sum(s)\r\n    print(number)","sub_path":"hw7/hw7_01.py","file_name":"hw7_01.py","file_ext":"py","file_size_in_byte":2017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"562587685","text":"from functools import reduce\nimport cv2\nimport matplotlib.pyplot as plt\nimport time\nfrom scipy.io import *\nimport spectral\nfrom spectral import *\nimport joblib\nfrom sklearn.model_selection import *\nimport numpy as np\nfrom sklearn.svm import SVC\nfrom sklearn import *\nimport pandas as pd\n\n'''\n    在SVM.py分类的基础上，将错误的分类像素点标记出来，实现可视化\n'''\n\ninput_image = loadmat('../dataset/origin/PaviaU.mat')['paviaU']\noutput_image = loadmat('../dataset/origin/PaviaU_gt.mat')['paviaU_gt']\n\n# 导入数据集切割训练与测试数据\ndata = pd.read_csv('../dataset/PaviaU_gt_band_label_loc.csv', header=None)\ndata = data.as_matrix()\n\n# 获取特征矩阵\ndata_content = data[:, :-2]\n# 获取标记矩阵\ndata_label = data[:, -2]\n# 获取位置矩阵\ndata_loc = data[:, -1]\n\n\n# 计算正确率\ndef show_accuracy(a, b, tip):\n    acc = a.ravel() == b.ravel()\n    acc = ('%.4f' % np.mean(acc))\n    print(tip + '正确率：', (float(acc) * 100), '%')\n\n\n# 返回错误坐标\ndef return_wrong(a, b):\n    acc = a.ravel() == b.ravel()\n    wrong = list()\n    i = 0\n    for cur in acc:\n        if cur:\n            pass\n        else:\n            cur_loc = data_loc[i]\n            wrong.append(cur_loc)\n        i = i + 1\n    return wrong\n\n\n'''\n    SVC+CV\n'''\ntime_start = time.time()\n\n# 加载模型\nmodel = joblib.load('./models/SVC.m')\n\n# 预测gt全集\npred = model.predict(data_content)\n\n# 计算gt全集正确率\nshow_accuracy(pred, data_label, 'SVC')  # SVC正确率： 97.05 %\n\n# 获取gt错误分类坐标\nwrong = return_wrong(pred, data_label)\n\n# print(wrong)\nprint(len(wrong))  # 1264\n\ntime_end = time.time()\nprint('totally cost', time_end - time_start)  # 24s\n\n''' \n    单颜色画图并将错误的分类像素标记SVM_WrongClass.png\n'''\n\n# 读取标记图片paviaU_gt\noutput_image = loadmat('../dataset/origin/PaviaU_gt.mat')['paviaU_gt']  # (610, 340)\n\n# 单颜色（黄色）显示标记图片\n\n# 初始化个通道，用于生成新的paviaU_gt\nc1 = loadmat('../dataset/origin/PaviaU_gt.mat')['paviaU_gt']\nc2 = loadmat('../dataset/origin/PaviaU_gt.mat')['paviaU_gt']\nc3 = loadmat('../dataset/origin/PaviaU_gt.mat')['paviaU_gt']\n\n# 现将全部分类坐标用黄色标记\nfor i in range(610):\n    for j in range(340):\n        if (output_image[i][j] == 0):\n            c1[i][j] = 255\n            c2[i][j] = 255\n            c3[i][j] = 255\n        if (output_image[i][j] == 1):\n            c1[i][j] = 0\n            c2[i][j] = 255\n            c3[i][j] = 255\n        if (output_image[i][j] == 2):\n            c1[i][j] = 0\n            c2[i][j] = 255\n            c3[i][j] = 255\n        if (output_image[i][j] == 3):\n            c1[i][j] = 0\n            c2[i][j] = 255\n            c3[i][j] = 255\n        if (output_image[i][j] == 4):\n            c1[i][j] = 0\n            c2[i][j] = 255\n            c3[i][j] = 255\n        if (output_image[i][j] == 5):\n            c1[i][j] = 0\n            c2[i][j] = 255\n            c3[i][j] = 255\n        if (output_image[i][j] == 6):\n            c1[i][j] = 0\n            c2[i][j] = 255\n            c3[i][j] = 255\n        if (output_image[i][j] == 7):\n            c1[i][j] = 0\n            c2[i][j] = 255\n            c3[i][j] = 255\n        if (output_image[i][j] == 8):\n            c1[i][j] = 0\n            c2[i][j] = 255\n            c3[i][j] = 255\n        if (output_image[i][j] == 9):\n            c1[i][j] = 0\n            c2[i][j] = 255\n            c3[i][j] = 255\n\n# 将错误分类坐标用红色标记\nfor value in wrong:\n    i = int(value // 340)\n    j = int(value % 340)\n    c1[i][j] = 255\n    c2[i][j] = 0\n    c3[i][j] = 255\n\n# 合并三个通道，组成三通道RGB图片\nsingle_merged = cv2.merge([c1, c2, c3])\n# 存储图片\ncv2.imwrite('../imgs/SVM_WrongClass.png', single_merged)\n# 显示图片\ncv2.imshow(\"output\", single_merged)\n# 不闪退\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n'''\n    多颜色画图并将错误的分类像素标记SVM_WrongClass_multicolor.png\n'''\n# 导入gt数据集\npavia_gt = pd.read_csv('../dataset/PaviaU_gt_band_label_loc.csv', header=None)\npavia_gt = pavia_gt.as_matrix()\n\n# 获取特征矩阵\npavia_gt_content = pavia_gt[:, :-2]\n# 获取标记矩阵\npavia_gt_label = pavia_gt[:, -2]\n# 获取位置矩阵\npavia_gt_loc = pavia_gt[:, -1]\n\n# 导入全部数据集\npavia = pd.read_csv('../dataset/PaviaU_band_label_loc.csv', header=None)\npavia = pavia.as_matrix()\n\n# 获取通道矩阵\npavia_content = pavia[:, :-2]\n# 获取标记矩阵\npavia_label = pavia[:, -2]\n# 获取位置矩阵\npavia_loc = pavia[:, -1]\n\n# 初始化个通道，用于生成新的paviaU_gt\nc1 = loadmat('../dataset/origin/PaviaU_gt.mat')['paviaU_gt']\nc2 = loadmat('../dataset/origin/PaviaU_gt.mat')['paviaU_gt']\nc3 = loadmat('../dataset/origin/PaviaU_gt.mat')['paviaU_gt']\n\ncursor = 0\n\n# 现将全部分类坐标用9种彩色标记，0背景类别用白色\nfor i in range(610):\n    for j in range(340):\n        if (output_image[i][j] == 0):\n            c1[i][j] = 255\n            c2[i][j] = 255\n            c3[i][j] = 255\n            continue\n        cur_band = pavia_gt_content[cursor]\n        cur_pred = int(model.predict([cur_band]))\n        if (cur_pred == 1):\n            c1[i][j] = 20\n            c2[i][j] = 104\n            c3[i][j] = 82\n        if (cur_pred == 2):\n            c1[i][j] = 40\n            c2[i][j] = 200\n            c3[i][j] = 160\n        if (cur_pred == 3):\n            c1[i][j] = 60\n            c2[i][j] = 240\n            c3[i][j] = 111\n        if (cur_pred == 4):\n            c1[i][j] = 80\n            c2[i][j] = 77\n            c3[i][j] = 190\n        if (cur_pred == 5):\n            c1[i][j] = 14\n            c2[i][j] = 80\n            c3[i][j] = 90\n        if (cur_pred == 6):\n            c1[i][j] = 120\n            c2[i][j] = 60\n            c3[i][j] = 150\n        if (cur_pred == 7):\n            c1[i][j] = 140\n            c2[i][j] = 200\n            c3[i][j] = 255\n        if (cur_pred == 8):\n            c1[i][j] = 160\n            c2[i][j] = 5\n            c3[i][j] = 100\n        if (cur_pred == 9):\n            c1[i][j] = 180\n            c2[i][j] = 180\n            c3[i][j] = 255\n        cursor = cursor + 1\n\n# 将错误分类坐标用黑色标记\nfor value in wrong:\n    j = int(value % 340)\n    i = int(value // 340)\n    c1[i][j] = 0\n    c2[i][j] = 0\n    c3[i][j] = 0\n\n# 合并三个通道，组成三通道RGB图片\nsingle_merged = cv2.merge([c1, c2, c3])\n# 存储图片\ncv2.imwrite('../imgs/SVM_WrongClass_multicolor.png', single_merged)\n# 显示图片\ncv2.imshow(\"output\", single_merged)\n# 不闪退\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","sub_path":"SVM/5.SVM_visual.py","file_name":"5.SVM_visual.py","file_ext":"py","file_size_in_byte":6548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"416042697","text":"import torch\nimport torch.nn.functional as F\nimport numpy as np\nimport os\nimport random\nfrom dataset import PF_WILLOW\nfrom model_noise import PMDNet\n#import matplotlib.pyplot as plt\nimport argparse\n\nparser = argparse.ArgumentParser(description=\"SFNet evaluation\")\nparser.add_argument('--num_workers', type=int, default=4, help='number of workers for data loader')\nparser.add_argument('--feature_h', type=int, default=20, help='height of feature volume')\nparser.add_argument('--feature_w', type=int, default=20, help='width of feature volume')\nparser.add_argument('--test_csv_path', type=str, default='data/PF_WILLOW/test_pairs_pf.csv', help='directory of test csv file')\nparser.add_argument('--test_image_path', type=str, default='/data/PF_WILLOW/', help='directory of test data')\nparser.add_argument('--beta', type=float, default=50, help='inverse temperature of softmax @ kernel soft argmax')\nparser.add_argument('--kernel_sigma', type=float, default=5, help='standard deviation of Gaussian kerenl @ kernel soft argmax')\nparser.add_argument('--eval_type', type=str, default='bounding_box', choices=('bounding_box','image_size'), help='evaluation type for PCK threshold (bounding box | image size)')\nargs = parser.parse_args()\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"2\"\n\n# Data Loader\nprint(\"Instantiate dataloader\")\ntest_dataset = PF_WILLOW(args.test_csv_path, args.test_image_path, args.feature_h, args.feature_w, args.eval_type)\ntest_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n                                           batch_size=1,\n                                           shuffle=False, num_workers = args.num_workers)\n\n\n# Instantiate model\nprint(\"Instantiate model\")\nnet = PMDNet(args.feature_h, args.feature_w, beta=args.beta, kernel_sigma = args.kernel_sigma)\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nnet.to(device)\n\n# Load weights\nprint(\"Load pre-trained weights\")\nbest_weights = torch.load(\"./models/pascal_weakly.pt\")\nadap3_dict = best_weights['state_dict1']\nadap4_dict = best_weights['state_dict2']\nchn4_dict = best_weights['chn4_dict']\nnet.adap_layer_feat3.load_state_dict(adap3_dict, strict=True)\nnet.adap_layer_feat4.load_state_dict(adap4_dict, strict=True)\nnet.chn4.load_state_dict(chn4_dict, strict=True)\n\n\n# PCK metric from 'https://github.com/ignacio-rocco/weakalign/blob/master/util/eval_util.py'\ndef correct_keypoints(source_points, warped_points, L_pck, alpha=0.1):\n    # compute correct keypoints\n    p_src = source_points[0,:]\n    p_wrp = warped_points[0,:]\n\n    N_pts = torch.sum(torch.ne(p_src[0,:],-1)*torch.ne(p_src[1,:],-1))\n    point_distance = torch.pow(torch.sum(torch.pow(p_src[:,:N_pts]-p_wrp[:,:N_pts],2),0),0.5)\n    L_pck_mat = L_pck[0].expand_as(point_distance)\n    correct_points = torch.le(point_distance, L_pck_mat * alpha)\n    pck = torch.mean(correct_points.float())\n    return pck\n\nwith torch.no_grad():\n    print('Computing PCK@Test set...')\n    net.eval()\n    total_correct_points = 0\n    total_points = 0\n    for i, batch in enumerate(test_loader):\n        src_image = batch['image1'].to(device)\n        tgt_image = batch['image2'].to(device)\n        output = net(src_image, tgt_image, train=False)\n\n        small_grid = output['grid_T2S'][:,:,:,:]\n        small_grid[:,:,:,0] = small_grid[:,:,:,0] * (args.feature_w//2)/(args.feature_w//2 - 0)\n        small_grid[:,:,:,1] = small_grid[:,:,:,1] * (args.feature_h//2)/(args.feature_h//2 - 0)\n        src_image_H = int(batch['image1_size'][0][0])\n        src_image_W = int(batch['image1_size'][0][1])\n        tgt_image_H = int(batch['image2_size'][0][0])\n        tgt_image_W = int(batch['image2_size'][0][1])\n        small_grid = small_grid.permute(0,3,1,2)\n        grid = F.interpolate(small_grid, size = (tgt_image_H,tgt_image_W), mode='bilinear', align_corners=True)\n        grid = grid.permute(0,2,3,1)\n        grid_np = grid.cpu().data.numpy()\n\n        image1_points = batch['image1_points'][0]\n        image2_points = batch['image2_points'][0]\n\n        est_image1_points = np.zeros((2,image1_points.size(1)))\n        for j in range(image2_points.size(1)):\n            point_x = int(np.round(image2_points[0,j]))\n            point_y = int(np.round(image2_points[1,j]))\n\n            if point_x == -1 and point_y == -1:\n                continue\n\n            if point_x == tgt_image_W:\n                point_x = point_x - 1\n\n            if point_y == tgt_image_H:\n                point_y = point_y - 1\n\n            est_y = (grid_np[0,point_y,point_x,1] + 1)*(src_image_H-1)/2\n            est_x = (grid_np[0,point_y,point_x,0] + 1)*(src_image_W-1)/2\n            est_image1_points[:,j] = [est_x,est_y]\n\n        total_correct_points += correct_keypoints(batch['image1_points'], torch.FloatTensor(est_image1_points).unsqueeze(0), batch['L_pck'], alpha=0.1)\n    PCK = total_correct_points / len(test_dataset)\n    print('PCK: %5f' % PCK)\n                \n","sub_path":"eval_willow.py","file_name":"eval_willow.py","file_ext":"py","file_size_in_byte":4893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"345419905","text":"import os\nimport math\nimport pygame\nimport util\n\nlocation = os.path.join(\"chromadude\", \"res\", \"JellyJump\")\n\n\ndef get_image(name):\n    return Images.images[name]\n\n\ndef load_image(file_name, alpha, size, alpha_value=None):\n    size = int(math.ceil(size[0])), int(math.ceil(size[1]))\n    if alpha:\n        image = pygame.transform.scale(pygame.image.load(os.path.join(location, file_name)).convert_alpha(), size)\n    else:\n        image = pygame.transform.scale(pygame.image.load(os.path.join(location, file_name)).convert(), size)\n    if alpha_value is not None:\n        image.set_alpha(alpha_value)\n    return image\n\n\ndef load_game_images():\n    Images.images = {\n        'char': load_image(\"char.png\", True, (util.get_dec_w(0.07), util.get_dec_w(0.07))),\n        'tile': load_image(\"jellytile.png\", False, (util.get_dec_w(0.1), util.get_dec_w(0.1))),\n        'chardead': load_image(\"chardead.png\", True, (util.get_dec_w(0.07), util.get_dec_h(0.07))),\n    }\n\n\nclass Images(object):\n    images = None\n    menu_images = None","sub_path":"src/chromadude/jellyjump/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"392652009","text":"\"\"\"\nListen to the notifications that are send by the NRC\n\"\"\"\nfrom vng_api_common.models import APICredential\nfrom vng_api_common.notifications.handlers import RoutingHandler, auth, log\nfrom zds_client.client import Client\n\nfrom drc_cmis.client import cmis_client\n\n\nclass ZakenHandler:\n    def handle(self, data: dict) -> None:\n        if data.get('actie') == 'create' and data.get('resource') == 'zaak':\n            zaaktype_url = data.get('kenmerken', {}).get('zaaktype')\n            client = Client.from_url(zaaktype_url)\n            client.auth = APICredential.get_auth(zaaktype_url)\n            zaaktype_data = client.retrieve('zaaktype', url=zaaktype_url)\n            print(zaaktype_data)\n            zaaktype_folder = cmis_client.get_or_create_zaaktype_folder(zaaktype_data)\n\n            zaak_url = data.get('resource_url')\n            client = Client.from_url(zaak_url)\n            client.auth = APICredential.get_auth(zaak_url)\n            zaak_data = client.retrieve('zaak', url=zaak_url)\n            print(zaak_data)\n            cmis_client.get_or_create_zaak_folder(zaak_data, zaaktype_folder)\n\n\ndefault = RoutingHandler({'autorisaties': auth, 'zaken': ZakenHandler()}, default=log)\n","sub_path":"drc_cmis/notifications.py","file_name":"notifications.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"466383258","text":"import numpy as np\n\n\ndef gaussian_pos_prob(X, Mu, Sigma, Phi):\n    \"\"\"\n    GAUSSIAN_POS_PROB Posterior probability of GDA.\n    Compute the posterior probability of given N data points X\n    using Gaussian Discriminant Analysis where the K gaussian distributions\n    are specified by Mu, Sigma and Phi.\n    Inputs:\n        'X'     - M-by-N numpy array, N data points of dimension M.\n        'Mu'    - M-by-K numpy array, mean of K Gaussian distributions.\n        'Sigma' - M-by-M-by-K  numpy array (yes, a 3D matrix), variance matrix of\n                  K Gaussian distributions.\n        'Phi'   - 1-by-K  numpy array, prior of K Gaussian distributions.\n    Outputs:\n        'p'     - N-by-K  numpy array, posterior probability of N data points\n                with in K Gaussian distribsubplots_adjustutions.\n    \"\"\"\n    N = X.shape[1]\n    K = Phi.shape[0]\n    p = np.zeros((N, K))\n    # 先计算likelihood\n    likelihood = np.zeros((N, K))\n    for i in range(N):\n        p_x = 0\n        for j in range(K):\n            x_minus_mu = X[:, i] - Mu[:, j]\n            sigma = Sigma[:, :, j]\n            det_sigma = np.linalg.det(sigma)\n            inv_sigma = np.linalg.inv(sigma)\n            base = 1.0 / (2 * np.pi * np.sqrt(np.abs(det_sigma)))\n            exponent = np.matmul(np.matmul(x_minus_mu.T, inv_sigma), x_minus_mu) * -0.5\n            likelihood[i, j] = base * np.exp(exponent)\n            p_x += likelihood[i, j] * Phi[j]\n        for j in range(K):\n            p[i, j] = likelihood[i, j] * Phi[j] / p_x\n    return p\n","sub_path":"gaussian_discriminant/gaussian_pos_prob.py","file_name":"gaussian_pos_prob.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"229348807","text":"from threading import Thread\nimport socket\n\n\n# 收数据\ndef receive_data(udp_socket):\n    while True:\n        receive_info = udp_socket.recvfrom(1024)\n        print(receive_info)\n\n\n# 发数据\ndef send_data(udp_socket, send_addr,send_port):\n    send_info = input('<<')\n    udp_socket.sendto(send_info.encode('utf-8'), (send_addr, send_port))\n\n\ndef main():\n    udp_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    udp_socket.bind(('', 9998))\n    send_addr = input(\"对方ip\\n\")\n    send_port = int(input('对方port\\n'))\n    ts = Thread(target=send_data, args=(udp_socket, send_addr, send_port))\n    tr = Thread(target=receive_data, args=(udp_socket, ))\n    ts.start()\n    tr.start()\n    ts.join()\n    tr.join()\nif __name__ == '__main__':\n    main()","sub_path":"python3/网络编程概述/4-多线程聊天室.py","file_name":"4-多线程聊天室.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"599586048","text":"\nimport math\n\n# Sprawdza czy podana liczba jest liczbą\n#       Armstronga (narcystyczną)\n# \n#  (n-cyfrowa liczba naturalna która jest\n# sumą swoich cyfr podniesionych do potęgi n)\n\ndef czyLiczbaArmstronga(foo):\n    fooList = list(map(int, str(foo)))\n    bar = 0\n    \n    for digit in fooList:\n        bar += digit ** len(fooList)\n    \n    if foo-bar==0:\n        return True\n    else:\n        return False\n\n\n# Zamiana miedzy systemami liczbowymi\n#\n\ndef zamianaNaPietnastkowy(foo):\n    list = []\n    bar = int(foo)\n    wynik = \"\"\n    switcher = [0,1,2,3,4,5,6,7,8,9,\"A\",\"B\",\"C\",\"D\",\"E\"]\n\n    while divmod(bar, 15)[0] != 0:\n        list.append(divmod(bar, 15)[1])\n        bar = divmod(bar, 15)[0]\n\n    if divmod(bar, 15)[0] == 0:\n        list.append(divmod(bar, 15)[1])\n    \n    for digit in list:\n        wynik += str(switcher[digit])\n\n    return wynik\n\n# Sprawdzenie czy podana liczba jest liczba pierwsza\n#     (podzielna tylko przez 2 liczby: 1 i n)\n\ndef czyPierwsza(foo):\n    bar = int(foo)\n\n    for x in range(2, round(math.sqrt(bar))):\n        if(bar%x==0):\n            return False\n    return True\n\n# Sprawdzenie czy podana liczba jest liczba doskonala \n#        (suma dzielnikow rowna liczbie)\n\ndef czyDoskonala(foo):\n    bar = int(foo)\n    list = []\n    wynik = 0\n    for x in range(1, bar):\n        if bar % x == 0: \n            list.append(x)\n    for x in list:\n        wynik += x\n\n    if wynik == bar:\n        return True\n    else:\n        return False\n\n# Rozkład liczby na czynniki pierwsze\n\ndef rozkladCzynnikiPierwsze(foo):\n    bar = int(foo)\n    wynik = []\n\n    if bar < 1:\n        return \"Error\"\n\n    while bar != 1:\n        for x in range(2, bar+1):\n            if bar % x == 0:\n                bar = bar // x\n                wynik.append(x)\n                break\n                \n    return wynik\n\n# Wspólny najmniejszy dzielnik dwóch liczb\n\ndef najmniejszyDzielnik2Liczb(foo1, foo2):\n    a = int(foo1)\n    b = int(foo2)\n\n    while True:\n        if a > b:\n            a -= b\n        if b > a:\n            b -= a\n        if a == b:\n            return a\n\n# Obliczenie n liczby fibonacci-ego\n\ndef fibonacci(foo):\n    bar = int(foo)\n\n    a, b = 0, 1\n    wynik = []\n\n    for x in range(1, bar):\n        wynik.append(b)\n        b += a\n        a = b - a\n\n    return wynik\n\n# Obliczanie reszty metodą zachłankową\n\ndef metodaZachlankowa(foo, tab):\n    bar = int(foo)\n\n    i = 0\n    wynik = []\n    while not bar <= 0:\n        if bar >= tab[i]:\n            a = bar // tab[i]\n            bar -= (tab[i]*a)\n            wynik.append([a, tab[i]])\n        i += 1\n    return wynik\n\n# Wyszukiwanie wzorca w ciagu znaków \n#          metodą naiwną\n\ndef wyszukiwanieNaiwne(wzorzec, tekst):\n    m = len(wzorzec)\n    n = len(tekst)\n    \n    wynik = []\n    i = 0\n    \n    while i<=n-m:\n        j = 0\n        while j < m and wzorzec[j] == tekst[i+j]:\n            j += 1\n        if j == m:\n            wynik.append(i)\n        i += 1\n    \n    return wynik\n\ndef sortowanieBabelkowe(foo):\n    tab = list(foo)\n    n = len(tab)\n    while n >= 1:\n        for i in range(0, n-1):\n            if tab[i] > tab[i+1]:\n                tab[i], tab[i+1] = tab[i+1], tab[i]            \n        n -= 1\n    return tab\n\ndef sortowaniePrzezWybieranie(foo):\n    tab = list(foo)\n    n = len(tab)\n    \n    for i in range(0, n-1):\n        min = i\n        for j in range(i+1, n):\n            if tab[j] <= tab[min]:\n                min = j\n        tab[i], tab[min] = tab[min], tab[i]\n    return tab\n\ndef sortowaniePrzezWstawianie(foo):\n    tab = list(foo)\n\n    for i in range(1, len(tab)):\n        current = tab[i]\n        tab.pop(i)\n        j = i - 1\n        while j >= 0 and current < tab[j]:\n            j -= 1\n        tab.insert(j+1, current)\n    return tab\n\nvar1 = input(\"Var1: \")\nvar2 = input(\"Var2: \")\n\ntab = [500, 200, 100, 50, 20, 10, 5, 2, 1]\n\nprint(sortowaniePrzezWstawianie(tab))\n","sub_path":"algorytmy.py","file_name":"algorytmy.py","file_ext":"py","file_size_in_byte":3872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"242117990","text":"cost = float(input(\"Input the cost of the item in $: \"))\nmonth = 0\ntotalInterestPaid = 0\ninterestRate = 0.02\nif cost <= 1000:\n    interestRate = 0.015\ndebt = cost\nwhile debt > 0:\n    month+= 1\n    monthsInterest = debt * interestRate\n    debt += monthsInterest\n    payment = 50.0\n    if debt < 50:\n        payment = debt\n    debt -= payment\n    print(\"Month: \"+str(month)+\n          \" Payment: \" +str(round(payment, 2))+\n          \" Interest paid: \"+str(round(monthsInterest, 2))+\n          \" Remaining debt: \"+str(round(debt, 2)))\n    totalInterestPaid += monthsInterest\n   \nprint(\"\\nNumber of months: \"+str(month))\nprint(\"Total interest paid: \"+str(round(totalInterestPaid, 2)))","sub_path":"Skilaverkefni/loan.py","file_name":"loan.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"521228632","text":"#!/usr/bin/env python3\nfrom sys import argv\nimport pdb\n\ndef isnum(n):\n    if n == None:\n        return False\n    try:\n        int(n)\n        return True\n    except ValueError:\n        return False\n\ndef unzip(n):\n    shl = []\n    elb = []\n    for i in n:\n        nums = [x.strip() for x in i.split(\",\")]\n        if not all(list(map(isnum, nums))):\n            shl.append(None)\n            elb.append(None)\n        else:\n            shl.append(nums[0])\n            elb.append(nums[1])\n    return {\"shl\": shl, \"elb\": elb}\n\ndef generate_pair(pair):\n    if pair[0] == None or pair[1] == None:\n        return \"\"\n    return \"{\" + str(pair[0]) + \", \" + str(pair[1]) + \"}, \"\n\ndef generate_line(shl_angle, pairs):\n    str = \"{\"\n    str += shl_angle + \", {\"\n    for s in map(generate_pair, pairs):\n        str += s\n    str += \"}},\"\n    return str\n\nshl_angles = []\nelb_angles = []\nshl_pws = []\nelb_pws = []\n\nshl_strings = []\nelb_strings = []\n\nwith open(\"calibration.org\") as f:\n    # skip the first line\n    next(f)\n\n    # read the header\n    tokens = [x.strip() for x in f.readline().split(\"|\")]\n    elb_angles = [x for x in tokens if isnum(x)]\n\n    # skip the -------\n    next(f)\n\n    for line in f:\n        # end condition\n        if \"+\" in line:\n            break\n        tokens = [x.strip() for x in line.split(\"|\")]\n        shl_angles.append(tokens[1])\n        shlelb = unzip(tokens[2:])\n        shl_pws.append(shlelb[\"shl\"])\n        elb_pws.append(shlelb[\"elb\"])\n\n    assert(len(shl_angles) == len(shl_pws) == len(elb_pws) and\n           len(shl_pws[0]) == len(shl_pws[0]) == len(shl_pws[0]))\n\n    # write the file\n    with open(\"include/lynxmotion_tm4c/joint_maps.hpp\", \"w\") as w:\n        for i in range(len(shl_angles)):\n            pw_entries = list(zip(elb_angles, shl_pws[i]))\n            shl_strings.append(generate_line(shl_angles[i], pw_entries))\n        for i in range(len(elb_angles)):\n            pw_entries = list(zip(elb_angles, elb_pws[i]))\n            elb_strings.append(generate_line(shl_angles[i], pw_entries))\n\n        w.write(\"    std::map > m_shl = {\\n\")\n        for i in shl_strings:\n            w.write(i + \"\\n\")\n        w.write(\"};\\n\\n\")\n\n        w.write(\"    std::map > m_elb = {\\n\")\n        for i in elb_strings:\n            w.write(i + \"\\n\")\n        w.write(\"};\\n\\n\")\n\n        with open(\"joint_map.m\", \"w\") as w:\n            def encapsulaten(arr):\n                return \"[\" +  \", \".join(map(str, arr)) + \"]\\n\"\n            def encapsulate(arr):\n                return \"[\" +  \", \".join(map(lambda x: str(x) if isnum(x) or isinstance(x, str) else \"0\", arr)) + \"]\\n\"\n            w.write(\"shl_angles = \")\n            w.write(encapsulate(shl_angles));\n            w.write(\"elb_angles = \")\n            w.write(encapsulate(elb_angles));\n\n            w.write(\"shl_pws = \")\n            w.write(encapsulaten([encapsulate(x) for x in shl_pws]))\n            w.write(\"elb_pws = \")\n            w.write(encapsulaten([encapsulate(x) for x in elb_pws]))\n","sub_path":"export-calibration.py","file_name":"export-calibration.py","file_ext":"py","file_size_in_byte":3018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"64098940","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2012-2019 Snowflake Computing Inc. All right reserved.\n#\n\nimport pytest\nimport random\nimport string\n\ntry:\n    from pyarrow import RecordBatch\n    import pyarrow\n    from snowflake.connector.arrow_iterator import FixedColumnConverter\n    from snowflake.connector.arrow_iterator import ColumnConverter\nexcept ImportError:\n    pass\n\n\n@pytest.mark.skip(\n    reason=\"Cython is not enabled in build env\")\ndef test_convert_from_fixed():\n\n    column_foo = (\"foo\", \"FIXED\", None, None, 1000, 0, True)\n    expected_val = []\n    array_len = 1000\n\n    for i in range(0, array_len):\n        data = None if bool(random.getrandbits(1)) else random.randint(-1000, 1000)\n        expected_val.append(data)\n\n    rb = RecordBatch.from_arrays([pyarrow.array(expected_val)], ['column_foo'])\n\n    for col_array in rb:\n        converter = FixedColumnConverter(col_array, column_foo)\n        for i in range(0, array_len):\n            py_val = converter.to_python_native(i)\n            assert py_val == expected_val[i]\n\n\n@pytest.mark.skip(\n    reason=\"Cython is not enabled in build env\")\ndef test_convert_from_binary():\n\n    column_foo = (\"foo\", \"TEXT\", None, None, 1000, 0, True)\n    column_bar = (\"bar\", \"BINARY\", None, None, 1000, 0, True)\n    column_metas = [column_foo, column_bar]\n\n    expected_val = []\n    array_len = 1000\n\n    string_val = []\n    for i in range(0, array_len):\n        data = None if bool(random.getrandbits(1)) else generate_random_string()\n        string_val.append(data)\n    expected_val.append(string_val)\n\n    binary_val = []\n    for i in range(0, array_len):\n        data = None if bool(random.getrandbits(1)) else generate_random_string().encode('utf-8')\n        binary_val.append(data)\n    expected_val.append(string_val)\n\n    rb = RecordBatch.from_arrays([pyarrow.array(expected_val[0]),\n                                  pyarrow.array(expected_val[1])],\n                                 ['col_foo', 'col_bar'])\n\n    for i, col_array in enumerate(rb):\n        converter = ColumnConverter(col_array, column_metas[i])\n        for j in range(0, array_len):\n            py_val = converter.to_python_native(j)\n            assert py_val == expected_val[i][j]\n\n\ndef generate_random_string():\n    return ''.join([random.choice(string.ascii_letters + string.digits) for n in range(0, 32)])\n","sub_path":"test/test_unit_arrow_converter.py","file_name":"test_unit_arrow_converter.py","file_ext":"py","file_size_in_byte":2354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"288577967","text":"import csv\nimport json\nfrom io import StringIO\nfrom django.shortcuts import render\n\n\nclass BaseConvertor:\n    def __init__(self, content):\n        self.content = content\n\n    def get_result(self):\n        res = self.parse_content()\n        return self.to_edi(res)\n\n    @staticmethod\n    def to_edi(val):\n        if isinstance(val, str):\n            return val\n        res = ''\n        for i in val:\n            res = res + '*'.join(i) + '\\n'\n        return res\n\n\nclass CsvConvertor(BaseConvertor):\n    def parse_content(self):\n        f = StringIO(self.content)\n        return csv.reader(f, delimiter=',')\n\n\nclass JsonConvertor(BaseConvertor):\n    def parse_content(self):\n        return json.loads(self.content)\n\n\nclass ExcelConvertor(BaseConvertor):\n    def parse_content(self):\n        raise Exception(\"Excel parser is not implemented\")\n\n\nclass EdiConvertor(BaseConvertor):\n    def parse_content(self):\n        return self.content\n\n\ndef index(request):\n    if request.method != \"POST\":\n        return render(request, 'index.html')\n\n    file_type = request.POST.get('file-type')\n    input_file = request.FILES.get('input-file')\n    if input_file.name.split('.')[-1] != file_type:\n        return render(request, 'index.html', {\n            \"err\": 'File format does not match file type'\n        })\n    content = input_file.read().decode('utf8')\n    convertor_cls = {\n        \"csv\": CsvConvertor,\n        \"json\": JsonConvertor,\n        \"xls\": ExcelConvertor,\n        \"edi\": EdiConvertor,\n    }[file_type]\n    convertor = convertor_cls(content)\n    try:\n        res = convertor.get_result()\n    except Exception as e:\n        return render(request, 'index.html', {\"err\": str(e)})\n    else:\n        return render(request, 'index.html', {\"res\": res})\n","sub_path":"edi/fileconvert/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"536618323","text":"# Copyright 2021 The Bazel Authors. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#    http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Tests for RPM generation analysis\"\"\"\n\nload(\"@bazel_skylib//lib:unittest.bzl\", \"analysistest\", \"asserts\")\nload(\"//experimental:rpm.bzl\", \"pkg_rpm\")\nload(\n    \"//:mappings.bzl\",\n    \"pkg_filegroup\",\n    \"pkg_files\",\n    \"pkg_mkdirs\",\n    \"pkg_mklink\",\n)\n\n# Generic negative test boilerplate\n#\n# TODO: create an internal test library containing this function, and maybe the second one too\ndef _generic_neg_test_impl(ctx):\n    env = analysistest.begin(ctx)\n\n    asserts.expect_failure(env, ctx.attr.reason)\n\n    return analysistest.end(env)\n\ngeneric_neg_test = analysistest.make(\n    _generic_neg_test_impl,\n    attrs = {\n        \"reason\": attr.string(\n            default = \"\",\n        ),\n    },\n    expect_failure = True,\n)\n\ndef _generic_base_case_test_impl(ctx):\n    env = analysistest.begin(ctx)\n\n    # Nothing here intentionally, this is simply an attempt to verify successful\n    # analysis.\n\n    return analysistest.end(env)\n\ngeneric_base_case_test = analysistest.make(\n    _generic_base_case_test_impl,\n    attrs = {},\n)\n\ndef _declare_pkg_rpm(name, srcs_ungrouped, tags = None, **kwargs):\n    pfg_name = \"{}_pfg\".format(name)\n    _tags = tags or [\"manual\"]\n\n    pkg_filegroup(\n        name = pfg_name,\n        srcs = srcs_ungrouped,\n        tags = _tags,\n    )\n\n    pkg_rpm(\n        name = name,\n        srcs = [\":\" + pfg_name],\n        version = \"1.0\",\n        release = \"1\",\n        license = \"N/A\",\n        summary = \"A test\",\n        description = \"very much a test\",\n        tags = _tags,\n        **kwargs\n    )\n\ndef _declare_conflicts_test(name, srcs, **kwargs):\n    rpm_name = name + \"_rpm\"\n    _declare_pkg_rpm(\n        name = rpm_name,\n        srcs_ungrouped = srcs,\n        tags = [\"manual\"],\n    )\n\n    generic_neg_test(\n        name = name,\n        target_under_test = \":\" + rpm_name,\n    )\n\ndef _test_conflicting_inputs(name):\n    # The test here is to confirm if pkg_rpm rejects conflicting inputs\n    #\n    # The structure of the code is such that it's only necessary to test any one\n    # packaged item conflicts with all others; order is irrelevant.\n    #\n    # So, we test how everything would conflict with a \"file\" entry\n    pkg_files(\n        name = \"{}_file_base\".format(name),\n        srcs = [\"foo\"],\n        tags = [\"manual\"],\n    )\n\n    _declare_pkg_rpm(\n        name = name + \"_base\",\n        srcs_ungrouped = [\":{}_file_base\".format(name)],\n    )\n\n    generic_base_case_test(\n        name = name + \"_base_case_passes_analysis\",\n        target_under_test = \":\" + name + \"_base\",\n    )\n\n    ##################################################\n    # file vs conflicting file\n    ##################################################\n\n    pkg_files(\n        name = \"{}_file_conflict\".format(name),\n        srcs = [\"foo\"],\n        tags = [\"manual\"],\n    )\n\n    _declare_conflicts_test(\n        name = name + \"_conflict_with_file\",\n        srcs = [\n            \":{}_file_base\".format(name),\n            \":{}_file_conflict\".format(name),\n        ],\n    )\n\n    ##################################################\n    # file vs conflicting dir\n    ##################################################\n\n    pkg_mkdirs(\n        name = \"{}_dir_conflict\".format(name),\n        dirs = [\"foo\"],\n        tags = [\"manual\"],\n    )\n\n    _declare_conflicts_test(\n        name = name + \"_conflict_with_dir\",\n        srcs = [\n            \":{}_file_base\".format(name),\n            \":{}_dir_conflict\".format(name),\n        ],\n    )\n\n    ##################################################\n    # file vs conflicting symbolic link\n    ##################################################\n\n    pkg_mklink(\n        name = \"{}_symlink_conflict\".format(name),\n        dest = \"foo\",\n        src = \"bar\",\n        tags = [\"manual\"],\n    )\n\n    _declare_conflicts_test(\n        name = name + \"_conflict_with_symlink\",\n        srcs = [\n            \":{}_file_base\".format(name),\n            \":{}_symlink_conflict\".format(name),\n        ],\n    )\n\n    native.test_suite(\n        name = name,\n        tests = [\n            \":{}_{}\".format(name, test_name)\n            for test_name in [\n                \"base_case_passes_analysis\",\n                \"conflict_with_file\",\n                \"conflict_with_dir\",\n                \"conflict_with_symlink\",\n            ]\n        ],\n    )\n\ndef analysis_tests(name, **kwargs):\n    # Need to test:\n    #\n    # - Mutual exclusivity of certain options (low)\n    #\n    _test_conflicting_inputs(name = name + \"_conflicting_inputs\")\n    native.test_suite(\n        name = name,\n        tests = [\n            name + \"_conflicting_inputs\",\n        ],\n    )\n","sub_path":"pkg/experimental/tests/rpm/analysis_tests.bzl","file_name":"analysis_tests.bzl","file_ext":"bzl","file_size_in_byte":5175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"411802998","text":"#coding=utf8\n\nimport sys,math\nimport codecs\nimport pickle\nimport random,json\n\n\n\nclass HMMModel():\n    def __init__(self):\n        self.path=dict()\n        self.dic1=json.load(file('rs1.json','r'))\n        self.dic2=json.load(file('rs2.json','r'))\n        self.dic0=json.load(file('rs0.json','r'))\n\n    \n    def prob1(self,label_w,label_u,label_v):\n        if label_w in self.dic1 and label_u in self.dic1[label_w] and label_v in self.dic1[label_w][label_u]:\n            value=self.dic1[label_w][label_u][label_v]*1.0/self.dic1[label_w][label_u]['#num#']\n        else:\n            value= self.dic0[label_u]*0.4/self.dic0['#num#']\n        return value\n\n    def prob2(self,word,label_v):\n        if word not in self.dic2[label_v]:\n            return 0.4/self.dic0['#num#']\n        return self.dic2[label_v][word]*1./self.dic2[label_v]['#num#']\n\n    def prob3(self,label):\n        if label in self.dic1['#START#']:\n            return self.dic1[label]['#num#']*1./self.dic1['#START#']['#num#']\n        else:\n            return 0.4/self.dic1['#START#']['#num#']\n\n\n    def get_thepath(self,path1,_len):\n        rs=[]\n        max_value=-10000\n        max_label=''\n        count=_len\n        now_label='#END#'\n        while count>0:\n            for j2 in path1[count][now_label]:\n                if path1[count][now_label][j2]>max_value:\n                    max_value=path1[count][now_label][j2]\n                    max_label=j2\n            rs.insert(0,max_label)\n            now_label=max_label\n            count-=1\n        return rs\n        \n    def write_2rs(self,rs):\n        with open(self.result_file_name,'a') as f:\n            for i in rs:\n                f.write(i+'\\n')\n            f.write('\\n')\n\n    def predict(self,test_file,result_file_name):\n        self.result_file_name=result_file_name\n        test_f=codecs.open(test_file,'r','utf8')\n        word_sentence=[]\n        for i in test_f.readlines():\n            word=i.strip()\n            if len(word)==0:\n                if len(word_sentence)==0:break\n                self.predict_sentence(word_sentence)\n                word_sentence=[]\n                self.path=dict()\n                continue\n            word_sentence.append(word)\n        self.predict_sentence(word_sentence)\n    \n\n\n\n    def predict_sentence(self,sentence):\n        label_set=pickle.load(file('set.dat','rb'))\n        i=len(sentence)\n        label_v='#END#'\n        self.path[i]=dict()\n        self.path[i][label_v]=dict()\n        if i==1:\n            rs=['UNKNOWN']\n            max_value=0\n            for x in self.dic2:\n                if sentence[0] in self.dic2[x]:\n                    vv=self.dic2[x][sentence[0]]\n                    if vv>max_value:\n                        max_value=vv\n                        rs[0]=x\n            self.write_2rs(rs)\n            return \n\n        for label_u in label_set:\n                self.path[i][label_v][label_u]=-10000000\n                for label_w in label_set:\n                        value=self.pi(sentence,i-1,label_w,label_u)+\\\n                        math.log10(self.prob1(label_w, label_u, label_v))\n                        if self.path[i][label_v][label_u]= self.totalBoardChecks):\n            self.checkNumber = 0\n        elif (self.checkNumber + direction < 0):\n            self.checkNumber = self.totalBoardChecks - 1\n        else: self.checkNumber += direction\n\n        # load the current frame\n        self.load_frame(self.boardCheck_frames[self.checkNumber], self.connection_images[self.checkNumber],\n                                 self.checkBox_frames[self.checkNumber], self.checkBoxButtons[self.checkNumber],\n                                 self.boardCheckToggleButtons)\n\n    # This method actually updates the screen by configuring the graphics\n    def load_frame(self, mainFrame, image, buttonFrame, checkButtonList, toggleButtons):\n        self.boardCheck_mainFrame.pack(fill=tk.BOTH, expand = 1)\n        self.activeProcess_4.pack(side = tk.TOP)\n        mainFrame.pack(expand=True,fill=tk.BOTH)\n        self.util.renderImage(self.canvases[self.checkNumber],tk.LEFT,\n                              self.list_int_imagePadding_connections[self.checkNumber][0],\n                              self.list_int_imagePadding_connections[self.checkNumber][1])\n        buttonFrame.pack(expand=True,fill=tk.X, side = tk.RIGHT)\n        for i in range(len(checkButtonList)):\n            checkButtonList[i].pack(expand=True,fill=tk.X)\n        toggleButtons[0].pack(side = tk.RIGHT)\n        toggleButtons[1].pack(side = tk.RIGHT)\n        self.master.master.update()\n        \n    def finishProcess_4(self):\n        self.finishProcess = 1\n    \n    def logic(self):\n        self.master.activeProcess.pack_forget()\n        # starting the test from beginning\n        if not self.bool_hasBeenCalled:\n                # load the frame of the current check, should be 0\n                self.load_frame(self.boardCheck_frames[self.checkNumber], self.connection_images[self.checkNumber],\n                                 self.checkBox_frames[self.checkNumber], self.checkBoxButtons[self.checkNumber],\n                                 self.boardCheckToggleButtons)\n\n                # update events and count boxes before entering loop\n                self.master.master.update()\n                self.countSubCheckedBoxes_live()\n                self.countTotalCheckedBoxes_live()\n                self.countTotalBoxes()\n                \n                # run this loop until the check passes and click finish\n                while not((self.totalChecked_live is self.totalBoxes) and self.finishProcess):\n                    # update events and count boxes at start of every loop\n                    self.master.master.update()\n                    self.countSubCheckedBoxes_live()\n                    self.countTotalCheckedBoxes_live()\n\n                    # check completion for a certain check\n                    if  self.checkedBoxes_live[self.checkNumber] is len(self.checkVars[self.checkNumber]):\n                         self.activeProcess_4.config(text = \"Please complete all checks before moving on    \\n\\nBoard Connection check \" + str(self.checkNumber+1) +\n                                                    \":                COMPLETE\\nCurrent count for this check:                     \"\n                                                    + str(self.checkedBoxes_live[self.checkNumber]) + \"             \",\n                                                    fg = \"green\")\n                    else:\n                         self.finishButton.pack_forget()\n                         self.activeProcess_4.config(text = \"Please complete all checks before moving on    \\n\\nBoard Connection check \" + str(self.checkNumber+1) +\n                                                    \":            INCOMPLETE\\nCurrent count for this check:                     \"\n                                                    + str(self.checkedBoxes_live[self.checkNumber]) + \"             \",\n                                                    fg = \"red\")\n                         self.activeProcess_4.pack(side = tk.TOP)\n                    # if everything is checked, display the finish button    \n                    if self.totalChecked_live is self.totalBoxes:\n                        self.activeProcess_4.config(text = \"Board Connection check is complete\\n press the finish button to confirm\\n\", fg = \"green\" )\n                        self.finishButton.pack(side = tk.BOTTOM)\n                        self.master.master.update()\n                    self.master.master.update()\n\n                # disable the finish button\n                self.finishButton.config(state=tk.DISABLED)\n                \n                # once break from loop then disable the checkbox buttons\n                for i in range(len(self.checkBoxButtons)):\n                        for j in range(len(self.checkBoxButtons[i])):\n                            self.checkBoxButtons[i][j].config(state = tk.DISABLED)\n                self.bool_hasBeenCalled = True\n                \n        elif self.bool_hasBeenCalled:\n                # load the frame of the current check, should be 0\n               self.load_frame(self.boardCheck_frames[self.checkNumber], self.connection_images[self.checkNumber],\n                                 self.checkBox_frames[self.checkNumber], self.checkBoxButtons[self.checkNumber],\n                                 self.boardCheckToggleButtons)\n    def clear(self):\n        self.boardCheck_mainFrame.pack_forget()\n","sub_path":"python_files/process_4.pyw","file_name":"process_4.pyw","file_ext":"pyw","file_size_in_byte":13055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"45333011","text":"#!/bin/env python3.5\n\nimport quandl\nimport appConfig\nimport traceback\nimport numpy\nimport math\nfrom scipy import signal\nfrom StockDetails import StockDetails\nfrom StockDataCache import StockDataCache\nfrom Cluster import Cluster\n\nclass StockPivotCalculator:\n\n    def __init__(self, stockDataCache, stockDetails, numDays):\n        self.cluster=Cluster()\n        self.stockDataCache=stockDataCache\n        self.stockDetails=stockDetails\n        self.numDays=numDays\n\n    def getHeader(self, symbol):\n        data=self.stockDataCache.get(symbol.replace(\"-\", \"_\"), self.numDays)         \n        detailsHeader=self.stockDetails.getDetailsHeader()\n        generatedFields=\"LastPrice,Direction,Upward Pivots,Downward Pivots,Pivots,Volatility,Pivots-Grouped,Nearest Pivot, Nearest Pivot (Low), Nearest Pivot (High), Distance from Pivot(%), Distance from Pivot (Low)(%), Distance from Pivot (High)(%), Number of Pivots Crossed, Crossed Pivots, Dist(CP to LP), Dist(CP to HP), isPivotDistLessVolat\"\n        return \"%s,%s\" % (detailsHeader, generatedFields)\n\n    def findNearest(self, pivotPoints, point, direction):\n        x=pivotPoints.tolist()\n        y=[]\n        distances=[]\n        if direction==\"DOWN\":\n            x.sort(reverse=True)\n            for e in x:\n                if e < point:\n                    y.append(e)\n                    distances.append(self.calcDistance(point, e))\n        elif direction==\"UP\":\n            x.sort()\n            for e in x:\n                if e > point:\n                    y.append(e)\n                    distances.append(self.calcDistance(point, e))\n        return (y, distances)\n              \n    def findNearest3(self, pivotPoints, point, direction):\n        x=pivotPoints.tolist()\n        x.sort()\n        y=[]\n        distances=[]\n        if direction==\"DOWN\":\n            idx=-1\n            for i in range(0, len(x)):\n                if (x[i]<=point and idx==-1) or (x[i]<=point and x[i] >= x[idx]):\n                    idx=i \n        elif direction==\"UP\":\n            idx=len(x)\n            for i in range(0, len(x)):\n                if (x[i]>=point and idx==len(x)) or (x[i]>=point and x[i] <= x[idx]):\n                    idx=i\n\n        if idx==-1 or idx==len(x):\n            y=[-1.0]        \n            distances=[-1.0]\n        else:\n            y=[x[idx]]\n            distances=[self.calcDistance(point, x[idx])]\n    \n        if idx > 0:\n            y.append(x[idx-1])\n            distances.append(self.calcDistance(point, x[idx-1]))\n        else:\n            y.append(-1.0)\n            distances.append(-1.0)\n        if idx < len(x)-1:\n            y.append(x[idx+1])\n            distances.append(self.calcDistance(point, x[idx+1]))        \n        else:\n            y.append(-1.0)\n            distances.append(-1.0)\n\n        return (y, distances)\n\n    def calcDistance(self, p1, p2):\n        return math.fabs((p2-p1)*100.0/p1)\n\n    def findCrossed(self, pivots, prev, cur):\n        crossed=[]\n        for p in pivots:\n            if p>=prev and p<=cur:\n                crossed.append(p)\n            elif p>=cur and p<=prev:\n                crossed.append(p)\n        return crossed  \n\n    def getDirection(self, closedPrices):\n        curPrice=closedPrices[-1]\n        prevPrice=closedPrices[-2]\n        if curPrice > prevPrice:\n            return \"UP\"\n        else:\n            return \"DOWN\" \n\n    def calcVolat(self, prices):\n        totalChanges=0.0\n        for i in range(1, len(prices)):\n            totalChanges+= math.fabs((prices[i]-prices[i-1])*100.0/prices[i-1])\n        return totalChanges/(len(prices)-1)\n            \n  \n    def calc(self, symbol, direction):\n        data=self.stockDataCache.get(symbol.replace(\"-\", \"_\"), self.numDays)         \n        closedPrices=numpy.asarray(list(data['Close']))\n        #direction=self.getDirection(closedPrices)\n        closedPricesSmooth = signal.savgol_filter(closedPrices, 3, 1)\n        upwardPivots=closedPrices[signal.argrelmax(closedPricesSmooth, 0, 3)]\n        downwardPivots=closedPrices[signal.argrelmin(closedPricesSmooth, 0, 3)]\n        print(\"upwardPivots:\", upwardPivots)\n        print(\"downwardPivots:\", downwardPivots)\n        volat=self.calcVolat(closedPrices)\n        print(\"volatility:\", volat)\n        pivots2=numpy.concatenate((upwardPivots, downwardPivots))\n        pivots2.sort()\n        print(\"pivots2:\", pivots2)\n        pivots=self.cluster.create(pivots2, volat)\n        print(\"grouped pivots:\", pivots)\n\n        lastPrice=closedPrices[-1]\n        lastPricePrev=closedPrices[-2]\n        (nearestPivots, nearestDistances)=self.findNearest3(pivots, lastPrice, direction)    \n        distCP2LP=0.0 if nearestPivots[0]==-1 or nearestPivots[1]==-1 else (nearestPivots[1]-nearestPivots[0])*100.0/nearestPivots[0]\n        distCP2HP=0.0 if nearestPivots[0]==-1 or nearestPivots[2]==-1 else (nearestPivots[2]-nearestPivots[0])*100.0/nearestPivots[0]\n        isPivotDistLessVolat= False if nearestPivots[0]==-1 or nearestDistances[0] > volat else True\n        crossedPivots=self.findCrossed(pivots, lastPricePrev, lastPrice) \n\n        print(\"symbol:\", symbol)\n        print(\"closedPrices:\", closedPrices)\n        print(\"direction:\", direction)\n        print(\"upwardPivots:\", upwardPivots)\n        print(\"downwardPivots:\", downwardPivots)\n        print(\"pivots:\", pivots)\n        print(\"nearestPivots:\", nearestPivots)\n        print(\"nearestDistances:\", nearestDistances)\n        print(\"crossedPivots:\", crossedPivots)\n        detailsData=self.stockDetails.getDetailsData(symbol)\n        return \"%s,%f,%s,%s,%s,%s,%.2f,%s,%s,%s,%d,%s,%.2f,%.2f,%s\" % (detailsData, lastPrice, direction, \"|\".join(map(lambda x: str(x), upwardPivots.tolist())), \"|\".join(map(lambda x: str(x), downwardPivots.tolist())), \"|\".join(map(lambda x: str(x), pivots2.tolist())), volat,\"|\".join(map(lambda x: str(x), pivots.tolist())), \",\".join(map(lambda x: \"%.2f\" % x, nearestPivots[:3])), \",\".join(map(lambda x: \"%.2f\" % x, nearestDistances[:3])), len(crossedPivots), \"|\".join(map(lambda x: str(x), crossedPivots)), distCP2LP, distCP2HP, isPivotDistLessVolat) \n        \n    def calculate(self, inputFile, outputFile):\n        #ACC LIMITED,ACC\n        fpr=open(inputFile)\n        fpw=open(outputFile, \"w\")\n        c=1\n        for line in fpr:\n            (security, symbol)=line.strip().split(\",\")\n            print(\"fetching for %s\" % symbol)\n            if c==1:\n                header=self.getHeader(symbol)\n                fpw.write(\"%s,%s,%s\\n\" % (\"security\", \"symbol\", header))                 \n            try:\n                data=self.calc(symbol, \"UP\")\n                fpw.write(\"%s,%s,%s\\n\" % (security, symbol, data))\n                data=self.calc(symbol, \"DOWN\")\n                fpw.write(\"%s,%s,%s\\n\" % (security, symbol, data))\n            except:\n                print(\"exception while fetching data for %s\" % symbol)\n                traceback.print_exc()\n            c+=1\n        fpw.close()\n        fpr.close()        \n\nif __name__=='__main__':\n    import sys\n    if len(sys.argv)!=4:\n        print(\"%s   \" % (sys.argv[0]))\n        sys.exit(0)\n    numDays=60\n    stockSymbolFile=sys.argv[1]\n    stockDetailsFile=sys.argv[2]\n    stockPivotFile=sys.argv[3]\n    print(\"stockSymbolFile:%s\" % stockSymbolFile)\n    print(\"stockDetailsFile:%s\" % stockDetailsFile)\n    print(\"stockPivotFile:%s\" % stockPivotFile)  \n    sDetails=StockDetails(stockDetailsFile)\n    sDataCache=StockDataCache(appConfig)\n    spc=StockPivotCalculator(sDataCache, sDetails, numDays)\n    spc.calculate(stockSymbolFile, stockPivotFile)       \n","sub_path":"util-scripts/stockPivotCalculator2.py","file_name":"stockPivotCalculator2.py","file_ext":"py","file_size_in_byte":7577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"320072814","text":"#! /usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author: Tdcqma\nfrom core import admin,user\n\n\nfunc_dic = {\n    '1':admin.admin_view,\n    '2':user.user_view,\n}\n\ndef run():\n    while True:\n        print(\n            '''\n            1 管理员视图\n            2 用户视图\n            '''\n        )\n\n        choose = input('please choose>>:').strip()\n        if 'q' == choose:break\n        if choose not in func_dic:continue\n        func_dic[choose]()","sub_path":"000_Project/oldboy_project/优酷项目/youkuClient/core/src.py","file_name":"src.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"303246846","text":"from django.http import HttpResponse\nfrom ModME.models import (\n    Metadata,\n    Event,\n    Session\n)\nimport json\n\n\ndef serialize(obj):\n    # Sorry; serializers.serialize doesn't go deep enough into the tree (doesn't serialize the session object) and\n    # json.dumps by default says metadata is not serializable.  This avoids bringing in another library, though we\n    # could... but the last one that went deep enough without us writing anything is apparently now not maintained.\n    if isinstance(obj, Metadata):\n        return {\n            'id': obj.id,\n            'allowEventReuse': obj.allowEventReuse,\n            'condition_id': obj.condition_id,  # Not serializing condition here... tree gets large.\n            'session': serialize(obj.session),\n            'startTime': obj.startTime,\n            'participant_id': obj.participant_id,\n            'duration': obj.duration\n        }\n    elif isinstance(obj, Session):\n        return {\n            'id': obj.id,\n            'name': obj.name\n        }\n    # Ugly. Eh.\n    return obj.__dict__\n\n\ndef getReusableSessions(request):\n    metadata = '[]'\n    condition = request.GET.get('condition')\n\n    if condition:\n        metadata = Metadata.objects.filter(condition=condition, allowEventReuse=True)\n        metadata = json.dumps(list(metadata), default=serialize)\n\n    return HttpResponse(metadata, content_type='application/json')\n\n\ndef getAlertsForMetadata(request):\n    serializedAlerts = '[]'\n    metadataId = request.GET.get('metadataId')\n\n    if metadataId:\n        alertList = list(Event.objects.filter(metadata=metadataId, eventType='alert').order_by('time'))\n        flatAlertList = [{\n            'arg': json.loads(alert.arg),\n            'time': alert.time,\n            'eventType': alert.eventType,\n            'chart': alert.chart,\n            'domID': alert.domID,\n            'table': \"Event\",\n        } for alert in alertList]\n        serializedAlerts = json.dumps(flatAlertList, indent=2)\n\n    return HttpResponse(serializedAlerts, content_type='application/json')\n","sub_path":"ModME/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":2042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"53074237","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport board_members.models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('board_members', '0004_auto_20161121_1601'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='boardmember',\n            name='image',\n            field=models.ImageField(verbose_name='Bild', upload_to=board_members.models.get_image_path, help_text='Bilden ska vara kvadratisk, helst inte så stor (ca 100 KB borde vara tillräckligt) och om man vill byta bild så får man ta bort instansen och lägga till en ny istället.'),\n        ),\n    ]\n","sub_path":"navitas/board_members/migrations/0005_auto_20161122_1607.py","file_name":"0005_auto_20161122_1607.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"599508775","text":"from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n    url(r'^$', views.index, name='indice'),\n\n    url(r'^unidade/list$', views.unidade_list, name='unidade_list'),\n    url(r'^unidade/detail/(?P\\d+)$', views.unidade_detail, name='unidade_detail'),\n    url(r'^unidade/new/$',views.unidade_new,name='unidade_new'),\n    url(r'^unidade/update/(?P\\d+)$',views.unidade_update,name='unidade_update'),\n    url(r'^unidade/delete/(?P\\d+)$',views.unidade_delete,name='unidade_delete'),\n\n]","sub_path":"exemplos_dj_1.9/projetoenquete_com_crud_simples/enquetes/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"412757439","text":"#!/usr/bin/env python\r\n'''mission waypoint generator'''\r\n\r\nimport time, threading, sys, os, numpy, Queue\r\n\r\n# use the mission generator code from the cuav repo (see githib.com/stephendade)\r\nsys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..', '..', 'cuav', 'lib'))\r\nsys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), 'lib'))\r\nimport mavutil, cuav_missiongenerator\r\n\r\n\r\nmpstate = None\r\n\r\ndef name():\r\n    '''return module name'''\r\n    return \"MissionGen\"\r\n\r\ndef description():\r\n    '''return module description'''\r\n    return \"Mission search area waypoint generator\"\r\n\r\ndef cmd_MissionGen():\r\n    '''create the mission waypoints'''\r\n    path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..', '..',\r\n                            'cuav', 'data', 'OBC Waypoints.kml')\r\n\r\n    gen = cuav_missiongenerator.MissionGenerator(path)\r\n    gen.Process('SA-', 'MB-')\r\n    gen.CreateEntryExitPoints('EL-1,EL-2', 'EL-3,EL-4')\r\n\r\n    gen.CreateSearchPattern(width = 150, overlap=50, offset=10, wobble=1, alt=90)\r\n    gen.altitudeCompensation(heightAGL = 90)\r\n    #gen.ExportSearchPattern()\r\n\r\n    mpstate.status.wploader.target_system = mpstate.status.target_system\r\n    mpstate.status.wploader.target_component = mpstate.status.target_component\r\n\r\n    #export the waypoints to MAVProxy\r\n    gen.exportToMAVProxy(mpstate.status.wploader)\r\n\r\n    #and upload the new waypoints to the APM\r\n    mpstate.status.loading_waypoints = True\r\n    mpstate.status.loading_waypoint_lasttime = time.time()\r\n    mpstate.master().waypoint_count_send(mpstate.status.wploader.count())\r\n\r\n    '''and exit'''\r\n    unload()\r\n\r\ndef init(_mpstate):\r\n    '''initialise module'''\r\n    global mpstate\r\n    mpstate = _mpstate\r\n    mpstate.MissionGen_state = cmd_MissionGen()\r\n    print(\"Mission Generator initialised\")\r\n\r\ndef unload():\r\n    '''unload module'''\r\n    mpstate.MissionGen_state = None\r\n    print('Mission Generator unload OK')\r\n\r\n\r\ndef mavlink_packet(m):\r\n    '''handle an incoming mavlink packet'''\r\n\r\n\r\n","sub_path":"Tools/MAVLink/MAVProxy/modules/CUAV/MissionGen.py","file_name":"MissionGen.py","file_ext":"py","file_size_in_byte":2056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"401060862","text":"import sys\nimport os\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\n\nimport data_load.load_stocks as stocks\nfrom data_load.replay_buffer import ReplayBuffer\nfrom data_load.batchifier import Batchifier\nfrom models.lstm_model import LSTMModel, tf\nfrom literals import ASSET_LIST\nimport numpy as np\nfrom get_metrics import get_metrics\nfrom sklearn.utils.extmath import softmax\n\n\n\n\nDATA_PATH = \"../dataset/Poloneix_Preprocessednew\"\nBSZ=32\nBPTT=10\nasset_list=ASSET_LIST\nIDX=0\nNUM_EPOCHS = 100\nINIT_PV=1000\nNUM_HID=20\nASSETS = ASSET_LIST\nLR = 1e-4\nrandomize_train=False\noverlapping_train=True\nRB_FACTOR=10\nreplay=BSZ*RB_FACTOR\n\nif __name__ == '__main__':\n\n    batch_gen = Batchifier(data_path=DATA_PATH, bsz=1, bptt=BPTT, idx=IDX,\n                           asset_list=ASSETS, randomize_train=randomize_train,\n                           overlapping_train=overlapping_train)\n\n    model = LSTMModel(num_hid=NUM_HID, bptt=BPTT, num_assets=len(asset_list), lr=LR, clip_norm=5.0)\n    buffer = ReplayBuffer(buffer_size=replay)\n\n    with tf.Session() as sess:\n        sess.run(model.tf_init())\n        losses = []\n        for epoch in range(1,NUM_EPOCHS + 1):\n            batch_losses = 0.0\n            for bTrainX, bTrainY in batch_gen.load_train():\n                if buffer.size < buffer.max_size:\n                    buffer.add(state=bTrainX, action=bTrainY)\n                    continue\n                else:\n                    buffer.add(state=bTrainX, action=bTrainY)\n                    state, reward, action = buffer.get_batch(bsz=BSZ)\n                    loss = model.optimize(sess, state, action)\n\n                    losses.append(loss)\n\n\n            print(\"Epoch {} Average Train Loss: {}, validating...\".format(epoch, np.mean(losses)))\n            losses = []\n            allocation_wts = []\n            price_change_vec = []\n            for bEvalX, bEvalYFat in batch_gen.load_test():\n                bEvalY = bEvalYFat[:,-1,:]\n                pred_allocations = model.predict_allocation(sess, bEvalX)\n                assert bEvalY.shape == pred_allocations.shape\n                price_change_vec.append(bEvalY)\n                allocation_wts.append(pred_allocations)\n\n            true_change_vec = np.concatenate(price_change_vec)\n            allocation_wts = np.concatenate(allocation_wts)\n\n            random_alloc_wts = softmax(np.random.random(allocation_wts.shape))\n            test_date = \"_\".join(batch_gen.dp.test_dates[IDX])\n            m = get_metrics(dt_range=test_date)\n            print(\"Our Policy:\")\n            m.apv_multiple_asset(true_change_vec, allocation_wts, get_graph=True, pv_0=INIT_PV, tag=\"epoch_{}\".format(epoch))","sub_path":"examples/lab_lstm_rb.py","file_name":"lab_lstm_rb.py","file_ext":"py","file_size_in_byte":2660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"493183325","text":"#1. Create a greeting for your program.\nprint(\"Welcome to the Band  Name Generator\")\n\n#2. Ask the user for the city that they grew up in.\ncity = input(\"Which city did you grew up in?\\n\")\n\n#3. Ask the user for the name of a pet.\npet = input(\"What is the name of the pet?\\n\")\n\n#4. Combine the name of their city and pet and show them their band name.\nBand_name = city + \" \" + pet\n\n#5. Make sure the input cursor shows on a new line:\nprint(\"Your band name could be \", Band_name)\n\n","sub_path":"Bandname.py","file_name":"Bandname.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"207406516","text":"import cherrypy\nimport sys\nfrom random import choice\nimport random\n\nclass Server:\n    @cherrypy.expose\n    @cherrypy.tools.json_in()\n    @cherrypy.tools.json_out()\n    \n    def move(self):\n        # Deal with CORS\n        cherrypy.response.headers['Access-Control-Allow-Origin'] = '*'\n        cherrypy.response.headers['Access-Control-Allow-Methods'] = 'GET, POST, OPTIONS'\n        cherrypy.response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization, X-Requested-With'\n        if cherrypy.request.method == \"OPTIONS\":\n            return ''\n            \n        #Messages affichés \n        messages = [\"omae wa mou shindeiru\",\" La roue tourne tkt\",\" Tu comptais vraiment me battre avec ça ?\",\"Tu veux des lunettes ?\",\"Nani ?!\",\"Noob\",\"On ne compare pas une F1 à un Karting\",\"ԅ(≖‿≖ԅ)\",\"(ง’̀-‘́)ง\",\"Je t'aimais Anakin...\"]\n        w = random.choice(messages)\n        self.body = cherrypy.request.json\n        return {\"move\":self.MonIA, \"message\": w}\n\n#Fonction permettant d'identifier les joueurs à partir du body\n    @property\n    def identifier_joueur(self): \n        if self.body[\"players\"][0] == self.body[\"you\"]:\n            self.joueur = 0 \n            self.adversaire = 1\n        else:\n            self.joueur = 1\n            self.adversaire = 0\n        return self.joueur\n\n#Fonction qui détermine tous les mouvements possibles.\n    @property\n    def coup_possible(self):\n            mouv_possibles=[]\n            mouv_interdits = []\n            bons_mouvs = []\n            for lignes in range(9):\n                for colonnes in range(9):\n                    if len(self.body[\"game\"][lignes][colonnes]) != 0:\n                        # Carré centrale du game\n                        for elem in self.body[\"game\"][lignes][colonnes]:\n                            if 0 5:\t\n            return \"no\"\n        if a == 0:\n            return \"no\"\n        if b == 0:\n            return \"no\"\n        if A == B:\n            return\"no\"\n        else:\n            return \"yes\"\n# Fonction qui détermine les meilleurs mouvements parmi les bons mouvements\n    @property\n    def meilleursmouvements(self):\n        meilleurs_mouvements = []\n        for elem in self.coup_possible:\n            A= elem.get(\"from\")\n            B= elem.get(\"to\")\n            a1=A[0]\n            a2=A[1]\n            b1=B[0]\n            b2=B[1]\n            fromvaleur= self.body[\"game\"][a1][a2]\n            tovaleur= self.body[\"game\"][b1][b2]\n\n            # Stratégie 1 \n            i = len(fromvaleur)-1\n            if fromvaleur[i]== self.identifier_joueur: \n                if len(fromvaleur)+len(tovaleur)== 5:\n                    meilleurs_mouvements.append(elem)\n        return meilleurs_mouvements\n\n    # Fonction IA qui choisit quelle liste renvoyer\n    @property\n    def MonIA(self):\n        if len(self.meilleursmouvements) != 0: \n            return random.choice(self.meilleursmouvements)\n        else:\n            return random.choice(self.coup_possible)\n\nif __name__ == \"__main__\":\n    if len(sys.argv) > 1:\n        port=int(sys.argv[1])\n    else:\n        port=5031\n\n    cherrypy.config.update({'server.socket_host': '0.0.0.0', 'server.socket_port': port})\n    cherrypy.quickstart(Server())\n","sub_path":"AvalamAI.py","file_name":"AvalamAI.py","file_ext":"py","file_size_in_byte":6841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"233439886","text":"import os\n\n\nif __name__ == \"__main__\":\n    rootDir = \".\"\n\n    for dirName, subdirList, fileList in os.walk(rootDir):\n        print(\"Found directory: %s\" % dirName)\n        for file in fileList:\n            if file.endswith(\".cpp\"):\n                full_file_name = os.path.join(dirName, file)\n                print(\"\\t%s\" % full_file_name)\n                try:\n                    input_file = open(full_file_name)\n                    input_lines = input_file.readlines()\n                    input_file.close()\n\n                    output_file = open(full_file_name, \"w\")\n\n                    output_lines = []\n\n                    for line in input_lines:\n                        if line.count(\"libProfiler.h\") == 0 and line.count(\"PROFILER_F()\") == 0:\n                            output_lines.append(line)\n\n                    output_file.writelines(output_lines)\n                    output_file.close()\n\n                except:\n                    print(\"Error processing: \" + full_file_name)\n","sub_path":"tools/remove_libprofiler_rec.py","file_name":"remove_libprofiler_rec.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"440899806","text":"import struct\nfrom typing import BinaryIO, List\n\nimport numpy as np\n\nfrom .packbits import unpack_bits\n\n\nCOMPRESSION_RAW = 0\nCOMPRESSION_RLE = 1\nCOMPRESSION_ZIP_WITHOUT_PREDICTION = 2\nCOMPRESSION_ZIP_PREDICTION = 3\n\n\ndef get_image_data(file: BinaryIO, compression: int, width: int, height: int, channels: int, depth: int = 8) -> np.ndarray:\n    \"\"\" Read image data from a chunk of bytes. Returns a numpy array.\"\"\"\n    # Convert photoshop color depth to c type\n    ps_to_c_depth = {1: \"B\", 8: \"B\", 16: \"H\", 32: \"I\"}\n    c_data_type = ps_to_c_depth[depth]\n\n    # Convert photoshop color depth to numpy data type\n    ps_to_np_depth = {1: np.bool_, 8: np.uint8, 16: np.uint16, 32: np.uint32}\n    np_data_type = ps_to_np_depth[depth]\n\n    # Initialize empty array\n    image_data = np.zeros((height, width, channels), dtype=np_data_type)\n\n    if compression == COMPRESSION_RAW:\n        scanlines = _read_raw(file=file, width=width, height=height, channels=channels, data_format=c_data_type)\n    elif compression == COMPRESSION_RLE:\n        scanlines = _read_rle(file=file, width=width, height=height, channels=channels, data_format=c_data_type)\n    elif compression == COMPRESSION_ZIP_WITHOUT_PREDICTION:\n        raise NotImplementedError(\"Unsupported compression method: ZIP without prediction\")\n    elif compression == COMPRESSION_ZIP_PREDICTION:\n        raise NotImplementedError(\"Unsupported compression method: ZIP with prediction\")\n    else:\n        raise NotImplementedError(f\"Unknown compression method: {compression}\")\n\n    # Write scanlines into image data\n    row = 0\n    channel = 0\n    for i, scanline in enumerate(scanlines):\n        if row >= height:\n            row -= height\n            channel += 1\n        image_data[row, :, channel] = scanline\n        row += 1\n\n    return image_data\n\n\ndef _read_raw(file: BinaryIO, width: int, height: int, channels: int, data_format: str) -> List[int or float]:\n    \"\"\" Raw image data. \"\"\"\n    scanlines = []\n    for channel in range(channels):\n        for row in range(height):\n            data = file.read(width)\n            scanline = struct.unpack(f'>{width}{data_format}', data)\n            scanlines.append(scanline)\n    return scanlines\n\n\ndef _read_rle(file: BinaryIO, width: int, height: int, channels: int, data_format: str) -> List[int or float]:\n    \"\"\" RLE data is stored with the PackBits compression scheme. \"\"\"\n    # First part of RLE image data stores the lengths of each data segment\n    data_lengths = []\n    for channel in range(channels):\n        for row in range(height):\n            length = struct.unpack('>H', file.read(2))[0]\n            data_lengths.append(length)\n\n    # Decompress each scanline\n    scanlines = []\n    for length in data_lengths:\n        data = unpack_bits(file.read(length))\n        scanline = struct.unpack(f'>{width}{data_format}', data)\n        scanlines.append(scanline)\n\n    return scanlines\n","sub_path":"photoshoppy/utilities/image_data.py","file_name":"image_data.py","file_ext":"py","file_size_in_byte":2891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"73062928","text":"import feelyng.encoders.count as count\nimport feelyng.encoders.tfidf as tfidf\nimport feelyng.encoders.word2vec as word2vec\nimport feelyng.utils.logging as l\n\nlogger = l.get_logger(__name__)\n\n\nclass Encoder:\n    \"\"\"An Encoder class is responsible for receiving raw data and\n    enconding it on a representation (i.e., count vectorizer, tfidf, word2vec).\n\n    Properties:\n        type (str): The type of the encoder.\n        encoder (obj): An encoder generic object depending on its type (inherit objects from \n        learning algorithms).\n        encoded_data (np.array): A numpy array holding the encoded data representation.\n\n    Methods:\n        learn(data_to_learn): Learns an encoding representation for its parameter.\n        encode(data_to_encode): Enconde its parameter based on previous learning.\n\n    \"\"\"\n\n    def __init__(self, type='count'):\n        \"\"\"Initialization method.\n\n        Args:\n            type (str): The type of the encoder.\n\n        \"\"\"\n\n        logger.info('Initializing Encoder ...')\n\n        # One should also declare the type of the encoder\n        self.type = type\n\n        # The encoder object will be initialized as None\n        self.encoder = None\n\n        # We initially set the encoded data as None\n        self.encoded_data = None\n\n        # We will log some important information\n        logger.info('Encoder created.')\n        logger.info('Encoder type: ' + self.type)\n\n    def learn(self, data_to_learn):\n        \"\"\"The method for learning an encoding representation. Currently, a bunch\n        of 'ifs' statements.\n\n        Args:\n            data_to_learn (df): A Panda's dataframe column holding sentences to be learned.\n\n        \"\"\"\n\n        logger.debug('Running method: learn()')\n\n        # We need to check the encoder type prior to its learning process\n        if self.type == 'count':\n            # Count Vectorizer\n            self.encoder = count.learn_count(data_to_learn)\n\n        elif self.type == 'tfidf':\n            # TFIDF Vectorizer\n            self.encoder = tfidf.learn_tfidf(data_to_learn)\n\n        elif self.type == 'word2vec':\n            # Word2Vec\n            self.encoder = word2vec.learn_word2vec(data_to_learn)\n\n    def encode(self, data_to_encode):\n        \"\"\"The method for encoding new data based on previous learning. Note that,\n        to invoke this class you need to call learn() first and certify thay your\n        'self.enconder' object exists.\n\n        Args:\n            data_to_encode (df): A Panda's dataframe column holding sentences to be encoded.\n\n        \"\"\"\n\n        # Check if there is an encoder that actually exists\n        if not self.encoder:\n            e = 'You need to call learn() prior to encode() method.'\n            logger.error(e)\n            raise RuntimeError(e)\n\n        logger.debug('Running method: encode()')\n\n        # We need to check the encoder type prior to its encoding process\n        if self.type == 'count':\n            # Count Vectorizer\n            self.encoded_data = count.encode_count(\n                self.encoder, data_to_encode)\n\n        elif self.type == 'tfidf':\n            # TFIDF Vectorizer\n            self.encoded_data = tfidf.encode_tfidf(\n                self.encoder, data_to_encode)\n\n        elif self.type == 'word2vec':\n            # Word2Vec\n            self.encoded_data = word2vec.encode_word2vec(\n                self.encoder, data_to_encode)\n","sub_path":"feelyng/core/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":3384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"8465552","text":"\"\"\" 加载训练后的模型 \"\"\"\n\"\"\" \n数据集来源\n模型训练的代码等\nhttps://github.com/skygongque/captcha_crack_demo/blob/master/01_introduction/captcha_sina.md \n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.transforms.functional import to_tensor, to_pil_image\nimport os\nimport random\nimport numpy as np\nfrom collections import OrderedDict\nimport string\nfrom PIL import Image\n\n\ncharacters = '-' + string.digits + string.ascii_lowercase\nwidth, height, n_len, n_classes = 100, 40, 5, len(characters)\nn_input_length = 12\n# print(characters, width, height, n_len, n_classes)\n\n\n\nclass Model(nn.Module):\n    def __init__(self, n_classes, input_shape=(3, 64, 128)):\n        super(Model, self).__init__()\n        self.input_shape = input_shape\n        channels = [32, 64, 128, 256, 256]\n        layers = [2, 2, 2, 2, 2]\n        kernels = [3, 3, 3, 3, 3]\n        # pools = [2, 2, 2, 2, (2, 1)]\n        # 减少一个池化层\n        pools = [2, 2, 2, (2, 1)]\n        modules = OrderedDict()\n        \n        def cba(name, in_channels, out_channels, kernel_size):\n            modules[f'conv{name}'] = nn.Conv2d(in_channels, out_channels, kernel_size,\n                                               padding=(1, 1) if kernel_size == 3 else 0)\n            modules[f'bn{name}'] = nn.BatchNorm2d(out_channels)\n            modules[f'relu{name}'] = nn.ReLU(inplace=True)\n        \n        last_channel = 3\n        for block, (n_channel, n_layer, n_kernel, k_pool) in enumerate(zip(channels, layers, kernels, pools)):\n            for layer in range(1, n_layer + 1):\n                cba(f'{block+1}{layer}', last_channel, n_channel, n_kernel)\n                last_channel = n_channel\n            modules[f'pool{block + 1}'] = nn.MaxPool2d(k_pool)\n        modules[f'dropout'] = nn.Dropout(0.25, inplace=True)\n        \n        self.cnn = nn.Sequential(modules)\n        self.lstm = nn.LSTM(input_size=self.infer_features(), hidden_size=128, num_layers=2, bidirectional=True)\n        self.fc = nn.Linear(in_features=256, out_features=n_classes)\n    \n    def infer_features(self):\n        x = torch.zeros((1,)+self.input_shape)\n        x = self.cnn(x)\n        x = x.reshape(x.shape[0], -1, x.shape[-1])\n        return x.shape[1]\n\n    def forward(self, x):\n        x = self.cnn(x)\n        x = x.reshape(x.shape[0], -1, x.shape[-1])\n        x = x.permute(2, 0, 1)\n        x, _ = self.lstm(x)\n        x = self.fc(x)\n        return x\n\n\n# model = Model(n_classes, input_shape=(3, height, width))\n# inputs = torch.zeros((32, 3, height, width))\n# outputs = model(inputs)\n# print(outputs.shape)\n\n\ndef decode(sequence):\n    a = ''.join([characters[x] for x in sequence])\n    s = ''.join([x for j, x in enumerate(a[:-1]) if x != characters[0] and x != a[j+1]])\n    if len(s) == 0:\n        return ''\n    if a[-1] != characters[0] and s[-1] != a[-1]:\n        s += a[-1]\n    return s\n\ndef decode_target(sequence):\n    return ''.join([characters[x] for x in sequence]).replace(' ', '')\n\n# def calc_acc(target, output):\n#     output_argmax = output.detach().permute(1, 0, 2).argmax(dim=-1)\n#     target = target.cpu().numpy()\n#     output_argmax = output_argmax.cpu().numpy()\n#     a = np.array([decode_target(true) == decode(pred) for true, pred in zip(target, output_argmax)])\n#     return a.mean()\n\n\n\nmodel = Model(n_classes, input_shape=(3, height, width))\nmodel.load_state_dict(torch.load('ctc_625_22.pth',map_location=torch.device('cpu')))\nmodel.eval()\n\nif __name__ == \"__main__\":\n    for i in range(20):\n        # 更新验证码\n        from get_new_captcha import get_pin\n        get_pin()\n        pic = Image.open(r'pin_img.jpg')\n        # 转换成3通道\n        image = to_tensor(pic.convert(\"RGB\"))\n        # image = my_transforms(pic)\n        output = model(image.unsqueeze(0))\n        output_argmax = output.detach().permute(1, 0, 2).argmax(dim=-1)\n        print('pred:', decode(output_argmax[0]))\n        pic.show()\n        input('enter:')\n\n\n","sub_path":"captcha_sina/training_process/captha_predict.py","file_name":"captha_predict.py","file_ext":"py","file_size_in_byte":3973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"573900970","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import division\n\nimport argparse\nimport logging\nimport math\nimport os\nimport time\n\nimport dipy.core.geometry as gm\nimport nibabel as nib\nimport numpy as np\nimport tractconverter as tc\n\nfrom scilpy.tracking.dataset import Dataset\nfrom scilpy.tracking.localTracking import track\nfrom scilpy.tracking.mask import BinaryMask\nfrom scilpy.tracking.seed import Seed\nfrom scilpy.tracking.tools import (compute_average_streamlines_length,\n                                   get_max_angle_from_curvature,\n                                   save_streamlines_fibernavigator,\n                                   save_streamlines_tractquerier)\nfrom scilpy.tracking.tracker import (probabilisticTracker,\n                                     deterministicMaximaTracker)\nfrom scilpy.tracking.trackingField import MaximaField\n\n\ndef buildArgsParser():\n    p = argparse.ArgumentParser(\n        formatter_class=argparse.RawTextHelpFormatter,\n        description='Local streamline deterministic tractography using peaks. '\n        + 'The tracking is done '\n        + 'inside a binary mask. Streamlines greater than minL and shorter '\n        + 'than maxL are outputted. The tracking direction is chosen in the '\n        + 'aperture cone defined by the previous tracking direction and the '\n        + 'angular constraint. The relation between theta and the curvature '\n        + \"is theta=2*arcsin(step_size/(2*R)).\"\n        + \"\\n\\nAlgo 'det': the peak the most closely aligned to the previous \"\n        + \"direction.\"\n        + \"\\nAlgo 'prob': a \"\n        + 'peak drawn from the empirical distribution function defined '\n        + 'from the norm of peaks.',\n        epilog='References: [1] Girard, G., Whittingstall K., Deriche, R., and '\n        + 'Descoteaux, M. (2014). Towards quantitative connectivity analysis: '\n        + 'reducing tractography biases. Neuroimage, 98, 266-278.')\n    p._optionals.title = \"Options and Parameters\"\n\n    p.add_argument(\n        'peaks_file', action='store', metavar='peaks_file', type=str,\n        help=\"Peaks file. Data must be aligned with seed_file and \\n\" +\n        \"must have an isotropic resolution. \\n\" +\n        \"(nifti shape=(X,Y,Z,3*N), e.g. N=2 : \\n(X,Y,Z,x1,y1,z1,x2,y2,z2) \" +\n        \"or (X,Y,Z,x1,y1,z1,0,0,0).\")\n    p.add_argument(\n        'seed_file', action='store', metavar='seed_file', type=str,\n        help=\"Seeding mask (isotropic resolution, nifti).\")\n    p.add_argument(\n        'mask_file', action='store', metavar='mask_file', type=str,\n        help=\"Tracking mask (isotropic resolution, nifti).\")\n    p.add_argument(\n        'output_file', action='store', metavar='output_file', type=str,\n        help=\"Streamline output file (must be trk or tck).\")\n\n    p.add_argument(\n        '--algo', dest='algo', action='store', metavar='ALGO', type=str,\n        default='det', choices=['det', 'prob'],\n        help=\"Algorithm to use (must be 'det' or 'prob'). [%(default)s]\")\n\n    seeding_group = p.add_mutually_exclusive_group()\n    seeding_group.add_argument(\n        '--npv', dest='npv', action='store', metavar='NBR', type=int,\n        help='Number of seeds per voxel. [1]')\n    seeding_group.add_argument(\n        '--nt', dest='nt', action='store', metavar='NBR', type=int,\n        help='Total number of seeds. Replaces --npv and --ns.')\n    seeding_group.add_argument(\n        '--ns', dest='ns', action='store', metavar='NBR', type=int,\n        help='Number of streamlines to estimate. Replaces --npv and \\n--nt. ' +\n        'No multiprocessing used.')\n\n    p.add_argument(\n        '--skip', dest='skip', action='store',\n        metavar='NBR', type=int,\n        default=0, help='Skip the first NBR generated seeds / NBR seeds per ' +\n        'voxel \\n(--nt / --npv). Not working with --ns. [%(default)s]')\n    p.add_argument(\n        '--random', dest='random', action='store',\n        metavar='RANDOM', type=int,\n        default=0, help='Initial value for the random number generator.' +\n        ' [%(default)s]')\n\n    p.add_argument(\n        '--step', dest='step_size', action='store',\n        metavar='STEP', type=float, default=0.5,\n        help='Step size in mm. [%(default)s]')\n\n    p.add_argument(\n        '--rk_order', action='store', metavar='ORDER', type=int, default=2,\n        choices=[1, 2, 4],\n        help='The order of the Runge-Kutta integration used for \\nthe step ' +\n             'function. Must be 1, 2 or 4. [%(default)s]\\nAs a rule of thumb' +\n             ', doubling the rk_order will double \\nthe computation time ' +\n             'in the worst case.')\n\n    deviation_angle_group = p.add_mutually_exclusive_group()\n    deviation_angle_group.add_argument(\n        '--theta', dest='theta', action='store', metavar='ANGLE',\n        type=float, help=\"Maximum angle between 2 steps. [45]\")\n    deviation_angle_group.add_argument(\n        '--curvature', dest='curvature', action='store',\n        metavar='RADIUS', type=float,\n        help='Minimum radius of curvature R in mm. Replaces --theta.')\n    p.add_argument(\n        '--maxL_no_dir', dest='maxL_no_dir', action='store',\n        metavar='MAX', type=float, default=1,\n        help='Maximum length without valid direction, in mm. [%(default)s]')\n\n    p.add_argument(\n        '--sfthres', dest='sf_threshold', action='store',\n        metavar='THRES', type=float, default=0.0,\n        help='Spherical function relative threshold. [%(default)s]')\n    p.add_argument(\n        '--sfthres_init', dest='sf_threshold_init', action='store',\n        metavar='THRES', type=float, default=0.0,\n        help='Spherical function relative threshold value for ' +\n        '\\nthe initial direction. [%(default)s]')\n    p.add_argument(\n        '--minL', dest='min_length', action='store',\n        metavar='MIN', type=float, default=10,\n        help='Minimum length of a streamline in mm. [%(default)s]')\n    p.add_argument(\n        '--maxL', dest='max_length', action='store',\n        metavar='MAX', type=int, default=300,\n        help='Maximum length of a streamline in mm. [%(default)s]')\n\n    p.add_argument(\n        '--mask_interp', dest='mask_interp', action='store',\n        metavar='INTERP', type=str, default='nn', choices=['nn', 'tl'],\n        help=\"Mask interpolation: \\n'nn' (nearest-neighbor) or 'tl' \" +\n        \"(trilinear). [%(default)s]\")\n\n    p.add_argument(\n        '--single_direction', dest='is_single_direction', action='store_true',\n        help=\"If set, tracks in one direction only (forward or\\n\" +\n        \"backward) given \" +\n        \"the initial seed. The direction is \\nrandomly drawn from the ODF. \")\n    p.add_argument(\n        '--processes', dest='nbr_processes', action='store',\n        metavar='NBR', type=int, default=0,\n        help='Number of sub processes to start. [cpu count]')\n    p.add_argument(\n        '--load_data', action='store_true', dest='isLoadData',\n        help='If set, loads data in memory for all processes. \\nIncreases ' +\n        'the speed, and the memory requirements.')\n    p.add_argument(\n        '--compress', action='store', dest='compress', type=float,\n        help='If set, will compress streamlines. The parameter\\nvalue is ' +\n        'the distance threshold. A rule of thumb\\nis to set it to ' +\n        '0.1mm for deterministic\\nstreamlines and ' +\n        '0.2mm for probabilitic streamlines.')\n    p.add_argument(\n        '--tq', action='store_true', dest='outputTQ',\n        help=\"If set, outputs in the track querier format.\")\n\n    p.add_argument(\n        '-f', action='store_true', dest='isForce',\n        help='If set, overwrites output file.')\n    p.add_argument(\n        '-v', action='store_true', dest='isVerbose',\n        help='If set, produces verbose output.')\n    return p\n\n\ndef main():\n    parser = buildArgsParser()\n    args = parser.parse_args()\n    param = {}\n\n    if args.isVerbose:\n        logging.basicConfig(level=logging.DEBUG)\n\n    if args.outputTQ:\n        filename_parts = os.path.splitext(args.output_file)\n        output_filename = filename_parts[0] + '.tq' + filename_parts[1]\n    else:\n        output_filename = args.output_file\n\n    out_format = tc.detect_format(output_filename)\n    if out_format not in [tc.formats.trk.TRK, tc.formats.tck.TCK]:\n        parser.error(\"Invalid output streamline file format (must be trk or \" +\n                     \"tck): {0}\".format(output_filename))\n        return\n\n    if os.path.isfile(output_filename):\n        if args.isForce:\n            logging.debug('Overwriting \"{0}\".'.format(output_filename))\n        else:\n            parser.error('\"{0}\" already exists! Use -f to overwrite it.'\n                         .format(output_filename))\n\n    if not args.min_length > 0:\n        parser.error('minL must be > 0, {0}mm was provided.'\n                     .format(args.min_length))\n    if args.max_length < args.min_length:\n        parser.error('maxL must be > than minL, (minL={0}mm, maxL={1}mm).'\n                     .format(args.min_length, args.max_length))\n\n    if not np.any([args.nt, args.npv, args.ns]):\n        args.npv = 1\n\n    if args.theta is not None:\n        theta = gm.math.radians(args.theta)\n    elif args.curvature > 0:\n        theta = get_max_angle_from_curvature(args.curvature, args.step_size)\n    else:\n        theta = gm.math.radians(45)\n\n    if args.mask_interp == 'nn':\n        mask_interpolation = 'nearest'\n    elif args.mask_interp == 'tl':\n        mask_interpolation = 'trilinear'\n    else:\n        parser.error(\"--mask_interp has wrong value. See the help (-h).\")\n        return\n\n    param['random'] = args.random\n    param['skip'] = args.skip\n    param['algo'] = args.algo\n    param['mask_interp'] = mask_interpolation\n    param['field_interp'] = 'nearest'\n    param['theta'] = theta\n    param['sf_threshold'] = args.sf_threshold\n    param['sf_threshold_init'] = args.sf_threshold_init\n    param['step_size'] = args.step_size\n    param['rk_order'] = args.rk_order\n    param['max_length'] = args.max_length\n    param['min_length'] = args.min_length\n    param['max_nbr_pts'] = int(param['max_length'] / param['step_size'])\n    param['min_nbr_pts'] = int(param['min_length'] / param['step_size']) + 1\n    param['is_single_direction'] = args.is_single_direction\n    param['nbr_seeds'] = args.nt if args.nt is not None else 0\n    param['nbr_seeds_voxel'] = args.npv if args.npv is not None else 0\n    param['nbr_streamlines'] = args.ns if args.ns is not None else 0\n    param['max_no_dir'] = int(math.ceil(args.maxL_no_dir / param['step_size']))\n    param['is_all'] = False\n    param['is_keep_single_pts'] = False\n    # r+ is necessary for interpolation function in cython who\n    # need read/write right\n    param['mmap_mode'] = None if args.isLoadData else 'r+'\n\n    logging.debug('Tractography parameters:\\n{0}'.format(param))\n\n    seed_img = nib.load(args.seed_file)\n    seed = Seed(seed_img)\n    if args.npv:\n        param['nbr_seeds'] = len(seed.seeds) * param['nbr_seeds_voxel']\n        param['skip'] = len(seed.seeds) * param['skip']\n    if len(seed.seeds) == 0:\n        parser.error('\"{0}\" does not have voxels value > 0.'\n                     .format(args.seed_file))\n\n    mask = BinaryMask(\n        Dataset(nib.load(args.mask_file), param['mask_interp']))\n\n    dataset = Dataset(nib.load(args.peaks_file), param['field_interp'])\n    field = MaximaField(dataset,\n                        param['sf_threshold'],\n                        param['sf_threshold_init'],\n                        param['theta'])\n\n    if args.algo == 'det':\n        tracker = deterministicMaximaTracker(field, param)\n    elif args.algo == 'prob':\n        tracker = probabilisticTracker(field, param)\n    else:\n        parser.error(\"--algo has wrong value. See the help (-h).\")\n        return\n\n    start = time.time()\n    if args.compress:\n        if args.compress < 0.001 or args.compress > 1:\n            logging.warn('You are using an error rate of {}.\\n'.format(args.compress) +\n                         'We recommend setting it between 0.001 and 1.\\n' +\n                         '0.001 will do almost nothing to the tracts while ' +\n                         '1 will higly compress/linearize the tracts')\n\n        streamlines = track(tracker, mask, seed, param, compress=True,\n                            compression_error_threshold=args.compress,\n                            nbr_processes=args.nbr_processes, pft_tracker=None)\n    else:\n        streamlines = track(tracker, mask, seed, param,\n                            nbr_processes=args.nbr_processes, pft_tracker=None)\n\n    if args.outputTQ:\n        save_streamlines_tractquerier(streamlines, args.seed_file,\n                                      output_filename)\n    else:\n        save_streamlines_fibernavigator(streamlines, args.seed_file,\n                                        output_filename)\n\n    str_ave_length = \"%.2f\" % compute_average_streamlines_length(streamlines)\n    str_time = \"%.2f\" % (time.time() - start)\n    logging.debug(str(len(streamlines)) + \" streamlines, with an average \" +\n                  \"length of \" + str_ave_length + \" mm, done in \" +\n                  str_time + \" seconds.\")\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"Scilpy/scripts/scil_compute_tracking_peaks.py","file_name":"scil_compute_tracking_peaks.py","file_ext":"py","file_size_in_byte":13109,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"411903222","text":"import pytest\nimport tensorflow as tf\nimport numpy as np\nimport tfs.core.layer.ops as ops\n\nfrom tfs.core.layer.normalization import LRN,BN\nfrom tfs.network import Network\nnet = Network()\n\n@pytest.fixture\ndef l():\n  l = LRN(\n    net,\n    radius=1,\n    alpha=.1,\n    beta=0.01,\n    bias=1.0,\n    name=None\n  )\n  return l\nclass TestLRN:\n  def test_build_inverse(self,l):\n    _in = tf.zeros([1,10,10,4])\n    _out=l.build(_in)\n    assert _out.get_shape().as_list()==[1,10,10,4]\n\n@pytest.fixture\ndef l():\n  l = BN(\n    net,\n    scale_offset=True,\n    activation=ops.relu,\n  )\n  return l\nclass TestBN:\n  def test_build_inverse(self,l):\n    _in = tf.zeros([1,10,10,4])\n    _out=l.build(_in)\n    assert _out.get_shape().as_list()==[1,10,10,4]\n\n\n","sub_path":"tests/core/layer/normalization_test.py","file_name":"normalization_test.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"424558661","text":"infile=open('lcs.in','r')\noutfile=open('lcs.out','w')\n\nimport sys\nsys.setrecursionlimit(100000)\n\nstrings=[]\nmissed=infile.readline()\nstrings.append(infile.readline())\nmissed=infile.readline()\nstrings.append(infile.readline())\n\na=strings[0].split()\nb=strings[1].split()\n\n\n\narray=[]\nfor x in range(len(a)+1):\n\tarray.append([0]*(len(b)+1))\nhowget=[]\nfor x in range(len(a)+1):\n\thowget.append([0]*(len(b)+1))\n\n\ndef leng(alen, blen):\n\tglobal array\n\tglobal howget\n\tfor i in range(1,alen+1):\n\t\tfor j in range(1,blen+1):\n\t\t\tif a[i-1]==b[j-1]:\n\t\t\t\tarray[i][j]=array[i-1][j-1] + 1\n\t\t\t\thowget[i][j] = 3\n\t\t\telif array[i-1][j] >= array[i][j-1]:\n\t\t\t\tarray[i][j] = array[i-1][j]\n\t\t\t\thowget[i][j] = 1\n\t\t\telse:\n\t\t\t\tarray[i][j] = array[i][j-1]\n\t\t\t\thowget[i][j] = 2\ndef ans(i,j):\n\tanswer=[]\n\tglobal howget\n\twhile i!=0 and j!=0:\n\t\tif howget[i][j] == 3:\n\t\t\tanswer.append(b[j-1])\n\t\t\ti-=1\n\t\t\tj-=1\n\t\telif howget[i][j] == 2:\n\t\t\tj-=1\n\t\telse:\n\t\t\ti-=1\t\n\treturn answer\n\nleng(len(a), len(b))\nanswer=ans(len(a), len(b))\n\n\noutfile.write(str(str(len(answer)) + '\\n' + ' '.join(answer[::-1])))\n\n\ninfile.close()\noutfile.close()\n\n","sub_path":"dynamic/substring.py","file_name":"substring.py","file_ext":"py","file_size_in_byte":1093,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"440501671","text":"import scrapy\nfrom scrapy.spiders import CrawlSpider, Rule\nfrom scrapy.linkextractors import LinkExtractor\nfrom scrapy.selector import HtmlXPathSelector\nfrom urlparse import urlparse\n\nfrom storeeventsscraper.items import UniandesItem\n\nclass UniandesSpider(CrawlSpider):\n    name=\"uniandes\"\n    allowed_domains = [\"uniandes.edu.co\"]\n    start_urls = [\n#        \"http://uniandes.edu.co/\",\n#        \"http://www.uniandes.edu.co/mapa-del-sitio-1\",\n         \"https://economia.uniandes.edu.co/\",\n#        \"http://eventos.uniandes.edu.co/\",\n#        \"http://eventos.uniandes.edu.co/s/1384/events/social2.aspx?sid=1384&gid=26&sitebuilder=1&pgid=1250&sitebuilder=1&contentbuilder=1\",\n        \"http://ingenieria.uniandes.edu.co/paginas/home.aspx\",\n         \"http://administracion.uniandes.edu.co/\",\n    ]\n    \n    rules = (\n        #Rule(LinkExtractor(allow=(), deny=('eventos\\.uniandes\\.edu\\.co'), restrict_xpaths=('//*[contains(@href,\"facultades\")]',))),\n        Rule(LinkExtractor(allow=(), deny=('eventos\\.uniandes\\.edu\\.co'), restrict_xpaths=('//*[contains(@title,\"Facultad\")]',))),\n        #Rule(LinkExtractor(allow=(), deny=('eventos\\.uniandes\\.edu\\.co'), restrict_xpaths=('//*[contains(@title,\"Facultad\") or contains(@title,\"Departamento\")]',))),\n        Rule(LinkExtractor(allow=(), deny=('eventos\\.uniandes\\.edu\\.co'), restrict_xpaths=('//div[contains(@class,\"eventListing\")]//tr[contains(@align,\"top\")]',)), callback=\"parse_items\"),#, follow= True),\n        Rule(LinkExtractor(allow=(), deny=('eventos\\.uniandes\\.edu\\.co'), restrict_xpaths=('//*[contains(@href,\"icalrepeat.detail\")]',)),callback=\"parse_items\"),\n        Rule(LinkExtractor(allow=(), deny=('eventos\\.uniandes\\.edu\\.co'), restrict_xpaths=('//*[contains(translate(@href, \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\", \"abcdefghijklmnopqrstuvwxyz\"),\"detalleeventos\")]',)),callback=\"parse_items\"),\n        Rule(LinkExtractor(allow=(), deny=('eventos\\.uniandes\\.edu\\.co'), restrict_xpaths=('//*[contains(translate(@class, \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\", \"abcdefghijklmnopqrstuvwxyz\"),\"ev_link_row\")]',)),callback=\"parse_items\"),\n        Rule(LinkExtractor(allow=(), deny=('eventos\\.uniandes\\.edu\\.co'), restrict_xpaths=('//*[contains(translate(@href, \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\", \"abcdefghijklmnopqrstuvwxyz\"),\"layout=event\")]',)),callback=\"parse_items\"),\n        Rule(LinkExtractor(allow=(), deny=('eventos\\.uniandes\\.edu\\.co',\"facultad\\/noticias-economia\",\"facultad\\/destacados\"), restrict_xpaths=('//*[contains(translate(@href, \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\", \"abcdefghijklmnopqrstuvwxyz\"),\"layout=detailevents\")]',)),callback=\"parse_items\"),\n        Rule(LinkExtractor(allow=(), deny=('eventos\\.uniandes\\.edu\\.co'), restrict_xpaths=('//*[contains(translate(@href, \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\", \"abcdefghijklmnopqrstuvwxyz\"),\"event\")]',))),\n        Rule(LinkExtractor(allow=(), deny=('eventos\\.uniandes\\.edu\\.co'), restrict_xpaths=('//*[contains(translate(@href, \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\", \"abcdefghijklmnopqrstuvwxyz\"),\"icagenda\")]',))),\n    )\n    \n    def parse_items(self, response):\n        parsed_uri = urlparse(response.url)\n        domain = '{uri.scheme}://{uri.netloc}/'.format(uri=parsed_uri)\n        #print domain\n        #Eventos institucionales\n        for sel in response.xpath('//div[contains(@class, \"eventWrapper\")]'):\n            item = UniandesItem()\n            item['domain'] = domain\n            item['title'] = sel.xpath('div[contains(@class,\"title\")]//text()').extract()\n            item['dates'] = sel.xpath('div[contains(@class,\"dateLoc\")]//text()').extract()\n            item['desc'] = sel.xpath('div[contains(@class,\"description\")]//text()').extract()\n            item['owner'] = sel.xpath('div[contains(@class,\"contacts\")]//text()').extract()\n            yield item\n        #Eventos ingenieria\n        for sel in response.xpath('//div[contains(@class, \"dual event\")]'):\n            if(sel.xpath('//div[@id=\"CuerpoEvento\"]').extract() is not None):\n                item = UniandesItem()\n                item['domain'] = domain\n                item['title'] = sel.xpath('//h2[@id=\"titulo\"]//text()').extract()\n                item['dates'] = sel.xpath('//div[contains(@class,\"detail-date\")]//text()').extract()\n                item['desc'] = sel.xpath('//div[@id=\"CuerpoEvento\"]//text()').extract()\n                item['owner'] = sel.xpath('//a[starts-with(@href, \"mailto\")]/text()').extract()\n                yield item\n        #Eventos administracion\n        for sel in response.xpath('//div[contains(concat(\" \", @class, \" \"), \" event-detail \")]'):\n            item = UniandesItem()\n            item['domain'] = domain\n            item['title'] = sel.xpath('.//div[contains(@class,\"header\")]//h2/text()').extract()\n            item['dates'] = sel.xpath('.//div[contains(@class,\"date\")]//div/text()').extract()\n            item['desc'] = sel.xpath('.//div[contains(@class,\"description\")]//*/text()').extract()\n            item['owner'] = sel.xpath('//a[starts-with(@href, \"mailto\")]/text()').extract()\n            yield item\n        #Eventos Artes\n        for sel in response.xpath('//div[@id=\"jevents_body\"]'):\n            item = UniandesItem()\n            item['domain'] = domain\n            item['title'] = response.xpath('//div[position()=1]/span[position()=1]/text()').extract()\n            item['dates'] = sel.xpath('.//span[contains(@class,\"hf_event\")]/text()').extract()\n            item['desc'] = sel.xpath('.//p/text()').extract()\n            item['owner'] = sel.xpath('//a[starts-with(@href, \"mailto\")]/text()').extract()\n            yield item\n        #Eventos CienciasSociales\n        for sel in response.xpath('//div[@id=\"icagenda\"]'):\n            item = UniandesItem()\n            item['domain'] = domain\n            item['title'] = response.xpath('.//h2/text()').extract()\n            item['dates'] = sel.xpath('.//div[contains(@class,\"details\")]//text()').extract()\n            item['desc'] = sel.xpath('.//div[@id=\"detail-desc\"]//text()').extract()\n            item['owner'] = sel.xpath('//a[starts-with(@href, \"mailto\")]/text()').extract()\n            yield item\n        #Eventos Derecho\n        for sel in response.xpath('//table[@id=\"jevents_body\"]'):\n            item = UniandesItem()\n            item['domain'] = domain\n            item['title'] = sel.xpath('.//tr[contains(@class,\"headingrow\")]//text()').extract()\n            item['dates'] = sel.xpath('.//td[contains(@class,\"ev_detail repeat\")]//text()').extract()\n            item['desc'] = sel.xpath('.//td[contains(concat(\" \", @class, \" \"), \" ev_detail \") and not (contains(@class,\"repeat\"))]//text()').extract()\n            item['owner'] = sel.xpath('.//a[starts-with(@href, \"mailto\")]/text()').extract()\n            yield item\n        #Eventos Economia\n        for sel in response.xpath('//div[contains(@class,\"wrapper_centrar_contenido row\")]'):\n            item = UniandesItem()\n            item['domain'] = domain\n            item['title'] = sel.xpath('(.//div[contains(@class,\"titulo\") and contains(@class,\"col-xs\")])[1]/text()').extract()\n            item['dates'] = sel.xpath('(.//div[contains(@class,\"titulo\") and contains(@class,\"col-xs\")])[2]/text()').extract()\n            item['desc'] = sel.xpath('./div[position()=5]/div//text()').extract()\n            item['owner'] = sel.xpath('.//a[starts-with(@href, \"mailto\")]/text()').extract()\n            yield item\n    custom_settings = {\n        'DEPTH_LIMIT': '3',\n    }\n","sub_path":"storeeventsscraper/build/lib.linux-x86_64-2.7/storeeventsscraper/spiders/uniandes_spider.py","file_name":"uniandes_spider.py","file_ext":"py","file_size_in_byte":7382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"133209466","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar 18 10:48:55 2021\n\n@author: Hadar\n\"\"\"\n\n# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\n\n    \ndef XtimesY(x:float, y:float):\n    if x<=0:\n        return 0.0\n    r=exponent(y*Ln(x))\n    result= float('%0.6f' % r)\n    return result\n\ndef exponent(x:float):\n    n=1\n    ex=1.0\n    wowy=1.0\n    powy=1.0\n    while (n<100):\n        powy=powy*x\n        wowy=wowy*n\n        ex=ex+ powy/wowy\n        n=n+1\n    return ex\n\n  \ndef Ln(x:float):\n    if x<=0:\n        return 0.0\n    yn=x-1.0\n    yn1=yn+2*(x-exponent(yn))/(x+exponent(yn))\n   \n    while (x==x):\n        dif= yn1-yn\n        if dif<0:\n            dif=-dif\n        if dif>0.000001:\n            yn=yn1\n            yn1=yn+2*(x-exponent(yn))/(x+exponent(yn))\n            dif=yn1-yn\n        else:\n            return yn1\n\n  \n \ndef sqrt(x:float, y:float):\n    if y<=0:\n        return 0.0\n    return XtimesY(y, 1/x)\n\n\ndef calculate(x:float):\n    if x<=0:\n        return 0.0\n    r=float('%0.6f' % exponent(x))*XtimesY(7,x)*XtimesY(x,-1)*sqrt(x,x)\n    return r\n","sub_path":"equations.py","file_name":"equations.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"71837559","text":"population_dict = {}\n\nf1 = open(\"County_Data/county_list.txt\", \"r\")\nfor line in f1:\n    tokens = line.split(\"\\t\")\n    population_dict[tokens[0]] = int(tokens[1].replace(\"\\n\", \"\"))\n\nf = open(\"Result/out0.txt\", \"r\")\nr = []\nfor line in f:\n    r.append(line.replace(\"\\n\", \"\"))\n\nr = r[len(r) - 1]\nr = r.replace(\"{\", \"\").replace(\"}\", \"\")\ntokens = r.split(\", \")\n\nd = {}\nfor token in tokens:\n    data = token.split(\": \")\n    district = int(data[1])\n    prev = d.get(district, 0)\n    d[district] = prev + population_dict[data[0].replace(\"\\'\", \"\").replace(\"\\'\", \"\")]\n\nprint(d)","sub_path":"Phase-4/Parse_by_sub_county/get_population.py","file_name":"get_population.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"469419667","text":"'''A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,\n\n                        a^2 + b^2 = c^2\nFor example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.\n\nThere exists exactly one Pythagorean triplet for which a + b + c = 1000.\nFind the product abc.'''\n\n\nfound = False\n\n\nfor m in range(1, 101):\n    for n in range(1, 101):\n        if found is False:\n            # a, b, c are the squares of side of the triangles\n            a = m ** 2 - n ** 2\n            b = 2 * m * n\n            c = m ** 2 + n ** 2\n\n            if a + b + c == 1000:\n                print(a * b * c)\n                found = True\n\n            m += 4\n            n += 4\n","sub_path":"Project EULER/Problem_09.py","file_name":"Problem_09.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"335451678","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCalculating loss using cross entropy\n\nCreated on Tue Dec 10 10:52:55 2019\n\n@author: -https://campus.datacamp.com/courses/deep-learning-with-pytorch/artificial-neural-networks?ex=7\n\"\"\"\nimport torch\n\n# Initialize the scores and ground truth\nlogits = torch.tensor([[-1.2, 0.12, 4.8]])\nground_truth = torch.tensor([2])\n\n# Instantiate cross entropy loss\ncriterion = nn.CrossEntropyLoss()\n\n# Compute and print the loss\nloss = criterion(logits, ground_truth)\nprint(loss)\n\n\n\n\n","sub_path":"cross_entropy.py","file_name":"cross_entropy.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"236389777","text":"# Copyright (C) 2017 Tran Quan Pham\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 logging as l\nFORMAT = \"[%(filename)s:%(lineno)s - %(funcName)20s() ] %(message)s\"\nl.basicConfig(level=l.DEBUG, format=FORMAT)\n\n\nimport spotipy\n\n\n# database & constants\nclient_id = '67292b56265946c1963408a7bcb5fffa'\nclient_secret = 'c5af3d371927415eb88d48778efb5035'\nredirect_uri = 'http://localhost:8888/callback'\nscopes = 'user-read-private user-read-email'\n\n\nimport sys\nimport spotipy\nimport spotipy.util as util\n\ndef show_tracks(tracks):\n    for i, item in enumerate(tracks['items']):\n        track = item['track']\n        print(\"   %d %32.32s %s\" % (i, track['artists'][0]['name'],\n                              track['name']))\n\nif __name__ == '__main__':\n    if len(sys.argv) > 1:\n        username = sys.argv[1]\n    else:\n        print(\"Whoops, need your username!\")\n        print(\"usage: python user_playlists.py [username]\")\n        sys.exit()\n\n    token = util.prompt_for_user_token(username)\n\n    if token:\n        sp = spotipy.Spotify(auth=token)\n        playlists = sp.user_playlists(username)\n        for playlist in playlists['items']:\n            if playlist['owner']['id'] == username:\n                print\n                print\n                playlist['name']\n                print\n                '  total tracks', playlist['tracks']['total']\n                results = sp.user_playlist(username, playlist['id'],\n                                           fields=\"tracks,next\")\n                tracks = results['tracks']\n                show_tracks(tracks)\n                while tracks['next']:\n                    tracks = sp.next(tracks)\n                    show_tracks(tracks)\n    else:\n        print\n        \"Can't get token for\", username\n","sub_path":"apps/spotify.py","file_name":"spotify.py","file_ext":"py","file_size_in_byte":2252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"91848615","text":"import os\nfrom allennlp.predictors import Predictor\n\nclass Ner_Allen:\n    def __init__(self):\n        cwd = os.getcwd()\n        file_name = \"/ner-model-2018.04.26.tar.gz\"\n        full_path = cwd + \"/\" + file_name\n        self.predictor = Predictor.from_path(full_path)\n\n    def get_entities(self, text):\n        if(text == \"\"):\n            print(\"empty\")\n            return []\n        else:\n            results = self.predictor.predict(sentence=text)\n            zipped = zip(results[\"words\"], results[\"tags\"])\n            tuple_list = list(zipped)\n            print(tuple_list)\n            i=0\n            final_list = []\n            while i < len(tuple_list):                \n                 \n                if(tuple_list[i][1] == 'U-PER'):\n                    final_list.append(tuple_list[i][0])\n                elif(tuple_list[i][1] == 'B-MISC' or tuple_list[i][1] == 'B-ORG'):\n                    composto = tuple_list[i][0]\n                    i = i+1\n                    #Append de midle part, if there is one\n                    if(tuple_list[i][1] == 'I-ORG' or tuple_list[i][1] == 'I-MISC'):\n                        composto.join([\" \", tuple_list[i][0]])\n                        # i = i+1\n                    #Append de final part\n                    composto.join([\" \", tuple_list[i][0]])\n                    final_list.append(composto)\n\n                i = i+1\n\n            print(\"/////////////////////\")\n            for item in final_list:\n                print(item)\n            if final_list == [] :\n                return \n            return  final_list\n","sub_path":"bot/ner/ner_allen.py","file_name":"ner_allen.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"254311317","text":"import re\n\nregex = re.compile(r\"^(?P\\S+)\\s+(?P[+-]\\d+)$\")\n\nwith open(\"input.txt\", mode=\"r\") as f:\n    code = list(map(lambda x: (x.group(\"op\"), int(x.group(\"arg\"))), [regex.match(x) for x in f.readlines()]))\n\nstate = { \"a\": 0, \"pc\": 0 }\nvisited = set()\n\nwhile True:\n    if state[\"pc\"] in visited:\n        print(\"Infinite Loop detected, last value of a:\")\n        print(state[\"a\"])\n        break\n    insn = code[state[\"pc\"]]\n    visited.add(state[\"pc\"])\n    if insn[0] == \"nop\":\n        # NOP\n        state[\"pc\"] += 1\n        continue\n    if insn[0] == \"acc\":\n        # modify accumulator\n        state[\"a\"] += insn[1]\n        state[\"pc\"] += 1\n        continue\n    if insn[0] == \"jmp\":\n        # Jump\n        state[\"pc\"] += insn[1]\n        continue\n    print(\"Illegal instruction {} at pc={}\".format(insn[0], state[\"pc\"]))\n\n","sub_path":"2020/08/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"473239048","text":"# -*- coding: utf-8 -*-\n#\n# Kanji Coach for Anki\n# Copyright (C) 2017-Present  Dorian DAUDIER\n#\n# This module implements functions to communicate with Kanjax database\n\nimport sqlite3 as db\n\nfrom .exceptions import DatabaseError\nfrom .. import kanjax_db\n\n__all__ = ['get_strokes', 'get_keyword', 'get_description', 'get_kanji_from_keyword']\n\ndef get_strokes(kanji):\n    \"\"\"Returns the filename stored in the 'strokes' field for a given Kanji in\n    kanjax database. None is returned if no result can be found.\"\"\"\n    return get_field('strokes', kanji)\n\ndef get_keyword(kanji):\n    \"\"\"Returns the keyword of a given kanji in kanjax database.\n    None is returned if no result can be found.\"\"\"\n    return get_field('keyword', kanji)\n\ndef get_description(kanji):\n    \"\"\"Returns a description of the kanji's meaning in kanjax database.\n    None is returned if no result can be found.\"\"\"\n    return get_field('desc', kanji)\n\ndef get_field(field, kanji):\n    \"\"\"Returns a field corresponding to a kanji in kanjax database.\n    None is returned if no result can be found.\"\"\"\n\n    try:\n        conn = db.connect(kanjax_db)\n        cur = conn.cursor()\n        cur.execute(\"SELECT %s FROM KanjiIinfo WHERE kanji=?\" % field, (kanji,))\n        res = cur.fetchone()\n    except:\n        raise DatabaseError(\n            \"Error while trying to connect with kanjax database\")\n    finally:\n        conn.close()\n    # if a result could be found, return it. None will be returned otherwise\n    if res:\n        return res[0]\n    else:\n        raise Exception(\"SELECT %s FROM KanjiIinfo WHERE kanji=%s failed\" % (field, kanji))\n\ndef get_kanji_from_keyword(keyword):\n    \"\"\"Returns the kanji strored kanjax database matching a given meaning.\"\"\"\n    try:\n        conn = db.connect(kanjax_db)\n        cur = conn.cursor()\n        cur.execute(\"SELECT kanji FROM KanjiIinfo WHERE keyword=?\", (keyword,))\n        res = cur.fetchone()\n    except:\n        raise DatabaseError(\n            \"Error while trying to connect with kanjax database\")\n    finally:\n        conn.close()\n    # if a result could be found, return it. None will be returned otherwise\n    if res:\n        return res[0]\n","sub_path":"kanjicoach/core/kanjax_api.py","file_name":"kanjax_api.py","file_ext":"py","file_size_in_byte":2157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"243565855","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 11 12:48:32 2017\n\n@author: Chenli Zhang\n\"\"\"\nimport TextUtils\n\ndef doadd(dct,c):\n    if c in dct:\n        dct[c]=dct[c]+1.0\n    else:\n        dct[c]=1.0\n\nclass Fenci:\n    def __init__(self):\n        self.char={}#字符出现的总次数\n        self.char2={}#词 xx出现的总次数\n        \n    def fillDict(self,file):\n        f=open(file,'r',encoding='utf-8')\n        for line in f:\n            sents=TextUtils.splitWithNotations(line)\n            for s in sents:\n                for ch in s:\n                    doadd(self.char,ch)\n                for i in range(0,len(s)-1):\n                    doadd(self.char2,line[i]+line[i+1])\n                \n\n        f.close()\n        print('char len :'+str(len(self.char)))\n        print('word len :'+str(len(self.char2)))\n                \n    def getPosibility(self,word):#计算词的概率\n        if len(word)!=2:\n            return 0.0\n            \n        num=0.0;\n        if word in self.char2:\n            num=self.char2[word]\n        else:\n            return 0.0\n\n        return max(num/5.0,1.0)\n        \n        if word[0] not in self.char or word[1] not in self.char:\n            return 0\n        a=b=0.0\n        #print(word)\n        a=self.char[word[0]]\n        b=self.char[word[1]]\n        return num/max(min(a,b),5.0)\n        \n\n        \n        \n    def printWord(self,outputfile,thresh=0.0):\n        f=open(outputfile,'w',encoding='utf-8')\n        for item in self.char2.items():\n            prob=self.getPosibility(item[0])\n            if prob>thresh:\n                f.write(item[0]+' - '+str(prob)+'\\n')\n        f.close()\n        \n    def SpliteSentence(self,sent,thresh=0.8):\n        ret=[]\n        lst=TextUtils.splitWithNotations(sent)\n        for s in lst:\n            word=''\n            for i in range(0,len(s)-1):\n                word=word+s[i]\n                if self.getPosibility(s[i]+s[i+1])>thresh:\n                    continue;\n                else:\n                    ret.append(word)\n                    word=''\n            word=word+s[-1]\n            ret.append(word)\n        return ret","sub_path":"BookReader/Fenci.py","file_name":"Fenci.py","file_ext":"py","file_size_in_byte":2114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"418834213","text":"#!/usr/bin/env python3\n# # -*- coding: utf-8 -*-\n\n\"\"\"\nUnit test a Kafka deployment.\n - Test Producer from command lines. This will autocreate a topic named 'utester'\n - Test Delete topic\n - Test Describe resource with filter\n\nTo test consumer is working, on kafka server try:\n    kafka-console-consumer --bootstrap-server localhost:9092 --topic utester --from-beginning\n\nExample:\n    Create lines\n        python utKafka.py -b 192.168.56.51:9092 -pl\n\n    Describe topic\n        python utKafka.py -b 192.168.56.51:9092 -d topic -cf utester\n\n    Delete Topic\n        python utKafka.py -b 192.168.56.51:9092 -dt\n\n\"\"\"\n\nimport argparse\nimport logging\nfrom argparse import RawTextHelpFormatter\nfrom confluent_kafka import KafkaException\nfrom confluent_kafka import Producer\nfrom confluent_kafka.admin import ConfigResource\n\nfrom helpers.kafka import *\nfrom helpers.utils import *\n\nlog = logging.getLogger(os.path.splitext(__file__)[0])\nlogfile = 'operations.log'\nversion = \"1.0\"\n\n\ndef send_to_kafka(config):\n    log.debug(\"------------------ Begin send_to_kafka ------------------\")\n    log_trace = 'None'\n    status = 'Ok'\n\n    try:\n        conf = {'bootstrap.servers': config['broker']}\n        producer = Producer(**conf)\n\n    except Exception as ex:\n        e, _, ex_traceback = sys.exc_info()\n        log_traceback(log, ex, ex_traceback)\n        return {\"logtrace\": \"HOST UNREACHABLE\", \"status\": \"UNKNOWN\"}\n\n    # ------------------------- Switch options ------------------------- #\n    if config['producelines']:\n        publish_lines(producer, config['topic'])\n\n    if config['listtopics']:\n        a = create_admin_client(config['broker'])\n        list_topics(a)\n\n    if config['deletetopic']:\n        a = create_admin_client(config['broker'])\n        delete_topic(a, config['topic'])\n\n    if config['describe'] != 'unknown':\n        a = create_admin_client(config['broker'])\n        describe_configs(a, config['describe'], config['configfilter'])\n    # ------------------------------------------------------------------ #\n\n    log_trace = \"Send \" + status + \" | \" + log_trace\n    log.debug(\"------------------ End send_to_kafka ------------------\")\n    return {\"logtrace\": log_trace, \"status\": status}\n\n\n\ndef describe_configs(a, config, filter):\n    \"\"\" describe configs\n    Resource types:\n    UNKNOWN = RESOURCE_UNKNOWN #: Resource type is not known or not set.\n    ANY     = RESOURCE_ANY     #: Match any resource, used for lookups.\n    TOPIC   = RESOURCE_TOPIC   #: Topic resource. Resource name is topic name\n    GROUP   = RESOURCE_GROUP   #: Group resource. Resource name is group.id\n    BROKER  = RESOURCE_BROKER  #: Broker resource. Resource name is broker id\n    \"\"\"\n\n    # resources = [ConfigResource(restype, resname) for\n    #              restype, resname in zip(args[0::2], args[1::2])]\n    resources = [ConfigResource(config, filter)]\n\n    fs = a.describe_configs(resources)\n\n    # Wait for operation to finish.\n    for res, f in fs.items():\n        try:\n            configs = f.result()\n            for config in iter(configs.values()):\n                print_config(config, 1)\n\n        except KafkaException as e:\n            print(\"Failed to describe {}: {}\".format(res, e))\n        except Exception:\n            raise\n\n\ndef list_topics(a):\n    topic_list = a.list_topics()\n    print(topic_list.topics)\n\n\ndef delete_topic(a, topic):\n    topics = [topic]\n    # Returns a dict of .\n    fs = a.delete_topics(topics, operation_timeout=30)\n\n    # Wait for operation to finish.\n    for topic, f in fs.items():\n        try:\n            f.result()  # The result itself is None\n            print(\"Topic {} deleted\".format(topic))\n        except Exception as e:\n            print(\"Failed to delete topic {}: {}\".format(topic, e))\n\n\ndef publish_lines(producer, topic):\n    # Read lines from stdin, produce each line to Kafka\n    print(\"Type some lines... [ctrl-c] to exit.\")\n    for line in sys.stdin:\n        try:\n            # Produce line (without newline)\n            producer.produce(topic, line.rstrip(), callback=acked)\n        except BufferError:\n            sys.stderr.write('%% Local producer queue is full (%d messages awaiting delivery): try again\\n' %\n                             len(p))\n        producer.poll(0)\n\n    print(\"go out\")\n\n\ndef main(args, loglevel):\n    if args.logging:\n        logging.basicConfig(filename=logfile, format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s', level=loglevel)\n    logging.info('Started send_to_kafka')\n    log.debug(\"------------------ Reading config ------------------\")\n\n\n    config = {'broker': args.broker, 'topic': args.topic,\n              'producelines': args.producelines,\n              'listtopics': args.listtopics,\n              'deletetopic': args.deletetopic,\n              'describe': args.describe,\n              'configfilter': args.configfilter\n              }\n    config['root_dir'] = os.path.dirname(os.path.abspath(__file__))\n\n    nodes_info = send_to_kafka(config)\n\n    print(\"Done.\")\n    logging.info('Finished send_to_kafka')\n    exit_to_icinga(nodes_info)\n\n\ndef parse_args():\n    \"\"\"Parse command line arguments.\"\"\"\n    parser = argparse.ArgumentParser(description=__doc__, formatter_class=RawTextHelpFormatter)\n    parser.add_argument('-V', '--version', action='version', version='%(prog)s '+version)\n\n    parser.add_argument('-b', '--broker', help='Broker', type=str, default=\"none\", required=True)\n    parser.add_argument('-t', '--topic', help='Topic (default=utester)', type=str, default=\"utester\")\n    parser.add_argument('-pl', '--producelines', help='Produce from command line inputs', action='store_const', const=True, default=False)\n    parser.add_argument('-lt', '--listtopics', help='List Topics', action='store_const', const=True, default=False)\n    parser.add_argument('-dt', '--deletetopic', help='Delete Topic (if topic not specified, default topic will be deleted)', action='store_const', const=True, default=False)\n    # parser.add_argument('-sc', '--showconfig', help='Show Config', action='store_const', const=True, default=False)\n    parser.add_argument('-d', '--describe', default='unknown', const='all', nargs='?', choices=['unknown', 'any', 'topic', 'group', 'broker'], help='Resource type from list (default: %(default)s)')\n    # parser.add_argument('filter', metavar='N', type=str, nargs='+', help='an integer for the accumulator')\n    parser.add_argument('-cf', '--configfilter', type=str, help='A value to filter Resources. Required if ShowConfig is present', required='-sc' in sys.argv)\n\n    parser.add_argument('-l', '--logging', help='create log output in current directory', action='store_const', const=True, default=False)\n    verbosity = parser.add_mutually_exclusive_group()\n    verbosity.add_argument('-v', '--verbose', help='increase output verbosity', action='store_const', const=logging.DEBUG, default=logging.INFO)\n    verbosity.add_argument('-q', '--quiet', help='hide any debug exit', dest='verbose', action='store_const', const=logging.WARNING)\n    return parser.parse_args()\n\n\nif __name__ == '__main__':\n    args = parse_args()\n    main(args, args.verbose)\n\n","sub_path":"utKafka.py","file_name":"utKafka.py","file_ext":"py","file_size_in_byte":7108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"638482135","text":"import os, sys, Terminal, Character, time, GameState, Npc, Field, Quest, Party\n\nif not any(\"SunCat\" in s for s in sys.path):\n\tsys.path.append(os.getcwd() + \"\\SunCat\")\n\ntry:\n\timport SunCat, SCHotkey, SCLib\nexcept:\n\tprint(\"Couldn't find SunCat module\")\n\nfrom AioAttackSettings import *\njob = Character.GetJob()\n\n#SCLib.PersistVar(\"KillHorntail\", DoHorntail)\n\n\nTheCaveOfTrialEasy1 = 240060002\nTheCaveOfTrialEasy2 = 240060102\nTheCaveOfTrialNormal1 = [240060005,240060000]\nTheCaveOfTrialNormal2 = [240060105,240060100]\nHorntailsCaveNormal = [240060200,240060205]\nHorntailsCaveEasy = 240060300\nChaosHorntailsCave = [x for x in range(240060201,240060230)]\nTheCaveOfTrialChaos1 = [x for x in range(240060001,240060030)]\nTheCaveOfTrialChaos2 = [x for x in range(240060101,240060130)] \nCaveOfLifeEntrance = 240050000\nEntranceToHorntailsCave = 240050400\nCaveOfLifeEntrance1 = 240040700\nPeakOfTheBigNest = 240040600\n\nHorntailsLeftHeadEasy = 8810200\nHorntailsRightHeadEasy = 8810201\nHorntailsLeftHeadNormal = 8810000\nHorntailsRightHeadNormal = 8810001\nNormalHorntail = 8810018\nEasyHorntail = 8810214\nChaosHorntail = 8810118\nChaosHorntail1 = 8810119\nChaosHorntail2 = 8810120\nChaosHorntail3 = 8810121\nChaosHorntail4 = 8810122\nChaosHorntailsLeftHead = 8810100\nChaosHorntailsRightHead = 8810101\nEncryptedSlateOfTheSquad = 2083000\n\ndef ToggleKami(indicator):\n    Terminal.SetCheckBox(\"Kami Vac\",indicator)\n\ndef ToggleHyperTeleportRock(indicator):\n    Terminal.SetCheckBox(\"map/maprusher/hypertelerock\",indicator)\n\ndef ToggleFaceLeft(indicator):\n    Terminal.SetCheckBox(\"flacc\",indicator)\n\ndef GotoHorntail():\n    ToggleKami(False)\n    ToggleHyperTeleportRock(True)\n    print(\"Going to Horntail\")\n    if Field.GetID() != CaveOfLifeEntrance:\n        if Field.GetID() != PeakOfTheBigNest:\n            Terminal.Rush(PeakOfTheBigNest)\n        else:\n            ToggleHyperTeleportRock(False)\n            time.sleep(0.5)\n            Terminal.Rush(CaveOfLifeEntrance)\n    else:\n        Party.CreateParty()\n        Npc.ClearSelection()\n        Character.TalkToNpc(2083000)\n\ndef LeaveHorntail():\n    if (Field.GetID() == EntranceToHorntailsCave or Field.GetID() == CaveOfLifeEntrance or Field.GetID() == CaveOfLifeEntrance1):\n        ToggleKami(False)\n        ToggleHyperTeleportRock(False)\n        \n        if Field.GetID() == EntranceToHorntailsCave:\n            Character.TalkToNpc(2083002)\n        elif Field.GetID() == CaveOfLifeEntrance:\n            if Character.GetPos().x != -335:\n                Character.Teleport(-335, 255)\n            else:\n                Character.EnterPortal()\n        elif Field.GetID() == CaveOfLifeEntrance1:\n            if Character.GetPos().x != -206:\n                Character.Teleport(-206, 312)\n            else:\n                Character.EnterPortal()\n\ndef ToggleLoot(indicator):\n    Terminal.SetCheckBox(\"Kami Loot\",indicator)\n    Terminal.SetCheckBox(\"Auto Loot\",indicator)\n\ndef MoveToXLocation(xPos):\n    while Character.GetPos().x not in range(xPos-60,xPos+60):\n        Character.AMoveX(xPos)\n\ndef ResetNowLockedFunction():\n    SCLib.UpdateVar(\"NowLockedVar\", False)\ndef NowLockedFunction():\n    SCLib.UpdateVar(\"NowLockedVar\", True)\ndef DidSpawn():\n    SCLib.UpdateVar(\"HasSpawned\", True)\ndef ResetSpawn():\n    SCLib.UpdateVar(\"HasSpawned\", False)\n\ndef KillHorntail(bossDifficulty):\n    \n    SCLib.PersistVar(\"HasSpawned\", False)\n    SCLib.PersistVar(\"NowLockedVar\", False)\n    HasSpawned = SCLib.GetVar(\"HasSpawned\")\n    NowLockedVar = SCLib.GetVar(\"NowLockedVar\")\n    SCLib.StartVars()\n    if bossDifficulty == 0:\n        HorntailEasy = True\n        HorntailNormal = False\n        HorntailChaos = False\n    elif bossDifficulty == 1:\n        HorntailEasy = False\n        HorntailNormal = True\n        HorntailChaos = False\n    else:\n        HorntailEasy = False\n        HorntailNormal = False\n        HorntailChaos = True\n\n    HorntailPreQuest = Quest.GetQuestState(7313)\n    if HorntailPreQuest == 0:\n        print(\"Horntail Prequest not started or done, Starting quest before entery\")\n        if Field.GetID() != CaveOfLifeEntrance1:\n            Terminal.Rush(CaveOfLifeEntrance1)\n        else:\n            Quest.StartQuest(7313, 2081006)\n            print(\"Horntail Prequest started\")\n    else:\n        #ToggleKami(False)\n        print(\"Doing Horntail\")\n        if HorntailEasy:\n            print(\"Easy\")\n            if Field.GetID() != HorntailsCaveEasy:\n                if Field.GetID() != TheCaveOfTrialEasy2:\n                    if Field.GetID() != TheCaveOfTrialEasy1:\n                        if Field.GetID() != EntranceToHorntailsCave:\n                            GotoHorntail()\n                        else:\n                            if not NowLockedVar:\n                                Npc.ClearSelection()\n                                Npc.RegisterSelection(\"Easy Mode (Level 130 or above)\")\n                                time.sleep(1)\n                                Character.TalkToNpc(2083004)\n                                time.sleep(1)\n                            else:\n                                print(\"Seems like you diddnt finish your last attempt and are locked. Continueing other bosses\")\n                                SCLib.UpdateVar(\"KillHorntail\", False)\n                                ResetNowLockedFunction()\n                    else:\n                        NowLockedFunction()\n                        boss = Field.FindMob(HorntailsLeftHeadEasy)\n                        if boss.valid:\n                            ToggleKami(False)\n                            \n                            if Character.GetPos().x != 522:\n                                Character.Teleport(522, -40)\n                            print(\"Horntails left head still alive standby\")\n                        else:\n                            ToggleFaceLeft(True)\n                            ToggleKami(False)\n                            \n                            if Character.GetPos().x != 840:\n                                Character.Teleport(840, -165)\n                            else:\n                                Character.EnterPortal()\n                else:\n                    boss = Field.FindMob(HorntailsRightHeadEasy)\n                    if boss.valid:\n                        ToggleKami(False)\n                        ToggleAttack(True)\n                        if Character.GetPos().x != 9:\n                            Character.Teleport(9, -40)\n                        print(\"Horntails right head still alive standby\")\n                    else:\n                        ToggleFaceLeft(False)\n                        ToggleKami(False)\n                        \n                        if Character.GetPos().x != -307:\n                            Character.Teleport(-307, -165)\n                        else:\n                            Character.EnterPortal()\n            else:\n                boss = Field.FindMob(EasyHorntail)\n                if boss.valid:\n                    ToggleAttack(True)\n                    \n                    DidSpawn()\n                    ToggleKami(True)\n                    print(\"Horntail still alive Standby\")\n                else:\n                    if HasSpawned:\n                        ToggleKami(False)\n                        ToggleLoot(True)\n                        print(\"Horntail Easy Is dead waiting 10 sec before continueing\")\n                        time.sleep(10)\n                        Character.TalkToNpc(2083002)\n                        time.sleep(1)\n                        SCLib.UpdateVar(\"KillHorntail\", False)\n                        ToggleLoot(False)\n                        ResetSpawn()\n                        ResetNowLockedFunction()\n                    else:\n                        ToggleKami(False)\n                        ToggleAttack(False)\n                        \n                        crystal = Field.FindReactor(2401300)\n                        if crystal.valid:\n                            if Character.GetPos().x != 540:\n                                Character.Teleport(540, 15)\n                            else:\n                                Character.BasicAttack()\n                                time.sleep(2)\t\t\t\n        elif HorntailNormal:\n            print(\"Normal\")\n            if Field.GetID() not in HorntailsCaveNormal:\n                if Field.GetID() not in TheCaveOfTrialNormal2:\n                    if Field.GetID() not in TheCaveOfTrialNormal1:\n                        if Field.GetID() != EntranceToHorntailsCave:\n                            GotoHorntail()\n                        else:\n                            if not NowLockedVar:\n                                Npc.ClearSelection()\n                                Npc.RegisterSelection(\"Normal Mode (Level 130 or above)\")\n                                time.sleep(1)\n                                Character.TalkToNpc(2083004)\n                                time.sleep(1)\n                            else:\n                                print(\"Seems like you diddnt finish your last attempt and are locked. Continueing other bosses\")\n                                SCLib.UpdateVar(\"KillHorntail\", False)\n                                ResetNowLockedFunction()\n                    else:\n                        NowLockedFunction()\n                        boss = Field.FindMob(HorntailsLeftHeadNormal)\n                        if boss.valid:\n                            ToggleKami(False)\n                            ToggleAttack(True)\n                            if Character.GetPos().x != 522:\n                                Character.Teleport(522, -40)\n                            print(\"Horntails left head still alive standby\")\n                        else:\n                            ToggleFaceLeft(True)\n                            ToggleKami(False)\n                            \n                            if Character.GetPos().x != 840:\n                                Character.Teleport(840, -165)\n                            else:\n                                Character.EnterPortal()\n                else:\n                    boss = Field.FindMob(HorntailsRightHeadNormal)\n                    if boss.valid:\n                        ToggleKami(False)\n                        \n                        if Character.GetPos().x != 9:\n                            Character.Teleport(9, -40)\n                        print(\"Horntails right head still alive standby\")\n                    else:\n                        ToggleFaceLeft(False)\n                        ToggleKami(False)\n                        \n                        if Character.GetPos().x != -307:\n                            Character.Teleport(-307, -165)\n                        else:\n                            Character.EnterPortal()\n            else:\n                boss = Field.FindMob(NormalHorntail)\n                if boss.valid:\n                    ToggleAttack(True)\n                    ToggleKami(True)\n                    \n                    DidSpawn()\n                    print(\"Horntail Normal still alive Standby\")\n                else:\n                    if HasSpawned:\n                        ToggleKami(False)\n                        ToggleLoot(True)\n                        print(\"Horntail Normal Is dead waiting 10 sec before continueing\")\n                        time.sleep(10)\n                        Character.TalkToNpc(2083002)\n                        time.sleep(1)\n                        ToggleLoot(False)\n                        SCLib.UpdateVar(\"KillHorntail\", False)\n                        ResetSpawn()\n                        ResetNowLockedFunction()\n                    else:\n                        ToggleAttack(False)\n                        ToggleKami(False)\n                        \n                        crystal = Field.FindReactor(2401000)\n                        if crystal.valid:\n                            if Character.GetPos().x != 540:\n                                Character.Teleport(540, 15)\n                            else:\n                                Character.BasicAttack()\n                                time.sleep(2)\n        elif HorntailChaos:\n            print(\"Chaos\")\n            if Field.GetID() not in ChaosHorntailsCave:\n                if Field.GetID() not in TheCaveOfTrialChaos2:\n                    if Field.GetID() not in TheCaveOfTrialChaos1:\n                        if Field.GetID() != EntranceToHorntailsCave:\n                            GotoHorntail()\n                        else:\n                            if not NowLockedVar:\n                                Npc.ClearSelection()\n                                Npc.RegisterSelection(\"Chaos Mode (Level 135 or above)\")\n                                time.sleep(1)\n                                Character.TalkToNpc(2083004)\n                                time.sleep(1)\n                            else:\n                                print(\"Seems like you diddnt finish your last attempt and are locked. Continueing other bosses\")\n                                SCLib.UpdateVar(\"KillHorntail\", False)\n                                ResetNowLockedFunction()\n                    else:\n                        NowLockedFunction()\n                        boss = Field.FindMob(ChaosHorntailsLeftHead)\n                        if boss.valid:\n                            ToggleKami(False)\n                            ToggleAttack(True)\n                            while Character.GetPos().x not in range(500,570):\n                                Character.AMoveX(522)\n                            print(\"Horntails left head still alive standby\")\n                        else:\n                            ToggleFaceLeft(True)\n                            ToggleKami(False)\n                            \n                            if Character.GetPos().x != 840:\n                                Character.Teleport(840, -165)\n                            else:\n                                Character.EnterPortal()\n                else:\n                    boss = Field.FindMob(ChaosHorntailsRightHead)\n                    if boss.valid:\n                        ToggleKami(False)\n                        ToggleAttack(True)\n                        while Character.GetPos().x not in range(-40,40):\n                            Character.AMoveX(9)\n                        print(\"Horntails right head still alive standby\")\n                    else:\n                        ToggleFaceLeft(False)\n                        ToggleKami(False)\n                        \n                        if Character.GetPos().x != -307:\n                            Character.Teleport(-307, -165)\n                        else:\n                            Character.EnterPortal()\n            else:\n                boss = Field.FindMob(ChaosHorntail)\n                boss1 = Field.FindMob(ChaosHorntail1)\n                boss2 = Field.FindMob(ChaosHorntail2)\n                boss3 = Field.FindMob(ChaosHorntail3)\n                boss4 = Field.FindMob(ChaosHorntail4)\n                if boss.valid or boss1.valid or boss2.valid or boss3.valid or boss4.valid:\n                    ToggleAttack(True)\n                    \n                    #ToggleKami(True)\n                    DidSpawn()\n                    while Character.GetPos().x not in range(140,220):\n                        Character.AMoveX(183)\n                    print(\"Horntail still alive, Standby\")\n                else:\n                    if HasSpawned:\n                        ToggleKami(False)\n                        ToggleLoot(False)\n                        #print(\"Horntail Is dead waiting 10 sec before continueing\")\n                        print(\"Looting\")\n                        Terminal.SetCheckBox(\"Auto Loot\",True)\n                        MoveToXLocation(Field.GetRect().left)\n                        time.sleep(1.5)\n                        MoveToXLocation(Field.GetRect().right)\n                        time.sleep(1.5)\n                        MoveToXLocation(Field.GetRect().left)\n                        time.sleep(1.5)\n                        MoveToXLocation(Field.GetRect().right)\n                        time.sleep(1.5)\n                        MoveToXLocation(Field.GetRect().left)\n                        time.sleep(1.5)\n                        #time.sleep(10)\n                        Character.TalkToNpc(2083002)\n                        time.sleep(1)\n                        SCLib.UpdateVar(\"KillHorntail\", False)\n                        ToggleLoot(False)\n                        ResetSpawn()\n                        ResetNowLockedFunction()\n                    else:\n                        ToggleAttack(False)\n                        ToggleKami(False)\n                        \n                        crystal = Field.FindReactor(2401100)\n                        if crystal.valid:\n                            if Character.GetPos().x != 540:\n                                Character.Teleport(540, 15)\n                            else:\n                                Character.BasicAttack()\n                                time.sleep(2)\n\nif GameState.IsInGame():\n    KillHorntail(2)","sub_path":"dependencies/AutoHorntail.py","file_name":"AutoHorntail.py","file_ext":"py","file_size_in_byte":17052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"594383339","text":"import pandas as pd\nimport numpy as np\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nimport nltk\nfrom nltk.corpus import stopwords, wordnet\nimport re\nimport string\nfrom nltk.tokenize import word_tokenize\nfrom keras.layers import Embedding, LSTM, SpatialDropout1D, Dense\nfrom keras.initializers import Constant\nfrom keras.models import Sequential\nfrom keras.optimizers import Adam\nfrom sklearn.model_selection import train_test_split\nfrom keras.callbacks.callbacks import ModelCheckpoint, ReduceLROnPlateau\nfrom sklearn.metrics import accuracy_score, classification_report, f1_score, precision_score, recall_score\n\nGLOVE = './glove/glove.6B.100d.txt'\nGLOVE_2 = './glove/glove.twitter.27B.100d.txt'\nDIMS = 100\nTRAINING_FILE_NAME = 'dataset/train.csv'\nTEST_PREDICT_FILE = './dataset/test.csv'\nSUBMISSION_FILE = './submission/disaster_dl.csv'\nSAMPLE_SUBMISSION_FILE = './dataset/sample_submission.csv'\n\ndef read_csv(file_name):\n    data = pd.read_csv(file_name)\n\n    # create dataframe\n    train_df = pd.DataFrame(data)\n\n    return train_df\n\n\ndef pre_process_text(df):\n    words = set(nltk.corpus.words.words())\n    stop_words = set(stopwords.words('english'))\n    texts = []\n    for _, row in df.iterrows():\n        text = row['text']\n        # remove word that is not in English corpus and transform them to lower case\n        text = \" \".join(w.lower()\n                        for w in nltk.wordpunct_tokenize(text) if w.lower() in words)\n\n        # remove http tag\n        text = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\\(\\),]|'\n                      '(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', text)\n\n        # remove number\n        text = re.sub(r'\\d+', '', text)\n\n        # remove punctuation mark\n        text = text.translate(str.maketrans('', '', string.punctuation))\n\n        # remove extra white space\n        text = text.strip()\n        texts.append(text)\n\n    df['text'] = texts\n    return df\n\n\ndef metrics(pred_tag, y_test):\n    print(\"F1-score: \", f1_score(pred_tag, y_test))\n    print(\"Precision: \", precision_score(pred_tag, y_test))\n    print(\"Recall: \", recall_score(pred_tag, y_test))\n    print(\"Acuracy: \", accuracy_score(pred_tag, y_test))\n    print(\"-\"*50)\n    print(classification_report(pred_tag, y_test))\n\n\ndf = read_csv(TRAINING_FILE_NAME)\ndf = pre_process_text(df)\nX = df['text'].values\ny = df['target'].values\n\ntokenizer = Tokenizer(lower=True)\ntokenizer.fit_on_texts(X)\nvocab_length = len(tokenizer.word_index) + 1\n\n# read GloVe and save into embedding_dict\nembedding_dict = {}\nwith open(GLOVE_2, 'r', encoding='utf-8') as f:\n    for line in f:\n        values = line.split()\n        word = values[0]\n        vectors = np.asarray(values[1:], 'float32')\n        embedding_dict[word] = vectors\nprint(\"words loaded!\")\nf.close()\n\n# store word that is in GloVe in embedding_matrix\nembedding_matrix = np.zeros((vocab_length, DIMS))\n# print(tokenizer.word_index.items())\nfor word, i in tokenizer.word_index.items():\n    embedding_vector = embedding_dict.get(word)\n\n    # words not found in embedding index will be all-zeros.\n    if embedding_vector is not None:\n        embedding_matrix[i] = embedding_vector\n\nprint(embedding_matrix.shape)\n\n# find longest tokenenized sentence and convert other sentence to the same length\nlongest_train = max(X, key=lambda sentence: len(word_tokenize(sentence)))\nlength_long_sentence = len(word_tokenize(longest_train))\npadded_sentence = pad_sequences(tokenizer.texts_to_sequences(\n    X), length_long_sentence, padding='post')\n\nembedding = Embedding(input_dim=embedding_matrix.shape[0], output_dim=embedding_matrix.shape[1], embeddings_initializer=Constant(\n    embedding_matrix), input_length=length_long_sentence)\n\nmodel = Sequential()\nmodel.add(embedding)\n# regularization technique, which aims to reduce the complexity of the model with the goal to prevent overfitting.\nmodel.add(SpatialDropout1D(0.2))\nmodel.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))\n# simply a layer where each unit or neuron is connected to each neuron in the next layer.\nmodel.add(Dense(1, activation='sigmoid'))\noptimizer = Adam(learning_rate=3e-4)\nmodel.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n\nprint(model.summary())\nX_train, X_test, y_train, y_test = train_test_split(padded_sentence, y,test_size=0.25)\n\ncheckpoint = ModelCheckpoint(\n    'model_2.h5',\n    monitor = 'val_loss',\n    verbose = 1,\n    save_best_only = True\n)\n\nreduce_lr = ReduceLROnPlateau(\n    monitor = 'val_loss',\n    factor = 0.2,\n    verbose = 1,\n    patience = 5,\n    min_lr = 0.001\n)\n\nhistory = model.fit(X_train\n                    ,y_train\n                    ,epochs=20\n                    ,batch_size=32\n                    ,validation_data=[X_test, y_test]\n                    ,verbose = 1\n                    ,callbacks= [reduce_lr, checkpoint])\n\nloss, accuracy = model.evaluate(X_test, y_test)\nprint('Loss:', loss)\nprint('Accuracy:', accuracy)\n\npreds = model.predict_classes(X_test)\nmetrics(preds, y_test)\n\nmodel.load_weights('model_2.h5')\npreds = model.predict_classes(X_test)\nprint('-------------------------------;;;---------')\nprint(preds)\nmetrics(preds, y_test)\n\n# prepare file to submission to kaggle\nmodel.load_weights('model_2.h5')\ntest = read_csv(TEST_PREDICT_FILE)\nsample_sub = read_csv(SAMPLE_SUBMISSION_FILE)\n\ntest = pre_process_text(test)\ntest_input = test['text'].values\npadded_sentence_test = pad_sequences(tokenizer.texts_to_sequences(test_input), length_long_sentence, padding='post')\n\nlabel_results = model.predict_classes(padded_sentence_test)\nsample_sub['target'] = label_results \nsample_sub.to_csv(SUBMISSION_FILE, index=False)","sub_path":"disaster_dl.py","file_name":"disaster_dl.py","file_ext":"py","file_size_in_byte":5665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"287918913","text":"# Definition for binary tree with next pointer.\nclass TreeLinkNode:\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n        self.next = None\n\nclass Solution:\n    # @param root, a tree link node\n    # @return nothing\n    def connect(self, root):\n        if not root:\n            return\n        self.helper(root)\n        \n        \n    def helper(self, root):\n        \n        if not root:\n            return\n        if root.next:\n            if root.right:\n                root.right.next = self.find_next(root.next, True)\n        if root.left:\n            root.left.next = self.find_next(root, False)\n        self.helper(root.left)\n        self.helper(root.right)\n        \n    def find_next(self, root, check_both):\n        \"\"\" finds the next pointer \"\"\"\n        \n        if root == None:\n            return None\n            \n        if check_both:\n            if root.left:\n                return root.left\n        if root.right:\n                return root.right\n        return self.find_next(root.next, True)\ntree = TreeLinkNode(10)   \ntree.left = TreeLinkNode(5)   \ntree.right = TreeLinkNode(15)   \nsolution1 = Solution()\nsolution1.connect(tree)\n","sub_path":"next_right_tree2.py","file_name":"next_right_tree2.py","file_ext":"py","file_size_in_byte":1200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"627244811","text":"#!/bin/env python3\n\nfrom urllib.parse import urlparse\nimport sys\n\n\n# This demo program illustrates the `urlparse()` function \n#\n# The `urlparse()` function easily evaluates whether a URL is absolute and\n# suitable for use with the Requests library.\n\ndef anatomyOfAUrl(url):\n    parsed = urlparse(url)\n    print(f\"'{url}' breaks down like this:\")\n\n    if parsed.scheme:\n        print(f\"\\tScheme        = '{parsed.scheme}'\")\n    else:\n        print(\"\\tNO scheme\")\n\n    if parsed.netloc:\n        print(f\"\\tLocation      = '{parsed.netloc}'\")\n    else:\n        print(\"\\tNO location\")\n\n    if parsed.path:\n        print(f\"\\tPath          = '{parsed.path}'\")\n    else:\n        print(\"\\tNO path\")\n\n    if parsed.params:\n        print(f\"\\tParameters    = '{parsed.params}'\")\n    else:\n        print(\"\\tNO parameters\") \n\n    if parsed.query:\n        print(f\"\\tQuery         = '{parsed.query}'\")\n    else:\n        print(\"\\tNO query\")\n\n    if parsed.fragment:\n        print(f\"\\tFragment      = '{parsed.fragment}'\")\n    else:\n        print(\"\\tNO fragment\")\n\n\nif len(sys.argv) < 2:\n    for url in [ 'http://unnovative.net/levels/level1.html',\n            'unnovative.net/levels/level1.html',\n            '//unnovative.net/?key=value',\n            'unnovative.net',\n            'https://usu.instructure.com/courses/518265/pages/5-readings-and-resources?module_item_id=3263688#approach', ]:\n        anatomyOfAUrl(url)\n        print()\nelse:\n    for url in sys.argv[1:]:\n        anatomyOfAUrl(url)\n        print()\n","sub_path":"demo/demo_urlparse.py","file_name":"demo_urlparse.py","file_ext":"py","file_size_in_byte":1498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"580239426","text":"\"\"\"\nUnion-Find unit tests\n\"\"\"\nfrom .union_find import UnionFindBoundsException\nfrom .quick_find import QuickFind\nfrom .quick_union import QuickUnion\nfrom .quick_union_weighted import WeightedQuickUnion\nfrom .quick_union_weighted_pc import WeightedQuickUnionWithPathCompression\nfrom ...test.test_case import TestCase, test_all_implementations\n\nN = 12\nCOMPONENTS = [\n    range(0, 1), # {0}\n    range(1, 4), # {1,2,3}\n    range(4, 8), # {4,5,6,7}\n    range(8, N)  # {8,9,10,11}\n]\nNUMBER_OF_COMPONENTS = len(COMPONENTS)\n\n#pylint: disable=C0103\ndef setup(Implementation):\n    \"\"\"Setup an implementation of the Union-Find data-structure for testing\n\n    Defines the following components:\n        {0}, {1, 2, 3}, {4,5,6,7}, {8,9,10,11}\n    \"\"\"\n    implementation = Implementation(N)\n\n    implementation.union(2, 1)\n    implementation.union(3, 1)\n\n    implementation.union(6, 5)\n    implementation.union(7, 5)\n    implementation.union(5, 4)\n\n    implementation.union(9, 8)\n    implementation.union(10, 9)\n    implementation.union(11, 10)\n\n    return implementation\n\ndef makeConnectedAssertion(implementation, component_a_nodes, component_b_nodes, makeAssertion):\n    \"\"\"Make an assertion about about connections between components in the specified implementation of UnionFind\"\"\"\n    for component_a_node in component_a_nodes:\n        for component_b_node in component_b_nodes:\n            makeAssertion(implementation.connected(component_a_node, component_b_node))\n#pylint: enable=C0103\n\nclass UnionFindTestCase(TestCase):\n    def setUp(self):\n        self.implementations = [\n            setup(QuickFind),\n            setup(QuickUnion),\n            setup(WeightedQuickUnion),\n            setup(WeightedQuickUnionWithPathCompression)\n        ]\n\n        super(UnionFindTestCase, self).setUp()\n\n    def tearDown(self):\n        self.implementations = []\n        super(UnionFindTestCase, self).tearDown()\n\n    @test_all_implementations\n    def test_connected_nodes(self, implementation=None):\n        \"\"\"Ensure that all of a component's nodes are connected to each other\n\n        Methods tested:\n            union(), connected(), find()\n        \"\"\"\n        for node_id in range(N):\n            # All nodes are connected to themselves\n            node = range(node_id, node_id + 1)\n            makeConnectedAssertion(implementation, node, node, self.assertTrue)\n\n        for component in COMPONENTS:\n            # Check that each node in a component is connected to each other node in the component\n            makeConnectedAssertion(implementation, component, component, self.assertTrue)\n\n    @test_all_implementations\n    def test_not_connected_nodes(self, implementation=None):\n        \"\"\"Ensure the nodes of one component are not connected to any nodes in any other component\n\n        Methods tested:\n            union(), connected(), find()\n        \"\"\"\n        components = list(COMPONENTS)\n\n        for index in range(len(components)):\n            component = components.pop(index)\n\n            for other_component in components:\n                makeConnectedAssertion(implementation, component, other_component, self.assertFalse)\n\n            components.insert(index, component)\n\n    @test_all_implementations\n    def test_count(self, implementation=None):\n        \"\"\"Ensure that the correct number of unique components is returned\"\"\"\n        self.assertEqual(implementation.count(), NUMBER_OF_COMPONENTS)\n\n    @test_all_implementations\n    def test_bounds(self, implementation=None):\n        \"\"\"Ensure that an error is thrown if a passed in node is outside the bounds of the data structure\n\n        Methods tested:\n            union(), connected(), find()\n        \"\"\"\n        valid_node = 0\n        invalid_nodes = [N + 1, N + 2]\n\n        self.assertRaises(UnionFindBoundsException, implementation.find, invalid_nodes[0])\n\n        self.assertRaises(UnionFindBoundsException, implementation.union, invalid_nodes[0], invalid_nodes[1])\n        self.assertRaises(UnionFindBoundsException, implementation.union, valid_node, invalid_nodes[0])\n\n        self.assertRaises(UnionFindBoundsException, implementation.connected, invalid_nodes[0], invalid_nodes[1])\n        self.assertRaises(UnionFindBoundsException, implementation.connected, valid_node, invalid_nodes[0])\n","sub_path":"src/data_structures/union_find/tests_unit.py","file_name":"tests_unit.py","file_ext":"py","file_size_in_byte":4255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"478894986","text":"\"\"\"\nModul zum hinzufügen von Blobs in eine sqllite3 Datenbank. Die \neingefügten Blobs werden versioniert.\n\"\"\"\n\nimport sqlite3\nimport os\nimport datetime\nimport logging\n\n\ndef create_or_open_db(db_file):\n    \"\"\" Erstellt oder öffnet eine sqlite3 Datenbank. \n\n    Arguments:\n        db_file -- Name der Datenbank\n\n    Returns:\n        Eine sqlite3 Datenbank connection.\n    \"\"\"\n    db_is_new = not os.path.exists(db_file)\n    conn = sqlite3.connect(db_file)\n    if db_is_new:\n        logging.info(\"Creating tables...\")\n\n        sql = \"\"\"CREATE TABLE IF NOT EXISTS BLOBS(\n                 ID INTEGER PRIMARY KEY AUTOINCREMENT,\n                 BLOBNAME TEXT,\n                 DATETIME TEXT,\n                 OPAQUE TEXT,\n                 STORAGE_ID INTEGER);\"\"\"\n\n        logging.debug(\"conn.execute(%s)\", sql)\n        conn.execute(sql)\n\n        sql = \"\"\"CREATE TABLE IF NOT EXISTS BLOB_STORAGE(\n                 ID INTEGER PRIMARY KEY AUTOINCREMENT,\n                 CONTENT BLOB);\"\"\"\n\n        logging.debug(\"conn.execute(%s)\", sql)\n        conn.execute(sql)\n\n    else:\n        logging.info(\"Tables exists.\")\n\n    return conn\n\n\ndef insert_blob(cursor, blobname, blob, opaque=\"\"):\n    \"\"\" Fügt einen Blob unter den Namen blobname einer sqllite3 Datenbank hinzu. \n\n    Es wird überprüft ob unter den gleichen Namen bereits Daten gespeichert wurden.\n    Falls ja, werden die Daten nicht erneut hinzugefügt sondern die alten Daten\n    verwendet.\n\n    Arguments:\n        cursor -- Datenbank Cursor\n        blobname -- Name des Blobs\n        blob -- Der eigentliche Blob\n        opaque -- Beliebige Daten als String gepackt\n\n    Returns:\n        [int] --  Die id unter welche der blob in der Datenbank gespeichert wurde.\n    \"\"\"\n\n    (storage_id, ablob_last) = extract_last_blob(cursor, blobname)\n\n    if (blob != ablob_last):\n        logging.info(\n            \"The blob \\\"%s\\\" is new. Insert it into BLOB_STORAGE.\", blobname)\n        sql = \"INSERT INTO BLOB_STORAGE (CONTENT) VALUES(?);\"\n        cursor.execute(sql, [sqlite3.Binary(blob)])\n        storage_id = int(cursor.lastrowid)\n    else:\n        logging.info(\n            \"The blob \\\"%s\\\" was inserted beforehand. Using this blob instead.\", blobname)\n\n    sql = \"INSERT INTO BLOBS (BLOBNAME, DATETIME, OPAQUE, STORAGE_ID) VALUES(?, ?, ?, ?);\"\n    dt = datetime.datetime.now().isoformat()\n\n    logging.info(\"Insert blob with blobname=\\\"%s\\\" into BLOBS.\", blobname)\n    cursor.execute(sql, [blobname, dt, opaque, storage_id])\n\n    lastrowid = int(cursor.lastrowid)\n    logging.debug(\n        \"The blob \\\"%s\\\" was inserted with into BLOBS with id=%i\", blobname, lastrowid)\n    return lastrowid\n\n\ndef list_dataset_for_blobname(cursor, blobname):\n    \"\"\"Listet alle gespeicherten Datensätze in der Datenbank auf die \n    unter filename gespeichert wurden\n\n    Arguments:\n        cursor -- Datenbank Cursor\n        blobname -- Name unter dem der gesuchte blob gespeichert ist.\n\n    Returns:\n        Eine Liste von Dictionaries die die gefundenen Einträge enthalten.\n    \"\"\"\n\n    sql = \"SELECT ID, DATETIME, STORAGE_ID FROM BLOBS WHERE BLOBNAME = :filename\"\n    param = {'filename': blobname}\n    cursor.execute(sql, param)\n    data = [\n        {\n            'id': x[0],\n            'datatime': x[1],\n            'storage_id':x[2]\n        }\n        for x in cursor.fetchall()]\n\n    return data\n\n\ndef extract_blob_from_storage(cursor, storage_id):\n    \"\"\" Extrahiert den unter der storage_id in der Datenbank abgelegten blob.\n\n    Arguments:\n        cursor -- Datenbank Cursor\n        storage_id -- Die Storage id des gewünschten blobs.\n\n    Returns:\n        Der blob der unter storage_id gespeichert wurde. Dieser wird\n        als bytearray zurückgegeben.\n    \"\"\"\n    logging.debug(\n        \"Extract blob with storage_id=%i from BLOB_STORAGE.\", storage_id)\n    sql = \"SELECT CONTENT FROM BLOB_STORAGE WHERE id = :id\"\n    param = {'id': storage_id}\n    cursor.execute(sql, param)\n    return cursor.fetchone()[0]\n\n\ndef extract_last_blob(cursor, blobname):\n    \"\"\" Extrahiert den letzten unter blobname gespeicherten blob.\n\n    Arguments:\n        cursor -- Datenbank Cursor\n        blobname -- Der Name des blobs der gesucht wird.\n\n    Returns:\n        Ein tuple welches die storage_id sowie das bytearray des gefundenen\n        blobs enthält. Falls kein blob gefunden wird (-1, None) zurückgegeben.\n    \"\"\"\n    dataset = list_dataset_for_blobname(cursor, blobname)\n\n    if len(dataset) == 0:\n        logging.debug(\"Found no blob with name \\\"%s\\\" in BLOBS.\", blobname)\n        return (-1, None)\n    else:\n        logging.debug(\"Found blobs with name \\\"%s\\\" in BLOBS. The last one has the id %i.\",\n                      blobname, dataset[-1]['storage_id'])\n    laststorageid = dataset[-1]['storage_id']\n    return laststorageid, extract_blob_from_storage(cursor, laststorageid)\n\n\ndef extract_blob(cursor, blobid):\n    \"\"\" Extrahiert einen Blob aus der sqllite3 Datenbank. Die extrahiert Datei\n    wird über die blobid identifziert.\n\n    Arguments:\n        cursor -- Datenbank Cursor\n        blobid -- Die id des Blobs der extrahiert werden soll.\n\n    Returns:\n        [(blobname, opaque, blob)] \n            blobname -- Der Name unter dem der blob gespeichert ist.\n            opaque -- Die gespeicherten beliebigen Datem-\n            blob -- Der eigentlich Blob als bytearray.\n    \"\"\"\n\n    sql = \"SELECT BLOBNAME, OPAQUE, STORAGE_ID FROM BLOBS WHERE id = :id\"\n    param = {'id': blobid}\n    cursor.execute(sql, param)\n    filename_db, opaque, storage_id = cursor.fetchone()\n\n    blob = extract_blob_from_storage(cursor, storage_id)\n\n    return filename_db, opaque, blob\n","sub_path":"sqlite3/sqlliteblob.py","file_name":"sqlliteblob.py","file_ext":"py","file_size_in_byte":5619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"571690237","text":"import pandas as pd\nimport numpy as np\nfrom time import time\n\n\ndef reversator(value):\n    \"\"\"\" every value will be inverted by letter\"\"\"\n    return value[::-1]\n\n\nmovie = pd.read_csv('C:/Users/ediga/Projects/Python_Ln/Data/Files/7181_10279_bundle_archive.zip', compression='zip')\n\ngenres = movie[['movie_title', 'genres']]\n# for i in genres.columns:\n#     print(i)\n# for i in genres.values:\n#     print(i)\n# for row in genres.values:\n#     for value in row:\n#         print(value)\n# for i, row in genres.iterrows():\n#     print(row.map(reversator))\n# for i, col in genres.iteritems():\n#     print(col.map(reversator))\n\nbudget = movie[['budget', 'duration']]\nbudget.applymap(lambda x: x + 1)\n# apply map for the whole dataframe\n\n# print(budget.apply(np.mean, axis=0))\n# print(budget.apply(lambda x: x + 1))\n# print(budget.transform(lambda x: x + 1))\n# print(budget.mean() + 2)\n# print(budget.values)\n# extract array of numpy\nnp.mean(budget['budget'].dropna().values)\n# necessary to apply dropna() (delete name of columns) before using of numpy\n\ndf = pd.read_csv('C:/Users/ediga/Projects/Python_Ln/Data/Files/iris.csv')\n\nbefore = time()\ndf.apply('mean')\nafter = time()\nprint(after - before)\n\nbefore = time()\ndf.mean(axis=0)\nafter = time()\nprint(after - before)\n# the best in speed\n\nbefore = time()\ndf.apply(np.mean)\nafter = time()\nprint(after - before)\n\nbefore = time()\ndf.describe().loc['mean']\nafter = time()\nprint(after - before)\n\n","sub_path":"Code/pandas8.py","file_name":"pandas8.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"277867989","text":"\"\"\"\n使用numpy 生成的椭圆\n\"\"\"\nimport pyqtgraph.opengl as gl\nimport pyqtgraph as pg\nimport numpy as np\n\npg.setConfigOptions(antialias=True)\n\n\ndef get_cylinder(radius, height, num_segments=30):\n    angle = np.linspace(0, 2 * np.pi, num_segments + 1)[:-1].reshape(1, -1)\n    x = np.cos(angle) * radius\n    y = np.sin(angle) * radius\n    z = height\n\n    vertices = []\n    faces = []\n\n    for i in range(len(height) - 1):\n        for j in range(num_segments):\n            next_j = (j + 1) % num_segments\n\n            base_index = len(vertices)\n            vertices.extend(\n                [\n                    (x[i][j], y[i][j], height[i]),\n                    (x[i + 1][j], y[i + 1][j], height[i + 1]),\n                    (x[i][next_j], y[i][next_j], height[i]),\n                    (x[i + 1][next_j], y[i + 1][next_j], height[i + 1]),\n                ]\n            )\n\n            faces.extend(\n                [\n                    (base_index, base_index + 1, base_index + 2),\n                    (base_index + 1, base_index + 3, base_index + 2),\n                ]\n            )\n    vertices = np.array(vertices, dtype=np.float32)\n    faces = np.array(faces, dtype=np.uint32)\n\n    return vertices, faces\n\n\n# 旋转顶点\ndef rotate_vertices(vertices, angle):\n    rotation_matrix = np.array(\n        [\n            [1, 0, 0],\n            [0, np.cos(angle), -np.sin(angle)],\n            [0, np.sin(angle), np.cos(angle)],\n        ]\n    )\n\n    return np.dot(vertices, rotation_matrix)\n\n\nclass CylinderPlot(gl.GLViewWidget):\n    def __init__(self):\n        super().__init__()\n\n        self.show()\n        self.setCameraPosition(distance=60)\n\n        # 创建圆柱\n        # self.cylinder = gl.MeshData.cylinder(\n        #     rows=10, cols=20, radius=[5.0, 5], length=20.0\n        # )\n        # 将圆柱整体下移10个单位\n        # self.cylinder._vertexes[:, 2] = self.cylinder._vertexes[:, 2] - 10\n\n        # 设置圆柱的颜色\n        # self.set_cylinder_colormap()\n        # m = gl.GLMeshItem(meshdata=self.cylinder, smooth=True)\n\n        num = 10\n        r = np.random.uniform(30.8, 31.2, num)\n        radius = np.array(r).reshape(-1, 1)\n        height = (np.array(range(num)) - len(r) // 2) * 100\n        vertices, faces = get_cylinder(radius, height, num_segments=20)\n\n        vertices = rotate_vertices(vertices, np.pi / 2)  # 绕x轴旋转90度（π/2弧度）\n        mesh_data = gl.MeshData(vertexes=vertices, faces=faces)\n        cylinder = gl.GLMeshItem(\n            meshdata=mesh_data,\n            smooth=True,\n            color=(0.5, 0.5, 1, 0.8),\n            shader=\"shaded\",  # normalColor heightColor edgeHilight shaded\n            glOptions=\"opaque\",\n        )\n        # window.addItem(cylinder)\n        self.addItem(cylinder)\n\n\nif __name__ == \"__main__\":\n    app = pg.mkQApp()\n    w = CylinderPlot()\n    pg.exec()\n","sub_path":"python/python绘图库/pyqtgraph/examples/3d/1_2_numpy_cylinder.py","file_name":"1_2_numpy_cylinder.py","file_ext":"py","file_size_in_byte":2844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"571321482","text":"#!/usr/bin/env python\n\nimport json\nimport re\nimport subprocess\n\nimport boto3\n\n\n# Describe the spack refs to include in the cache.spack.io website\nREF_REGEXES = [\n    re.compile(r\"^develop-[\\d]+-[\\d]+-[\\d]+$\"), # dev snapshot mirrors\n    re.compile(r\"^develop$\"),                   # main develop mirror\n    re.compile(r\"^v[\\d]+\\.[\\d]+\\.[\\d]+$\"),      # mirrors for point releases\n]\n\n# Stacks or other \"subdirectories\" to ignore under *any* ref\nSUBREF_IGNORE_REGEXES = [\n    re.compile(r\"^deprecated$\"),\n    re.compile(r\"^e4s-mac$\"),\n]\n\n\ndef get_label(subref):\n    if subref == \"build_cache\":\n        return \"root\" # or top-level?\n    for regex in SUBREF_IGNORE_REGEXES:\n        if regex.match(subref):\n            return None\n    return subref\n\n\ndef get_matching_ref(ref):\n    for regex in REF_REGEXES:\n        if regex.match(ref):\n            return ref\n    return None\n\n\ndef build_json(bucket_name, index_paths):\n    json_data = {}\n\n    for p in index_paths:\n        parts = p.split(\"/\")\n        ref = get_matching_ref(parts[0])\n        if ref:\n            if ref not in json_data:\n                json_data[ref] = []\n            mirror_label = get_label(parts[1])\n            if mirror_label:\n                json_data[ref].append({\n                    \"label\": mirror_label,\n                    \"url\": f\"s3://{bucket_name}/{p}\",\n                })\n\n    return json_data\n\n\ndef query_bucket(bucket_name):\n    client = boto3.client(\"s3\")\n    paginator = client.get_paginator(\"list_objects_v2\")\n    pages = paginator.paginate(Bucket=bucket_name)\n    results = []\n    for page in pages:\n        for obj in page[\"Contents\"]:\n            if obj[\"Key\"].endswith(\"/build_cache/index.json\"):\n                results.append(obj[\"Key\"])\n\n    return results\n\n\nif __name__ == \"__main__\":\n    bucket_name = \"spack-binaries\"\n\n    results = query_bucket(bucket_name)\n    json_data = build_json(bucket_name, results)\n\n    # with open(\"results.txt\") as fd:\n    #     results = [l.strip() for l in fd]\n    # json_data = build_json(root_url, results)\n\n    with open(\"output.json\", \"w\") as fd:\n        fd.write(json.dumps(json_data))\n\n    client = boto3.client(\"s3\")\n    client.upload_file(\"output.json\", bucket_name, \"cache_spack_io_index.json\")\n","sub_path":"images/cache-indexer/cache_indexer.py","file_name":"cache_indexer.py","file_ext":"py","file_size_in_byte":2229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"191382315","text":"from xcp.enum.command_code import PageSwitchingCommand\nfrom xcp.enum.command_code import StandardCommandCode\nfrom xcp.enum.parameter_bit import SetCalPageBit\n\nclass GetCalPageRequest(object):\n    \n    def __init__(self):\n        self._code                        = PageSwitchingCommand.GET_CAL_PAGE\n        self._mode                        =  SetCalPageBit(0xFF)\n        self._logical_data_segment_number = 0xFF\n\nclass GetCalPageResponse(object):\n    \n    def __init__(self):\n        self._code                     = StandardCommandCode.CONNECT\n        self._logical_data_page_number = 0xFF","sub_path":"protocols/xcp/pdu/command/pag/get_cal_page.py","file_name":"get_cal_page.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"531750908","text":"#!/usr/bin/env python\n# encoding: utf-8\n\n\n\"\"\"\n@version: 0.1\n@author: Yang Reid\n@license: Apache Licence \n@contact: yangtao584@126.com\n@site: https://github.com/yangr5/python\n@software: PyCharm Community Edition\n@file: test_1.py\n@time: 2017/11/17 15:08\n\"\"\"\nimport random\n\ndef isPrime(n):\n    import math\n    flag = True\n    if n == 2:\n        return flag\n    for i in range(2,math.ceil(math.sqrt(n)+1)):\n        # print(i)\n        if n % i == 0:\n            flag = False\n            break\n    return flag\n\nclass Puke:\n    def __init__(self,kind,number):\n        self.kind = kind\n        self.number = number\n\n    def sortByNumber(self):\n        return self.number\n\ndef isTongHuaShun(puke_list):\n    if checkTheSameKind(puke_list):\n        if checkNumberIsTongHuaShun(puke_list):\n            printPukeList(puke_list)\n            return True\n        else:\n            return False\n\n\ndef checkTheSameKind(puke_list):\n    kind_list = []\n    for item in puke_list:\n        kind_list.append(item.kind)\n    kind_set = set(kind_list)\n    if kind_set.__len__() == 1:\n        return True\n    else:\n        return False\n\ndef checkNumberIsTongHuaShun(puke_list):\n    number_list = []\n    for item in puke_list:\n        number_list.append(item.number)\n        number_list.sort()\n    unique_list = list(set(number_list))\n    unique_list.sort()\n    if unique_list.__len__() != 5:\n        return False\n    min_number = min(unique_list)\n    max_number = max(unique_list)\n    if max_number-min_number == 4 :\n        return True\n    elif min_number ==1:\n        second_min = unique_list[1]\n        if second_min ==10:\n            return True\n        return False\n    else:\n        return False\n\n\ndef printPukeList(puke_list):\n    for item in puke_list:\n        print(\"Kind {} with Number {}\".format(item.kind,item.number))\n\n\ndef unique(list_data):\n    new_list = list(set(list_data))\n    new_list.sort(key = list_data.index)\n    return new_list\n\nif __name__ == '__main__':\n    # print(isPrime(13))\n    puke_list = []\n    p1 = Puke(1,1)\n    puke_list.append(p1)\n    p2 = Puke(1,10)\n    puke_list.append(p2)\n    p3 = Puke(1,11)\n    puke_list.append(p3)\n    p4 = Puke(1,12)\n    puke_list.append(p4)\n    p5 = Puke(1,13)\n    puke_list.append(p5)\n    # for i in range(5):\n    #     print('----------{}-------'.format(i))\n    #     p = Puke(random.randint(1,4),random.randint(1,13))\n    #     puke_list.append(p)\n    puke_list.sort(key=Puke.sortByNumber)\n    # print(checkTheSameKind(puke_list))\n    # checkNumberIsTongHuaShun(puke_list)\n    print(isTongHuaShun(puke_list))\n    list_data = [random.randint(1, 5) for i in range(10)]\n    print(list_data)\n    print(unique(list_data))\n\n    words = [\n        'look', 'into', 'my', 'eyes', 'look', 'into', 'my', 'eyes',\n        'the', 'eyes', 'the', 'eyes', 'the', 'eyes', 'not', 'around', 'the',\n        'eyes', \"don't\", 'look', 'around', 'the', 'eyes', 'look', 'into',\n        'my', 'eyes', \"you're\", 'under'\n    ]\n\n    dict_num = {i: words.count(i) for i in set(words)}\n    print(dict_num)\n    print(words.count('look'))\n    A0 = dict(zip(('a', 'b', 'c', 'd', 'e'), (1, 2, 3, 4, 5)))\n    A1 = range(10)\n    A2 = [i for i in A1 if i in A0]\n    A3 = [A0[s] for s in A0]\n    A4 = [i for i in A1 if i in A3]\n    A5 = {i: i * i for i in A1}\n    A6 = [[i, i * i] for i in A1]\n    print(A0)\n    print(A1)\n    print(A2)\n    print(A3)\n    print(A4)\n    print(A5)\n    print(A6)\n    for it ,va in A0.items():\n        print('(--{}--{}--)'.format(it,va))\n    s ='''this {};\n    and I am {} old'''\n    for p in['reid','merry']:\n        print(s.format(p, random.randint(28,32)))\n    f_str = f'this is A1:{A3}'\n    print(f_str)\n    ips = ('10.109.52.23','10.109.52.24')\n    print(ips *5)\n    shares = ['base_fs_'+str(i) for i in range(5)]\n    print(shares)\n\n    for ip in ips:\n        print((ip)*5)\n        dict_o = dict(zip(shares,[ip]*5))\n        # print(dict_o)\n        for fs,ip in dict_o.items():\n            print(f'''\n                net use {ip}\\{fs}\n                ''')","sub_path":"daily/test_1.py","file_name":"test_1.py","file_ext":"py","file_size_in_byte":3984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"2645398","text":"from tkinter import*\r\nfrom SEPM_LIB_SNP import *\r\nfrom SEPM_LIB_LG import *\r\n\r\nimport mysql.connector\r\nconn=mysql.connector.connect(host='localhost',database='name_of_the_database',user='root',password='add_password')\r\ncursor=conn.cursor()\r\nconn.commit()\r\n\r\nclass startpg:\r\n    def __init__(self,root):\r\n        self.root=root\r\n        self.f=Frame(root.title(\"Start Page\"),height=500,width=600,bg='dodgerblue3')\r\n\r\n        self.f.propagate(0)\r\n\r\n        self.f.pack()\r\n\r\n        self.n1=Label(text='ABC Library',bg='dodgerblue3',font=('Bold Calibri',30))\r\n    \r\n        self.b1=Button(text='Login',fg='white',bg='dark red',width=20,height=2,font=('Calibri',15),command=lambda: self.buttonclick(0))\r\n        self.b2=Button(text='Sign Up',fg='white',bg='dark red',width=20,height=2,font=('Calibri',15),command=lambda: self.buttonclick(1))\r\n\r\n        self.n1.place(x=200,y=50)\r\n        self.b1.place(x=200,y=150)\r\n        self.b2.place(x=200,y=250)\r\n\r\n\r\n    def buttonclick(self,num):\r\n        if(num==1):\r\n            self.root.destroy()\r\n            SEPM_LIB_SNP()\r\n            cursor.close()\r\n            conn.close()\r\n            \r\n        elif(num==0):\r\n            self.root.destroy()\r\n            SEPM_LIB_LG()\r\n            cursor.close()\r\n            conn.close()\r\n            \r\n            \r\n\r\nroot=Tk()\r\n\r\nmb=startpg(root)\r\n\r\nroot.mainloop()\r\n","sub_path":"lib sys/SEPM_LIB_LG_SNP.py","file_name":"SEPM_LIB_LG_SNP.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"342348741","text":"###import pyDOE\nimport matplotlib.pyplot as plt\n\n\n###lh = pyDOE.lhs(2, samples=20, criterion='centermaximin')\n#“center” or “c”: center the points within the sampling intervals\n#“maximin” or “m”: maximize the minimum distance between points, but place the point in a randomized location within its interval\n#“centermaximin” or “cm”: same as “maximin”, but centered within the intervals\n#“correlation” or “corr”: minimize the maximum correlation coefficient\n\n\n\n#%\n\"\"\"\nx = []\ny = []\nfor entry in lh:\n    x.append(entry[0])\n    y.append(entry[1])\n\n\nplt.scatter(x,y)\nplt.show()\n\"\"\"\n#%%\nimport numpy as np\nimport pickle as pkl\nimport matplotlib.pyplot as plt\n\ndata = []\n\nimg1 = np.array([[0,1],[2,3]])\nimg2 = np.array([[0,0],[0,0]])\n\n\n#%%\ndata = []\nfor fake in range(100):\n    if fake%1 == 0:\n        print(fake)\n    for z in range(7,12):\n        img = []\n        angle = np.pi*np.random.uniform()\n        shift = np.pi*np.random.uniform()\n        for y in range(256): # image size\n            row = []\n            for x in range(256): # image size\n                if (np.abs(x-14) < 9) and (np.abs(y-14) < 9):\n                    r = np.cos(angle)*x + np.sin(angle)*y\n                    pixel = np.sin((r/63.*6.28*(z-6)) + shift ) + np.random.uniform()\n                    #pixel = np.sin((x/63.*6.28*(z-6))) + np.random.uniform()\n                else:\n                    r = np.cos(angle)*x + np.sin(angle)*y\n                    pixel = np.sin((r/63.*6.28*(z-6)) + shift) + np.random.uniform()\n                row.append(pixel)\n            img.append(row)\n\n\n        if fake == 0:\n            plt.imshow(img)\n            #plt.savefig(\"images/fakeimg_%d.png\" % z)\n            plt.show()\n            plt.close()\n\n\n        #if z == 7:\n        #    img = np.repeat(np.repeat(img1,128, axis=0), 128, axis=1)\n        #else:\n        #    img = np.repeat(np.repeat(img2,128, axis=0), 128, axis=1)\n        #print(img)\n\n        data.append([img, 'something_z'+str(z) + '_something'])\n\n\npkl.dump(data, open(\"faketest_images_256.pkl\", \"wb\")) # image size\nprint('duneÄ)')\n\n\n#%%\nimport random\n#x = [r = 5, (random.uniform(0,1) < 0.5)*random.uniform(0.9, 1.) for _ in range(10)]\nx = [random.uniform(0.9,1.) if (random.uniform(0,1)<0.95) else random.uniform(0.,0.1) for _ in range(1, 100) ]\nprint(x)\n","sub_path":"lhc_test.py","file_name":"lhc_test.py","file_ext":"py","file_size_in_byte":2318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"350298978","text":"import sys\n\nimport numpy\nfrom pyspark import SparkContext\nfrom numpy import genfromtxt\nfrom pyspark.streaming import StreamingContext\nimport os\nos.environ['PYSPARK_PYTHON'] = 'virt/bin/python'\n\n\nsc = SparkContext(appName=\"FacialRecognizer\")\nssc = StreamingContext(sc, 5)\nsocket_stream = ssc.socketTextStream(\"0.0.0.0\", 5555)\n\n\ndef run_visible(data, weights, hidden_nodes):\n    \"\"\"\n    Assuming the RBM has been trained (so that weights for the network have been learned),\n    run the network on a set of visible units, to get a sample of the hidden units.\n\n    Taken from https://github.com/echen/restricted-boltzmann-machines/blob/master/rbm.py\n    Parameters\n    ----------\n    data: A matrix where each row consists of the states of the visible units.\n\n    Returns\n    -------\n    hidden_states: A matrix where each row consists of the hidden units activated from the visible\n    units in the data matrix passed in.\n    \"\"\"\n\n    num_examples = data.shape[0]\n\n    # Create a matrix, where each row is to be the hidden units (plus a bias unit)\n    # sampled from a training example.\n    hidden_states = numpy.ones((num_examples, hidden_nodes + 1))\n\n    # Insert bias units of 1 into the first column of data.\n    data = numpy.insert(data, 0, 1)\n\n    # Calculate the activations of the hidden units.\n    hidden_activations = numpy.dot(data, weights)\n    # Calculate the probabilities of turning the hidden units on.\n    hidden_probs = _logistic(hidden_activations)\n    # Turn the hidden units on with their specified probabilities.\n    hidden_states[:,:] = hidden_probs > numpy.random.rand(num_examples, hidden_nodes + 1)\n    # Always fix the bias unit to 1.\n    # hidden_states[:,0] = 1\n\n    # Ignore the bias units.\n    hidden_states = hidden_states[:,1:]\n    return hidden_states\n\ndef _logistic(x):\n    return 1.0 / (1 + numpy.exp(-x))\n\n\nweights = genfromtxt('rbmWeights.csv', delimiter=',')\nnumpy.set_printoptions(threshold=sys.maxsize)\n# socket_stream.map(lambda v: \"Embedding: \" + v).pprint() # prints the embedding sent on the socket\n# prints the hidden units calculated from the weights\nsocket_stream.map(lambda v: run_visible(numpy.fromstring(v, count=128, sep=\", \"), weights, 20)).pprint()\n\n# Next steps would be to take these hidden states, backpropogate, and look at the resulting visible states.\n# Run the cost function to determine if the states are sufficiently similar to be the same person as the neural neetwork\n# was trained on\n\nssc.start()\nssc.awaitTermination()\n","sub_path":"stream_reader.py","file_name":"stream_reader.py","file_ext":"py","file_size_in_byte":2483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"231131885","text":"import tsk\n\n\ni=0\nwhile i!=6:\n\tprint(tsk.vivod_menu())\n\ta = int(input(''))\n\t\n\tif a == 6:\n\t\ti=6\n\t\tbreak\n\n\tif a == 1:\n\t\ttsk.spisok()\n\n\tif a == 2:\n\t\ttsk.add()\n\n\tif a == 3:\n\t\tprint('            ')\n\t\tprint('Отредактировать задачу')\n\t\tprint('            ')\n\t\tprint('Нажмите любую клавишу для продолжения')\n\n\tif a ==4:\n\t\ttsk.edit()\n\n\tif a == 5:\n\t\ttsk.restart()\n\n\twhile not input(): #Пока не нажмем не пойдем дальше\n\t\tpass\n","sub_path":"eged../main_4_1.py","file_name":"main_4_1.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"589623398","text":"import pygame\nimport pygame_gui\n\nfrom Active_MVC.Model.IObserver import IObserver\nfrom Active_MVC.Model.Piece import Piece\nfrom Active_MVC.Model.Pieces import Knight\n\n\nclass GameGUI(IObserver):\n    WINDOW_HEIGHT = 600\n    WINDOW_WIDTH = 600\n    DIMENSION = 8\n    SQUARE_SIZE = WINDOW_HEIGHT // DIMENSION\n    PIECES_IMAGES = {}\n    MAX_FPS = 15\n\n    pygame.init()\n    pygame.display.set_caption('Chess')\n    screen = pygame.display.set_mode((WINDOW_WIDTH + 200, WINDOW_HEIGHT))\n    background = pygame.Surface((800, 600))\n    background.fill(pygame.Color('#edebe4'))\n    manager = pygame_gui.UIManager((800, 600))\n    clock = pygame.time.Clock()\n\n    startGameButton = pygame_gui.elements.UIButton(relative_rect=pygame.Rect((650, 180), (100, 50)), text='START',\n                                                   manager=manager)\n    restartGameButton = pygame_gui.elements.UIButton(relative_rect=pygame.Rect((650, 380), (100, 50)), text='RESTART',\n                                                     manager=manager)\n    pvpButton = pygame_gui.elements.UIButton(relative_rect=pygame.Rect((615, 280), (50, 50)), text='PvP',\n                                             manager=manager)\n    pvAIButton = pygame_gui.elements.UIButton(relative_rect=pygame.Rect((675, 280), (50, 50)), text='PvAI',\n                                              manager=manager)\n    AIvAIButton = pygame_gui.elements.UIButton(relative_rect=pygame.Rect((735, 280), (50, 50)), text='AIvAI',\n                                               manager=manager)\n\n    lang_ro = pygame_gui.elements.UIButton(relative_rect=pygame.Rect((605, 50), (40, 40)), text='RO', manager=manager)\n    lang_en = pygame_gui.elements.UIButton(relative_rect=pygame.Rect((655, 50), (40, 40)), text='EN', manager=manager)\n    lang_fr = pygame_gui.elements.UIButton(relative_rect=pygame.Rect((705, 50), (40, 40)), text='FR', manager=manager)\n    lang_ch = pygame_gui.elements.UIButton(relative_rect=pygame.Rect((755, 50), (40, 40)), text='CH', manager=manager)\n\n    # lang_ch.set\n    def __init__(self):\n        self.screen.fill(pygame.Color(\"White\"))\n        self.loadImages()\n\n    def loadImages(self):\n        piecesNames = [\"bR\", \"bN\", \"bB\", \"bQ\", \"bK\", \"bB\", \"bN\", \"bR\", \"bP\",\n                       \"wP\", \"wR\", \"wN\", \"wB\", \"wQ\", \"wK\", \"wB\", \"wN\", \"wR\"]\n        for currentPiece in piecesNames:\n            self.PIECES_IMAGES[currentPiece] = pygame.image.load(\"../images/\" + currentPiece + \".png\")\n\n    def drawBoard(self, screen):\n        global colors\n        colors = [pygame.Color(\"white\"), pygame.Color(\"gray\")]\n        for row in range(self.DIMENSION):\n            for column in range(self.DIMENSION):\n                color = colors[((row + column) % 2)]\n                pygame.draw.rect(screen, color,\n                                 pygame.Rect(column * self.SQUARE_SIZE, row * self.SQUARE_SIZE, self.SQUARE_SIZE,\n                                             self.SQUARE_SIZE))\n\n    def highlightSquares(self, screen, gameState, validMoves, selectedSquare):\n        if selectedSquare != ():\n            row, column = selectedSquare\n            if 0 <= row <= 7 and 0 <= column <= 7:\n                if isinstance(gameState.board[row][column], Piece) and gameState.board[row][column].color == (\n                        \"white\" if gameState.whiteToMove else \"black\"):\n                    selectedSurface = pygame.Surface((self.SQUARE_SIZE, self.SQUARE_SIZE))\n                    selectedSurface.set_alpha(80)\n                    selectedSurface.fill(pygame.Color(\"blue\"))\n                    screen.blit(selectedSurface, (column * self.SQUARE_SIZE, row * self.SQUARE_SIZE))\n\n                    for currentMove in validMoves:\n                        if currentMove.startRow == row and currentMove.startCol == column:\n                            selectedSurface.fill(pygame.Color('yellow'))\n                            screen.blit(selectedSurface,\n                                        (currentMove.endCol * self.SQUARE_SIZE, currentMove.endRow * self.SQUARE_SIZE))\n                            if isinstance(gameState.board[currentMove.endRow][currentMove.endCol], Piece) and \\\n                                    gameState.board[currentMove.endRow][currentMove.endCol].color == (\n                                    \"black\" if gameState.whiteToMove else \"white\"):\n                                selectedSurface.fill(pygame.Color('red'))\n                                screen.blit(selectedSurface,\n                                            (currentMove.endCol * self.SQUARE_SIZE,\n                                             currentMove.endRow * self.SQUARE_SIZE))\n\n    def drawText(self, screen, textObject, textLocation):\n        screen.blit(textObject, textLocation)\n\n    def parsePieceToKey(self, piece):\n        if isinstance(piece, Piece):\n            if isinstance(piece, Knight.Knight):\n                return piece.color[0] + \"N\"\n            return piece.color[0] + type(piece).__name__[0]\n\n    def getClickLocation(self):\n        mouseClickLocation = pygame.mouse.get_pos()\n        columnClickLocation = mouseClickLocation[0] // self.SQUARE_SIZE\n        rowClickLocation = mouseClickLocation[1] // self.SQUARE_SIZE\n        return columnClickLocation, rowClickLocation\n\n    def update(self, screen, gameState, validMoves, selectedSquare):\n        time_delta = self.clock.tick(60) / 1000.0\n        self.manager.update(time_delta)\n        self.screen.blit(self.background, (0, 0))\n        self.drawBoard(screen)\n        self.highlightSquares(screen, gameState, validMoves, selectedSquare)\n        # controller.drawPieces(screen, gameState.board)\n        self.manager.draw_ui(self.screen)\n","sub_path":"Active_MVC/View/GameGUI.py","file_name":"GameGUI.py","file_ext":"py","file_size_in_byte":5659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"635759712","text":"# -*- coding:utf-8 -*-\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\nclass Solution:\n    def HasSubtree(self, pRoot1, pRoot2):\n        # write code here\n        if not pRoot1 or not pRoot2:\n            return False\n        res = self.is_sub_tree(pRoot1, pRoot2)\n        if not res:\n            res = self.is_sub_tree(pRoot1.left, pRoot2)\n        if not res:\n            res = self.is_sub_tree(pRoot1.right, pRoot2)\n        return res\n    def is_sub_tree(self, p1, p2):\n        if not p2:\n            return True\n        elif not p1:\n            return False\n        elif p1.val != p2.val:\n            return False\n        else:\n            return self.is_sub_tree(p1.left, p2.left) and self.is_sub_tree(p1.right, p2.right)","sub_path":"17_树的子结构.py","file_name":"17_树的子结构.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"322354019","text":"import builtins\nfrom RestrictedPython import compile_restricted\nfrom RestrictedPython import utility_builtins\nimport time\n\nerror = False\n\ndef limited_range(iFirst, *args):\n    # limited range function from Martijn Pieters\n    RANGELIMIT = 1000\n    if not len(args):\n        iStart, iEnd, iStep = 0, iFirst, 1\n    elif len(args) == 1:\n        iStart, iEnd, iStep = iFirst, args[0], 1\n    elif len(args) == 2:\n        iStart, iEnd, iStep = iFirst, args[0], args[1]\n    else:\n        raise AttributeError('range() requires 1-3 int arguments')\n    if iStep == 0:\n        raise ValueError('zero step for range()')\n    iLen = int((iEnd - iStart) / iStep)\n    if iLen < 0:\n        iLen = 0\n    if iLen >= RANGELIMIT:\n        raise ValueError(\n            'To be created range() object would be to large, '\n            'in our IDE we only allow {limit} '\n            'elements in a range.'.format(limit=str(RANGELIMIT)),\n        )\n    return range(iStart, iEnd, iStep)\n\n\nclass PrintCollector(object):\n    \"\"\"Collect written text, and return it when called.\"\"\"\n\n    def __init__(self, _getattr_=None):\n        self.txt = []\n        self._getattr_ = _getattr_\n\n    def write(self, text):\n        self.txt.append(text)\n\n    def __call__(self):\n        return ''.join(self.txt)\n\n    def _call_print(self, *objects, **kwargs):\n        if kwargs.get('file', None) is None:\n            kwargs['file'] = self\n        else:\n            self._getattr_(kwargs['file'], 'write')\n\n        print(*objects, **kwargs)\n        printList.append(objects)\n        sendString(*objects, False)\n\n\n#a fuction that sends the string to the frontend using django to be displayed in realtime without a url change\ndef sendString(string, error):\n    pass\n    \n    \ndef input(string):\n    PrintCollector(string)\n    answer = getInput()\n    return answer\n\ndef getInput():\n    string = \"\"\n    #gets input from frontend with django without a url change\n    return string\n\nrestricted_globals = dict(__builtins__=utility_builtins)\n\nMAX_ITER_LEN = 100\n\nclass MaxCountIter:\n    def __init__(self, dataset, max_count):\n        self.i = iter(dataset)\n        self.left = max_count\n\n    def __iter__(self):\n        return self\n\n    def __next__(self):\n        if self.left > 0:\n            self.left -= 1\n            return next(self.i)\n        else:\n            raise StopIteration()\n\ndef _getiter(ob):\n    return MaxCountIter(ob, MAX_ITER_LEN)\n\n\ndef inputTest(string):\n    return inputTestValue\n\n\ndef compileRun(userInput, inputTestValue=\"\"):\n    printList = []\n    __builtins__['printList'] = printList\n    __builtins__['inputTestValue'] = inputTestValue\n    restricted_globals.update({\n    \"_getiter_\": _getiter,\n    '_print_': PrintCollector,\n    \"_getattr_\": getattr,\n    \"range\": limited_range,\n    \"input\": input,\n    \"inputTest\": inputTest,\n    })\n\n    printList = __builtins__['printList']\n\n    error = False\n    try:\n        byte_code = compile_restricted(userInput, '', 'exec')\n        tic = time.perf_counter()\n        exec(byte_code, restricted_globals)\n        toc = time.perf_counter()\n        timeTaken = toc - tic\n\n    except SyntaxError as e:\n        printList.append(e)\n        sendString(e, True)\n        error = True\n\n    except ImportError:\n        printList.append(\"You are trying to import a module that isn't allowed\")\n        sendString(\"You are trying to import a module that isn't allowed\", True)\n        error = True\n\n    except NameError as e:\n        printList.append(e)\n        sendString(e, True)\n        printList.append(\"You may not be using a function you're allowed to\")\n        sendString(\"You may not be using a function you're allowed to\", True)\n        error = True\n    #print(__builtins__['printList'])\n    return printList, timeTaken","sub_path":"codeRunner.py","file_name":"codeRunner.py","file_ext":"py","file_size_in_byte":3736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"317728093","text":"from django.db import models\nfrom django.db.models.signals import post_save\nfrom django.contrib.auth.models import User\nfrom django.dispatch import receiver\n\n# Create your models here.\nclass Profile(models.Model):\n    user = models.OneToOneField(User, on_delete=models.CASCADE)\n    uid = models.CharField(max_length=70, unique=True, null=True, default=None)\n\n    def __str__(self):\n        return f'{self.user.username}\\'s profile'\n\n@receiver(post_save, sender=User)\ndef create_profile(sender, instance, created, **kwargs):\n    if created:\n        if instance.is_superuser == False:\n            Profile.objects.create(user=instance, uid=instance.uid)\n        else:\n            Profile.objects.create(user=instance, uid=None)\n\n@receiver(post_save, sender=User)\ndef save_profile(sender, instance, **kwargs):\n    instance.profile.save()","sub_path":"SmartMailboxSystem/users/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"79626745","text":"import os\nimport subprocess\n\ndef main():\n    curve_directories = [\"curves/I/\", \"curves/V/\"]\n\n    [process_dir(d) for d in curve_directories]\n\ndef process_dir(directory):\n    [process_file(directory, f) for f in sorted(os.listdir(directory))]\n\ndef process_file(directory, f):\n    \"\"\"\n    1) Add the header\n    2) Remove excess spacing\n    3) Replace delimiting spaces with commas\n    4) Remove any commas at the end of lines\n    5) Remove any commas at beginning of lines\n    \"\"\"\n    f = f[:-4]\n    f = os.path.join(directory, f)\n\n    print(f)\n\n    command = \"echo 'time,mag,magerror' > '%s.csv' && cat '%s.dat' | tr -s ' ' | sed 's/ /,/g' | sed 's/,$$//g' | sed 's/^,//g' >> '%s.csv'\" % (f, f, f)\n\n    subprocess.call(command, shell=True)\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"ogle/ogle3/OIII-CVS/lmc/lpv/process_light_curves.py","file_name":"process_light_curves.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"21092271","text":"#!/usr/bin/env python3\n\nimport sys, os\nimport socket, threading\nimport argparse\n\nimport interface_pb2\n\nclass thrServer(threading.Thread):\n\t\n\tdef __init__(self, port, host='localhost'):\n\t\t\n\t\tthreading.Thread.__init__(self)\n\t\t\n\t\tself.port = port\n\t\tself.host = host\n\n\t\tself.server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\t\tself.server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\t\t\n\t\ttry:\n\t\t\tself.server.bind((self.host, self.port))\n\t\t\n\t\texcept socket.error:\n\t\t\tprint('Oops. %s' % (socket.error))\n\t\t\tsys.exit()\n\n\t\tself.server.listen(5)\n\t\t\n\tdef run_thread(self, conn, addr, handle):\n\t\tprint('Accepted client: ', addr)\n\n\t\thandle(conn, addr);\n\n\t\tconn.close()\n\t\tprint( 'Closed: ', addr )\n\n\tdef run(self, handle):\n\t\tprint('Listening on port: %s.' % (self.port))\n\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\tconn, addr = self.server.accept()\n\t\t\t\tthreading.Thread( target=self.run_thread, \n\t\t\t\t\targs=(conn, addr, handle) ).start()\n\t\t\t\n\t\t\texcept KeyboardInterrupt:\n\t\t\t\tself.server.close()\n\t\t\t\t\n\t\t\t\tprint('\\nConnection closed.')\n\t\t\t\tbreak\n\ndef handle(conn, addr):\n\n\tdata_r = conn.recv( 1024*16 )\t\n\n\tnew = interface_pb2.Tag()\n\tnew.ParseFromString( data_r )\n\n\tprint(\"read tag: \", new.text)\n\n\twith lock:\n\t\titems.tag.add().CopyFrom(new)\n\n\tdata_s = items.SerializeToString()\n\n\tconn.sendall(data_s)\n\n\tprint(\"sent list to: \", addr)\n\t\n    \nif __name__ == '__main__':\n\n\tparser = argparse.ArgumentParser()\n\n\tparser.add_argument('-p', '--port', default=2015)\n\tparser.add_argument('-f', '--file', default=\"tag_cloud.bin\")\n\n\targs = parser.parse_args()\n\n\tPORT = int(args.port)\n\tFILE = args.file\n\n\titems = interface_pb2.TagCloud()\n\n\tif os.path.isfile(FILE):\n\t\twith open(FILE, 'rb+') as f:\n\t\t\titems.ParseFromString( f.read() )\n\n\tlock = threading.Lock()\n\n\tserver = thrServer(PORT)\n\tserver.run(handle)\n\n\tif len(items.tag) > 0:\n\t\t\n\t\tfout = open(FILE, 'wb+')\n\t\tfout.write(items.SerializeToString())\n\t\tfout.close()\n","sub_path":"task2-protobuf/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"182879031","text":"from scipy.ndimage.filters import median_filter\nimport colorsys\nfrom scipy.integrate import trapz\n\nimport numpy as np\n\nfrom skimage.segmentation import slic, quickshift\nfrom skimage.feature import greycomatrix, greycoprops\n\nfrom scipy.misc import imread, imresize\n\n# =========================================================\n# ================ subfunctions ===========================\n# =========================================================\n\n\n# =========================================================\ndef get_calib_matrix(classim, gim, red, green, blue, energy, homo, contrast, dissim, hue, sat, auto, classes, factor):\n   \"\"\"\n   generate calibration matrix per feature type.\n   channels: 1. greyscale intensity, 2. red, 3. green, 4. blue, 5. energy, 6. homogeneity\n             7. contrast, 8. dissimilarity, 9. hue, 10. saturation\n   uses median values with outliers removed\n   \"\"\"\n   X = [];\n   counter = 1\n   for k in range(1,5):\n      i = gim.flatten()[np.where((classim==k).flatten())[0]]\n      r = red.flatten()[np.where((classim==k).flatten())[0]]\n      g = green.flatten()[np.where((classim==k).flatten())[0]]\n      b = blue.flatten()[np.where((classim==k).flatten())[0]]\n      e = energy.flatten()[np.where((classim==k).flatten())[0]]\n      h = homo.flatten()[np.where((classim==k).flatten())[0]]\n      c = contrast.flatten()[np.where((classim==k).flatten())[0]]\n      d = dissim.flatten()[np.where((classim==k).flatten())[0]]\n      h2 = hue.flatten()[np.where((classim==k).flatten())[0]]\n      s = sat.flatten()[np.where((classim==k).flatten())[0]]\n\n      #X.append(np.c_[i, r, g, b, e, h, c, d, np.ones(np.shape(g))*counter ])\n      X.append(np.c_[np.median(i), np.median(r), np.median(g), np.median(b), np.median(e), np.median(h), np.median(c), np.median(d), np.median(h2), np.median(s), counter ])\n      counter = counter+1\n\n   Xr = np.concatenate(X)\n\n   X = Xr[:,:np.shape(Xr)[1]-1]\n   y = Xr[:,np.shape(Xr)[1]-1:]\n\n   Xc, yc = remove_outliers(X, y, factor)\n\n   return Xc, yc\n\n# =========================================================\ndef get_calib_matrix_texture(classim, energy, homo, contrast, dissim, auto, factor):\n   \"\"\"\n   generate calibration matrix per feature type.\n   channels: 1. energy, 2. homogeneity, 3. contrast, 4. dissimilarity, 5. autocorrelation\n   uses median values with outliers removed\n   \"\"\"\n   X = [];\n   counter = 1\n   for k in range(1,5):\n      e = energy.flatten()[np.where((classim==k).flatten())[0]]\n      h = homo.flatten()[np.where((classim==k).flatten())[0]]\n      c = contrast.flatten()[np.where((classim==k).flatten())[0]]\n      d = dissim.flatten()[np.where((classim==k).flatten())[0]]\n      a1 = auto[:,:,0].flatten()[np.where((classim==k).flatten())[0]]\n      a2 = auto[:,:,1].flatten()[np.where((classim==k).flatten())[0]]\n      a3 = auto[:,:,2].flatten()[np.where((classim==k).flatten())[0]]\n      a4 = auto[:,:,3].flatten()[np.where((classim==k).flatten())[0]]\n      a5 = auto[:,:,4].flatten()[np.where((classim==k).flatten())[0]]\n      a6 = auto[:,:,5].flatten()[np.where((classim==k).flatten())[0]]\n      a7 = auto[:,:,6].flatten()[np.where((classim==k).flatten())[0]]\n      a8 = auto[:,:,7].flatten()[np.where((classim==k).flatten())[0]]\n      a9 = auto[:,:,8].flatten()[np.where((classim==k).flatten())[0]]\n      a10 = auto[:,:,9].flatten()[np.where((classim==k).flatten())[0]]\n\n      #X.append(np.c_[i, r, g, b, e, h, c, d, np.ones(np.shape(g))*counter ])\n      #X.append(np.c_[np.median(e), np.median(h), np.median(c), np.median(d), np.median(a), counter ])\n      X.append(np.c_[np.median(e), np.median(h), np.median(c), np.median(d), np.median(a1[a1>0]), np.median(a2[a2>0]), np.median(a3[a3>0]), np.median(a4[a4>0]), np.median(a5[a5>0]), np.median(a6[a6>0]), np.median(a7[a7>0]), np.median(a8[a8>0]), np.median(a9[a9>0]), np.median(a10[a10>0]), counter ])\n\n      counter = counter+1\n\n   Xr = np.concatenate(X)\n\n   X = Xr[:,:np.shape(Xr)[1]-1]\n   y = Xr[:,np.shape(Xr)[1]-1:]\n\n   return X, y #remove_outliers(X, y, factor)\n\n\n# =========================================================\ndef get_calib_matrix_color(classim, gim, red, green, blue, hue, sat, factor):\n   \"\"\"\n   generate calibration matrix per feature type.\n   channels: 1. greyscale intensity, 2. red, 3. green, 4. blue, 5. hue, 6. saturation\n   uses median values with outliers removed\n   \"\"\"\n   X = [];\n   counter = 1\n   for k in range(1,5):\n      i = gim.flatten()[np.where((classim==k).flatten())[0]]\n      r = red.flatten()[np.where((classim==k).flatten())[0]]\n      g = green.flatten()[np.where((classim==k).flatten())[0]]\n      b = blue.flatten()[np.where((classim==k).flatten())[0]]\n      h2 = hue.flatten()[np.where((classim==k).flatten())[0]]\n      s = sat.flatten()[np.where((classim==k).flatten())[0]]\n\n      #X.append(np.c_[i, r, g, b, e, h, c, d, np.ones(np.shape(g))*counter ])\n      X.append(np.c_[np.median(i), np.median(r), np.median(g), np.median(b), np.median(h2), np.median(s), counter ])\n      #0,2,4,5\n      #X.append(np.c_[np.median(i), np.median(g), np.median(h2), np.median(s), counter ])\n      counter = counter+1\n\n   Xr = np.concatenate(X)\n\n   X = Xr[:,:np.shape(Xr)[1]-1]\n   y = Xr[:,np.shape(Xr)[1]-1:]\n\n   return X, y #remove_outliers(X, y, factor)\n\n# =========================================================\ndef ascol( arr ):\n    '''\n    reshapes row matrix to be a column matrix (N,1).\n    '''\n    if len( arr.shape ) == 1: arr = arr.reshape( ( arr.shape[0], 1 ) )\n    return arr\n\n# =========================================================\ndef remove_outliers(X, y, k):\n   \"\"\"\n   simple outlier removal based on deviation from mean\n   \"\"\"\n   mu, sigma = np.mean(X, axis=0), np.std(X, axis=0, ddof=1)\n   index = np.all(np.abs((X - mu) / sigma) < k, axis=1)\n   return X[index], y[index]\n\n# =========================================================\ndef radial_data(data, annulus_width=1):\n\n       npix, npiy = np.shape(data)\n\n       r = np.empty((npix, npiy),dtype=np.float64)\n       x = np.empty((npix, npiy),dtype=np.float64)\n       y = np.empty((npix, npiy),dtype=np.float64)  \n       x1 = np.empty(npix,dtype=np.float64)\n       y1 = np.empty(npiy,dtype=np.float64)\n\n       minrad = np.empty(1,dtype=np.float64)\n       maxrad = np.empty(1,dtype=np.float64)\n       dr = np.empty(1,dtype=np.float64)\n\n       x1 = np.arange(-np.float64(npix/2),np.float64(npix/2))\n       y1 = np.arange(-np.float64(npiy/2),np.float64(npiy/2))\n       x,y = np.meshgrid(y1,x1)\n       r = np.abs(x+1j*y)\n\n       rmax = np.max(r)\n       dr = np.abs([x[0,0] - x[0,1]]) * annulus_width\n    \n       sizeout = np.ceil(rmax/dr)\n\n       radial = np.empty(sizeout,dtype=np.float64)\n       radialdatamean = np.empty(sizeout,dtype=np.float64)\n\n       radial = np.arange(rmax/dr)*dr + np.float64(dr/2)\n       nrad = len(radial)\n\n       # Loop through the bins\n       for irad in xrange(nrad):\n         minrad = irad*dr\n         maxrad = minrad + dr\n         radialdatamean[irad] = data[(r>=minrad) * (r= 1:\n        imagelink = data['data'][random.randint(0, len(data['data']) - 1)]['images']['original']['url']\n        bot.sendChatAction(chat_id=chat_id, action=telegram.ChatAction.UPLOAD_DOCUMENT)\n        bot.sendDocument(chat_id=chat_id,\n                         filename=requestText.encode('utf-8') + '.gif',\n                         document=imagelink.encode('utf-8'))\n        return True\n    else:\n        bot.sendMessage(chat_id=chat_id, text='I\\'m sorry ' + (user if not user == '' else 'Dave') + \\\n                                              ', I\\'m afraid I can\\'t find a giphy gif for ' + \\\n                                              string.capwords(requestText.encode('utf-8')) + '.')","sub_path":"commands/giphy.py","file_name":"giphy.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"417015793","text":"import FWCore.ParameterSet.Config as cms\n\nflashggPhotons = cms.EDProducer('FlashggPhotonProducer',\n                                PhotonTag = cms.untracked.InputTag('slimmedPhotons'),\n                                reducedBarrelRecHitCollection = cms.InputTag('reducedEgamma','reducedEBRecHits'),\n                                reducedEndcapRecHitCollection = cms.InputTag('reducedEgamma','reducedEERecHits'),\n                                reducedPreshowerRecHitCollection = cms.InputTag('reducedEgamma','reducedESRecHits'),\n                                VertexCandidateMapTag = cms.InputTag(\"flashggVertexMapNonUnique\"),\n                                rhoFixedGridCollection = cms.InputTag('fixedGridRhoAll'),\n                                photonIdMVAweightfile_EB = cms.FileInPath(\"flashgg/MicroAODProducers/data/2013FinalPaper_PhotonID_Barrel_BDT_TrainRangePT15_8TeV.weights.xml\"),\n                                photonIdMVAweightfile_EE = cms.FileInPath(\"flashgg/MicroAODProducers/data/2013FinalPaper_PhotonID_Endcap_BDT_TrainRangePT15_8TeV.weights.xml\"),\n                                regressionWeightFile = cms.FileInPath(\"HiggsAnalysis/GBRLikelihoodEGTools/data/regweights_v8_8TeV_forest_ph.root\"),\n                                doOverlapRemovalForIsolation = cms.bool(True),\n                                extraCaloIsolations = cms.VPSet()\n                                )\n","sub_path":"MicroAODProducers/python/flashggPhotons_cfi.py","file_name":"flashggPhotons_cfi.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"115891497","text":"\n# Implement an iterator over a binary search tree (BST). Your iterator will be initialized with the root node of a BST.\n#\n# Calling next() will return the next smallest number in the BST.\n#\n#\n#\n# Example:\n#\n#\n#\n# BSTIterator iterator = new BSTIterator(root);\n# iterator.next();    // return 3\n# iterator.next();    // return 7\n# iterator.hasNext(); // return true\n# iterator.next();    // return 9\n# iterator.hasNext(); // return true\n# iterator.next();    // return 15\n# iterator.hasNext(); // return true\n# iterator.next();    // return 20\n# iterator.hasNext(); // return false\n#\n#\n# Note:\n#\n# next() and hasNext() should run in average O(1) time and uses O(h) memory,\n# where h is the height of the tree.\n# You may assume that next() call will always be valid, that is,\n# there will be at least a next smallest number in the BST when next() is called.\n\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\n\nclass BSTIterator:\n    def __init__(self, root):\n        \"\"\"\n        :type root: TreeNode\n        \"\"\"\n        self.stack = []\n        self.cur = root\n\n    def next(self):\n        \"\"\"\n        @return the next smallest number\n        :rtype: int\n        \"\"\"\n        while self.hasNext():\n            if self.cur:\n                self.stack.append(self.cur)\n                self.cur = self.cur.left\n            else:\n                ret = self.stack.pop()\n                self.cur = ret.right\n                return ret.val\n\n    def hasNext(self):\n        \"\"\"\n        @return whether we have a next smallest number\n        :rtype: bool\n        \"\"\"\n        return self.cur is not None or len(self.stack) > 0\n\n# Your BSTIterator object will be instantiated and called as such:\n# obj = BSTIterator(root)\n# param_1 = obj.next()\n# param_2 = obj.hasNext()\n\nif __name__ == '__main__':\n    from LeetcodeProblems.tree.tree_node import TreeNode\n    tree = TreeNode([5, 3, 6, 2, 4, None, 8])\n    it = BSTIterator(tree)\n    while True:\n        try:\n            print(it.next())\n            print(it.hasNext())\n        except StopIteration:\n            pass\n","sub_path":"LeetcodeProblems/tree/173_binary_search_tree_iterator.py","file_name":"173_binary_search_tree_iterator.py","file_ext":"py","file_size_in_byte":2163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"82475283","text":"import hashlib \nimport random\nimport json\nimport string \nimport binascii\nimport numpy as np\nimport pandas as pd\nimport pylab as pl\nimport logging\nimport datetime\nimport collections\nimport time\n\nimport Crypto\nimport Crypto.Random\nfrom Crypto.Hash import SHA\nfrom Crypto.PublicKey import RSA\nfrom Crypto.Signature import PKCS1_v1_5\n\ntransactions = []\n\nclass Client:\n    def __init__(self):\n        random = Crypto.Random.new().read\n        self._private_key = RSA.generate(1024, random)\n        self._public_key = self._private_key.publickey()\n        self._signer = PKCS1_v1_5.new(self._private_key)\n\n    @property\n    def identity(self):\n        return binascii.hexlify(self._public_key.exportKey(format='DER')).decode('ascii')\n\nclass Transaction:\n\n    def __init__(self, sender, recipient, value):\n        '''\n        takes in senders public key, recipient's public key, and amount to be sent\n        '''\n        self.sender = sender\n        self.recipient = recipient\n        self.value = value\n        self.time = datetime.datetime.now()\n\n    def to_dict(self):\n        if self.sender == 'Genisis':\n            identity = 'Genisis'\n        else:\n            identity = self.sender.identity\n\n        return collections.OrderedDict({\n            'sender': identity,\n            'recipient': self.recipient,\n            'value': self.value,\n            'time': self.time\n        })\n\n    def sign_transaction(self):\n        private_key = self.sender._private_key\n        signer = PKCS1_v1_5.new(private_key)\n        h = SHA.new(str(self.to_dict()).encode('utf8'))\n        return binascii.hexlify(signer.sign(h)).decode('ascii')\n\n\ndef display_transaction(transaction):\n    dict = transaction.to_dict()\n    print (\"sender: \" + dict['sender'])\n    print ('-----')\n    print (\"recipient: \" + dict['recipient'])\n    print ('-----')\n    print (\"value: \" + str(dict['value']))\n    print ('-----')\n    print (\"time: \" + str(dict['time']))\n    print ('-----')\n","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"620651393","text":"from region.models import Region\nfrom commune.models import Commune\nfrom building.models import Building\nfrom dbase.globals import *\nfrom appraisal.models import Appraisal\nimport datetime\nimport re\nimport unidecode\nimport requests\nfrom lxml import html\nimport dateutil.parser\n\ndef get_value(ws,cell):\n    if ws[cell].value == None:\n        return None\n    if type(ws[cell].value) == type(\"\"):\n        if ws[cell].value == \"\":\n            return None\n        else:\n            return ws[cell].value\n\ndef parse_date(string, **kwargs):\n    try:\n        date = dateutil.parser.parse(string).strftime(\"%d/%m/%Y %H:%M\")\n    except ValueError:\n        return None\n    return date\n\ndef parse_email(string, **kwargs):\n    if '<' in string:\n        iii = string.index('<')+1\n        iif = string.index('>')\n        return string[iii:iif].strip().lower()\n    if \"@\" not in string:\n        return None\n    else:\n        return string.strip().lower()\n\ndef parse_telephone(string, **kwargs):\n    if re.search('[a-zA-Z]', string):\n        return None\n    else:\n        return string.strip().replace(' ','')\n\ndef parse_address(address, **kwargs):\n    addressNumber2 = None\n    addressNumber = None\n\n    address = address.lower().strip()\n\n    commune = kwargs.pop(\"addressCommune\",None)\n    if commune:\n        commune = Commune.objects.get(id=commune).name.lower()\n        if address.endswith(commune):\n            address = address[:address.find(commune)].strip()\n        commune = unidecode.unidecode(commune)\n        if address.endswith(commune):\n            address = address[:address.find(commune)].strip()\n\n    dpto_strings = [\"dpto.\",\"dpto\",\"depto.\",\"depto\",\"departamento\",\"oficina\"]\n    for dpto_string in dpto_strings:\n        if dpto_string in address:\n            match = re.search(dpto_string+' ?(\\d+)', address)\n            if match:\n                addressNumber2 = match.group(1)\n                print(address)\n                address = address[:address.index(dpto_string)]+address[address.index(dpto_string)+len(dpto_string):]\n                print(address)\n                break\n\n    casa_strings = [\"casa.\",\"casa \"]\n    for casa_string in casa_strings:\n        if casa_string in address:\n            match = re.search(casa_string+' ?[a-zA-Z0-9]', address)\n            if match:\n                addressNumber2 = match.group(0).title()\n                address = address[:address.index(casa_string)]\n                break\n        \n    km_strings = [\"km\"]\n    for km_string in km_strings:\n        if km_string in address:\n            print(\"AA:A:A:A\")\n            match = re.search(km_string+' ?(\\d+)', address)\n            if match:\n                addressNumber = \"Km. \"+match.group(1)\n                address = address[:address.index(km_string)]\n                break\n\n    address = address.strip()\n    if address[-1] == ',' or address[-1] == '.' or address[-1] == '-':\n        address = address[:-1].strip()\n\n    match = re.search('(\\d+)$', address) \n    if match:\n        addressNumber = match.group(0)\n        address = address[:address.index(addressNumber)]\n    address = address.strip()\n    address = address.replace(\"avenida\",\"av.\")\n    address = address.replace('aven','av.')\n    address = address.replace('avnda','av.')\n\n    addressStreet = address\n\n    no_strings = [\"no.\",\"nº\"]\n    for no_string in no_strings:\n        if address.endswith(no_string):\n            addressStreet = address[:address.find(no_string)]\n            break\n\n    if addressStreet.startswith('calle'):\n        addressStreet = addressStreet[5:].strip()\n\n    addressStreet = addressStreet.title()\n\n    return {'addressStreet':addressStreet,'addressNumber':addressNumber,'addressNumber2':addressNumber2}\n\ndef parse_rut(rut, **kwargs):\n    rut = rut.replace('.','').replace(',','').replace('-','').lower()\n    return rut[:-1].strip()+'-'+rut[-1].strip()\n\ndef parse_commune(string, **kwargs):\n    commune = string.strip().title()\n    if '(' in commune:\n        commune = commune[:commune.index('(')].strip()\n    if commune in COMMUNE_NAME_ASCII__UTF.keys():\n        commune = COMMUNE_NAME_ASCII__UTF[commune]\n    commune = Commune.objects.get(name=commune)\n    return commune.id\n\ndef parse_solicitante_ejecutivo(string, **kwargs):\n    return string.strip().title()\n\ndef parse_solicitante_sucursal(string, **kwargs):\n    sucursal = string.strip().title()\n    print(sucursal)\n    if sucursal == \"Nlc\":\n        print(sucursal)\n        return \"NLC\"\n    return sucursal\n\ndef parse_tipo_tasacion(string, **kwargs):\n    if string == \"Operación\":\n        return None\n    else:\n        if string in ['CRÉDITO HIPOTECARIO']:\n            return Appraisal.HIPOTECARIA\n        elif string in ['CRÉDITO COMERCIAL']:\n            return Appraisal.COMERCIAL\n        elif string in ['INSTACOB']:\n            return Appraisal.TYPE_REMATE\n\ndef parse_finalidad(string, **kwargs):\n    string = string.strip()\n    if string == \"Operación\":\n        return None\n    else:\n        if string == 'ACTUALIZAR GARANTÍA':\n            return Appraisal.GARANTIA\n        elif string == 'COMPRA INMUEBLE':\n            return Appraisal.CREDITO\n        elif string == 'LIQUIDACIÓN FORZADA':\n            return Appraisal.LIQUIDACION\n        elif string == 'DACIÓN EN PAGO':\n            return Appraisal.DACION_EN_PAGO\n\ndef parse_time_request(string, **kwargs):\n    return parse_date(string)\n\ndef parse_cliente(string, **kwargs):\n    return string.strip().title()\n\ndef parse_contacto(string, **kwargs):\n    return string.strip().title()\n\ndef parse_property_type(string, **kwargs):\n    if string == None:\n        return None\n    else:\n        if string == 'CASAS':\n            return Building.TYPE_CASA\n        elif string == 'DEPARTAMENTOS':\n            return Building.TYPE_DEPARTAMENTO\n        elif string == 'OFICINAS':\n            return Building.TYPE_OFICINA\n        elif string == 'TERRENO PROYECTO INMOBILIARIO':\n            return Building.TYPE_TERRENO\n        elif string == 'SITIOS Y TERRENOS URBANOS':\n            return Building.TYPE_TERRENO\n        elif string == 'LOCALES COMERCIALES':\n            return Building.TYPE_LOCAL_COMERCIAL\n        elif string == 'CONSTRUCCIONES INDUSTRIALES':\n            return Building.TYPE_INDUSTRIA\n        elif 'BODEGAS' in string:\n            return Building.TYPE_BODEGA\n        elif 'ESTACIONAMIENTOS' in string:\n            return Building.TYPE_ESTACIONAMIENTO\n        elif 'BIENES RAICES RURALES' in string:\n            return Building.TYPE_PARCELA\n        elif 'PREDIOS' in string:\n            return Building.TYPE_TERRENO\n        else:\n            return Building.TYPE_OTRO\n\ndef parse_rol(string, **kwargs):\n    return string\n\ndef parse_comment(string, **kwargs):\n    return string\n\ndef parse_with_dictionary(ws,dictionary):\n    data = {}\n    for variable, info in dictionary.items():\n        function = info[1]\n        coords = info[0]\n        if type(coords) == type([]):\n            for coord in coords:\n                value = get_value(ws,coord)\n                if value != None:\n                    if len(info) == 3:\n                        kwargs = {info[2]:data[info[2]]}\n                    else:\n                        kwargs = {}\n                    ret = function(value,**kwargs)\n                    print(coord,coords,variable,ret)\n                    if ret != None:\n                        if type(ret) == type({}):\n                            for key, value in ret.items():\n                                data[key] = value\n                        else:\n                            data[variable] = ret\n                        break\n    return data\n\ndef parseItau(ws):\n    '''\n    Devuelve datos de solicitud ITAU\n    '''\n\n    dictionary = {\n            'appraisalTimeRequest':[[\"M3\",\"N3\"],parse_time_request],\n            'solicitanteEjecutivo':[[\"C7\"],parse_solicitante_ejecutivo],\n            'solicitanteEjecutivoEmail':[[\"J7\",\"K7\"],parse_email],\n            'solicitanteEjecutivoTelefono':[[\"O7\",\"P7\"],parse_telephone],\n            'solicitanteSucursal':[[\"C9\"],parse_solicitante_sucursal],\n            'tipoTasacion':[[\"G9\",\"H9\",\"H7\"],parse_tipo_tasacion],\n            'finalidad':[[\"J9\"],parse_finalidad],\n            'cliente':[[\"C14\"],parse_cliente],\n            'clienteEmail':[[\"C22\"],parse_email],\n            'clienteTelefono':[[\"C24\"],parse_telephone],\n            'contacto':[[\"C26\"],parse_contacto],\n            'contactoEmail':[[\"C28\"],parse_email],\n            'contactoTelefono':[[\"C30\"],parse_telephone],\n            'propertyType':[[\"C37\"],parse_property_type],\n            'addressCommune':[[\"C45\"],parse_commune],\n            'addressStreet':[[\"C41\"],parse_address,'addressCommune'],\n            'rol':[[\"C43\"],parse_rol],\n            'comments':[[\"B54\"],parse_comment]\n        }\n    \n    data = parse_with_dictionary(ws,dictionary)\n\n    print(data)\n\n    for c in Appraisal.petitioner_choices:\n        if c[1] == 'Itaú':\n            data['solicitante'] = c[0]\n\n    if 'addressRegion' not in data.keys():\n        if 'addressCommune' in data.keys() and data['addressCommune'] != None:\n            commune = Commune.objects.get(id=data['addressCommune'])\n            data['addressRegion'] = commune.region.code\n\n    if ws['C16'].value != None:\n        if '-' in ws['C16'].value:\n            # Rut viene todo en la celda\n            data['clienteRut'] = parse_rut(ws['C16'].value)\n        else:\n            if ws['F16'].value != None and ws['F16'].value != '-':\n                data['clienteRut'] = parse_rut(ws['C16'].value+ws['F16'].value)\n            elif ws['G16'].value != None:\n                data['clienteRut'] = parse_rut(ws['C16'].value+ws['G16'].value)\n            else:\n                data['clienteRut'] = parse_rut(ws['C16'].value)\n\n    return data\n\ndef parseBancoDeChileAvance(wb):\n    '''\n    Sacar información de solicitu de Banco de Chile, para tasaciones\n    de avance de obras. Recibe un archivo excel.\n    '''\n\n    ws = wb.worksheets[0]\n\n    data = {}\n\n    for c in Appraisal.petitioner_choices:\n        if c[1] == 'Banco de Chile':\n            data['solicitante'] = c[0]\n\n    solicitanteEjecutivo = ws['E61'].value\n    if isinstance(solicitanteEjecutivo,type('')):\n        if solicitanteEjecutivo != '':\n            data['solicitanteEjecutivo'] = solicitanteEjecutivo.strip().title()\n\n    solicitanteSucursal = ws['E62'].value\n    if isinstance(solicitanteSucursal,type('')):\n        if solicitanteSucursal != '':\n            data['solicitanteSucursal'] = solicitanteSucursal.strip().title()\n\n    solicitanteEjecutivoEmail = ws['E63'].value\n    if isinstance(solicitanteEjecutivoEmail,type('')):\n        if solicitanteEjecutivoEmail != '':\n            data['solicitanteEjecutivoEmail'] = solicitanteEjecutivoEmail.strip().lower()\n\n    solicitanteEjecutivoTelefono = ws['M62'].value\n    if solicitanteEjecutivoTelefono != '':\n        data['solicitanteEjecutivoTelefono'] = str(solicitanteEjecutivoTelefono)\n\n    solicitanteEjecutivoRut = str(ws['M61'].value)+str(ws['N61'].value)\n    if isinstance(solicitanteEjecutivoRut,type('')):\n        if solicitanteEjecutivoRut != '':\n            data['solicitanteEjecutivoRut'] = parse_rut(solicitanteEjecutivoRut)\n\n    cliente = ws['H9'].value\n    if isinstance(cliente,type('')):\n        if cliente != '':\n            data['cliente'] = cliente.strip().title()\n\n    clienteRut = ws['H10'].value\n    clienteRutDF = ws['J10'].value\n    if clienteRut != '' and clienteRutDF != '':\n        data['clienteRut'] = parse_rut(str(clienteRut)+''+clienteRutDF)\n\n    contacto = ws['E56'].value\n    if isinstance(contacto,type('')):\n        if contacto != '':\n            data['contacto'] = contacto.strip().title()\n\n    contactoEmail = ws['E58'].value\n    if isinstance(contactoEmail,type('')):\n        if contactoEmail != '':\n            data['contactoEmail'] = contactoEmail.strip().lower()\n\n    contactoTelefono = ws['M56'].value\n    if contactoTelefono != '':\n        data['contactoTelefono'] = str(contactoTelefono)\n\n    tipoTasacion = ws['I12'].value.strip()\n    if 'ESTADO de AVANCE' in tipoTasacion:\n        data['tipoTasacion'] = Appraisal.AVANCE_DE_OBRA\n        data['finalidad'] = Appraisal.OTRO\n        data['visita'] = Appraisal.COMPLETA\n\n\n    tipo = ws['H17'].value\n    if isinstance(tipo,type('')):\n        tipo = tipo.strip()\n        if tipo == 'Casas':\n            data['propertyType'] = Building.TYPE_CONDOMINIO\n        if tipo == 'CASA':\n            data['propertyType'] = Building.TYPE_CASA\n        elif tipo == 'DEPARTAMENTO':\n            data['propertyType'] = Building.TYPE_DEPARTAMENTO\n\n    address = ws['K17'].value\n    if isinstance(address,type('')):\n        if address != '':\n            addressStreet, addressNumber, addressNumber2 = parse_address(address)\n            data['addressStreet'] = addressStreet\n            if addressNumber:\n                data['addressNumber'] = addressNumber\n            if addressNumber2:\n                data['addressNumber2'] = addressNumber2\n\n    try :\n        commune = ws['M17'].value\n        if commune == 'EST. CENTRAL':\n            commune = \"Estación Central\"\n        commune = Commune.objects.get(name=commune.strip().title())\n        data['addressCommune'] = commune.id\n        data['addressRegion'] = commune.region.code\n    except Commune.DoesNotExist:\n        pass\n\n    if ws['N6'].value:\n        data['appraisalTimeRequest'] = ws['N6'].value.strftime('%d/%m/%Y %H:%M')\n\n    ws = wb.worksheets[1]\n\n    data['appraisalTimeDue'] = ws['J41'].value.strftime('%d/%m/%Y %H:%M')\n\n    data['solicitanteCodigo'] = ws['E37'].value\n\n    return data\n\ndef parseBancoDeChile(text):\n    '''\n    Devuelve datos de solicitud de Banco de Chile (general)\n    Recibe un arreglo de strings que es el .pdf cortado.\n    '''\n    data = {}\n    \n    for c in Appraisal.petitioner_choices:\n        if c[1] == 'Banco de Chile':\n            data['solicitante'] = c[0]\n    for i, line in enumerate(text):\n        if 'ID' == line.strip():\n            data['solicitanteCodigo'] = text[i+6].strip()\n        if 'TIPO OPERACION' in line.strip():\n            if text[i+6].strip() == \"Crédito Hipotecario\":\n                data['tipoTasacion'] = Appraisal.HIPOTECARIA\n                data['finalidad'] = Appraisal.CREDITO\n        if 'TIPO DE BIEN' in line.strip():\n            if text[i+6].strip() == \"DEPARTAMENTO\":\n                data['propertyType'] = Building.TYPE_DEPARTAMENTO\n            if text[i+6].strip() == \"CASA\":\n                data['propertyType'] = Building.TYPE_CASA\n        if 'COMUNA' in line.strip():\n            comuna = text[i+6].strip().title()\n            print(comuna)\n            try:\n                commune = Commune.objects.get(name_simple=unidecode.unidecode(comuna))\n                data['addressCommune'] = commune.id\n                data['addressRegion'] = commune.region.code\n            except Commune.DoesNotExist:\n                data['addressCommune'] = \"\"\n                data['addressRegion'] = \"\"\n        if 'ROL' in line.strip():\n            data['rol'] = text[i+6+c-1].strip()\n        if 'DIRECCION' in line.strip():\n            address = ''\n            c = 0\n            while not 'De propiedad de' in text[i+6+c+1].strip():\n                address += text[i+6+c].strip().title()\n                c += 1\n            addressStreet, addressNumber, addressNumber2 = parse_address(address)\n            data['addressStreet'] = addressStreet\n            if addressNumber:\n                data['addressNumber'] = addressNumber\n            if addressNumber2:\n                data['addressNumber2'] = addressNumber2\n        if 'Cliente' in line.strip():\n            data['cliente'] = text[i+2].strip().title()\n        if 'Rut' == line.strip():\n            data['clienteRut'] = parse_rut(text[i+2])\n        if 'Nombre' == line.strip():\n            data['solicitanteEjecutivo'] = text[i+2].strip().title()\n        if 'Teléfono' == line.strip():\n            data['solicitanteEjecutivoTelefono'] = text[i+2].strip().replace(' ','')\n        if 'E - Mail' == line.strip():\n            data['solicitanteEjecutivoEmail'] = text[i+2].strip().lower()\n        if 'Unidad' in line.strip():\n            data['solicitanteSucursal'] = text[i+2].strip().title()\n        if 'INFORMACION ADICIONAL' in line.strip():\n            data['comments'] = ''\n            c = 1\n            while not 'Antecedentes' in text[i+c].strip() or 'Información de Contacto' in text[i+c].strip():\n                data['comments'] += text[i+c].strip()\n                c += 1\n        if 'Información de Contacto' in line.strip():\n            line = text[i+1]\n            if '@' in text[i+2]:\n                line += text[i+2]\n                data['appraisalTimeRequest'] = text[i+3]+' '+text[i+4]\n            else:\n                data['appraisalTimeRequest'] = text[i+2]+' '+text[i+3]\n            c = line.split('/')\n            data['contacto'] = c[0]\n            for cc in c[1:]:\n                if 'Fono' in cc:    \n                    data['contactoTelefono'] = cc.split(':')[1].strip().replace(' ','')\n                elif 'E-Mail' in cc:\n                    data['contactoEmail'] = cc.split(':')[1].strip()\n\n    return data\n\ndef parseSantander(text):\n    '''\n    Devuelve datos de solicitud del Santander\n    Recibe un arreglo de strings que es el .pdf cortado.\n    '''\n    \n    data = {}\n    \n    address = ''\n\n    for c in Appraisal.petitioner_choices:\n        if c[1] == 'Santander':\n            data['solicitante'] = c[0]\n    for i, line in enumerate(text):\n        if 'Nº Req' in line.strip():\n            data['solicitanteCodigo'] = line.split(':')[1].strip()\n        elif 'Fecha de asignación' in line.strip():\n            data['appraisalTimeRequest'] = text[i+5].strip()\n        elif 'Entregar informe de' in line.strip():\n            data['appraisalTimeDue'] = text[i+5].strip()\n        elif 'Nombre Cliente' in line.strip():\n            data['cliente'] = line.split(':')[1].strip().title()\n            c = 1\n            while not 'RUT Cliente' in text[i+c].strip():\n                if text[i+c].strip() == '':\n                    c += 1\n                    continue\n                data['cliente'] += ' '+text[i+c].strip().title()\n                c += 1\n        elif 'RUT Cliente' in line.strip():\n            data['clienteRut'] = parse_rut(line.split(':')[1])\n        elif 'Nombre Propietario' in line.strip():\n            data['propietario'] = line.split(':')[1].strip().title()\n            c = 1\n            while not 'RUT Propietario' in text[i+c].strip():\n                if text[i+c].strip() == '':\n                    c += 1\n                    continue\n                data['propietario'] += ' '+text[i+c].strip().title()\n                c += 1\n        elif 'RUT Propietario' in line.strip():\n            data['propietarioRut'] = parse_rut(line.split(':')[1])\n        elif 'Nombre Contacto' in line.strip():\n            data['contacto'] = line.split(':')[1].strip().title()\n        elif 'Telefono movil' in line.strip():\n            data['contactoTelefono'] = text[i+1].split(':')[1].strip().replace(' ','')\n        elif 'Direccion' in line.strip():\n            address = line.split(':')[1].strip()            \n        elif 'Rubro :' in line.strip():\n            tipoTasacion = line.split(':')[1].strip()\n            if tipoTasacion == \"HIPOTECARIO\":\n                data['tipoTasacion'] = Appraisal.HIPOTECARIA\n                data['finalidad'] = Appraisal.CREDITO\n            elif tipoTasacion == \"GARANTIAS GENERALES\":\n                data['finalidad'] = Appraisal.GARANTIA\n        elif 'Grupo :' in line.strip():\n            propertyType = line.split(':')[1].strip()\n            if 'DEPARTAMENTO' in propertyType:\n                data['propertyType'] = Building.TYPE_DEPARTAMENTO\n            elif 'VIVIENDA' in propertyType:\n                pass # Puede ser casa o departamento\n            elif 'TERRENO' in propertyType:\n                data['propertyType'] = Building.TYPE_TERRENO\n            elif 'LOCAL COMERCIAL' in propertyType:\n                data['propertyType'] = Building.TYPE_LOCAL_COMERCIAL\n                data['tipoTasacion'] = Appraisal.COMERCIAL\n            elif 'LOCALCOMERCIAL' in propertyType:\n                data['propertyType'] = Building.TYPE_LOCAL_COMERCIAL\n                data['tipoTasacion'] = Appraisal.COMERCIAL\n            elif 'AVANCE' in propertyType:\n                data['tipoTasacion'] = Appraisal.AVANCE_DE_OBRA\n        elif 'Rol :' in line.strip():\n            data['rol'] = line.split(':')[1].strip()\n        elif 'Comuna' in line.strip():\n            communes = line.split(':')[1].strip().title()\n            commune, region = parse_commune(communes)\n            data['addressCommune'] = commune.id\n            data['addressRegion'] = region.code\n            addressStreet, addressNumber, addressNumber2 = parse_address(address,commune=commune.name)\n            data['addressStreet'] = addressStreet\n            if addressNumber:\n                data['addressNumber'] = addressNumber\n            if addressNumber2:\n                data['addressNumber2'] = addressNumber2\n        elif 'Nombre Ejecutivo' in line.strip():\n            data['solicitanteEjecutivo'] = line.split(':')[1].strip().title()\n            c = 1\n            while not 'E-Mail Ejecutivo' in text[i+c].strip():\n                if text[i+c].strip() == '':\n                    c += 1\n                    continue\n                data['solicitanteEjecutivo'] += ' '+text[i+c].strip().title()\n                c += 1\n        elif 'Telefono Ejecutivo' in line.strip():\n            data['solicitanteEjecutivoTelefono'] = line.split(':')[1].strip().replace(' ','')\n        elif 'E-Mail Ejecutivo' in line.strip():\n            data['solicitanteEjecutivoEmail'] = line.split(':')[1].strip()\n        elif 'Sucursal' in line.strip():\n            data['solicitanteSucursal'] = line.split(':')[1].strip().title()\n        elif 'Centro de Costo' in line.strip():\n            c = 1\n            data['comments'] = ''\n            while i+c < len(text) and not 'Página 1 de 2' in text[i+c].strip() and not 'Incidencia' in text[i+c].strip():\n                if len(text[i+c]) > 1 and text[i+c].strip()[1:-1] not in data['comments']:\n                    data['comments'] += text[i+c].strip()\n                c += 1\n\n    return data\n\ndef parseSantanderUrl(url):\n\n    data = {}\n    \n    with requests.Session() as s:\n\n        login_url = \"https://extranet.gruposantander.cl/autentica.aspx\"\n        login_data = {\"user\": \"70149761\", \"clave\": \"protasa2018\"}\n        response = s.post(login_url, data=login_data, headers=dict(referer=\"https://extranet.gruposantander.cl/\"))\n        # These are to populate the session with the right cookies\n        response = s.get(\"https://extranet.gruposantander.cl/gateW.aspx?dst_url=https://tasaciones.extranetsantander.cl/segesta/login_prov_adm.aspx?qry=ok&UrlToken=&Target=principal&UsaToken=SI\")\n        response = s.get(\"https://tasaciones.extranetsantander.cl/SEGESTA/VISTAS/PERFILES/INI_Generico.aspx\",headers=dict(referer=\"https://extranet.gruposantander.cl/arriba2.aspx\"))\n        # Now that we have the right cookies, really get the appraisal\n        response = s.get(url,headers=dict(referer=url))\n        tree = html.fromstring(response.content)\n        for c in Appraisal.petitioner_choices:\n            if c[1] == 'Santander':\n                data['solicitante'] = c[0]\n        data['solicitanteCodigo'] = tree.xpath('//*[@id=\"lbl_requerimientoID\"]/text()')\n        data['solicitanteSucursal'] = tree.xpath('//*[@id=\"suc_ejecutivo\"]/text()')\n\n        ejecutivo = tree.xpath('//*[@id=\"lbl_nomEjecutivo\"]/text()')\n        if len(ejecutivo) > 0:\n            data['solicitanteEjecutivo'] = ejecutivo[0]\n        data['solicitanteEjecutivoEmail'] = tree.xpath('//*[@id=\"lbl_mailEjecutivo\"]/text()')\n        data['appraisalTimeRequest'] = tree.xpath('//table//td[contains(text(),\"Fecha Ingreso\")]/../following-sibling::tr[1]/td[1]/text()')[0].replace(\"-\",\"/\")\n        data['appraisalTimeDue'] = tree.xpath('//table//td[contains(text(),\"Entrega en Estándar Real\")]/../following-sibling::tr[1]/td[6]/text()')[0].replace(\"-\",\"/\")\n\n        rubro = tree.xpath('//*[@id=\"lbl_rubro\"]/text()')\n        if \"Garantía General\" in rubro:\n            data['tipoTasacion'] = Appraisal.GARANTIA\n            data['finalidad'] = Appraisal.GARANTIA\n\n        data['cliente'] = tree.xpath('//*[@id=\"lbl_nomSolicitante\"]/text()')\n        data['clienteRut'] = tree.xpath('//*[@id=\"lbl_rutSolicitante\"]/text()')\n\n        propietario = tree.xpath('//*[@id=\"lbl_nomPropietario\"]/text()')\n        if len(propietario) > 0:\n            data['propietario'] = propietario[0].title()\n        propietarioRut = tree.xpath('//*[@id=\"lbl_rutPropietario\"]/text()')\n        if len(propietarioRut) > 0:\n            data['propietarioRut'] = propietarioRut[0]\n\n        data['contacto'] = tree.xpath('//*[@id=\"lbl_nomContacto\"]/text()')\n        data['contactoEmail'] = tree.xpath('//*[@id=\"lbl_emailContacto\"]/text()')\n        data['contactoTelefono'] = tree.xpath('//*[@id=\"lbl_fono1Contacto\"]/text()')\n\n        rubro = tree.xpath('//*[@id=\"lbl_rubro\"]/text()')\n        if len(rubro) > 0:\n            rubro = rubro[0].strip()\n            if \"Local Comercial\" in rubro:\n                data['propertyType'] = Building.TYPE_LOCAL_COMERCIAL\n                data['tipoTasacion'] = Appraisal.COMERCIAL\n            if \"Leasing\" in rubro:\n                data['tipoTasacion'] = Appraisal.LEASING\n            if \"Convenio Hipotecario\" in rubro:\n                data['tipoTasacion'] = Appraisal.CONVENIO_HIPOTECARIO\n\n        grupo = tree.xpath('//*[@id=\"lbl_grupo\"]/text()')\n        if len(grupo) > 0:\n            grupo = grupo[0].strip()\n            if \"Oficina\" in grupo:\n                data['propertyType'] = Building.TYPE_OFICINA\n\n        commune, region = parse_commune(tree.xpath('//*[@id=\"lbl_comuna\"]/text()')[0])\n        data['addressCommune'] = commune.id\n        data['addressRegion'] = region.id\n\n        address = tree.xpath('//*[@id=\"lbl_direccion\"]/text()')\n        if len(address) > 0:\n            street, number, number2 = parse_address(address[0])\n            data['addressStreet'] = street\n            data['addressNumber'] = number\n            data['addressNumber2'] = number2\n\n        roles = tree.xpath('//*[@id=\"lbl_roles\"]/text()')\n        if len(roles) > 0:\n            if roles[0] != \"\":\n                data[\"rol\"] = roles[0].split(',')[0]\n\n    return data","sub_path":"web/create/parse.py","file_name":"parse.py","file_ext":"py","file_size_in_byte":26324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"86679897","text":"\"\"\"\nBreadth First Search\nsource : https://www.educative.io/edpresso/how-to-implement-a-breadth-first-search-in-python\nO(V + E) time \n\"\"\"\n\ngraph = {\n  'A' : ['B','C'],\n  'B' : ['D', 'E'],\n  'C' : ['F'],\n  'D' : [],\n  'E' : ['F'],\n  'F' : []\n}\n\nvisited = []\nqueue = []\n\ndef breadth_first_search(visited, graph, node):\n    visited.append(node)\n    queue.append(node)\n\n    while queue:\n        vertex = queue.pop(0)\n        print(vertex, end = \" \")\n\n        for neighbor in graph[vertex]:\n            if neighbor not in visited:\n                visited.append(neighbor)\n                queue.append(neighbor)","sub_path":"graphs-problems/breadth-first-search.py","file_name":"breadth-first-search.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"628809496","text":"# Copyright 2019 Alibaba Cloud Inc. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#    http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom alibabacloud.client import AlibabaCloudClient\nfrom alibabacloud.request import APIRequest\nfrom alibabacloud.utils.parameter_validation import verify_params\n\n\nclass DybaseapiClient(AlibabaCloudClient):\n\n    def __init__(self, client_config, credentials_provider=None, retry_policy=None,\n                 endpoint_resolver=None):\n        AlibabaCloudClient.__init__(self, client_config,\n                                    credentials_provider=credentials_provider,\n                                    retry_policy=retry_policy,\n                                    endpoint_resolver=endpoint_resolver)\n        self.product_code = 'Dybaseapi'\n        self.api_version = '2017-05-25'\n        self.location_service_code = 'dybaseapi'\n        self.location_endpoint_type = 'openAPI'\n\n    def query_token_for_mns_queue(\n            self,\n            queue_name=None,\n            resource_owner_id=None,\n            resource_owner_account=None,\n            message_type=None,\n            owner_id=None):\n        api_request = APIRequest('QueryTokenForMnsQueue', 'GET', 'http', 'RPC', 'query')\n        api_request._params = {\n            \"QueueName\": queue_name,\n            \"ResourceOwnerId\": resource_owner_id,\n            \"ResourceOwnerAccount\": resource_owner_account,\n            \"MessageType\": message_type,\n            \"OwnerId\": owner_id}\n        return self._handle_request(api_request).result\n","sub_path":"alibabacloud/clients/dybaseapi_20170525.py","file_name":"dybaseapi_20170525.py","file_ext":"py","file_size_in_byte":2002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"89070979","text":"########################################################################################################################\n#    File: bank_view.py\n#  Author: Dan Huckson, https://github.com/unodan\n#    Date: 2018-10-03\n########################################################################################################################\n\nfrom tkinter import Toplevel, Label, Entry, StringVar, IntVar, LabelFrame\n\n\nclass BankView(Toplevel):  # A View\n    def __init__(self, master, title):\n        Toplevel.__init__(self, master)\n        self.title(title)\n\n        self.protocol('WM_DELETE_WINDOW', self.master.destroy)\n\n        frame = LabelFrame(self, text='Interest')\n\n        self.rate = StringVar()\n        self.rate.set('0.0125')\n        Label(frame, text=' Rate ').grid(sticky='e')\n        self.interest_rate = Entry(frame, width=8, textvariable=self.rate)\n        self.interest_rate.grid(padx=(0, 10), pady=(0, 10), row=0, column=1, sticky='w')\n\n        self.period = IntVar()\n        self.period.set(1000)\n        Label(frame, text=' Time (APR) ').grid()\n        self.interest_period = Entry(frame, width=25, textvariable=self.period)\n        self.interest_period.grid(padx=(0, 10), pady=(0, 10), row=1, column=1)\n\n        frame.grid(pady=(0, 5), sticky='ew')\n\n    def get_interest_rate(self):\n        return float(self.rate.get())\n\n    def get_interest_period(self):\n        try:\n            period = self.period.get()\n        except:\n            period = 0\n\n        return int(period)\n","sub_path":"tkmvc2/views/bank_view.py","file_name":"bank_view.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"494887706","text":"# encoding: utf-8\n\n\"\"\"\nCreated on 2016/07/04\n\n@author: ted\n\"\"\"\n\nimport logging\nimport random\nimport json_utils\nfrom zuzu_env import Constants\n\nlogger = logging.getLogger(\"common_utils\")\n\nDICE = \"dice\"\nretry_item_dict = dict()\n\ndef roll_dice_in_json(chosen, json_file, full_dice_arr=range(1,7)):\n    json_data = json_utils.load_json_file(json_file, json_utils.UTF8_ENCODE)\n    dice_arr = json_data.get(DICE)\n    if not dice_arr or len(dice_arr) <= 0:\n        dice_arr = full_dice_arr\n    dice_result = random.choice(dice_arr)\n    dice_arr.remove(dice_result)\n    json_data[DICE] = dice_arr\n    json_utils.update_json_file(json_file, json_data, json_utils.UTF8_ENCODE)\n    if dice_result == chosen:\n        return True\n    else:\n        return False\n\n\ndef write_body(content, file_name):\n    logname = Constants.LOG_FOLDER + file_name\n    try:\n        with open(logname, \"a+\") as logFile:\n            s = content\n            logFile.write(s)\n            logFile.close()\n    except:\n        pass\n\ndef retry_req(failure):\n    url = failure.request.url\n    retry_times = retry_item_dict.get(url)\n    if retry_times:\n        retry_times += 1\n        if retry_times >= 3:\n            return None\n        else:\n            retry_item_dict[url] = retry_times\n            logger.error(\"Resending the page request with different proxy\")\n            return failure.request\n    else:\n        retry_item_dict[url] = 1\n        logger.error(\"First resending the page request with different proxy\")\n        return failure.request","sub_path":"RHC/RHC/common_utils.py","file_name":"common_utils.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"494908330","text":"#! /usr/bin/env python3\n\nimport io\nimport re\nfrom datetime import date\nimport math\nfrom functools import reduce\nfrom collections import deque\n\nfrom wand.image import Image as WImage\nfrom PIL import Image\n\nimport pyocr\nimport pyocr.builders\n\nfrom PyPDF2 import PdfFileReader, PdfFileWriter\n\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen.canvas import Canvas\n\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\n\nfrom skimage.transform import hough_ellipse\nfrom skimage.draw import ellipse_perimeter\n\nfrom rdp import rdp\n\nimport dropbox\n\nimport PIL\n\nimport constants\nimport cvutils\n\n\ndef _indexByFilterFrom(li, start, func, relative=False):\n    if type(start) is not int:\n        start = li.index(start)\n    for k, v in enumerate(li[start:]):\n        if func(v):\n            return start+k if not relative else k\n    raise Exception(\"No Elements passed filter\")\n\ndef _indexByFilter(li, func):\n    return _indexByFilterFrom(li, 0, func)\n\ndef _spliceAt(li, start, range):\n    return li[start+range[0]:start+range[1]+1]\n\ndef grabDiagnoses(pdfPath):\n    tool = pyocr.get_available_tools()[0]\n    lang = tool.get_available_languages()[0]\n\n    inputpdf = []\n    parsed = []\n    with WImage(filename=pdfPath, resolution=300) as img:\n        converted = img.convert('jpeg')\n        for page in converted.sequence:\n            inputpdf.append(WImage(image=page).make_blob('jpeg'))\n\n    for page in inputpdf:\n        parsed.append(tool.image_to_string(\n            Image.open(io.BytesIO(page)),\n            lang=lang,\n            builder=pyocr.builders.TextBuilder()\n        ))\n\n    parsed = \"\\n\".join(parsed).split(\"\\n\")\n    diags = [diag.replace(\" \", \"\") for diag in re.findall(r'\\d\\)(.+)\\;', \"\\n\".join(_spliceAt(parsed, _indexByFilter(parsed, lambda x: \"ICD-10\" in x), (1,8))), flags=re.M)]\n    print(diags)\n    return diags\n\ndef getSigPosition(pdfPath):\n    tool = pyocr.get_available_tools()[0]\n    lang = tool.get_available_languages()[0]\n\n    page = io.BytesIO()\n    pageToImage(pdfPath, -1, page)\n\n    with WImage(blob=page.getvalue()) as img:\n        sigCrop = WImage(image=converted.sequence[-1])[75:500, -200:-100]\n\n    text = tool.image_to_string(\n        Image.open(io.BytesIO(sigCrop.make_blob('jpeg'))),\n        lang=lang,\n        builder=pyocr.builders.TextBuilder()\n    )\n    if \"Physician Extender\" in text:\n        return (297, 454)\n    else:\n        return (27, 184)\n\ndef signAndDate(srcPath, destPath):\n    pos = getSigPosition(srcPath)\n\n    packet = io.BytesIO()\n    c = Canvas(packet, pagesize=letter)\n    c.drawImage(\"./sig.png\", pos[0], 45, width=126, height=40, preserveAspectRatio=True, mask=[255]*6)\n    c.drawString(pos[1], 58, \"08/17/1998\")\n    c.showPage()\n    c.save()\n\n    packet.seek(0)\n    signed = PdfFileReader(packet)\n\n    inputStream = open(srcPath, \"rb\")\n    inputPDF = PdfFileReader(inputStream)\n\n    output = PdfFileWriter()\n\n    page = inputPDF.getPage(-1)\n    page.mergePage(signed.getPage(0))\n\n    for i in range(inputPDF.numPages):\n        if i != inputPDF.numPages-1:\n            output.addPage(inputPDF.getPage(i))\n        else:\n            output.addPage(page)\n    with open(destPath, \"wb+\") as outputStream:\n        output.write(outputStream)\n    inputStream.close()\n\ndef maskTables(img, inverted=False, both=False):\n    mask = np.zeros(img.shape, np.uint8)\n    kernel1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(11,11))\n\n    close = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel1)\n    div = np.float32(img)/close\n    res = np.uint8(cv2.normalize(div, div, 0, 255, cv2.NORM_MINMAX))\n\n    thresh = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)\n    _, contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n    boxes = sorted(contours, key=cv2.contourArea)[-3:-1]\n\n    cv2.drawContours(mask, boxes, 0,255,-1)\n    cv2.drawContours(mask, boxes, 1,255,-1)\n    cv2.drawContours(mask, boxes, 0,0,2)\n    cv2.drawContours(mask, boxes, 1,0,2)\n\n    if inverted or both:\n        inverted_mask = np.bitwise_not(mask)\n        if both:\n            return cv2.bitwise_and(res, mask), cv2.bitwise_and(res, invered_mask)\n        return cv2.bitwise_and(res, inverted_mask)\n\n    return cv2.bitwise_and(res, mask)\n\ndef autocrop(img, threshold=0, invert=False):\n    if invert and threshold == 0:\n        threshold = 255\n    if len(img.shape) == 3:\n        flatImage = np.max(img, 2)\n    else:\n        flatImage = img\n    assert len(flatImage.shape) == 2\n\n    if not invert:\n        rows = np.where(np.max(flatImage, 0) > threshold)[0]\n        if rows.size:\n            cols = np.where(np.max(flatImage, 1) > threshold)[0]\n            img = img[cols[0]:cols[-1]+1, rows[0]:rows[-1]+1]\n        else:\n            img = img[:1,:1]\n        return img\n    else:\n        rows = np.where(np.min(flatImage, 0) < threshold)[0]\n        if rows.size:\n            cols = np.where(np.min(flatImage, 1) < threshold)[0]\n            img = img[cols[0]:cols[-1]+1, rows[0]:rows[-1]+1]\n        else:\n            img = img[:1,:1]\n        return img\n\ndef getCircles(img):\n    img_circles = np.zeros(img.shape, np.uint8)\n    circleMask = img_circles.copy()\n    inverted_mask = maskTables(img, inverted=True)\n\n    template = cv2.imread(\"./diff.jpg\", 0)\n    masked_template = autocrop(maskTables(template))\n\n    w, h = masked_template.shape[::-1]\n    res = cv2.matchTemplate(img, masked_template, cv2.TM_CCOEFF_NORMED)\n    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)\n    top_left = max_loc\n    bottom_right = (top_left[0] + w, top_left[1] + h)\n    cropped_img = img[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0]]\n\n    _, b_cropped_img = cv2.threshold(cropped_img, 255, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n    _, b_mask_diff = cv2.threshold(masked_template, 255, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n    _, b_inverted_mask = cv2.threshold(inverted_mask, 255, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n\n    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))\n\n    dilated_diff = cv2.morphologyEx(b_mask_diff, cv2.MORPH_DILATE, kernel)\n\n    diff = cv2.subtract(b_cropped_img, dilated_diff)\n    b_inverted_mask[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0]] = diff\n    diff = b_inverted_mask[top_left[1]:bottom_right[1], :]\n    for _ in range(4):\n        diff = cv2.morphologyEx(diff, cv2.MORPH_DILATE, kernel)\n    # _, diff = cv2.threshold(cv2.GaussianBlur(diff, (49, 49), 10), 0, 255, cv2.THRESH_BINARY)\n    res = cv2.morphologyEx(diff, cv2.MORPH_ERODE, kernel)\n\n    img_test = cv2.cvtColor(img.copy()[top_left[1]:bottom_right[1], :], cv2.COLOR_GRAY2BGR)\n    edges = cv2.Canny(res, 100, 200)\n    img_test = houghE(edges)\n    # _, mask_contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n    # passed_contours = []\n    # for contour in mask_contours:\n       # # if contourIsCurved(contour) and len(contour) >= 5 and all([not cvutils.contourIsInside(contour, other) for other in [cnt for cnt in mask_contours if cnt is not contour]]):\n        # if len(contour) >= 5:\n            # img_test = contourIsElliptical(contour, img_test)\n            # passed_contours.append(contour)\n            # break\n    nl_hulls = [cv2.convexHull(cnt) for cnt in passed_contours]\n    cv2.fillPoly(circleMask[top_left[1]:bottom_right[1], :], nl_hulls, 255)\n\n    img_masked = cv2.bitwise_and(img.copy(), circleMask)\n    return img_test\n\ndef houghE(img):\n    return_img = img.copy()\n    result = hough_ellipse(img, accuracy=20, threshold=250, min_size=100)\n    result.sort(order='accumulator')\n    for e in result:\n        yc, xc, a, b = [int(round(x)) for x in e[1:5]]\n        orientation = e[5]\n        cy, cx = ellipse_perimeter(yc,xc,a,b,orientation)\n        return_img[cy, cx] = (255, 255, 255)\n    \n    return return_img\n    \ndef getSuperbill(date, officeID):\n    client = dropbox.client.DropboxClient(utils.getAuth(\"DB\"))\n    possible = []\n    for office in constants.officeMap[officeID]:\n        possible += client.search(\"./Westshore Superbills\", office+\" \"+date.strftime(\"%m-%d-%y\"))\n    if len(possible) == 1:\n        path = possible[0][\"path\"]\n    elif len(possible) > 1:\n        print(\"Ambiguous Superbill Search, Please Choose one of the following:\")\n        for i, search in enumerate(possible):\n            print(\"\\t(\"+i+\") \"+\"/\".join(search[\"path\"].split(\"/\")[-2:]))\n        chosen = int(input(\"\\t\"))\n        path = possible[chosen][\"path\"]\n    elif len(possible) == 0:\n        raise Exception(\"Could not find a superbill fitting seach -> \"+office+\" \"+date.strftime(\"%m-%d-%y\"))\n    return io.BytesIO(client.get_file(path).read())\n\ndef simpleContour(contour, epsilon):\n    result = rdp(contour.reshape(len(contour), 2), epsilon=epsilon)\n    return result.reshape(len(result), 1, 2)\n\ndef contourIsCurved(contour):\n    avg_slope_change = 0\n    ratio = 1/len(contour)\n    for i in range(1, len(contour)-1):\n        prevPoint = contour[i-1][0]\n        currPoint = contour[i][0]\n        nextPoint = contour[i+1][0]\n\n        prevSlope = currPoint[1]-prevPoint[1]/currPoint[0]-prevPoint[0]\n        nextSlope = nextPoint[1]-currPoint[1]/nextPoint[0]-currPoint[0]\n\n        avg_slope_change = (nextSlope-prevSlope)*ratio\n    return abs(avg_slope_change) < 0.1\n\ndef contourIsElliptical(contour, img):\n    center, (w, h), theta = cv2.fitEllipse(contour)\n    # print(theta)\n    # area = cv2.contourArea(contour)\n    print(contour.shape)\n    translated = contour - center\n    thetaR = np.radians(-theta)\n    c, s = np.cos(thetaR), np.sin(thetaR)\n    rotM = np.matrix(\"{} {}; {} {}\".format(c, -s, s, c))\n\n    rotated = (translated * rotM)\n    # print(rotated, end=\"\\n\\n\")\n    xcoords = rotated.T[0]\n    ycoords = rotated.T[1]\n    xmin = np.amin(xcoords)\n    xmax = np.amax(xcoords)\n    ymin = np.amin(ycoords)\n    ymax = np.amax(ycoords)\n\n    width = xmax - xmin\n    height = ymax - ymin\n    ellipse = (center, (width, height), theta)\n    # print(xmin, xmax, ymin, ymax)\n    # img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)\n    rot_rect = cv2.minAreaRect(contour)\n    box = cv2.boxPoints(rot_rect)\n    box = np.int0(box)\n    cv2.ellipse(img, ellipse, (0,255,0), thickness=3)\n    cv2.ellipse(img, (center, (w,h), theta), (0,0,255), thickness=3)\n    cv2.drawContours(img, [contour], 0, (255, 0, 0), thickness=3)\n    cv2.drawContours(img, [simpleContour(contour, 30)], 0, (0,0,255), thickness=3)\n    a = width / 2\n    b = height / 2\n    avg = 0\n    ratio = 1/len(contour)\n    for point in rotated:\n        x = point[0,0]\n        y = point[0,1]\n        avg += abs(1-((x**2/b**2) + (y**2/a**2))) * ratio\n        # avg = (x**2/a**2)+(y**2/b**2)\n    print(avg)\n    # return avg <=1\n    # rotated = np.dot(rotM, translated)\n    # print(contour)\n    # print(translated)\n    # print(rotated)\n    # print(\"\\n\")\n    # return avg\n    return img\n\ndef calculateCurvature(points):\n    # x1, y1 = points[0]\n    # x2, y2 = points[1]\n    # x3, y3 = points[2]\n    AB = np.linalg.norm(points[0]-points[1])\n    BC = np.linalg.norm(points[1]-points[2])\n    CA = np.linalg.norm(points[2]-points[0])\n    p = (AB+BC+CA)/2\n    area = np.sqrt(p*(p-AB)*(p-BC)*(p-CA))\n    # print(\"AB: \"+str(AB), end=\", \")\n    # print(\"BC: \"+str(BC), end=\", \")\n    # print(\"CA: \"+str(CA))\n    # print(\"area: \"+str(area))\n    if area == 0:\n        curvature = 0\n    else:\n        curvature = (AB*BC*CA)/(area*4)\n    return curvature\n\ndef getCenterline(contour):\n    x, y, w, h = cv2.boundingRect(contour)\n    temp = np.zeros((w, h), np.uint8)\n\n    cv2.drawContours(temp, [contour], 0, 255, -1, offset=(-x, -y))\n    temp = cv2.blur(temp, (8,8))\n    _, temp = cv2.threshold(temp, 255, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n    # cv2.drawContours(temp, [contour], 0, 0, 2)\n    return np.vstack((temp, dest))\n\ndef maskSuperbillPage(pdfStream, page):\n    pageToImage(pdfStream, page, \"./temp.jpg\")\n    img = cv2.imread(\"./temp.jpg\", 0)\n\n    circleMask = np.zeros(img.shape, np.uint8)\n    circles = getCircles(img)\n\n    # _, mask_contours, _ = cv2.findContours(circles, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n    # mask_hulls = [cv2.convexHull(cnt) for cnt in mask_contours]\n    # cv2.fillPoly(circleMask, mask_hulls, 255)\n    #\n    # template = cv2.imread(\"./codes.jpg\", 0)\n    # scaled_template = cv2.resize(template, img.shape[::-1], interpolation=cv2.INTER_LANCZOS4)\n    # bkg = np.full(circleMask.shape, 255, dtype=np.uint8)\n    # img_masked = cv2.bitwise_and(scaled_template, circleMask)\n    # bkg_masked = cv2.bitwise_and(bkg, np.bitwise_not(circleMask))\n\n    # return autocrop(np.bitwise_or(img_masked, bkg_masked), invert=True)\n    return circles\n\ndef parseSuperbill(img):\n    tool = pyocr.get_available_tools()[0]\n    lang = tool.get_available_languages()[0]\n\n    im = Image.fromarray(np.uint8(cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)))\n    im.save(\"./test.jpg\")\n\n    parsed = tool.image_to_string(\n        im,\n        lang=lang,\n        builder=pyocr.builders.TextBuilder()\n    )\n    print(parsed)\n    codeDict = {code:False for code in [\"99214\", \"99080\", \"99081\", \"97612\", \"\\D93\\D\", \"99354\", \"99355\", \"99358\"]}\n    for code in codeDict:\n        if len(re.findall(\"(\"+code+\")\", parsed)) >= 1:\n            codeDict[code] = True\n    for k,v in codeDict.items():\n        print(k+\":\", v)\n\ndef pageToImage(inputStream, pageNum, outputStream):\n    closeInput = False\n    closeOutput = False\n    if type(inputStream) is str:\n        closeInput = True\n        inputStream = open(inputStream, \"rb\")\n    if type(outputStream) is str:\n        closeOutput = True\n        outputStream = open(outputStream, \"wb+\")\n\n    pdf = PdfFileReader(inputStream)\n    page = io.BytesIO()\n    writer = PdfFileWriter()\n    writer.addPage(pdf.getPage(pageNum))\n    writer.write(page)\n    with WImage(blob=page.getvalue(), resolution=300) as img:\n        # img.resize(2535, 3300)\n        converted = img.make_blob('jpeg')\n\n    outputStream.write(converted)\n    if closeInput:\n        inputStream.close()\n    if closeOutput:\n        outputStream.close()\n\nif __name__ == \"__main__\":\n    # SB = getSuperbill(date(2016, 8, 31), \"CC\")\n    masked = maskSuperbillPage(\"./SB test.pdf\", 24)\n    # _, masked = cv2.threshold(masked, 255, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n    # masked = cv2.morphologyEx(masked, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)))\n\n    # parseSuperbill(masked)\n    plt.imshow(masked, cmap=\"gray\", interpolation='none'),plt.xticks([]),plt.yticks([])\n    plt.show()\n","sub_path":"pdf.py","file_name":"pdf.py","file_ext":"py","file_size_in_byte":14431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"477887855","text":"'''\n20. Valid Parentheses\n\nQuestion: \nGiven a string containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.\nAn input string is valid if:\n    - Open brackets must be closed by the same type of brackets.\n    - Open brackets must be closed in the correct order.\nNote that an empty string is also considered valid.\n\nSolution: \n    - Use a stack. \n        - Add if it is open brace. \n        - Pop if it is a close brace. \n    - Use a dictionary with open brace as keys and close brace as values\n    - Corner cases:\n        - Stack has to be empty at the end (Braces shouldnt be left open)\n        - Stack should not be empty when we are popping (Trying to close more braces than those that have been opened)\n    \nCreated on Apr 5, 2019\n\n@author: smaiya\n'''\n\nclass Solution:\n    def isValid(self, s: str) -> bool:\n        if s is None:\n            return True\n        mapping = {'(':')', '[':']', '{':'}'}\n        stack = []\n        for brace in s:\n            if brace in mapping:\n                stack.append(brace)\n            else:\n                if stack:\n                    last_brace = stack.pop()\n                else: # Closing more braces than those that have been opened\n                    return False\n                if mapping[last_brace] != brace:\n                    return False\n        if stack: # Have left braces opened\n            return False\n        return True\n    \ninp = '{[]}'\nop = Solution().isValid(inp)\nprint ('Is Valid Pranthesis? = ', op)","sub_path":"LeetCode_python/src/strings/isValid_paranthesis_E.py","file_name":"isValid_paranthesis_E.py","file_ext":"py","file_size_in_byte":1516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"651447988","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport sqlite3\n\nclass TemperatureDatabase:\n\n  def __init__(self, path_to_database):\n    self.conn = sqlite3.connect(path_to_database)\n    self.cur = self.conn.cursor()\n\n  def dataEntry(self, data, tableName):\n    with self.conn:\n      self.cur.execute(\"INSERT INTO \" + tableName + \" (dateTime, celsiusTemperature, farenheitTemperature) VALUES( ?, ?, ?)\", (data[0], data[1], data[2]))\n      self.conn.commit()\n      print(\"Current temperature is \" + str(data[1]) + \" F\")\n      print(\"Temperature logged\")\n","sub_path":"code/homeworks/hw2/temperatureDatabase.py","file_name":"temperatureDatabase.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"3408374","text":"from pick import pick\nfrom scripts import MongoDBInterface, DataImportMain, Config, createChart\nimport os\n\n\nOPTION_BACK = \"Back\"\n\ndef get_first_level(options):\n    result = []\n    if type(options) is str:\n        #This is the command itself.\n        return options\n    for key in options:\n        result.append(key)\n    return sorted(result)\n\ndef find_menu(options,selected):\n    result = None\n    if selected == []:\n        result = get_first_level(options)\n    else:\n        opt = options\n        for item in selected:\n\n            opt = opt[item]\n        result = get_first_level(opt)\n        if type(result) is list:\n            result.append(OPTION_BACK)\n    return result\n\ndef combine_selected(selected):\n    str = \"root\"\n    for item in selected:\n        str += \"->\"\n        str += item\n    return str\n\ndef get_choice(options,selected):\n    title = \"Current selection: \"+combine_selected(selected)\n    true_options = find_menu(options,selected)\n    if type(true_options) is str:\n        return selected\n    option,index = pick(true_options,title,indicator=\"->\")\n    result = selected\n    result.append(option)\n    return result\n\noptions_list = {\n    \"1.Read Instructions\":{\n        \"1.Introduction\":\"instruction\",\n        \"2.Dependencies\":\"dependencies\"\n    },\n    \"2.Setup data\":{\n        \"1.Download data\":\"download_data\",\n        \"2.Instructions on installing MongoDB\":\"install_db\",\n        \"3.Test DB connection\":\"test_db\",\n        \"4.Importing data into database\":\"import_db\"\n    },\n    \"3.Sample Classification\":{\n        \"1.Do Audio Classification\":\"do_audio\",\n        \"2.Do Pitch Classification(unused)\":\"do_pitch\"\n    },\n    \"4.Playable Classification\":{\n        \"1.Generate dataset for playable classification\":\"gen_pc_data\",\n        \"2.Train using our feedforward+summary model\":\"train_ff\",\n        \"3.Train using our LSTM model\":\"train_lstm\",\n        \"4.Delete trained models\":\"del_states\"\n    },\n    \"5.Evaluation\":{\n        \"1.Evaluate per-chart accuracy on Playable Classification(Feed forward)\":\"eval_ff\",\n        \"2.Evaluate per-chart accuracy on Playable Classification(LSTM)\": \"eval_lstm\",\n        \"3.Get reconstructed chart\":\"reconstruct\"\n    },\n    \"6.Generation (Experimental)\":{\n        \"1.Train step generation model\":\"train_cols\",\n        \"2.Evaluate step generation model\":\"eval_cols\",\n        \"3.Mix two charts\":\"mix\"\n    },\n    \"7.Exit\":\"exit\"\n}\n\ndef main():\n    selected = []\n    while True:\n        selected = get_choice(options_list,selected)\n        if selected[-1] == OPTION_BACK:\n            selected = selected[:-2]\n        if type(find_menu(options_list,selected)) is str:\n            # print(\"Final selection:\"+str(combine_selected(selected)))\n            # exit()\n            call_submodules(find_menu(options_list,selected))\n            input(\"Press Enter to continue...\")\n            selected = []\n\ndef call_submodules(command):\n    #clear the screen before executing a sub command\n    print(\"\\033[H\\033[J\")\n\n    if command == \"exit\":\n        exit()\n    elif command == \"instruction\":\n        print(\"This is the entry point for GenerationMania, a chart generator.\")\n    elif command == \"download_data\":\n        print(\"Create a new folder named 'rawdata' in the same directory of this file. In this folder create a new folder named `bof2011`.\")\n        print(\"Download data using the torrent provided in the root folder or use this magnet link, to rawdata/bof2011:\")\n        print(\"magnet:?xt=urn:btih:d133a79e03ff1c11c9512739542fe25a1cd2f03d&dn=%5BBMS%5D%5BPACK%5D%20THE%20BMS%20OF%20FIGHTERS%202011%20-%20Intersection%20of%20conflict%20-&tr=http%3A%2F%2Fwww.ceena.net%2Fannounce.php\")\n        print(\"After download is finished, extract every zip file so that they have their individual folders.\")\n    elif command == \"dependencies\":\n        f = open(\"DEPENDENCIES\", \"r\")\n        print(f.read())\n    elif command == \"install_db\":\n        print(\"Visit https://docs.mongodb.com/manual/installation/ for how to get MongoDB.\")\n    elif command == \"test_db\":\n        MongoDBInterface.test_database()\n    elif command == \"import_db\":\n        DataImportMain.import_data(\"rawdata/\")\n    elif command == \"do_audio\":\n        print(\"Wait for a while, importing tensorflow...\")\n        from scripts import AudioInterface\n        AudioInterface.do_audio()\n    elif command == \"do_pitch\":\n        print(\"Wait for a while, importing tensorflow...\")\n        from scripts import AudioInterface\n        AudioInterface.do_audio(type=\"pitch\")\n    elif command == \"gen_pc_data\":\n        print(\"Wait for a while, importing pytorch...\")\n        from scripts import DBtoLearn as DL\n        outfile = Config.config.training_file\n        outfile_pc = Config.config.training_file_per_chart\n        DL.preparePlayableWithLookbackFile(\n            dict(outfile=outfile, outfile_per_chart=outfile_pc, extra_info='yeah', per_note_diff_osu='yeah')\n            , algorithm=\"osu\")\n    elif command == \"train_ff\":\n        print(\"Wait for a while, importing pytorch...\")\n        from scripts import model_feedforward as ff\n        ff.train_model()\n    elif command == \"eval_ff\":\n        print(\"Wait for a while, importing pytorch...\")\n        from scripts import generatePerChartResult as gp\n        gp.eval_all()\n    elif command == \"train_lstm\":\n        print(\"Wait for a while, importing pytorch...\")\n        from scripts import model_lstm as lstm\n        lstm.train()\n    elif command == \"eval_lstm\":\n        print(\"Wait for a while, importing pytorch...\")\n        from scripts import model_lstm as lstm\n        lstm.eval()\n    elif command == \"del_states\":\n        print(\"Deleting states...\")\n        try:\n            os.remove(Config.config.training_state_feedforward)\n        except FileNotFoundError:\n            print(\"State for feedforward network missing.\")\n        try:\n            os.remove(Config.config.training_state_LSTM)\n        except FileNotFoundError:\n            print(\"State for LSTM network missing.\")\n        # from scripts import model_lstm as lstm\n        # lstm.train()\n        print(\"Done.\")\n    elif command == \"train_cols\":\n        print(\"Wait for a while, importing pytorch...\")\n        from scripts import model_ff_cols as ffc\n        ffc.train_model()\n    elif command == \"eval_cols\":\n        print(\"Wait for a while, importing pytorch...\")\n        from scripts import generatePerChartResult_column as gc\n        gc.eval_all()\n    elif command == \"mix\":\n        print(\"Wait for a while, importing pytorch & tensorflow...\")\n        from scripts import createChart as cc\n        aud = input(\"Mix Base: Enter path to a BMS where its audio content would be used: \")\n        sty = input(\"Mix With: Enter path to a BMS where its style would be used: \")\n        result = cc.mix_two_charts(audio_provider=aud,style_provider=sty)\n        cc.create_bmson_package(bmson=result,audio_source=aud)\n\n\n    else:\n        print(\"%s:Coming soon!\"%command)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"136540881","text":"from pronomial.utils import predict_gender, pos_tag, word_tokenize, is_plural\n\n\nclass PronomialCoreferenceSolver:\n    @staticmethod\n    def solve_corefs(sentence, lang=\"en\"):\n        # universal tagset, langs can override these depending on pos tagger\n        # model\n        PRONOUN_TAG = ['PRON']\n        NOUN_TAG = ['NOUN']\n        JJ_TAG = ['ADJ']\n        PLURAL_NOUN_TAG = ['NOUN']\n        SUBJ_TAG = ['NOUN']\n        WITH = WITH_FOLLOWUP = THAT = THAT_FOLLOWUP = []\n        NEUTRAL_WORDS = []\n        SUBJ_INDICATORS = []\n        NAME_JOINER = \"+\"  # symbol used to merge Nouns to replace plurals\n\n        if lang.startswith(\"en\"):\n            from pronomial.lang.en import NOUN_TAG_EN, PLURAL_NOUN_TAG_EN, \\\n                PRONOUNS_EN, PRONOUN_TAG_EN, SUBJ_TAG_EN, JJ_TAG_EN, WITH_EN,\\\n                GENDERED_WORDS_EN, WITH_FOLLOWUP_EN, THAT_EN, \\\n                THAT_FOLLOWUP_EN, NEUTRAL_WORDS_EN, SUBJ_INDICATORS_EN, NAME_JOINER_EN\n            GENDERED_WORDS = GENDERED_WORDS_EN\n            NOUN_TAG = NOUN_TAG_EN\n            SUBJ_TAG = SUBJ_TAG_EN\n            PRONOUN_TAG = PRONOUN_TAG_EN\n            PRONOUNS = PRONOUNS_EN\n            PLURAL_NOUN_TAG = PLURAL_NOUN_TAG_EN\n            JJ_TAG = JJ_TAG_EN\n            WITH = WITH_EN\n            WITH_FOLLOWUP = WITH_FOLLOWUP_EN\n            THAT = THAT_EN\n            THAT_FOLLOWUP = THAT_FOLLOWUP_EN\n            NEUTRAL_WORDS = NEUTRAL_WORDS_EN\n            SUBJ_INDICATORS = SUBJ_INDICATORS_EN\n            NAME_JOINER = NAME_JOINER_EN\n        elif lang.startswith(\"pt\"):\n            from pronomial.lang.pt import PRONOUNS_PT, GENDERED_WORDS_PT\n            GENDERED_WORDS = GENDERED_WORDS_PT\n            PRONOUNS = PRONOUNS_PT\n        elif lang.startswith(\"es\"):\n            from pronomial.lang.es import PRONOUNS_ES, GENDERED_WORDS_ES\n            GENDERED_WORDS = GENDERED_WORDS_ES\n            PRONOUNS = PRONOUNS_ES\n        elif lang.startswith(\"ca\"):\n            from pronomial.lang.ca import PRONOUNS_CA, GENDERED_WORDS_CA\n            GENDERED_WORDS = GENDERED_WORDS_CA\n            PRONOUNS = PRONOUNS_CA\n        else:\n            raise NotImplementedError\n\n        tags = pos_tag(sentence, lang=lang)\n        pron_list = [p for k, p in PRONOUNS.items()]\n        flatten = lambda l: [item for sublist in l for item in sublist]\n        pron_list = flatten(pron_list)\n\n        prev_names = {\n            \"male\": [],\n            \"female\": [],\n            \"first\": [],\n            \"neutral\": [],\n            \"plural\": [],\n            \"subject\": [],\n            \"verb_subject\": []\n        }\n        candidates = []\n\n        for idx, (w, t) in enumerate(tags):\n            next_w, next_t = tags[idx + 1] if idx < len(tags) - 1 else (\"\", \"\")\n            prev_w, prev_t = tags[idx - 1] if idx > 0 else (\"\", \"\")\n            idz = -1\n            if prev_w.lower() in WITH and w.lower() in WITH_FOLLOWUP:\n                idz = -2\n            elif prev_w.lower() in THAT and w.lower() in THAT_FOLLOWUP:\n                idz = -2\n\n            if t in NOUN_TAG:\n                if prev_w in NEUTRAL_WORDS:\n                    prev_names[\"neutral\"].append(w)\n                else:\n                    gender = predict_gender(w, prev_w, lang=lang)\n                    if w in PRONOUNS[\"female\"] or\\\n                            w.lower() in GENDERED_WORDS[\"female\"]:\n                        prev_names[\"female\"].append(w)\n                    elif w in PRONOUNS[\"male\"] or \\\n                            w.lower() in GENDERED_WORDS[\"male\"]:\n                        prev_names[\"male\"].append(w)\n                    elif w[0].isupper() or prev_t in [\"DET\"]:\n                        prev_names[gender].append(w)\n                    prev_names[\"neutral\"].append(w)\n                    prev_names[\"subject\"].append(w)\n\n                if next_t.startswith(\"V\") and not prev_t.startswith(\"V\"):\n                    prev_names[\"verb_subject\"] = w\n\n            elif t in SUBJ_TAG:\n                prev_names[\"subject\"].append(w)\n                gender = predict_gender(w, prev_w, lang=lang)\n                prev_names[\"neutral\"].append(w)\n                if gender == \"female\":\n                    prev_names[\"female\"].append(w)\n                if gender == \"male\":\n                    prev_names[\"male\"].append(w)\n            elif (t in PRONOUN_TAG and w.lower() in pron_list) or any(w in items\n                                         for k, items in PRONOUNS.items()):\n                w = w.lower()\n                if w in PRONOUNS[\"male\"]:\n                    # give preference to verb subjects\n                    n = prev_names[\"male\"]\n                    if (w in SUBJ_INDICATORS or prev_w in SUBJ_INDICATORS) and \\\n                            prev_names[\"verb_subject\"]:\n                        n = [_ for _ in prev_names[\"male\"]\n                             if _ in prev_names[\"verb_subject\"]] or n\n                    if n:\n                        if abs(idz) > len(n):\n                            idz = 0\n                        candidates.append((idx, w, n[idz]))\n                    elif prev_names[\"subject\"]:\n                        if abs(idz) > len(prev_names[\"subject\"]):\n                            idz = 0\n                        candidates.append((idx, w, prev_names[\"subject\"][idz]))\n                elif w in PRONOUNS[\"female\"]:\n                    # give preference to verb subjects\n                    n = prev_names[\"female\"]\n                    if (w in SUBJ_INDICATORS or prev_w in SUBJ_INDICATORS) and prev_names[\"verb_subject\"]:\n                        n = [_ for _ in prev_names[\"female\"]\n                             if _ in prev_names[\"verb_subject\"]] or n\n                    if n:\n                        if abs(idz) > len(n):\n                            idz = 0\n                        candidates.append((idx, w, n[idz]))\n                    elif prev_names[\"subject\"]:\n                        if abs(idz) > len(prev_names[\"subject\"]):\n                            idz = 0\n                        candidates.append((idx, w, prev_names[\"subject\"][idz]))\n                elif w in PRONOUNS[\"neutral\"]:\n                    # give preference to verb subjects\n                    n = prev_names[\"neutral\"]\n                    if prev_names[\"verb_subject\"]:\n                        n = [_ for _ in prev_names[\"neutral\"]\n                             if _ in prev_names[\"verb_subject\"]] or n\n                    if n:\n                        if abs(idz) > len(n):\n                            idz = 0\n                        candidates.append((idx, w, n[idz]))\n                    elif prev_names[\"subject\"]:\n                        if abs(idz) > len(prev_names[\"subject\"]):\n                            idz = 0\n                        candidates.append((idx, w, prev_names[\"subject\"][idz]))\n                elif w in PRONOUNS[\"plural\"]:\n                    plural_subjs = [_ for _ in prev_names[\"subject\"] if\n                                    is_plural(_, lang)]\n                    names = prev_names[\"male\"] + prev_names[\"female\"]\n                    if prev_names[\"plural\"]:\n                        if abs(idz) > len(prev_names[\"plural\"]):\n                            idz = 0\n                        candidates.append((idx, w, prev_names[\"plural\"][idz]))\n                    elif plural_subjs:\n                        if abs(idz) > len(plural_subjs):\n                            idz = 0\n                        candidates.append((idx, w, plural_subjs[idz]))\n                    elif t in [\"WP\"] and prev_names[\"subject\"]:\n                        if abs(idz) > len(prev_names[\"subject\"]):\n                            idz = 0\n                        candidates.append((idx, w, prev_names[\"subject\"][idz]))\n                    elif len(names) == 2:\n                        merged_names = NAME_JOINER.join([_ for _ in names if\n                                                     _[0].isupper()])\n                        candidates.append((idx, w, merged_names))\n                else:\n                    for k, v in PRONOUNS.items():\n                        if prev_names[k] and w in v:\n                            if abs(idz) > len(prev_names[k]):\n                                idz = 0\n                            candidates.append((idx, w, prev_names[k][idz]))\n                    else:\n                        if prev_names[\"subject\"] and w not in PRONOUNS[\"first\"]:\n                            if abs(idz) > len(prev_names[\"subject\"]):\n                                idz = 0\n                            candidates.append(\n                                (idx, w, prev_names[\"subject\"][idz]))\n            elif t in PLURAL_NOUN_TAG:\n                prev_names[\"plural\"].append(w)\n                if w[0].isupper():\n                    gender = predict_gender(w, prev_w, lang=lang)\n                    if not prev_names[gender]:\n                        prev_names[gender].append(w)\n            elif t in JJ_TAG:  # common tagger error\n                if w[0].isupper():\n                    gender = predict_gender(w, prev_w, lang=lang)\n                    if not prev_names[gender]:\n                        prev_names[gender].append(w)\n                if not prev_names[\"neutral\"]:\n                    prev_names[\"neutral\"].append(w)\n                if not prev_names[\"subject\"]:\n                    prev_names[\"subject\"].append(w)\n        return candidates\n\n    @classmethod\n    def replace_corefs(cls, text, lang=\"en\"):\n        tokens=word_tokenize(text)\n        for idx, _, w in cls.solve_corefs(text, lang=lang):\n            tokens[idx] = w\n        return \" \".join(tokens)\n\n\ndef replace_corefs(text, lang=\"en\"):\n    return PronomialCoreferenceSolver.replace_corefs(text, lang=lang)\n","sub_path":"pronomial/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":9654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"3138812","text":"class Animal():\n\n    def __init__(self, name, kind):\n        self.name = name\n        self.kind = kind\n\n    def fly(self):\n        if self.kind == bird:\n            return \"I am flying\"\n        else:\n            return \"I can not fly\"\n\n\n    def walk(self):\n        if self.kind == bird:\n            return \"I am walking\"\n        else:\n            return \"I can not walk\"\n\n\n    def swim(self):\n        if self.kind != bird:\n            return \"I am swimming\"\n        else:\n            return \"I can not swim\"\n\n\n\nbird = Animal(\"louro\", \"papagaio\")\ncat = Animal(\"lilica\", \"cat\")\n\nprint(bird.name)\n\n#print(bird.walk())\n#print(bird.fly())\n#print(bird.swim())\n","sub_path":"python/animals_class.py","file_name":"animals_class.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"264010325","text":"from bs4 import BeautifulSoup\r\nimport tkinter as tk \r\nfrom googlesearch import search\r\nimport threading\r\nimport time\r\nimport urllib\r\n\r\n\r\nroot = tk.Tk()\r\nphoto = tk.PhotoImage(file='Filosoft-01.png')\r\ndef retrieve_input(textBox):\r\n    time.sleep(2)\r\n    inputValue=textBox.get(\"1.0\",\"end-1c\")\r\n    Buscar(inputValue)\r\n\r\nroot.title('Buscador de Torrents')\r\nroot.geometry(\"400x400\")\r\ntk.Label(root, text=\"Nombre del torrent:\").grid(row=0)\r\ntextBox= tk.Text(root, height=1, width=10)\r\ntextBox.grid(row=0, column=8)\r\n\r\ntk.Button(root, text='Buscar', command= lambda: Hilos() ).grid(row=0, column=12)\r\n\r\n\r\ndef Hilos():\r\n    Res = threading.Thread(target=retrieve_input, args=(textBox,))\r\n    Res.start()\r\n\r\n\r\ndef Buscar(Enter):\r\n    num = 0\r\n    for url in search(Enter + \"Torrent gratis\", stop=5):\r\n        time.sleep(2)\r\n        content = urllib.request.urlopen(url).read()\r\n        contentHTML = BeautifulSoup(content, \"html.parser\")\r\n        for link in contentHTML.select('a[href*=\".torrent\"]'):\r\n            href = BeautifulSoup(str(link), \"html.parser\")\r\n            downloadLink = href.a['href']\r\n            Descargar(downloadLink)\r\n\r\ndef Descargar(url):\r\n    url = \"https:\" + url\r\n    Directorio = \"C:/Users/AntonioSalaices/Downloads/Torrents\"\r\n    nameRar = \"Torrent.torrent\"\r\n    urllib.request.urlretrieve(url, filename= Directorio + \"/\" + nameRar)\r\n\r\nroot.mainloop()\r\n\r\n\r\n    \r\n","sub_path":"Parser.py","file_name":"Parser.py","file_ext":"py","file_size_in_byte":1386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"481876106","text":"import tkinter as tk\nimport numpy as np\nfrom tkinter import *\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nfrom matplotlib.figure import Figure\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg\nimport matplotlib.animation as animation\n\n\n#define font normIntype for GUI\nLARGE_FONT = (\"Verdana\", 12)\n\n\ndef animate(i, genome, numX, numY, f, a):\n    pullData = open(\"genome_info.txt\", \"r\").read()\n    dataList = pullData.split(\"\\n\")\n    foundConnections = False\n    nodeCounter = 0\n    connectionCounter = 0\n\n    for line in dataList:\n        if(not foundConnections and \"Activation\" in line):\n            activationValue = int(line[12:])\n            #print(activationValue)\n            genome.nodeList[nodeCounter].activationKey = activationValue\n            nodeCounter += 1\n        if(foundConnections and \"Weight\" in line):\n            weightValue = float(line[8:])\n            #print(weightValue)\n            genome.connectionList[connectionCounter].weight  = weightValue\n            #now must update weight in nodeList\n            n_out = genome.nodeList[genome.connectionList[connectionCounter].nodeOut]\n            n_in = genome.nodeList[genome.connectionList[connectionCounter].nodeIn]\n            #searches connecting nodes and updates weight for correct input node\n            for node_data in n_out.connectingNodes:\n                if(node_data[0] == n_in):\n                    node_data[1] = weightValue\n            connectionCounter += 1\n\n        elif(\"CONNECTIONS\" in line):\n            foundConnections = True\n    # graphs genotype each time it is updated so changes can be observed\n    genome.graphGenotype()\n    \"\"\"CREATES LIST OF INPUTS TO RUN NETWORK\"\"\"\n    inputs = []\n    #creates input values for CPPN for spring optimization\n    for x in range(1, numX + 1):\n        inputs.append(x)\n\n    tmp = np.array(inputs, copy = True)\n    MEAN = np.mean(tmp)\n    STD = np.std(tmp)\n\n    #list of normalized inputs\n    normIn = [] \n\n    #creates input list with normalized vectors, values of input are of form (x,y) in a list of tuples\n    for y in range(0,numY):\n        for x in range(0,numX):\n            tup = ((x - MEAN)/STD, (y-MEAN)/STD, 1)\n            normIn.append(tup)\n    \"\"\"*******************\"\"\"\n\n    output_list = []\n    for x in range(len(normIn)):\n        output_list.append(genome.evaluate([normIn[x][0],normIn[x][1], normIn[x][2]])[0])\n\n    #creates numpy array and resizes it for graphing\n    g = np.array(output_list, copy=True)\n\n    #want to clear data each time so that the points do not add up every time they are changed\n    a.clear()\n\n    g = np.reshape(g, (numX,numY))\n    a.imshow(g, cmap='Greys')\n    \n        \n#creates class for starting page\n#inherits from Frame class\nclass GUIFrame(tk.Frame):\n    def __init__(self, f):\n        super().__init__()\n        self.initUI(f)\n\n    def initUI(self, f):\n        label = Label(text = \"Graph Page\", font=LARGE_FONT)\n        #easiest to use pack when you're only including a few things in the window\n        label.pack(pady=10,padx=10)\n        self.master.title(\"CPPN Playground\")\n\n        quit_button = Button(self.master, text =\"Quit\", command = self._quit)\n        quit_button.pack(side = BOTTOM)\n\n        canvas = FigureCanvasTkAgg(f,self.master)\n        canvas.show()\n        canvas.get_tk_widget().pack()\n\n    #root can be accessed with master, this is used to exit out of page\n    def _quit(self):\n        self.master.quit()\n        self.master.destroy()\n\n\ndef main(g_param, numX, numY, f, a):\n    #initialize main window\n    root = Tk()\n    root.geometry(\"500x300+200+100\")\n    #initialize frame\n    app = GUIFrame(f)\n    #link animation to figure, pass in the animation function, and specify an update interval in milliseconds\n    ani = animation.FuncAnimation(f,lambda x: animate(1000, g_param, numX, numY, f, a))\n    root.mainloop()\n\n\n\nif __name__ == '__main__':\n    main()","sub_path":"tk_cppn.py","file_name":"tk_cppn.py","file_ext":"py","file_size_in_byte":3898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"260623085","text":"from flask import flash\nfrom markdown import markdown\n\n\ndef to_html(raw):\n    html = markdown(raw, output_format='html',\n                    extensions=['markdown.extensions.extra',\n                                'markdown.extensions.codehilite'])\n\n    return html\n\n\ndef flash_errors(form):\n    for field, errors in form.errors.items():\n        for error in errors:\n            flash(u\"Error in the %s field - %s\" % (\n                getattr(form, field).label.text, error)\n                  )\n","sub_path":"quickchat/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"614482465","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nAssign an hexadecimal RGB color to a Trackvis TRK tractogram.\nThe hexadecimal RGB color should be formatted as 0xRRGGBB or\n\"#RRGGBB\".\n\"\"\"\n\nimport argparse\n\nfrom dipy.io.stateful_tractogram import StatefulTractogram\nfrom dipy.io.streamline import save_tractogram\nimport numpy as np\n\nfrom scilpy.io.streamlines import load_tractogram_with_reference\nfrom scilpy.io.utils import (assert_inputs_exist,\n                             assert_outputs_exist,\n                             add_overwrite_arg,\n                             add_reference_arg)\n\n\ndef _build_arg_parser():\n    p = argparse.ArgumentParser(\n        description=__doc__,\n        formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n    p.add_argument('in_tractogram',\n                   help='Tractogram.')\n\n    p.add_argument('out_tractogram',\n                   help='Colored TRK tractogram.')\n    p.add_argument('color',\n                   help='Can be either hexadecimal (ie. \"#RRGGBB\" '\n                        'or 0xRRGGBB).')\n\n    add_reference_arg(p)\n    add_overwrite_arg(p)\n\n    return p\n\n\ndef main():\n    parser = _build_arg_parser()\n    args = parser.parse_args()\n\n    assert_inputs_exist(parser, args.in_tractogram)\n    assert_outputs_exist(parser, args, args.out_tractogram)\n\n    if not args.out_tractogram.endswith('.trk'):\n        parser.error('Output file needs to end with .trk.')\n\n    if len(args.color) == 7:\n        args.color = '0x' + args.color.lstrip('#')\n\n    if len(args.color) == 8:\n        color_int = int(args.color, 0)\n        red = color_int >> 16\n        green = (color_int & 0x00FF00) >> 8\n        blue = color_int & 0x0000FF\n    else:\n        parser.error('Hexadecimal RGB color should be formatted as \"#RRGGBB\"'\n                     ' or 0xRRGGBB.')\n\n    sft = load_tractogram_with_reference(parser, args, args.in_tractogram)\n\n    sft.data_per_point[\"color\"] = [np.tile([red, green, blue],\n                                           (len(i), 1)) for i in sft.streamlines]\n\n    sft = StatefulTractogram.from_sft(sft.streamlines, sft,\n                                      data_per_point=sft.data_per_point)\n\n    save_tractogram(sft, args.out_tractogram)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"scripts/scil_assign_color_to_trk.py","file_name":"scil_assign_color_to_trk.py","file_ext":"py","file_size_in_byte":2245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"531012365","text":"from src.dspn.DSPN_copy import hungarian_loss\nfrom src.dspn.FSPool import FSPool\n\nimport scipy.optimize\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport random\nimport math\nimport numpy as np\nfrom torch.utils.data import Dataset\nimport pandas as pd\nimport matplotlib\nfrom matplotlib import pyplot as plt\n\nimport pytorch_lightning as pl\n\nclass SetEncoder(pl.LightningModule):\n    def __init__(self, env_len=6, obj_in_len=9, env_hidden_dim=64, obj_hidden_dim=256):\n        super().__init__()\n        self.save_hyperparameters()\n\n        self.env_hidden_dim = env_hidden_dim\n        self.obj_hidden_dim = obj_hidden_dim\n        self.obj_in_len = obj_in_len\n        self.env_len = env_len\n\n        self.dropout = nn.Dropout(p=0.1)\n\n        self.obj_embed = nn.Sequential(\n            nn.Linear(obj_in_len, 128),\n            self.dropout,\n        )\n\n        if env_len == 0:\n            hidden_dim = obj_hidden_dim\n        else:\n            self.env_embed = nn.Sequential(\n                nn.Linear(env_len, env_hidden_dim),\n                nn.ReLU()\n            )\n            hidden_dim = obj_hidden_dim + env_hidden_dim\n\n        self.encoder = nn.Sequential(\n            nn.Linear(192, hidden_dim),\n            nn.ReLU()\n        )\n\n    def forward(self, objs, env=None):\n        # Calculate the set embedding\n        objs = objs.view((1, -1, self.obj_in_len))\n        h_objs = self.obj_embed(objs)  # Shape: [BS, N, 64]\n        h_set_vector, _ = torch.max(h_objs, dim=1)\n\n        # Calculate environment embedding\n        if env is not None:\n            h_env_vector = self.env_embed(env)\n\n            # Concatenate\n            h = torch.cat((h_set_vector, h_env_vector), dim=1)\n        else:\n            h = h_set_vector\n\n        h = self.encoder(h)\n        return h","sub_path":"src/dspn/SetEncoder.py","file_name":"SetEncoder.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"76008243","text":"# Author:zhang\n# -*- coding:utf-8 -*-\nimport hashlib\nm=hashlib.md5()\nn=hashlib.md5()\nm.update(b\"test\")  #你好呀\nm.update(b\"zhang\")\nprint(m.hexdigest())\nprint(n.update(b\"testzhang\"))\nprint(n.hexdigest())","sub_path":"review_code/day8/hash.py","file_name":"hash.py","file_ext":"py","file_size_in_byte":205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"187542097","text":"#!/usr/bin/env python3\n# TODO: _NET_WM_STRUT_PARTIAL\n# t = namedtuple(\"STRUT\", \"left right top bottom left_start_y left_end_y right_start_y right_end_y top_start_x top_end_x bottom_start_x bottom_end_x\".split())\n# t(0, 0, 0, 18, 0, 0, 0, 0, 0, 0, 0, 1919)\n# WM_CLASS(STRING) = \"dzen2\", \"dzen\"\n\nfrom useful.log import Log\n\nfrom utils import run_\nfrom wm import WM\nfrom desktop import Desktop\nfrom window import Window\nfrom myosd import OSD\n# from textgui import gui\nimport asyncio\nimport os.path\nimport sys\n\n\nfrom useful.prettybt import prettybt\nsys.excepthook = prettybt\n\n# USEFUL ALIASES\nup, down, left, right = 'Up', 'Down', 'Left', 'Right'\nwin = fail = 'mod4'\nctrl = control = 'control'\nshift = 'shift'\ncaps = 'Caps_Lock'\nalt = 'mod1'\ntab = 'Tab'\nMouseL = 1\nMouseC = 2\nMouseR = 3\nlog = Log(\"USER HOOKS\")\nosd = OSD()\n\nmod = win\n\n# PRE-INIT\n# switch to english just in case\nrun_(\"setxkbmap -layout en\")\n\n# create event loop and setup text GUI\nloop = asyncio.new_event_loop()\n# logwidget = gui(loop=loop)\n# Log.file = logwidget\n\n\n# INIT\nnum_desktops = 4\ndesktops = [Desktop(id=i, name=str(i + 1)) for i in range(num_desktops)]\nwm = WM(desktops=desktops, loop=loop)\n\n\n# MOUSE STUFF\norig_pos = None\norig_geometry = None\n\n# move\n\n\n@wm.hook(wm.grab_mouse([mod], MouseL))\ndef on_mouse_move(evhandler, evtype, xcb_ev):\n    global orig_pos\n    global orig_geometry\n    cur_pos = xcb_ev.root_x, xcb_ev.root_y\n    window = wm.cur_desktop.cur_focus\n    if evtype == \"ButtonPress\":\n        orig_pos = cur_pos\n        orig_geometry = window.geometry\n        log.on_mouse_move.debug(\n            \"orig_pos: {}, orig_geom: {}\".format(orig_pos, orig_geometry))\n    elif evtype == \"ButtonRelease\":\n        orig_pos = None\n        orig_geometry = None\n    elif evtype == \"MotionNotify\":\n        dx = cur_pos[0] - orig_pos[0]\n        dy = cur_pos[1] - orig_pos[1]\n        x = max(0, orig_geometry[0] + dx)\n        y = max(0, orig_geometry[1] + dy)\n        window.move(x=x, y=y)\n\n# resize\n\n\n@wm.hook(wm.grab_mouse([mod, alt], MouseL))\ndef on_mouse_resize(evhandler, evtype, xcb_ev):\n    global orig_pos\n    global orig_geometry\n    cur_pos = xcb_ev.root_x, xcb_ev.root_y\n    window = wm.cur_desktop.cur_focus\n    if evtype == \"ButtonPress\":\n        orig_pos = cur_pos\n        orig_geometry = window.geometry\n        log.on_mouse_resize.debug(\n            \"orig_pos: {}, orig_geom: {}\".format(orig_pos, orig_geometry))\n    elif evtype == \"ButtonRelease\":\n        orig_pos = None\n        orig_geometry = None\n    elif evtype == \"MotionNotify\":\n        dw = cur_pos[0] - orig_pos[0]\n        dh = cur_pos[1] - orig_pos[1]\n        width = max(0, orig_geometry[2] + dw)\n        height = max(0, orig_geometry[3] + dh)\n        window.resize(x=width, y=height)\n\n\n# DESKTOP SWITCHING\ncur_desk_idx = 0\n\nfor i in range(1, num_desktops + 1):\n    @wm.hook(wm.grab_key([mod], str(i)))\n    def switch_to(event, i=i):\n        cur_desk_idx = i - 1\n        wm.switch_to(cur_desk_idx)\n        osd.write(cur_desk_idx)\n\n    @wm.hook(wm.grab_key([shift, mod], str(i)))\n    def teleport_window(event, i=i):\n        window = wm.cur_desktop.cur_focus\n        if not window:\n            log.teleport_window.error(\"window is NONE!!!\")\n            return\n        wm.relocate_to(window, desktops[i - 1])\n\n\n@wm.hook(wm.grab_key([mod], right))\ndef next_desktop(event):\n    global cur_desk_idx\n    cur_desk_idx += 1\n    cur_desk_idx %= num_desktops\n    wm.switch_to(cur_desk_idx)\n    osd.write(cur_desk_idx)\n\n\n@wm.hook(wm.grab_key([mod], left))\ndef prev_desktop(event):\n    global cur_desk_idx\n    cur_desk_idx -= 1\n    cur_desk_idx %= num_desktops\n    wm.switch_to(cur_desk_idx)\n    osd.write(cur_desk_idx)\n\n\n# CUSTOMIZE WINDOWS\n@wm.hook(\"new_window\")\ndef on_window_create(event, window: Window):\n    if window.name in [\"dzen title\", \"XOSD\", \"panel\"]:\n        window.sticky = True\n        window.can_focus = False\n        window.above_all = True\n        # log.critical(\"PANEL!\")\n    if window.type in [\"dropdown\", \"menu\", \"notification\", \"tooltip\"]:\n        window.can_focus = False\nprev_handler = None\n\n\n@wm.hook(\"new_window\")\ndef print_new_window_props(event, window: Window):\n    run_(\"xprop -id %s\" % window.wid)\n\n\n@wm.hook('window_unmap')\ndef on_window_unmap(event, window):\n    window = find_next()\n    if not window:\n        return\n    wm.focus_on(window)\n\n# TODO: dirty hack, WM should not have unknown windows\n\n\n@wm.hook(\"unknown_window\")\ndef unknown_window(event, wid):\n    run_(\"xprop -id %s\" % wid)\n    wm.on_new_window(wid)\n\n\n@wm.hook(\"window_enter\")\ndef on_window_enter(event, window):\n    global prev_handler\n\n    if prev_handler:\n        prev_handler.cancel()\n\n    if window == wm.root:\n        return\n\n    if window == wm.cur_desktop.cur_focus:\n        log.notice(\"we do not focus on the same window {}\".format(window))\n        return\n\n    log._switch.debug(\"delaying activation of %s\" % window)\n\n    def _switch(window=window):\n        log._switch.debug(\"okay, it's time to switch to %s\" % window)\n        wm.focus_on(window)\n        window.rise()\n    prev_handler = loop.call_later(0.15, _switch)\n\n\n# TODO: get rid of this function in favor of wm.focus_on()\ndef switch_focus(event, window, warp=False):\n    # do not switch focus when moving over root window\n    if window == wm.root:\n        return\n    wm.focus_on(window)\n    osd.write(window)\n\n\ndef get_edges(windows, vert=False):\n    vstart, vstop, hstart, hstop = [], [], [], []\n    for window in windows:\n        if not window.mapped:\n            continue\n        x, y, w, h = window.geometry\n        vstart.append(x)\n        vstop.append(x + w)\n        hstart.append(y)\n        hstop.append(y + h)\n    return vstart, vstop, hstart, hstop\n\n\ndef snap_to(cur, step, edges):\n    edges = sorted(edges)\n    for edge in edges:\n        if min(cur, cur + step) < edge < max(cur, cur + step):\n            return edge\n    return cur + step\n\n# TODO: rename cur_focus to focus, cur_desktop to desktop\n\n\ndef smart_snap(attr, step):\n    windows = wm.cur_desktop.windows\n    window = wm.cur_desktop.cur_focus\n    x, y, w, h = window.geometry\n    vstart, vstop, hstart, hstop = get_edges(\n        w for w in windows if w != window and w.mapped)\n    if attr == 'width':\n        cur = x + w\n        snap = snap_to(cur, step, vstart + vstop)\n        window.set_geometry(**{attr: (snap - x)})\n    elif attr == 'height':\n        cur = y + h\n        snap = snap_to(cur, step, hstart + hstop)\n        window.set_geometry(**{attr: (snap - y)})\n    elif attr == 'x':\n        cur = x\n        snap = snap_to(cur, step, vstart + vstop)\n        window.set_geometry(x=snap)\n    return snap\n\n\n# RESIZE\nstep = 200\n\n\n@wm.hook(wm.grab_key([mod, alt], right))\ndef expand_width(event):\n    smart_snap('width', step)\n\n\n@wm.hook(wm.grab_key([mod, alt], left))\ndef shrink_width(event):\n    smart_snap('width', -step)\n\n\n@wm.hook(wm.grab_key([mod, alt], up))\ndef expand_height(event):\n    # wm.cur_desktop.cur_focus.resize(dy=-step).warp()\n    smart_snap('height', -step)\n\n\n@wm.hook(wm.grab_key([mod, alt], down))\ndef shrink_height(event):\n    # wm.cur_desktop.cur_focus.resize(dy=step).warp()\n    smart_snap('height', step)\n\n\n@wm.hook(wm.grab_key([mod], 'm'))\ndef maximize(event):\n    wm.cur_desktop.cur_focus.toggle_maximize()\n\n\n# MOVE\n\n@wm.hook(wm.grab_key([alt], right))\ndef move_right(event):\n    # wm.cur_desktop.cur_focus.move(dx=step).warp()\n    smart_snap('x', step)\n\n\n@wm.hook(wm.grab_key([alt], left))\ndef move_left(event):\n    # wm.cur_desktop.cur_focus.move(dx=-step).warp()\n    smart_snap('x', -step)\n    # wm.mouse.move(dx=-snap)\n    # wm.xsync()\n\n\n@wm.hook(wm.grab_key([alt], up))\ndef move_up(event):\n    # step = 5\n    wm.cur_desktop.cur_focus.move(dy=-step).warp()\n\n\n@wm.hook(wm.grab_key([alt], down))\ndef move_down(event):\n    # step = 5\n    wm.cur_desktop.cur_focus.move(dy=step).warp()\n\n\n# FOCUS\ndef cycle_from(l, pos):\n    from itertools import chain\n    for e in chain(l[pos:], l[:pos]):\n        yield e\n\n\ndef find_next(inc=1):\n    desktop = wm.cur_desktop\n    windows = desktop.windows  # type: List[Window]\n    cur = desktop.cur_focus\n    idx = windows.index(cur)\n    tot = len(windows)\n    for i in range(tot):\n        idx -= inc\n        window = windows[idx % tot]\n        if not window.can_focus:\n            continue\n        if window.skip:\n            continue\n        if not window.mapped:\n            continue\n        return window\n    return None\n\n\n@wm.hook(wm.grab_key([alt], tab))\ndef next_window(event):\n    window = find_next()\n    if window:\n        wm.focus_on(window, warp=False)\n        window.rise()\n\n\n@wm.hook(wm.grab_key([mod], 'n'))\ndef prev_window(event):\n    window = find_next(-1)\n    if window:\n        wm.focus_on(window, warp=False)\n        window.rise()\n\n\n# SPAWN\n# terminals, etc\nwm.hotkey(\n    ([mod],\n     'x'),\n    'urxvtcd -rv -fade 50 -fn \"xft:Terminus:size=16\" -fb \"xft:Terminus:bold:size=16\" -sl 10000 -si -tn xterm')\nwm.hotkey(([mod], 'y'), 'xterm')\nwm.hotkey(([mod], 'd'), \"dmenu_run\")\nwm.hotkey(([mod], 'l'), \"mylock\")\n# kbd layout\nwm.hotkey(([], caps), \"setxkbmap -layout us\")\nwm.hotkey(([shift], caps), \"setxkbmap -layout ru\")\n# volumeal\nwm.hotkey(([mod], 'period'), \"sound_volume up\")\nwm.hotkey(([mod], 'comma'), \"sound_volume down\")\n# brightness\nwm.hotkey(([shift, alt], down), \"asus-kbd-backlight down\")\nwm.hotkey(([shift, alt], up), \"asus-kbd-backlight up\")\nwm.hotkey(([ctrl, win], up), \"value.py --set /sys/class/backlight/intel_backlight/brightness  \\\n                                       --min=10  \\\n                                       --max /sys/class/backlight/intel_backlight/max_brightness  \\\n                                       -- +10%\")\nwm.hotkey(([ctrl, win], down), \"value.py --set /sys/class/backlight/intel_backlight/brightness  \\\n                                         --min=10  \\\n                                         --max /sys/class/backlight/intel_backlight/max_brightness  \\\n                                         -- -10%\")\n\n# OTHER\n\n\n# TODO: rewrite it to use wm.hide\n@wm.hook(wm.grab_key([mod], 'h'))\ndef hide_window(event):\n    desktop = wm.cur_desktop\n    # windows = desktop.windows\n    cur = desktop.cur_focus\n    # cur_idx = windows.index(cur)\n    cur.hide()\n    next_window()\n\n\n@wm.hook(wm.grab_key([mod, shift], 'k'))\ndef kill_window(event):\n    wm.cur_desktop.cur_focus.kill()\n\n\n@wm.hook(wm.grab_key([mod], 'o'))\ndef osd_test(event):\n    osd.write(\"OSD test output\")\n\n\n@wm.hook(wm.grab_key([mod], 'e'))\ndef log_print_separator(event):\n    log.notice('========')\n    log.notice(\"        \")\n\n\n@wm.hook(wm.grab_key([mod], 's'))\ndef status(event):\n    root = wm.root\n    focus = wm.cur_desktop.cur_focus\n    log.status.debug(\"root: {root}, focus: {focus}\".format(\n        root=root, focus=focus))\n    for wid in sorted(wm.windows):\n        window = wm.windows[wid]\n        # log.status.debug(\"{wid:<10} {window.name:<20} {window.mapped:<10}\n        # {window.can_focus}  {window.skip} {window.type}\n        # {window.flags}\".format(wid=\"WID\", namipyth    ))\n        log.status.debug(\n            \"{wid:<10} {window.name:<20} {window.mapped:<10} {window.can_focus}  {window.skip} {window.type} {window.flags}\".format(\n                wid=wid, window=window))\n\n# restore windows, otherwise they will stay invisible\n\n\n@wm.hook(wm.grab_key([mod, shift], 'q'))\ndef quit(event):\n    wm.stop()\n\n\n@wm.hook(wm.grab_key([mod, shift], 'r'))\ndef restart(event):\n    log.notice(\"restarting WM\")\n    path = os.path.abspath(__file__)\n    wm.replace(execv_args=(path, [path]))\n\n\n@wm.hook(\"on_exit\")\ndef on_exit(*args, **kwargs):\n    # restore windows, otherwise they will stay invisible\n    for window in wm.windows.values():\n        window.show()\n\n# set background\nrun_(\"xsetroot -solid Teal\")\n# replace default X-shaped cursor with something more suitable\nrun_(\"xsetroot -cursor_name left_ptr\")\n\n\n# DO NOT PUT ANY CONFIGURATION BELOW THIS LINE\n# because wm.loop is blocking.\nwm.loop()\n","sub_path":"myconfig.py","file_name":"myconfig.py","file_ext":"py","file_size_in_byte":11862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"249821451","text":"# -*- coding: utf-8 -*-\nimport datetime\nimport logging\n\nimport cherrypy\nimport cv2\nimport telebot\n\nimport config\nimport processing\nfrom detector import Detector\nfrom processing import prepare_url, url_to_cv2, process_photo_message\nfrom user import user\n\n# initialization\nlogger = telebot.logger\ntelebot.logger.setLevel(logging.INFO)\nbot = telebot.TeleBot(config.token)\n\n\n# WebhookServer, process webhook calls\nclass WebhookServer(object):\n    @cherrypy.expose\n    def index(self):\n        if 'content-length' in cherrypy.request.headers and \\\n                'content-type' in cherrypy.request.headers and \\\n                cherrypy.request.headers['content-type'] == 'application/json':\n            length = int(cherrypy.request.headers['content-length'])\n            json_string = cherrypy.request.body.read(length).decode(\"utf-8\")\n            update = telebot.types.Update.de_json(json_string)\n            bot.process_new_updates([update])\n            return ''\n        else:\n            raise cherrypy.HTTPError(403)\n\n\ndetector = Detector()\nusers = []\n\n\n# handlers\n@bot.message_handler(content_types=[\"text\"])\ndef repeat_all_text(message):\n    pass\n    # bot.send_message(message.chat.id, \"Пришлите фотографию, исходя из которой нужно сделать фото профиля\")\n\n\n@bot.message_handler(func=lambda message: True, content_types=['photo'])\ndef photo(message, is_callback=False):\n    cur_user: user = next((usr for usr in users if usr.chat_id == message.chat.id), False)\n\n    cv_mat = url_to_cv2(prepare_url(message))\n    path = '/root/profile_pics/originals/'\n    if is_callback:\n        path_delta = processing.generate_current_path(path, message.chat.id)\n    else:\n        path_delta = processing.generate_next_path(path, message.chat.id)\n    cv2.imwrite(filename=path + path_delta,\n                img=cv_mat)\n    if not cur_user:\n        cur_user = user(message.chat.id)\n        users.append(cur_user)\n    if is_callback:\n        while not process_photo_message(message, cur_user, detector, bot, cv_mat, path_delta) \\\n                and cur_user.tries <= len(detector.haarcascades):\n            pass\n    else:\n        process_photo_message(message, cur_user, detector, bot, cv_mat, path_delta)\n\n\n# В большинстве случаев целесообразно разбить этот хэндлер на несколько маленьких\n@bot.callback_query_handler(func=lambda call: True)\ndef callback_inline(call):\n    # call.message.chat.id\n    chat_id = call.message.chat.id\n    # Если сообщение из чата с ботом\n    if call.message:\n        cur_user: user = next((usr for usr in users if usr.chat_id == call.message.chat.id), False)\n        if not cur_user:\n            return\n        path = '/root/profile_pics/'\n        if call.data == \"true\":\n            path_delta = processing.generate_next_path(path, call.message.chat.id)\n            cv2.imwrite(filename=path + path_delta,\n                        img=url_to_cv2(prepare_url(call.message)))\n            # bot.edit_message_text(text='', chat_id=chat_id, message_id=call.message.message_id)\n            bot.send_message(chat_id, \"Хорошо, приятно было с вами работать\")\n            processing.write_log(datetime.datetime.now().isoformat(),\n                                 call.message.chat.id,\n                                 call.from_user.first_name,\n                                 call.from_user.last_name,\n                                 call.from_user.username,\n                                 \"http://%s:%s/?id=%s\" % (config.WEBHOOK_HOST, config.IMAGES_PORT, path_delta),\n                                 \"accepted our cropping\")\n            users.remove(cur_user)\n            if cur_user:\n                cur_user.tries = 0\n        elif call.data == \"false\":\n            if (cur_user.tries + 1 >= len(detector.haarcascades)):\n                bot.send_message(chat_id, \"К сожалению лицо не было найдено! Может попробуем другую фотографию?\")\n\n                users.remove(cur_user)\n                # bot.edit_message_text(text='', chat_id=chat_id, message_id=call.message.message_id)\n                cur_user.tries = 0\n                processing.write_log(datetime.datetime.now().isoformat(),\n                                     call.message.chat.id,\n                                     call.from_user.first_name,\n                                     call.from_user.last_name,\n                                     call.from_user.username,\n                                     '-',\n                                     \"didn\\'t accept our cropping and he\\'s ran out of tries\")\n            else:\n                cv_mat = url_to_cv2(prepare_url(call.message))\n                path = '/root/profile_pics/originals/'\n                path_delta = processing.generate_current_path(path, call.message.chat.id)\n\n                res_user = next(usr for usr in users if usr.chat_id == chat_id)\n                detector.next_haarcascade_for_user(res_user)\n                process_photo_message(call.message, cur_user, detector, bot, cv_mat, path_delta)\n        bot.edit_message_reply_markup(chat_id, call.message.message_id)\n\n\n# starting service\n\nbot.remove_webhook()\nbot.set_webhook(url=config.WEBHOOK_URL_BASE + config.WEBHOOK_URL_PATH,\n                certificate=open(config.WEBHOOK_SSL_CERT, 'r'))\naccess_log = cherrypy.log.access_log\nfor handler in tuple(access_log.handlers):\n    access_log.removeHandler(handler)\ncherrypy.config.update({\n    'server.socket_host': config.WEBHOOK_LISTEN,\n    'server.socket_port': config.WEBHOOK_PORT,\n    'server.ssl_module': 'builtin',\n    'server.ssl_certificate': config.WEBHOOK_SSL_CERT,\n    'server.ssl_private_key': config.WEBHOOK_SSL_PRIV\n})\ncherrypy.quickstart(WebhookServer(), config.WEBHOOK_URL_PATH, {'/': {}})\n","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":5902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"643467012","text":"from flask import Flask, request, jsonify\nfrom joblib import load\nfrom nltk.tokenize.regexp import regexp_tokenize\nfrom main import  clean_text, tokenize, getTags\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom nltk.stem import WordNetLemmatizer\nimport nltk\nimport pickle\n\n\n# Found solution at https://stackoverflow.com/questions/50465106/attributeerror-when-reading-a-pickle-file\nclass MyCustomUnpickler(pickle.Unpickler):\n    def find_class(self, module, name):\n        if module == \"__main__\":\n            module = \"application\"\n        return super().find_class(module, name)\n\nwith open('vect.pkl', 'rb') as f:\n    unpickler = MyCustomUnpickler(f)\n    vect = unpickler.load()\n\napplication = Flask(__name__)\n\nlemmatizer = WordNetLemmatizer()\ntokeni_zer = tokenize\nencoder = load('labeller.pkl')\nmodel = load('randomforest.pkl')\npersonal_model = load('logreg.pkl')\npersonal_vect = load('basic_tfidf.pkl')\n\ndef tag_email(text):\n    text = vect.transform([text])\n    pred = model.predict_proba(text)\n    new_tag = getTags(pred[0], encoder)\n    return new_tag\n\n@application.route('/')\ndef home():\n    return 'You made it'\n\n@application.route('/api/tags', methods=['POST'])\ndef api():\n    data = request.get_json()\n    uid = data['id']\n    sender = data['sender']\n    subject = data['subject']\n    message = data['message']\n    text = sender + ' ' + subject + ' ' + message\n    tag = tag_email(text)\n    text_clean = clean_text(text)\n    text_vect = personal_vect.transform([text_clean])\n    predict = personal_model.predict(text_vect)\n    tagged_email = {'message-id': uid, 'from': sender, 'subject': subject,\n                    'message': message, 'tag': tag, 'personal': predict.tolist()}\n    \n    return jsonify(tagged_email)\n\n\nif __name__ == \"__main__\":\n    application.run()","sub_path":"RF multi-label/application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":1796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"127234976","text":"\"\"\"\nGreenscreen photo booth GUI\n\"\"\"\nimport time\n\nimport wx\nfrom glob import glob\nfrom os import path\nimport greenscreen\nfrom PIL import Image\nimport os\nfrom numpy import random\nimport shutil\nimport datetime\nimport subprocess\n\n\n# GUI layout\nnBGSelectorPreviewPanels = 2  # number of 'preview' items in BG selector, each forwards and backwards\nBGPreviewScaleFac = 0.2  # scale factor for background preview images\nBGImageThumbnailScaleFac = 0.2  # scale factor for background image thumbnails\nFGImageThumbnailScaleFac = 0.3  # scale factor for foreground image thumbnails\nBGSelectedImageBorderWidth = 4  # border width around the selected background image\nnFGSelectorPreviewPanels = 3  # number of 'preview' items in FG selector, each backwards and forwards\nnFGTotalPreviewPanels = 2 * nFGSelectorPreviewPanels + 1\niMainFGPanel = 2 * nFGSelectorPreviewPanels + 1\niMidFGPanel = nFGSelectorPreviewPanels\nnFGThumbnailCache = 30  # cache size for thumbnail cache\nSelectedImageBorderWidth = 6  # border around selected composite image\n\n\n# file name conventions:\n# - P1234567.JPG: input image\n# - Background123.JPG background image\n# - C1234567___Background123.JPG: compound image to above input and background image\n\n\ndef PILImageToWxBitmap(img):\n    image = wx.EmptyImage(img.size[0], img.size[1])\n    image.SetData(img.convert(\"RGB\").tostring())\n    return wx.BitmapFromImage(image)\n\n\nclass GreenieGUI(wx.Frame):\n\n    def __init__(self,\n                 BGImagesDir,\n                 CompoundImagesDir,\n                 PrintedImagesDir,\n                 ReferenceImage,\n                 PrinterOptions,\n                 GreenScreenTol):\n\n        wx.Frame.__init__(self, None, title=\"Greenie GUI\", pos=(0, 22), size=(1276, 778),\n                          style=wx.CAPTION | wx.CLOSE_BOX | wx.CLIP_CHILDREN | wx.SYSTEM_MENU)\n\n        self.BGImagesPath = BGImagesDir\n        self.CompoundImagesPath = CompoundImagesDir\n        self.PrintedImagesPath = PrintedImagesDir\n        self.ReferenceImagePath = ReferenceImage\n        self.PrinterOptions = PrinterOptions\n        self.GreenScreenTol = GreenScreenTol\n\n        self.Bind(wx.EVT_CLOSE, self.OnClose)\n\n        mainPanel = wx.Panel(self)\n        mainBox = wx.BoxSizer(wx.HORIZONTAL)\n        mainPanel.SetSizer(mainBox)\n\n        BGSelectorPanel = wx.Panel(mainPanel)\n        mainBox.Add(BGSelectorPanel, 1, wx.ALL | wx.EXPAND, border=5)\n        BGSelectorBox = wx.BoxSizer(wx.VERTICAL)\n        BGSelectorPanel.SetSizer(BGSelectorBox)\n\n        BGSelectorTitleText = wx.StaticText(BGSelectorPanel, -1, \"Background selection:\")\n        BGSelectorTitleText.SetFont(wx.Font(14, wx.SWISS, wx.NORMAL, wx.BOLD))\n        BGSelectorTitleText.SetSize(BGSelectorTitleText.GetBestSize())\n        BGSelectorBox.Add(BGSelectorTitleText, 0, wx.ALL | wx.ALIGN_CENTER, 5)\n\n        BGSelectorBox.AddStretchSpacer(1)\n\n        BGUpButton = wx.Button(BGSelectorPanel, label=\"Up\")\n        BGSelectorBox.Add(BGUpButton, 0, wx.ALL | wx.ALIGN_CENTER, 5)\n        BGUpButton.Bind(wx.EVT_BUTTON, lambda evt, idx=nBGSelectorPreviewPanels - 1: self.OnBGImageClick(evt, idx))\n\n        self.BGSelectorImagePanels = []\n        for i in range(2 * nBGSelectorPreviewPanels + 1):\n            panel = wx.Panel(BGSelectorPanel)\n            self.BGSelectorImagePanels.append(panel)\n            panel.SetSize((30, 20))\n            BGSelectorBox.Add(panel, 0, wx.ALL | wx.EXPAND | wx.SHAPED | wx.ALIGN_CENTER | wx.ALIGN_CENTER_VERTICAL, 5)\n            panel.Bind(wx.EVT_LEFT_DOWN, lambda evt, idx=i: self.OnBGImageClick(evt, idx))\n            if i == nBGSelectorPreviewPanels:\n                panel.BackgroundColour = ((255, 100, 0))\n            panel.Bind(wx.EVT_PAINT, lambda evt, idx=i: self.OnBGPanelPaint(evt, idx))\n            panel.Bind(wx.EVT_ERASE_BACKGROUND, self.OnBGPanelEraseBackground)\n\n        BGDownButton = wx.Button(BGSelectorPanel, label=\"Down\")\n        BGSelectorBox.Add(BGDownButton, 0, wx.ALL | wx.ALIGN_CENTER, 5)\n        BGDownButton.Bind(wx.EVT_BUTTON, lambda evt, idx=nBGSelectorPreviewPanels + 1: self.OnBGImageClick(evt, idx))\n\n        BGSelectorBox.AddStretchSpacer(1)\n\n        BGSelectButton = wx.Button(BGSelectorPanel, label=\"Change background for selected photo\")\n        BGSelectButton.Bind(wx.EVT_BUTTON, self.DoBGSelection)\n        BGSelectButton.SetFont(wx.Font(16, wx.DEFAULT, wx.NORMAL, wx.NORMAL, 0, \"\"))\n        BGSelectorBox.Add(BGSelectButton, 0, wx.ALL | wx.ALIGN_CENTER, 5)\n\n        mainBox.Add(wx.StaticLine(mainPanel, style=wx.LI_VERTICAL), 0, flag=wx.EXPAND | wx.ALL, border=5)\n\n        PhotoControlSelectorPanel = wx.Panel(mainPanel)\n        mainBox.Add(PhotoControlSelectorPanel, 8, wx.ALL | wx.EXPAND, border=5)\n        PhotoControlSelectorBox = wx.BoxSizer(wx.VERTICAL)\n        PhotoControlSelectorPanel.SetSizer(PhotoControlSelectorBox)\n\n        ImageViewerPanel = wx.Panel(PhotoControlSelectorPanel)\n        PhotoControlSelectorBox.Add(ImageViewerPanel, 5, wx.ALL | wx.EXPAND, border=5)\n        ImageViewerPanelBox = wx.BoxSizer(wx.VERTICAL)\n        ImageViewerPanel.SetSizer(ImageViewerPanelBox)\n\n        ImageViewerPreviewRowPanel = wx.Panel(ImageViewerPanel)\n        ImageViewerPreviewRowPanelBox = wx.BoxSizer(wx.HORIZONTAL)\n        ImageViewerPreviewRowPanel.SetSizer(ImageViewerPreviewRowPanelBox)\n        ImageViewerPreviewRowPanel.SetMinSize((1, 100))\n\n        self.FGSelectorImagePanels = []\n        for i in range(nFGTotalPreviewPanels + 1):\n            if i < nFGTotalPreviewPanels:\n                panel = wx.Panel(ImageViewerPreviewRowPanel)\n                panel.SetSize((30, 20))\n                ImageViewerPreviewRowPanelBox.Add(\n                    panel, 0,\n                    wx.ALL | wx.EXPAND | wx.SHAPED | wx.ALIGN_CENTER | wx.ALIGN_CENTER_VERTICAL,\n                    5)\n            else:  # center panel\n                panel = wx.Panel(ImageViewerPanel)\n                panel.SetSize((30, 20))\n                ImageViewerPanelBox.Add(panel, 0,\n                                        wx.ALL | wx.EXPAND | wx.SHAPED | wx.ALIGN_CENTER | wx.ALIGN_CENTER_VERTICAL, 5)\n            self.FGSelectorImagePanels.append(panel)\n            panel.Bind(wx.EVT_LEFT_DOWN, lambda evt, idx=i: self.OnFGImageClick(evt, idx))\n            # panel.BackgroundColour = ( ( 255, 100, 20 * i ) )\n            panel.Bind(wx.EVT_PAINT, lambda evt, idx=i: self.OnFGPanelPaint(evt, idx))\n            panel.Bind(wx.EVT_ERASE_BACKGROUND, self.OnFGPanelEraseBackground)\n\n        # must be added here instead of above to get correct order\n        ImageViewerPanelBox.Add(ImageViewerPreviewRowPanel, 1, wx.ALL | wx.EXPAND, border=5)\n\n        ImageViewerButtonPanel = wx.Panel(PhotoControlSelectorPanel)\n        PhotoControlSelectorBox.Add(ImageViewerButtonPanel, 0, wx.ALL | wx.EXPAND, border=5)\n        ImageViewerButtonPanelBox = wx.BoxSizer(wx.HORIZONTAL)\n        ImageViewerButtonPanel.SetSizer(ImageViewerButtonPanelBox)\n\n        ImageViewerButtonPanelBox.AddStretchSpacer(1)\n\n        ImageFirstButton = wx.Button(ImageViewerButtonPanel, label=\"First\")\n        ImageViewerButtonPanelBox.Add(ImageFirstButton, 0, wx.ALL | wx.ALIGN_CENTER, 5)\n        ImageFirstButton.Bind(wx.EVT_BUTTON, lambda evt, idx=iMidFGPanel - 1000000: self.OnFGImageClick(evt, idx))\n\n        ImageMinus10Button = wx.Button(ImageViewerButtonPanel, label=\"-10\")\n        ImageViewerButtonPanelBox.Add(ImageMinus10Button, 0, wx.ALL | wx.ALIGN_CENTER, 5)\n        ImageMinus10Button.Bind(wx.EVT_BUTTON, lambda evt, idx=iMidFGPanel - 10: self.OnFGImageClick(evt, idx))\n\n        ImageViewerButtonPanelBox.AddStretchSpacer(1)\n\n        ImageBackButton = wx.Button(ImageViewerButtonPanel, label=\"Back\")\n        ImageViewerButtonPanelBox.Add(ImageBackButton, 0, wx.ALL | wx.ALIGN_CENTER, 5)\n        ImageBackButton.Bind(wx.EVT_BUTTON, lambda evt, idx=iMidFGPanel - 1: self.OnFGImageClick(evt, idx))\n\n        ImageForwardButton = wx.Button(ImageViewerButtonPanel, label=\"Forward\")\n        ImageViewerButtonPanelBox.Add(ImageForwardButton, 0, wx.ALL | wx.ALIGN_CENTER, 5)\n        ImageForwardButton.Bind(wx.EVT_BUTTON, lambda evt, idx=iMidFGPanel + 1: self.OnFGImageClick(evt, idx))\n\n        ImageViewerButtonPanelBox.AddStretchSpacer(1)\n\n        ImagePlus10Button = wx.Button(ImageViewerButtonPanel, label=\"+10\")\n        ImageViewerButtonPanelBox.Add(ImagePlus10Button, 0, wx.ALL | wx.ALIGN_CENTER, 5)\n        ImagePlus10Button.Bind(wx.EVT_BUTTON, lambda evt, idx=iMidFGPanel + 10: self.OnFGImageClick(evt, idx))\n\n        ImageLastButton = wx.Button(ImageViewerButtonPanel, label=\"Last\")\n        ImageViewerButtonPanelBox.Add(ImageLastButton, 0, wx.ALL | wx.ALIGN_CENTER, 5)\n        ImageLastButton.Bind(wx.EVT_BUTTON, lambda evt, idx=iMidFGPanel + 1000000: self.OnFGImageClick(evt, idx))\n\n        ImageViewerButtonPanelBox.AddStretchSpacer(3)\n\n        self.ImagePrintButton = wx.Button(ImageViewerButtonPanel, label=\"Print current photo\")\n        self.ImagePrintButton.SetFont(wx.Font(16, wx.DEFAULT, wx.NORMAL, wx.NORMAL, 0, \"\"))\n        ImageViewerButtonPanelBox.Add(self.ImagePrintButton, 0, wx.ALL | wx.ALIGN_CENTER, 5)\n        self.ImagePrintButton.Bind(wx.EVT_BUTTON, self.PrintImage)\n\n        ImageViewerButtonPanelBox.AddStretchSpacer(1)\n\n        self.RefreshBGImageList()\n\n        self.CompoundImageList = []\n        self.FGImageList = []\n        self.selectedFGImageIdx = -1\n        self.ShownFGImagePaths = [None] * len(self.FGSelectorImagePanels)\n        self.FGImageCache = {}\n\n        self.GreenScreenRefColors = greenscreen.GetRefColor(Image.open(self.ReferenceImagePath))\n\n        mainPanel.Layout()\n\n    def RefreshBGImageList(self):\n        # find all BG images\n        self.BGImageFiles = glob(path.join(self.BGImagesPath, \"*.[jJ][pP][gG]\"))\n        self.BGImageBitmaps = [None] * len(self.BGSelectorImagePanels)\n        self.selectedBGImageIdx = min(nBGSelectorPreviewPanels, len(self.BGImageFiles) - 1)\n\n        # store re-sized versions of all BG images\n        self.PreviewBGImags = []\n        for imgPath in self.BGImageFiles:\n            bmp = wx.Bitmap(imgPath)\n            image = wx.ImageFromBitmap(bmp).Scale(bmp.Size[0] * BGImageThumbnailScaleFac,\n                                                  bmp.Size[1] * BGImageThumbnailScaleFac, wx.IMAGE_QUALITY_HIGH)\n            self.PreviewBGImags.append(wx.BitmapFromImage(image))\n        self.RefreshGUI()\n\n    def AddFGImage(self, FGImagePath):\n        # if no compound image does exist yet, create it\n        ImageName = path.split(FGImagePath)[1]\n        CompoundImagePattern = path.join(self.CompoundImagesPath, \"C\" + ImageName[1: -4] + \"*.[jJ][pP][gG]\")\n        curCompoundImages = glob(CompoundImagePattern)\n        newFile = False\n        if len(curCompoundImages) == 0:\n            newFile = True\n            BGImagePath = self.BGImageFiles[self.selectedBGImageIdx]\n            BGImageName = path.split(BGImagePath)[1]\n            CompoundImagePath = path.join(self.CompoundImagesPath, \"C\" + ImageName[1: -4] + \"___\" + BGImageName[:])\n        else:\n            CompoundImagePath = curCompoundImages[0]\n        self.CompoundImageList.append(CompoundImagePath)\n        self.FGImageList.append(FGImagePath)\n        self.selectedFGImageIdx = len(self.CompoundImageList) - 1\n        if newFile:\n            self.MakeCompoundImage()\n\n    def MakeCompoundImage(self):\n        BGImagePath = self.BGImageFiles[self.selectedBGImageIdx]\n        BGImageName = path.split(BGImagePath)[1]\n        bgImage = Image.open(BGImagePath)\n        FGImagePath = self.FGImageList[self.selectedFGImageIdx]\n        FGImageName = path.split(FGImagePath)[1]\n        fgImage = Image.open(FGImagePath)\n        CompoundImagePath = path.join(self.CompoundImagesPath, \"C\" + FGImageName[1: -4] + \"___\" + BGImageName[:])\n\n        # delete any compound images already present for that FG image\n        compoundImage = greenscreen.Overlay(fgImage, bgImage, self.GreenScreenRefColors, tolA=self.GreenScreenTol[0],\n                                            tolB=self.GreenScreenTol[1])\n        CompoundImagePattern = path.join(self.CompoundImagesPath, \"C\" + FGImageName[1: -4] + \"___*.[jJ][pP][gG]\")\n        for f in glob(CompoundImagePattern):\n            os.remove(f)\n        compoundImage.save(CompoundImagePath)\n        self.CompoundImageList[self.selectedFGImageIdx] = CompoundImagePath\n\n    def OnBGImageClick(self, event, panelIdx):\n        self.selectedBGImageIdx += panelIdx - nBGSelectorPreviewPanels\n        self.selectedBGImageIdx = max(0, min(len(self.BGImageFiles) - 1, self.selectedBGImageIdx))\n        # refresh all panels\n        for panel in self.BGSelectorImagePanels:\n            panel.Refresh()\n\n    def DoBGSelection(self, event):\n        # create new compound image\n        self.MakeCompoundImage()\n        self.FGSelectorImagePanels[iMainFGPanel].Refresh()\n        self.FGSelectorImagePanels[iMidFGPanel].Refresh()\n        pass\n\n    def OnFGImageClick(self, event, panelIdx):\n        self.selectedFGImageIdx += panelIdx - iMidFGPanel\n        self.selectedFGImageIdx = max(0, min(len(self.CompoundImageList) - 1, self.selectedFGImageIdx))\n        # refresh all panels\n        for panel in self.FGSelectorImagePanels:\n            panel.Refresh()\n        pass\n\n    def RefreshGUI(self):\n        for panel in self.BGSelectorImagePanels:\n            panel.Refresh()\n        for panel in self.FGSelectorImagePanels:\n            panel.Refresh()\n\n    def OnClose(self, event):\n        dlg = wx.MessageDialog(self,\n                               \"Do you really want to close this application?\",\n                               \"Confirm Exit\", wx.OK | wx.CANCEL | wx.ICON_QUESTION)\n        result = dlg.ShowModal()\n        dlg.Destroy()\n        if result == wx.ID_OK:\n            dlg = wx.MessageDialog(self,\n                                   \"Do you really think you should close this application?\",\n                                   \"Confirm Exit if you mean it\", wx.YES_NO | wx.NO_DEFAULT | wx.ICON_QUESTION)\n            result = dlg.ShowModal()\n            dlg.Destroy()\n            if result == wx.ID_YES:\n                dlg = wx.MessageDialog(self,\n                                       \"REALLY close? Don't say I didn't warn you!\",\n                                       \"Confirm Exit, but don't complain later\", wx.OK | wx.CANCEL | wx.ICON_QUESTION)\n                result = dlg.ShowModal()\n                dlg.Destroy()\n                if result == wx.ID_OK:\n                    self.Destroy()\n\n    def OnBGPanelPaint(self, evt, panelIdx):\n        panel = self.BGSelectorImagePanels[panelIdx]\n        dc = wx.BufferedPaintDC(panel)\n        dc.Clear()\n        imgIdx = self.selectedBGImageIdx + panelIdx - nBGSelectorPreviewPanels\n        if imgIdx >= 0 and imgIdx < len(self.BGImageFiles):\n            self.BGImageBitmaps[panelIdx] = self.PreviewBGImags[imgIdx]\n            panelScalFac = (1.0 - BGPreviewScaleFac) ** abs(panelIdx - nBGSelectorPreviewPanels)\n            imgSize = [panel.Size[0] * panelScalFac, panel.Size[1] * panelScalFac]\n            if panelIdx == nBGSelectorPreviewPanels:\n                imgSize[0] -= 2 * BGSelectedImageBorderWidth\n                imgSize[1] -= 2 * BGSelectedImageBorderWidth\n            image = wx.ImageFromBitmap(self.BGImageBitmaps[panelIdx]).Scale(imgSize[0], imgSize[1])\n            dc.DrawBitmap(wx.BitmapFromImage(image), 0.5 * (panel.Size[0] - imgSize[0]),\n                          0.5 * (panel.Size[1] - imgSize[1]))\n        else:\n            self.BGImageBitmaps[panelIdx] = None\n\n    def OnBGPanelEraseBackground(self, event):\n        \"\"\" Handles the wx.EVT_ERASE_BACKGROUND event for CustomCheckBox. \"\"\"\n        # This is intentionally empty, because we are using the combination\n        # of wx.BufferedPaintDC + an empty OnEraseBackground event to\n        # reduce flicker\n        pass\n\n    def CacheFGImage(self, imgPath):\n        \"\"\" Add image to cache; make space in cache if necessary\"\"\"\n        if imgPath in self.FGImageCache.keys():\n            # image is already cached\n            return\n        # make space in cache if necessary\n        while len(self.FGImageCache) >= nFGThumbnailCache:\n            # try to remove a random element\n            iRand = random.randint(0, len(self.FGImageCache))\n            # only remove images not currently shown\n            imgNameRand = self.FGImageCache.keys()[iRand]\n            if not imgNameRand in self.ShownFGImagePaths:\n                del self.FGImageCache[imgNameRand]\n        # cache the new image\n        bmp = wx.Bitmap(imgPath)\n        image = wx.ImageFromBitmap(bmp).Scale(bmp.Size[0] * FGImageThumbnailScaleFac,\n                                              bmp.Size[1] * FGImageThumbnailScaleFac)\n        self.FGImageCache[imgPath] = wx.BitmapFromImage(image)\n\n    def OnFGPanelPaint(self, evt, panelIdx):\n        panel = self.FGSelectorImagePanels[panelIdx]\n        dc = wx.BufferedPaintDC(panel)\n        dc.Clear()\n        if panelIdx < nFGTotalPreviewPanels:\n            imgIdx = self.selectedFGImageIdx + panelIdx - iMidFGPanel\n        else:\n            # main panel\n            imgIdx = self.selectedFGImageIdx\n        if imgIdx >= 0 and imgIdx < len(self.CompoundImageList) and path.exists(self.CompoundImageList[imgIdx]):\n            imgPath = self.CompoundImageList[imgIdx]\n            FGImageName = self.FGImageList[imgIdx]\n            self.CacheFGImage(imgPath)\n            self.ShownFGImagePaths[panelIdx] = imgPath\n            imgSize = panel.Size\n            if panelIdx != iMainFGPanel:\n                bmp = self.FGImageCache[imgPath]\n            else:\n                # always reload full-res version of main image from disk\n                bmp = wx.Bitmap(self.CompoundImageList[imgIdx])\n            image = wx.ImageFromBitmap(bmp).Scale(imgSize[0], imgSize[1], wx.IMAGE_QUALITY_HIGH)\n            dc.DrawBitmap(wx.BitmapFromImage(image), 0.5 * (panel.Size[0] - imgSize[0]),\n                          0.5 * (panel.Size[1] - imgSize[1]))\n            if panelIdx == iMidFGPanel:\n                # draw a border around current image in preview\n                dc.SetPen(wx.Pen((0, 255, 50), 2 * SelectedImageBorderWidth))\n                dc.DrawLines(((0, 0), (panel.Size[0], 0), (panel.Size[0], panel.Size[1]), (0, panel.Size[1]), (0, 0)))\n            dc.SetTextForeground((255, 255, 255))\n            dc.SetFont(wx.Font(16, wx.SWISS, wx.NORMAL, wx.BOLD))\n            dc.DrawText(str(imgIdx + 1), 4, 4)\n            dc.SetTextForeground((0, 0, 100))\n            dc.DrawText(str(imgIdx + 1), 3, 3)\n        else:\n            self.ShownFGImagePaths[panelIdx] = None\n\n    def OnFGPanelEraseBackground(self, event):\n        \"\"\" Handles the wx.EVT_ERASE_BACKGROUND event for CustomCheckBox. \"\"\"\n        # This is intentionally empty, because we are using the combination\n        # of wx.BufferedPaintDC + an empty OnEraseBackground event to\n        # reduce flicker\n        pass\n\n    def PrintImage(self, event):\n        \"\"\" Copy current compound image to PrintedImages folder, send to printer \"\"\"\n        timeStr = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n        compoundImgPath = self.CompoundImageList[self.selectedFGImageIdx]\n        destFile = path.join(self.PrintedImagesPath, timeStr + \"_\" + path.split(compoundImgPath)[1])\n        shutil.copy(compoundImgPath, destFile)\n\n        ret = subprocess.call([\"lpr\"] + self.PrinterOptions + [destFile])\n        if ret == 0:\n            msg = \"Sent photo to printer.\"\n        else:\n            msg = \"Error printing!\"\n        dlg = wx.MessageDialog(self, \"\", msg, wx.OK)\n        dlg.ShowModal()\n","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":19474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"461285186","text":"# -*- conding:utf-8 -*-\n\n\"\"\" \n@author: Salted fish \n@file: data.py\n@time: 2019/01/12 \n\"\"\"\n\nimport config.config as config\nimport modbus.modbus as modbus\n\nprotocol_address = config.configs.MODBUS_PROTOCOL_ADDRESS\n\"\"\"\n遥测数据相关配置\n\"\"\"\nTELEMETRY_START_ADDRESS = protocol_address.telemetry_start_address  # 遥测数据报文开始地址\nTELEMETRY_END_ADDRESS = protocol_address.telemetry_end_address  # 遥测数据报文结束地址\nGET_TELEMETRY_STEP = protocol_address.get_telemetry_step  # 遥测数据获取数据步��\n\n\"\"\"\n遥信数据相关配置\n\"\"\"\nTELECOMMAND_START_ADDRESS = protocol_address.telecommand_start_address  # 遥信数据报文开始地址\nTELECOMMAND_END_ADDRESS = protocol_address.telecommand_end_address  # 遥信数据报文结束地址\nGET_TELECOMMAND_STEP = protocol_address.get_telecommand_step  # 遥信数据获取数据步长\n\n\"\"\"\n电度数据相关配置\n\"\"\"\nELECTRICAL_START_ADDRESS = protocol_address.electrical_start_address  # 电度数据报文开始地址\nELECTRICAL_END_ADDRESS = protocol_address.electrical_end_address  # 电度数据报文结束地址\nGET_ELECTRICAL_STEP = protocol_address.get_electrical_step  # 电度数据获取数据步长\n\n\"\"\"\n寄存器类型\n\"\"\"\nHOLDING_REGISTERS_TYPE = 40001  # 保持寄存器\nDISCRETE_INPUTS_TYPE = 10001  # 离散输入寄存器\n\n\ndef get_data(start_address, end_address, func, type, step):\n    \"\"\"\n    获取modbusTCP数据\n    :param start_address:   报文开始地址\n    :param end_address:     报文结束地址\n    :param func:            获取数据方法\n    :param type:            寄存器类型\n    :param step:            步数(每次获取数据量 最大125)\n    :return:                dict 遥测数据\n    \"\"\"\n    data = ()\n    num = step\n    for i in range(start_address, end_address, step):\n        if i + num >= end_address:\n            num = end_address - i\n        data += func(i, num)\n\n    list1 = list(data)\n    list2 = [i for i in range(start_address + type, end_address + type)]\n    return dict(zip(list2, list1))\n\n\ndef get_telemetry_data():\n    \"\"\"\n    获取遥测数据\n    :return: dict 遥测数据\n    \"\"\"\n    func = modbus.get_keep_data\n    return get_data(TELEMETRY_START_ADDRESS, TELEMETRY_END_ADDRESS, func, HOLDING_REGISTERS_TYPE, GET_TELEMETRY_STEP)\n\n\ndef get_telecommand_data():\n    \"\"\"\n    获取遥信数据\n    :return: dict 遥信数据\n    \"\"\"\n    func = modbus.get_digital_data\n    return get_data(TELECOMMAND_START_ADDRESS, TELECOMMAND_END_ADDRESS, func, DISCRETE_INPUTS_TYPE,\n                    GET_TELECOMMAND_STEP)\n\n\ndef get_electrical_data():\n    \"\"\"\n    获取电度数据\n    :return: dict 电度数据\n    \"\"\"\n    func = modbus.get_keep_data\n    return get_data(ELECTRICAL_START_ADDRESS, ELECTRICAL_END_ADDRESS, func, HOLDING_REGISTERS_TYPE,\n                    GET_ELECTRICAL_STEP)\n\n\ndef get_devices_data():\n    \"\"\"\n    获取设备所有数据\n    :return:\n    \"\"\"\n    # 获取遥测数据\n    telemetry_data = get_telemetry_data()\n    # 获取电度数据\n    electrical_data = get_electrical_data()\n    # 获取遥信数据\n    telecommand_data = get_telecommand_data()\n    # 合并数据\n    devices_data = {}\n    devices_data.update(telemetry_data)\n    devices_data.update(electrical_data)\n    devices_data.update(telecommand_data)\n    return devices_data\n","sub_path":"tb-gateway1/modbus/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":3319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"111848273","text":"# In[7]:\nfrom collections import namedtuple\nimport numpy as np\nimport pandas as pd\nimport time\nimport math\n\n# KdTree 的节点\n\n\nclass KdNode:\n    def __init__(self, axis, point, left, right):\n        \"\"\"\n        :param axis 决定这个节点以哪一个轴进行分类\n        :param point 保存这个节点的值\n        :param left 左节点\n        :param right 右节点\n        :param val 类别\n        \"\"\"\n        self.axis = axis\n        self.point = point\n        self.left = left\n        self.right = right\n        \n\n\n# KdTree 类\nclass KdTree:\n    def load_data(self, feature):\n        # 加载数据\n        self.data = feature\n\n    def init_args(self):\n        \"\"\"\n        通过递归的方法创建 KdTree\n        \"\"\"\n        data = self.data\n        # k 表示数据的维度\n        k = 3\n        # node_num 记录数据集的长度\n        self.node_num = len(data)\n        # 递归创建节点\n\n        def create_node(axis, data_set):\n            # 递归终止条件，数据结构为空，这一层递归返回，也就是说叶节点的再往下的时候，得到节点为 None\n            if len(data_set) == 0:\n                return None\n            data_set = sorted(data_set,key=lambda x: x[axis]) \n            point_pos = len(data_set) // 2 # 排序取中点\n            point_media = data_set[point_pos]\n            next_axis = (axis + 1) % k\n            return KdNode(axis, point_media, # 递归创建节点\n                          create_node(next_axis, data_set[0:point_pos]),\n                          create_node(next_axis, data_set[point_pos+1:]))\n        # 根节点\n        self.root = create_node(0, data)\n\n    # 前序遍历树\n    def pre_order(self, ele):\n        if not ele:\n            return\n        self.pre_order(ele.left)\n        print(ele.point)\n        self.pre_order(ele.right)\n\n# 计算欧式距离方法一\ndef compute_dist(l1, l2):\n    # 兼容数组和 np.array\n    try:\n        return np.linalg.norm(l1 - l2)\n    except:\n        return np.linalg.norm(np.array(l1) - np.array(l2))\n\n# 计算欧式距离方法二\ndef Euclidean(vec1, vec2):\n    npvec1, npvec2 = np.array(vec1), np.array(vec2)\n    return math.sqrt(((npvec1 - npvec2)**2).sum())\n\n# 距离的高斯加权函数\ndef gaussian(dist, a=1, b=0, c=0.3):\n    return a * math.e ** (-(dist - b) ** 2 / (2 * c ** 2))\n\n\nclass KNN:\n    def __init__(self, kdtree, point):\n        \"\"\"\n        :param kdtree 已加载好数据的 kdtree\n        :param point 目标点\n        \"\"\"\n        self.kdtree = kdtree\n        self.point = point\n\n    def add_node(self, point, num=-1):\n        \"\"\"\n        :param num \n        如果要求的最近点的数目大于已有最近点的数目，则直接向最近点中加入这个点，此时 num = -1\n        如果要求的最近点的数目已经满足已有最近点的数目，则与距离最远的比较，距离比他大就不变，比他小就替换掉\n        \"\"\"\n        # 给 tupple 取名字。比如_Result((1, 2)) 输出_Result(dist=1, point=2)\n        _Result = namedtuple('_Result', 'dist point')\n        # 计算距离\n        #dist = compute_dist(self.point[:3], point[:3])\n        dist = Euclidean(self.point[:3], point[:3])\n        # 组成点和距离的 tupple\n        r = _Result(dist, point[3])\n        # 加入这个点\n        self.close_nodes.append(r)\n        # 排序\n        self.close_nodes.sort(key=lambda x: x.dist)\n\n        # 判断这个点的是否进入最近点集合中\n        if num == -1:\n            return\n        else:\n            self.close_nodes = self.close_nodes[:num]\n\n    def find_nearest_with_num(self, num=1):\n        if num > self.kdtree.node_num:\n            print('要找的节点数目，大于树节点的数目')\n            return\n        self.close_nodes = []\n        #k = len(self.point)\n        target_point = self.point\n\n        def travel(current):\n            if not current:\n                # 如果当前点是 None\n                return\n            axis = current.axis\n            current_point = current.point\n            # 选择更近的一个点\n            near_point, far_point = [current.left, current.right] if target_point[\n                axis] <= current_point[axis] else [current.right, current.left]\n\n            travel(near_point)\n            # 递归遍历回归以后\n            if len(self.close_nodes) < num:\n                self.add_node(current_point)\n            else:\n                # 检查当前节点及远边节点满不满足加入的条件\n                max_dist = self.close_nodes[num-1].dist\n                # 这里算的是垂直轴的距离，如果最远点比这个垂直轴的距离还要小，那么当前点和另一边的点\n                # 一定更远\n                if max_dist <= abs(current_point[axis] - target_point[axis]):\n                    return\n\n                self.add_node(current_point, num)\n                travel(far_point)\n        # 遍历根节点\n        travel(self.kdtree.root)\n        return self.close_nodes\n\n\ndef main():\n    # 创建 100 个点\n\n    TRAIN_DATA = \"filled_data.csv\"\n    TEST_DATA = \"filled_data.csv\"\n    train_data = pd.read_csv(TRAIN_DATA,encoding='gb2312')\n    test_data = pd.read_csv(TEST_DATA,encoding='gb2312')\n    train_data = pd.DataFrame(train_data,dtype= np.float)\n    test_data = pd.DataFrame(test_data,dtype= np.float)\n\n    train_feature = train_data[['a','b','c','t']].values # (7000,3)\n    train_feature.tolist()\n    \n    test_feature = test_data[['a','b','c']].values\n    test_label = test_data[['t']].values.reshape(-1)\n    # 创建 KdTree\n    kd = KdTree()\n    kd.load_data(train_feature)\n    kd.init_args()\n    # 选择一个点\n    #point = [100, 100,100]\n    acc = 0\n    count_1 = 0\n    count_0 = 0\n    for i in range(0,len(test_feature)):\n        #print(i)\n        cur_label = test_label[i]\n        #print(cur_label)\n        k = 9 # K value\n        point = test_feature[i]\n        # 创建 KNN\n        knn = KNN(kd, point)\n        # 找最近的 3 个点\n        r = knn.find_nearest_with_num(num=k)\n        count = 0\n        for i in range(0,len(r)):\n            #print(r[i][1])\n            #print(r)\n            if r[i][1] > 0.0:\n                count += 1 \n        #print(count)\n        if count*2 > k:\n            cur_class = 1.0\n            count_1 += 1\n        else:\n            cur_class = 0.0\n            count_0 += 1\n        #print(cur_class)\n        if cur_class == cur_label:\n            acc += 1\n            #print(\"2:\",cur_label)\n    print(acc)\n\n\n        \n\n    \n\n\nif __name__ == \"__main__\":\n    time_start=time.time()\n    main()\n    time_end=time.time()\n    print('totally cost',time_end-time_start)","sub_path":"ML_Homework/knn_optim.py","file_name":"knn_optim.py","file_ext":"py","file_size_in_byte":6635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"379564265","text":"# -*- encoding: UTF-8 -*-\n'''\n4. A seqüência de Fibonacci é a seguinte: 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, ... Sua regra de\nformação é simples: os dois primeiros elementos são 1; a partir de então, cada elemento é a\nsoma dos dois anteriores. Faça um algoritmo que leia um número inteiro calcule o seu número\nde Fibonacci. F 1 = 1, F 2 = 1, F 3 = 2, etc.\n'''\n\nnum = int(raw_input('Informe um numero: '))\nfibonacci = [1, 1]\ncont = 1\ni = 0\n\nwhile i < (num - 2):\n    fibonacci.append(fibonacci[cont - 1] + fibonacci[cont])\n    cont += 1\n    i += 1\nprint(fibonacci)\n","sub_path":"Python Para Zumbis/Lista 3/ex4.py","file_name":"ex4.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"165999053","text":"from src.black_jack.resources.classes_full import Deck\nfrom src.black_jack.resources.classes_full import Chips\nfrom src.black_jack.resources.classes_full import Hand\n\n\nclass Game:\n\n    def __init__(self):\n        pass\n\n    def take_bet(self, chips):\n        while True:\n\n            try:\n                chips.bet = int(input(\"How many chips \"\n                                      \"would you like to bet? \"))\n            except ValueError:\n                print(\"Sorry, please insert an integer\")\n            else:\n                if chips.bet > chips.total:\n                    print(f\"Sorry,you don`t have enough money.\"\n                          f\" You have only {chips.total}\")\n                else:\n                    break\n\n    def hit(self, deck, hand):\n        single_card = deck.deal()\n        hand.add_card(single_card)\n        hand.adjust_for_ace()\n\n    def show_some(self, player, dealer):\n        print(\"Dealer`s hand:\")\n        print(\"One card hidden\")\n        print(dealer.cards[1])\n        print('\\n')\n        print(\"Players hand:\")\n        for card in player.cards:\n            print(card)\n        print(player.value)\n\n    def show_all(self, player, dealer):\n        print(\"Dealer`s hand:\")\n        for card in dealer.cards:\n            print(card)\n        print(dealer.value)\n        print('\\n')\n        print(\"Players hand:\")\n        for card in player.cards:\n            print(card)\n        print(player.value)\n\n    def player_busts(self, player, dealer, chips):\n        print(\"Player busts\")\n        chips.loose_bet()\n\n    def player_wins(self, player, dealer, chips):\n        print(\"Player wins!!!\")\n        chips.win_bet()\n\n    def dealer_busts(self, player, dealer, chips):\n        print(\"Dealer busts, Player wins\")\n        chips.win_bet\n\n    def dealer_wins(self, player, dealer, chips):\n        print(\"Dealer wins\")\n        chips.loose_bet()\n\n    def tie(self, player, dealer, chips):\n        print(\"Tie. Nobody wins\")\n\n    def play(self):\n\n        playing = True\n\n        while playing:\n\n            self.deck = Deck()\n            self.deck.shuffle()\n\n            self.player_hand = Hand()\n            self.dealer_hand = Hand()\n\n            for _ in range(2):\n                self.player_hand.add_card(self.deck.deal())\n                self.dealer_hand.add_card(self.deck.deal())\n\n            player_chips = Chips()\n\n            self.take_bet(player_chips)\n\n            self.show_some(self.player_hand, self.dealer_hand)\n\n            while True and self.player_hand.value < 21:\n                choice = input(\"Please choose h or s\")\n                if choice[0].lower() not in ['s', 'h']:\n                    continue\n\n                elif choice[0] == 'h':\n                    self.player_hand.add_card(self.deck.deal())\n                    self.show_some(self.player_hand,\n                                   self.dealer_hand)\n                    continue\n\n                elif choice[0] == 's':\n                    break\n\n            self.show_all(self.player_hand,\n                          self.dealer_hand)\n\n            if self.player_hand.value == 21:\n                self.player_wins(self.player_hand,\n                                 self.dealer_hand,\n                                 player_chips)\n\n            elif self.player_hand.value > 21:\n                self.player_busts(self.player_hand,\n                                  self.dealer_hand,\n                                  player_chips)\n            elif self.player_hand.value < 21:\n                while self.dealer_hand.value <= 17:\n                    self.dealer_hand.add_card(self.deck.deal())\n\n                self.show_all(self.player_hand,\n                              self.dealer_hand)\n\n                if self.dealer_hand.value > 21:\n                    self.dealer_busts(self.player_hand,\n                                      self.dealer_hand,\n                                      player_chips)\n\n                elif self.dealer_hand.value > self.player_hand.value:\n                    self.dealer_wins(self.player_hand,\n                                     self.dealer_hand,\n                                     player_chips)\n\n                elif self.dealer_hand.value < self.player_hand.value:\n                    self.player_wins(self.player_hand,\n                                     self.dealer_hand,\n                                     player_chips)\n\n                else:\n                    self.tie(self.player_hand,\n                             self.dealer_hand,\n                             player_chips)\n\n            print(f'\\n Players total money {player_chips.total}')\n\n            again = input(\"Play Again? [Y/N] \")\n            while again.lower() not in [\"y\", \"n\"]:\n                again = input(\"Please enter Y or N \")\n            if again.lower() == \"n\":\n                print(\"Thanks for playing!\")\n                playing = False\n            else:\n                playing = True\n\n\nif __name__ == '__main__':\n    game = Game()\n    game.play()\n","sub_path":"src/black_jack/resources/class_game.py","file_name":"class_game.py","file_ext":"py","file_size_in_byte":4979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"122065934","text":"'''\n(C) 2014-2016 Roman Sirokov and contributors\nLicensed under BSD license\n\nhttp://github.com/r0x0r/pywebview/\n'''\n\nimport logging\nimport os\nimport sys\nimport base64\nfrom threading import Semaphore, Event\nfrom cefpython3 import cefpython as cef\nimport platform\n\nfrom webview import OPEN_DIALOG, FOLDER_DIALOG, SAVE_DIALOG\nfrom webview import _parse_api_js, _js_bridge_call\nfrom webview.localization import localization\n\n\n# Fix for PyCharm hints warnings when using static methods\nWindowUtils = cef.WindowUtils()\n\n\n\nlogger = logging.getLogger(__name__)\n\n\n# Try importing Qt5 modules\ntry:\n    from PyQt5 import QtCore\n\n    # Check to see if we're running Qt > 5.5\n    from PyQt5.QtCore import QT_VERSION_STR\n    _qt_version = [int(n) for n in QT_VERSION_STR.split('.')]\n\n    # if _qt_version >= [5, 5]:\n    #\n    #     # # from webview.WebViewonlink import WebViewonlink as QWebView\n    #     # from PyQt5.QtWebEngineWidgets import  QWebEngineSettings as QWebSettings\n    #     # from PyQt5.QtWebChannel import QWebChannel\n    #     # from widget.mainwindow import MainWindow\n    # else:\n    #     from PyQt5.QtWebKitWidgets import QWebView\n\n    from PyQt5.QtGui import *\n    # noinspection PyUnresolvedReferences\n    from PyQt5.QtCore import *\n    # noinspection PyUnresolvedReferences\n    from PyQt5.QtWidgets import *\n\n    logger.debug('Using Qt5')\nexcept ImportError as e:\n    logger.exception('PyQt5 or one of dependencies is not found')\n    _import_error = True\nelse:\n    _import_error = False\n\n\nclass BrowserView(QMainWindow):\n    instances = {}\n\n    create_window_trigger = QtCore.pyqtSignal(object)\n    set_title_trigger = QtCore.pyqtSignal(str)\n    load_url_trigger = QtCore.pyqtSignal(str)\n    html_trigger = QtCore.pyqtSignal(str, str)\n    dialog_trigger = QtCore.pyqtSignal(int, str, bool, str, str)\n    destroy_trigger = QtCore.pyqtSignal()\n    fullscreen_trigger = QtCore.pyqtSignal()\n    current_url_trigger = QtCore.pyqtSignal()\n    evaluate_js_trigger = QtCore.pyqtSignal(str)\n\n    sys.excepthook = cef.ExceptHook  # To shutdown all CEF processes on error\n\n    class JSBridge(QtCore.QObject):\n        api = None\n        parent_uid = None\n\n        try:\n            qtype = QtCore.QJsonValue  # QT5\n        except AttributeError:\n            qtype = str  # QT4\n\n        def __init__(self):\n            super(BrowserView.JSBridge, self).__init__()\n\n        @QtCore.pyqtSlot(str, qtype, result=str)\n        def call(self, func_name, param):\n            func_name = BrowserView._convert_string(func_name)\n            param = BrowserView._convert_string(param)\n\n            return _js_bridge_call(self.parent_uid, self.api, func_name, param)\n\n    def __init__(self, uid, title, url, width, height, resizable, fullscreen,\n                 min_size, confirm_quit, background_color, debug, js_api, webview_ready):\n        super(BrowserView, self).__init__()\n        BrowserView.instances[uid] = self\n        self.uid = uid\n\n        self.is_fullscreen = False\n        self.confirm_quit = confirm_quit\n\n        self._file_name_semaphore = Semaphore(0)\n        self._current_url_semaphore = Semaphore()\n        self._evaluate_js_semaphore = Semaphore(0)\n        self.load_event = Event()\n\n        self._evaluate_js_result = None\n        self._current_url = None\n        self._file_name = None\n\n        self.resize(width, height)\n        self.title = title\n        self.setWindowTitle(title)\n\n        # Set window background color\n        self.background_color = QColor()\n        self.background_color.setNamedColor(background_color)\n        palette = self.palette()\n        palette.setColor(self.backgroundRole(), self.background_color)\n        self.setPalette(palette)\n\n        if not resizable:\n            self.setFixedSize(width, height)\n\n        self.setMinimumSize(min_size[0], min_size[1])\n\n        window_info = cef.WindowInfo()\n        rect = [0, 0, self.width(), self.height()]\n        window_info.SetAsChild(int(self.winId()), rect)\n        print(title)\n        self.check_versions()\n\n        setting={\"default_encoding\":\"utf-8\",\"plugins_disabled\":True,\"tab_to_links_disabled\":True,\"web_security_disabled\":True}\n        if url!=None:\n            self.view = cef.CreateBrowserSync(window_info,url=url,settings=setting)\n        else:\n            self.view = cef.CreateBrowserSync(window_info,url=\"about:blank\",settings=setting)\n\n\n        # self.browser.SetClientHandler(LoadHandler(self.parent.navigation_bar))\n        # self.browser.SetClientHandler(FocusHandler(self))\n\n        self.create_window_trigger.connect(BrowserView.on_create_window)\n        self.load_url_trigger.connect(self.on_load_url)\n        self.html_trigger.connect(self.on_load_html)\n        self.dialog_trigger.connect(self.on_file_dialog)\n        self.destroy_trigger.connect(self.on_destroy_window)\n        self.fullscreen_trigger.connect(self.on_fullscreen)\n        self.current_url_trigger.connect(self.on_current_url)\n        self.evaluate_js_trigger.connect(self.on_evaluate_js)\n        self.set_title_trigger.connect(self.on_set_title)\n\n        self.js_bridge = BrowserView.JSBridge()\n        self.js_bridge.api = js_api\n        self.js_bridge.parent_uid = self.uid\n\n        # if _qt_version >= [5, 5]:\n        #     self.channel = QWebChannel(self.view.page())\n        #     self.view.page().setWebChannel(self.channel)\n\n        self.load_event.set()\n\n        if fullscreen:\n            self.toggle_fullscreen()\n\n        # self.view.setContextMenuPolicy(QtCore.Qt.NoContextMenu)  # disable right click context menu\n\n        self.move(QApplication.desktop().availableGeometry().center() - self.rect().center())\n\n        # self.view.settings().setAttribute(QWebSettings.LocalContentCanAccessFileUrls, True);\n        #\n        # self.view.settings().setAttribute(QWebSettings.LocalContentCanAccessRemoteUrls, True);\n\n        self.activateWindow()\n        self.raise_()\n        webview_ready.set()\n\n    def on_set_title(self, title):\n        self.setWindowTitle(title)\n\n    def on_file_dialog(self, dialog_type, directory, allow_multiple, save_filename, file_filter):\n        if dialog_type == FOLDER_DIALOG:\n            self._file_name = QFileDialog.getExistingDirectory(self, localization['linux.openFolder'], options=QFileDialog.ShowDirsOnly)\n        elif dialog_type == OPEN_DIALOG:\n            if allow_multiple:\n                self._file_name = QFileDialog.getOpenFileNames(self, localization['linux.openFiles'], directory, file_filter)\n            else:\n                self._file_name = QFileDialog.getOpenFileName(self, localization['linux.openFile'], directory, file_filter)\n        elif dialog_type == SAVE_DIALOG:\n            if directory:\n                save_filename = os.path.join(str(directory), str(save_filename))\n\n            self._file_name = QFileDialog.getSaveFileName(self, localization['global.saveFile'], save_filename)\n\n        self._file_name_semaphore.release()\n\n    def on_current_url(self):\n        self._current_url = self.view.GetUrl()\n        self._current_url_semaphore.release()\n\n    def on_load_url(self, url):\n        self.view.LoadUrl(url)\n        self.load_event.set()\n\n    def on_load_html(self, html, js_callback=None):\n        # This function is called in two ways:\n        # 1. From Python: in this case value is returned\n        # 2. From Javascript: in this case value cannot be returned because\n        #    inter-process messaging is asynchronous, so must return value\n        #    by calling js_callback.\n        html = html.encode(\"utf-8\", \"replace\")\n        b64 = base64.b64encode(html).decode(\"utf-8\", \"replace\")\n        ret = \"data:text/html;base64,{data}\".format(data=b64)\n        if js_callback:\n            self.view.js_print(js_callback.GetFrame().GetBrowser(),\n                     \"Python\", \"html_to_data_uri\",\n                     \"Called from Javascript. Will call Javascript callback now.\")\n            js_callback.Call(ret)\n        else:\n             self.view.LoadUrl(ret)\n        self.load_event.set()\n\n\n\n    def check_versions(self):\n        ver = cef.GetVersion()\n        print(\"[tutorial.py] CEF Python {ver}\".format(ver=ver[\"version\"]))\n        print(\"[tutorial.py] Chromium {ver}\".format(ver=ver[\"chrome_version\"]))\n        print(\"[tutorial.py] CEF {ver}\".format(ver=ver[\"cef_version\"]))\n        print(\"[tutorial.py] Python {ver} {arch}\".format(\n        ver=platform.python_version(),\n        arch=platform.architecture()[0]))\n        assert cef.__version__ >= \"57.0\", \"CEF Python v57.0+ required to run this\"\n\n    def closeEvent(self, event):\n        if self.confirm_quit:\n            reply = QMessageBox.question(self, self.title, localization['global.quitConfirmation'],\n                                         QMessageBox.Yes, QMessageBox.No)\n\n            if reply == QMessageBox.No:\n                event.ignore()\n                return\n\n        event.accept()\n        # del BrowserView.instances[self.uid]\n\n        super(BrowserView, self).closeEvent(event)\n\n    def setCookie(self,cookie):\n        cookieStore = self.view.page().profile().cookieStore()\n        cookieStore.setCookie(cookie)\n\n    def on_destroy_window(self):\n        self.close()\n\n    def on_fullscreen(self):\n        if self.is_fullscreen:\n            self.showNormal()\n        else:\n            self.showFullScreen()\n\n        self.is_fullscreen = not self.is_fullscreen\n\n    def on_evaluate_js(self, script):\n\n        self._evaluate_js_semaphore.release()\n\n        self.view.GetMainFrame().ExecuteJavascript(script)\n\n        # self._evaluate_js_result = self.view.ExecuteJavascript(script)\n\n\n    def on_load_finished(self):\n        if self.js_bridge.api:\n            self._set_js_api()\n        else:\n            self.load_event.set()\n\n    def set_title(self, title):\n        self.set_title_trigger.emit(title)\n\n    def get_current_url(self):\n        self.current_url_trigger.emit()\n        self._current_url_semaphore.acquire()\n\n        return self._current_url\n\n    def load_url(self, url):\n        self.load_event.clear()\n        self.load_url_trigger.emit(url)\n\n    def load_html(self, content, base_uri):\n        self.load_event.clear()\n        self.html_trigger.emit(content, base_uri)\n\n    def create_file_dialog(self, dialog_type, directory, allow_multiple, save_filename, file_filter):\n        self.dialog_trigger.emit(dialog_type, directory, allow_multiple, save_filename, file_filter)\n        self._file_name_semaphore.acquire()\n\n        if _qt_version >= [5, 0]:  # QT5\n            if dialog_type == FOLDER_DIALOG:\n                file_names = (self._file_name,)\n            elif dialog_type == SAVE_DIALOG or not allow_multiple:\n                file_names = (self._file_name[0],)\n            else:\n                file_names = tuple(self._file_name[0])\n\n        else:  # QT4\n            if dialog_type == FOLDER_DIALOG:\n                file_names = (BrowserView._convert_string(self._file_name),)\n            elif dialog_type == SAVE_DIALOG or not allow_multiple:\n                file_names = (BrowserView._convert_string(self._file_name[0]),)\n            else:\n                file_names = tuple([BrowserView._convert_string(s) for s in self._file_name])\n\n        # Check if we got an empty tuple, or a tuple with empty string\n        if len(file_names) == 0 or len(file_names[0]) == 0:\n            return None\n        else:\n            return file_names\n\n    def destroy_(self):\n        self.destroy_trigger.emit()\n\n    def toggle_fullscreen(self):\n        self.fullscreen_trigger.emit()\n\n    def evaluate_js(self, script):\n        self.load_event.wait()\n        print(\"evaluate_js\")\n        self.evaluate_js_trigger.emit(script)\n        self._evaluate_js_semaphore.acquire()\n\n        return self._evaluate_js_result\n\n    def js_api(self, content):\n        # Execute Javascript function \"js_print\"\n        self.view.ExecuteFunction(\"eval\", content)\n\n    def _set_js_api(self):\n        def _register_window_object():\n            frame.addToJavaScriptWindowObject('external', self.js_bridge)\n\n        script = _parse_api_js(self.js_bridge.api)\n\n        if _qt_version >= [5, 5]:\n            qwebchannel_js = QtCore.QFile('://qtwebchannel/qwebchannel.js')\n            if qwebchannel_js.open(QtCore.QFile.ReadOnly):\n                source = bytes(qwebchannel_js.readAll()).decode('utf-8')\n                self.view.page().runJavaScript(source)\n                self.channel.registerObject('external', self.js_bridge)\n                qwebchannel_js.close()\n        elif _qt_version >= [5, 0]:\n            frame = self.view.page().mainFrame()\n            _register_window_object()\n        else:\n            frame = self.view.page().mainFrame()\n            _register_window_object()\n\n        try:    # PyQt4\n            self.view.page().mainFrame().evaluateJavaScript(script)\n        except AttributeError:  # PyQt5\n            self.view.page().runJavaScript(script)\n\n        self.load_event.set()\n\n\n    @staticmethod\n    def _convert_string(qstring):\n        try:\n            qstring = qstring.toString() # QJsonValue conversion\n        except:\n            pass\n\n        if sys.version < '3':\n            return unicode(qstring)\n        else:\n            return str(qstring)\n\n    @staticmethod\n    # Receive func from subthread and execute it on the main thread\n    def on_create_window(func):\n        func()\n\n\ndef create_window(uid, title, url, width, height, resizable, fullscreen, min_size,\n                  confirm_quit, background_color, debug, js_api, webview_ready):\n    # app = QApplication.instance() or QApplication([])\n\n    app = CefApplication(sys.argv)\n    settings = {\n        'context_menu': {\n            'enabled': False}}\n    cef.Initialize(settings)\n\n\n    def _create():\n        browser = BrowserView(uid, title, url, width, height, resizable, fullscreen,\n                              min_size, confirm_quit, background_color, debug, js_api,\n                              webview_ready)\n        browser.show()\n\n    if uid == 'master':\n        _create()\n        app.exec_()\n        app.stopTimer()\n        # del browser  # Just to be safe, similarly to \"del app\"\n        del app  # Must destroy app object before calling Shutdown\n        cef.Shutdown()\n    else:\n        i = list(BrowserView.instances.values())[0]     # arbitary instance\n        i.create_window_trigger.emit(_create)\n\n\ndef set_title(title, uid):\n    BrowserView.instances[uid].set_title(title)\n\n\ndef get_current_url(uid):\n    return BrowserView.instances[uid].get_current_url()\n\n\ndef load_url(url, uid):\n    BrowserView.instances[uid].load_url(url)\n\ndef setCookie(cookie,uid):\n    BrowserView.instances[uid].setCookie(cookie)\n\n\ndef load_html(content, base_uri, uid):\n    BrowserView.instances[uid].load_html(content, base_uri)\n\n\ndef destroy_window(uid):\n    BrowserView.instances[uid].destroy_()\n\n\ndef toggle_fullscreen(uid):\n    BrowserView.instances[uid].toggle_fullscreen()\n\n\ndef create_file_dialog(dialog_type, directory, allow_multiple, save_filename, file_types):\n    # Create a file filter by parsing allowed file types\n    file_types = [s.replace(';', ' ') for s in file_types]\n    file_filter = ';;'.join(file_types)\n\n    i = list(BrowserView.instances.values())[0]\n    return i.create_file_dialog(dialog_type, directory, allow_multiple, save_filename, file_filter)\n\n\ndef evaluate_js(script, uid):\n    return BrowserView.instances[uid].evaluate_js(script)\n\n\nclass CefApplication(QApplication):\n    def __init__(self, args):\n        super(CefApplication, self).__init__(args)\n        self.timer = self.createTimer()\n        # self.setupIcon()\n\n    def createTimer(self):\n        timer = QTimer()\n        # noinspection PyUnresolvedReferences\n        timer.timeout.connect(self.onTimer)\n        timer.start(10)\n        return timer\n\n    def onTimer(self):\n        cef.MessageLoopWork()\n\n    def stopTimer(self):\n        # Stop the timer after Qt's message loop has ended\n        self.timer.stop()\n\n    def setupIcon(self):\n        icon_file = os.path.join(os.path.abspath(os.path.dirname(__file__)),\n                                 \"resources\", \"{0}.png\".format(sys.argv[1]))\n        if os.path.exists(icon_file):\n            self.setWindowIcon(QIcon(icon_file))\n","sub_path":"webview/qt.py","file_name":"qt.py","file_ext":"py","file_size_in_byte":16102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"251366094","text":"#-*- coding: utf-8 -*-\r\nfrom django.contrib import admin\r\nfrom django.http import HttpResponseRedirect\r\nfrom models import Parceiro\r\nfrom modulos.batchadmin.admin import BatchModelAdmin\r\nfrom modulos.utils import cropar_imagem_view\r\n  \r\nclass ParceiroAdmin(BatchModelAdmin) :\r\n    list_display = ['nome', 'link', 'admin_thumbnail']\r\n    search_fields = ['nome']\r\n    actions_on_bottom = False\r\n    \r\n    def response_add(self, request, obj, post_url_continue=\"../%s/\") :\r\n        redirect = super(ParceiroAdmin, self).response_add(request, obj) \r\n        if obj.imagem:\r\n            obj.criar_miniatura()\r\n            obj.criar_miniatura_admin()\r\n        request.method = \"GET\"    \r\n        request.session['obj_crop'] = obj\r\n        return cropar_imagem_view(request, obj.imagem.name, redirect)    \r\n\r\n    def response_change(self, request, obj) :\r\n        redirect = super(ParceiroAdmin, self).response_change(request, obj)\r\n        if obj.imagem:\r\n            obj.criar_miniatura()\r\n            obj.criar_miniatura_admin()\r\n        request.method = \"GET\"    \r\n        request.session['obj_crop'] = obj\r\n        return cropar_imagem_view(request, obj.imagem.name, redirect)\r\n    \r\nadmin.site.register(Parceiro, ParceiroAdmin)","sub_path":"modulos/parceiros/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"348255805","text":"# coding=utf-8\n\n# Username for MELcloud\nMELCLOUD_USER = \"xx@yy.com\"\n\n# Password for MELcloud\nMELCLOUD_PWD  = \"secret\"\n\n# Connection details of InfluxDB database\nINFLUXDB_HOST = \"influxhost.xxx.com\"\nINFLUXDB_PORT = 8086\nINFLUXDB_USER = \"raspi\"\nINFLUXDB_PWD  = \"secret\"\nINFLUXDB_DATABASE = \"raspidata\"\n\n","sub_path":"my_config.py","file_name":"my_config.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"575440708","text":"# Import libraries\nfrom dataclasses import dataclass\n\nimport numpy as np\nfrom h5py import File\nfrom pandas import DataFrame\nfrom sklearn.decomposition import PCA\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom tensorflow.keras import Input, Model\nfrom tensorflow.keras.callbacks import History, ModelCheckpoint, CSVLogger\nfrom tensorflow.keras.constraints import unit_norm\nfrom tensorflow.keras.layers import Conv1D, Dense, Dropout, Flatten, LeakyReLU\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.utils import to_categorical\n\nfrom scene import HyperspectralScene\n\n\n# Data class for a 3D-CNN\n@dataclass(init=False)\nclass Train1DCNN(HyperspectralScene):\n    X_scale: np.ndarray\n    X_PCA: np.ndarray\n    X_all: np.ndarray\n    X_train: np.ndarray\n    X_test: np.ndarray\n    X_valid: np.ndarray\n    y_train: np.ndarray\n    y_test: np.ndarray\n    y_valid: np.ndarray\n    y_pred: np.ndarray\n    y_test_pred: np.ndarray\n    model: Model\n    history: History\n\n    # Remove unlabeled data from X and y\n    def check_remove_unlabeled(self):\n        if self.remove_unlabeled:\n            self.X = self.X[self.y != 0, :]\n            self.y = self.y[self.y != 0] - 1\n\n    # Scale each feature to a given range\n    def fit_scaler(self, feature_range):\n        model_scale = MinMaxScaler(feature_range=feature_range)\n        self.X_scale = model_scale.fit_transform(X=self.X)\n\n    # Fit a PCA model\n    def fit_PCA(self, n_components, whiten):\n        model_PCA = PCA(n_components=n_components, whiten=whiten)\n        self.X_PCA = model_PCA.fit_transform(X=self.X_scale)\n        self.bands = self.X_PCA.shape[1]\n\n    # Split data into 60% training, 20% testing, and 20% validation\n    def prepare_data(self):\n        X_train, X_test, y_train, y_test = train_test_split(self.X_PCA,\n                                                            self.y,\n                                                            test_size=0.4,\n                                                            random_state=42,\n                                                            stratify=self.y)\n        X_test, X_valid, y_test, y_valid = train_test_split(X_test,\n                                                            y_test,\n                                                            test_size=0.5,\n                                                            random_state=42,\n                                                            stratify=y_test)\n        self.X_all = np.reshape(a=self.X_PCA, newshape=(-1, self.bands, 1))\n        self.X_train = np.reshape(a=X_train, newshape=(-1, self.bands, 1))\n        self.X_test = np.reshape(a=X_test, newshape=(-1, self.bands, 1))\n        self.X_valid = np.reshape(a=X_valid, newshape=(-1, self.bands, 1))\n        self.y_train = to_categorical(y=y_train)\n        self.y_test = y_test\n        self.y_valid = to_categorical(y=y_valid)\n\n    # Design a 1D-CNN model\n    def design_CNN_1D(self):\n        inputs = Input(shape=self.X_train.shape[1:])\n        x = Conv1D(filters=32,\n                   kernel_size=11,\n                   padding='causal',\n                   bias_constraint=unit_norm())(inputs)\n        x = LeakyReLU()(x)\n        x = Conv1D(filters=32,\n                   kernel_size=5,\n                   padding='causal',\n                   bias_constraint=unit_norm())(x)\n        x = LeakyReLU()(x)\n        x = Flatten()(x)\n        x = Dense(units=256, activation='relu')(x)\n        x = Dropout(rate=0.4)(x)\n        x = Dense(units=128, activation='relu')(x)\n        x = Dropout(rate=0.4)(x)\n        outputs = Dense(units=len(self.labels), activation='softmax')(x)\n        self.model = Model(inputs=inputs, outputs=outputs)\n\n    # Fit a 1D-CNN model and save the best model\n    def fit_CNN_1D(self, model_dir):\n        self.model.compile(optimizer=Adam(learning_rate=0.001),\n                           loss='categorical_crossentropy',\n                           metrics=['accuracy'])\n        best_weights = ModelCheckpoint(filepath=f\"{model_dir}/weights.hdf5\",\n                                       monitor='val_loss',\n                                       verbose=1,\n                                       save_best_only=True)\n        log = CSVLogger(filename=f\"{model_dir}/history.hdf5\")\n        self.history = self.model.fit(x=self.X_train,\n                                      y=self.y_train,\n                                      batch_size=256,\n                                      epochs=200,\n                                      verbose=2,\n                                      callbacks=[best_weights, log],\n                                      validation_data=(self.X_valid,\n                                                       self.y_valid))\n\n    # Predict data using the best model and save testing data and predictions\n    def predict_data(self, weights_path, data_dir):\n        self.model.load_weights(filepath=weights_path)\n        y_pred = self.model.predict(self.X_all)\n        y_test_pred = self.model.predict(self.X_test)\n        self.y_pred = np.argmax(a=y_pred, axis=1)\n        self.y_test_pred = np.argmax(a=y_test_pred, axis=1)\n        if self.remove_unlabeled:\n            self.y_test += 1\n            self.y_pred += 1\n            self.y_test_pred += 1\n        with File(name=f\"{data_dir}/y_test.hdf5\", mode='w') as file:\n            file.create_dataset(name='y_test', data=self.y_test)\n        with File(name=f\"{data_dir}/y_pred.hdf5\", mode='w') as file:\n            file.create_dataset(name='y_pred', data=self.y_pred)\n        with File(name=f\"{data_dir}/y_test_pred.hdf5\", mode='w') as file:\n            file.create_dataset(name='y_test_pred', data=self.y_test_pred)\n\n    # Save model training history\n    def save_history(self, history_dir):\n        accuracy = {'Training': self.history.history['accuracy'],\n                    'Validation': self.history.history['val_accuracy']}\n        loss = {'Training': self.history.history['loss'],\n                'Validation': self.history.history['val_loss']}\n        DataFrame.to_hdf(DataFrame.from_dict(accuracy),\n                         path_or_buf=f\"{history_dir}/accuracy.hdf5\",\n                         key='history',\n                         mode='w')\n        DataFrame.to_hdf(DataFrame.from_dict(loss),\n                         path_or_buf=f\"{history_dir}/loss.hdf5\",\n                         key='history',\n                         mode='w')\n\n    # Initialize other class attributes\n    def __post_init__(self):\n        self.check_remove_unlabeled()\n","sub_path":"scripts/CNN_1D.py","file_name":"CNN_1D.py","file_ext":"py","file_size_in_byte":6549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"224694757","text":"list_res_position = []\ndict_res_position_base = {}\n\nwith open(\"MTB_Base_Calibration_List.vcf\", 'r') as f:\n    for line in f:\n        if line.startswith(\"#\"):\n            next(f)\n        else:\n            list_line = line.split(\"\\t\")\n            position = list_line[1]\n            nucleotide = list_line[4].upper()\n            resistance = list_line[-1].split(\";RES=\")[-1]\n            list_res_position.append(position)\n            dict_res_position_base[position] = [nucleotide, resistance] \n            #print(position, nucleotide, resistance)\n\nwith open(\"MTB_Resistance_Mediating.txt\", 'r') as f:\n    for _ in range(1):\n        next(f)\n    for line in f:\n        list_line = line.split(\"\\t\")\n        if list_line[2] == \"SNP\":\n            position = list_line[1]\n            nucleotide = list_line[5].upper()\n            resistance = list_line[21]\n            if position not in list_res_position and not resistance.startswith(\"phylo\") :\n                list_res_position.append(position)\n                dict_res_position_base[position] = [nucleotide, resistance]\n\nprint(dict_res_position_base)","sub_path":"annotation/resistance/res_to_dict.py","file_name":"res_to_dict.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"278743126","text":"\n\nfrom xai.brain.wordbase.nouns._chord import _CHORD\n\n#calss header\nclass _CHORDS(_CHORD, ):\n\tdef __init__(self,): \n\t\t_CHORD.__init__(self)\n\t\tself.name = \"CHORDS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"chord\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_chords.py","file_name":"_chords.py","file_ext":"py","file_size_in_byte":231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"362186254","text":"DEFAULT_BOARD_SIZE = 3\n\nclass Board(object):\n    ''' Simple class which represents a TicTacToe game board.  '''\n    OPEN = ' '\n    X = 'X'\n    O = 'O'\n\n    class InvalidMove(ValueError): pass\n\n    def __init__(self, x_first=True, size=DEFAULT_BOARD_SIZE):\n        assert(size > 1)\n        self.x_first = x_first\n        self.size = size\n        self._state = self.OPEN * (size ** 2)\n        self._players = (self.O, self.X) if x_first else (self.X, self.O)\n        self._moves = []\n\n    def _mark_position(self, position, marker):\n        assert(len(marker) == 1)\n        self._state = self._state[:position] + marker + self._state[position + 1:]\n\n    def move(self, position, player):\n        ''' Places a move on the board at the provided position.\n            position is an integer in the range 0-(board size ** 2)\n            player is a boolean indicating the player who went first (True)\n            or second (False) '''\n        try:\n            if(self._state[position] != self.OPEN):\n                raise self.InvalidMove('That board position has already been played')\n        except IndexError:\n            raise self.InvalidMove('Invalid move location')\n        self._mark_position(position, self._players[player])\n        self._moves.append((position, player)) # add to the stack\n\n    def undo(self):\n        ''' Undo the last move made '''\n        position, player = self._moves.pop()\n        self._mark_position(position, self.OPEN)\n\n    def open_moves(self):\n        ''' Returns a tuple of state indices for board positions that have\n            not yet been played.  '''\n        s = self._state\n        return tuple(i for i in range(len(s)) if s[i] == self.OPEN)\n\n    def winner(self):\n        ''' Determines if the board state contains a winning condition.\n            Returns the winner (X or O) or None if no win.  '''\n        # Not optimized for speed.  If the AI is too slow calculating moves\n        # this is probably the first place to look.\n        state = self._state\n        board_size = self.size\n        diag1 = ''\n        diag2 = ''\n        for i in range(board_size):\n            # Check for horizontal win\n            s = state[i * board_size:(i + 1) * board_size] # One row\n            first = s[0]\n            if((first != self.OPEN) and (s.count(first) == len(s))):\n                return first\n            # Check for vertical win\n            s = ''.join(state[(j * board_size) + i] for j in range(board_size))\n            first = s[0]\n            if((first != self.OPEN) and (s.count(first) == len(s))):\n                return first\n            # Accumulate diagonals\n            diag1 += state[(board_size * i) + i]\n            diag2 += state[(board_size * i) + (board_size - i - 1)]\n\n        # Check diagonals\n        for s in (diag1, diag2):\n            first = s[0]\n            if((first != self.OPEN) and (s.count(first) == len(s))):\n                return first\n\n        return None\n\n    def draw(self):\n        ''' Is this game a draw?  '''\n        return not self.OPEN in self._state\n\n    def _board_string(self, state):\n        return '-----\\n'.join(\n            '|'.join(\n                state[i * self.size:(i + 1) * self.size]) + '\\n' for i in range(self.size))\n\n    def get_layout(self):\n        ''' Return a string showing the board layout with available moves\n            indicated by the move number for that spot. '''\n        state = self._state\n        return self._board_string(\n            ''.join(\n                state[i] if state[i] != self.OPEN else str(i + 1) for i in range(self.size ** 2)))\n\n    def __str__(self):\n        return self._board_string(self._state)\n\n\n","sub_path":"board.py","file_name":"board.py","file_ext":"py","file_size_in_byte":3630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"470684925","text":"import numpy as np\n\ndef write_mat(Rt, fp):\n   for i in range(Rt.shape[0]-1):\n      for j in range(Rt.shape[1]):\n         fp.write(str(Rt[i,j]) + \" \")\n   fp.write(\"\\n\")\n\nfp = open(\"path.txt\", \"w\")\nlength = 175.0 # meters\nnframes = 632-446 + 1\n\ndelta_z = length / nframes\n\nRt = np.eye(4,4)\n\nfor i in range(nframes):\n   write_mat(Rt, fp)\n   Rt[2,3] += delta_z\n\n\n","sub_path":"scripts/stereo_model/generate_straight_path.py","file_name":"generate_straight_path.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"303421546","text":"from django.db import models\nfrom wagtail.snippets.models import register_snippet\n# Create your models here.\n\n@register_snippet\nclass Testimonial (models.Model):\n\n    quote = models.TextField(\n        blank = False,\n        null = False,\n        max_length = 500,\n    )\n\n    attribution = models.CharField(\n        max_length = 50,\n        blank = False,\n        null = False,\n    )\n\n    def __str__(self):\n        return f\"{self.quote} by {self.attribution}\"\n\n    class Meta:\n        verbose_name = \"Testimonial\"\n        verbose_name_plural = \"Testimonials\"","sub_path":"testimonials/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"461377395","text":"from keras.models import Sequential\nfrom keras.layers.core import Dense, Activation\nfrom keras.optimizers import SGD\nimport numpy as np\n\ndef initialize_NN():\n\t# as first layer in a sequential model:\n\tmodel = Sequential()\n\t#model.add(Dense(80, init='uniform', activation='sigmoid', input_shape=(198,), batch_input_shape=(198,1)))\n\t#model.add(Dense(80, init='uniform', activation='sigmoid', input_shape=(1,198)))\n\tmodel.add(Dense(80, init='uniform', activation='sigmoid', input_dim=198))\n\tmodel.add(Dense(1,  activation='sigmoid', init='uniform'))\n\t# pass optimizer by name: default parameters will be used\n\tmodel.compile(loss='mean_absolute_error', optimizer=SGD(lr=0.01, momentum=0.7))\n\treturn model\n\ndef train_NN(model, board_t0, board_t1):\n\t#print(\"board_t1 in train_NN: \", board_t1)\n\tdata = board_t0\n\tif board_t1[0][0] == 123:\n\t\tlabel = np.array([1])\n\telif board_t1[0][0] == 321:\n\t\tlabel = np.array([0])\n\telse:\n\t\tlabel = model.predict(board_t1, batch_size=1)\n\t#print(\"train_NN label: \", label.shape)\n\tmodel.fit(data, label, batch_size=1, nb_epoch=1, verbose=1, callbacks=[], validation_split=0.0, shuffle=False)\n\treturn model","sub_path":"01_198_Array/old_backgammon_code/tdgammon_NN.py","file_name":"tdgammon_NN.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"351922253","text":"\ndef is_prime(num):\n\ti = 2\n\twhile i < num:\n\t\tif num % i == 0:\n\t\t\treturn False\n\t\ti += 1\n\treturn True\n\n# lpf - largest prime factor\ndef lpf(num):\n\ti = 2\n\twhile i <= num/2:\n\t\tif num % i == 0:\n\t\t\tif is_prime(num/i):\n\t\t\t\treturn num/i\n\t\ti += 1\n\nresult = lpf(600851475143)\nprint(result)","sub_path":"3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"263187917","text":"# function for checking character type\ndef ch_check(str1):\n    if \"a\" <= str1 <= \"z\":\n        return \"Lower\"\n    elif \"A\" <= str1 <= \"Z\":\n        return \"Upper\"\n    elif \"0\" <= str1 <= \"9\":\n        return \"Digit\"\n    else:\n        return None\n\n\n# opening file\nfob = open(input(\"Enter the file name :\")+\".txt\",\"r\")\nstr1 = fob.read().split()\nlower = 0\nupper = 0\ndigit = 0\n\n# checking character type for each letter\nfor i in str1:\n    lst1 = list(i.strip(\"\\n\").strip())\n    for j in lst1:\n        typ = ch_check(j)\n        if typ == \"Lower\":\n            lower += 1\n        elif typ == \"Upper\":\n            upper += 1\n        elif typ == \"Digit\":\n            digit += 1\n        else:\n            pass\n\nprint(\"Total Uppercase =\", upper)\nprint(\"Total Lowercase =\", lower)\nprint(\"Total digits =\", digit)","sub_path":"Q10/Q10.py","file_name":"Q10.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"500761670","text":"\"\"\"\n    Tests for case_study.routes.products. Note these are integration tests with only the external\n    calls to the product service mocked. Even the database is loaded with test data. This is mostly\n    to provide solid integration tests that tests the entire API all of the way through. Perhaps\n    after the prototyping period one may way to look into an in memory MongoDB mock service. Perhaps\n    similar to moto onto boto for AWS services.\n\"\"\"\n\nimport json\nimport unittest\nimport logging\nimport requests_mock\nfrom urllib.parse import urljoin\nfrom case_study.app import app\nfrom case_study.utils import load_docs\nfrom case_study.configure import app_config\n\nclass TestProductRoutes(unittest.TestCase):\n    def setUp(self):\n        # Lazily disabling logging for now for better deciphering of test output.\n        config = {}\n        app_config(config)\n        logging.disable(logging.CRITICAL)\n        self.app = app.test_client()\n        load_docs()\n        self.valid_doc = {\n            \"id\": 13860428,\n            \"name\": \"The Big Lebowski (Blu-ray)\",\n            \"current_price\": {\n                \"currency_code\": \"USD\",\n                \"value\": 13.49\n            }\n        }\n        self.product_url = urljoin(config[\"product_endpoint\"], str(self.valid_doc[\"id\"])) + \"?excludes={}\".format(config[\"product_endpoint_exclude_fields\"])\n        self.valid_redsky_response = {\n            \"product\": { \"item\": { \"product_description\": { \"title\": \"The Big Lebowski (Blu-ray)\"}}}\n        }\n\n    def test_get_entry(self):\n        with requests_mock.mock() as m:\n            m.get(self.product_url, text=json.dumps(self.valid_redsky_response))\n            r = self.app.get(\"/products/{}\".format(self.valid_doc[\"id\"]))\n\n        self.assertEqual(json.loads(r.get_data()), self.valid_doc)\n\n    def test_get_entry_non_existant(self):\n        r = self.app.get(\"/products/{}\".format(1234))\n\n        self.assertEqual(json.loads(r.get_data()), {\"code\": \"PRODUCT_NOT_FOUND\", \"message\": 'product with id \"1234\" was not found'})\n\n    def test_put_entry(self):\n        data = {\n            \"id\": 13860428,\n            \"current_price\": {\n                \"currency_code\": \"USD\",\n                \"value\": 100.00\n            }\n        }\n        self.valid_doc[\"current_price\"][\"value\"] = 100.00\n\n        with requests_mock.mock() as m:\n            m.get(self.product_url, text=json.dumps(self.valid_redsky_response))\n            r = self.app.put(\"/products/{}\".format(data[\"id\"]), data=json.dumps(data))\n\n        self.assertEqual(json.loads(r.get_data()), self.valid_doc)\n\n    def test_put_entry_invalid_requests(self):\n        data = {\n            \"id\": 13860428,\n            \"current_price\": {\n                \"the_money_thingy_ya_know_that_thing\": \"USD\",\n                \"value\": 100.00\n            }\n        }\n\n        r = self.app.put(\"/products/{}\".format(data[\"id\"]), data=json.dumps(data))\n\n        self.assertEqual(r.status_code, 400)\n\nif __name__ == \"__main__\":\n    unittest.main()\n","sub_path":"tests/routes/products.py","file_name":"products.py","file_ext":"py","file_size_in_byte":2980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"554996470","text":"from collections import namedtuple\nfrom contextlib import contextmanager\nfrom io import StringIO\nimport os\nfrom pathlib import Path\nfrom poethepoet.app import PoeThePoet\nfrom poethepoet.virtualenv import Virtualenv\nimport pytest\nimport shutil\nfrom subprocess import PIPE, Popen\nimport sys\nfrom tempfile import TemporaryDirectory\nimport tomlkit\nfrom typing import Any, List, Mapping, Optional\nimport venv\nimport virtualenv\n\nPROJECT_ROOT = Path(__file__).resolve().parent.parent\nPROJECT_TOML = PROJECT_ROOT.joinpath(\"pyproject.toml\")\n\n\n@pytest.fixture\ndef is_windows():\n    return sys.platform == \"win32\"\n\n\n@pytest.fixture\ndef pyproject():\n    with PROJECT_TOML.open(\"r\") as toml_file:\n        return tomlkit.parse(toml_file.read())\n\n\n@pytest.fixture\ndef poe_project_path():\n    return PROJECT_ROOT\n\n\n@pytest.fixture\ndef dummy_project_path():\n    return PROJECT_ROOT.joinpath(\"tests\", \"fixtures\", \"dummy_project\")\n\n\n@pytest.fixture\ndef venv_project_path():\n    return PROJECT_ROOT.joinpath(\"tests\", \"fixtures\", \"venv_project\")\n\n\n@pytest.fixture\ndef simple_project_path():\n    return PROJECT_ROOT.joinpath(\"tests\", \"fixtures\", \"simple_project\")\n\n\n@pytest.fixture\ndef scripts_project_path():\n    return PROJECT_ROOT.joinpath(\"tests\", \"fixtures\", \"scripts_project\")\n\n\n@pytest.fixture(scope=\"function\")\ndef temp_file(tmp_path):\n    # not using NamedTemporaryFile here because it doesn't work on windows\n    tmpfilepath = tmp_path / \"tmp_test_file\"\n    tmpfilepath.touch()\n    yield tmpfilepath\n\n\nPoeRunResult = namedtuple(\"PoeRunResult\", (\"code\", \"capture\", \"stdout\", \"stderr\"))\n\n\n@pytest.fixture(scope=\"function\")\ndef run_poe_subproc(dummy_project_path, temp_file, tmp_path, is_windows):\n    coverage_setup = (\n        \"from coverage import Coverage;\"\n        fr'Coverage(data_file=r\\\"{PROJECT_ROOT.joinpath(\".coverage\")}\\\").start();'\n    )\n    shell_cmd_template = (\n        'python -c \"'\n        \"{coverage_setup}\"\n        \"import tomlkit;\"\n        \"from poethepoet.app import PoeThePoet;\"\n        \"from pathlib import Path;\"\n        r\"poe = PoeThePoet(cwd=r\\\"{cwd}\\\", config={config}, output={output});\"\n        \"poe([{run_args}]);\"\n        '\"'\n    )\n\n    def run_poe_subproc(\n        *run_args: str,\n        cwd: str = dummy_project_path,\n        config: Optional[Mapping[str, Any]] = None,\n        coverage: bool = not is_windows,\n    ) -> str:\n        if config is not None:\n            config_path = tmp_path.joinpath(\"tmp_test_config_file\")\n            with config_path.open(\"w+\") as config_file:\n                toml.dump(config, config_file)\n                config_file.seek(0)\n            config_arg = fr\"tomlkit.parse(open(r\\\"{config_path}\\\", \\\"r\\\").read())\"\n        else:\n            config_arg = \"None\"\n\n        shell_cmd = shell_cmd_template.format(\n            coverage_setup=(coverage_setup if coverage else \"\"),\n            cwd=cwd,\n            config=config_arg,\n            run_args=\",\".join(f'r\\\\\"{arg}\\\\\"' for arg in run_args),\n            output=fr\"open(r\\\"{temp_file}\\\", \\\"w\\\")\",\n        )\n\n        env = dict(os.environ)\n        if coverage:\n            env[\"COVERAGE_PROCESS_START\"] = str(PROJECT_TOML)\n\n        poeproc = Popen(shell_cmd, shell=True, stdout=PIPE, stderr=PIPE, env=env)\n        task_out, task_err = poeproc.communicate()\n\n        with temp_file.open(\"rb\") as output_file:\n            captured_output = output_file.read().decode().replace(\"\\r\\n\", \"\\n\")\n\n        result = PoeRunResult(\n            code=poeproc.returncode,\n            capture=captured_output,\n            stdout=task_out.decode().replace(\"\\r\\n\", \"\\n\"),\n            stderr=task_err.decode().replace(\"\\r\\n\", \"\\n\"),\n        )\n        print(result)  # when a test fails this is usually useful to debug\n        return result\n\n    return run_poe_subproc\n\n\n@pytest.fixture(scope=\"function\")\ndef run_poe(capsys, dummy_project_path):\n    def run_poe(\n        *run_args: str,\n        cwd: str = dummy_project_path,\n        config: Optional[Mapping[str, Any]] = None,\n    ) -> str:\n        output_capture = StringIO()\n        poe = PoeThePoet(cwd=cwd, config=config, output=output_capture)\n        result = poe(run_args)\n        output_capture.seek(0)\n        return PoeRunResult(result, output_capture.read(), *capsys.readouterr())\n\n    return run_poe\n\n\n@pytest.fixture\ndef esc_prefix(is_windows):\n    \"\"\"\n    When executing on windows it's not necessary to escape the $ for variables\n    \"\"\"\n    if is_windows:\n        return \"\"\n    return \"\\\\\"\n\n\n@pytest.fixture(scope=\"function\")\ndef run_poe_main(capsys, dummy_project_path):\n    def run_poe_main(\n        *cli_args: str,\n        cwd: str = dummy_project_path,\n        config: Optional[Mapping[str, Any]] = None,\n    ) -> str:\n        from poethepoet import main\n\n        prev_cwd = os.getcwd()\n        os.chdir(cwd)\n        sys.argv = (\"poe\", *cli_args)\n        result = main()\n        os.chdir(prev_cwd)\n        return PoeRunResult(result, \"\", *capsys.readouterr())\n\n    return run_poe_main\n\n\n@pytest.fixture\ndef install_into_virtualenv():\n    def install_into_virtualenv(location: Path, contents: List[str]):\n        venv = Virtualenv(location)\n        Popen(\n            (venv.resolve_executable(\"pip\"), \"install\", *contents),\n            env=venv.get_env_vars(os.environ),\n            stdout=PIPE,\n            stderr=PIPE,\n        ).wait()\n\n    return install_into_virtualenv\n\n\n@pytest.fixture\ndef use_venv(install_into_virtualenv):\n    @contextmanager\n    def use_venv(\n        location: Path,\n        contents: Optional[List[str]] = None,\n        require_empty: bool = False,\n    ):\n        did_exist = location.is_dir()\n        assert not require_empty or not did_exist, (\n            f\"Test requires no directory already exists at {location}, \"\n            \"maybe try delete it and run again\"\n        )\n\n        # create new venv\n        venv.EnvBuilder(symlinks=True, with_pip=True,).create(str(location))\n\n        if contents:\n            install_into_virtualenv(location, contents)\n\n        yield\n        # Only cleanup if we actually created it to avoid this fixture being a bit dangerous\n        if not did_exist:\n            shutil.rmtree(location)\n\n    return use_venv\n\n\n@pytest.fixture\ndef use_virtualenv(install_into_virtualenv):\n    @contextmanager\n    def use_virtualenv(\n        location: Path,\n        contents: Optional[List[str]] = None,\n        require_empty: bool = False,\n    ):\n        did_exist = location.is_dir()\n        assert not require_empty or not did_exist, (\n            f\"Test requires no directory already exists at {location}, \"\n            \"maybe try delete it and run again\"\n        )\n\n        # create new virtualenv\n        virtualenv.cli_run([str(location)])\n\n        if contents:\n            install_into_virtualenv(location, contents)\n\n        yield\n        # Only cleanup if we actually created it to avoid this fixture being a bit dangerous\n        if not did_exist:\n            shutil.rmtree(location)\n\n    return use_virtualenv\n\n\n@pytest.fixture\ndef with_virtualenv_and_venv(use_venv, use_virtualenv):\n    def with_virtualenv_and_venv(\n        location: Path, contents: Optional[List[str]] = None,\n    ):\n        with use_venv(location, contents, require_empty=True):\n            yield\n\n        with use_virtualenv(location, contents, require_empty=True):\n            yield\n\n    return with_virtualenv_and_venv\n","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":7307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"483751767","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('blog', '0012_remove_entry_slug'),\n    ]\n\n    operations = [\n        migrations.RenameModel(\n            old_name='Entry',\n            new_name='Post',\n        ),\n        migrations.AlterModelOptions(\n            name='post',\n            options={'ordering': ['-created'], 'verbose_name_plural': 'Blog Entries', 'verbose_name': 'Blog Post'},\n        ),\n    ]\n","sub_path":"blog/blog/migrations/0013_auto_20150702_1025.py","file_name":"0013_auto_20150702_1025.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"494576964","text":"#!/usr/bin/env python\n\nfrom pyquery import PyQuery as pq\nfrom bike import bike\nfrom copy import deepcopy\nimport re\n\ndef extract_urls(req):\n\turls = set()\n\n\tdomain = 'https://www.bikes2race.de'\n\tw = pq(req[\"html\"])\n\n\tnext_pages = w(\"div.articlenav>a\")\n\tfor next_page in next_pages:\n\t\tnext_page = w(next_page)\n\t\tif \"next\" in next_page.attr(\"rel\"):\n\t\t\turls.add(next_page.attr(\"href\"))\n\n\tbikes = w(\"div.content_highlights div.produkt_info div.produkt_picture a\")\n\tfor item in bikes:\n\t\titem = pq(item)\n\t\turls.add(domain+item.attr(\"href\"))\n\n\treturn list(urls)\n\n\ndef extract_data(req):\n\tdata = deepcopy(bike)\n\tdomain = 'https://www.bikes2race.de'\n\tw = pq(req[\"html\"])\n\n\tis_bike = w(\"div.singlearticleheader h1\")\n\tif is_bike:\n\t\tname = w(\"div.singlearticleheader h1\").text().strip()\n\t\tif name:\n\t\t\tdata[\"name\"] = name\n\t\t\tyear = re.search(r\"(\\d{4})\", name)\n\t\t\tif year:\n\t\t\t\tdata[\"year\"] = year.group(1)\n\t\tdata[\"currency\"] = \"EUR\"\n\t\tdata[\"external_source_id\"] = w(\"div.infovalue\").eq(2).text().strip()\n\t\tbrand = w(\"div.infovalue a font font\").text().strip()\n\t\tif brand:\n\t\t\tdata[\"brand\"] = brand\n\t\timage = w(\"a.zoom img\").attr(\"src\")\n\t\tif image:\n\t\t\tdata[\"image\"] = domain+image\n\t\told_price = w(\"span.strike font font\").text().strip()\n\t\tif old_price:\n\t\t\tdata[\"price\"] = old_price[4:-3]\n\t\t\tdata[\"discounted_price\"] = w(\"div.singleprice span\").text().strip()[:-3]\n\t\telse:\n\t\t\tdata[\"discounted_price\"] = w(\"div.singleprice span\").text().strip()[:-3]\n\t\t\tdata[\"price\"] = w(\"div.singleprice span\").text().strip()[:-3]\n\n\t\tavailability = dict()\n\t\tsizes = w(\"div.PlentyFormContainer.ArticleAttrTd_1.PlentyWebAttributeSelect select option\")\t\n\t\tfor size in sizes:\n\t\t\titem = pq(size)\n\t\t\tsize = item.text()\n\t\t\t\t\t\t\n\t\t\tif size:\n\t\t\t\t#avail = w(\"div.infovalue span font font\").text().strip()\n\t\t\t\tavailability[size] = w(\"div.infovalue span\").text().strip()\n\t\tdata[\"availability\"] = availability\t\n\n\t\ttech_specs = str()\n\t\tbike_specs = w(\"table.bike-specifications tr\")\n\t\tfor item in bike_specs:\n\t\t\titem = pq(item)\n\t\t\t#print item\n\t\t\tlabel = item.find('th').text().strip()\n\t\t\titem_data = item.find('td').text().strip()\n\t\t\ttech_specs += label + \":\" + item_data + \"||\"\n\t\t\t\n\t\tdata[\"tech_specs\"] = tech_specs\n\t\tdesc = w(\"div#articledescription ul li\").text().strip()\n\t\tif desc:\n\t\t\tdata[\"description\"] = desc + \"; \"\n\t\telif w(\"div.content.lv-verdana font font\"):\n\t\t\tdata[\"description\"] = desc + \"; \"\n\t\telif w(\"div#articledescription div li font font\"):\n\t\t\tdata[\"description\"] = desc + \"; \"\n\t\t#elif w(\"div#articledescription ul li\"):\n\t\t#\tdata[\"description\"] = desc\n\t\telif w(\"div.lv-verdana ul li\"):\n\t\t\tdata[\"description\"] = desc + \"; \"\n\t#make that > 3\n\tif len([item for item in data if data[item] != \"N/A\"]) >= 1:\n\t\treturn data\n\treturn {}\n","sub_path":"extractors/bikes2race.py","file_name":"bikes2race.py","file_ext":"py","file_size_in_byte":2693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"276485733","text":"# coding: utf8\n\n\"\"\"\n    Web spiders, for crawling free ip proxies.\n\"\"\"\n\nimport time\n\nimport requests\nfrom bs4 import BeautifulSoup\n\nimport conn\nimport const\n\n\nclass Spider(object):\n    url = 'http://www.xicidaili.com/wt'\n\n    def __init__(self, page_num=5):\n        \"\"\"\n        :param page_num:\n        \"\"\"\n        self.page_num = page_num\n\n    def request_html(self, url):\n        \"\"\"\n        :param url:\n        :return:\n        \"\"\"\n        headers = {\n            'user-agent': const.USER_AGENT,\n            'referer': const.REFER_URL\n        }\n        resp = None\n        try:\n            r = requests.get(url, headers=headers)\n            if r.status_code == 200:\n                resp = r.text\n        except requests.RequestException as e:\n            print(e)\n        finally:\n            time.sleep(2)\n\n        return resp\n\n    @property\n    def proxies(self):\n        \"\"\"\n        :return:\n        \"\"\"\n        urls = ['{url}/{page}'.format(url=self.url, page=i + 1) for i in xrange(self.page_num)]\n        for url in urls:\n            html = self.request_html(url)\n            if not html:\n                print('fetch html of url %s failed.' % url)\n                continue\n            soup = BeautifulSoup(html, 'html.parser')\n            tr = soup.find('table', {'id': 'ip_list'}).find_all('tr')[1:]\n            for proxy in tr:\n                td = proxy.find_all('td')\n                ip = td[1].get_text()\n                port = td[2].get_text()\n                yield ':'.join([ip, port])\n\n    def publish(self):\n        \"\"\"\n        :return:\n        \"\"\"\n        proxies = [proxy for proxy in self.proxies]\n        conn.RedisConnection().sadd('proxies', *proxies)\n","sub_path":"backend/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":1677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"637269569","text":"# -*- coding:utf-8 -*-\r\nfrom django import forms\r\nfrom django.forms.fields import ChoiceField, BooleanField\r\nfrom django.forms.models import ModelChoiceField\r\nfrom django.utils.translation import ugettext_lazy as _\r\n\r\nfrom channel.models import Channel\r\nfrom group.models import Theme\r\nfrom language.models import Language\r\n\r\nTEMPLATE_FAC_WITH_1_NEWS = 1\r\nTEMPLATE_FAC_WITH_1_NEWS_LARGE = 7\r\nTEMPLATE_FAC_WITH_3_NEWS = 6\r\nTEMPLATE_FAC_WITH_4_NEWS = 3\r\n\r\nTEMPLATE_LAB_WITH_3_NEWS = 4\r\nTEMPLATE_LAB_WITH_ALL_NEWS = 10 \r\n\r\nTEMPLATE_TEXT_LAYOUT = 2\r\nTEMPLATE_RIGHT_COLUMN = 5\r\n\r\nTEMPLATE_HOMEPAGE_FACULTY_RESPONSIVE = 11\r\nTEMPLATE_RESPONSIVE = 12\r\n\r\n\r\nNB_CHOICES_TEMPATES = [\r\n    (TEMPLATE_LAB_WITH_3_NEWS, _('Laboratories/Services layout with 3 News')),\r\n    (TEMPLATE_LAB_WITH_ALL_NEWS,_('Laboratories/Services layout with all News')),\r\n    \r\n    (TEMPLATE_FAC_WITH_1_NEWS, _('Faculty layout with 1 news')),\r\n    (TEMPLATE_FAC_WITH_1_NEWS_LARGE, _('Faculty layout with 1 news - large')),\r\n    (TEMPLATE_FAC_WITH_3_NEWS, _('Faculty layout with 3 news')),\r\n    (TEMPLATE_FAC_WITH_4_NEWS, _('Faculty layout with 4 news')),\r\n    \r\n    (TEMPLATE_TEXT_LAYOUT, _('Text layout')),\r\n    #(TEMPLATE_RIGHT_COLUMN, _('Right column')),\r\n\r\n    #(TEMPLATE_RESPONSIVE, _('Responsive Template')),\r\n]\r\n\r\nclass WebserviceForm(forms.Form):\r\n    \r\n    language = forms.ModelChoiceField(widget=forms.RadioSelect, queryset=Language.objects.all(), empty_label=None, initial=1)\r\n    themes = forms.ModelMultipleChoiceField(required=False, widget=forms.CheckboxSelectMultiple, queryset=Theme.objects.all())\r\n    projects = forms.MultipleChoiceField(required=False, widget=forms.CheckboxSelectMultiple)\r\n    templates = forms.ChoiceField(widget=forms.RadioSelect, choices=NB_CHOICES_TEMPATES, initial=4)\r\n\t#category = ChoiceField(required=False)\r\n    sticker = BooleanField(required=False)\r\n    \r\n    def set_projects(self, id_channel):\r\n \r\n        selected_channel = Channel.objects.get(id=id_channel)\r\n        \r\n        projects_of_selected_channel = selected_channel.get_projects('fr')\r\n        projects_of_selected_channel.insert(0, [0, _(\"Don't filter by section\")])\r\n                \r\n        self.fields[\"projects\"].choices = projects_of_selected_channel\r\n        self.fields[\"projects\"].initial = '0'\r\n        \r\n    def clean(self):\r\n        \"\"\" Clean project field \"\"\"\r\n        \r\n        cleaned_data = self.cleaned_data\r\n        data = self.data\r\n        \r\n        # If channel has no project, we don't need of project field\r\n        if \"projects\" in cleaned_data:\r\n            del cleaned_data[\"projects\"]\r\n            \r\n        # If channel has projects, we must add project field\r\n        # and check if the selected channel has the selected project\r\n        if \"projects\" in data:\r\n            \r\n            projects = data.getlist(\"projects\")\r\n            \r\n            for id_project in projects:          \r\n                channel = Channel.objects.get(id=data[\"id_channel\"])\r\n                \r\n                if int(id_project) == 0:\r\n                    # User has selected all projects\r\n                    pass  \r\n                elif channel.has_project(id_project):\r\n                    pass\r\n                else:\r\n                    raise forms.ValidationError(\"The selected channel and the selected project are incompatible\")\r\n            \r\n            cleaned_data[\"projects\"] = projects\r\n            \r\n        # Validation is done by us so we can delete the Django validation\r\n        if \"projects\" in self.errors:\r\n            del self.errors[\"projects\"]\r\n            \r\n        return cleaned_data\r\n    \r\n############################################################################\r\n         \r\nNB_CHOICES_TEMPATES_SCOTCH = [\r\n    (3, 'Scotch'),\r\n    (1, 'Scotch_portail')\r\n]                \r\nclass WebserviceJahiaScotchForm(forms.Form):\r\n    lang     = ChoiceField(choices=Language.objects.all())\r\n    channel  = ChoiceField(choices=())\r\n    template = ChoiceField(choices=NB_CHOICES_TEMPATES_SCOTCH)\r\n","sub_path":"src/webservice/form.py","file_name":"form.py","file_ext":"py","file_size_in_byte":4005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"159094882","text":"import pandas as pd\ndf = pd.read_csv('rel2.csv',dtype=str)\ndf = df.rename(columns={'subject':'source', 'object':\"target\", 'relation':'weight'})\ndf = df.drop_duplicates(['source', 'target']).reset_index().dropna()\n\nG = nx.from_pandas_edgelist(df.dropna(), \"source\", 'target')\n\n\nimport networkx as nx\n\nfrom bokeh.io import output_file, show\nfrom bokeh.models import CustomJSTransform, LabelSet\nfrom bokeh.models.graphs import from_networkx\n\nfrom bokeh.plotting import figure\n\n#G=nx.nx.barbell_graph(3,2)\n\np = figure(x_range=(-3.5,3.5), y_range=(-3.5,3.5),  plot_height=1000, plot_width=1800)\np.grid.grid_line_color = None\n\nr = from_networkx(G, nx.spring_layout, scale=3, center=(0,0))\nr.node_renderer.glyph.size=15\nr.edge_renderer.glyph.line_alpha=0.2\n\np.renderers.append(r)\n\nfrom bokeh.transform import transform\n\n# add the labels to the edge renderer data source\nsource = r.edge_renderer.data_source\n#source.data['names'] = [\"{x}-{y}\".format(x=x, y=y) for (x,y) in zip(source.data['start'], source.data['end'])]\nsource.data['names'] = df['weight'].values\n# create a transform that can extract and average the actual x,y positions\ncode = \"\"\"\n    var result = new Float64Array(xs.length)\n    coords = provider.get_edge_coordinates(source)[%s]\n    for (var i = 0; i < xs.length; i++) {\n        result[i] = (coords[i][0] + coords[i][1])/2\n    }\n    return result\n\"\"\"\nxcoord = CustomJSTransform(v_func=code % \"0\", args=dict(provider=r.layout_provider, source=source))\nycoord = CustomJSTransform(v_func=code % \"1\", args=dict(provider=r.layout_provider, source=source))\n\n# Use the transforms to supply coords to a LabelSet\nlabels = LabelSet(x=transform('start', xcoord),\n                  y=transform('start', ycoord),\n                  text='names', text_font_size=\"12px\",\n                  x_offset=5, y_offset=5,\n                  source=source, render_mode='canvas')\n\np.add_layout(labels)\noutput_file('edges.html')\nshow(p)\n","sub_path":"graphes/edges.py","file_name":"edges.py","file_ext":"py","file_size_in_byte":1920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"134897995","text":"# 寻找最长回文子串\n\n# 方法一：\n\n# 找到字符串的所有子串，遍历每一个子串以验证它们是否为回文串。\n# 一个子串由子串的起点和终点确定，因此对于一个长度为n的字符串，共有n^2个子串。\n# 这些子串的平均长度大约是n/2，因此这个解法的时间复杂度是O(n^3)。\n# class Solution(object):\n#     def longestPalindrome(self, s):\n#         \"\"\"\n#         :type s: str\n#         :rtype: str\n#         \"\"\"\n#         result = ''\n#         strs = ''\n        \n#         for i in range(0, len(s)):\n#             n = i + 1\n#             while(n <= len(s)):\n#                 if not self.isPalindromic(s[i: n]):\n#                     n = n + 1\n#                     continue\n#                 else:\n#                     strs = s[i: n]\n#                     n = n + 1\n#             if len(strs) > len(result):\n#                 result = strs\n#             strs = ''\n#         return result\n    \n#     def isPalindromic(self, s):\n#         index = len(s)\n#         if index % 2 == 0:\n#             return s[0: index//2] == s[index//2:][::-1]\n#         else:\n#             return s[0: (index - 1)// 2] == s[(index + 1)// 2:][::-1]\n\n\n# 方法二：\n \nclass Solution:\n    # @return a string\n    def longestPalindrome(self, s):\n        if len(s)==0:\n            return 0\n        maxLen=1\n        start=0\n        for i in range(len(s)):\n            print(s[i], i, maxLen, start, s[i-maxLen-1:i+1], s[i-maxLen-1:i+1][::-1],s[i-maxLen:i+1],s[i-maxLen:i+1][::-1])\n            if i-maxLen >=1 and s[i-maxLen-1:i+1]==s[i-maxLen-1:i+1][::-1]:\n                start=i-maxLen-1\n                maxLen+=2\n                continue\n\n            if i-maxLen >=0 and s[i-maxLen:i+1]==s[i-maxLen:i+1][::-1]:\n                start=i-maxLen\n                maxLen+=1\n        return s[start:start+maxLen]\np = Solution()\nprint(p.longestPalindrome('acccccdddaaaaaa'))","sub_path":"LetCode/005-Longest-Palindromic-Substring.py","file_name":"005-Longest-Palindromic-Substring.py","file_ext":"py","file_size_in_byte":1912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"509733137","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom graph.weights_initializer import weights_init\n\n\nclass ResNetBlock(nn.Module):\n    def __init__(self, _in, _out):\n        super(ResNetBlock, self).__init__()\n\n        self.conv_seq = nn.Sequential(\n            nn.Conv2d(_in, _in, kernel_size=3, stride=1, padding=1, bias=False),\n            nn.BatchNorm2d(_in),\n            nn.LeakyReLU(0.2, inplace=True),\n            nn.Conv2d(_in, _in, kernel_size=3, stride=1, padding=1, bias=False),\n            nn.BatchNorm2d(_in),\n            nn.LeakyReLU(0.2, inplace=True),\n            nn.Conv2d(_in, _out, kernel_size=3, stride=2, padding=1, bias=False),\n            nn.BatchNorm2d(_out),\n            nn.LeakyReLU(0.2, inplace=True),\n        )\n\n        self.apply(weights_init)\n\n    def forward(self, x):\n        return self.conv_seq(x)\n\n\nclass ReduceBlock(nn.Module):\n    def __init__(self, _in, _out):\n        super(ReduceBlock, self).__init__()\n\n        self.conv_seq = nn.Sequential(\n            nn.Conv2d(_in, _in, kernel_size=3, stride=1, padding=1, bias=False),\n            nn.BatchNorm2d(_in),\n            nn.LeakyReLU(0.2, inplace=True),\n            nn.Conv2d(_in, _in, kernel_size=3, stride=1, padding=1, bias=False),\n            nn.BatchNorm2d(_in),\n            nn.LeakyReLU(0.2, inplace=True),\n            nn.Conv2d(_in, _out, kernel_size=3, stride=[2, 1], padding=1, bias=False),\n            nn.BatchNorm2d(_out),\n            nn.LeakyReLU(0.2, inplace=True),\n        )\n\n        self.apply(weights_init)\n\n    def forward(self, x):\n        return self.conv_seq(x)\n\n\nclass Reduce(nn.Module):\n    def __init__(self, _in):\n        super(Reduce, self).__init__()\n\n        self.conv_seq = nn.Sequential(\n            ReduceBlock(_in, _in // 2),\n            ReduceBlock(_in // 2, _in // 2),\n            ReduceBlock(_in // 2, _in // 4),\n            ReduceBlock(_in // 4, _in // 8),\n        )\n\n        self.apply(weights_init)\n\n    def forward(self, x):\n        return self.conv_seq(x)\n\n\nclass FeatureExtract(nn.Module):\n    def __init__(self):\n        super(FeatureExtract, self).__init__()\n\n        self.block0 = ResNetBlock(3, 128)\n        self.block1 = ResNetBlock(128, 256)\n        self.block2 = ResNetBlock(256, 512)\n        self.block3 = ResNetBlock(512, 1024)\n        self.block4 = ResNetBlock(1024, 2048)\n\n        self.reduce1 = Reduce(256)\n        self.reduce2 = Reduce(512)\n        self.reduce3 = Reduce(1024)\n        self.reduce4 = Reduce(2048)\n\n        self.apply(weights_init)\n\n    def forward(self, x):\n        inter = self.block0(x)\n\n        inter_128 = self.block1(inter)\n        inter_64 = self.block2(inter_128)\n        inter_32 = self.block3(inter_64)\n        inter_16 = self.block4(inter_32)\n        \n        reduce1 = self.reduce1(inter_128).view(-1, 256, 1, 256)\n        reduce2 = F.interpolate(self.reduce2(inter_64), size=(4, 256),\n                                mode='bilinear', align_corners=False).view(-1, 256, 1, 256)\n        reduce3 = F.interpolate(self.reduce3(inter_32), size=(2, 256),\n                                mode='bilinear', align_corners=False).view(-1, 256, 1, 256)\n        reduce4 = F.interpolate(self.reduce4(inter_16), size=(1, 256),\n                                mode='bilinear', align_corners=False).view(-1, 256, 1, 256)\n        \n        feature = torch.cat((reduce1, reduce2, reduce3, reduce4), dim=1)    # 1024 1 256\n        feature = feature.reshape(-1, 1024, 256).permute(2, 0, 1)\n\n        return feature\n\n\nclass HorizonBase(nn.Module):\n    def __init__(self):\n        super(HorizonBase, self).__init__()\n\n        self.feature_extract = FeatureExtract()\n\n        self.lstm = nn.LSTM(input_size=1024,\n                            hidden_size=512,\n                            num_layers=2,\n                            dropout=0.5,\n                            batch_first=False,\n                            bidirectional=True)\n        self.drop_out = nn.Dropout(0.5)\n\n        self.linear = nn.Linear(in_features=2 * 512,\n                                out_features=12)\n\n        self.lrelu = nn.LeakyReLU(0.2, inplace=True)\n\n        self.apply(weights_init)\n\n    def forward(self, x):\n        batch_size = x.size(0)\n\n        feature = self.feature_extract(x)\n        \n        output, _ = self.lstm(feature)  # 256 b 1024\n        output = self.drop_out(output)\n\n        output = self.lrelu(self.linear(output))\n        output = output.view(256, batch_size, 3, 4) # 256 b 12\n        output = output.permute(1, 2, 0, 3) # b 3 256 4\n\n        return output   # b 3 256 4\n\n\nclass Corner(nn.Module):\n    def __init__(self):\n        super(Corner, self).__init__()\n\n        self.conv1 = nn.Conv2d(3, 2, kernel_size=[1, 3], stride=1, padding=[0, 1], bias=False)\n        self.conv2 = nn.Conv2d(2, 1, kernel_size=[1, 3], stride=1, padding=[0, 1], bias=False)\n        \n        self.lrelu = nn.LeakyReLU(0.2, inplace=True)\n        self.sigmoid = nn.Sigmoid()\n\n        self.apply(weights_init)\n\n    def forward(self, horizon_output):\n        horizon_output = horizon_output.contiguous().view(horizon_output.size(0), 3, 1, -1) # b 3 1 1024\n        \n        corner = self.lrelu(self.conv1(horizon_output))\n        corner = self.sigmoid(self.conv2(corner))\n        \n        return corner.view(-1, 1, 1024)\n\n\nclass FloorMap(nn.Module):\n    def __init__(self):\n        super().__init__()\n\n        self.deconv_1 = nn.ConvTranspose2d(in_channels=3, out_channels=3, kernel_size=[1, 8], stride=[1, 8], bias=False)\n        self.deconv_2 = nn.ConvTranspose2d(in_channels=3, out_channels=2, kernel_size=[1, 8], stride=[1, 8], bias=False)\n        self.deconv_3 = nn.ConvTranspose2d(in_channels=2, out_channels=1, kernel_size=4, stride=2,\n                                           padding=1, bias=False)\n\n        self.sigmoid = nn.Sigmoid()\n        self.lrelu = nn.LeakyReLU(0.2, inplace=True)\n\n        self.apply(weights_init)\n\n    def forward(self, horizon_output):\n        output = self.lrelu(self.deconv_1(horizon_output))\n        output = self.lrelu(self.deconv_2(output))\n        output = self.sigmoid(self.deconv_3(output))\n\n        return output","sub_path":"graph/model/horizon_base.py","file_name":"horizon_base.py","file_ext":"py","file_size_in_byte":6096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"649544499","text":"#Q.No.1\r\n\r\nprint(\"The exception which occurred in this program is 'ZeroDivisionError'\")\r\ntry:\r\n    a=3\r\n    if a<4:\r\n        a=a/(a-3)\r\n        print(a)\r\nexcept ZeroDivisionError:\r\n    print(\"can't divide by zero, please enter value either greater than 3 or less than 3\")\r\nelse:\r\n    print(\"value of a is: \",a)\r\n\r\n#Q.NO.2\r\n\r\nprint(\"The exception which occurred in this program is 'IndexError'\")\r\ntry:\r\n    l=[1,2,3]\r\n    l1=l[3]\r\n    print(l1)\r\nexcept IndexError:\r\n    print(\"please enter valid index i.e. less than or equal to 2\")\r\nelse:\r\n    print(\"value of extracted list item is: \",l1)\r\n\r\n#Q.NO.3\r\n\r\ntry:\r\n    raise NameError(\"hi there\")\r\nexcept NameError:\r\n    print(\"an exception\")\r\nprint(\"output of given above program is 'an exception'\")\r\n\r\n#Q.NO.4\r\n\r\ndef AbyB(a, b):\r\n    try:\r\n        c = ((a+b)/(a-b))\r\n    except ZeroDivisionError:\r\n        print(\"a/b result in 0\")\r\n    else:\r\n        print(c)\r\n\r\nAbyB(2.0, 3.0)\r\nAbyB(3.0, 3.0)\r\n\r\nprint(\"output on first calling of function is '-5.0'\")\r\nprint(\"output on second calling of function is 'a/b result in 0'\")\r\n\r\n#Q.NO.5.1\r\n\r\ntry:\r\n    import rakesh\r\nexcept ImportError:\r\n    print(\"can't import this module, please import valid module\")\r\n\r\n#Q.NO.5.2\r\n\r\ntry :\r\n    a = int(input(\"enter a number: \"))\r\nexcept ValueError:\r\n    print(\"please enter valid integer\")\r\nelse:\r\n    print(\"value of a is \",a)\r\n\r\n#Q.NO.5.3\r\n\r\ntry:\r\n    a=[1,2,3,4,5]\r\n    b=a[5]\r\n    print(b)\r\nexcept IndexError:\r\n    print(\"please enter valid index i.e. less than or equal to 2\")\r\nelse:\r\n    print(\"value of extracted list item is: \",b)\r\n\r\n#Q.NO.6\r\n\r\nclass AgeTooSmall(Exception):\r\n    pass\r\n\r\nvalue = True\r\nwhile value:\r\n    try:\r\n        a = int(input(\"enter age: \"))\r\n        if a<18:\r\n            raise AgeTooSmall\r\n    except ValueError:\r\n        print(\"please enter age in integer\")\r\n    except AgeTooSmall:\r\n        print(\"your age is less than 18\")\r\n    else:\r\n        print(\"your are eligible\")\r\n        value = False\r\n\r\n\r\n","sub_path":"assignment12.py","file_name":"assignment12.py","file_ext":"py","file_size_in_byte":1962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"597069607","text":"import bson.json_util\nfrom bson.objectid import ObjectId\nimport json\nfrom pymongo import MongoClient\n\n\ndef run(host=None, db=None, coll=None, spec=None):\n    # Connect to the mongo collection.\n    client = MongoClient(host)\n    db = client[db]\n    graph = db[coll]\n\n    spec = json.loads(spec)\n    spec2 = {\"type\": \"node\"}\n    for k, v in spec.iteritems():\n        if k == \"key\":\n            spec2[\"_id\"] = ObjectId(v)\n        else:\n            spec2[\"data.%s\" % (k)] = v\n\n    result = list(graph.find(spec2))\n\n    return bson.json_util.dumps(result)\n","sub_path":"tangelo/mongo/web/findNodes.py","file_name":"findNodes.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"113535480","text":"# import the necessary packages\nimport gc\nimport io\nimport os\nimport time\nfrom threading import Thread\nfrom timeit import default_timer as timer\n\nimport flask\nimport numpy as np\nimport tensorflow as tf\nfrom PIL import Image\nfrom keras import backend as K\nfrom keras.applications import ResNet50\nfrom keras.applications import imagenet_utils\nfrom keras.models import load_model\nfrom keras.preprocessing.image import img_to_array\n\nfrom dead_ends.yolo_handler import run_on_image, run_on_single_crop\nfrom dead_ends.yolo_handler import use_path_which_exists\n\n# Thanks to the tutorial at: https://blog.keras.io/building-a-simple-keras-deep-learning-rest-api.html\n\napp = flask.Flask(__name__)\ndarkflow_model = None\nmodel = None\nREUSE = False\n\ndef load_model_resnet():\n    global model\n    model = ResNet50(weights=\"imagenet\")\n    global graph\n    graph = tf.get_default_graph()\n\ndef load_model_yolo(model_path):\n    #global graph\n    #graph = tf.get_default_graph()\n\n    model_path = os.path.expanduser(model_path)\n    print(model_path)\n    assert model_path.endswith('.h5'), 'Keras model must be a .h5 file.'\n\n    global darkflow_model\n    yolo_model = load_model(model_path)\n    print('{} model, anchors, and classes loaded.'.format(model_path))\n\n    global sess\n    sess = K.get_session()\n\n    global graph\n    graph = tf.get_default_graph()\n\n    global REUSE\n    REUSE = False\n\ndef ReuseTrue():\n    global REUSE\n    REUSE = True\n\ndef prepare_image(image, target):\n   # if the image mode is not RGB, convert it\n   if image.mode != \"RGB\":\n      image = image.convert(\"RGB\")\n\n   # resize the input image and preprocess it\n   image = image.resize(target)\n   image = img_to_array(image)\n   image = np.expand_dims(image, axis=0)\n   image = imagenet_utils.preprocess_input(image)\n\n   # return the processed image\n   return image\n\n@app.route(\"/time_transfer\", methods=[\"POST\"])\ndef time_transfer():\n   # Time transferring and loading the file\n\n   data = {\"success\": False}\n   start = timer()\n\n   if flask.request.method == \"POST\":\n      if flask.request.files.get(\"image\"):\n         image = flask.request.files[\"image\"].read()\n         image = Image.open(io.BytesIO(image))\n\n         data[\"imageshape\"] = image.size\n\n         end = timer()\n         data[\"internal_time\"] = end - start\n         data[\"success\"] = True\n\n\n   # return the data dictionary as a JSON response\n   return flask.jsonify(data)\n\n\n@app.route(\"/predict\", methods=[\"POST\"])\ndef predict():\n   # Evaluate data\n   data = {\"success\": False}\n\n   if flask.request.method == \"POST\":\n      if flask.request.files.get(\"image\"):\n         # read the image in PIL format\n         image = flask.request.files[\"image\"].read()\n         image = Image.open(io.BytesIO(image))\n\n         image = prepare_image(image, target=(224, 224))\n\n         with graph.as_default():\n            # evaluate image\n            preds = model.predict(image)\n            results = imagenet_utils.decode_predictions(preds)\n            data[\"predictions\"] = []\n\n            for (imagenetID, label, prob) in results[0]:\n               r = {\"label\": label, \"probability\": float(prob)}\n               data[\"predictions\"].append(r)\n\n            # indicate that the request was a success\n            data[\"success\"] = True\n\n   # return the data dictionary as a JSON response\n   return flask.jsonify(data)\n\n@app.route(\"/yolo_full\", methods=[\"POST\"])\ndef yolo_full():\n    # Evaluate data\n    data = {\"success\": False}\n    if flask.request.method == \"POST\":\n        if flask.request.files.get(\"image\"):\n            # read the image in PIL format\n            image = flask.request.files[\"image\"].read()\n            image = Image.open(io.BytesIO(image))\n\n            with graph.as_default():\n                # evaluate image\n                bboxes = run_on_image(image, darkflow_model, sess) # aka many crops\n\n                data[\"bboxes\"] = bboxes\n\n                # indicate that the request was a success\n                data[\"success\"] = True\n\n    return flask.jsonify(data)\n\n\n@app.route(\"/yolo_single_crop\", methods=[\"POST\"])\ndef yolo_single_crop():\n    # Evaluate data\n    data = {\"success\": False}\n    if flask.request.method == \"POST\":\n        if flask.request.files.get(\"image\"):\n            # read the image in PIL format\n            image = flask.request.files[\"image\"].read()\n            image = Image.open(io.BytesIO(image))\n\n            with tf.variable_scope(\"model\", reuse=REUSE):\n\n                with graph.as_default():\n                    # evaluate image\n                    bboxes = run_on_single_crop(image, darkflow_model, sess)\n\n                    data[\"bboxes\"] = bboxes\n\n                    # indicate that the request was a success\n                    data[\"success\"] = True\n\n            ReuseTrue()\n            gc.collect()\n\n    return flask.jsonify(data)\n\ndef mem_monitor_deamon():\n    import subprocess\n    while (True):\n        \"\"\"\n        #import psutil\n        #process = psutil.Process(os.getpid())\n        #mem = process.get_memory_info()[0] / float(2 ** 20)\n        #return mem\n\n        rusage_denom = 1024.\n        if sys.platform == 'darwin':\n            # ... it seems that in OSX the output is different units ...\n            rusage_denom = rusage_denom * rusage_denom\n        mem = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / rusage_denom\n        #return mem\n        \"\"\"\n\n        out = subprocess.Popen(['ps', 'v', '-p', str(os.getpid())],\n                               stdout=subprocess.PIPE).communicate()[0].split(b'\\n')\n        vsz_index = out[0].split().index(b'RSS')\n        mem = float(out[1].split()[vsz_index]) / 1024\n\n        print(\"Memory:\", mem)\n        time.sleep(2.0) # check every 2 sec\n\n\n# if this is the main thread of execution first load the model and\n# then start the server\nif __name__ == \"__main__\":\n    print((\"* Loading Keras model and Flask starting server...\"\n        \"please wait until server has fully started\"))\n    #load_model_resnet()\n\n    yolo_paths = [\"/home/ekmek/YAD2K/\", \"/home/vruzicka/storage_pylon2/YAD2K/\"]\n    path_to_yolo = use_path_which_exists(yolo_paths)\n    model_h5 = 'yolo.h5'\n    yolo_path = path_to_yolo + 'model_data/' + model_h5\n\n    load_model_yolo(yolo_path)\n\n    t = Thread(target=mem_monitor_deamon, args=())\n    t.daemon = True\n    t.start()\n\n\n    app.run()\n    # On server:\n    #app.run(host='0.0.0.0', port=8123)\n","sub_path":"dead_ends/server_yolo.py","file_name":"server_yolo.py","file_ext":"py","file_size_in_byte":6336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"86160920","text":"import mock\n\nfrom django.test import TestCase\n\nfrom bluebottle.clients.middleware import TenantProperties, TenantPropertiesMiddleware\nfrom bluebottle.clients.middleware import properties\n\nMock = mock.Mock\n\n\nclass TestProperties(TestCase):\n    def test_property_match(self):\n        \"\"\" A match found in the client properties \"\"\"\n        with mock.patch(\"bluebottle.clients.middleware.settings\", foo=1):\n            p = TenantProperties()\n            p.tenant_properties = {'foo': 2}\n\n            self.failUnless(p.foo == 2)\n            self.failUnless(hasattr(p, 'foo'))\n\n    def test_settings_match(self):\n        \"\"\" No match in properties but match in settings \"\"\"\n        with mock.patch(\"bluebottle.clients.middleware.settings\", foo=1):\n            p = TenantProperties()\n\n            self.failUnless(p.foo == 1)\n            self.failUnless(hasattr(p, 'foo'))\n\n    def test_nomatch(self):\n        \"\"\" No match in either properties or settings \"\"\"\n        with mock.patch(\"bluebottle.clients.middleware.settings\", Mock(spec_set=[])):\n            p = TenantProperties()\n            with self.assertRaises(AttributeError):\n                p.foo == 1\n            self.failIf(hasattr(p, 'foo'))\n\n    def test_verify_settings(self):\n        with mock.patch(\"bluebottle.clients.middleware.settings\",\n                        MULTI_TENANT_DIR='/tmp/') as settings, \\\n                mock.patch(\"__builtin__.execfile\") as execfile:\n            properties.set_tenant(Mock(client_name='testtenant'))\n            self.assertEquals(execfile.call_args[0][1]['settings'], settings)\n\n\nclass TestTenantMiddleware(TestCase):\n    def setUp(self):\n        self.middleware = TenantPropertiesMiddleware()\n\n    def test_no_tenant(self):\n        \"\"\" verify that ordinary settings resolving just works \"\"\"\n        with mock.patch(\"bluebottle.clients.middleware.settings\", foo=42):\n            self.middleware.process_request(Mock())\n            self.assertEquals(properties.foo, 42)\n\n    def test_invalid_tenant(self):\n        \"\"\" verify that with an invalid tenant default settings resolving\n            works \"\"\"\n        with mock.patch(\"bluebottle.clients.middleware.settings\", foo=42), \\\n             mock.patch(\"bluebottle.clients.middleware.connection\",\n                        Mock(**{\"tenant.client_name\": \"dontexist\"})):\n            self.middleware.process_request(Mock())\n            self.assertEquals(properties.foo, 42)\n\n    def test_valid_tenant(self):\n        \"\"\" verify that the correct properties are loaded\"\"\"\n        with mock.patch(\"bluebottle.clients.middleware.settings\",\n                        MULTI_TENANT_DIR=\"/some/client/path/\"), \\\n             mock.patch(\"bluebottle.clients.middleware.connection\",\n                        Mock(**{\"tenant.client_name\": \"valid\"})), \\\n             mock.patch(\"__builtin__.execfile\") as execfile:\n            self.middleware.process_request(Mock())\n            self.assertEquals(execfile.call_args_list[0][0][0],\n                              \"/some/client/path/valid/settings.py\")\n","sub_path":"bluebottle/clients/tests/test_properties.py","file_name":"test_properties.py","file_ext":"py","file_size_in_byte":3020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"17648262","text":"#!/usr/bin/env python3\n# author          : Alina Kutlushina\n# date            : 01.05.2018\n# license         : BSD-3\n#==============================================================================\n\nimport os, sys\nimport time\nimport json\nimport pandas as pd\nimport argparse\n\n\ndef get_external_stat(mol_act, mol_inact, ts_act, ts_inact, in_pma, in_act_screen, in_inact_screen):\n\n    act_screen = []\n    inact_screen = []\n\n    ts_active_mol = []\n    ts_inactive_mol = []\n    all_active_mol = len(open(mol_act).readlines())\n    all_inactive_mol = len(open(mol_inact).readlines())\n    # ts_inactive_mol = len(open(ts_inact).readlines())\n\n    model = os.path.basename(in_pma)\n    with open(in_pma) as fpma:\n        d = json.loads(fpma.readline().strip())\n        labels = ''.join(i[0] for i in d['feature_coords'])\n\n    with open(ts_act) as f:\n        for x in f:\n            ts_active_mol.append(x.strip().split('\\t')[0])\n        \n    with open(in_act_screen) as fs:\n        for column in fs:\n            act_screen.append(column.strip().split('\\t')[0])\n\n    act_screen = set(act_screen).difference(ts_active_mol)\n\n    with open(ts_inact) as f:\n        for x in f:\n            ts_inactive_mol.append(x.strip().split('\\t')[0])\n\n    with open(in_inact_screen) as fs:\n        for column in fs:\n            inact_screen.append(column.strip().split('\\t')[0])\n\n    inact_screen = set(inact_screen).difference(ts_inactive_mol)\n\n    TP = len(act_screen)\n    FP = len(inact_screen)\n    FN = all_active_mol - len(ts_active_mol) - TP\n    TN = all_inactive_mol - len(ts_inactive_mol) - FP\n\n    try:\n        precision = TP / (TP + FP)\n        recall = TP / (TP + FN)\n        FPR = FP / (TN + FP)\n        spc = TN / (TN + FP)\n        f1 = (2 * precision * recall) / (precision + recall)\n        f2 = (5 * precision * recall) / (4 * precision + recall)\n        f05 = (1.25 * precision * recall) / (0.25 * precision + recall)\n        ba = round((recall + spc) / 2, 3)\n        ef = (TP / (TP + FP)) / (all_active_mol / (all_inactive_mol + all_active_mol))\n\n        return [model,\n                TP,\n                FP,\n                round(precision, 3),\n                round(FPR, 3),\n                round(recall, 3),\n                round(f1, 3),\n                round(f2, 3),\n                round(f05, 3),\n                round(ba, 3),\n                round(ef, 3),\n                labels]\n    \n    except ZeroDivisionError:\n        return [model,\n                TP,\n                FP,\n                '-',\n                '-',\n                '-',\n                '-',\n                '-',\n                '-',\n                '-',\n                '-',\n                labels]\n\n\ndef main(mol_act, mol_inact, ts_act, ts_inact, path_to_pma, path_to_screen, out_external):\n    \n    start_time = time.time()\n\n    df_result = pd.DataFrame(columns=['model', 'TP', 'FP', 'precision', 'FPR', 'recall', 'F1', 'F2', 'F05', 'BA', 'EF', 'features'])\n\n\n    for enum, in_pma in enumerate(sorted(os.listdir(path_to_pma))):\n        ppath = (os.path.abspath(os.path.join(os.path.abspath(path_to_screen), 'screen_active_{}.txt'.format(in_pma.split('.')[0]))),\n                 os.path.abspath(os.path.join(os.path.abspath(path_to_screen), 'screen_inactive_{}.txt'.format(in_pma.split('.')[0]))),\n                 os.path.abspath(os.path.join(path_to_pma, in_pma)))\n\n        result = get_external_stat(mol_act, mol_inact, ts_act, ts_inact, ppath[2], ppath[0], ppath[1])\n        if result:\n            df_result.loc[enum] = result\n        else:\n            continue\n\n    df_result = df_result.sort_values(by=['recall', 'F05', 'F2'], ascending=False)\n\n    df_result.to_csv(out_external, index=None, sep='\\t')\n    sys.stderr.write('{}: ({}s)\\n\\n'.format(os.path.basename(out_external), round(time.time()-start_time,3)))\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='', formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n    parser.add_argument('-mol_act', '--input_active_mol', metavar='active.smi', required=True,\n                        help='.smi file with active molecules.')\n    parser.add_argument('-mol_inact', '--input_inactive_mol', metavar='inactive.smi', required=True,\n                        help='.smi file with inactive molecules.')\n    # parser.add_argument('-idb', '--in_active_database', metavar='active.db', required=True,\n    #                     help='input DB SQLite file with activity moleculs')\n    parser.add_argument('-ts_act', '--trainset_active_mol', metavar='active.smi', required=True,\n                        help='txt file with active molecules from training set.')\n    parser.add_argument('-ts_inact', '--trainset_inactive_mol', metavar='inactive.smi', required=True,\n                        help='txt file with inactive molecules from training set.')\n    parser.add_argument('-ppma', '--path_to_pma', metavar='models/pYY/', required=True,\n                        help='path to pma files')\n    parser.add_argument('-pscreen', '--path_to_screen', metavar='screen/pYY/', required=True,\n                        help='path to screen')\n    parser.add_argument('-o', '--out_external', metavar='external_subsetXX_pYY.txt', default=None,\n                        help='output text file, which will contain: model, n_act, n_inact, precision, recall, '\n                             'F1, BA, EF, features, n_act_train, n_inact_train. ')\n\n    args = vars(parser.parse_args())\n    for o, v in args.items():\n        if o == \"input_active_mol\": mol_act = v\n        if o == \"input_inactive_mol\": mol_inact = v\n        # if o == \"in_active_database\": in_adb = v\n        if o == \"trainset_active_mol\": ts_act = v\n        if o == \"trainset_inactive_mol\": ts_inact = v\n        if o == \"path_to_pma\": path_to_pma = v\n        if o == \"path_to_screen\": path_to_screen = v\n        if o == \"out_external\": out_external = v\n\n    if out_external is None:\n        out_external = os.path.join(os.path.split(os.path.dirname(mol_act))[0], 'result.txt')\n\n\n    main(mol_act, mol_inact, ts_act, ts_inact, path_to_pma, path_to_screen, out_external)\n    \n","sub_path":"external_statistics.py","file_name":"external_statistics.py","file_ext":"py","file_size_in_byte":6056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"431254392","text":"\"\"\"\nThis file is a Eploy spider created on top of the ATSSpider\nscrapy crawl eploy -a mining_job_id=9999 -a iteration=1 -a url=\"http://careers.eploy.co.uk/vacancies/vacancy-search-results.aspx\" -a extract=1\n\nsample url:\n    http://careers.eploy.co.uk/vacancies/vacancy-search-results.aspx\n\"\"\"\n\nfrom re import compile\nfrom urlparse import urlparse\nfrom scrapy.http import Request, FormRequest\nfrom scrapy.selector import Selector\n\nfrom brightcorp.base.atsspiders import ATSSpider\nfrom brightcorp.items import BrightcorpItemLoader\nfrom brightcorp.processors import ConvertDateString, NormalizedJoin, Prefix, Replace\n\npattern = {\n    'ref_number': compile(r'vacancies\\/(\\d+)'),\n    'ref_number_1': compile(r'VacancyID=(\\d+)'),\n    'pager': compile(r'doPostBack\\(\\'([^\\']*)'),\n}\n\n\nclass Eploy(ATSSpider):\n\n    name = \"eploy\"\n    page = 1\n\n    def parse(self, response):\n        sel = Selector(response)\n        if not self.expected_job_count_set:\n            expected_count = sel.xpath(\n                '//div[@class=\"paginator\"]/div[@class=\"PageNoInfo\"]/text()'\n            ).extract()\n            if expected_count:\n                self.expected_job_count = expected_count[0].split(' ')[-1]\n\n        jobs = sel.xpath(\n            \"//div[contains(@class,'VacancyDetails')]//a[contains(text(), 'More Info')]/@href |\"\n            \"//div[@class='vacancy-results']/div//a[contains(text(), 'More Info')]/@href |\"\n            \"//div[contains(@id, 'pnVacancyResults')]//a[contains(text(), 'More Info')]/@href\"\n        )\n        for job in jobs:\n            request = Request(job.extract(), callback=self.parse_job)\n            yield request\n\n        # pagination\n        next_page = sel.xpath(\n            \"//div[@class='paginator']/a[contains(text(), 'Next')]/@href |\"\n            \"//div[@class='paginator']/a[contains(text(), '>')]/@href\"\n        ).extract()\n        if next_page:\n            match = pattern['pager'].search(next_page[0])\n            if match:\n                self.page += 1\n                formdata = {\n                    \"__EVENTTARGET\": match.group(1),\n                    \"__EVENTARGUMENT\": str(self.page),\n                    \"__EVENTVALIDATION\": sel.xpath(\"//input[@id='__EVENTVALIDATION']/@value\")[0].extract(),\n                    \"__LASTFOCUS\": \"\",\n                    \"__VIEWSTATE\": sel.xpath(\"//input[@id='__VIEWSTATE']/@value\")[0].extract(),\n                }\n                # POST requests redirect to the same URL so we stop filtering dupes for this spider\n                request = FormRequest(\n                    url=response.url,\n                    formdata=formdata,\n                    callback=self.parse,\n                    dont_filter=True\n                )\n\n                yield request\n\n    def parse_job(self, response):\n        sel = Selector(response)\n        loader = BrightcorpItemLoader(selector=sel)\n        url_parse = urlparse(response.url)\n\n        company_name = url_parse.netloc.split('.')[1]\n        # hotfix: 30-Oct-2014\n        # JIRA: MNY:JOBS / JOBS-7555 / All Kier jobs mapping to Kidderminster, GB\n        # https://careers.kier.co.uk/vacancies/vacancy-search-results.aspx\n        # Resolved: Location is scraped by Kier, and also added title, location, company_name more accurate.\n        # referencenumber is also dynamically changed for different urls (i.e eploy-18, kier-4425).\n        loader.add_xpath(\n            \"title\",\n            \"//div[@id='div_VacV_Title']//text() |\"\n            \"//div[@id='mainContainer']/div/h1/text() |\"\n            \"//div[contains(@id, 'FormContent_jobDetails')]/h1/text() |\"\n            \"//div/h1[@class='standardpagetitle' or @class='media__title']/text() |\"\n            \"//div[contains(@id, 'mainContent_jobDetails')]/div/h2[@class='media__title']/text()\"\n        )\n        loader.add_xpath(\n            \"description\", \"//div[@id='div_VacV_Description']\"\n        )\n        loader.add_xpath(\n            \"qualifications\", \"//div[@id='div_VacV_Qualifications']\"\n        )\n        loader.add_xpath(\n            \"benefits\", \"//div[@id='div_VacV_Benefits']\"\n        )\n        loader.add_xpath(\n            'location',\n            [\n                \"//li/div/span[contains(text(), 'Work Location:')]/../following-sibling::div[1]/span/text()\",\n                \"//li/div/span[contains(text(), 'State:')]/../following-sibling::div[1]/span/text()\",\n                \"//li/div/span[contains(text(), 'Country:')]/../following-sibling::div[1]/span/text()\"\n            ],\n            Replace('Not Specified'),\n            NormalizedJoin(', '),\n        )\n        if not loader.get_output_value('location'):\n            loader.add_xpath(\n                \"location\",\n                \"//li[@id='li_VacV_IndustryID']/div[@class='content']/span/text() |\"\n                \"//div[@id='div_content_VacV_LocationID']/span/text() |\"\n                \"//li[@id='li_VacV_LocationID']/div[@class='content']/span/text() |\"\n                \"//div/span[contains(text(), 'Location:')]/../following-sibling::div[1]/span/text()\"\n            )\n        loader.add_xpath(\n            \"baseSalary\",\n            \"//li[@id='li_VacV_DisplaySalary']/div[@class='content']/span/text() |\"\n            \"//div/span[contains(text(), 'Salary:')]/../following-sibling::div[1]/span/text()\"\n        )\n        loader.add_xpath(\n            \"jobcategory\",\n            \"//li[@id='li_VacV_PositionID']/div[@class='content']/span/text() |\"\n            \"//div/span[contains(text(), 'Job Function:')]/../following-sibling::div[1]/span/text() |\"\n            \"//div/span[contains(text(), 'Position:')]/../following-sibling::div[1]/span/text()\"\n        )\n        loader.add_xpath(\n            \"jobtype\",\n            \"//li[@id='li_VacV_VacancyTypeID']/div[@class='content']/span/text() |\"\n            \"//div[@id='div_VacV_VacancyTypeID']/div[@class='content']/span/text()\"\n        )\n        loader.add_value(\"company\", company_name)\n        loader.add_value(\n            \"referencenumber\",\n            response.url,\n            Prefix(\"%s-\" % company_name),\n            re=pattern[\"ref_number\"]\n        )\n        if not loader.get_output_value(\"referencenumber\"):\n            loader.add_value(\n                \"referencenumber\",\n                response.url,\n                Prefix(\"%s-\" % company_name),\n                re=pattern[\"ref_number_1\"]\n            )\n        if not loader.get_output_value('location'):\n            loader.add_value(\n                \"location\", \"Kidderminster Worcestershire England United Kingdom\"\n            )\n        loader.add_xpath(\n            \"date\",\n            \"//div[@id='div_content_VacV_DatePosted']/span/text() |\"\n            \"//li[@id='li_VacV_DatePosted']/div[@class='content']/span/text() |\"\n            \"//div[@id='div_VacV_DatePosted']/div[@class='content']/span/text()\",\n            ConvertDateString(\"%d %b %Y\")\n        )\n        loader.add_xpath(\n            \"expiration_date\",\n            \"//li/div[@id='div_content_VacV_AdvertisingEndDate']/span/text() |\"\n            \"//li[@id='li_VacV_AdvertisingEndDate']/div[@class='content']/span/text() |\"\n            \"//div[@id='div_VacV_AdvertisingEndDate']/div[@class='content']/span/text()\",\n            ConvertDateString(\"%d %b %Y\")\n        )\n        loader.add_xpath(\n            \"workhours\",\n            \"//li/div[@id='div_content_VacV_HoursPerWeek']/span/text()\"\n        )\n        loader.add_value(\"url\", response.url)\n        loader.add_value(\"apply_url\", response.url)\n\n        yield loader.load_item()\n","sub_path":"brightcorp/brightcorp/spiders/eploy.py","file_name":"eploy.py","file_ext":"py","file_size_in_byte":7435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"383442100","text":"import collections\nimport datetime\nimport logging\nimport os\nimport random\nimport re\nimport sre_constants\nimport time\n\nimport xlrd\nfrom tornado.iostream import IOStream\n\nimport cqpapi\nfrom wcatgcr_events import EventGroupMsg\n\n_dir = os.path.dirname(__file__)\n_config_dir = os.path.join(_dir, os.path.pardir, 'config')\n\n_MATCH_TYPES = ('简单字符串', '正则表达式')\n_CASE_FLAGS = ('否', '是')\n_REPLY_TYPES = ('单条', '多条', '随机', '星期', '捕获')\n_REPLY_TO = ('群', 'QQ')\n\n_Pattern = collections.namedtuple('_Pattern', (\n    'match_type',  # index of _MATCH_TYPES\n    'case_sensitive',  # False or True\n    'match_pattern',  # tuple(size:3) or _sre.SRE_Pattern\n    'reply_type',  # index of _REPLY_TYPES\n    'reply_message',  # str or list or dict\n    'reply_to',  # index of _REPLY_TO\n    'reply_to_id',  # tuple\n))\n\n\ndef _load_config_file():\n    patterns = collections.defaultdict(lambda: collections.defaultdict(list))\n    with xlrd.open_workbook(os.path.join(_config_dir, 'configurations.xlsx'), on_demand=True) as book:\n        sheet = book.sheet_by_name('wcatgcr_pattern')\n\n        for idx, cell in enumerate(sheet.col(0)):\n            if cell.value == '序号':\n                seq_no_idx = idx + 1\n                break\n        else:\n            logging.error('表\"wcatgcr_pattern\"中未能找到表头。')\n            return\n\n        succeed_count = 0\n        for row_no in range(seq_no_idx, sheet.nrows):\n            row = sheet.row(row_no)\n\n            if not row[:1]:\n                break\n\n            seq_no = str(row[0].value).strip()\n            if not seq_no:\n                break\n            try:\n                seq_no = int(float(seq_no))\n            except ValueError:\n                logging.error('表\"wcatgc_pattern\"第{}行，序号为非法字符：{}'.format(row_no + 1, seq_no))\n                continue\n\n            if seq_no > 0 and len(row) >= 10:\n                group_id = str(row[1].value).strip()\n                qq_id = str(row[2].value).strip()\n                match_type = str(row[3].value).strip()\n                case_sensitive = str(row[4].value).strip()\n                match_pattern = str(row[5].value).strip()\n                reply_type = str(row[6].value).strip()\n                reply_message = str(row[7].value).strip()\n                reply_to = str(row[8].value).strip()\n                reply_to_id = str(row[9].value).strip()\n\n                try:\n                    group_ids = tuple(map(int, filter(bool, group_id.split('|'))))\n                    if not group_ids:\n                        logging.error('表\"wcatgcr_pattern\"第{}行，未指定群号！'.format(row_no + 1))\n                        continue\n                except ValueError:\n                    logging.error('表\"wcatgcr_pattern\"第{}行，群号格式有误：{}'.format(row_no + 1, group_id))\n                    continue\n\n                try:\n                    qq_ids = tuple(map(int, filter(bool, qq_id.split('|'))))\n                except ValueError:\n                    logging.error('表\"wcatgcr_pattern\"第{}行，QQ号格式有误：{}'.format(row_no + 1, qq_id))\n                    continue\n\n                if match_type not in _MATCH_TYPES:\n                    logging.error('表\"wcatgcr_pattern\"第{}行，匹配类型有误：{}'.format(row_no + 1, match_type))\n                    continue\n                match_type_idx = _MATCH_TYPES.index(match_type)\n\n                if case_sensitive not in _CASE_FLAGS:\n                    logging.error('表\"wcatgcr_pattern\"第{}行，大小写敏感有误：{}'.format(row_no + 1, case_sensitive))\n                    continue\n                case_flag = bool(_CASE_FLAGS.index(case_sensitive))\n\n                if not match_pattern:\n                    logging.error('表\"wcatgcr_pattern\"第{}行，未输入匹配模式！'.format(row_no + 1))\n                    continue\n                match_pattern_data = None\n                if match_type == _MATCH_TYPES[0]:\n                    if match_pattern.count('|') < 2:\n                        logging.error('表\"wcatgcr_pattern\"第{}行，匹配模式（简单字符串）格式有误：{}'\n                                      .format(row_no + 1, match_pattern))\n                        continue\n                    match_pattern_data = match_pattern.split('|')\n                elif match_type == _MATCH_TYPES[1]:\n                    try:\n                        match_pattern_data = re.compile(match_pattern, 0 if case_flag else re.IGNORECASE)\n                    except sre_constants.error as e:\n                        logging.error('表\"wcatgcr_pattern\"第{}行，匹配模式（正则表达式）格式有误：{} => {}'\n                                      .format(row_no + 1, match_pattern, e))\n                        continue\n\n                if reply_type not in _REPLY_TYPES:\n                    logging.error('表\"wcatgcr_pattern\"第{}行，回复类型有误：{}'.format(row_no + 1, reply_type))\n                    continue\n                reply_type_idx = _REPLY_TYPES.index(reply_type)\n\n                if not reply_message:\n                    logging.error('表\"wcatgcr_pattern\"第{}行，未输入回复内容！'.format(row_no + 1))\n                    continue\n                reply_message_data = None\n                if reply_type == _REPLY_TYPES[0]:\n                    reply_message_data = reply_message\n                elif reply_type in _REPLY_TYPES[1:4]:\n                    reply_message_data = []\n                    try:\n                        for line in reply_message.split('\\n'):\n                            m = re.fullmatch(r'^(\\d*)\\|(.*)$', line)\n                            if m:\n                                delay_secs, message_body = m.groups()\n                                reply_message_data.append([\n                                    int(delay_secs) if delay_secs else 0,\n                                    message_body\n                                ])\n                            else:\n                                if not reply_message_data:\n                                    logging.error('表\"wcatgcr_pattern\"第{}行，回复内容（{}）格式有误：{}'\n                                                  .format(row_no + 1, reply_type, line))\n                                    raise StopIteration\n                                else:\n                                    reply_message_data[-1][-1] = '\\n'.join((reply_message_data[-1][-1], line))\n                    except StopIteration:\n                        continue\n                    if reply_type == _REPLY_TYPES[3] and len(reply_message_data) != 7:\n                        logging.error('表\"wcatgcr_pattern\"第{}行，星期类型回复内容必须有7条！'.format(row_no + 1))\n                        continue\n                elif reply_type == _REPLY_TYPES[4]:\n                    if match_type != _MATCH_TYPES[1]:\n                        logging.error('表\"wcatgcr_pattern\"第{}行，回复类型为捕获时匹配类型必须为正则表达式！'\n                                      .format(row_no + 1))\n                        continue\n                    reply_message_dict = {}\n                    try:\n                        lines = reply_message.split('\\n')\n                        reply_message_key = lines[0]\n                        if not re.search(r'\\{regroup_\\d+\\}', reply_message_key):\n                            logging.error('表\"wcatgcr_pattern\"第{}行，回复内容（捕获）选择关键词未包含任何捕获组：{}'\n                                          .format(row_no + 1, reply_message_key))\n                            raise StopIteration\n                        message_key = None\n                        for line in lines[1:]:\n                            m = re.fullmatch(r'^(\\d*)\\|(.*?)\\|(.*)$', line)\n                            if m:\n                                delay_secs, message_key, message_body = m.groups()\n                                reply_message_dict[message_key] = [\n                                    int(delay_secs) if delay_secs else 0,\n                                    message_body\n                                ]\n                            else:\n                                if not message_key:\n                                    logging.error('表\"wcatgcr_pattern\"第{}行，回复内容（捕获）格式有误：{}'\n                                                  .format(row_no + 1, line))\n                                    raise StopIteration\n                                else:\n                                    reply_message_dict[message_key][-1] = '\\n'.join(\n                                        (reply_message_dict[message_key][-1], line))\n                        reply_message_data = (reply_message_key, reply_message_dict)\n                    except StopIteration:\n                        continue\n\n                if reply_to not in _REPLY_TO:\n                    logging.error('表\"wcatgcr_pattern\"第{}行，回复对象有误：{}'.format(row_no + 1, reply_to))\n                    continue\n                reply_to_idx = _REPLY_TO.index(reply_to)\n\n                try:\n                    reply_to_ids = tuple(map(int, filter(bool, reply_to_id.split('|'))))\n                except ValueError:\n                    logging.error('表\"wcatgcr_pattern\"第{}行，回复对象群号或QQ号格式有误：{}'\n                                  .format(row_no + 1, reply_to_id))\n                    continue\n\n                pattern = _Pattern(\n                    match_type_idx,\n                    case_flag,\n                    match_pattern_data,\n                    reply_type_idx,\n                    reply_message_data,\n                    reply_to_idx,\n                    reply_to_ids\n                )\n\n                for gid in group_ids:\n                    if qq_ids:\n                        for qid in qq_ids:\n                            patterns[gid][qid].append(pattern)\n                    else:\n                        patterns[gid][0].append(pattern)\n\n                succeed_count += 1\n\n        logging.info('表\"wcatgcr_pattern\"中成功读入{}条模式。'.format(succeed_count))\n        return patterns\n\n\n_patterns = _load_config_file()\n\n\n# logging.debug(_patterns)\n\n\ndef _do_match(event: EventGroupMsg, pattern: _Pattern):\n    if pattern.match_type == 0:\n        event_msg = event.msg if pattern.case_sensitive else event.msg.lower()\n        if all((event_msg.startswith(pattern.match_pattern[0]),\n                all(map(lambda p: event_msg.find(p) >= 0,\n                        pattern.match_pattern[1:-1])),\n                event_msg.endswith(pattern.match_pattern[-1]))):\n            return True\n    elif pattern.match_type == 1:\n        m = pattern.match_pattern.search(event.msg)\n        if m:\n            return dict(re_groups=m.groups())\n    return False\n\n\nasync def _do_reply(result, ios: IOStream, event: EventGroupMsg, pattern: _Pattern):\n    if not result:\n        return\n\n    target_id = pattern.reply_to_id\n\n    if pattern.reply_to == 0:\n        send_func = cqpapi.send_group_msg\n        if not target_id:\n            target_id = (event.from_group,)\n    elif pattern.reply_to == 1:\n        send_func = cqpapi.send_private_msg\n        if not target_id:\n            target_id = (event.from_qq,)\n    else:\n        raise AssertionError('非法回复对象：{}'.format(pattern.reply_to))\n\n    event_time = time.localtime(event.send_time)\n\n    # TODO str.format parameters: group_name\n    fmt_params = dict(\n        qq=event.from_qq,\n        nick=event.from_member_name,\n        group=event.from_group,\n        group_name='dummy',\n        YYYY='{:04}'.format(event_time.tm_year),\n        MM='{:02}'.format(event_time.tm_mon),\n        DD='{:02}'.format(event_time.tm_mday),\n        hh='{:02}'.format(event_time.tm_hour),\n        mm='{:02}'.format(event_time.tm_min),\n        ss='{:02}'.format(event_time.tm_sec),\n    )\n    if isinstance(result, dict) and 're_groups' in result:\n        re_groups = result['re_groups']\n        fmt_params.update(dict(zip(map(lambda n: 'regroup_{}'.format(n), range(len(re_groups))),\n                                   re_groups)))\n\n    if pattern.reply_type == 0:\n        message = pattern.reply_message.format(**fmt_params)\n        for id in target_id:\n            await ios.write(send_func(id, message))\n    elif pattern.reply_type == 1:\n        for delay, message in pattern.reply_message:\n            message = message.format(**fmt_params)\n            for id in target_id:\n                await ios.write(send_func(id, message, delay))\n    elif pattern.reply_type == 2:\n        delay, message = random.choice(pattern.reply_message)\n        message = message.format(**fmt_params)\n        for id in target_id:\n            await ios.write(send_func(id, message, delay))\n    elif pattern.reply_type == 3:\n        delay, message = pattern.reply_message[datetime.date.today().weekday()]\n        message = message.format(**fmt_params)\n        for id in target_id:\n            await ios.write(send_func(id, message, delay))\n    elif pattern.reply_type == 4:\n        message_key, message_dict = pattern.reply_message\n        message_key = message_key.format(**fmt_params)\n        if message_key in message_dict:\n            delay, message = message_dict[message_key]\n            message = message.format(**fmt_params)\n        elif '' in message_dict:\n            delay, message = message_dict['']\n            message = message.format(**fmt_params)\n        else:\n            delay, message = 0, None\n        if message:\n            for id in target_id:\n                await ios.write(send_func(id, message, delay))\n\n\nasync def procedure(ios: IOStream, event: EventGroupMsg):\n    logging.debug(event)\n\n    if event.from_group not in _patterns:\n        return\n    group_pattern = _patterns[event.from_group]\n\n    if event.from_qq in group_pattern:\n        for personal_pattern in group_pattern[event.from_qq]:\n            result = _do_match(event, personal_pattern)\n            # logging.debug('Personal pattern matching: {}'.format(result))\n            await _do_reply(result, ios, event, personal_pattern)\n\n    for default_pattern in group_pattern[0]:\n        result = _do_match(event, default_pattern)\n        # logging.debug('Default pattern matching: {}'.format(result))\n        await _do_reply(result, ios, event, default_pattern)\n","sub_path":"python/wcatgcr_pattern.py","file_name":"wcatgcr_pattern.py","file_ext":"py","file_size_in_byte":14386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"74258426","text":"def main():\n    # Este programa calcula el saldo mensual de una cuenta en este banco tomando en cuenta el saldo del mes anterior,\n    # los ingresos, los egresos y el número de cheques expedidos.\n\n    saldo = float(input(\"Dame el saldo del mes anterior: \"))\n    ingresos = float(input(\"Dame los ingresos: \"))\n    egresos = float(input(\"Dame los egresos: \"))\n    cheques = int(input(\"Dame el número de cheques: \"))\n\n    saldo_final = saldo + ingresos - ((cheques * 13) + egresos)\n    interes = saldo_final * 0.925\n\n    print(\"El saldo final de la cuenta es:\", interes)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"assignments/18CuentaBancaria/src/exercise.py","file_name":"exercise.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"400020306","text":"\n# coding: utf-8\n\n# Takes in data from /dors/capra_lab/users/yand1/te_ml/data/2018_06_12_te_enhancers_ml\n# to predict if transposable elements overlap with enhancers given a set of transcription\n# factors. This version runs a SVC with a rbf kernel and C value of 10 on the full set of data.\n\n# In[ ]:\n\n\n# Import needed libraries\nimport pandas as pd # For getting data\nfrom sklearn import metrics # Get model metrics\nfrom sklearn.svm import SVC # Support vector classifier.\nfrom sklearn.model_selection import cross_val_score, train_test_split # Split set\n\n\n# In[ ]:\n\n\n# Class constants\nDATE = \"2018_06_22_svc_rbf/\" \nDIRECTORY = \"/dors/capra_lab/users/yand1/te_ml/\" # Root directory\nLOC = \"accre/\" # local or accre cluster\nDATA_FILE = \"full.tsv\" # Name of data file to process\nCHROMOSOME = 0 # Column for the chromosome number of transposable element\nSTART = 1 # Column for the start location of transposable element\nEND = 2 # Column for the end location of transposable element\nTF = 8 # Column for the transcription factor intersecting with transposable element\nENHANCER = 13 # Column for if enhancer is present. 1 means enhancer is present\nCROSS_VAL = 5 # Number of subdivisions of data for cross validation\nRAM = 10000 # MB used.\n\n\n# In[ ]:\n\n\ndef transform_df (old_df):\n    \"\"\"transform_tf updates the columns of transcription factors in new_df by cross referencing old_df\n    \n    Each row in the old data frame is matched to the corresponding location in the new\n    data frame.The column of the the transcription factor in the new data frame that corresponds\n    to the old data frame is incremented by 1. The enhancer column in the new data frame is\n    set to 1 if that column in the old data frame is 1.\n    \n    Args:\n        old_df(pd.DataFrame): Data frame that contains the information about transcription factors.\n        \n    Return:\n        new_df(pd.DataFrame): New data frame that has columns with the number of times\n            each transposable element in different locations intersects with each\n            transcription factor, as well as if an enhancer site is present.\n    \"\"\"\n    # Create groups based on chromosome, start location, end location, transcription factor, and if\n    # transcription factor is present. Get the size of each of those groups, and use unstack to \n    # change the transcription factors to column indices to create matrix for machine learning input.\n    # Use reset_index to bring all other labels to top level.\n    new_df = te_df.groupby([CHROMOSOME, START, END, TF, ENHANCER], sort = False).size().unstack(TF, fill_value = 0).reset_index()\n\n    # Reformat enhancer column to have 1 or 0 value.\n    new_df[ENHANCER] = new_df[ENHANCER].apply(lambda x: 1 if x == \"1\" else 0)\n\n    # Rename the columns\n    new_df.rename(columns = {CHROMOSOME: \"chr\", START: \"start\", END: \"end\", ENHANCER: \"enhancer\"}, inplace = True)\n\n    # Sum any repeated rows (in case any rows were identical other than enhancer presence)\n    new_df.groupby(new_df.index).sum()\n\n    # Move row with enhancer to the end.\n    enhancer_df = new_df.copy()[\"enhancer\"]\n    new_df.drop(labels = [\"enhancer\"], axis = 1, inplace = True)\n    new_df.insert(len(new_df.columns), \"enhancer_actual\", enhancer_df)\n    \n    return new_df\n\n\n# In[ ]:\n\n\ndef create_predictions(model, x_df, y_df):\n    \"\"\"create_predictions splits the data into a training and testing set,\n        oversamples the training set, performs cross validation and predicts the testing set.\n        \n    Args:\n        model(sklearn.ensemble.SVC): The machine learning model to train and predict with\n        x_df(pd.DataFrame): Input \"x\" vector to predict with\n        y_df(pd.DataFrame): Output \"y\" vector with real y values\n    \"\"\"  \n    # Use model to predict the testing set\n    y_pred = model.predict(x_df)\n    \n    # Create a confusion matrix and write to file.\n    cm_df = pd.DataFrame(metrics.confusion_matrix(y_df, y_pred), index = [\"actual_negative\", \"actual_positive\"]\n                    , columns = [\"predicted_negative\", \"predicted_positive\"])\n    cm_df.to_csv((DIRECTORY + \"results/\" + DATE + LOC + \"confusion_matrix.csv\"), sep = '\\t', mode = \"w+\")\n    \n    # Create a file to store metrics.\n    metrics_file = open((DIRECTORY + \"results/\" + DATE + LOC + \"metrics.txt\"), \"w+\")\n    metrics_file.write(metrics.classification_report(y_df, y_pred))\n\n\n# In[ ]:\n\n\n## Main\n\n# Open the transposable elements data as a dataframe.\nte_df = pd.read_table((DIRECTORY + \"data/2018_06_12_te_enhancers_ml/\" + DATA_FILE), header = None)\n\n# Create new data frame for machine learning model by setting columns as the different transcription\n# factors from the original data frame. Each row will now have the location of the transposable \n# element, the number of intersections with each transcription factor, and if there is an overlap\n# with an enhancer.\nte_new_df = transform_df(te_df)\n\n# Get index number for the \"y\" vector for machine learning model.\nend_index = len(te_new_df.columns) - 1\n# Set the machine learning input vector as all columns of transcription factors.\nx_df = te_new_df.iloc[:,3:end_index]\n# Set the machine learning prediction vector as the last column, which tells if enhancer is present.\ny_df = te_new_df.iloc[:,end_index]\n\n# Create model\nmodel = SVC(kernel = \"rbf\", cache_size = RAM, C = 10)\n\n# Perform cross validation\ncvs = cross_val_score(model, x_df, y_df, cv = CROSS_VAL, scoring = \"f1_macro\")\n# Print the cross validation scores to a file.\ncvs_df = pd.DataFrame(data = cvs, columns = [\"f1_macro_score\"])\ncvs_df.to_csv((DIRECTORY + \"results/\" + DATE + LOC + \"cross_val_scores.csv\"), sep = '\\t', index = False, mode = \"w+\")\n\n# Split the data into training and testing data.\nx_train, x_test, y_train, y_test = train_test_split(x_df, y_df)\n# Create predictions with SVM model and get metrics.\nmodel.fit(x_train, y_train)\ncreate_predictions(model = model, x_df = x_test, y_df = y_test)\n\n","sub_path":"bin/2018_06_22_svc_rbf/accre/svc_rbf.py","file_name":"svc_rbf.py","file_ext":"py","file_size_in_byte":5886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"209116794","text":"from tkinter import *\nimport tkinter.ttk as ttk\nimport datetime\nimport pickle\nimport random\nimport webbrowser\n\nimport csv\n\nfrom datetime import datetime\n\n#google drive connect\nimport googleconnect as gg\n\nr = datetime.today().weekday()\ntoday = datetime.today()\nt=['월','화','수','목','금','토','일']\n\n\ntop = Tk()\ntop.title(\"My Wunderlist\")\ntop.geometry(\"315x470+1000+100\")\ntop.resizable(True,True)\n\nnotebook = ttk.Notebook(top, width=315, height=470) #Notebook을 열고\nnotebook.pack()\n\n#====================================================================================================================\n\n'''\nframe1 \"work\" 에 대한 부분\n\n일정관리에 대한 부분을 다룬다.\n'''\nframe1=Frame(top)\nnotebook.add(frame1, text=\"work\") #notebook에 frame를 채워 넣는다.\n\n\nlabel1 = Label(frame1, text=\"목록추가\")\nlabel2 = Label(frame1, text=\"목록\")\nlabel3 = Label(frame1, text=\"검색\")\nlabel4 = Label(frame1, text=\"random : \")\nlabel5 = Label(frame1)\n\nframe=Frame(frame1, width=40,height=50)\nscrollbar=Scrollbar(frame)\n\nlistbox = Listbox(frame,  width=40, height=13, yscrollcommand=scrollbar.set)\nscrollbar['command']=listbox.yview\n\nurl_list = [\"https://www.google.co.kr/search?q=\",\n            \"https://search.naver.com/search.naver?where=nexearch&sm=top_hty&fbm=1&ie=utf8&query=\"]\n\nlist_position='listfile.txt'\nradio_var = IntVar()\n\n\nradio1=Radiobutton(frame1, text=\"구글\",variable=radio_var,value=1) #value 1 하고 value 2를 써줘야 둘이 묶임\nradio2=Radiobutton(frame1, text=\"네이버\",variable=radio_var,value=2) #value 1 하고 value 2를 써줘야 둘이 묶임\n\n\na=0\n\ndef addlist(event):\n    time = datetime.now()\n    time = time.year, time.month, time.day\n    s=entry1.get()\n    listbox.insert(a,\"{0} : {1}\".format(time,s))\n\n    entry1.delete(0,len(s))\n\ndef dellist(event):\n    select=listbox.curselection()\n    select=select[0]\n    listbox.delete(select)\n\ndef savelist():\n    listbox_size=listbox.size()\n    data=listbox.get(0,listbox_size)\n    print(type(data))\n    with open(list_position,'wb') as file:\n        pickle.dump(data, file)\n\n    #code 복붙인데 어쩌나... 실행은 잘됨\n    gg.save(list_position)\n\n\ndef loadlist():\n    try:\n        with open(list_position,'rb') as file:\n            data=pickle.load(file)\n            for i in range(len(data)):\n                listbox.insert(i, data[i])\n            return data\n    except:\n        print(\"I can't find list file!\")\n\ndef random_select():\n    try:\n        listbox_size = listbox.size()\n        data = listbox.get(0, listbox_size)\n        choice=random.choice(data)\n        global label5\n        label5 = Label(frame1, text=\"                                                              \").place(x=60,y=300)\n        label5 = Label(frame1, text=choice).place(x=60,y=300)\n    except:\n        print(\"error\")\n\n\ndef clear_list():\n    listbox_size = listbox.size()\n    listbox.delete(0,listbox_size)\n\ndef web_search(event):\n    select_url=radio_var.get()\n    s = entry2.get()\n    if select_url == 1:\n        webbrowser.open(url_list[0] + s)\n    if select_url == 2:\n        webbrowser.open(url_list[1] + s)\n\n\nentry1 = Entry(frame1,width=40)\nentry1.bind(\"\",addlist)\nlistbox.bind(\"\",dellist)\n\nentry2 = Entry(frame1,width=30)\nentry2.bind(\"\",web_search)\n\n\n\nbtn1=Button(frame1,text=\"저장\",command=savelist)\nbtn2=Button(frame1,text=\"불러오기\",command=loadlist)\nbtn3=Button(frame1,text=\"랜덤추출\",command=random_select)\nbtn4=Button(frame1,text=\"Clear\", command=clear_list)\n\n#--------위젯 위치 선정 -----------------\nbtn1.place(x=10,y=325,width=65)\nbtn2.place(x=80,y=325,width=65)\nbtn3.place(x=150,y=325,width=65)\nbtn4.place(x=220,y=325,width=65)\nscrollbar.pack(side='right', fill='y')\nlabel1.place(x=8,y=7)\nlabel2.place(x=8,y=55)\nlabel3.place(x=8, y=365)\nlabel4.place(x=8, y=300)\nentry1.place(x=10,y=30)\nframe.place(x=10,y=75)\nentry2.place(x=10,y=410)\n#listbox.place(x=10,y=30)\nradio1.place(x=10, y=385)\nradio2.place(x=60, y=385)\nlistbox.pack()\n\n#======================================================================================================================\n'''\nframe2 \"plus\"에 대한 부분\n시간변환에 대한 부분을 다룬다.\n변환하고자 하는 시간과 원하는 시간대를 입력하면 \n변환된 시간이 나온다.\n\n입력 : 변환을 원하는 시간, 변환을 원하는 시간대\n출력 : 변환된 시간 \n'''\n\nframe2=Frame(top)\nnotebook.add(frame2, text=\"plus\")\n\nplus_label1 = Label(frame2, text=\"시간입력\")\nplus_label1.place(x=8,y=7)\n\nmenubutton=Menubutton(frame2,text=\"메뉴 메뉴버튼\", relief=\"raised\", direction=\"right\")\nmenubutton.pack()\n\nmenu=Menu(menubutton, tearoff=0)\nmenu.add_command(label=\"하위메뉴-1\")\nmenu.add_separator()\nmenu.add_command(label=\"하위메뉴-2\")\nmenu.add_command(label=\"하위메뉴-3\")\n\nmenubutton[\"menu\"]=menu\n\n#====================================================================================================================\n'''\nframe3는 구글 캘린더 일정 확인을 진행한다.\n매주 반복업무를 설정하고 잘 지켜지고 있는지 체크한다. \n\n'''\n\nframe3=Frame(top)\nnotebook.add(frame3, text=\"google\")\n\n\n\n#====================================================================================================================\n'''\nframe4는 누군가 나에게 해야하는 일이라고 전달 해준 것이 들어온다.\n\n'''\n\nframe4=Frame(top)\nnotebook.add(frame4, text=\"일정관리\")\n\n\n\nif __name__==\"__main__\" :\n    data = loadlist()\n    top.mainloop()\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"36769351","text":"\nfrom django.http import JsonResponse\nfrom django.shortcuts import render\nfrom rest_framework import status\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.response import Response\n\nfrom .models import Category, Tag, Product\n# Create your views here.\n\n\n\n# def product_list_view(request):\n#     products = Product.objects.all()\n#     product_list = []\n#     for i in products:\n#         product_list.append(\n#             {\n#                 'title': i.title,\n#                 'description': i.description,\n#                 'price': i.price,\n#                 'category': i.category,\n#             }\n#         )\n#     data = {\n#         'title': product_list\n#     }\n#     return JsonResponse(data=data, safe=False)\n\n\nfrom rest_framework.decorators import api_view, permission_classes\nfrom .serializers import ProductSerializer, CategorySerializer, ProductCreateSerializer, ProductUpdateSerializer, \\\n    LoginValidateSerializer\n\n\n# @api_view(['GET', 'POST'])\n# def product_rest_list_view(request):\n#     products = Product.objects.all()\n#     data = ProductSerializer(products, many=True).data\n#     return Response(data=data)\n\n@api_view(['GET', 'POST'])\n@permission_classes([IsAuthenticated])\ndef product_rest_list_view(request):\n    if request.method == \"GET\":\n        print(request.user)\n        return Response(data=ProductSerializer(Product.objects.all(), many=True).data)\n    elif request.method == \"POST\":\n        serializer = ProductCreateSerializer(data=request.data)\n        if not serializer.is_valid():\n            return Response(\n                data={\n                    'message': 'error',\n                    'errors': serializer.errors\n                }\n            )\n        title = request.data['title']\n        description = request.data['description']\n        price = request.data['price']\n        category_id = request.data['category_id']\n        product = Product.objects.create(\n            title=title, description=description, price=price,\n             category_id=category_id)\n        tags = request.data['tags']\n        for i in tags:\n            product.tags.add(i)\n        product.save()\n        return Response(data={'message': 'OK',\n                              'product': ProductSerializer(product).data})\n\n\n@api_view(['GET', 'PUT'])\ndef product_item(request,id):\n    try:\n        products = Product.objects.get(id=id)\n    except Product.DoesNotExist:\n        return Response(data={'message': 'Takogo producta net'},\n                        status=status.HTTP_404_NOT_FOUND)\n    if request.method == 'PUT':\n        serializer = ProductUpdateSerializer(data=request.data)\n        if not serializer.is_valid():\n            return Response(\n                data={\n                    'message': 'error',\n                    'errors': serializer.errors\n                }\n            )\n        products.title = request.data['title']\n        products.description = request.data['description']\n        products.price = request.data['price']\n        products.category_id = request.data['category_id']\n        products.tags.clear()\n        for i in request.data['tags']:\n            products.tags.add(i)\n        products.save()\n    data = ProductSerializer(products, many=False).data\n    return Response(data=data)\n\n\n\n@api_view(['GET', 'POST'])\ndef categories_list(request):\n    categories = Category.objects.all()\n    data = CategorySerializer(categories, many=True).data\n    return Response(data=data)\n\n@api_view(['GET'])\ndef categories_id(request, id):\n    categories =Category.objects.get(id=id)\n    data = CategorySerializer(categories).data\n    return Response(data=data)\n\n@api_view(['POST'])\ndef test(request):\n    title = request.data.get('title', 'Mango')\n    Product.objects.create(title=title)\n    return Response(data={'massage': 'received'})\n\nfrom django.contrib import auth\n@api_view(['POST'])\ndef login(request):\n    if request.method == 'POST':\n        serializer = LoginValidateSerializer(data=request.data)\n        if not serializer.is_valid():\n            return Response(\n                data={\n                    'message': 'error',\n                    'errors': serializer.errors\n                },\n                status=status.HTTP_406_NOT_ACCEPTABLE\n            )\n        print(serializer.validated_data)\n        user = auth.authenticate(**serializer.validated_data)\n        if user:\n            try:\n                token = Token.objects.get(user=user)\n                print('GET TOKEN')\n            except Token.DoesNotExist:\n                print('CREATE TOKEN')\n                token = Token.objects.create(user=user)\n            return Response(data={'key': token.key})\n        else:\n            return Response(\n                data={'message': 'User not found!!!'},\n                status=status.HTTP_404_NOT_FOUND\n            )\n\n\n            # Token.objects.filter(user=user).delete()   - ЭТОТ ИСПОЛЬЗУЕМ TOKEN ЕСЛИ НУЖЕН 1 ПОЛЬЗОВАТЕЛЬ - ОДНО ПРИЛОЖЕНИЕ\n            # token = Token.objects.create(user=user)\n            # return Response(data={'key': token.key})\n\n\n\n\n","sub_path":"distributor/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"439935570","text":"# coding=utf8\n'''\nВся работа происходит в памяти, без промежуточных файлов\npython 2.6\n\nФункционал работы со списками кеев и ссылок:\n+ число строк\n+ базовая чистка, в т.ч. приведение к нижнему регистру\n+ сортировка по алфавиту/по длине/по параметру (разные алгоритмы)\n+ перемешивание\n+ удаление дубликатов (разные алгоритмы, меняется число строк)\n+ выборка строк по кейвордам/блэк-листу (разные алгоритмы, меняется число строк)\n+ удаление строк по кейвордам/блэк-листу (разные алгоритмы, меняется число строк)\n'''\n\nimport random, time, re, codecs\n\nclass Kwk8:\n    '''Базовый класс'''\n    def __init__(self, path, verbose=False, encoding='cp1251'):\n        '''path - входной файл, isLinks - кеи или ссылки'''\n        self.pathOriginal = path  # исходный файл\n        self.countOriginal = 0  # число строк в исходном файле\n        self.verbose=verbose\n        self.lines = []\n        self._Load(self.pathOriginal, encoding)\n        \n    def _Load(self, path, encoding='cp1251'):\n        '''Чтение файла'''\n        self._Print('Loading file %s...' % path)\n        self._TimeStart()\n        with codecs.open(path, 'r', encoding, 'ignore') as fd:\n            self.lines = fd.readlines()\n        self.countOriginal = self.Count()\n        self._Print(\"- %d lines loaded %s\" % (self.Count(), self._TimeFinish()))\n        return self\n        \n    def Extend(self, newItems):\n        '''Добавляем кеи из другого списка'''\n        self.lines.extend(newItems)\n        return self\n        \n    def Save(self, path = '', encoding='cp1251'):\n        '''Записывает строки в файл'''\n        if path == '':  # если path == '', то записывает в исходный файл\n            path = self.pathOriginal\n        self._Print('Saving to file %s...' % path)\n        self._TimeStart()\n        with codecs.open(path, 'w', encoding, 'ignore') as fd:\n            fd.writelines(self.lines)\n        self._Print(\"- {0} lines saved {1}\".format(self.Count(), self._TimeFinish()))\n        return self\n    \n    def Items(self):\n        '''Доступ к кеям'''\n        return self.lines\n    \n    def Count(self):\n        '''Число строк'''\n        return len(self.lines)\n        \n    def _Print(self, s):\n        '''Выводит строку'''\n        if self.verbose:\n            print(s)\n    \n    def _TimeStart(self):\n        '''Засекает текущее время'''\n        self.timetoken = time.time()\n        \n    def _TimeFinish(self):\n        '''Возвращает сколько времени прошло'''\n        return '({0:.2f} sec.)'.format(time.time() - self.timetoken)\n        \n    def _ProcessLine(self, line):\n        '''Ключ строки. Переопределяется в классах-потомках'''\n        return line\n    \n    def _ProcessLineLen(self, line):\n        '''Вспомогательная функция, используется при сортировке'''\n        return len(self._ProcessLine(line))\n        \n    def Basic(self, makeLowerCase=False, stripSpaces=False, allowedChars=''):\n        '''Базовая чистка кеев'''\n        self._Print('Basic processing...')\n        self._TimeStart()\n        rxStripSpaces = re.compile(r'\\s+')\n        if allowedChars != '':\n            rxAllowedChars = re.compile(r'[^' + re.escape(allowedChars) + ']')\n        newLines = []\n        for line in self.lines:\n            line = line.strip()\n            if makeLowerCase:\n                line = line.lower()\n            if stripSpaces:\n                line = rxStripSpaces.sub(' ', line)\n            if allowedChars != '':\n                line = rxAllowedChars.sub('', line).strip()\n            if line != '':\n                newLines.append(line + '\\n')\n        self._Print('- done %s' % self._TimeFinish())\n        self.lines = newLines\n        return self\n    \n    def Sort(self, mode = 'alpha'):\n        '''Сортировка, mode: (alfa|length)'''\n        self._Print('Sorting by {0}...'.format(mode))\n        self._TimeStart()\n        if mode == 'alpha':\n            self.lines.sort(key=self._ProcessLine)\n        if mode == 'length':\n            self.lines.sort(key=self._ProcessLineLen)\n        self._Print('- done %s' % self._TimeFinish())\n        return self\n    \n    def Shuffle(self):\n        '''Перемешивание'''\n        self._Print('Shuffling...')\n        self._TimeStart()\n        random.shuffle(self.lines)\n        self._Print('- done %s' % self._TimeFinish())\n        return self\n    \n    def Duplicates(self):\n        '''Удаление дубликатов'''\n        self._Print('Duplicates removing...')\n        self._TimeStart()\n        hashes = set()\n        newLines = []\n        for line in self.lines:\n            x = self._ProcessLine(line)\n            if not x in hashes:\n                hashes.add(x)\n                newLines.append(line)\n        if self.Count() != 0:\n            self._Print('- %d lines (%.2f%%) %s' % (len(newLines), len(newLines) * 100.0 / self.Count(), self._TimeFinish()))\n        self.lines = newLines\n        return self\n    \n    def SelectByList(self, keysList):\n        '''Выборка по кеям из списка'''       \n        self._Print('Selecting by keys...')\n        self._TimeStart()\n        newLines = []\n        for line in self.lines:\n            x = self._ProcessLine(line)\n            for key in keysList:\n                if x.find(key) >= 0:\n                    newLines.append(line)\n                    break\n        if self.Count() != 0:\n            self._Print('- %d lines (%.2f%%) %s' % (len(newLines), len(newLines) * 100.0 / self.Count(), self._TimeFinish()))\n        self.lines = newLines\n        return self\n    \n    def DeleteByList(self, keysList):\n        '''Чистка по кеям из списка'''       \n        self._Print('Clearing by keys...')\n        self._TimeStart()\n        newLines = []\n        for line in self.lines:\n            found = False\n            x = self._ProcessLine(line)\n            for key in keysList:\n                if x.find(key) >= 0:\n                    found = True\n                    break\n            if not found:\n                newLines.append(line)\n        if self.Count() != 0:\n            self._Print('- %d lines (%.2f%%) %s' % (len(newLines), len(newLines) * 100.0 / self.Count(), self._TimeFinish()))\n        self.lines = newLines\n        return self\n    \n    def SelectByFile(self, keysFile, encoding='cp1251'):\n        '''Выборка по кеям из файла'''\n        if keysFile:\n            keysList = []\n            for line in codecs.open(keysFile, 'r', encoding, 'ignore'):\n                keysList.append(self._ProcessLine(line))\n            return self.SelectByList(keysList)\n        else:\n            return self\n        \n    def DeleteByFile(self, keysFile, encoding='cp1251'):\n        '''Чистка по кеям из файла'''\n        if keysFile:\n            keysList = []\n            for line in codecs.open(keysFile, 'r', encoding, 'ignore'):\n                keysList.append(self._ProcessLine(line))\n            return self.DeleteByList(keysList)\n        else:\n            return self\n\nclass Kwk8Keys(Kwk8):\n    def _ProcessLine(self, line):\n        return line.strip().lower()\n\nclass Kwk8Links(Kwk8):\n    def _ProcessLine(self, line):\n        '''Возвращает имя хоста вида \"subdomain.domain.com/\"'''\n        line = line.strip().lower()\n        if line.startswith('http://'):\n            line = line[7:]\n        if line.startswith('www.'):\n            line = line[4:]\n        line, _, _ = line.partition('/')\n        return line + '/'\n\n    def _PostProcessLine(self, line):\n        '''Пост-обработка ссылки'''\n        while line.find('/./') >= 0:\n            line = line.replace('/./', '/')\n        ''' index.php '''\n        featuresList = '''/action=profile;u=\n/index.php?action=\n/index.php?do=\n/index.php?showforum=\n/index.php?showtopic=\n/index.php?showuser=\n/index.php?topic=\n/member.php\n/memberlist.php\n/newreply.php\n/posting.php\n/profile.php\n/showthread.php\n/topic.php\n/viewthread.php\n/viewtopic.php'''.split('\\n')\n        for feature in featuresList:\n            if line.find(feature) >= 0:\n                return line[:line.find(feature)] + '/index.php\\n'\n        ''' forum.php '''\n        featuresList = '''/forum.php'''.split('\\n')\n        for feature in featuresList:\n            if line.find(feature) >= 0:\n                return line[:line.find(feature)] + '/forum.php\\n'\n        ''' yabb.pl '''\n        featuresList = '''/yabb.pl'''.split('\\n')\n        for feature in featuresList:\n            if line.find(feature) >= 0:\n                return line[:line.find(feature)] + '/yabb.pl\\n'\n        return line\n    \n    def PostProcessing(self):\n        '''Пост-обработка ссылок с фильтрацией'''\n        self._Print('Post processing...')\n        self._TimeStart()\n        newLines = []\n        for line in self.lines:\n            x = self._ProcessLine(line)\n            isGood = True\n            level = x.count('.') + 1\n            if (level <= 1) or (level >= 4):  # удаляем домены 1 и 4 и более уровней\n                isGood = False\n            else:\n                zone1 = x.split('.')[-1]\n                if zone1 in ['cc', 'tk']:  # удаляем фридомены\n                    isGood = False\n                else:\n                    zone2 = x.split('.')[-2]\n                    if (level == 3) and (len(zone2) > 3):  # удаляем домены 3-го уровня\n                        isGood = False\n            if isGood:\n                newLines.append(self._PostProcessLine(line))\n        if self.Count() != 0:\n            self._Print('- %d lines (%.2f%%) %s' % (len(newLines), len(newLines) * 100.0 / self.Count(), self._TimeFinish()))\n        self.lines = newLines\n        return self\n    \ndef ProcessKeys(inPathKeywords, outPathKeywords, pathStopwords = None):\n    '''Стандартная обработка кеев'''\n    return Kwk8Keys(inPathKeywords, False).Basic(True).DeleteByFile(pathStopwords).Duplicates().Shuffle().Save(outPathKeywords).Count()\n\ndef ProcessLinks(inPathLinks, outPathLinks):\n    '''Стандартная обработка ссылок'''\n    return Kwk8Links(inPathLinks, False).Basic().Duplicates().Shuffle().Save(outPathLinks).Count()\n\ndef ProcessSnippets(inPathKeywords, outPathKeywords, pathStopwords = [], snippetsStopWords = ['http://', '[url', '.ru', '.com', '.html', '.php']):\n    '''Стандартная обработка сниппетов'''\n    return Kwk8Keys(inPathKeywords, False).DeleteByList(snippetsStopWords).DeleteByList(pathStopwords).Duplicates().Shuffle().Save(outPathKeywords).Count()\n\nif __name__ == '__main__':\n    #ProcessKeys('/home/sasch/temp/list/list1.txt', '/home/sasch/temp/list/list1_out1.txt', '/home/sasch/temp/list/list1_stop.txt')\n    #ProcessLinks('/home/sasch/temp/list/list1.txt', '/home/sasch/temp/list/list1_out2.txt')\n    #ProcessSnippets('/home/sasch/temp/list/text.txt', '/home/sasch/temp/list/text-out.txt', '/home/sasch/temp/list/stopwords.txt')\n    Kwk8Links(r'c:\\Work\\links\\LinksList id266a.txt', True).PostProcessing().Save(r'c:\\Work\\links\\LinksList id266b.txt')\n    pass\n","sub_path":"tools/kwk8.py","file_name":"kwk8.py","file_ext":"py","file_size_in_byte":11770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"13773847","text":"import nltk.data\nfrom nltk.tokenize import word_tokenize\nfrom nltk.tokenize import sent_tokenize\nfrom random import choice\nimport json\n\ndb = {}\n\nreflect = {         #Used to convert from past to present tense\n    \"was\": \"is\",\n    \"were\": \"are\",\n    \"became\": \"will become\",\n    \"you\": \"me\",\n    \"started\": \"starts\",\n    \"remained\": \"remains\",\n}\n\n\ndef parseWords(db, sentenceWords):          #Create the chains between words in a sentence\n\n    for i, word in enumerate(sentenceWords):\n        try:\n            word1 = sentenceWords[i]\n            word2 = sentenceWords[i + 1]\n            word3 = sentenceWords[i + 2]\n            word4 = sentenceWords[i + 3]\n        except IndexError:          #Reached end of sentence\n            break\n        key = (word1, word2, word3)\n        if key not in db:\n            db[key] = []\n        db[key].append(word4)\n    return\n\n\ndef tryReflect(segment):            #Convert from past tense to present tense\n    words = segment.lower().split()\n    for x, word in enumerate(words):        #Iterate through each word and try to convert to present tense\n        if word in reflect:\n            words[x] = reflect[word]\n    return ' '.join(words)\n\n\ndef generateSentence(db, present):      #Generate a sentence from the chains of words\n    li = [key for key in db.keys() if key[0][0].isupper()]\n    keychoice = [(\"It\", \"was\", \"a\"), (\"It\", \"was\", \"another\"), (\"There\", \"were\", \"some\")]       #Starting point for the chain\n    key = choice(keychoice)\n    sentence = []\n    firstWord, secondWord, thirdWord = key\n    sentence.append(firstWord)      #Begin sentence construction\n    sentence.append(secondWord)\n    sentence.append(thirdWord)\n    while True:     #Loop until we reach the end of the sentence\n        try:\n            fourthWord = choice(db[key])\n        except KeyError:\n            break\n        if fourthWord[-1] == \".\":\n            break\n        sentence.append(fourthWord)\n        key = (secondWord, thirdWord, fourthWord)\n        firstWord, secondWord, thirdWord = key\n    if present:     #Convert sentence to present tense\n        for x, g in enumerate(sentence):\n            if(g in reflect):\n                sentence[x] = reflect[g]\n    return ' '.join(sentence) + \".\"\n\n\ndef generate(filePath):     #Generate dictionary from text file\n    with open(filePath, \"r\", encoding='utf-8') as file:\n        text = file.read()\n    for sentence in sent_tokenize(text):\n        parseWords(db, word_tokenize(sentence))\n    return db","sub_path":"Final/src/MarkovGenerator.py","file_name":"MarkovGenerator.py","file_ext":"py","file_size_in_byte":2470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"146241665","text":"from abc import ABC, abstractmethod\nfrom pyspark.sql import SparkSession\n\n\nclass SparkJob(ABC):\n    \"\"\"\n    Interface that serves as a base for any Spark job\n    The method run must be implemented by the child class\n    Finally, the start method is used for starting the Job\n    \"\"\"\n\n    @abstractmethod\n    def run(self): raise NotImplementedError\n\n    @staticmethod\n    def get_spark_session(app_name=\"PMP Batch\"):\n        return SparkSession.builder\\\n            .master(\"local[*]\")\\\n            .config('spark.executor.memory', '40g')\\\n            .config('spark.driver.memory', '40g')\\\n            .config(\"spark.sql.shuffle.partitions\", 10)\\\n            .appName(app_name)\\\n            .getOrCreate()\n","sub_path":"resources/spark.py","file_name":"spark.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"424984035","text":"\"\"\"\nFunctions for plotting LOFAR stations with matplotlib\n\"\"\"\nfrom .db import LofarAntennaDatabase\nfrom .geo import (\n    localnorth_to_etrs,\n    geographic_array_from_xyz,\n    xyz_from_geographic,\n)\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.patches import Circle, Polygon\n\n\ndb = LofarAntennaDatabase()\n\n__all__ = [\n    \"plot_hba\",\n    \"plot_lba\",\n    \"plot_station\",\n    \"plot_superterp\",\n    \"plot_core\",\n    \"add_background\",\n]\n\n\ndef plot_hba(\n    station_name, ax=None, centre=None, subfield=\"\", labels=False, tilestyle=\"lines\"\n):\n    \"\"\"\n    Plot LOFAR HBA tiles for one station\n\n    Args:\n        station_name: Station name, without suffix. E.g. \"CS001\"\n        ax: existing matplotlib axes object to use\n        centre: etrs coordinates of origin. Default: HBA phase centre of station.\n        subfield: '0', '1' or ''to suffix after 'HBA' for e.g. CS002HBA1)\n        labels: add labels\n\n    Example:\n        >>> from lofarantpos.plotutil import plot_hba\n        >>> plot_hba(\"CS001\")\n    \"\"\"\n    if centre is None:\n        centre = db.phase_centres[station_name + \"HBA\"]\n\n    if ax is None:\n        fig, ax = plt.subplots()\n        ax.set_xlabel(\"Local East (m)\")\n        ax.set_ylabel(\"Local North (m)\")\n        ax.set_aspect(\"equal\")\n\n    etrs_to_xyz = localnorth_to_etrs(centre).T\n\n    if station_name + \"HBA\" + subfield not in db.hba_rotations:\n        plot_hba(\n            station_name,\n            ax=ax,\n            centre=centre,\n            subfield=\"0\",\n            labels=labels,\n            tilestyle=tilestyle,\n        )\n        plot_hba(\n            station_name,\n            ax=ax,\n            centre=centre,\n            subfield=\"1\",\n            labels=labels,\n            tilestyle=tilestyle,\n        )\n        return\n\n    etrs_delta = db.antenna_etrs(station_name + \"HBA\" + subfield) - centre\n    xys = (etrs_to_xyz @ etrs_delta.T)[:2, :].T\n\n    theta = db.hba_rotations[station_name + \"HBA\" + subfield]\n    c, s = np.cos(theta), np.sin(theta)\n    rot_mat = db.pqr_to_localnorth(station_name + \"HBA\")[:2, :2] @ np.array(\n        ((c, s), (-s, c))\n    )\n    x_dir = rot_mat @ [5.15, 0]\n    y_dir = rot_mat @ [0, 5.15]\n\n    tile = np.array(\n        [\n            -0.5 * x_dir - 0.5 * y_dir,\n            +0.5 * x_dir - 0.5 * y_dir,\n            +0.5 * x_dir + 0.5 * y_dir,\n            -0.5 * x_dir + 0.5 * y_dir,\n            -0.5 * x_dir - 0.5 * y_dir,\n        ]\n    )\n\n    for num, xy in enumerate(xys):\n        x, y = xy\n        if tilestyle == \"lines\":\n            ax.plot((x + tile)[:, 0], (y + tile)[:, 1], \"k\")\n        else:\n            # Plot transparent lines, to keep extent\n            ax.plot((x + tile)[:, 0], (y + tile)[:, 1], \"k\", alpha=0)\n        if tilestyle == \"filled\":\n            ax.add_patch(Polygon((tile + [x, y]), fill=True, facecolor=\"k\"))\n        if labels:\n            if subfield == \"1\":\n                num += 24\n            if tilestyle == \"filled\":\n                ax.text(x, y, str(num), va=\"center\", ha=\"center\", color=\"w\")\n            else:\n                ax.text(x, y, str(num), va=\"center\", ha=\"center\")\n\n\ndef plot_lba(station_name, ax=None, centre=None, labels=False):\n    \"\"\"\n    Plot LOFAR LBA locations for one station\n\n    Args:\n        station_name: Station name, without suffix. E.g. \"CS001\"\n        ax: existing matplotlib axes object to use\n        centre: etrs coordinates of origin. Default: LBA phase centre of station.\n        labels: add labels\n\n    Example:\n        >>> from lofarantpos.plotutil import plot_lba\n        >>> plot_lba(\"IE613\", labels=True)\n    \"\"\"\n    if centre is None:\n        centre = db.phase_centres[station_name + \"LBA\"]\n\n    if ax is None:\n        fig, ax = plt.subplots()\n        ax.set_xlabel(\"Local East (m)\")\n        ax.set_ylabel(\"Local North (m)\")\n        ax.set_aspect(\"equal\")\n\n    etrs_to_xyz = localnorth_to_etrs(centre).T\n    etrs_delta = db.antenna_etrs(station_name + \"LBA\") - centre\n    xys = (etrs_to_xyz @ etrs_delta.T)[:2, :].T\n\n    ax.plot(xys[:, 0], xys[:, 1], \"k.\")\n\n    if labels:\n        for num, xy in enumerate(xys):\n            x, y = xy\n            ax.text(x, y, str(num))\n\n\ndef plot_cabinet(station_name, ax=None, centre=None, labels=False):\n    \"\"\"\n    Plot LOFAR cabinet location for one station\n\n    Args:\n        station_name: Station name, without suffix. E.g. \"CS001\"\n        ax: existing matplotlib axes object to use\n        centre: etrs coordinates of origin. Default: LBA phase centre of station.\n        labels: add label\n\n    Example:\n        >>> from lofarantpos.plotutil import plot_hba, plot_cabinet\n        >>> import matplotlib.pyplot as plt\n        >>> fig, ax = plt.subplots()\n        >>> plot_hba(\"CS002\", ax=ax)\n        >>> plot_cabinet(\"CS002\", ax=ax, labels=True)\n    \"\"\"\n    if centre is None:\n        centre = db.phase_centres[station_name + \"LBA\"]\n\n    if ax is None:\n        fig, ax = plt.subplots()\n        ax.set_aspect(\"equal\")\n\n    etrs_to_xyz = localnorth_to_etrs(centre).T\n    etrs_delta = db.cabinet_etrs[station_name] - centre\n    x, y, _ = etrs_to_xyz @ etrs_delta\n\n    ax.plot(x, y, \"ks\")\n    if labels:\n        ax.text(x, y, station_name + \" cabinet\")\n\n\ndef plot_station(\n    station_name, ax=None, centre=None, labels=False, tilestyle=\"lines\", background=None\n):\n    \"\"\"\n    Plot a LOFAR station\n\n    Args:\n        station_name: Station name, without suffix. E.g. \"CS001\"\n        ax: existing matplotlib axes object to use\n        labels: add labels\n        centre: centre of projection, as ETRS xyz coordinate. Default is station's LBA phase centre.\n        tilestyle: style for HBA tiles (\"filled\", \"lines\" or None)\n        background: name of background to draw, e.g. \"openstreetmap\" or \"luchtfoto\" (Dutch stations only)\n\n    Example:\n        >>> from lofarantpos.plotutil import plot_station\n        >>> plot_station(\"CS002\")\n        >>> plot_station(\"CS011\", background=\"openstreetmap\")\n        >>> plot_station(\"RS210\", background=\"luchtfoto\")\n        >>> plot_station(\"IE613\", background=\"Stamen_Toner\")\n    \"\"\"\n    if centre is None:\n        centre = db.phase_centres[station_name + \"LBA\"]\n\n    if ax is None:\n        fig, ax = plt.subplots()\n        ax.set_aspect(\"equal\")\n        ax.set_xlabel(\"Local East (m)\")\n        ax.set_ylabel(\"Local North (m)\")\n        ax.set_title(\"LOFAR station \" + station_name)\n\n    plot_lba(station_name, ax=ax, centre=centre, labels=labels)\n    plot_hba(station_name, ax=ax, centre=centre, labels=labels, tilestyle=tilestyle)\n    plot_cabinet(station_name, ax=ax, centre=centre, labels=labels)\n\n    if background is not None:\n        add_background(ax, centre, background)\n\n\ndef plot_superterp(\n    ax=None, labels=False, circle=True, centre=None, tilestyle=\"lines\", background=None\n):\n    \"\"\"\n    Plot the LOFAR superterp\n\n    Args:\n        ax: existing matplotlib axes object to use\n        labels: add labels\n        circle: plot a surrounding circle\n        centre: ETRS xyz coordinates of centre (default is the centre of the superterp)\n        tilestyle: style for HBA tiles (\"lines\", \"filled\" or None)\n        background: name of background to draw, e.g. \"openstreetmap\" or \"luchtfoto\" (Dutch stations only)\n\n    Example:\n        >>> from lofarantpos.plotutil import plot_superterp\n        >>> plot_superterp(background='openstreetmap', tilestyle=None)\n    \"\"\"\n    if ax is None:\n        fig, ax = plt.subplots()\n        ax.set_aspect(\"equal\")\n        ax.set_xlabel(\"Local East (m)\")\n        ax.set_ylabel(\"Local North (m)\")\n        ax.set_title(\"LOFAR superterp\")\n\n    if circle:\n        ax.add_patch(Circle((0, 0), radius=185, fill=False, edgecolor=\"k\"))\n\n    if centre is None:\n        centre = db.phase_centres[\"CS002LBA\"]\n    for station_name in \"CS002\", \"CS003\", \"CS004\", \"CS005\", \"CS006\", \"CS007\":\n        plot_station(\n            station_name,\n            ax=ax,\n            centre=centre,\n            labels=labels,\n            tilestyle=tilestyle,\n            background=None,\n        )\n\n    if background is not None:\n        add_background(ax, centre, background)\n\n\ndef plot_core(\n    ax=None, labels=False, circle=True, centre=None, tilestyle=\"lines\", background=None\n):\n    \"\"\"\n    Plot the LOFAR core\n\n    Args:\n        ax: existing matplotlib axes object to use\n        labels: add labels for each dipole\n        circle: plot a circle around the superterp\n        centre: ETRS xyz coordinates of centre (default is centre of the superterp)\n        tilestyle: style for HBA tiles (\"lines\", \"filled\", or None)\n        background: name of background to draw, e.g. \"openstreetmap\" or \"luchtfoto\"\n\n    Example:\n        >>> from lofarantpos.plotutil import plot_core\n        >>> plot_core()\n    \"\"\"\n    if ax is None:\n        fig, ax = plt.subplots()\n        ax.set_aspect(\"equal\")\n        ax.set_xlabel(\"Local East (m)\")\n        ax.set_ylabel(\"Local North (m)\")\n        ax.set_title(\"LOFAR core\")\n\n    if centre is None:\n        centre = db.phase_centres[\"CS002LBA\"]\n\n    plot_superterp(\n        ax=ax,\n        labels=labels,\n        circle=circle,\n        centre=centre,\n        tilestyle=tilestyle,\n        background=None,\n    )\n\n    core_stations = set(\n        [\n            station_name[:5]\n            for station_name in db.phase_centres.keys()\n            if station_name[:2] == \"CS\"\n        ]\n    )\n    superterp_stations = set([\"CS002\", \"CS003\", \"CS004\", \"CS005\", \"CS006\", \"CS007\"])\n    lofar_centre = db.phase_centres[\"CS002LBA\"]\n\n    for station in core_stations - superterp_stations:\n        plot_station(\n            station,\n            ax=ax,\n            centre=centre,\n            labels=labels,\n            tilestyle=tilestyle,\n            background=None,\n        )\n\n    add_background(ax, centre, background, zoom=15)\n\n\ndef _full_extent_to_xy(plotter, centre):\n    \"\"\"\n    Convert plotter extents to local north coordinates w.r.t. centre\n\n    Args:\n        plotter: tilemapbase.plotter object\n        centre: coordinates of centre, in ETRS xyz\n    \"\"\"\n    try:\n        import tilemapbase.mapping\n    except ImportError:\n        raise RuntimeError(\"To plot background maps, tilemapbase is required\")\n\n    scale = 2 ** plotter.zoom\n    xmin_deg, ymin_deg = tilemapbase.mapping.to_lonlat(\n        *plotter.extent.project(plotter.xtilemin / scale, plotter.ytilemin / scale)\n    )\n    xmax_deg, ymax_deg = tilemapbase.mapping.to_lonlat(\n        *plotter.extent.project(\n            (plotter.xtilemax + 1) / scale, (plotter.ytilemax + 1) / scale\n        )\n    )\n\n    lower_left_etrs = xyz_from_geographic(np.deg2rad(xmin_deg), np.deg2rad(ymin_deg), 0)\n    upper_right_etrs = xyz_from_geographic(\n        np.deg2rad(xmax_deg), np.deg2rad(ymax_deg), 0\n    )\n\n    etrs_to_xyz = localnorth_to_etrs(centre).T\n    lower_left_xyz = etrs_to_xyz @ (lower_left_etrs - centre)\n    upper_right_xyz = etrs_to_xyz @ (upper_right_etrs - centre)\n\n    return (\n        lower_left_xyz[0],\n        upper_right_xyz[0],\n        upper_right_xyz[1],\n        lower_left_xyz[1],\n    )\n\n\ndef add_background(ax, centre, background, zoom=18):\n    \"\"\"\n    Add openstreetmap background to axes. Assumes the axes extents are given in metres w.r.t. a local coordinates system\n    with origin centre (given in ETRS XYZ)\n\n    Args:\n        ax: existing matplotlib Axes object\n        centre: ETRS XYZ coordinates of the centre\n        background: name of background to draw, e.g. \"openstreetmap\" or \"luchtfoto\" (Dutch stations only)\n        zoom: zoom level for the background tiles\n\n    Example:\n        >>> from lofarantpos.plotutil import plot_station, add_background\n        >>> from lofarantpos.db import LofarAntennaDatabase\n        >>> db = LofarAntennaDatabase()\n        >>> centre = db.phase_centres[\"CS002LBA\"]\n        >>> fig, ax = plt.subplots()\n        >>> plot_station(\"CS002\", ax=ax)\n        >>> plot_station(\"CS001\", ax=ax, centre=centre)\n        >>> add_background(ax, centre, 'OSM')\n    \"\"\"\n    try:\n        import tilemapbase\n    except ImportError:\n        raise RuntimeError(\"To plot background maps, tilemapbase is required\")\n\n    if background == \"osm\" or background == \"openstreetmap\":\n        t = tilemapbase.tiles.build_OSM()\n    elif background in [\n        \"Carto_Dark\",\n        \"Carto_Dark_Labels\",\n        \"Carto_Dark_No_Labels\",\n        \"Carto_Light\",\n        \"Carto_Light_Labels\",\n        \"Carto_Light_No_Labels\",\n        \"Stamen_Terrain\",\n        \"Stamen_Terrain_Background\",\n        \"Stamen_Terrain_Labels\",\n        \"Stamen_Terrain_Lines\",\n        \"Stamen_Toner\",\n        \"Stamen_Toner_Background\",\n        \"Stamen_Toner_Hybrid\",\n        \"Stamen_Toner_Labels\",\n        \"Stamen_Toner_Lines\",\n        \"Stamen_Toner_Lite\",\n        \"Stamen_Watercolour\",\n    ]:\n        t = getattr(tilemapbase.tiles, background)\n    elif background == \"luchtfoto\":\n        t = tilemapbase.tiles.Tiles(\n            \"https://service.pdok.nl/hwh/luchtfotorgb/wmts/v1_0/2022_ortho25/EPSG:3857/{zoom}/{x}/{y}.jpeg\",\n            \"LUFO2022\",\n        )\n    elif isinstance(background, tilemapbase.tiles.Tiles):\n        t = background\n    else:\n        raise ValueError(\"Background not recognized: \" + str(background))\n\n    xmin, xmax = ax.get_xlim()\n    ymin, ymax = ax.get_ylim()\n\n    tilemapbase.init(create=True)\n\n    xmin_deg, ymin_deg, _ = np.rad2deg(\n        geographic_array_from_xyz(\n            centre + (localnorth_to_etrs(centre) @ [xmin, ymin, 0])\n        )\n    )[0]\n    xmax_deg, ymax_deg, _ = np.rad2deg(\n        geographic_array_from_xyz(\n            centre + (localnorth_to_etrs(centre) @ [xmax, ymax, 0])\n        )\n    )[0]\n\n    extent = tilemapbase.Extent.from_lonlat(xmin_deg, xmax_deg, ymin_deg, ymax_deg)\n\n    plotter = tilemapbase.Plotter(extent, t, zoom=zoom)\n\n    im = plotter.as_one_image()\n\n    ax.imshow(im, extent=_full_extent_to_xy(plotter, centre), zorder=0)\n\n    ax.set_xlim(xmin, xmax)\n    ax.set_ylim(ymin, ymax)\n","sub_path":"lofarantpos/plotutil.py","file_name":"plotutil.py","file_ext":"py","file_size_in_byte":13781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"622405342","text":"import re\nfrom pkgutil import get_data\nfrom .board_util import GoBoardUtil, BLACK, WHITE, EMPTY, BORDER\n\n\nclass WeightUtil:\n\n    def __init__(self, file):\n        weightlist = str(get_data('nogo4', 'simulation/weights').decode('utf-8')).strip().split('\\n')\n        self.weightdict = {}\n        for line in weightlist:\n            splitline = re.split(\" \", line)\n            index, weight = splitline\n            self.weightdict[index] = weight\n\n    def getweight(self, index):\n        \"\"\"\n        Takes a base 4 index string and returns a float weight for that move\n        \"\"\"\n        indexb10 = str(int(index, 4))\n        return float(self.weightdict[indexb10])\n\n    def getindex(self, board, move):\n        \"\"\"\n        Takes a move on a board and produces an index for use with the weight file\n        \"\"\"\n        index = ''\n        index = index + str(board.board[move + board.NS + 1])  # Bottom Right\n        index = index + str(board.board[move + board.NS])  # Bottom\n        index = index + str(board.board[move + board.size])  # Bottom Left\n        index = index + str(board.board[move + 1])  # Right\n        index = index + str(board.board[move - 1])  # Left\n        index = index + str(board.board[move - board.size])  # Top Right\n        index = index + str(board.board[move - board.NS])  # Top\n        # Diagonal Top Left\n        index = index + str(board.board[move - board.NS - 1])\n        if board.current_player == WHITE:  # Swap 1's and 2's if player is not black\n            # This could be done much more efficiently by doing a little arithetic up top, but less readable\n            invert_index = index.replace(\"1\", \"b\")\n            invert_index = invert_index.replace(\"2\", \"1\")\n            invert_index = invert_index.replace(\"b\", \"2\")\n            return invert_index\n        return index\n","sub_path":"nogo4/simulation/weighting.py","file_name":"weighting.py","file_ext":"py","file_size_in_byte":1810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"470773629","text":"# SPDX-FileCopyrightText: 2017 Fermi Research Alliance, LLC\n# SPDX-License-Identifier: Apache-2.0\n\nimport multiprocessing\nimport pickle\nimport threading\nimport time\nimport uuid\n\nimport structlog\n\nfrom kombu import Connection, Queue\nfrom kombu.pools import producers\n\nfrom decisionengine.framework.modules import Module\nfrom decisionengine.framework.modules.logging_configDict import LOGGERNAME\nfrom decisionengine.framework.modules.Source import Source\nfrom decisionengine.framework.taskmanager.module_graph import _create_module_instance\nfrom decisionengine.framework.taskmanager.ProcessingState import State\nfrom decisionengine.framework.util.metrics import Gauge\n\n_DEFAULT_SCHEDULE = 300  # 5 minutes\n\nSOURCE_ACQUIRE_GAUGE = Gauge(\n    \"de_source_last_acquire_timestamp_seconds\",\n    \"Last time a source successfully ran its acquire function\",\n    [\n        \"source_name\",\n    ],\n)\n\n\nclass SourceWorker(multiprocessing.Process):\n    \"\"\"\n    Provides interface to loadable modules an events to sycronise\n    execution\n    \"\"\"\n\n    def __init__(self, key, config, channel_name, exchange, broker_url):\n        \"\"\"\n        :type config: :obj:`dict`\n        :arg config: configuration dictionary describing the worker\n        \"\"\"\n        super().__init__(name=f\"SourceWorker-{key}\")\n        self.module_instance = _create_module_instance(config, Source, channel_name)\n        self.config = config\n        self.module = self.config[\"module\"]\n        self.key = key\n        self.name = self.module_instance.__class__.__name__\n        SOURCE_ACQUIRE_GAUGE.labels(self.name)\n\n        self.logger = structlog.getLogger(LOGGERNAME)\n        self.logger = self.logger.bind(module=__name__.split(\".\")[-1], source=self.name)\n\n        self.exchange = exchange\n        self.connection = Connection(broker_url)\n\n        # We use a random name to avoid queue collisions when running tests\n        queue_id = self.key + \"-\" + str(uuid.uuid4()).upper()\n        self.logger.debug(f\"Creating queue {queue_id} with routing key {self.key}\")\n        self.queue = Queue(\n            queue_id,\n            exchange=self.exchange,\n            routing_key=self.key,\n            auto_delete=True,\n        )\n        self.use_count = multiprocessing.Value(\"i\", 1)\n        self.schedule = config.get(\"schedule\", _DEFAULT_SCHEDULE)\n\n        self.logger.debug(\n            f\"Creating worker: module={self.module} name={self.key} class_name={self.name} parameters={config['parameters']} schedule={self.schedule}\"\n        )\n\n    def should_stop(self):\n        with self.use_count.get_lock():\n            return self.use_count.value == 0\n\n    def increment_use_count(self):\n        with self.use_count.get_lock():\n            self.use_count.value += 1\n\n    def decrement_use_count(self):\n        with self.use_count.get_lock():\n            self.use_count.value -= 1\n\n    def take_offline(self):\n        with self.use_count.get_lock():\n            self.use_count.value = 0\n\n    def run(self):\n        \"\"\"\n        Get the data from source\n        \"\"\"\n        self.logger.info(f\"Starting source loop for {self.key}\")\n        SOURCE_ACQUIRE_GAUGE.labels(self.key)\n        with producers[self.connection].acquire(block=True) as producer:\n            # If task manager is in offline state, do not keep executing sources.\n            while not self.should_stop():\n                try:\n                    self.logger.info(f\"Source {self.name} calling acquire\")\n                    data = self.module_instance.acquire()\n                    Module.verify_products(self.module_instance, data)\n                    self.logger.info(f\"Source {self.name} acquire returned\")\n                    SOURCE_ACQUIRE_GAUGE.labels(self.name).set_to_current_time()\n                    self.logger.debug(\n                        f\"Publishing data to queue {self.key} with routing key {self.key}\"\n                        + f\" ({len(pickle.dumps(data))} pickled bytes)\"\n                    )\n                    producer.publish(\n                        dict(source_module=self.module, class_name=self.name, data=data),\n                        routing_key=self.key,\n                        exchange=self.exchange,\n                        serializer=\"pickle\",\n                        declare=[\n                            self.exchange,\n                            self.queue,\n                        ],\n                    )\n                    self.logger.info(f\"Source {self.name} {self.module} finished cycle\")\n                except Exception:\n                    self.logger.exception(f\"Exception running source {self.name} \")\n                    producer.publish(\n                        dict(source_module=self.module, class_name=self.name, data=State.SHUTDOWN),\n                        routing_key=self.key,\n                        exchange=self.exchange,\n                        serializer=\"pickle\",\n                        declare=[\n                            self.exchange,\n                            self.queue,\n                        ],\n                    )\n                    break\n                if self.schedule > 0:\n                    time.sleep(self.schedule)\n                else:\n                    self.logger.info(f\"Source {self.name} runs only once\")\n                    break\n        self.logger.info(f\"Stopped {self.name}\")\n\n\nclass SourceWorkers:\n    def __init__(self, exchange, broker_url, logger=structlog.getLogger(LOGGERNAME)):\n        self._exchange = exchange\n        self._broker_url = broker_url\n        self._logger = logger\n        self._workers = {}\n        self._lock = threading.Lock()\n\n    def update(self, channel_name, source_configs):\n        workers = {}\n\n        # Reuse already existing sources\n        with self._lock:\n            existing_sources = set(self._workers.keys()).intersection(source_configs.keys())\n            for src_name in existing_sources:\n                new_src_config = source_configs.pop(src_name)\n                src_worker = self._workers[src_name]\n                if new_src_config != src_worker.config:\n                    err_msg = (\n                        f\"Channel {channel_name} will not be loaded due to the following error:\\n\"\n                        f\"Mismatched configurations for source with name {src_name}\\n\"\n                        f\"New configuration\\n -> {new_src_config}\\n\"\n                        f\"Cached configuration\\n -> {src_worker.config}\"\n                    )\n                    raise RuntimeError(err_msg)\n                self._logger.info(f\"Using existing source {src_name} for channel {channel_name}\")\n                src_worker.increment_use_count()\n                workers[src_name] = src_worker\n\n            # The remaining configuration correspond to new sources\n            for key, config in source_configs.items():\n                self._logger.info(f\"Creating source {key} for channel {channel_name}\")\n                worker = SourceWorker(key, config, channel_name, self._exchange, self._broker_url)\n                self._workers[key] = worker\n                workers[key] = worker\n\n        return workers\n\n    def prune(self, source_names):\n        with self._lock:\n            for source_name in source_names:\n                src_worker = self._workers[source_name]\n                src_worker.decrement_use_count()\n                if src_worker.should_stop():\n                    src_worker.join()\n                    del self._workers[source_name]\n                    self._logger.debug(f\"Removed source {source_name}\")\n\n    def remove_all(self, timeout):\n        with self._lock:\n            for worker in self._workers.values():\n                worker.take_offline()\n                if worker.is_alive():\n                    worker.join(timeout)\n            self._workers.clear()\n","sub_path":"src/decisionengine/framework/engine/SourceWorkers.py","file_name":"SourceWorkers.py","file_ext":"py","file_size_in_byte":7740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"188110023","text":"import sys\nsys.path.append(\"..\")\nimport unittest\n\nsys.path.append(\"..\")\n\ntestmodules = [\n    'allergies_test',\n    'apache_aps_test',\n    'apache_patient_result_test',\n    'apache_pred_var_test',\n    'care_plan_care_provider_test',\n    'care_plan_eol_test',\n    'care_plan_general_test',\n    'care_plan_goal_test',\n    'care_plan_infectious_disease_test',\n    'custom_lab_test',\n    'diagnosis_test',\n    'dna_test',\n    'drugs_test',\n    'dx_test',\n    'hospital_test',\n    'infusion_drug_test',\n    'intake_output_test',\n    'lab_test',\n    'medication_test',\n    'micro_lab_test',\n    'note_test',\n    'nurse_assessment_test',\n    'nurse_care_test',\n    'nurse_charting_test',\n    'past_history_test',\n    'patient_test',\n    'physical_exam_test',\n    'respiratory_care_test',\n    'respiratory_charting_test',\n    'treatment_test',\n    'vital_aperiodic_test',\n    'vital_periodic_test',\n]\n\nsuite = unittest.TestSuite()\n\nfor t in testmodules:\n    try:\n        # If the module defines a suite() function, call it to get the suite.\n        mod = __import__(t, globals(), locals(), ['suite'])\n        suitefn = getattr(mod, 'suite')\n        suite.addTest(suitefn())\n    except (ImportError, AttributeError):\n        # else, just load all the test cases from the module.\n        suite.addTest(unittest.defaultTestLoader.loadTestsFromName(t))\n\nunittest.TextTestRunner().run(suite)\n","sub_path":"tests/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"76748278","text":"#!/usr/bin/env python\n\"\"\"\nDraws a static plot of bessel functions, oriented vertically, side-by-side.\n\nYou can experiment with using different containers (uncomment lines 30-31)\nor different orientations on the plots (comment out line 41 and uncomment 42).\n\"\"\"\n\n# Major library imports\nfrom numpy import arange\nfrom scipy.special import jn\n\nfrom chaco.example_support import COLOR_PALETTE\n\n# Enthought library imports\nfrom enable.api import Component, ComponentEditor\nfrom traits.api import HasTraits, Instance\nfrom traitsui.api import Item, Group, View\nfrom chaco.api import PlotLabel, HPlotContainer, create_line_plot\n\n#===============================================================================\n# # Create the Chaco plot.\n#===============================================================================\ndef _create_plot_component():\n    numpoints = 100\n    low = -5\n    high = 15.0\n    x = arange(low, high, (high-low)/numpoints)\n\n    container = HPlotContainer(resizable = \"hv\", bgcolor=\"lightgray\",\n                               fill_padding=True, padding = 10)\n    # container = VPlotContainer(resizable = \"hv\", bgcolor=\"lightgray\",\n    #                            fill_padding=True, padding = 10)\n\n\n    # Plot some bessel functions\n    value_range = None\n    for i in range(10):\n        y = jn(i, x)\n        plot = create_line_plot((x,y), color=tuple(COLOR_PALETTE[i]), width=2.0,\n                                orientation=\"v\")\n                               # orientation=\"h\")\n        plot.origin_axis_visible = True\n        plot.origin = \"top left\"\n        plot.padding_left = 10\n        plot.padding_right = 10\n        plot.border_visible = True\n        plot.bgcolor = \"white\"\n        if value_range is None:\n            value_range = plot.value_mapper.range\n        else:\n            plot.value_range = value_range\n            value_range.add(plot.value)\n        if i%2 == 1:\n            plot.line_style = \"dash\"\n        container.add(plot)\n\n    container.padding_top = 50\n    container.overlays.append(PlotLabel(\"More Bessels\",\n                                        component=container,\n                                        font = \"swiss 16\",\n                                        overlay_position = \"top\"))\n\n    return container\n\n#===============================================================================\n# Attributes to use for the plot view.\nsize=(800,600)\ntitle=\"Vertical Line Plot\"\n\n#===============================================================================\n# # Demo class that is used by the demo.py application.\n#===============================================================================\nclass Demo(HasTraits):\n    plot = Instance(Component)\n\n    traits_view = View(\n                    Group(\n                        Item('plot', editor=ComponentEditor(size=size),\n                             show_label=False),\n                        orientation = \"vertical\"),\n                    resizable=True, title=title,\n                    width=size[0], height=size[1]\n                    )\n\n    def _plot_default(self):\n         return _create_plot_component()\n\ndemo = Demo()\n\nif __name__ == \"__main__\":\n    demo.configure_traits()\n\n# EOF\n","sub_path":"examples/demo/vertical_plot.py","file_name":"vertical_plot.py","file_ext":"py","file_size_in_byte":3186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"333111328","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\n\"\"\"\n-------------------- Copyright --------------------\nDate    : 2018-12-14 09:35:11\nAuthor  : zhangwei (potaski@qq.com)\nDescribe: xlsx writer\nVersion : 1.0.0\n-------------------- End --------------------\n\"\"\"\n\n\nimport xlsxwriter\nimport uuid\nimport os\n\n\ndef parser_xlsx_data(input=[]):\n    \"\"\" 生成xlsx数据\n    input = [\n        ['col_1', 'col_2', 'col_3'],  # row1\n        ['col_1', 'col_2', 'col_3'],  # row2\n    ]\n    output = open(xlsx, 'rb').read()\n    \"\"\"\n    xlsx = '{}-{}.xlsx'.format(uuid.uuid4(), uuid.uuid4())\n    workbook = xlsxwriter.Workbook(xlsx)\n    worksheet = workbook.add_worksheet()\n    row = 0\n    for line in input:\n        col = 0\n        for item in line:\n            worksheet.write(row, col, item)\n            col += 1\n        row += 1\n    workbook.close()\n    output = open(xlsx, 'rb').read()\n    os.remove(xlsx)\n    return output\n\n\nif __name__ == '__main__':\n    ret = parser_xlsx_data(input=[['1', '2', '3'], ['4', '5', '6', '7']])\n    # print(ret)\n    with open('test.xlsx', 'wb') as f:\n        f.write(ret)","sub_path":"xlsx_file/how_to.py","file_name":"how_to.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"121216372","text":"import urllib2, math, json, glob, rdflib\nfrom flask import Flask, abort, redirect, request, make_response\nfrom flask_negotiation import provides\nfrom rdflib import Graph, URIRef, BNode, Literal\nfrom rdflib.namespace import RDF, VOID, XSD\nfrom rdflib.plugin import register, Serializer\nfrom itertools import izip_longest\nfrom uritemplate import URITemplate, expand\nfrom os.path import isfile\n\n# Define a Flask app\napp = Flask(__name__)\n\n# Register the json-ld plugin for the rdflib serializer\nrdflib.plugin.register('application/ld+json', Serializer,\n    'rdflib_jsonld.serializer', 'JsonLDSerializer')\nrdflib.plugin.register('json-ld', Serializer,\n    'rdflib_jsonld.serializer', 'JsonLDSerializer')\n\n# Load the configuration information contained in config.json\nCONFIG = json.load(open('config.json', 'r'))\n\n# With the context and subcontext (optional) names specified in config.json,\n# use URI templates to define the relative URIs for which the Flask app will\n# handle requests\nDATASET_RELATIVE_URI = URITemplate(\"{/context,subcontext}/\").expand(\n    context=CONFIG['context'], subcontext=CONFIG['subcontext'])\nWELL_KNOWN_VOID_RELATIVE_URI = URITemplate(\"{/context,subcontext}/.well-known/void\").expand(\n    context=CONFIG['context'], subcontext=CONFIG['subcontext'])\nRESOURCE_RELATIVE_URI = URITemplate(\"{/context,subcontext}/\").expand(\n    context=CONFIG['context'], subcontext=CONFIG['subcontext'])\nTPF_RELATIVE_URI = URITemplate(\"{/context,subcontext}/fragment{?s,p,o,page}\").expand(\n    context=CONFIG['context'], subcontext=CONFIG['subcontext'])\n\n# Define a namespace for the Hydra vocabulary\nHYDRA = rdflib.Namespace(\"http://www.w3.org/ns/hydra/core#\")\n\n# Create a graph containing the data from the void.ttl file\nVOID_GRAPH = Graph().parse('void.ttl', format=\"n3\")\n\n# Create a graph to contain the dataset triples\nDATASET_GRAPH = Graph()\n\n# Load data from the data directory into DATASET_GRAPH\nfor entitytype in CONFIG['entitytypes']:\n    for ld_doc in glob.glob('data{}{}/*.ttl'.format(DATASET_RELATIVE_URI, entitytype)):\n        DATASET_GRAPH.parse(ld_doc, format=\"n3\")\n\n# Given an iterable and a page size (n), return an iterable that pages through\n# what the first iterable provides (taken verbatim from the Python 2.x version\n# of the itertools package documentation)\ndef grouper(iterable, n, fillvalue=None):\n    \"Collect data into fixed-length chunks or blocks\"\n    # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx\n    args = [iter(iterable)] * n\n    return izip_longest(fillvalue=fillvalue, *args)\n\n# Given a graph and a media type, return a Flask response with the graph's data\n# serialized according to the media type\ndef accepted_graph_serialization(graph, media_type):\n    if media_type == 'application/rdf+xml':\n        response = make_response(graph.serialize(None, format='xml'))\n        response.headers['content-type'] = 'application/rdf+xml'\n    elif media_type == 'text/plain':\n        response = make_response(graph.serialize(None, format='ntriples'))\n        response.headers['content-type'] = 'text/plain'\n    elif media_type == 'application/ld+json':\n        response = make_response(graph.serialize(None, format='json-ld', indent=4))\n        response.headers['content-type'] = 'application/ld+json'\n    else: # return text/turtle\n        response = make_response(graph.serialize(None, format='turtle'))\n        response.headers['content-type'] = 'text/turtle'\n    return response\n\n# Return the parameter values provided in a request to the TPF_RELATIVE_URI route\n# i.e., a request for a triple pattern fragment page\ndef tpf_params():\n    s = request.args.get('s')\n    if s:\n        s = URIRef(s)\n    p = request.args.get('p')\n    if p:\n        p = URIRef(p)\n    o = request.args.get('o')\n    if o:\n        if o.startswith('http'):\n            o = URIRef(o)\n        else:\n            o = Literal(o)\n    triple_pattern = ( s, p, o )\n    page = request.args.get('page')\n    if page:\n        page = int(page)\n    else:\n        page = 0\n    return s, p, o, page\n\n# Given the parameters for a triple patern fragment page request, return a graph\n# containing triples for the requested page and the total number of triples in\n# matching the requested pattern\ndef tpf_data(s, p, o, page):\n    data = Graph()\n    triples = [ triple for triple in DATASET_GRAPH.triples( (s, p, o) ) ]\n    n_triples = len(triples)\n    for i, page_triples in enumerate(grouper(triples, CONFIG['pagesize'])):\n        if i == page:\n            for triple in page_triples:\n                if triple:\n                    data.add(triple)\n            break\n    return data, n_triples\n\n# Given the number of triples in the requested data, return a graph containing\n# the metadata for the requested triple pattern fragment page\ndef tpf_metadata(n_triples):\n    metadata = Graph()\n    fragment = URIRef(request.url)\n    metadata.add( (fragment, VOID.triples, Literal(n_triples, datatype=XSD.integer)) )\n    return metadata\n\n# Given the data for a requested triple pattern fragment page, the total number\n# of triples in DATASET_GRAPH that match the triple pattern, and the page number,\n# return a graph containing the Hydra hypermedia controls triples for\n# the requested page\ndef tpf_controls(data, n_triples, page):\n    controls = Graph()\n    page_size = CONFIG['pagesize']\n    max_page_no = math.ceil(n_triples / page_size)\n    dataset = URIRef(request.url_root)\n    fragment = URIRef(request.url)\n    controls.add( (dataset, VOID.subset, URIRef(request.url)) )\n    controls.add( (dataset, HYDRA.template, Literal(TPF_RELATIVE_URI+'{?s,p,o}')) )\n    s_var = BNode()\n    controls.add( (s_var, HYDRA.variable, Literal('s')) )\n    controls.add( (s_var, HYDRA.property, RDF.subject) )\n    p_var = BNode()\n    controls.add( (p_var, HYDRA.variable, Literal('p')) )\n    controls.add( (p_var, HYDRA.property, RDF.predicate) )\n    o_var = BNode()\n    controls.add( (o_var, HYDRA.variable, Literal('o')) )\n    controls.add( (o_var, HYDRA.property, RDF.object) )\n    controls.add( (dataset, HYDRA.mapping, s_var) )\n    controls.add( (dataset, HYDRA.mapping, p_var) )\n    controls.add( (dataset, HYDRA.mapping, o_var) )\n    controls.add( (fragment, RDF.type, HYDRA.Collection) )\n    controls.add( (fragment, RDF.type, HYDRA.PagedCollection) )\n    controls.add( (fragment, HYDRA.totalItems, Literal(n_triples, datatype=XSD.integer)) )\n    controls.add( (fragment, HYDRA.itemsPerPage, Literal(page_size, datatype=XSD.integer)) )\n    if page >= 1:\n        controls.add( (fragment, HYDRA.firstPage, Literal(0, datatype=XSD.integer)) )\n    if page > 1:\n        controls.add( (fragment, HYDRA.previousPage, Literal(page - 1, datatype=XSD.integer)) )\n    if page < max_page_no:\n        controls.add( (fragment, HYDRA.nextPage, Literal(page + 1, datatype=XSD.integer)) )\n    return controls\n\n# Given a request matching DATASET_RELATIVE_URI, return the VOID description of\n# the dataset\n@app.route(DATASET_RELATIVE_URI)\n@provides('text/html', 'text/turtle', 'application/rdf+xml', 'text/plain',\n    'application/x-turtle', 'text/rdf+n3', 'application/ld+json', to='media_type')\ndef get_root(media_type):\n    return accepted_graph_serialization(VOID_GRAPH, media_type)\n\n# Given a request matching WELL_KNOWN_VOID_RELATIVE_URI (which captures the\n# /.well-known/void access convention, return the VOID description of the dataset\n@app.route(WELL_KNOWN_VOID_RELATIVE_URI)\n@provides('text/html', 'text/turtle', 'application/rdf+xml', 'text/plain',\n    'application/x-turtle', 'text/rdf+n3', 'application/ld+json', to='media_type')\ndef get_well_known_void(media_type):\n    return accepted_graph_serialization(VOID_GRAPH, media_type)\n\n# Given a request for RESOURCE_RELATIVE_URI, return the data associated with\n# the requested resource using the coorresponding Turtle file in the data directory\n# structure, or a 404 HTTP error if no such file exists\n@app.route(RESOURCE_RELATIVE_URI)\n@provides('text/html', 'text/turtle', 'application/rdf+xml', 'text/plain',\n    'application/x-turtle', 'text/rdf+n3', 'application/ld+json', to='media_type')\ndef get_resource(resource, media_type):\n    ld_document_file = 'data{}{}.ttl'.format(request.script_root, request.path)\n    if isfile(ld_document_file):\n        graph = Graph().parse(ld_document_file, format='n3')\n    else:\n        abort(404)\n    return accepted_graph_serialization(graph, media_type)\n\n# Given a request for a TPF_RELATIVE_URI, return the requested triple pattern\n# fragment page\n@app.route(TPF_RELATIVE_URI)\n@provides('text/html', 'text/turtle', 'application/rdf+xml', 'text/plain',\n    'application/x-turtle', 'text/rdf+n3', 'application/ld+json', to='media_type')\ndef get_tpf(media_type):\n    (s, p, o, page) = tpf_params()\n    (data, n_triples) = tpf_data(s, p, o, page)\n    metadata = tpf_metadata(n_triples)\n    controls = tpf_controls(data, n_triples, page)\n    graph = data + metadata + controls\n    return accepted_graph_serialization(graph, media_type)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":8907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"369445220","text":"#!/bin/python3\n\nimport requests\nimport re\nimport json\nimport pprint\nimport yaml\nimport datetime\n\n\njspayload = ''\nif not os.path.exists('apidoc.js'):\n    url_to_parse = \"https://pve.proxmox.com/pve-docs/api-viewer/apidoc.js\"\n    jspayload = requests.get(url_to_parse).text\nelse:\n    jspayload = open('apidoc.js').read()\n\nsearch = re.search(r'(?<=var pveapi =)[\\s\\S]*?(?=;\\n)', jspayload, re.MULTILINE)\n\nif not search:\n    raise Exception(\"failed to find api doc definition\")\n\ndata = json.loads(search.group())\n\ntodo = []\n\ntodecoded = data[:]\n\n# flatten the api tree\nwhile todecoded:\n    item = todecoded.pop()\n    \n    if item['leaf'] != 1:\n        todecoded.extend(item['children'])\n        del item['children']\n    todo.append(item)\n\npaths = {}\n\n\ndef convertReturns(returns):\n    if not returns:\n        return\n\n    returnType = returns.get('type')\n\n    if not returnType:\n        pass\n\n\n\ndef convertParameters(method, path, parameters):\n    if not parameters or not parameters.get('properties'):\n        return [], {}\n\n    ret = []\n    bodyParameters = {}\n\n    for name, definition in parameters['properties'].items():\n        shouldGoToBody = False\n        param = {\n            'name': name,\n            'schema': {\n                'type': definition['type'],\n            },\n        }\n        if definition.get('optional', 0) != 1:\n            param['required'] = True\n\n        if 'description' in definition:\n            param['description'] = definition['description']\n\n        if f'{{{name}}}' in path:\n            param['in'] = 'path'\n        else:\n            if method not in ['POST', 'PUT']:\n                param['in'] = 'query'\n            else:\n                bodyParameters[name] = definition\n                continue\n\n        if 'format' in definition:\n            param['schema']['format'] = definition['format']\n        \n        if 'default' in definition:\n            param['schema']['default'] = definition['default']\n        ret.append(param)\n\n    return ret, bodyParameters\n\nfor item in todo:\n    methods = {}\n    for method, payload in item.get('info', {}).items():\n        parameters, bodyParameters = convertParameters(method, item['path'], payload['parameters'])\n\n        methods[method.lower()] = {\n            \"summary\": payload['description'],\n            \"parameters\": parameters,\n            \"responses\": {\n                '200': convertReturns(payload['returns'])\n            }\n        }\n        if bodyParameters:\n            methods[method.lower()]['requestBody'] = {\n                'content': {\n                    \"application/x-www-form-urlencoded\": {\n                        'schema': {\n                            'type': 'object',\n                            'properties': bodyParameters\n                        },\n                        'required': [i for i, j in bodyParameters.items() if j.get('optional', 0) != 1]\n                    }\n                }\n            }\n    paths[item['path']] = methods\n\nwith open('openapi.yaml', 'w') as fp:\n    fp.write(yaml.dump({\n        'openapi': '3.0.0',\n        'info': {\n            'title': 'Proxmox API',\n            'version': datetime.datetime.now().strftime('%Y%m%d'),\n        },\n        'externalDocs': {\n            'description': 'Official API Viewer',\n            'url': 'https://pve.proxmox.com/pve-docs/api-viewer/'\n        },\n        'paths': paths\n    }))","sub_path":"generate.py","file_name":"generate.py","file_ext":"py","file_size_in_byte":3356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"651541426","text":"from TwitterDriver.TwitterDriver import driver\nfrom TwitterDriver.TwitterDriver import TwitterDriver\nfrom TwitterDriver.GoBackHistoryDriver import GoBackHistoryDriver\nfrom TwitterDriver.TweetUrlCollectDriver import TweetUrlCollectDriver\nimport time\n\nif __name__==\"__main__\":\n    twitterDriver = TwitterDriver(driver)\n    twitterDriver.login()\n\n    goBackHistoryDriver = GoBackHistoryDriver(driver)\n\n    time.sleep(1)\n    goBackHistoryDriver.go_back()\n\n    time.sleep(1)\n    goBackHistoryDriver.scroll(3)\n    goBackHistoryDriver.scroll_top()\n    \n    tweetUrlCollectDriver = TweetUrlCollectDriver(driver)\n\n    for i in range(10):\n        tweetUrlCollectDriver.get_article_urls3(100)\n        goBackHistoryDriver.scroll(2)\n        print(tweetUrlCollectDriver.urls)\n        print(len(tweetUrlCollectDriver.urls))\n\n    \n\n    _ = input()\n    driver.close()\n\n\n","sub_path":"hello_everyone.py","file_name":"hello_everyone.py","file_ext":"py","file_size_in_byte":853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"208644349","text":"patch_size = 7\ndataset = \"IN\"  # \"IN\", \"UP\" or \"Salinas\"\n\nif(dataset == \"IN\"):\n    dataset_file_name = \"Indian_pines_corrected.mat\"\n    dataset_mat_name = \"indian_pines_corrected\"\n    dataset_gt_file_name = \"Indian_pines_gt.mat\"\n    dataset_gt_mat_name = \"indian_pines_gt\"\n    num_classes = 16\n    channels = 200\n    train_frac = 0.15\n    test_frac = 0.75\n    contextual_kernel_num = 128\n    ssdc_kernel_num = 48\nelif(dataset == \"UP\"):\n    dataset_file_name = \"PaviaU.mat\"\n    dataset_mat_name = \"paviaU\"\n    dataset_gt_file_name = \"PaviaU_gt.mat\"\n    dataset_gt_mat_name = \"paviaU_gt\"\n    num_classes = 9\n    channels = 103\n    train_frac = 0.05\n    test_frac = 0.80\n    contextual_kernel_num = 128\n    ssdc_kernel_num = 32\nelif(dataset == \"Salinas\"):\n    dataset_file_name = \"Salinas_corrected.mat\"\n    dataset_mat_name = \"salinas_corrected\"\n    dataset_gt_file_name = \"Salinas_gt.mat\"\n    dataset_gt_mat_name = \"salinas_gt\"\n    num_classes = 16\n    channels = 204\n    train_frac = 0.05\n    test_frac = 0.80\n    contextual_kernel_num = 192\n    ssdc_kernel_num = 16\nelse:\n    assert False, \"Dataset is not available.\"\n","sub_path":"SSDC_code/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1119,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"175295915","text":"# coding: utf-8\n\n\"\"\"\n    elepay API リファレンス\n\n    elepay APIはRESTをベースに構成された決済APIです。支払い処理、返金処理など、決済に関わる運用における様々なことができます。  # noqa: E501\n\n    The version of the OpenAPI document: 1.2.0\n    Contact: support@elestyle.jp\n    Generated by: https://openapi-generator.tech\n\"\"\"\n\nfrom datetime import date, datetime  # noqa: F401\nimport decimal  # noqa: F401\nimport functools  # noqa: F401\nimport io  # noqa: F401\nimport re  # noqa: F401\nimport typing  # noqa: F401\nimport typing_extensions  # noqa: F401\nimport uuid  # noqa: F401\n\nimport frozendict  # noqa: F401\n\nfrom elepay import schemas  # noqa: F401\n\n\nclass InvoiceItem(\n    schemas.AnyTypeSchema,\n):\n    \"\"\"NOTE: This class is auto generated by OpenAPI Generator.\n    Ref: https://openapi-generator.tech\n\n    Do not edit the class manually.\n\n    インボイスオブジェクト\n    \"\"\"\n\n\n    class MetaOapg:\n        \n        class properties:\n            id = schemas.StrSchema\n            object = schemas.StrSchema\n            name = schemas.StrSchema\n            unitPrice = schemas.IntSchema\n            currency = schemas.StrSchema\n            quantity = schemas.IntSchema\n            __annotations__ = {\n                \"id\": id,\n                \"object\": object,\n                \"name\": name,\n                \"unitPrice\": unitPrice,\n                \"currency\": currency,\n                \"quantity\": quantity,\n            }\n\n    \n    @typing.overload\n    def __getitem__(self, name: typing_extensions.Literal[\"id\"]) -> MetaOapg.properties.id: ...\n    \n    @typing.overload\n    def __getitem__(self, name: typing_extensions.Literal[\"object\"]) -> MetaOapg.properties.object: ...\n    \n    @typing.overload\n    def __getitem__(self, name: typing_extensions.Literal[\"name\"]) -> MetaOapg.properties.name: ...\n    \n    @typing.overload\n    def __getitem__(self, name: typing_extensions.Literal[\"unitPrice\"]) -> MetaOapg.properties.unitPrice: ...\n    \n    @typing.overload\n    def __getitem__(self, name: typing_extensions.Literal[\"currency\"]) -> MetaOapg.properties.currency: ...\n    \n    @typing.overload\n    def __getitem__(self, name: typing_extensions.Literal[\"quantity\"]) -> MetaOapg.properties.quantity: ...\n    \n    @typing.overload\n    def __getitem__(self, name: str) -> schemas.UnsetAnyTypeSchema: ...\n    \n    def __getitem__(self, name: typing.Union[typing_extensions.Literal[\"id\", \"object\", \"name\", \"unitPrice\", \"currency\", \"quantity\", ], str]):\n        # dict_instance[name] accessor\n        return super().__getitem__(name)\n    \n    \n    @typing.overload\n    def get_item_oapg(self, name: typing_extensions.Literal[\"id\"]) -> typing.Union[MetaOapg.properties.id, schemas.Unset]: ...\n    \n    @typing.overload\n    def get_item_oapg(self, name: typing_extensions.Literal[\"object\"]) -> typing.Union[MetaOapg.properties.object, schemas.Unset]: ...\n    \n    @typing.overload\n    def get_item_oapg(self, name: typing_extensions.Literal[\"name\"]) -> typing.Union[MetaOapg.properties.name, schemas.Unset]: ...\n    \n    @typing.overload\n    def get_item_oapg(self, name: typing_extensions.Literal[\"unitPrice\"]) -> typing.Union[MetaOapg.properties.unitPrice, schemas.Unset]: ...\n    \n    @typing.overload\n    def get_item_oapg(self, name: typing_extensions.Literal[\"currency\"]) -> typing.Union[MetaOapg.properties.currency, schemas.Unset]: ...\n    \n    @typing.overload\n    def get_item_oapg(self, name: typing_extensions.Literal[\"quantity\"]) -> typing.Union[MetaOapg.properties.quantity, schemas.Unset]: ...\n    \n    @typing.overload\n    def get_item_oapg(self, name: str) -> typing.Union[schemas.UnsetAnyTypeSchema, schemas.Unset]: ...\n    \n    def get_item_oapg(self, name: typing.Union[typing_extensions.Literal[\"id\", \"object\", \"name\", \"unitPrice\", \"currency\", \"quantity\", ], str]):\n        return super().get_item_oapg(name)\n    \n\n    def __new__(\n        cls,\n        *args: typing.Union[dict, frozendict.frozendict, str, date, datetime, uuid.UUID, int, float, decimal.Decimal, bool, None, list, tuple, bytes, io.FileIO, io.BufferedReader, ],\n        id: typing.Union[MetaOapg.properties.id, str, schemas.Unset] = schemas.unset,\n        object: typing.Union[MetaOapg.properties.object, str, schemas.Unset] = schemas.unset,\n        name: typing.Union[MetaOapg.properties.name, str, schemas.Unset] = schemas.unset,\n        unitPrice: typing.Union[MetaOapg.properties.unitPrice, decimal.Decimal, int, schemas.Unset] = schemas.unset,\n        currency: typing.Union[MetaOapg.properties.currency, str, schemas.Unset] = schemas.unset,\n        quantity: typing.Union[MetaOapg.properties.quantity, decimal.Decimal, int, schemas.Unset] = schemas.unset,\n        _configuration: typing.Optional[schemas.Configuration] = None,\n        **kwargs: typing.Union[schemas.AnyTypeSchema, dict, frozendict.frozendict, str, date, datetime, uuid.UUID, int, float, decimal.Decimal, None, list, tuple, bytes],\n    ) -> 'InvoiceItem':\n        return super().__new__(\n            cls,\n            *args,\n            id=id,\n            object=object,\n            name=name,\n            unitPrice=unitPrice,\n            currency=currency,\n            quantity=quantity,\n            _configuration=_configuration,\n            **kwargs,\n        )\n","sub_path":"elepay/model/invoice_item.py","file_name":"invoice_item.py","file_ext":"py","file_size_in_byte":5286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"86781844","text":"# Copyright (C) 2017 Canonical\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program.  If not, see \n\nimport json\nimport logging\nimport multiprocessing\nimport os\nimport shutil\n\nfrom testflinger_agent.job import TestflingerJob\nfrom testflinger_agent.errors import TFServerError\n\nlogger = logging.getLogger(__name__)\n\n\nclass TestflingerAgent:\n    def __init__(self, client):\n        self.client = client\n\n    def get_offline_file(self):\n        return os.path.join(\n            '/tmp',\n            'TESTFLINGER-DEVICE-OFFLINE-{}'.format(\n                self.client.config.get('agent_id')))\n\n    def check_offline(self):\n        return os.path.exists(self.get_offline_file())\n\n    def check_job_state(self, job_id):\n        job_data = self.client.get_result(job_id)\n        if job_data:\n            return job_data.get('job_state')\n\n    def mark_device_offline(self):\n        # Create the offline file, this should work even if it exists\n        open(self.get_offline_file(), 'w').close()\n\n    def process_jobs(self):\n        \"\"\"Coordinate checking for new jobs and handling them if they exists\"\"\"\n        TEST_PHASES = ['setup', 'provision', 'test', 'reserve']\n\n        # First, see if we have any old results that we couldn't send last time\n        self.retry_old_results()\n\n        job_data = self.client.check_jobs()\n        while job_data:\n            job = TestflingerJob(job_data, self.client)\n            logger.info(\"Starting job %s\", job.job_id)\n            rundir = os.path.join(\n                self.client.config.get('execution_basedir'), job.job_id)\n            os.makedirs(rundir)\n            # Dump the job data to testflinger.json in our execution directory\n            with open(os.path.join(rundir, 'testflinger.json'), 'w') as f:\n                json.dump(job_data, f)\n            # Create json outcome file where phases will store their output\n            with open(os.path.join(rundir, 'testflinger-outcome.json'),\n                      'w') as f:\n                json.dump({}, f)\n\n            for phase in TEST_PHASES:\n                # First make sure the job hasn't been cancelled\n                if self.check_job_state(job.job_id) == 'cancelled':\n                    logger.info(\"Job cancellation was requested, exiting.\")\n                    break\n                # Try to update the job_state on the testflinger server\n                try:\n                    self.client.post_result(job.job_id, {'job_state': phase})\n                except TFServerError:\n                    pass\n                proc = multiprocessing.Process(target=job.run_test_phase,\n                                               args=(phase, rundir,))\n                proc.start()\n                while proc.is_alive():\n                    proc.join(10)\n                    if (self.check_job_state(job.job_id) == 'cancelled' and\n                            phase != 'provision'):\n                        logger.info(\"Job cancellation was requested, exiting.\")\n                        proc.terminate()\n                exitcode = proc.exitcode\n\n                # exit code 46 is our indication that recovery failed!\n                # In this case, we need to mark the device offline\n                if exitcode == 46:\n                    self.mark_device_offline()\n                    self.client.repost_job(job_data)\n                    shutil.rmtree(rundir)\n                    # Return NOW so we don't keep trying to process jobs\n                    return\n                if phase != 'test' and exitcode:\n                    logger.debug('Phase %s failed, aborting job' % phase)\n                    break\n\n            # Always run the cleanup, even if the job was cancelled\n            proc = multiprocessing.Process(target=job.run_test_phase,\n                                           args=('cleanup', rundir,))\n            proc.start()\n            proc.join()\n\n            try:\n                self.client.transmit_job_outcome(rundir)\n            except Exception as e:\n                # TFServerError will happen if we get other-than-good status\n                # Other errors can happen too for things like connection\n                # problems\n                logger.exception(e)\n                results_basedir = self.client.config.get('results_basedir')\n                shutil.move(rundir, results_basedir)\n\n            if self.check_offline():\n                # Don't get a new job if we are now marked offline\n                break\n            job_data = self.client.check_jobs()\n\n    def retry_old_results(self):\n        \"\"\"Retry sending results that we previously failed to send\"\"\"\n\n        results_dir = self.client.config.get('results_basedir')\n        # List all the directories in 'results_basedir', where we store the\n        # results that we couldn't transmit before\n        old_results = [os.path.join(results_dir, d)\n                       for d in os.listdir(results_dir)\n                       if os.path.isdir(os.path.join(results_dir, d))]\n        for result in old_results:\n            try:\n                logger.info('Attempting to send result: %s' % result)\n                self.client.transmit_job_outcome(result)\n            except TFServerError:\n                # Problems still, better luck next time?\n                pass\n","sub_path":"testflinger_agent/agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":5783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"363235936","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Kevin Nguyen\n# GA-DAT-25: Final Assignment\n# Model Evaluation Script\n# Requirment: Python 3\n\n# time script runtime\nfrom datetime import datetime\nstartTime = datetime.now()\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.cross_validation import KFold, train_test_split\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.metrics import confusion_matrix\nfrom nltk.corpus import stopwords\nfrom sklearn.linear_model import LogisticRegression\n\n\n# function to create X Y vectors\ndef make_xy(df, vectorizer=None):\n    if vectorizer is None:\n        vectorizer = TfidfVectorizer(ngram_range=(1, 3))\n    X = vectorizer.fit_transform(df['Headline'] + df['Title'])\n    X = X.tocsc()  # some versions of sklearn return COO format\n    y = (df['Protection and Human Rights'] == 1).values.astype(np.int)\n    return X, y\n\n\n# function to run cross vaildation and return results\ndef cv_score(clf, X, y, scorefunc):\n    result = 0.\n    nfold = 5\n    for train, test in KFold(y.size, nfold):  # split data into groups\n        clf.fit(X[train], y[train])  # fit\n        result += scorefunc(clf, X[test], y[test])  # evaluate score function\n    return result / nfold  # average\n\n\n# function to get the log likelihood of a probalisitic classifer\ndef log_likelihood(clf, x, y):\n    prob = clf.predict_log_proba(x)\n    non_humanitian = y == 0\n    humanitian = ~non_humanitian\n    return prob[non_humanitian, 0].sum() + prob[humanitian, 1].sum()\n\nif __name__ == '__main__':\n    # load in the dataset\n    articles = pd.read_csv(\"../data/dataset.csv\")\n    df = articles.copy()\n    stopwrds = stopwords.words('english')\n\n    # create X, y\n    X, y = make_xy(df)\n\n    # create your train/test dataset\n    xtrain, xtest, ytrain, ytest = train_test_split(X, y)\n    clf = MultinomialNB().fit(xtrain, ytrain)\n    print(\"MN Accuracy: %0.2f%%\" % (100 * clf.score(xtest, ytest)))\n\n    # get train and test accuracy\n    training_accuracy = clf.score(xtrain, ytrain)\n    test_accuracy = clf.score(xtest, ytest)\n\n    print(\"Accuracy on training data: %0.2f\" % (training_accuracy))\n    print(\"Accuracy on test data:  %0.2f\" % (test_accuracy))\n\n    # set up cross validation with masks\n    index_train, index_test = train_test_split(range(df.shape[0]), train_size=0.7)\n    mask = np.ones(df.shape[0], dtype='int')\n    mask[index_train] = 1\n    mask[index_test] = 0\n    mask = (mask == 1)\n\n    # the grid of parameters to search over\n    alphas = [0, .1, 1, 5, 10, 50, 100, 200, 500]\n    min_dfs = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, .05, .1]\n\n    # Find the best value for alpha and min_df, and the best classifier\n    best_alpha = None\n    best_min_df = None\n    maxscore = -np.inf\n    for alpha in alphas:\n\n        for min_df in min_dfs:\n\n            vectorizer = TfidfVectorizer(min_df = min_df, ngram_range=(1, 3), stop_words=stopwrds)\n            Xthis, ythis = make_xy(df, vectorizer)\n            Xtrainthis = Xthis[mask]\n            ytrainthis = ythis[mask]\n            # your code here\n            clf = MultinomialNB(alpha=alpha)\n            cvscore = cv_score(clf, Xtrainthis, ytrainthis, log_likelihood)\n\n            if cvscore > maxscore:\n                maxscore = cvscore\n                best_alpha, best_min_df = alpha, min_df\n\n    print(\"alpha: %f\" % best_alpha)\n    print(\"min_df: %f\" % best_min_df)\n\n    vectorizer = TfidfVectorizer(min_df=best_min_df, ngram_range=(1, 3), stop_words=stopwrds)\n\n    # create a hold-out set for cross validation\n    X, y = make_xy(df, vectorizer)\n    xtrain = X[mask]\n    ytrain = y[mask]\n    xtest = X[~mask]\n    ytest = y[~mask]\n\n    clf = MultinomialNB(alpha=best_alpha).fit(xtrain, ytrain)\n\n    # Your code here. Print the accuracy on the test and training dataset\n    training_accuracy = clf.score(xtrain, ytrain)\n    test_accuracy = clf.score(xtest, ytest)\n\n    print(\"Accuracy on training data: {:.2f}\".format(training_accuracy))\n    print(\"Accuracy on test data: {:.2f}\".format(test_accuracy))\n    print(confusion_matrix(ytest, clf.predict(xtest)))\n\n    words = np.array(vectorizer.get_feature_names())\n    x = np.eye(xtest.shape[1])\n    probs = clf.predict_log_proba(x)[:, 0]\n    ind = np.argsort(probs)\n\n    good_words = words[ind[:20]]\n    bad_words = words[ind[-20:]]\n\n    good_prob = probs[ind[:20]]\n    bad_prob = probs[ind[-20:]]\n\n    print(\"Words with high indication of human rights violations\\n\\tP(violation | word)\")\n    for w, p in zip(good_words, good_prob):\n        print(\"%20s\" % w, \"%0.2f\" % (1 - np.exp(p)))\n\n    print(\"Words with low indication of human rights violations\\n\\tP(violation | word)\")\n    for w, p in zip(bad_words, bad_prob):\n        print(\"%20s\" % w, \"%0.2f\" % (1 - np.exp(p)))\n\n    x, y = make_xy(df, vectorizer)\n\n    prob = clf.predict_proba(x)[:, 0]\n    predict = clf.predict(x)\n\n    false_negative = np.argsort(prob[y == 0])[:20]\n    false_positive = np.argsort(prob[y == 1])[-20:]\n\n    print('\\n---------------------------\\n')\n    print(\"Mis-predicted non-risky headlines\")\n    print('\\n---------------------------\\n')\n    for row in false_negative:\n        print(df[y == 0]['Title'].iloc[row])\n        print(df[y == 0]['Headline'].iloc[row])\n        print(\"\\n~~~~~~~~~\\n\")\n\n    print('\\n---------------------------\\n')\n    print(\"Mis-predicted risky headlines\")\n    print('\\n---------------------------\\n')\n    for row in false_positive:\n        print(df[y == 1]['Title'].iloc[row])\n        print(df[y == 1]['Headline'].iloc[row])\n        print(\"\\n~~~~~~~~~\\n\")\n\n    print(\"\\nTest run on a headline form the New York Times:\")\n    print(\"Crimes Against Muslim Americans and Mosques Rise Sharply\")\n    print(clf.predict_proba(vectorizer.transform([\"Crimes Against Muslim Americans and Mosques Rise Sharply\"])))\n","sub_path":"scripts/evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":5778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"346829075","text":"\"\"\"\n    This script involves variables and specific functions\n    that will be utilized in main file (psl_matches)\n    :file: utilities.py\n    :platform: windows 10\n    :synopsis:\n        Script containing common variables and functions\n    :author:\n        Muhammad Faheem ur Rehman\n        email: faheemlasani1034@gmail.com\n\"\"\"\n\n# importing required libraries\nimport requests\nfrom bs4 import BeautifulSoup\nimport simplejson\nimport os\n\n# global variables to use\nPSL_URL_REQUEST = \"https://www.espncricinfo.com/scores/series/8679/season/2020/pakistan-super-league?view=results\"\n\n# requesting to access PSL website\nURL_RESPONSE = requests.get(PSL_URL_REQUEST)\nSOURCE_CODE = URL_RESPONSE.content\n\n# creating Beautiful siup object\nSOUP = BeautifulSoup(SOURCE_CODE, 'lxml')\n\n\ndef grab_match_number_date_venue(class_attribute):\n    \"\"\"\n       Function for getting number, date and venue of psl matches using soup object\n\n       :param string class_attribute: Soup class name under which matches number,venue and date is placed\n\n       :returns: matches number, their venue and date of occurrence\n       :return type: list\n    \"\"\"\n    match_number_date_venue = list()\n    match_number = list()\n    match_venue = list()\n    match_date = list()\n\n    number_date_venue_element = SOUP.find_all(class_=class_attribute)\n\n    for item in number_date_venue_element:\n        match_number_date_venue.append(item.text)\n\n    for index in range(0, 30):\n        grabbed_information = match_number_date_venue[index]\n        separated_information = grabbed_information.split(',')\n        match_number.append(separated_information[0])\n        match_venue.append(separated_information[1])\n        match_date.append(separated_information[2])\n\n    return match_number, match_venue, match_date\n\n\ndef grab_team_names(class_attribute_general, class_attribute_specific):\n    \"\"\"\n    Function for getting the names of playing teams using soup object\n\n    :param string class_attribute_general: Soup class name under which team name is placed\n    :param string class_attribute_specific: Soup class name under which team name is placed in text form\n\n    :returns:team names\n    :return type: list\n    \"\"\"\n    team_names = list()\n    team_a = list()\n    team_b = list()\n    # teams_element = soup.find_all(class_='row no-gutters')\n\n    for first_item in SOUP.find_all(class_=class_attribute_general):\n        for second_item in first_item.find_all(class_=class_attribute_specific):\n            team_names.append(second_item)\n\n    for index in range(0, 60):\n        if index % 2 == 0:\n            team_a.append(team_names[index].text)\n        else:\n            team_b.append(team_names[index].text)\n\n    return team_a, team_b\n\n\ndef grab_team_scores(class_attribute_general, class_attribute_specific):\n    \"\"\"\n    Function for getting the scores of  teams using soup object\n\n    :param string class_attribute_general: Soup class name under which team scores are placed\n    :param string class_attribute_specific: Soup class name under which team scores are placed in text form\n\n    :returns:team scores\n    :return type: list\n    \"\"\"\n    team_scores = list()\n    team_a_score = list()\n    team_b_score = list()\n\n    for first_item in SOUP.find_all(class_=class_attribute_general):\n        for second_item in first_item.find_all(class_=class_attribute_specific):\n            team_scores.append(second_item.text)\n\n    for index in range(0, 60):\n        if index % 2 == 0:\n            team_a_score.append(team_scores[index])\n        else:\n            team_b_score.append(team_scores[index])\n\n    return team_a_score, team_b_score\n\n\ndef grab_winning_team(class_attribute):\n    \"\"\"\n     Function for getting the names winning using soup object\n\n    :param string class_attribute: Soup class name under which winning team name is placed\n\n    :returns:winning team\n    :return type: list\n    \"\"\"\n    winning_team = list()\n    winning_team_element = SOUP.find_all(class_=class_attribute)\n\n    for item in winning_team_element:\n        winning_team.append(item.text)\n\n    return winning_team\n\n\ndef creating_psl_json_file(fetched_information):\n    \"\"\"\n    Function for converting the fetched information into json file format and saving it\n    in a separate data folder\n\n    :param list fetched_information: Contains information of all psl 2019 matches\n\n    :returns: None\n    \"\"\"\n\n    psl_json_data = simplejson.dumps(fetched_information)\n    data_folder_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"data\")\n\n    if not os.path.exists(data_folder_path):\n        os.mkdir(data_folder_path)\n    psl_json_data_file_path = os.path.join(data_folder_path, \"psl_json_data_file.json\")\n\n    psl_json_data_file = open(psl_json_data_file_path, 'w')\n    psl_json_data_file.write(psl_json_data)\n\n    print(\"PSL matches data saved in {}\".format('psl_json_data_file'))\n","sub_path":"projects/news_feed/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":4829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"136611501","text":"import pyautogui\nimport time\nimport os\nimport pyperclip\nimport PySimpleGUI as sg\nfrom colorama import Fore\ndef clear ():\n    if os.name == 'nt': \n        _ = os.system('cls') \n    else: \n        _ = os.system('clear')\n\ndef spam (repeats, delay):\n\tfor i in range(repeats):\n\t\tpyautogui.hotkey('ctrl', 'v')      \n\t\tpyautogui.press(\"enter\")\n\t\ttime.sleep(delay)\n\tprint(\"Дело сделано!\")\n\t\ndef main ():\n\tprint (Fore.GREEN+'''\n   █─█─█───████─████──████─███\n   █─█─█───█──█─█──██─█──█─█\n   █─█─█───████─█──██─█──█─███\n   ███─█───█──█─█──██─█──█───█\n   ─█──███─█──█─████──████─███\n\n\n███─████─███─███─█───█─████─████─███\n█───█──█─█────█──█───█─█──█─█──█─█\n███─█──█─███──█──█─█─█─████─████─███\n──█─█──█─���────█──█████─█──█─█─█──█\n███─████─█────█───█─█──█──█─█─█──███\n\t\t''')\n\tmessage = input(Fore.WHITE+\"Какое сообщение вы хотите отправлять: \")\n\trepeats = int(input(\"Сколько сообщений вы хотите отправить: \"))\n\tdelay = int(input(\"Задержка между сообщениями (в секундах): \"))\n\tsleeping = int(input(\"Время до начала спама (в секундах): \"))\n\tprint(\"Нажмите мышкой на поле ввода, а потом нажмите 'Enter'. Через \" + str(sleeping) + \" секунд запустится спам!\")\n\tpyperclip.copy(message)\n\tif pyperclip.paste() == message:\n\t\ttime.sleep(sleeping)\n\t\tspam(repeats, delay)\nlayout = [  [sg.Text('Какое сообщение вы хотите отправлять:',size=(35,1)),sg.Input(size=(50,1),key=\"input0\")],\n            [sg.Text('Сколько сообщений вы хотите отправить:',size=(35,1),key='gg'), sg.Input(size=(50,1),key=\"input1\")],\n            [sg.Text('Задержка между сообщениями (в секундах):',size=(35,1)),sg.Input(size=(50,1),key=\"input2\")],\n            [sg.Text('Время до начала спама (в секундах):',size=(35,1)),sg.Input(size=(50,1),key=\"input3\")],\n            [sg.Output(size=(90,1),key=\"output0\")],\n            [sg.Text(\"\",size=(35,1),key='good')],\n            [sg.Button('Ok'), sg.Button('Refresh'), sg.Button('Cancel')] ]\nwindow = sg.Window('Spamer by VladOS', layout, return_keyboard_events=True)\nif (__name__==\"__main__\"):\n    while True:                             # The Event Loop\n        event, values = window.read()\n        window.Refresh()\n        if event in ('Refresh'):\n            window['input0'].update(\"\")\n            window['input1'].update(\"\")\n            window['input2'].update(\"\")\n            window['input3'].update(\"\")\n            #window['output0'].update(\"\")\n        if event in ('Cancel'):\n            break\n        if event in ('Ok'):\n            print(\"Нажмите мышкой на поле ввода, а потом нажмите 'Enter'. Через \" + str(values['input3']) + \" секунд запустится спам!\")\n            window.Refresh()\n            pyperclip.copy(values['input0'])\n            if pyperclip.paste() == values['input0']:\n                time.sleep(int(values['input3']))\n                spam(int(values['input1']),int(values['input2']))\n        window['good'].update(event)\n    #clear ()\t\n    #main ()\n","sub_path":"spammer.py","file_name":"spammer.py","file_ext":"py","file_size_in_byte":3868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"2924347","text":"from WordSchema import Word\n\n\nclass Adverb(Word):\n\t\"\"\"\n\tWord : this is also the id\n\tTranslation : in greek\n\tGerman\n\tSynonyms []\n\tOpposites \n\t\"\"\"\n\n\n\tdef __init__(self,fr,gr,de):\n\t\tself.French=fr\n\t\tself.Greek=gr\n\t\tself.German=de\n\t\t\n\t\t\n\t\t\n\tdef ToString(self):\n\t\t\n\t\tobj= { '_id' :  self.French,\n\t\t\t   'Greek' :  self.Greek,\n\t\t\t   'German' :  self.German,\n\t\t\t   'Synonym' : [],\n\t\t\t   'Opposite' : []\n\t\t\t   \n\t\t\t   \n\t\t\t  }\n\t\t\n\t\treturn obj\n\t\t","sub_path":"Napoleon/src/main/NoSQLSchemas/AdverbSchema.py","file_name":"AdverbSchema.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"390978371","text":"import sys\nif len(sys.argv) == 3:\n    x = int(sys.argv[1])\n    y = int(sys.argv[2])\n    modx = x + 100\n    mody = y - 50\n    aFile = \"C:\\\\Users\\\\Peter\\\\Documents\\\\GitHub\\\\setup\\\\blender\\\\ahk\\\\myscript.ahk\"\n    f = open(aFile, \"r+\")\n    lines = f.read().split('\\n')\n    newwrite = list()\n    skipNext = False\n    for r,aline in enumerate(lines):\n        if skipNext: \n            skipNext = False\n            continue\n        newwrite.append(aline)\n        if \"XXX2\" in aline:\n            newwrite.append(\"MouseMove, {}, {}\".format(str(modx), str(mody)))\n            skipNext = True\n        elif \"XXX\" in aline:\n            newwrite.append(\"MouseMove, {}, {}\".format(str(x), str(y)))\n            skipNext = True\n    f.seek(0)\n    f.write('\\n'.join(newwrite))\n    f.close()\n","sub_path":"blender/scripts/regen_blender_script.py","file_name":"regen_blender_script.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"611341851","text":"import os\nimport shutil as sht\n\nsource_dir=\"/home/kuba/PycharmProjects/python_course/resources/lab2/files\"\noutput_dir=\"/home/kuba/PycharmProjects/python_course/mysolution/lab3_trening\"\n\nfor file in os.listdir(source_dir):\n    if os.path.isfile(source_dir+os.sep+file):\n        with open(source_dir+os.sep+file, \"r\") as read_file:\n            folder_name= read_file.read().replace(\" \",\"\").split(\":\")[1]\n            if not os.path.exists(output_dir+os.sep+folder_name):\n                os.mkdir(output_dir+os.sep+folder_name)\n            sht.copy(source_dir+os.sep+file, output_dir+os.sep+folder_name+os.sep+file)","sub_path":"mysolution/lab3_trening/zadanie1.py","file_name":"zadanie1.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"584697435","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Dec 11 11:36:55 2019\n\n@author: christian\n\"\"\"\n\nimport numpy as np\nimport time\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.animation as animation\nimport scipy.constants\nimport matplotlib\nmatplotlib.use('Agg')\nprint('NBody START')\n\nmatplotlib.rc('xtick', labelsize=21)\nmatplotlib.rc('ytick', labelsize=21)\n\nWriter = animation.writers['ffmpeg']\nwriter = Writer(fps=60, metadata=dict(artist='Christian CB'), bitrate=1800)\n\nshow_pos=True\ndim = 2\n\nduration= 3 #duration in seconds\nprecision  = 0.005 #precision in seconds\n\nduration+=precision\n\nnum_cycles = int(duration/precision)-1\n\n#mass = np.array([1,1,1,1,1,1,1],dtype=np.float64)\nmass = np.ones(8,dtype=np.float64)\n\nnp.random.seed(1)\npos = 2*np.random.rand(len(mass),dim)-1\nnp.random.seed(1)\nvel = 1*np.random.rand(len(mass),dim)-0.5\n\npos2=np.copy(pos)\n\npos2[0,0]+=0.01\npos2[0,1]-=0.01\n\n# '''Figure-8'''\n# pos = np.array([[0.9700436,-0.24308753],[-0.9700436,0.24308753],[0,0]],dtype=np.float64)\n# vel = np.array([[0.466203685,0.43236573],[0.466203685,0.43236573],[-2*0.466203685,-2*0.43236573]],dtype=np.float64)\n\nno_objects = len(mass)\n\nrows=[]\nfor i,row in enumerate(vel):\n    rows.append(row*mass[i])\nrows = np.array(rows)\n\n#vcentre = np.sum(np.multiply(mass,vel),axis=0)/np.sum(mass)\nvcentre=np.sum(rows,axis=0)/np.sum(mass)\n\n#print(np.sum(np.multiply(mass,vel),axis=1))\nfor obj in vel:\n    obj-=vcentre\n\n#translate so that COM at 0,0\ncom_coords=[]    \nfor i,elem in enumerate(pos):\n    com_coords.append(mass[i]*elem)\ncom_coords = np.sum(com_coords,axis=0)/np.sum(mass)\nfor obj in pos:\n    obj-=com_coords\n\ncom_coords2=[]    \nfor i,elem in enumerate(pos2):\n    com_coords2.append(mass[i]*elem)\ncom_coords2 = np.sum(com_coords2,axis=0)/np.sum(mass)\nfor obj in pos2:\n    obj-=com_coords2\n    \n    \n#We want to do a set number of cycles, so initialize the big arrays now.\npos = np.concatenate((pos[None,...],np.zeros((num_cycles,len(mass),dim),dtype=np.float64)),axis=0)\nvel = np.concatenate((vel[None,...],np.zeros((num_cycles,len(mass),dim),dtype=np.float64)),axis=0)\n\npos2 = np.concatenate((pos2[None,...],np.zeros((num_cycles,len(mass),dim),dtype=np.float64)),axis=0)\nvel2 = np.copy(vel)\n\ndt=precision\n\ndef dydt(y,mass,dim,timestep=dt):\n    soften = dt*10 #Soften the gravitational force dependent on the time-step.\n    posit = y[0:len(y)-1:2]\n    veloc = y[1:len(y):2]\n    \n    r_matrix = np.zeros((len(mass),len(mass)),dtype=np.object)\n    for i in range(len(mass)):\n        for j in range(len(mass)):\n            if i > j:\n                r_val = posit[i]-posit[j]\n                r_matrix[j][i] = r_val/(np.linalg.norm(r_val)**3+np.linalg.norm(r_val)*soften**2)\n                r_matrix[i][j] = -r_matrix[j][i]\n\n    deriv = np.zeros((len(mass)*2,dim))\n    deriv[0:len(deriv)-1:2] = veloc\n    deriv_index = np.arange(1,len(mass)*2,2)\n    \n    mult = np.dot(r_matrix,mass)\n    for i,item in enumerate(mult):\n        deriv[deriv_index[i]] += mult[i]\n#    deriv[1:len(deriv):2] *= scipy.constants.G  #Real-world correction\n    return deriv\n\nsim_times=[]\nsim_time=0\nstart_time=time.time()\nfor cyc_i in range(num_cycles):\n    if cyc_i==0:\n        now=time.time()\n    global linepos\n    \n    sim_time+=dt\n    sim_times.append(sim_time)\n    global prev_time\n    \n    y=[]\n    for i,elem in enumerate(pos[cyc_i]):\n        y.append(elem)\n        y.append(vel[cyc_i][i])\n    y=np.array(y)\n    bef = time.time()\n    k1 = dt*dydt(y,mass,dim); #print('k1:',k1)\n    k2 = dt*dydt(y+k1/2.0,mass,dim); #print('k2:',k2)\n    k3 = dt*dydt(y+k2/2.0,mass,dim); #print('k3:',k3)\n    k4 = dt*dydt(y+k3,mass,dim)\n    #print('k4:',k4)\n    dy = k1/6.0 + k2/3.0 +k3/3.0 + k4/6.0\n#    print('RK4 calculations: '+str(time.time()-bef))\n    \n    newpos = dy[0:len(dy)-1:2]\n    newvel = dy[1:len(dy):2]\n    \n    for i in range(len(newpos)):\n        pos[cyc_i+1,i] = pos[cyc_i,i] + newpos[i]\n        vel[cyc_i+1,i] = vel[cyc_i,i] + newvel[i]\n    if cyc_i==0:\n        looptime = time.time()-now\n        print('Time per loop: '+str(looptime))\n        print('Estimated duration: '+str(looptime*num_cycles)+'s for '+str(num_cycles)+' cycles.')\nprint('Model complete in: '+str(time.time()-start_time))\n\nsim_times=[]\nsim_time=0\nstart_time=time.time()\nfor cyc_i in range(num_cycles):\n    if cyc_i==0:\n        now=time.time()\n    \n    sim_time+=dt\n    sim_times.append(sim_time)\n    \n    y=[]\n    for i,elem in enumerate(pos2[cyc_i]):\n        y.append(elem)\n        y.append(vel2[cyc_i][i])\n    y=np.array(y)\n    bef = time.time()\n    k1 = dt*dydt(y,mass,dim); #print('k1:',k1)\n    k2 = dt*dydt(y+k1/2.0,mass,dim); #print('k2:',k2)\n    k3 = dt*dydt(y+k2/2.0,mass,dim); #print('k3:',k3)\n    k4 = dt*dydt(y+k3,mass,dim)\n    #print('k4:',k4)\n    dy = k1/6.0 + k2/3.0 +k3/3.0 + k4/6.0\n#    print('RK4 calculations: '+str(time.time()-bef))\n    \n    newpos = dy[0:len(dy)-1:2]\n    newvel = dy[1:len(dy):2]\n    \n    for i in range(len(newpos)):\n        pos2[cyc_i+1,i] = pos2[cyc_i,i] + newpos[i]\n        vel2[cyc_i+1,i] = vel2[cyc_i,i] + newvel[i]\n    if cyc_i==0:\n        looptime = time.time()-now\n        print('Time per loop: '+str(looptime))\n        print('Estimated duration: '+str(looptime*num_cycles)+'s for '+str(num_cycles)+' cycles.')\nprint('Model complete in: '+str(time.time()-start_time))\n\n\n\n#fig = plt.figure(figsize=(6,6))\nfig, axs = plt.subplots(1,2,sharey=True,figsize=(24,12))\nplt.subplots_adjust(wspace=0.05,left=0.05,right=0.975,bottom=0.05,top=0.975)\naxs[0].set_xlim([-1.5,1.5])\naxs[0].set_ylim([-1.5,1.5])\naxs[0].grid('on')\n\ncolour_matrix = ['blue','green','red','orange','magenta','cyan','yellow','brown','red','red','red','red','red','red','red']\n#colour_matrix = ['blue','blue','blue']\nbodies = []\nlines=[]\n\nfor ind,m in enumerate(mass):\n    body, = axs[0].plot([pos[0,ind,0]],[pos[0,ind,1]],'o',color=colour_matrix[ind],\n                     markersize=12*m,zorder=3)\n    bodies.append(body)\n    line, = axs[0].plot([pos[0,ind,0]],[pos[0,ind,1]],color=colour_matrix[ind],\n                     zorder=-1,linewidth=3,alpha=0.5)\n    lines.append(line)\ncom, = axs[0].plot([0],[0],'r+',markersize=20 )\ntime_text = plt.text(0.02,0.89,'hi',fontsize=32,transform=axs[0].transAxes)\n#fps_text = plt.text(0.02,0.85,'hi',fontsize=12,transform=ax1.transAxes)\n\nsim_time = 0\n\ndef animate_timedur(k):\n    global sim_time\n    sim_time= sim_time + (0.01/precision)*dt\n    global pos\n    global vel\n    \n    if k % 50 == 0:\n        print(k)\n       \n    for ind,body in enumerate(bodies):\n        body.set_data(pos[k,ind,0],pos[k,ind,1])\n        if k <= 100:\n            lines[ind].set_data(pos[:k,ind,0],pos[:k,ind,1])\n        elif k > 100:\n            lines[ind].set_data(pos[k-100:k,ind,0],pos[k-100:k,ind,1])\n            \n    for i,line2 in enumerate(lines2):\n        if k <= 100:\n            line2.set_data(pos2[:k,i,0],pos2[:k,i,1])\n        elif k >= 100:\n            line2.set_data(pos2[k-100:k,i,0],pos2[k-100:k,i,1])\n    for i,bod in enumerate(bods):\n        bod.set_data(pos2[k,i,0],pos2[k,i,1])\n        \n    com_coords=[]      \n    for i,elem in enumerate(pos[k,:,:]):\n        com_coords.append(mass[i]*elem)\n    com_coords = np.sum(com_coords,axis=0)/np.sum(mass)\n    com.set_data(com_coords[0],com_coords[1])\n\n    com_coords2=[]      \n    for i,elem in enumerate(pos2[k,:,:]):\n        com_coords2.append(mass[i]*elem)\n    com_coords2 = np.sum(com_coords2,axis=0)/np.sum(mass)\n    com2.set_data(com_coords2[0],com_coords2[1])\n\n\n    time_text.set_text(\"Time: {0:.2f}s\".format(sim_time)) #\\n{0:.1f}d\".format(sim_time/86400))\n#    fps_text.set_text(\"FPS: {0:.1f}\".format(1/np.mean(times[-5:])))\n\n\n\naxs[1].grid('on')\nlines2=[]    \nbods = []\nfor ind,coord in enumerate(pos2[0]):\n    bod, = axs[1].plot([coord[0]],[coord[1]],marker='o',color=colour_matrix[ind],\n                    markersize= 12*mass[ind],zorder=3)\n    bods.append(bod)\n    lin, = axs[1].plot([coord[0]],[coord[1]],color=colour_matrix[ind],\n                    zorder=-1,linewidth=3,alpha=0.5)\n    lines2.append(lin)\ncom2, = axs[1].plot([0],[0],'r+',markersize=20)\n    \naxs[1].set_xlim([-1.5,1.5])\naxs[1].set_ylim([-1.5,1.5]) \n\nif show_pos==True:\n    ani=animation.FuncAnimation(fig,animate_timedur,frames=range(0,int(duration/precision-1),int(0.01/precision)),interval=15)\n    ani.save('sim_states.mp4',writer=writer)","sub_path":"NBody_Set_Timespan_TwoStates.py","file_name":"NBody_Set_Timespan_TwoStates.py","file_ext":"py","file_size_in_byte":8320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"106102637","text":"#!/usr/bin/env python3\n#\n# Copyright (C) 2020 The Android Open Source Project\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\"\"\"test_monitor.py: Run test.py and report test summary.\n\n1. Support testing multiple devices at the same time.\n2. Support repeating tests.\n3. Continue test if test.py aborts in the middle.\n4. Handle both parallel tests and serialized tests.\n\n\"\"\"\n\nimport argparse\nimport collections\nimport fnmatch\nimport os\nimport re\nimport shutil\nimport subprocess\nimport sys\nimport time\n\nfrom utils import log_exit, log_info\n\nDevice = collections.namedtuple('Device', ['name', 'serial_number'])\n\nConfig = collections.namedtuple(\n    'Config', ['devices', 'python_interpreters', 'repeat_times', 'test_patterns', 'test_dir'])\n\nCONFIG = None\n\nTEST_TIMEOUT_IN_SEC = 180\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        '-d', '--device', nargs='+',\n        help='set devices used to run tests. Each device in format name:serial-number')\n    parser.add_argument('--python-version', choices=['2', '3', 'both'], default='both', help=\"\"\"\n                        Run tests on which python versions.\"\"\")\n    parser.add_argument('-r', '--repeat', type=int, default=1, help='times to repeat tests')\n    parser.add_argument('--test-dir', default='.', help='test dir')\n    parser.add_argument('-p', '--pattern', nargs='+',\n                        help='Run tests matching the selected pattern.')\n    args = parser.parse_args()\n\n    devices = []\n    if args.device:\n        for s in args.device:\n            name, serial_number = s.split(':')\n            devices.append(Device(name=name, serial_number=serial_number))\n    else:\n        devices.append(Device(name='default', serial_number=None))\n\n    if args.python_version == '2':\n        python_interpreters = ['python']\n    elif args.python_version == '3':\n        python_interpreters = ['python3']\n    else:\n        python_interpreters = ['python', 'python3']\n\n    global CONFIG\n    CONFIG = Config(devices=devices, python_interpreters=python_interpreters,\n                    repeat_times=args.repeat, test_patterns=args.pattern, test_dir=args.test_dir)\n    log_info('config = %s' % (CONFIG,))\n\ndef get_test_script_path():\n    return os.path.join(os.path.dirname(os.path.realpath(__file__)), 'test.py')\n\n\ndef is_serialized_test(test):\n    return test in ['TestExamplePureJava.test_run_simpleperf_without_usb_connection']\n\n\ndef get_tests():\n    res = subprocess.run('%s %s --list-tests' % (sys.executable, get_test_script_path()),\n                         check=True, shell=True, stdout=subprocess.PIPE, encoding='utf-8')\n    tests = [x.strip() for x in res.stdout.split('\\n') if x.strip()]\n    if CONFIG.test_patterns:\n        patterns = [re.compile(fnmatch.translate(x)) for x in CONFIG.test_patterns]\n        tests = [t for t in tests if any(pattern.match(t) for pattern in patterns)]\n    parallel_tests = [t for t in tests if not is_serialized_test(t)]\n    serialized_tests = [t for t in tests if is_serialized_test(t)]\n    if not parallel_tests and not serialized_tests:\n        log_exit('no test to run')\n    return parallel_tests, serialized_tests\n\n\nclass Task:\n    \"\"\" Run test.py one time for a device and python interpreter.\n    \"\"\"\n\n    def __init__(self, name, device, python_interpreter, tests, repeat_index):\n        self.name = name\n        self.device = device\n        self.python_interpreter = python_interpreter\n        self.tests = tests\n        self.repeat_index = repeat_index\n        self.try_time = 0\n        self.test_results = {}\n        self.test_proc = None\n        self.test_dir = None\n        self.progress_file = None\n        self.failed_to_setup_test = None\n        self.last_update_time = None\n\n    def start_task(self):\n        assert not self.test_proc\n        not_started_tests = [t for t in self.tests if t not in self.test_results]\n        assert not_started_tests\n        self.try_time += 1\n        self._create_test_dir()\n        with open(os.path.join(self.test_dir, 'tests.txt'), 'w') as fh:\n            fh.write('\\n'.join(not_started_tests) + '\\n')\n\n        self.progress_file = os.path.join(self.test_dir, 'progress_file.txt')\n        env = os.environ.copy()\n        if self.device.serial_number:\n            env['ANDROID_SERIAL'] = self.device.serial_number\n        if not self._is_device_available(env):\n            for t in self.tests:\n                if t not in self.test_results:\n                    self.test_results[t] = 'DEVICE_NOT_AVAILABLE'\n            return\n        args = [self.python_interpreter, get_test_script_path(),\n                '--progress-file', self.progress_file]\n        args += not_started_tests\n        self.test_proc = subprocess.Popen(args, cwd=self.test_dir, env=env)\n        self.last_update_time = time.time()\n        log_info('start task for %s' % self.test_dir)\n\n    def _create_test_dir(self):\n        test_name = '%s_%s_%s_repeat%d_try%d' % (\n            self.name, self.device.name, self.python_interpreter, self.repeat_index, self.try_time)\n        self.test_dir = os.path.abspath(test_name)\n        if os.path.isdir(self.test_dir):\n            shutil.rmtree(self.test_dir)\n        os.mkdir(self.test_dir)\n\n    def _is_device_available(self, env):\n        res = subprocess.run('adb shell pwd', env=env, stdout=subprocess.PIPE,\n                             stderr=subprocess.PIPE, shell=True)\n        return res.returncode == 0\n\n    def update_status(self):\n        if self.finished():\n            return\n        assert self.test_proc\n        has_update = False\n        if os.path.isfile(self.progress_file):\n            with open(self.progress_file, 'r', encoding='utf-8') as fh:\n                for line in fh.readlines():\n                    items = line.strip().split()\n                    if len(items) == 2 and items[1] in ['OK', 'FAILED']:\n                        if self.test_results.get(items[0]) != items[1]:\n                            has_update = True\n                            self.test_results[items[0]] = items[1]\n        if has_update:\n            self.last_update_time = time.time()\n        elif time.time() - self.last_update_time > TEST_TIMEOUT_IN_SEC:\n            self.test_proc.kill()\n\n        if self.test_proc.poll() is not None:\n            self.test_proc = None\n            if not self.finished():\n                # Test process ends without finishing all tests.\n                # It should be caused by failing to setup a test.\n                for t in self.tests:\n                    if t not in self.test_results:\n                        self._on_failed_to_setup_test(t)\n                        break\n\n    def _on_failed_to_setup_test(self, test):\n        if self.failed_to_setup_test is None or self.failed_to_setup_test[0] != test:\n            self.failed_to_setup_test = [test, 1]\n        else:\n            self.failed_to_setup_test[1] += 1\n            # Mark a test as failed if failing to setup it for multiple times.\n            if self.failed_to_setup_test[1] == 5:\n                self.test_results[test] = 'FAILED_TO_SETUP'\n\n    def started(self):\n        return self.test_proc is not None\n\n    def finished(self):\n        return all([t in self.test_results for t in self.tests])\n\n    def enumerate_new_task(self):\n        \"\"\" When one task finishes, it can enumerate a new task to run, for repeat testing or\n            another python version.\n        \"\"\"\n        if self.repeat_index < CONFIG.repeat_times:\n            return Task(\n                self.name, self.device, self.python_interpreter, self.tests, self.repeat_index + 1)\n        if self.python_interpreter != CONFIG.python_interpreters[-1]:\n            i = CONFIG.python_interpreters.index(self.python_interpreter)\n            return Task(self.name, self.device, CONFIG.python_interpreters[i+1], self.tests, 1)\n        return None\n\n    def force_stop(self):\n        if self.test_proc:\n            self.test_proc.kill()\n\n\nclass TestSummary:\n    def __init__(self, tests):\n        tests.sort()\n        self.results = {}\n        for device in CONFIG.devices:\n            for python_interpreter in CONFIG.python_interpreters:\n                results = self.results['%s_%s' % (device.name, python_interpreter)] = {}\n                for test in tests:\n                    for repeat_index in range(1, CONFIG.repeat_times + 1):\n                        test_name = '%s_repeat_%d' % (test, repeat_index)\n                        results[test_name] = 'None'\n\n    def update(self, task):\n        config_name = '%s_%s' % (task.device.name, task.python_interpreter)\n        results = self.results[config_name]\n        for t, status in task.test_results.items():\n            test_name = '%s_repeat_%d' % (t, task.repeat_index)\n            results[test_name] = status\n\n        self._write_summary()\n\n    def _write_summary(self):\n        with open('test_summary.txt', 'w') as fh:\n            for config_name in sorted(self.results):\n                fh.write('%s\\n' % config_name)\n                results = self.results[config_name]\n                for test_name in sorted(results):\n                    result = results[test_name]\n                    fh.write('\\t%s: %s\\n' % (test_name, result))\n\n        with open('failed_test_summary.txt', 'w') as fh:\n            for config_name in sorted(self.results):\n                results = self.results[config_name]\n                has_failure = any(result not in ['OK', 'None'] for result in results.values())\n                if not has_failure:\n                    continue\n                fh.write('%s\\n' % config_name)\n                for test_name in sorted(results):\n                    result = results[test_name]\n                    if result not in ['OK', 'None']:\n                        fh.write('\\t%s: %s\\n' % (test_name, result))\n\n\ndef run_tasks(tasks, test_summary):\n    try:\n        while tasks:\n            for task in tasks:\n                if not task.started():\n                    task.start_task()\n                task.update_status()\n                test_summary.update(task)\n            new_tasks = []\n            for task in tasks:\n                if task.finished():\n                    new_task = task.enumerate_new_task()\n                    if new_task:\n                        new_tasks.append(new_task)\n                else:\n                    new_tasks.append(task)\n            tasks = new_tasks\n            time.sleep(2)\n    except KeyboardInterrupt:\n        for task in tasks:\n            task.force_stop()\n\n\ndef main():\n    parse_args()\n    parallel_tests, serialized_tests = get_tests()\n    test_summary = TestSummary(parallel_tests + serialized_tests)\n    if parallel_tests:\n        tasks = []\n        for device in CONFIG.devices:\n            tasks.append(\n                Task(\n                    'test', device, CONFIG.python_interpreters[0],\n                    parallel_tests, 1))\n        run_tasks(tasks, test_summary)\n    if serialized_tests:\n        for device in CONFIG.devices:\n            tasks = [\n                Task(\n                    'serialized_test', device, CONFIG.python_interpreters[0],\n                    serialized_tests, 1)]\n            run_tasks(tasks, test_summary)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"simpleperf/scripts/test_monitor.py","file_name":"test_monitor.py","file_ext":"py","file_size_in_byte":11697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"383165486","text":"# -*- coding: utf-8 -*-\n# \n#  idl.py\n#  aopy\n#  \n#  Created by Alexander Rudy on 2013-07-27.\n#  Copyright 2013 Alexander Rudy. All rights reserved.\n# \n\n\"\"\"\n:mod:`wcao.io.idl` – Loading IDL data\n=====================================\n\n\"\"\"\n\nfrom __future__ import (absolute_import, unicode_literals, division,\n                        print_function)\n\nimport os, os.path\nimport warnings\n\nimport numpy as np\n\nfrom .core import BaseIO\n\nclass MapIO(BaseIO):\n    \"\"\"Read/Write IDL 3-extension maps.\"\"\"\n    \n    def read(self, path=\".\"):\n        \"\"\"Read wind map information from IDL\"\"\"\n        from astropy.io import fits\n        \n        filename = os.path.join(path,self.identifier + \".fits\")\n        \n        read_data = {}\n        \n        with fits.open(filename) as HDUs:\n            for HDU in HDUs:\n                if HDU.header[\"DTYPE\"] == 'Wind Map':\n                    read_data[\"map\"] = HDU.data.copy()\n                elif HDU.header[\"DTYPE\"] == 'Wind vx scale':\n                    read_data[\"vx\"] = HDU.data.copy()\n                elif HDU.header[\"DTYPE\"] == 'Wind vy scale':\n                    read_data[\"vy\"] = HDU.data.copy()\n                elif HDU.header[\"DTYPE\"] == 'Wind Layer List':\n                    layer_list = np.atleast_2d(HDU.data.copy())\n                    read_data[\"layers\"] = []\n                    for row in layer_list:\n                        read_data[\"layers\"].append({\n                            \"vx\" : row[0],\n                            \"vy\" : row[1],\n                            \"m\"  : row[2],\n                        })\n        \n        if read_data[\"map\"].shape != (read_data[\"vx\"].shape + read_data[\"vy\"].shape):\n            warnings.warn(\"Map scale data does not match map shape: {} != {}\".format(\n                read_data[\"map\"].shape, (read_data[\"vx\"].shape + read_data[\"vy\"].shape)\n            ))\n            \n        self.target._init_data(read_data)\n    \n    def write(self, path=\".\"):\n        \"\"\"Write wind map information to IDL\"\"\"\n        from astropy.io import fits\n        \n        HDU_map = fits.PrimaryHDU(self.target.map)\n        HDU_map.header[\"DTYPE\"] = ('Wind Map', 'In spatial domain')\n        \n        HDU_vx = fits.ImageHDU(self.target.vx)\n        HDU_vx.header[\"DTYPE\"] = ('Wind vx scale', 'in m/s')\n        \n        HDU_vy = fits.ImageHDU(self.target.vy)\n        HDU_vy.header[\"DTYPE\"] = ('Wind vy scale', 'in m/s')\n        \n        HDUs = fits.HDUList([HDU_map, HDU_vx, HDU_vy])\n        \n        if getattr(self.target, 'layers', False):\n            \n            shape = (len(self.target.layers),4)\n            layers = np.zeros(shape)\n            \n            for i,layer in enumerate(self.target.layers):\n                layers[i,0] = layer[\"vx\"]\n                layers[i,1] = layer[\"vy\"]\n                layers[i,2] = layer[\"m\"]\n            \n            HDU_layers = fits.ImageHDU(layers)\n            HDU_layers.header[\"DTYPE\"] = ('Wind Layer List', '[vx,vy,m,0]')\n            HDUs.append(HDU_layers)\n        \n        map(self.addheader,HDUs)\n        filename = os.path.join(path, self.identifier)\n        HDUs.writeto(filename, clobber=True)\n        \n    def addheader(self,hdu):\n        \"\"\"Add IDL type-headers\"\"\"\n        HDU.header[\"TSCOPE\"] = self.target.case.instrument\n        \n    \n\nclass TimeSeriesReader(BaseIO):\n    \"\"\"Read/Write IDL FITS files with time series.\"\"\"\n    \n    def read(self, path=\".\"):\n        \"\"\"Read IDL format timeseries data from FITS files.\"\"\"\n        from astropy.io import fits\n        \n        filename = os.path.join(path, self.identifier + '.fits')\n        \n        with fits.open(filename) as HDUs:\n            data = HDUs[0].data.copy()[:,:2,method_index]\n            data = data[:,np.newaxis,:]\n        self.target._init_data(data)\n        \n    \n    def write(self, path=\".\"):\n        \"\"\"Write IDL format timeseries data to FITS files\"\"\"\n        pass\n\n","sub_path":"wcao/io/idl.py","file_name":"idl.py","file_ext":"py","file_size_in_byte":3863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"492456084","text":"## Script (Python) \"ninetydays_entries\"\n##bind container=container\n##bind context=context\n##bind namespace=\n##bind script=script\n##bind subpath=traverse_subpath\n##parameters=\n##title=\n##\nfrom Products.PythonScripts.standard import html_quote\nfrom DateTime import DateTime\n\nrequest = container.REQUEST\nRESPONSE =  request.RESPONSE\nzc = context.portal_catalog\n\n# get the end date (today)\nnow = DateTime()\n\n# get the start date (today - 3 months)\nthen = now - 90\n\nobjects = []\n\n# find the blog container path\nblogpath = context.simpleblog_tool.getObjectPath(context.simpleblog_tool.getStartpoint(context))\n\nstartDate = then\nendDate = now\n\nresults = zc.searchResults(meta_type = 'BlogEntry', \n                           path=blogpath, \n                           review_state='published', \n                           sort_order='reverse', sort_on='effective'\n                           )\n\nfor result in results:\n   create_date = DateTime(str(result.getObject().getEffectiveDate()))\n   if create_date > startDate and \\\n      create_date < endDate:\n      objects.append(result)\n\nreturn objects\n","sub_path":"CcOrg/skins/CcOrg/ninetydays_entries.py","file_name":"ninetydays_entries.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"184336014","text":"import unittest\nfrom validictory.schemas import Graph\n\nclass GraphTest(unittest.TestCase):\n\n    def test_add_node(self):\n        gr = Graph()\n        gr.add_node(\"Hello\")\n        self.assertIn(\"Hello\", gr)\n\n    def test_add_nodes(self):\n        gr = Graph()\n        gr.add_node(\"Hello\", \"Goodbye\")\n        self.assertIn(\"Hello\", gr)\n        self.assertIn(\"Goodbye\", gr)\n\n    def test_bad_add_edge(self):\n        gr = Graph()\n        with self.assertRaises(KeyError):\n            gr.add_edge(\"Hello\", \"Goodbye\")\n\n    def test_add_edge(self):\n        gr = Graph()\n        gr.add_node(\"foo\", \"bar\")\n        gr.add_edge(\"foo\", \"bar\")\n        self.assertIn(\"bar\", gr[\"foo\"])\n\n    def test_add_edges(self):\n        gr = Graph()\n        with self.assertRaises(KeyError):\n            gr.add_edge(\"Hello\", \"Goodbye\")\n\n    def test_no_cycle(self):\n        gr = Graph()\n        gr.add_node(\"foo\", \"bar\")\n        gr.add_edge(\"foo\", \"bar\")\n        self.assertFalse(gr.has_cycle())\n\n\n    def test_has_cycle(self):\n        gr = Graph()\n        gr.add_node(\"foo\", \"bar\")\n        gr.add_edge(\"foo\", \"bar\")\n        gr.add_edge(\"bar\", \"foo\")\n        self.assertTrue(gr.has_cycle())\n","sub_path":"tests/test_graph.py","file_name":"test_graph.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"37908660","text":"#!/usr/bin/env python\r\n# -*- encoding: utf-8 -*-\r\n\"\"\"\r\n@File    :  config.py\r\n@Date    :  2021/7/27\r\n@Author  :  Yaronzz\r\n@Version :  1.0\r\n@Contact :  yaronhuang@foxmail.com\r\n@Desc    :\r\n\"\"\"\r\nimport json\r\nimport os\r\n\r\nimport aigpy\r\n\r\n_BASE_PATH = os.path.expanduser('~') + '/b2a/'\r\n_CONFIG_FILE_PATH = f\"{_BASE_PATH}auth.json\"\r\n_ALI_KEY = \"ali-refresh_token\"\r\n_BDY_KEY = \"bdy-cookies\"\r\n\r\n\r\nclass B2aConfig(object):\r\n    def __init__(self):\r\n        aigpy.path.mkdirs(_BASE_PATH)\r\n        self.aliKey = \"\"\r\n        self.bdyKey = \"\"\r\n        self.load()\r\n\r\n    def load(self):\r\n        authJson = aigpy.file.getJson(_CONFIG_FILE_PATH)\r\n        self.aliKey = authJson.get(_ALI_KEY)\r\n        self.bdyKey = authJson.get(_BDY_KEY)\r\n\r\n    def save(self) -> bool:\r\n        content = dict()\r\n        content[_ALI_KEY] = self.aliKey\r\n        content[_BDY_KEY] = self.bdyKey\r\n        if not aigpy.file.write(_CONFIG_FILE_PATH, json.dumps(content), 'w+'):\r\n            return False\r\n        return True\r\n","sub_path":"b2a/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"426389239","text":"#!/usr/bin/python\n\nfrom ansible.module_utils.basic import *\nimport os\nimport subprocess\nimport ast\n\ndef get_failure(req_lines):\n    Failure = []\n    ERROR = []\n    lines = req_lines.split('\\n')\n    for x in lines:\n        if \"Failed\" in x:\n           Failure.append(x)\n        elif \"ERROR\" in x:\n           ERROR.append(x)\n    return Failure,ERROR            \n\ndef check_log(module):\n    req_id = module.params[\"req_id\"]\n    path = module.params[\"log_path\"]\n    cmd = \"grep -ir {} {}\".format(req_id,path)\n    rc, stdout, stderr = module.run_command(cmd, use_unsafe_shell=True)\n    if rc != 0:\n        return False, {\"rc\": rc, \"error\": \"No logs present for the req-id provided\", \"req-id\": req_id}\n    else:\n        Failure, ERROR = get_failure(stdout)\n        return True, {\"Failure_message\": Failure, \"Error\": ERROR}\n\ndef main():\n    fields = {\n             \"req_id\": {\"required\": True, \"type\": \"str\"},\n             \"log_path\": {\"required\": True, \"type\": \"str\"}\n    }\n    module = AnsibleModule(argument_spec=fields)\n    result, msg = check_log(module)\n    if result:\n        module.exit_json(changed=False, meta=msg)\n    else:\n        module.fail_json(msg=msg)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"library/logs/check_log.py","file_name":"check_log.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"274477199","text":"'''\n\nDocstring for ReFPloT:\n\nThis Plotting tool takes in a GLImER receiver function `.mat` file as input and\noutputs a plot with sliders to go through the Receiver Functions.\n\n\nAuthor: Lucas Sawade\n\n'''\n\nimport numpy as np\nfrom scipy.io import loadmat\nimport matplotlib.pyplot as plt\nfrom matplotlib.widgets import Slider, Button, RadioButtons\nimport argparse\n\n\ndef main(filename, color):\n    \"\"\"\n\n    :param filename: string\n    :param color: string\n    :return:\n    \"\"\"\n\n    RF = loadmat(filename)\n\n    # Loading the necessary variables\n    rf = RF['rf']           # Receiver Functions\n    delta = RF['rdelta']    # Epicentral distance\n    baz = RF['rbaz']        # Back-azimuth\n    dt = RF['dt']           # Sampling Time\n\n    # Number of RFs in array\n    N = len(delta[0])\n    print(N)\n\n    # Create time Vector with dt\n    t0 = -30       # P-wave arrival is set to 30sec\n    tf = 120\n    t = np.arange(t0, tf, dt)\n\n    # Maximum\n    ampMax = np.max(np.abs(rf))\n\n    # Setting up the Figure\n    fig, ax = plt.subplots()\n    plt.subplots_adjust(left=0.25, bottom=0.25)\n\n    # Plotting the first receiver function\n    l, = plt.plot(t, rf[1, :], lw=2, color=color)\n\n    # Axis setup\n    plt.axis([0, 80, -ampMax , ampMax])\n\n    # Create Slider position and Style:\n    axcolor = 'lightgoldenrodyellow'\n    axrfn = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)\n\n    # Create Slider                low lim, upper lim, Initial Val, ValueFormat\n    srfn  = Slider(axrfn,  'RF #', 1,       N,         valinit=1,   valfmt='%d')\n\n\n    def update(val):\n        # Find number of RF round so the value is not decimal\n        no = int(srfn.val-1)\n        l.set_ydata(rf[no, :])\n        fig.canvas.draw_idle()\n\n    srfn.on_changed(update)\n\n    resetax = plt.axes([0.8, 0.025, 0.1, 0.04])\n    button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')\n\n\n    def reset(event):\n        srfn.reset()\n\n    button.on_clicked(reset)\n\n    # Possible colors for plot\n    colors = ('red', 'blue', 'green')\n\n    # get index of color entry\n    index = [i for i, x in enumerate([y == color for y in colors]) if x]\n\n    print(index)\n\n    # Create color radio\n    rax = plt.axes([0.025, 0.5, 0.15, 0.15], facecolor=axcolor)\n    radio = RadioButtons(rax, colors, active=index[0])\n\n    print(l.get_ydata() > 0)\n\n    def colorfunc(label):\n        l.set_color(label)\n        fig.canvas.draw_idle()\n\n\n    radio.on_clicked(colorfunc)\n\n    plt.show()\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"filename\", help=\"Filename of the file of to be loaded and displayed\",type=str)\n    parser.add_argument(\"-c\",\"--color\", help=\"Color of the trace, e.g. \"\"red\"\"\", type=str)\n    args = parser.parse_args()\n\n    main(args.filename, args.color)\n","sub_path":"refplot.py","file_name":"refplot.py","file_ext":"py","file_size_in_byte":2766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"366645660","text":"# coding: utf-8\n\nfrom CScanPoc.thirdparty import requests\nfrom CScanPoc import ABPoc, ABVuln, VulnLevel, VulnType\n\n\nclass Vuln(ABVuln):\n    vuln_id = 'DedeCMS_0044'  # 平台漏洞编号，留空\n    name = 'DedeCMS plus/recommend.php sql注入'  # 漏洞名称\n    level = VulnLevel.HIGH  # 漏洞危害级别\n    type = VulnType.INJECTION  # 漏洞类型\n    disclosure_date = '2014-02-28'  # 漏洞公布时间\n    desc = '''\n        DedeCMS 在/plus/recommend.php中存在注入漏洞，\n        只要变量名不含cfg_|GLOBALS|_GET|_POST|_COOKIE就行了，利用上面的代码可以覆盖$_FILES，\n        同时利用这句代码：\n        $$_key = $_FILES[$_key]['tmp_name'] = str_replace(\"\\\\\\\\\", \"\\\\\", $_FILES[$_key]['tmp_name']);[/php]\n        就可以覆盖任意变量了，结合str_replace的替换，就可以带入单引号了，造成注入漏洞。\n    '''  # 漏洞描述\n    ref = 'http://0day5.com/archives/1346/'  # 漏洞来源\n    cnvd_id = 'Unknown'  # cnvd漏洞编号\n    cve_id = 'Unknown'  # cve编号\n    product = 'DedeCMS(织梦CMS)'  # 漏洞应用名称\n    product_version = 'Unknown'  # 漏洞应用版本\n\n\nclass Poc(ABPoc):\n    poc_id = 'a8ae0a61-aa79-4fed-b9bf-47af17f64c67'\n    author = '47bwy'  # POC编写者\n    create_date = '2018-06-15'  # POC创建时间\n\n    def __init__(self):\n        super(Poc, self).__init__(Vuln())\n        self.option_schema = {\n            'properties': {\n                'base_path': {\n                    'type': 'string',\n                    'description': '部署路径',\n                    'default': '',\n                    '$default_ref': {\n                        'property': 'deploy_path'\n                    }\n                }\n            }\n        }\n\n    def verify(self):\n        self.target = self.target.rstrip(\n            '/') + '/' + (self.get_option('base_path').lstrip('/'))\n        try:\n            self.output.info('开始对 {target} 进行 {vuln} 的扫描'.format(\n                target=self.target, vuln=self.vuln))\n\n            payload = '/plus/recommend.php?action=&aid=1&_FILES[type][tmp_name]=\\' or mid=@`\\'` /*!50000union*//*!50000select*/1,2,3,(select CONCAT(0x7c,userid,0x7c,md5(c))+from+`%23@__admin` limit+0,1),5,6,7,8,9%23@`\\'`+&_FILES[type][name]=1.jpg&_FILES[type][type]=application/octet-stream&_FILES[type][size]=6873'\n            url = self.target + payload\n            r = requests.get(url)\n\n            if '4a8a08f09d37b73795649038408b5f33' in r.text:\n                self.output.report(self.vuln, '发现{target}存在{name}漏洞'.format(\n                    target=self.target, name=self.vuln.name))\n\n        except Exception as e:\n            self.output.info('执行异常{}'.format(e))\n\n    def exploit(self):\n        self.verify()\n\n\nif __name__ == '__main__':\n    Poc().run()\n","sub_path":"pocs/cms/DedeCMS(织梦CMS)/DedeCMS_0044.py","file_name":"DedeCMS_0044.py","file_ext":"py","file_size_in_byte":2805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"533769909","text":"import hashlib\nimport sqlite3\nfrom sqlite3 import Error\n\n# setting md5 var\nmd5 = hashlib.md5();\n\n# get filename\nprint(\"hiss, muchaho! you want me to create a sneksum for ya?\")\nfilename = input(\"no problemos just enter the name of the textfile (.txt) in quotes:\")\n\n# opening and closing a file in readonly mode with the filename the user provides \nsnekfile = open(filename,\"r\")\ncontent = snekfile.read()\nsnekfile.close()\n\n# generate & print checksum\nmd5.update(content)\nprint(\"thiss is your checksum:\")\nprint(md5.hexdigest())\n\n\n#fun with databses\ndef create_connection(db_file):\n    \"\"\" create a database connection to the SQLite database\n        specified by db_file\n    :param db_file: database file\n    :return: Connection object or None\n    \"\"\"\n    conn = None\n    try:\n        conn = sqlite3.connect(db_file)\n        return conn\n    except Error as e:\n        print(e)\n\n    return conn\n\n\ndef create_table(conn, create_table_sql):\n    \"\"\" create a table from the create_table_sql statement\n    :param conn: Connection object\n    :param create_table_sql: a CREATE TABLE statement\n    :return:\n    \"\"\"\n    try:\n        c = conn.cursor()\n        c.execute(create_table_sql)\n    except Error as e:\n        print(e)\n\ndef create_sneksumentry(conn, entry):\n    \"\"\"\n    Create a new checksum into the sneksum table\n    :param conn:\n    :param entry:\n    :return: entry id\n    \"\"\"\n    sql = ''' INSERT INTO sneksums(filename,checksum)\n              VALUES(?,?) '''\n    cur = conn.cursor()\n    cur.execute(sql, entry)\n    conn.commit()\n\ndef main():\n    database = r\"sneksum.db\"\n\n    sql_create_sneksums_table = \"\"\" CREATE TABLE IF NOT EXISTS sneksums (\n                                        id integer PRIMARY KEY,\n                                        filename text NOT NULL,\n                                        checksum NOT NULL\n                                    ); \"\"\"\n\n    # create a database connection\n    conn = create_connection(database)\n\n    # create tables\n    if conn is not None:\n        # create sneksums table\n        create_table(conn, sql_create_sneksums_table)\n    else:\n        print(\"Error! cannot create the database connection.\")\n\n    with conn:\n        # create a new entry\n        sneksum_entry = (filename, md5.hexdigest())\n        create_sneksumentry(conn, sneksum_entry)\n\nif __name__ == '__main__':\n    main()\n\nprint(\"thanksss and see ya soon matey!\")","sub_path":"sneksum.py","file_name":"sneksum.py","file_ext":"py","file_size_in_byte":2381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"597535799","text":"import config_accessor as cfg\nimport os\nimport csv\nfrom shutil import copyfile\nfrom pathlib import Path\nimport schedule\nimport time\nfrom threading import Thread, Lock\n\nfrom elasticsearch_dsl import Index\nfrom elasticsearch_dsl.connections import connections\nfrom tensorflow.python.keras.applications.resnet50 import ResNet50\nfrom tensorflow.python.keras.preprocessing import image\nfrom tensorflow.python.keras.models import Model\n\nimport numpy as np\n\nfrom keras_retinanet import models\nimport keras.preprocessing.image\nimport tensorflow as tf\nfrom models_es import EsAsset, EsAssetMeta, EsCropped\nimport faiss\n\nimport logging\n\nlogger = logging.getLogger('celum.settings')\n\nindex = None\ndb_asset = None\ndb_asset_meta = None\ndb_cropped = None\nmodel = None\nfeature_model = None\nblacklist = []\nblacklist_mutex = None\ncron_job_thread = None\nround_robin_backup_index = 0\n\n\ndef initialize_logging():\n    print('Initializing logging...')\n    # set up logging to file - see previous section for more details\n    logging.basicConfig(level=logging.DEBUG,\n                        format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',\n                        datefmt='%m-%d %H:%M',\n                        filename=cfg.resolve(cfg.DEFAULT, cfg.log_dir)+cfg.resolve(cfg.DEFAULT, cfg.log_name),\n                        filemode='a')\n    # define a Handler which writes INFO messages or higher to the sys.stderr\n    console = logging.StreamHandler()\n    console.setLevel(logging.INFO)\n    # set a format which is simpler for console use\n    formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')\n    # tell the handler to use this format\n    console.setFormatter(formatter)\n    # add the handler to the root logger\n    logging.getLogger('').addHandler(console)\n\n\ndef initialize_similarity_index():\n    global index\n    path = cfg.resolve(cfg.FAISS_SETTINGS, cfg.index_path)\n    if not os.path.exists(path):\n        os.mkdir(path)\n\n    file = os.path.join(path, cfg.resolve(cfg.FAISS_SETTINGS, cfg.index_file))\n    if not os.path.exists(file):\n        index = faiss.IndexFlatIP(cfg.resolve_int(cfg.FAISS_SETTINGS, cfg.index_size))\n        persist_similarity_index()\n    else:\n        try:\n            index = faiss.read_index(file)\n            logger.info(\"Faiss index loaded\")\n        except (OSError, TypeError, NameError):\n            index = faiss.read_index(file)\n            logger.error(\"Can't load index! Using default empty index\")\n\n\ndef persist_similarity_index():\n    if index is not None:\n        file = os.path.join(cfg.resolve(cfg.FAISS_SETTINGS, cfg.index_path),\n                            cfg.resolve(cfg.FAISS_SETTINGS, cfg.index_file))\n        faiss.write_index(index, file)\n        logger.info(\"Faiss index saved to disk\")\n    else:\n        logger.warning(\"Can't save, index was not loaded yet!\")\n\n\ndef initialize_elastic_search():\n    global db_asset, db_cropped, db_asset_meta\n    connections.create_connection(hosts=cfg.resolve(cfg.ELASTICSEARCH_SERVER, cfg.host),\n                                  port=cfg.resolve(cfg.ELASTICSEARCH_SERVER, cfg.port),\n                                  timeout=20)\n\n    db_asset = Index(cfg.resolve(cfg.ELASTICSEARCH_SERVER,\n                                 cfg.index_prefix) + cfg.resolve(cfg.ELASTICSEARCH_SERVER, cfg.index_asset))\n    if not db_asset.exists():\n        db_asset.doc_type(EsAsset)\n        db_asset.create()\n    db_asset_meta = Index(cfg.resolve(cfg.ELASTICSEARCH_SERVER,\n                                      cfg.index_prefix) + cfg.resolve(cfg.ELASTICSEARCH_SERVER, cfg.index_asset_meta))\n    if not db_asset_meta.exists():\n        db_asset_meta.doc_type(EsAssetMeta)\n        db_asset_meta.create()\n    db_cropped = Index(cfg.resolve(cfg.ELASTICSEARCH_SERVER,\n                                   cfg.index_prefix) + cfg.resolve(cfg.ELASTICSEARCH_SERVER, cfg.index_cropped))\n    if not db_cropped.exists():\n        db_cropped.doc_type(EsCropped)\n        db_cropped.create()\n\n    logger.info(\"Elastic search initialized!\")\n\n\ndef get_session():\n    cfg = tf.ConfigProto()\n    cfg.gpu_options.allow_growth = True\n    return tf.Session(config=cfg)\n\n\ndef initialize_retinanet():\n    global model\n    logger.info('Loading retinanet classification model...')\n    # os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n    logger.info('Setting keras session...')\n    keras.backend.tensorflow_backend.set_session(get_session())\n\n    logger.info('Loading model name...')\n    model = models.load_model(cfg.resolve(cfg.RETINANET_MODEL, cfg.model_path) +\n                              cfg.resolve(cfg.RETINANET_MODEL, cfg.model_name),\n                              backbone_name=cfg.resolve(cfg.RETINANET_MODEL, cfg.backbone_name))\n\n\ndef initialize_extraction_model():\n    global feature_model\n    logger.info('Loading extraction model...')\n    resnet = ResNet50(weights='imagenet')\n    output = resnet.layers[-2].output\n    feature_model = Model(resnet.input, output)\n\n\ndef initialize_blacklist():\n    global blacklist, blacklist_mutex\n    blacklist_mutex = Lock()\n\n    path = cfg.resolve(cfg.FAISS_SETTINGS, cfg.index_path)\n    if not os.path.exists(path):\n        os.mkdir(path)\n    file = os.path.join(path, cfg.resolve(cfg.FAISS_SETTINGS, cfg.index_blacklist_file))\n    if not os.path.exists(file):\n        Path(file).touch()\n    with open(file,'r') as f:\n        reader = csv.DictReader(f)\n        for row in reader:\n            blacklist.append(int(row['index']))\n\n\n# Multi-threading is currently not used/required - preventive method!\ndef threadsafe_blacklist_operation(delegate):\n    \"\"\"\n    Thread safe implementation for handling multiple blacklist operations at once.\n    :param delegate:\n    :return:\n    \"\"\"\n    blacklist_mutex.acquire()\n    try:\n        return delegate(blacklist)\n    finally:\n        blacklist_mutex.release()\n\n\ndef persist_blacklist_index():\n    path = cfg.resolve(cfg.FAISS_SETTINGS, cfg.index_path)\n    file = os.path.join(path, cfg.resolve(cfg.FAISS_SETTINGS,\n                                          cfg.index_blacklist_file))\n    with open(file, 'w') as f:\n        writer = csv.DictWriter(f, fieldnames=['index'])\n        writer.writeheader()\n        blacklist_dict = threadsafe_blacklist_operation(lambda bl: [{'index': id} for id in bl])\n        writer.writerows(blacklist_dict)\n\n\ndef predict_features(img_file):\n    x = image.load_img(img_file, target_size=(224, 224))\n    x = image.img_to_array(x)\n    x = np.expand_dims(x, axis=0)\n    return feature_model.predict(x)[0]\n\n\ndef backup_persisting_files():\n    global round_robin_backup_index\n    round_robin_backup_index += 1\n    round_robin_backup_index %= cfg.resolve_int(cfg.CRON_JOB, cfg.cron_job_round_robin_backups)\n\n    # copy faiss file\n    path = cfg.resolve(cfg.FAISS_SETTINGS, cfg.index_path)\n    file = os.path.join(path, cfg.resolve(cfg.FAISS_SETTINGS, cfg.index_file))\n    copyfile(file, file+'.backup_{}'.format(round_robin_backup_index))\n\n    # copy blacklist file\n    file = os.path.join(path, cfg.resolve(cfg.FAISS_SETTINGS, cfg.index_blacklist_file))\n    copyfile(file, file+'.backup_{}'.format(round_robin_backup_index))\n\n\ndef trigger_backup():\n    logging.info('Backup persisting files...')\n    backup_persisting_files()\n    logger.info('Persisting blacklist...')\n    persist_blacklist_index()\n    logger.info('Persisting faiss index...')\n    persist_similarity_index()\n\n\ndef initialize_cron_job():\n    global cron_job_thread\n\n    def cron_job_runner():\n        schedule.every(cfg.resolve_int(cfg.CRON_JOB, cfg.cron_job_interval)).minutes.do(trigger_backup)\n        while True:\n            schedule.run_pending()\n            time.sleep(1)\n\n    if not cron_job_thread:\n        cron_job_thread = Thread(target=cron_job_runner)\n        cron_job_thread.start()\n","sub_path":"core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":7729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"221074784","text":"#!/usr/bin/python3.5\nimport argparse\nimport argcomplete\nimport os\n\ndef directory_tree():\n\tos.system('rm -rf RemoteSystemsTempFiles')\n\tos.system('mkdir -p libsimitar/obj')\n\tos.system('mkdir -p tests/xml')\n\tos.system('mkdir -p pcaps')\n\tos.system('mkdir -p trace-analyzer/obj')\n\tos.system('mkdir -p data/config')\n\tos.system('mkdir -p data/db')\n\tos.system('mkdir -p data/log')\n\tos.system('mkdir -p data/test')\n\tos.system('mkdir -p data/xml')\n\n\ndef clean():\n\tprint_header(\"Building directory tree\");\n\tdirectory_tree()\n\tprint_header(\"Clean all components\");\n\tos.system('make -C libsimitar/ clean')\n\tos.system('make -C  trace-analyzer/ clean')\n\tos.system('make -C  tests/ clean')\n\tos.system('make -C simitar-gen/ clean')\n\tos.system('rm -rf data/log/*.log')\n\n\ndef build():\n\tprint_header(\"Directory tree\");\n\tdirectory_tree()\n\tprint_header(\"Libsimitar\");\n\tos.system('make -C libsimitar/')\n\tprint_header(\"Trace Analyzer trace-analyzer\");\n\tos.system('make -C trace-analyzer/')\n\tprint_header(\"Tests\");\n\tos.system('make -C tests/')\n\tprint_header(\"SIMITAR traffic generator simitar-gen\");\n\tos.system('make -C simitar-gen/')\n\n\ndef print_header(component_name):\n\tprint(\"\");\n\tprint(\"################################################################################\")\n\tprint(\"# \" + component_name)\n\tprint(\"################################################################################\")\t\n\n\n#def print_tutorial():\n#\tprint_header(\"Tutorial\")\n#\tprint('(1) Now run, at the workspace dir (SIMITAR/), run:\\n source data/config/simitar-workspace.config.sh\\nIt will set the enviroment variables needed.')\n#\tprint(\"(2) To install in the operational system runs:\\n ./build --install \\nIt will compile, save the enviroment variables in the .bashrc file, and create links to the binaries in the /usr/bin/ directory. So, there will be no need for running the simitar-workspace.config.sh script.\")\n\n\ndef install():\n\tprint(\"TODO\")\n\n\ndef unninstall():\n\tprint(\"TODO\")\n\n\ndef deps():\n\t# pip install argcomplete \n\tprint_header(\"Tools dependencies\")\n\tos.system('sudo apt-get install ostinato;')\n\tos.system('sudo apt-get install wireshark')\n\tos.system('sudo apt-get install tshark')\n\tos.system('')\n\tos.system('')\n\tos.system('')\n\tos.system('')\n\tos.system('')\n\tprint_header(\"Python dependencies\")\n\tos.system('sudo pip install sqlite3')\n\tos.system('sudo pip install pyshark')\n\tos.system('sudo pip install rstr')\n\tos.system('sudo pip install numpy')\n\tos.system('sudo pip install fnvhash')\n\tos.system('sudo pip install termcolor')\n\tos.system('sudo -H pip install python-ostinato;')\t\t\n\tprint_header(\"C/C++ lybraries\")\n\tos.system('sudo apt-get install librapidxml-dev ')\n\tos.system('sudo apt-get install sqlite3 ')\n\tos.system('sudo apt-get install libsqlite3-dev ')\n\tos.system('sudo apt-get install liblapack-dev ')\n\tos.system('sudo apt-get install libblas-dev ')\n\tos.system('sudo apt-get install libboost-dev ')\n\tos.system('sudo apt-get install libopenblas-base')\n\tos.system('apt-get install libarmadillo-dev')\n\tos.system('apt-get install libtclap-dev')\n\tos.system('')\n\tos.system('')\n\tos.system('')\n\tos.system('')\n\tos.system('')\n\n\nif __name__ == '__main__':\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument(\"--build\", help=\"Build project\", action=\"store_true\")\n\tparser.add_argument(\"--remake\", help=\"Clean and build\", action=\"store_true\")\n\tparser.add_argument(\"--clean\", help=\"Clean\", action=\"store_true\")\n\tparser.add_argument(\"--install\", help=\"TODO\", action=\"store_true\")\n\tparser.add_argument(\"--unninstall\", help=\"TODO\", action=\"store_true\")\n\tparser.add_argument(\"--deps\", help=\"TODO\", action=\"store_true\")\n\targcomplete.autocomplete(parser)\n\targs = parser.parse_args()\n\tif args.remake:\n\t\tclean()\n\t\tbuild()\n\t\t#print_tutorial()\n\telif args.clean:\n\t\tclean()\n\telif args.install:\n\t\t#build()\n\t\tinstall()\n\telif args.unninstall:\n\t\tunninstall()\n\telif args.build:\n\t\tbuild()\n\telif args.deps:\n\t\tdeps()\n\telse:\n\t\tbuild()\n\t\t#print_tutorial()\n\n\n","sub_path":"SIMITAR/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":3893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"251412862","text":"import numpy\nimport math\n\ndef change_ball_velocity(velocity_vec, paddle):\n    speed = numpy.linalg.norm(velocity_vec)\n    if math.fabs(velocity_vec[0]) < speed*math.cos(math.pi/3):\n        velocity_vec[0] = -velocity_vec[0]*4.\n    else:\n        velocity_vec[0] = -velocity_vec[0]\n    if paddle.direction == -1:\n        paddle_velocity = +paddle.velocity\n    elif paddle.direction == 0:\n        paddle_velocity = 0\n    elif paddle.direction == +1:\n        paddle_velocity = -paddle.velocity\n    velocity_vec[1] = velocity_vec[1] - paddle_velocity\n    return velocity_vec\n\n\ndef line_line_intersect(x1, y1, x2, y2, x3, y3, x4, y4):\n    # Taken from http://paulbourke.net/geometry/lineline2d/\n    # Denominator for ua and ub are the same, so store this calculation\n    d = float((y4 - y3) * (x2 - x1) - (x4 - x3) * (y2 - y1))\n    # n_a and n_b are calculated as seperate values for readability\n    n_a = float((x4 - x3) * (y1 - y3) - (y4 - y3) * (x1 - x3))\n    n_b = float((x2 - x1) * (y1 - y3) - (y2 - y1) * (x1 - x3))\n    # Make sure there is not a division by zero - this also indicates that\n    # the lines are parallel.  \n    # If n_a and n_b were both equal to zero the lines would be on top of each \n    # other (coincidental).  This check is not done because it is not \n    # necessary for this implementation (the parallel check accounts for this).\n    if d == 0:\n        return False\n    # Calculate the intermediate fractional point that the lines potentially intersect.\n    ua = n_a / d\n    ub = n_b / d\n    # The fractional point will be between 0 and 1 inclusive if the lines\n    # intersect.  If the fractional calculation is larger than 1 or smaller\n    # than 0 the lines would need to be longer to intersect.\n    if ua >= 0. and ua <= 1. and ub >= 0. and ub <= 1.:\n        return [x1 + (ua * (x2 - x1)), y1 + (ua * (y2 - y1))]\n    return False\n\n\ndef step_one_frame(ball_prev_position, bounds,ball,paddle_l,paddle_r):\n    ball_position_x = ball_prev_position[0]\n    ball_position_y = ball_prev_position[1]\n\n    hit_left=False\n    hit_right = False\n    \n    # Paddle collision check. Could probably just do a line-line intersect but I think I prefer having the pixel-pefect result of a rect-rect intersect test as well.\n\n    if ball.rect.x < bounds.centerx:\n        # Left side bullet-through-paper check on ball and paddle\n        if ball.velocity_vec[0] < 0:\n            intersect_point = line_line_intersect(\n                paddle_l.rect.right, paddle_l.rect.top,\n                paddle_l.rect.right, paddle_l.rect.bottom,\n                ball_position_x-ball.rect.width/2, ball_position_y+ball.rect.height/2,\n                ball.position_x-ball.rect.width/2, ball.position_y+ball.rect.height/2\n            )\n            if intersect_point:\n                # ball does collide with the paddle\n                ball.position_y = intersect_point[1]-ball.rect.height/2\n\n            if intersect_point or (paddle_l.rect.colliderect(ball.rect) and ball.rect.right > paddle_l.rect.right):\n                ball.position_x = paddle_l.rect.right\n                change_ball_velocity(ball.velocity_vec, paddle_l)\n                hit_left = True\n    else:\n        # Right side bullet-through-paper check on ball and paddle.\n        if ball.velocity_vec[0] > 0:\n            intersect_point = line_line_intersect(\n                paddle_r.rect.left, paddle_r.rect.top,\n                paddle_r.rect.left, paddle_r.rect.bottom,\n                ball_position_x-ball.rect.width/2, ball_position_y+ball.rect.height/2,\n                ball.position_x-ball.rect.width/2, ball.position_y+ball.rect.height/2\n            )\n            if intersect_point:\n                ball.position_y = intersect_point[1]-ball.rect.height/2\n            if intersect_point or (paddle_r.rect.colliderect(ball.rect) and ball.rect.x < paddle_r.rect.x):\n                ball.position_x = paddle_r.rect.x - ball.rect.width\n                change_ball_velocity(ball.velocity_vec, paddle_r)\n                hit_right = True\n    # Bounds collision check\n    if ball.rect.y < (bounds.top ):\n        ball.position_y = float(bounds.top)\n        ball.velocity_vec[1] = -ball.velocity_vec[1]\n    elif ball.rect.y > bounds.bottom:\n        ball.position_y = float(bounds.bottom)\n        ball.velocity_vec[1] = -ball.velocity_vec[1]\n\n    return hit_left, hit_right\n\n","sub_path":"pypong/kinematics.py","file_name":"kinematics.py","file_ext":"py","file_size_in_byte":4329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"101872786","text":"#Zahlenraten\nimport random\nimport sys\n\ndef guesser(cZ):\n    v = 0\n    while True:\n        try:\n            benutzerZahl = int(input(\">> \"))\n        except Exception:\n            print(\"Deine Eingabe ist nicht korrekt!\")\n        v += 1\n        if benutzerZahl == cZ:\n            print(\"Du hast die Zahl \" + str(cZ) + \" mit \" + str(v) + \" Versuchen erraten.\")\n            while True:\n                print(\"Nochmal? j/n\")\n                eingabe = input(\">>\")\n                if (eingabe == \"j\") or (eingabe == \"J\"):\n                    return True\n                elif (eingabe == \"n\") or (eingabe == \"N\"):\n                    return False\n                else:\n                    print(\"Falsche Eingabe\")\n        elif benutzerZahl < cZ:\n            print(\"Zu klein.\")\n        elif benutzerZahl > cZ:\n            print(\"Zu groß.\")\n        else:\n            print(\"ERROR\")\n            sys.exit()\na = True\nwhile a == True:\n    print(\"\\n\"*50)\n    a = guesser(random.randint(1,100))","sub_path":"python/Dateien/python025.py","file_name":"python025.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"70"}
+{"seq_id":"517691853","text":"#!/usr/bin/env python\nimport re\n\nexps = [\n        ('nVar', 'Number of variables:\\\\s*(\\\\d+)'),\n        ('nCls', r'Number of clauses:\\s*(\\d+)'),\n        ('time', r'CPU time\\s*:\\s*(?P