diff --git "a/448.jsonl" "b/448.jsonl" new file mode 100644--- /dev/null +++ "b/448.jsonl" @@ -0,0 +1,686 @@ +{"seq_id":"504769919","text":"import os\nimport torch\nimport torch.utils.data\nimport torchvision\nfrom PIL import Image\nfrom pycocotools.coco import COCO\n\nclass PascalVocDatasetClass(torch.utils.data.Dataset):\n def __init__(self, root, annotation, transforms=None):\n self.root = root\n self.transforms = transforms\n self.coco = COCO(annotation)\n self.ids = list(sorted(self.coco.imgs.keys()))\n\n def __getitem__(self, index):\n coco = self.coco\n img_id = self.ids[index]\n ann_ids = coco.getAnnIds(imgIds=img_id)\n coco_annotation = coco.loadAnns(ann_ids)\n path = coco.loadImgs(img_id)[0]['file_name']\n img = Image.open(os.path.join(self.root, path))\n '''\n coco_annotation has the following structure:\n [{'id': 1, 'image_id': 1, 'category_id': 15, 'bbox': [269, 90, 150, 284], 'area': 42600, 'segmentation': [], 'iscrowd': 0}]\n '''\n num_objs = len(coco_annotation)\n boxes = []\n # Bounding boxes for objects\n # In coco format, bbox = [xmin, ymin, width, height]\n # In pytorch, the input should be [xmin, ymin, xmax, ymax]\n for i in range(num_objs):\n xmin = coco_annotation[i]['bbox'][0]\n ymin = coco_annotation[i]['bbox'][1]\n xmax = xmin + coco_annotation[i]['bbox'][2]\n ymax = ymin + coco_annotation[i]['bbox'][3]\n boxes.append([xmin, ymin, xmax, ymax])\n boxes = torch.as_tensor(boxes, dtype=torch.float32)\n labels = []\n for i in range(num_objs):\n labels.append(coco_annotation[i]['category_id'])\n labels = torch.as_tensor(labels, dtype=torch.int64)\n img_id = torch.tensor([img_id])\n areas = []\n for i in range(num_objs):\n areas.append(coco_annotation[i]['area'])\n areas = torch.as_tensor(areas, dtype=torch.float32)\n iscrowds = []\n for i in range(num_objs):\n iscrowds.append(coco_annotation[i]['iscrowd'])\n iscrowds = torch.tensor(iscrowds, dtype=torch.int64)\n\n # Annotation is in dictionary format\n new_annotation = {}\n new_annotation[\"boxes\"] = boxes\n new_annotation[\"labels\"] = labels\n new_annotation[\"image_id\"] = img_id\n new_annotation[\"area\"] = areas\n new_annotation[\"iscrowd\"] = iscrowds\n\n if self.transforms is not None:\n img = self.transforms(img)\n\n return img, new_annotation\n\n def __len__(self):\n return len(self.ids)","sub_path":"DatasetPascalVoc.py","file_name":"DatasetPascalVoc.py","file_ext":"py","file_size_in_byte":2487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"115839682","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport re\nfrom glob import glob\nimport numpy as np\nfrom frequency_response import FrequencyResponse\n\nDIR = 'innerfidelity/data/onear'\nDIR = os.path.abspath(DIR)\nOUT_DIR = os.path.join('innerfidelity/data/avg/onear')\n\n\ndef main():\n models = {}\n for file_path in glob(os.path.join(DIR, '*')):\n model = os.path.split(file_path)[-1]\n if not (re.search(' sample [a-zA-Z0-9]$', model, re.IGNORECASE) or re.search(' sn[a-zA-Z0-9]+$', model, re.IGNORECASE)):\n # Skip measurements with sample or serial number, those have averaged results\n continue\n norm = re.sub(' sample [a-zA-Z0-9]$', '', model, 0, re.IGNORECASE)\n norm = re.sub(' sn[a-zA-Z0-9]+$', '', norm, 0, re.IGNORECASE)\n try:\n models[norm].append(model)\n except KeyError as err:\n models[norm] = [model]\n\n for norm, origs in models.items():\n if len(origs) > 1:\n print(norm, origs)\n avg = np.zeros(613)\n f = FrequencyResponse.generate_frequencies()\n for model in origs:\n fr = FrequencyResponse.read_from_csv(os.path.join(DIR, model, model + '.csv'))\n fr.interpolate()\n fr.center()\n avg += fr.raw\n avg /= len(origs)\n fr = FrequencyResponse(name=norm, frequency=f, raw=avg)\n d = os.path.join(OUT_DIR, norm)\n if not os.path.isdir(d):\n os.makedirs(d)\n fr.write_to_csv(os.path.join(d, norm + '.csv'))\n #fr.plot_graph()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"average.py","file_name":"average.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"92404476","text":"# Imports: standard library\nfrom uuid import uuid4\n\n# Imports: third party\nimport dash_auth\nfrom flask_caching import Cache\n\n# Imports: first party\nfrom visualizer import app\nfrom visualizer.properties import load_config\nfrom visualizer.static_callbacks import set_static_callbacks\n\n\n# pylint: disable=import-outside-toplevel\ndef run(debug, port, address, files_dir, options):\n app.title = \"HD5 visualizer\"\n\n random_token = str(uuid4())\n dash_auth.BasicAuth(\n app,\n {\"aguirrelab\": random_token},\n )\n\n cache = Cache(\n app.server,\n config={\n \"CACHE_TYPE\": \"filesystem\",\n \"CACHE_DIR\": \"cache-directory\",\n \"CACHE_THRESHOLD\": 200,\n },\n )\n\n load_config(user_files=options)\n # Imports: first party\n from visualizer.layout import create_layout\n from visualizer.graphs_callbacks import set_dynamic_callbacks\n\n app.layout = create_layout(files_dir)\n set_static_callbacks(app)\n set_dynamic_callbacks(app, cache)\n\n print(\n f\"\"\"\n ****************\n To log in use:\n\n \\t Username: aguirrelab\n \\t Password: {random_token}\n\n ****************\"\n \"\"\",\n )\n\n app.run_server(debug=debug, host=address, port=port)\n\n\ndef run_visualizer(args):\n run(args.debug, args.port, args.address, args.tensors, args.options_file)\n","sub_path":"visualizer/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"251686577","text":"import struct\nfrom collections import namedtuple\n\nfrom twisted.internet.protocol import DatagramProtocol\n\nfrom .protocol import RTheader, QRTPacketType\n\nQRTDiscoveryP1 = struct.Struct(\"H\")\nQRTDiscoveryPacketSize = QRTDiscoveryP1.size + QRTDiscoveryP2.size\nQRTDiscoveryBasePort = struct.Struct(\">H\")\n\nQRTDiscoveryResponse = namedtuple('QRTDiscoveryResponse', 'info host port')\n\n\nclass QRTDiscoveryProtocol(DatagramProtocol):\n\n def __init__(self, receiver=None):\n self.port = None\n self.receiver = receiver\n\n def startProtocol(self):\n self.transport.setBroadcastAllowed(True)\n self.port = self.transport.getHost().port\n\n def send_discovery_packet(self):\n if self.port is None:\n return\n\n self.transport.write(QRTDiscoveryP1.pack(QRTDiscoveryPacketSize,\n QRTPacketType.PacketDiscover.value) + QRTDiscoveryP2.pack(self.port),\n ('', 22226))\n\n def datagramReceived(self, datagram, address):\n size, type_ = RTheader.unpack_from(datagram, 0)\n info, = struct.unpack_from(\"{0}s\".format(size - 3 - 8), datagram, RTheader.size)\n base_port, = QRTDiscoveryBasePort.unpack_from(datagram, size-2)\n\n if self.receiver is not None:\n self.receiver(QRTDiscoveryResponse(info, address[0], base_port))\n","sub_path":"qtm/discovery.py","file_name":"discovery.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"401881061","text":"def plot_country(country, data, log='lin', end=None, start=None, limit=0):\n \n \"\"\"\n plot the cases of given country\n \n :param country: Country name, if there is \"countries\" column in the data - else use \"World or \"\" for all data\n :param data: dataframe with \"dates\" (datetime) and \"cases\" columns - coses is the number of daily new cases\n :param log: 'log' if logarithmic plot\n :param end: end datetime to plot x-range\n :param start: start datetime\n :param limit: first date when there is more than given value of cumulative cases\n :return:\n \"\"\"\n \n import matplotlib.pyplot as plt\n import matplotlib.ticker as ticker\n import seaborn as sns\n\n if country=='all' or country=='World' or len(country)==0:\n temp = data.sort_values('dates')\n temp['cases'] = temp.groupby(['dates'])['cases'].transform('sum')\n temp['deaths'] = temp.groupby(['dates'])['deaths'].transform('sum')\n temp.drop_duplicates(subset=['dates'], inplace=True)\n else:\n temp = data[data.countries == country].sort_values('dates')\n\n temp['cumcases']=temp.cases.cumsum().values\n temp['cumdeaths']=temp.deaths.cumsum().values\n first_date2 = next((ti['dates'] for ind, ti in temp.iterrows() if ti['cumcases'] > limit), None)\n \n if first_date2 == None:\n #print('no cumulative cases over the limit %f for country '%limit, country)\n return\n \n if not(start is None):\n first_date2 = max(start, first_date2)\n \n temp = temp[temp.dates>= first_date2]\n if end is None:\n end = temp.dates.max()\n #print('date range with non-zero data: \\n', first_date2, '-', end)\n else:\n #print('date range with non-zero data: \\n', first_date2, '-', end)\n temp = temp[temp.date <= end]\n\n fig, ax = plt.subplots(figsize=[12, 8])\n plt.plot_date(temp.dates, temp.cumcases.values, '', linewidth=3.5, label='cases', color='#005082', alpha=.5)\n plt.plot_date(temp.dates, temp.cumdeaths.values, '', linewidth=3, label='deaths', color='#FF1053', alpha=.5)\n\n\n if log == \"log\":\n ax.set_yscale('log')\n\n fig.autofmt_xdate()\n ax.xaxis.set_major_locator(ticker.AutoLocator())\n ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())\n ax.xaxis.set_tick_params(labelsize=10)\n plt.title(country + ': Cases and Deaths', fontsize=20)\n plt.xlabel('', fontsize=12, labelpad=8)\n plt.ylabel('total', fontsize=12, labelpad=8)\n plt.legend()\n\n ax.tick_params(axis='both', which='major', pad=8)\n\n sns.despine()\n\n return first_date2\n","sub_path":"plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":2556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"305540104","text":"import nuke\r\n\n\nnuke.menu('Nodes').addCommand('dgTools/PerspLines/dg_PerspLines','nuke.createNode(\"dg_PerspLines\")','')\n\nnuke.menu('Nodes').addCommand('dgTools/PerspLines/dg_Horizon','dg_PerspLines_Horizon()','')\n\nnuke.menu('Nuke').addCommand('dgTools/PerspLines/Align camera for 2 selected nodes','dg_PerspLines_AlignCamera()', 'Shift+V')\n\n\n\ndef dg_PerspLines_Horizon():\n\tnodes=nuke.selectedNodes()\n\tif not len(nodes)==2:\n\t\tnuke.message('Illegal amount of selected nodes.\\nPlease select exactly two dg_PerspLines nodes')\n\t\treturn\n\tfor n in nodes:\n\t\tif not n.Class()=='dg_PerspLines':\n\t\t\tnuke.message('One of selected nodes is not dg_PerspLines')\n\t\t\treturn\n\ti=1\n\tif nodes[0].input(0)==nodes[1]:\n\t\ti=0\n\t\n\tdg_PerspLines_selectOnly(nodes[i])\n\t\n\tn=nuke.createNode('dg_Horizon')\n\tn['vp1'].setExpression(nodes[0].name()+'.PT')\n\tn['vp2'].setExpression(nodes[1].name()+'.PT')","sub_path":"ToolSets/dg_PerspLines/menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"195551947","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 ('users', '0003_auto_20150216_2350'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Tshirt',\n fields=[\n ('payment_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='users.Payment')),\n ('size', models.CharField(max_length=3, choices=[(b'S', b'Small'), (b'M', b'Medium'), (b'L', b'Large'), (b'XL', b'X-Large'), (b'2XL', b'XX-Large'), (b'3XL', b'XXX-Large'), (b'S', b'Small'), (b'S', b'Small')])),\n ('back_name', models.CharField(max_length=40, blank=True)),\n ],\n options={\n },\n bases=('users.payment',),\n ),\n migrations.AlterField(\n model_name='payment',\n name='amount_payed',\n field=models.FloatField(default=0, validators=[django.core.validators.MinValueValidator(0), django.core.validators.MaxValueValidator(15.0)]),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='payment',\n name='year_payed',\n field=models.CharField(max_length=6, choices=[(b'2014', b'2014'), (b'2015', b'2015'), (b'2016', b'2016'), (b'2017', b'2017'), (b'2018', b'2018'), (b'2019', b'2019'), (b'2020', b'2020'), (b'2021', b'2021'), (b'2022', b'2022'), (b'2023', b'2023'), (b'2024', b'2024')]),\n preserve_default=True,\n ),\n ]\n","sub_path":"apps/users/migrations/0004_auto_20150218_0028.py","file_name":"0004_auto_20150218_0028.py","file_ext":"py","file_size_in_byte":1628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"104644281","text":"import os\nimport logging\nimport torch\nimport torch.nn as nn\nimport baseline as bl\nfrom baseline.utils import (\n export,\n Offsets,\n write_json,\n load_vectorizers,\n find_model_basename,\n)\nfrom baseline.model import load_model_for\nfrom baseline.vectorizers import (\n GOVectorizer,\n Dict1DVectorizer,\n Char2DVectorizer,\n Dict2DVectorizer,\n Char1DVectorizer,\n Token1DVectorizer,\n)\nfrom mead.utils import (\n get_output_paths,\n create_metadata,\n save_to_bundle,\n)\nfrom mead.exporters import Exporter, register_exporter\nfrom mead.pytorch.tagger_decoders import InferenceCRF, InferenceGreedyDecoder\n\n\n__all__ = []\nexporter = export(__all__)\nlogger = logging.getLogger('mead')\n\n\nVECTORIZER_SHAPE_MAP = {\n Token1DVectorizer: [1, 10],\n GOVectorizer: [1, 10],\n Dict1DVectorizer: [1, 10],\n Char2DVectorizer: [1, 10, 5],\n Dict2DVectorizer: [1, 10, 5],\n Char1DVectorizer: [1, 10],\n}\n\n\ndef create_fake_data(shapes, vectorizers, order, min_=0, max_=50,):\n data = {\n k: torch.randint(min_, max_, shapes[type(v)]) for k, v in vectorizers.items()\n }\n ordered_data = tuple(data[k] for k in order)\n lengths = torch.LongTensor([data[list(data.keys())[0]].shape[1]])\n return ordered_data, lengths\n\n\ndef monkey_patch_embeddings(model):\n order = tuple(k for k, _ in model.embeddings.items())\n logger.debug(\"Using %s as the feature order\", order)\n model.ordered_embeddings = tuple(model.embeddings[k] for k in order)\n\n def embed(self, x):\n res = []\n for i in range(len(x)):\n res.append(self.ordered_embeddings[i](x[i]))\n return torch.cat(res, dim=2)\n\n model.embed = embed.__get__(model)\n return order\n\n\nclass ExportingTagger(nn.Module):\n def __init__(self, tagger):\n super(ExportingTagger, self).__init__()\n self.tagger = tagger\n if hasattr(tagger, 'crf'):\n logger.debug(\"Found CRF, replacing with torch script decoder.\")\n self.decoder = InferenceCRF(\n self.tagger.crf.transitions.squeeze(0),\n self.tagger.crf.start_idx,\n self.tagger.crf.end_idx\n )\n else:\n if tagger.constraint is None:\n # This just calls torch.max, this is normally done in code for the tagger but we\n # wrap in a class here so that we can have a consistent forward.\n self.decoder = InferenceGreedyDecoder()\n else:\n logger.debug(\"Found constraints for decoding, replacing with torch script decoder.\")\n self.decoder = InferenceCRF(\n self.tagger.constraint.squeeze(0),\n Offsets.GO,\n Offsets.EOS\n )\n\n def forward(self, x, l):\n trans_x = []\n for i in range(len(x)):\n trans_x.append(x[i].transpose(0, 1))\n new_x = tuple(trans_x)\n x = self.tagger.compute_unaries(new_x, l)\n return self.decoder.decode(x, l)[0]\n\n\nclass ExportingClassifier(nn.Module):\n def __init__(self, classifier):\n super(ExportingClassifier, self).__init__()\n self.classifier = classifier\n\n def forward(self, x, l):\n x = self.classifier.embed(x)\n x = self.classifier.pool(x, l)\n x = self.classifier.stacked(x)\n return self.classifier.output(x)\n\n\n@exporter\nclass PytorchExporter(Exporter):\n def __init__(self, task, **kwargs):\n super(PytorchExporter, self).__init__(task, **kwargs)\n self.wrapper = None\n\n def run(self, basename, output_dir, project=None, name=None, model_version=None, **kwargs):\n logger.warning(\"Pytorch exporting is experimental and is not guaranteed to work for plugin models.\")\n client_output, server_output = get_output_paths(\n output_dir,\n project, name,\n model_version,\n kwargs.get('remote', True),\n )\n logger.info(\"Saving vectorizers and vocabs to %s\", client_output)\n logger.info(\"Saving serialized model to %s\", server_output)\n model, vectorizers, model_name = self.load_model(basename)\n order = monkey_patch_embeddings(model)\n data, lengths = create_fake_data(VECTORIZER_SHAPE_MAP, vectorizers, order)\n meta = create_metadata(\n order, ['output'],\n self.sig_name,\n model_name, model.lengths_key,\n exporter_type=self.preproc_type()\n )\n\n exportable = self.wrapper(model)\n logger.info(\"Tracing Model.\")\n traced = torch.jit.trace(exportable, (data, lengths))\n traced.save(os.path.join(server_output, 'model.pt'))\n\n logger.info(\"Saving metadata.\")\n save_to_bundle(client_output, basename, assets=meta)\n logger.info('Successfully exported model to %s', output_dir)\n\n\n def load_model(self, model_dir):\n model_name = find_model_basename(model_dir)\n vectorizers = load_vectorizers(model_dir)\n model = load_model_for(self.task.task_name(), model_name, device='cpu')\n model = model.cpu()\n model.eval()\n model_name = os.path.basename(model_name)\n return model, vectorizers, model_name\n\n\n@exporter\n@register_exporter(task='classify', name='default')\nclass ClassifyPytorchExporter(PytorchExporter):\n def __init__(self, task, **kwargs):\n super(ClassifyPytorchExporter, self).__init__(task)\n self.wrapper = ExportingClassifier\n self.sig_name = 'predict_text'\n\n\n@exporter\n@register_exporter(task='tagger', name='default')\nclass TaggerPytorchExporter(PytorchExporter):\n def __init__(self, task, **kwargs):\n super(TaggerPytorchExporter, self).__init__(task)\n self.wrapper = ExportingTagger\n self.sig_name = 'tag_text'\n\n\n@exporter\n@register_exporter(task='seq2seq', name='default')\nclass Seq2SeqPytorchExporter(PytorchExporter):\n def __init__(self, task, **kwargs):\n raise NotImplementedError\n","sub_path":"python/mead/pytorch/exporters.py","file_name":"exporters.py","file_ext":"py","file_size_in_byte":5946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"469540096","text":"from tkinter import *\n\n# Helper class to orgainize and handle all UAV orbit parameter controls\nclass OrbitControl:\n\t\n\t# The rows are where to display the controls, calcFlightPath is the Orbit class's function to recalculate its flight path\t\n\t#\tmoveCamera is the Orbit class's function to change the camera orientation\n\tdef __init__(self, topMenuRow, bottomMenuRow, initValues, calcFlightPath, orientCamera):\t\n\t\t\n\t\t# The input text initialization values need to be in this order\n\t\t[majorAxisInit, minorAxisInit, centerXInit, centerYInit, axisYawAngleInit, heightInit, cameraPanInit, cameraTiltInit, cameraUpAngleInit] = initValues\n\t\t\n\t\t# Instantiate and initialize the orbit parameters\n\t\tcenterX = StringVar()\n\t\tcenterX.set(centerXInit)\n\t\tcenterY = StringVar()\n\t\tcenterY.set(centerYInit)\n\t\tmajorAxis = StringVar()\n\t\tmajorAxis.set(majorAxisInit)\n\t\tminorAxis = StringVar()\n\t\tminorAxis.set(minorAxisInit)\n\t\taxisYawAngle = StringVar()\n\t\taxisYawAngle.set(axisYawAngleInit)\n\t\theight = StringVar()\n\t\theight.set(heightInit)\n\t\tcameraUpAngle = StringVar()\n\t\tcameraUpAngle.set(cameraUpAngleInit)\n\t\tcameraPan = StringVar()\n\t\tcameraPan.set(cameraPanInit)\n\t\tcameraTilt = StringVar()\n\t\tcameraTilt.set(cameraTiltInit)\n\t\t\n\t\t# major axis length \n\t\tLabel(topMenuRow, text = \"Orbit1 Major Axis Length\").pack(side=LEFT)\n\t\torbit1MajorSpinbox = Spinbox(topMenuRow, from_=20, to=500, increment=10, textvariable=majorAxis, command=calcFlightPath)\n\t\torbit1MajorSpinbox.pack(side=LEFT)\n\n\t\t# minor axis length \n\t\tLabel(topMenuRow, text = \"Orbit1 Minor Axis Length\").pack(side=LEFT)\n\t\torbit1MinorSpinbox = Spinbox(topMenuRow, from_=20, to=500, increment=10, textvariable=minorAxis, command=calcFlightPath)\n\t\torbit1MinorSpinbox.pack(side=LEFT)\n\n\t\t# center X\n\t\tLabel(topMenuRow, text = \"Orbit1 Center X\").pack(side=LEFT)\n\t\torbit1CenterXSpinbox = Spinbox(topMenuRow, from_=-200, to=200, increment=10, textvariable=centerX, command=calcFlightPath)\n\t\torbit1CenterXSpinbox.pack(side=LEFT)\n\n\t\t# center Y\n\t\tLabel(topMenuRow, text = \"Orbit1 Center Y\").pack(side=LEFT)\n\t\torbit1CenterYSpinbox = Spinbox(topMenuRow, from_=-200, to=200, increment=10, textvariable=centerY, command=calcFlightPath)\n\t\torbit1CenterYSpinbox.pack(side=LEFT)\n\n\t\t# orbit yaw angle\n\t\tLabel(topMenuRow, text = \"Orbit1 Yaw Angle\").pack(side=LEFT)\n\t\torbit1YawAngleSpinbox = Spinbox(topMenuRow, from_=0, to=180, increment=10, textvariable=axisYawAngle, command=calcFlightPath)\n\t\torbit1YawAngleSpinbox.pack(side=LEFT)\n\n\t\t# Orbit height\n\t\tLabel(topMenuRow, text = \"Orbit1 Height\").pack(side=LEFT)\n\t\torbit1HeightSpinbox = Spinbox(topMenuRow, from_=40, to=500, increment=20, textvariable=height, command=calcFlightPath)\n\t\torbit1HeightSpinbox.pack(side=LEFT)\n\t\t\t\t\n\t\t# Camera Pan\n\t\tLabel(bottomMenuRow, text = \"Orbit1 Camera Pan\").pack(side=LEFT)\n\t\tcamera1PanSpinbox = Spinbox(bottomMenuRow, from_=-90, to=90, increment=5, textvariable=cameraPan, command=orientCamera)\n\t\tcamera1PanSpinbox.pack(side=LEFT)\n\n\t\t# Camera Tilt\n\t\tLabel(bottomMenuRow, text = \"Orbit1 Camera Tilt\").pack(side=LEFT)\n\t\tcamera1TiltSpinbox = Spinbox(bottomMenuRow, from_=-180, to=180, increment=5, textvariable=cameraTilt, command=orientCamera)\n\t\tcamera1TiltSpinbox.pack(side=LEFT)\n\n\t\t# Camera Up Angle\n\t\tLabel(bottomMenuRow, text = \"Orbit1 Camera Up Angle\").pack(side=LEFT)\n\t\torbit1CameraUpAngleSpinbox = Spinbox(bottomMenuRow, from_=-90, to=90, increment=5, textvariable=cameraUpAngle, command=orientCamera)\n\t\torbit1CameraUpAngleSpinbox.pack(side=LEFT)\n\t\t\n\t\t# Need to share these with the associated orbit\n\t\tself.orbitVars = [majorAxis, minorAxis, centerX, centerY, axisYawAngle, height, cameraPan, cameraTilt, cameraUpAngle]\n\t\t\n\t\t# Store these for enable/disable\n\t\tself.orbitControls = [orbit1MajorSpinbox, orbit1MinorSpinbox, orbit1CenterXSpinbox, orbit1CenterYSpinbox, orbit1YawAngleSpinbox, \n\t\t\t\t\t\t\t orbit1HeightSpinbox, camera1PanSpinbox, camera1TiltSpinbox, camera1TiltSpinbox, orbit1CameraUpAngleSpinbox]\n\t\t\t\t\n\t# pass back the orbit parameters (as Tkinter StringVar's)\n\tdef returnControlValues(self):\n\t\treturn self.orbitVars\n\n\t# Shut down the parameter change widgets\n\tdef disable(self):\n\t\tfor control in self.orbitControls:\n\t\t\tcontrol.config(state=\"disabled\")\n\t\t\t\n\t# Activate the parameter change widgets\n\tdef enable(self):\n\t\tfor control in self.orbitControls:\n\t\t\tcontrol.config(state=\"normal\")\n","sub_path":"OrbitControl.py","file_name":"OrbitControl.py","file_ext":"py","file_size_in_byte":4299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"448454252","text":"# Copyright (c) 2021 Graphcore Ltd. All rights reserved.\n# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport glob\nimport os\nimport argparse\nfrom transformers import BertTokenizer, BertTokenizerFast, GPT2TokenizerFast\nimport pickle\nimport numpy as np\nfrom tqdm import tqdm\nimport random\n\n\nclass WikicorpusTextFormatting:\n def __init__(self, wiki_path, output_filename, recursive=False):\n self.wiki_path = wiki_path\n self.recursive = recursive\n self.output_filename = output_filename\n\n # This puts one article per line\n def merge(self):\n with open(self.output_filename, mode=\"w\", newline=\"\\n\") as ofile:\n for dirname in glob.glob(self.wiki_path + \"/*/\", recursive=False):\n for filename in glob.glob(dirname + \"wiki_*\", recursive=self.recursive):\n print(filename)\n article_lines = []\n article_open = False\n\n with open(filename, mode=\"r\", newline=\"\\n\") as file:\n for line in file:\n if \"\" in line:\n article_open = False\n for oline in article_lines[1:]:\n if oline != \"\\n\":\n ofile.write(oline.rstrip() + \" \")\n ofile.write(\"\\n\\n\")\n article_lines = []\n else:\n if article_open:\n article_lines.append(line)\n\n\ndef main(args):\n # Step 1: merge the data into one txt file\n wiki_path = args.input_file_path\n output_filename = args.output_file_path + \"/wikicorpus_en_one_article_per_line.txt\"\n # wiki_formatter = WikicorpusTextFormatting(wiki_path, output_filename, recursive=True)\n # wiki_formatter.merge()\n\n # Step 2: tokenize the articles\n output_path = args.output_file_path + \"/wikicorpus_en_one_article_per_line.pkl\"\n print(\"preprocessing data,data path:{}, save path:{}\".format(output_filename, output_path))\n\n if args.use_bpe:\n print(\"Generate and use BPE tokenizer...\")\n from tokenizers import Tokenizer, models, pre_tokenizers, decoders, trainers, processors\n\n # Initialize a tokenizer\n tokenizer = Tokenizer(models.BPE())\n\n # Customize pre-tokenization and decoding\n tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=True)\n tokenizer.decoder = decoders.ByteLevel()\n tokenizer.post_processor = processors.ByteLevel(trim_offsets=True)\n\n # And then train\n trainer = trainers.BpeTrainer(\n vocab_size=30522, min_frequency=2, initial_alphabet=pre_tokenizers.ByteLevel.alphabet()\n )\n tokenizer.train([output_filename], trainer=trainer)\n\n # And Save it\n tokenizer.save(\"gpt2-bpe-tokenizer.json\", pretty=True)\n\n # Use the generated file\n tokenizer = Tokenizer.from_file(\"gpt2-bpe-tokenizer.json\")\n else:\n tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\", add_prefix_space=False)\n\n data = open(output_filename, \"rb\")\n train_data = data.readlines()\n\n text_len = []\n text_list = []\n with open(output_path, \"w\", encoding=\"utf-8\") as f:\n for index, text in enumerate(tqdm(train_data)):\n utterances = text.decode(\"utf-8\").split(\"\\n\")\n\n input_ids = [] # begin with [CLS]\n for utterance in utterances:\n if args.use_bpe:\n input_ids += tokenizer.encode(utterance).ids\n else:\n input_ids += tokenizer.encode(utterance, add_special_tokens=False)\n # end with eod\n input_ids += tokenizer.encode(\"<|endoftext|>\")\n if len(input_ids) >= args.min_length:\n text_len.append(len(input_ids))\n text_list.append(input_ids)\n # text_list += input_ids\n random.shuffle(text_list)\n len_mean = np.mean(text_len)\n len_median = np.median(text_len)\n len_max = np.max(text_len)\n with open(output_path, \"wb\") as f:\n pickle.dump(text_list, f)\n print(\"finish preprocessing data,the result is stored in {}\".format(output_path))\n print(\"mean of text len:{},median of text len:{},max len:{}\".format(len_mean, len_median, len_max))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--input-file-path\", required=True, type=str)\n parser.add_argument(\"--output-file-path\", required=True, type=str)\n parser.add_argument(\"--use-bpe\", action=\"store_true\", help=\"use bpe or GPT2 tokenizer\")\n parser.add_argument(\"--min-length\", default=10, type=int, required=False, help=\"minimal length of dataset\")\n args = parser.parse_args()\n main(args)\n","sub_path":"nlp/gpt2/pytorch/data/wikipedia_preprocess.py","file_name":"wikipedia_preprocess.py","file_ext":"py","file_size_in_byte":5519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"439213350","text":"from __future__ import print_function\n\nfrom builtins import object, str\nfrom typing import Dict\n\nfrom empire.server.core.module_models import EmpireModule\nfrom empire.server.utils.module_util import handle_error_message\n\n\nclass Module(object):\n @staticmethod\n def generate(\n main_menu,\n module: EmpireModule,\n params: Dict,\n obfuscate: bool = False,\n obfuscation_command: str = \"\",\n ):\n # read in the common module source code\n script, err = main_menu.modulesv2.get_module_source(\n module_name=module.script_path,\n obfuscate=obfuscate,\n obfuscate_command=obfuscation_command,\n )\n\n if err:\n return handle_error_message(err)\n\n script_end = \"Invoke-DCSync -PWDumpFormat \"\n\n if params[\"Domain\"] != \"\":\n script_end += \" -Domain \" + params[\"Domain\"]\n\n if params[\"Forest\"] != \"\":\n script_end += \" -DumpForest \"\n\n if params[\"Computers\"] != \"\":\n script_end += \" -GetComputers \"\n\n if params[\"Active\"] == \"\":\n script_end += \" -OnlyActive:$false \"\n\n outputf = params.get(\"OutputFunction\", \"Out-String\")\n script_end += f\" | {outputf};\"\n\n script = main_menu.modulesv2.finalize_module(\n script=script,\n script_end=script_end,\n obfuscate=obfuscate,\n obfuscation_command=obfuscation_command,\n )\n return script\n","sub_path":"empire/server/modules/powershell/credentials/mimikatz/dcsync_hashdump.py","file_name":"dcsync_hashdump.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"626674033","text":"import numpy\nimport os\nimport tempfile\nimport wave\n\nfrom amen.exceptions import AmenError\n\nfrom ffmpeg import ffmpeg\n\nclass AudioRenderable(object):\n \"\"\"\n An object that gives an `AudioData` in response to a call to its `render`\\()\n method.\n Intended to be an abstract class that helps enforce the `AudioRenderable`\n protocol. Picked up a couple of convenience methods common to many descendants.\n\n Every `AudioRenderable` must provide three things:\n\n render()\n A method returning the `AudioData` for the object. The rhythmic duration (point\n at which any following audio is appended) is signified by the `endindex` accessor,\n measured in samples.\n source\n An accessor pointing to the `AudioData` that contains the original sample data of\n (a superset of) this audio object.\n duration\n An accessor returning the rhythmic duration (in seconds) of the audio object.\n \"\"\"\n def resolve_source(self, alt):\n \"\"\"\n Given an alternative, fallback `alt` source, return either `self`'s\n source or the alternative. Throw an informative error if no source\n is found.\n\n Utility code that ended up being replicated in several places, so\n it ended up here. Not necessary for use in the RenderableAudioObject\n protocol.\n \"\"\"\n if hasattr(self, 'source'):\n source = self.source\n else:\n if isinstance(alt, AudioData):\n source = alt\n else:\n raise AmenError(\"%s has no implicit or explicit source \\\n during rendering.\" %\n (self.__class__.__name__, ))\n return source\n\n @staticmethod\n def init_audio_data(source, num_samples):\n \"\"\"\n Convenience function for rendering: return a pre-allocated, zeroed\n `AudioData`.\n \"\"\"\n if source.numChannels > 1:\n newchans = source.numChannels\n newshape = (num_samples, newchans)\n else:\n newchans = 1\n newshape = (num_samples,)\n return AudioData32(shape=newshape, sampleRate=source.sampleRate,\n numChannels=newchans, defer=False)\n\n def sources(self):\n return set([self.source])\n\n def encode(self, filename):\n \"\"\"\n Shortcut function that takes care of the need to obtain an `AudioData`\n object first, through `render`.\n \"\"\"\n self.render().encode(filename)\n\nclass AudioData(AudioRenderable):\n \"\"\"\n Handles audio data transparently. A smart audio container\n with accessors that include:\n\n sampleRate\n samples per second\n numChannels\n number of channels\n data\n a `numpy.array`_\n\n .. _numpy.array: http://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html\n \"\"\"\n def __init__(self, filename=None, ndarray=None, shape=None, sampleRate=None, numChannels=None, defer=False, verbose=True):\n \"\"\"\n Given an input `ndarray`, import the sample values and shape\n (if none is specified) of the input `numpy.array`.\n\n Given a `filename` (and an input ndarray), use ffmpeg to convert\n the file to wave, then load the file into the data,\n auto-detecting the sample rate, and number of channels.\n\n :param filename: a path to an audio file for loading its sample\n data into the AudioData.data\n :param ndarray: a `numpy.array`_ instance with sample data\n :param shape: a tuple of array dimensions\n :param sampleRate: sample rate, in Hz\n :param numChannels: number of channels\n\n .. _numpy.array: http://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html\n \"\"\"\n self.verbose = verbose\n self.defer = defer\n self.filename = filename\n self.sampleRate = sampleRate\n self.numChannels = numChannels\n self.convertedfile = None\n self.endindex = 0\n if shape is None and isinstance(ndarray, numpy.ndarray) and not self.defer:\n self.data = numpy.zeros(ndarray.shape, dtype=numpy.int16)\n elif shape is not None and not self.defer:\n self.data = numpy.zeros(shape, dtype=numpy.int16)\n elif not self.defer and self.filename:\n self.data = None\n self.load()\n else:\n self.data = None\n if ndarray is not None and self.data is not None:\n self.endindex = len(ndarray)\n self.data[0:self.endindex] = ndarray\n\n def load(self):\n if isinstance(self.data, numpy.ndarray):\n return\n temp_file_handle = None\n if self.filename.lower().endswith(\".wav\") and (self.sampleRate, self.numChannels) == (44100, 2):\n file_to_read = self.filename\n elif self.convertedfile:\n file_to_read = self.convertedfile\n else:\n temp_file_handle, self.convertedfile = tempfile.mkstemp(\".wav\")\n self.sampleRate, self.numChannels = ffmpeg(self.filename, self.convertedfile, overwrite=True,\n numChannels=self.numChannels, sampleRate=self.sampleRate, verbose=self.verbose)\n file_to_read = self.convertedfile\n\n w = wave.open(file_to_read, 'r')\n numFrames = w.getnframes()\n raw = w.readframes(numFrames)\n sampleSize = numFrames * self.numChannels\n data = numpy.frombuffer(raw, dtype=\" 1:\n ndarray.resize((numFrames, self.numChannels))\n self.data = numpy.zeros(ndarray.shape, dtype=numpy.int16)\n self.endindex = 0\n if ndarray is not None:\n self.endindex = len(ndarray)\n self.data = ndarray\n if temp_file_handle is not None:\n os.close(temp_file_handle)\n w.close()\n\n def __getitem__(self, index):\n \"\"\"\n Fetches a frame or slice. Returns an individual frame (if the index\n is a time offset float or an integer sample number) or a slice if\n the index is an `AudioQuantum` (or quacks like one).\n \"\"\"\n if not isinstance(self.data, numpy.ndarray) and self.defer:\n self.load()\n if isinstance(index, float):\n index = int(index * self.sampleRate)\n elif hasattr(index, \"start\") and hasattr(index, \"duration\"):\n index = slice(float(index.start), index.start + index.duration)\n\n if isinstance(index, slice):\n if (hasattr(index.start, \"start\") and\n hasattr(index.stop, \"duration\") and\n hasattr(index.stop, \"start\")):\n index = slice(index.start.start, index.stop.start + index.stop.duration)\n\n if isinstance(index, slice):\n return self.getslice(index)\n else:\n return self.getsample(index)\n\n def getslice(self, index):\n \"Help `__getitem__` return a new AudioData for a given slice\"\n if not isinstance(self.data, numpy.ndarray) and self.defer:\n self.load()\n if isinstance(index.start, float):\n index = slice(int(index.start * self.sampleRate),\n int(index.stop * self.sampleRate), index.step)\n return AudioData(None, self.data[index], sampleRate=self.sampleRate,\n numChannels=self.numChannels, defer=False)\n\n def getsample(self, index):\n \"\"\"\n Help `__getitem__` return a frame (all channels for a given\n sample index)\n \"\"\"\n if not isinstance(self.data, numpy.ndarray) and self.defer:\n self.load()\n if isinstance(index, int):\n return self.data[index]\n else:\n #let the numpy array interface be clever\n return AudioData(None, self.data[index], defer=False)\n\n def pad_with_zeros(self, num_samples):\n if num_samples > 0:\n if self.numChannels == 1:\n extra_shape = (num_samples,)\n else:\n extra_shape = (num_samples, self.numChannels)\n self.data = numpy.append(self.data,\n numpy.zeros(extra_shape, dtype=numpy.int16), axis=0)\n\n def append(self, another_audio_data):\n \"Appends the input to the end of this `AudioData`.\"\n extra = len(another_audio_data.data) - (len(self.data) - self.endindex)\n self.pad_with_zeros(extra)\n self.data[self.endindex : self.endindex + len(another_audio_data)] += another_audio_data.data\n self.endindex += another_audio_data.endindex\n\n def sum(self, another_audio_data):\n extra = len(another_audio_data.data) - len(self.data)\n self.pad_with_zeros(extra)\n compare_limit = min(len(another_audio_data.data), len(self.data)) - 1\n self.data[: compare_limit] += another_audio_data.data[: compare_limit]\n\n def add_at(self, time, another_audio_data):\n \"\"\"\n Adds the input `another_audio_data` to this `AudioData` \n at the `time` specified in seconds. If `another_audio_data` has fewer channels than\n this `AudioData`, the `another_audio_data` will be resampled to match.\n In this case, this method will modify `another_audio_data`.\n\n \"\"\"\n offset = int(time * self.sampleRate)\n extra = offset + len(another_audio_data.data) - len(self.data)\n self.pad_with_zeros(extra)\n if another_audio_data.numChannels < self.numChannels:\n # Resample another_audio_data\n another_audio_data.data = numpy.repeat(another_audio_data.data, self.numChannels).reshape(len(another_audio_data), self.numChannels)\n another_audio_data.numChannels = self.numChannels\n self.data[offset : offset + len(another_audio_data.data)] += another_audio_data.data \n\n def __len__(self):\n if self.data is not None:\n return len(self.data)\n else:\n return 0\n\n def __add__(self, other):\n \"\"\"Supports stuff like this: sound3 = sound1 + sound2\"\"\"\n return assemble([self, other], numChannels=self.numChannels,\n sampleRate=self.sampleRate)\n\n def encode(self, filename=None, mp3=None):\n \"\"\"\n Outputs an MP3 or WAVE file to `filename`.\n Format is determined by `mp3` parameter.\n \"\"\"\n if not mp3 and filename.lower().endswith('.wav'):\n mp3 = False\n else:\n mp3 = True\n if mp3:\n foo, tempfilename = tempfile.mkstemp(\".wav\")\n os.close(foo)\n else:\n tempfilename = filename\n fid = open(tempfilename, 'wb')\n # Based on Scipy svn\n # http://projects.scipy.org/pipermail/scipy-svn/2007-August/001189.html\n fid.write('RIFF')\n fid.write(struct.pack('\" % self.id\n\n\n@app.route('/')\ndef index():\n return render_template(\"index.html\")\n\n\n@app.route('/news', methods=['GET'])\ndef news():\n articles = Article.query.order_by(Article.id.desc()).all()\n\n return render_template(\"news.html\", articles=articles)\n\n\n@app.route('/create', methods=['GET', 'POST'])\ndef create():\n if request.method == 'POST':\n title = request.form['title']\n text = request.form['text']\n\n article = Article(title=title, text=text)\n\n try:\n db.session.add(article)\n db.session.commit()\n return redirect('/success')\n\n except:\n return \"Error\"\n\n else:\n return render_template(\"create.html\")\n\n\n@app.route('/success')\ndef success_add():\n return render_template(\"success.html\")\n\n\n@app.route('/article', methods=['POST', 'GET'])\ndef article(id):\n article = Article.query.get(id)\n\n return render_template(\"article.html\", article=article)\n\n\n@app.route('/article/del')\ndef del_article(id):\n article = Article.query.get(id)\n db.session.delete(article)\n db.session.commit()\n return redirect('/news')\n\n\n@app.route('/article/edit', methods=['GET', 'POST'])\ndef edit_article(id):\n article = Article.query.get(id)\n\n if request.method == 'POST':\n article.title = request.form['title']\n article.text = request.form['text']\n\n try:\n db.session.commit()\n return redirect('/news')\n\n except:\n return \"При редактировании статьи произошла ошибка\"\n\n else:\n return render_template(\"edit.html\", article=article)\n\n\nif __name__ == '__main__':\n app.run()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"583516162","text":"import global_variables\nimport threading\nimport paramiko\nimport interactive\n\n\nclass Ssh:\n shell = None\n client = None\n transport = None\n\n def __init__(self, address, username):\n print(\"Connecting to server\", str(address) + \".\")\n self.client = paramiko.client.SSHClient()\n self.client.set_missing_host_key_policy(paramiko.client.AutoAddPolicy())\n self.client.connect(address, username=username)\n self.transport = paramiko.Transport((address, 22))\n self.transport.connect(username=username)\n\n thread = threading.Thread(target=self.process)\n thread.daemon = True\n thread.start()\n\n def close_connection(self):\n if self.client is not None:\n self.client.close()\n self.transport.close()\n\n def open_shell(self):\n self.shell = self.client.invoke_shell()\n\n def send_shell(self, command):\n if self.shell:\n self.shell.send(command + \"\\n\")\n else:\n print(\"Shell not opened.\")\n\n def process(self):\n global connection\n while True:\n # Print data when available\n if self.shell is not None and self.shell.recv_ready():\n alldata = self.shell.recv(1024)\n while self.shell.recv_ready():\n alldata += self.shell.recv(1024)\n strdata = str(alldata, \"utf8\")\n strdata.replace('\\r', '')\n print(strdata, end=\"\")\n if strdata.endswith(\"$ \"):\n print(\"\\n$ \", end=\"\")\n\n\ndef new_connection(server):\n connect = Ssh(server, global_variables.user)\n connect.open_shell()\n interactive.start_interactive()\n","sub_path":"ssh_connection.py","file_name":"ssh_connection.py","file_ext":"py","file_size_in_byte":1696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"446765221","text":"import tensorflow as tf\n\ndef triplet_loss(y_true, y_pred, alpha=0.25):\n\n anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]\n\n \"\"\"\n Formula : sum((anchor-positive)^2 + (anchor - negative)^2 + alpha) from i=1 to N\n \"\"\"\n\n positive_distance = tf.reduce_sum(tf.square(tf.subtract(anchor,positive)),axis=1)\n\n negative_distance = tf.reduce_sum(tf.square(tf.subtract(anchor,negative)), axis=1)\n\n loss = tf.reduce_sum(tf.maximum(tf.add(tf.subtract(positive_distance, negative_distance), alpha),0.0))\n\n return loss\n\n \n","sub_path":"facenet/triplet_loss.py","file_name":"triplet_loss.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"346726421","text":"def merge(left, right):\n result = []\n while len(left) > 0 and len(right) > 0:\n if left[0] <= right[0]:\n result.append(left.pop(0))\n else:\n result.append(right.pop(0))\n while len(left) > 0:\n result.append(left.pop(0))\n\n while len(right) > 0:\n result.append(right.pop(0))\n\n return result\n\n\ndef mergesort(list):\n if len(list) == 1:\n return list\n\n left = list[0: len(list) // 2]\n right = list[len(list) // 2:]\n\n left = mergesort(left)\n right = mergesort(right)\n\n return merge(left, right)\n\nwith open('/Users/matteocanegallo/PycharmProjects/Rosalind/rosalind_ms.txt','r') as file:\n content = file.read().splitlines()\nlst = content[1].split(' ')\nfor i in range(len(lst)):\n lst[i] = int(lst[i])\nsorted = mergesort(lst)\nfor i in range(len(sorted)):\n sorted[i] = str(sorted[i])\nprint(' '.join(sorted))","sub_path":"19-MS.py","file_name":"19-MS.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"499036070","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom django.db import models\nimport time\nfrom django.contrib.auth.models import User\n\n\nclass Appointment(models.Model):\n # посади\n appoint_name = models.CharField(max_length=100, verbose_name=u'посада')\n\n def __unicode__(self):\n return self.appoint_name\n\n class Meta:\n db_table = 'ch_appointment'\n verbose_name = u'посада'\n verbose_name_plural = u'посади'\n\n\nclass AcademicDegree(models.Model):\n degree_name = models.CharField(max_length=100, verbose_name=u'вчений ступінь')\n\n def __unicode__(self):\n return self.degree_name\n\n class Meta:\n db_table = 'ch_academic_degree'\n verbose_name = u'науковий ступінь'\n verbose_name_plural = u'наукові ступені'\n\n\nclass Teacher(models.Model):\n # user = models.ForeignKey(User, verbose_name='співробітник')\n last_name = models.CharField(max_length=50)\n first_name = models.CharField(max_length=50)\n patronymic = models.CharField(max_length=50)\n appointment = models.ForeignKey(Appointment, verbose_name=u'посада')\n academic_degree = models.ForeignKey(AcademicDegree, verbose_name=u'науковий ступінь')\n order = models.IntegerField()\n about_teacher = models.TextField(blank=True, default='')\n photo = models.ImageField(upload_to=u'teacher_img', null=True)\n\n def __unicode__(self):\n return u'%s %s' % (self.last_name, self.first_name)\n\n class Meta:\n db_table = u'ch_teacher'\n verbose_name = u'викладач'\n verbose_name_plural = u'викладачі'\n\n\nclass Education(models.Model):\n education_description = models.TextField(verbose_name=u'освіта')\n education_order = models.IntegerField()\n teacher = models.ForeignKey(Teacher, default=1)\n\n def __unicode__(self):\n return self.education_description\n\n class Meta:\n db_table = u'ch_education'\n verbose_name = u'отримана освіта'\n ordering = [u'education_order']\n\nclass Achievement(models.Model):\n achievement_description = models.TextField(verbose_name=u'досягнення')\n achievement_order = models.IntegerField()\n teacher = models.ForeignKey(Teacher, default=1)\n\n def __unicode__(self):\n return self.achievement_description\n\n class Meta:\n db_table = 'ch_achievement'\n verbose_name = u'досягнення'\n ordering = [u'achievement_order']\n\n\nclass Syllabus(models.Model):\n # назва курсів, які читаються викладачами\n syl_title = models.CharField(max_length=200, verbose_name='назва')\n syl_description = models.TextField(blank=True, default='')\n teacher = models.ManyToManyField(Teacher)\n#TODO: add Evaluation & Grading Policies, GRADING PLAN\n def __unicode__(self):\n return self.syl_title\n\n class Meta:\n db_table = u'ch_syllabus'\n ordering = [u'syl_title']\n\n\nclass Module(models.Model):\n module_title = models.CharField(max_length=300)\n syllabus = models.ForeignKey(Syllabus)\n semester = models.IntegerField()\n module_num = models.IntegerField()\n\n def __unicode__(self):\n return self.module_title\n\n class Meta:\n db_table = 'ch_module'\n ordering = ['semester', 'module_num']\n\n\nclass PlanLectures(models.Model):\n # план лекцій COURSE TOPICS\n lecture_title = models.CharField(max_length=300, verbose_name=u'назва лекції')\n order = models.IntegerField()\n module = models.ForeignKey(Module)\n\n def __unicode__(self):\n return self.lecture_title\n\n class Meta:\n db_table = 'ch_plan_lectures'\n ordering = ['order']\n\n\nclass Publication(models.Model):\n # публікації викладачів\n publ_title = models.CharField(max_length=200, verbose_name='назва')\n publ_authors = models.CharField(max_length=200, blank=True, default='')\n publ_annotation = models.TextField(blank=True, default='')\n publ_source = models.TextField(max_length=300, blank=True, default='')\n teacher = models.ManyToManyField(Teacher)\n\n def __unicode__(self):\n return self.publ_title\n\n class Meta:\n db_table = 'ch_publication'\n ordering = ['publ_title']\n\n\nclass Project(models.Model):\n # проекти, бюджетні теми, які виконували викладачі\n proj_title = models.CharField(max_length=500, verbose_name=u'назва')\n start_proj_year = models.IntegerField(verbose_name=u'початок', blank=True, default=0)\n end_proj_year = models.IntegerField(verbose_name=u'завершення', blank=True, default=0)\n teacher = models.ManyToManyField(Teacher)\n\n def __unicode__(self):\n return self.proj_title\n\n class Meta:\n db_table = 'ch_project'\n verbose_name = u'проект'\n verbose_name_plural = 'проекти'\n ordering = ['start_proj_year', 'proj_title']\n\n\nclass Scinterest(models.Model):\n # сфера наукових інтересів\n sci_name = models.CharField(max_length=200, verbose_name=u'назва')\n teacher = models.ManyToManyField(Teacher)\n\n def __unicode__(self):\n return self.sci_name\n\n class Meta:\n db_table = 'ch_scinterest'\n verbose_name = u'сфера наукових інтересів'\n verbose_name_plural = u'сфери наукових інтересів'\n ordering = ['sci_name']\n\n\nclass News(models.Model):\n news_title = models.CharField(max_length=250)\n news_date = models.DateField(null=True)\n news_content = models.CharField(max_length=1024)\n news_img = models.ImageField(upload_to='news_img', null=True)\n\n def __unicode__(self):\n return self.news_title\n\n class Meta:\n db_table = 'ch_news'\n verbose_name = u'новини'\n ordering = ['-news_date', 'news_title']\n\n\n#########################################################################\n# lecture schedule\n########################################################################\n\n\nclass Building(models.Model):\n building_name = models.CharField(max_length=100)\n building_abbrev = models.CharField(max_length=10)\n\n def __unicode__(self):\n return self.building_name\n\n class Meta:\n db_table = 'sch_building'\n ordering = ['building_name']\n\n\nclass LessonHours(models.Model):\n lesson_num = models.IntegerField()\n hours_from = models.TimeField()\n hours_to = models.TimeField()\n\n def __unicode__(self):\n return u'пара %i' % self.lesson_num\n\n class Meta:\n db_table = 'sch_lesson_hours'\n ordering = ['lesson_num']\n\n\nclass LessonType(models.Model):\n # лекция лабораторная, семинар,...\n lt_name = models.CharField(max_length=100)\n lt_abbrev = models.CharField(max_length=10)\n\n def __unicode__(self):\n return self.lt_name\n\n class Meta:\n db_table = 'sch_lesson_type'\n ordering = ['lt_name']\n\n\nclass Subject(models.Model):\n #перелік предметів\n subject_name = models.CharField(max_length=100)\n subject_abbrev = models.CharField(max_length=10)\n\n def __unicode__(self):\n return self.subject_name\n\n class Meta:\n db_table = 'sch_subject'\n ordering = ['subject_name']\n\n\nclass Lecturer(models.Model):\n # викладачі\n last_name = models.CharField(max_length=50, blank=True)\n first_name = models.CharField(max_length=50, blank=True)\n patronymic = models.CharField(max_length=50, blank=True)\n appointment = models.ForeignKey(Appointment, verbose_name=u'посада', null=True)\n academic_degree = models.ForeignKey(AcademicDegree, verbose_name=u'науковий ступінь', null=True)\n isvisible = models.BooleanField(default=True)\n\n def __unicode__(self):\n fio = self.last_name\n if len(self.first_name) > 0:\n fio += ' %s.' % self.first_name[0].upper()\n if len(self.patronymic) > 0:\n fio += '%s.' % self.patronymic[0].upper()\n return fio\n\n class Meta:\n db_table = 'sch_lecturer'\n ordering = ['last_name']\n\n\nclass StudentGroup(models.Model):\n group_name = models.CharField(max_length=100)\n group_abbrev = models.CharField(max_length=10)\n\n def __unicode__(self):\n return self.group_abbrev\n\n class Meta:\n db_table = 'sch_student_group'\n ordering = ['group_name']\n\n\nclass DayOfWeek(models.Model):\n dow_name = models.CharField(max_length=12)\n dow_abbrev = models.CharField(max_length=3)\n\n def __unicode__(self):\n return self.dow_name\n\n class Meta:\n db_table = 'sch_day_of_week'\n ordering = ['pk']\n\n\nclass LessonWeek(models.Model):\n week_name = models.CharField(max_length=25)\n\n def __unicode__(self):\n return self.week_name\n\n class Meta:\n db_table = 'sch_lesson_week'\n ordering = ['week_name']\n\n\nclass LessonSchedule(models.Model):\n subject = models.ForeignKey(Subject)\n lesson_type = models.ForeignKey(LessonType)\n course_num = models.IntegerField()\n student_group = models.ManyToManyField(StudentGroup)\n lecturer = models.ForeignKey(Lecturer)\n building = models.ForeignKey(Building)\n lecture_room = models.IntegerField()\n lesson_hours = models.ForeignKey(LessonHours)\n lesson_week = models.ForeignKey(LessonWeek) # 1 - кожний тиждень, 2- парний тиждень, 3 - непарний тиждень\n day_of_week = models.ForeignKey(DayOfWeek) # 1 - Mon, 2 - gTue...\n\n def __unicode__(self):\n groups_lst = [r.group_abbrev for r in self.student_group.all()]\n\n return u'%s: %s, %s, пара %i, к.%i, %s, %s, %s, %s, курс %i' % \\\n (', '.join(groups_lst),\n self.day_of_week.dow_abbrev,\n self.lesson_week.week_name,\n self.lesson_hours.lesson_num,\n self.lecture_room,\n self.building.building_abbrev,\n self.subject.subject_abbrev,\n self.lesson_type.lt_abbrev,\n self.lecturer,\n self.course_num\n )\n\n\n class Meta:\n db_table = 'sch_lesson_schedule'\n ordering = ['pk']\n\n\nclass CanceledLesson(models.Model):\n cancel_date = models.DateField()\n lesson_schedule = models.ManyToManyField(LessonSchedule)\n\n def __unicode__(self):\n return self.cancel_date.strftime('%d/%m/%y')\n\n class Meta:\n db_table = 'sch_canceled_lesson'\n ordering = ['cancel_date']\n\n\nclass MovedLesson(models.Model):\n from_date = models.DateField()\n to_date = models.DateField()\n # lesson_schedule = models.ManyToManyField(LessonSchedule)\n\n def __unicode__(self):\n return u'з %s %s на %s %s' % (self.from_date.strftime('%a'), self.from_date.strftime('%m/%d/%Y'),\n self.to_date.strftime('%a'), self.to_date.strftime('%m/%d/%Y'))\n\n class Meta:\n db_table = 'sch_moved_lesson'\n ordering = ['from_date']\n\n\nclass OneTimeLessonSchedule(models.Model):\n subject = models.ForeignKey(Subject)\n lesson_type = models.ForeignKey(LessonType)\n course_num = models.IntegerField()\n student_group = models.ManyToManyField(StudentGroup)\n lecturer = models.ForeignKey(Lecturer)\n building = models.ForeignKey(Building)\n lecture_room = models.IntegerField()\n lesson_hours = models.ForeignKey(LessonHours)\n lesson_date = models.DateField()\n\n def __unicode__(self):\n return '%s %s' % (self.subject.subject_abbrev, self.lesson_date.strftime('%d/%m/%Y'))\n\n class Meta:\n db_table = 'sch_onetime_lesson_schedule'\n\n\nclass SemesterInfo(models.Model):\n semester_num = models.IntegerField()\n semester_start = models.DateField()\n semester_end = models.DateField()\n first_week = models.DateField() # monday of the first week\n\n def __unicode__(self):\n return u'Семестр %i, початок занять: %s, закінчення: %s, понеділок першого тижня %s' % \\\n (self.semester_num, self.semester_start.strftime('%d/%m/%Y'), self.semester_end.strftime('%d/%m/%Y'),\n self.first_week.strftime('%d/%m/%Y'))","sub_path":"main/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":12562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"434159428","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n\"\"\"\n@fileName : utilities.py\n@desc : \n@dateTime : 2021/01/23 11:09:02\n@author : 5km\n@contact : 5km@smslit.cn\n\"\"\"\nimport os\nimport matplotlib\nfrom typing import List, Any, Dict, Tuple\n\nimport typer\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib import colors as mcolors\nfrom .consts import __description__, __version__\n\nmatplotlib.use(\"Agg\")\n\n\ndef classify_with_aspect_ratio(images: List[Dict[str, Any]]) -> Dict[str, int]:\n \"\"\"按照宽高比对图像进行分类\n\n Args:\n images (List[Dict[str, Any]]): 标注文件中图像数据\n\n Returns:\n Dict[str, int]: 统计后的图像数据\n \"\"\"\n result: Dict[str, List[int]] = {}\n for image in images:\n width = image.get(\"width\")\n height = image.get(\"height\")\n aspect_ratio_str = f\"{round(width / height, 2)}-({width}, {height})\"\n if aspect_ratio_str in result:\n result[aspect_ratio_str][\"count\"] += 1\n else:\n result[aspect_ratio_str] = {\n \"count\": 1,\n \"width\": width,\n \"height\": height\n }\n return result\n\n\ndef find_max_size_of_images(images: List[Dict[str, Any]]) -> Tuple[int, int]:\n \"\"\"找到图像中最大的宽高尺寸\n\n Args:\n images (List[Dict[str, Any]]): coco 中图像列表原数据\n\n Returns:\n Tuple[int, int]: 最大尺寸元组(宽, 高)\n \"\"\"\n max_width = 0\n max_height = 0\n for image in images:\n width = image.get(\"width\")\n height = image.get(\"height\")\n max_width = max(max_width, width)\n max_height = max(max_height, height)\n return (max_width, max_height)\n\n\ndef find_max_size_of_annotations(\n annotations: List[Dict[str, Any]]\n) -> Tuple[int, int]:\n \"\"\"找到标注中最大的宽高尺寸\n\n Args:\n annotations (List[Dict[str, Any]]): coco 中标注列表原数据\n\n Returns:\n Tuple[int, int]: 最大尺寸元组(宽, 高)\n \"\"\"\n max_width = 0\n max_height = 0\n for annotation in annotations:\n bbox = annotation.get(\"bbox\")\n width = bbox[2]\n height = bbox[3]\n max_width = max(max_width, width)\n max_height = max(max_height, height)\n return (max_width, max_height)\n\n\ndef plot_images_quantities(\n data: Dict[str, Dict[str, int]],\n title: str = \"Quantities of images with different width and height\",\n output_dir: str = \"plots\"\n):\n \"\"\"绘制图像按照宽高统计的信息\n\n Args:\n data (Dict[str, Dict[str, int]]): 图像的宽高统计数据，例如：\n {\n \"1.33-(2592, 1944)\": {\n \"count\": 1083,\n \"width\": 2592,\n \"height\": 1944\n }\n }\n \"\"\"\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n\n counts = []\n legends = []\n max_width, max_height, max_count = 0, 0, 0\n\n plt.figure(figsize=(8, 8))\n\n for value in data.values():\n count = value.get(\"count\")\n max_count = max(max_count, count)\n\n color = mcolors.CSS4_COLORS[\"blue\"]\n\n current_axis = plt.gca()\n\n for index, value in enumerate(data.values()):\n count = value.get(\"count\")\n width = value.get(\"width\")\n height = value.get(\"height\")\n counts.append(count)\n current_axis.add_patch(plt.Rectangle(\n (0, 0),\n width,\n height,\n linewidth=1,\n edgecolor=color,\n facecolor=\"none\",\n alpha=count*0.5/max_count + 0.1)\n )\n legends.append(f\"Pic({width},{height}) - {count}\")\n max_width = max(max_width, width)\n max_height = max(max_height, height)\n\n plt.legend(legends, ncol=3, loc=\"best\", fontsize=8)\n plt.title(\"test\")\n plt.xlabel(\"width\")\n plt.ylabel(\"height\")\n plt.xlim((-100, max_width + 100))\n plt.ylim((-100, 1.3 * max_height))\n image_name = f\"{title}.svg\"\n image_path = os.path.join(output_dir, image_name)\n\n # 保存图像\n plt.savefig(image_path)\n\n # 关闭 figure\n plt.close(\"all\")\n\n\ndef init_norm_scatter_data(step: float = 0.02) -> Dict[str, Dict[str, Any]]:\n \"\"\" annotation 散点图数据初始化（归一化）\n \"\"\"\n if step > 0.5:\n step = 0.02\n scatter_dict: Dict[str, Dict[str, Any]] = {}\n nums = [i / 100.0 for i in range(0, 100, int(step*100.0))]\n for i in nums:\n for j in nums:\n scatter_dict[f\"{i:.2f}-{j:.2f}\"] = {\n \"x\": i,\n \"y\": j,\n \"annotations\": []\n }\n return scatter_dict\n\n\ndef init_scatter_data(\n max_x: int = 4000,\n max_y: int = 4000,\n step: int = 50\n) -> Dict[str, Dict[str, Any]]:\n \"\"\" annotation 散点图数据初始化\n \"\"\"\n scatter_dict: Dict[str, Dict[str, Any]] = {}\n for x in range(0, max_x + step, step):\n for y in range(0, max_y + step, step):\n scatter_dict[f\"{x}-{y}\"] = {\n \"x\": x,\n \"y\": y,\n \"annotations\": []\n }\n return scatter_dict\n\n\ndef build_idx_table(items: List[Dict[str, Any]],\n table_name: str = \"images\") -> Dict[int, Dict[str, int]]:\n \"\"\"重建数据索引表\n\n Args:\n items (List[Dict[str, Any]]): 待建立索引的数据\n table_name str: 索引表标记名称\n\n Returns:\n Dict[int, Dict[str, int]]: 索引表\n \"\"\"\n idx_dict: Dict[int, Dict[str, int]] = {}\n typer.secho(f\"\\n建立 {table_name} 索引...\", fg=typer.colors.BRIGHT_BLACK)\n for item in items:\n item[\"data\"] = []\n idx_dict[item.get(\"id\")] = item\n typer.secho(\"完成!\", fg=typer.colors.BRIGHT_BLACK)\n\n return idx_dict\n\n\ndef plot_wh_normalization(raw_data: dict = {},\n step: float = 0.02,\n title: str = \"Annotation normalized size of all categories\",\n output_dir: str = \"plots\"):\n \"\"\" 绘制 annotation 宽高按图像宽高归一化统计热力分布\n\n Args:\n raw_data (dict, optional): 统计的数据. Defaults to {}.\n step (float, optional): 粒度. Defaults to 0.02.\n title (str, optional): 图像标题. Defaults to \"Annotation normalized size of all categories\".\n output_dir (str, optional): 图像保存目录. Defaults to \"plots\".\n \"\"\"\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n\n dw, dh = step, step\n\n h, w = np.mgrid[0:1+dh:dh, 0:1+dw:dw]\n quantity = round(1.0 / step)\n data = np.zeros((quantity, quantity))\n v_max = 0\n for i in range(0, quantity):\n for j in range(0, quantity):\n w_ij = w[i][j]\n h_ij = h[i][j]\n key = f\"{w_ij:.2f}-{h_ij:.2f}\"\n data[i][j] = len(raw_data[key][\"annotations\"])\n if data[i][j] > v_max:\n v_max = data[i][j]\n\n plt.figure(figsize=(10, 8))\n\n plt.pcolor(w, h, data, cmap='Blues', vmin=0, vmax=v_max)\n plt.title(title)\n plt.xlabel(\"width\")\n plt.ylabel(\"height\")\n plt.colorbar()\n\n image_name = f\"{title}.svg\"\n image_path = os.path.join(output_dir, image_name)\n\n # 保存图像\n plt.savefig(image_path, )\n\n # 关闭 figure\n plt.close(\"all\")\n\n\ndef plot_wh(raw_data: dict = {},\n step: int = 50,\n max_size: int = 4000,\n title: str = \"Annotation size of all categories\",\n output_dir: str = \"plots\"):\n \"\"\" 绘制 annotation 宽高按图像宽高统计热力分布\n\n Args:\n raw_data (dict, optional): 统计的数据. Defaults to {}.\n step (int, optional): 粒度. Defaults to 0.02.\n max_size (int, optional): 最大的尺寸. Defaults to 4000\n title (str, optional): 图像标题. Defaults to \"Annotation size of all categories\".\n output_dir (str, optional): 图像保存目录. Defaults to \"plots\".\n \"\"\"\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n\n dw, dh = step, step\n\n h, w = np.mgrid[0:max_size+dh:dh, 0:max_size+dw:dw]\n quantity = h.shape[0] - 1\n data = np.zeros((quantity, quantity))\n v_max = 0\n for i in range(0, quantity):\n for j in range(0, quantity):\n w_ij = w[i][j]\n h_ij = h[i][j]\n key = f\"{w_ij}-{h_ij}\"\n data[i][j] = len(raw_data[key][\"annotations\"])\n v_max = max(data[i][j], v_max)\n\n plt.figure(figsize=(10, 8))\n\n plt.pcolor(w, h, data, cmap='Blues', vmin=0, vmax=v_max)\n plt.title(f\"{title}-Grid({step}x{step})\")\n plt.xlabel(\"width\")\n plt.ylabel(\"height\")\n plt.colorbar()\n\n image_name = f\"{title}.svg\"\n image_path = os.path.join(output_dir, image_name)\n\n # 保存图像\n plt.savefig(image_path)\n\n # 关闭 figure\n plt.close(\"all\")\n\n\ndef plot_category_quantities(names: list = [],\n quantities: list = [],\n title: str = \"Quantities of annotations for all categories\",\n output_dir: str = \"plots\"):\n \"\"\" 绘制所有类别的数量统计直方图\n\n Args:\n names (list, optional): 类别名称列表. Defaults to [].\n quantities (list, optional): 对应类别的 annotations 数量. Defaults to [].\n title (str, optional): 直方图标题. Defaults to \"Quantities of all categories\".\n output_dir (str, optional): 直方图保存目录. Defaults to \"plots\".\n \"\"\"\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n\n plt.figure(figsize=(12, 8))\n\n plt.bar(names, quantities)\n # 绘制数字标签\n for a, b in zip(names, quantities):\n plt.text(a, b+0.05, f\"{b}\", ha=\"center\", va=\"bottom\", fontsize=8)\n plt.title(title)\n plt.xticks(rotation=90)\n plt.xlabel(\"category name\")\n plt.ylabel(\"category quantity\")\n\n image_name = f\"{title}.svg\"\n image_path = os.path.join(output_dir, image_name)\n\n # annotation_inches 解决 xlabels 内容显示不全的问题\n plt.tight_layout()\n plt.savefig(image_path)\n\n # 关闭 figure\n plt.close(\"all\")\n\n\nclass CoCoCallback:\n\n @staticmethod\n def check_file(value: str):\n \"\"\"检查文件路径\n \"\"\"\n if not os.path.exists(value):\n raise typer.BadParameter(\"文件不存在!\")\n if os.path.isdir(value) or (not value.endswith(\".json\")):\n raise typer.BadParameter(\"请指定有效的 coco json 文件!\")\n return value\n\n @staticmethod\n def check_img_dir(value: str):\n if not os.path.exists(value):\n raise typer.BadParameter(\"图像路径不存在!\")\n if os.path.isfile(value):\n raise typer.BadParameter(\"请指定有效的图像路径!\")\n return os.path.abspath(value)\n\n @staticmethod\n def version_echo(value: bool):\n if value:\n typer.secho(f\"\\n{__description__}\\n\",\n fg=typer.colors.BRIGHT_GREEN)\n echo_version_str = typer.style(\"版本号: \",\n fg=typer.colors.BRIGHT_BLACK,\n bold=True)\n echo_version_str += typer.style(\n f\" v{__version__} \", fg=typer.colors.YELLOW, bold=True)\n typer.echo(echo_version_str + \"\\n\")\n raise typer.Exit()\n","sub_path":"cocogo/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":11335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"487461754","text":"import random\nimport numpy as np\nimport copy\n\n\nfrom PySide2.QtCore import Qt, Signal, QRectF\nfrom PySide2.QtWidgets import (QGraphicsObject, QGraphicsItem,\n QAction, QMenu, QGraphicsView, QGraphicsScene, QActionGroup)\nfrom PySide2.QtGui import QPixmap, QBrush, QIcon, QTransform, QCursor, QImage\n\nfrom wezel import canvas, icons\nfrom wezel.canvas.utils import colormap_to_LUT\n\nclass Canvas(QGraphicsView):\n \"\"\"Wrapper for ImageItem displaying it in a scrollable Widget\"\"\"\n\n #imageUpdated = Signal(object)\n newMaskSeries = Signal(object)\n mousePositionMoved = Signal(int, int)\n arrowKeyPress = Signal(str)\n #maskChanged = Signal()\n\n def __init__(self, parent=None): \n super().__init__(parent)\n self.setScene(QGraphicsScene(self))\n self.setBackgroundBrush(QBrush(Qt.black))\n self.setTransformationAnchor(QGraphicsView.AnchorUnderMouse)\n self.setResizeAnchor(QGraphicsView.AnchorUnderMouse)\n self.toolBar = None\n\n def zoomTo(self, factor):\n self.setTransform(QTransform())\n self.scale(factor, factor)\n\n def item(self, n):\n for item in self.scene().items():\n if item.zValue() == n:\n return item\n\n def removeItem(self, item):\n if item is not None:\n self.scene().removeItem(item)\n\n @property\n def imageItem(self):\n return self.item(0)\n\n @property\n def maskItem(self):\n return self.item(1)\n \n @property\n def filterItem(self):\n return self.item(2)\n \n def setBlank(self):\n self.removeItem(self.imageItem)\n self.removeItem(self.maskItem)\n\n def setImage(self, array, center, width, cmap, lut=None):\n if lut is None:\n lut = colormap_to_LUT(cmap)\n self.removeItem(self.imageItem)\n item = ImageItem(array, center, width, lut)\n item._cmap = cmap\n self.scene().addItem(item)\n item.setZValue(0)\n filter = self.filterItem\n if filter is not None:\n filter.prepareGeometryChange()\n filter.boundingRectangle = item.boundingRectangle\n filter.initialize()\n self.setMask(None)\n return item\n\n def setMask(self, mask, color=0, opacity=0.5):\n self.removeItem(self.maskItem)\n if self.toolBar is not None:\n #opacity = self.toolBar.opacity()\n opacity = self.toolBar.actionOpacity.opacity()\n item = MaskItem(self.imageItem, mask, opacity=opacity, color=color)\n item.setZValue(1)\n item.maskChanged.connect(self.slotMaskChanged)\n return item\n\n def slotMaskChanged(self):\n if self.toolBar is not None:\n self.toolBar.maskChanged()\n\n def setFilter(self, filter=None):\n self.removeItem(self.filterItem)\n if filter is None:\n return\n if filter == 'Default':\n filter = canvas.PanFilter()\n self.scene().addItem(filter)\n self.scene().setFocusItem(filter)\n filter.setZValue(2)\n filter.prepareGeometryChange()\n if self.imageItem is None:\n filter.boundingRectangle = self.scene().sceneRect()\n else:\n filter.boundingRectangle = self.imageItem.boundingRectangle\n filter.initialize()\n\n def fitItem(self):\n item = self.imageItem\n if item is None:\n item = self.maskItem\n if item is not None:\n self.fitInView(item, Qt.KeepAspectRatio)\n\n def setColormap(self, cmap=None):\n if self.imageItem is None:\n return\n if cmap is None:\n cmap = 'Greyscale'\n RGB = colormap_to_LUT(cmap)\n self.imageItem._cmap = cmap\n self.imageItem.setLUT(RGB)\n self.imageItem.setDisplay()\n\n def setWindow(self, center=None, width=None):\n if self.imageItem is None:\n return\n if (center is None) or (width is None):\n array = self.imageItem._array\n min = np.min(array)\n max = np.max(array)\n if center is None:\n center = (max+min)/2\n if width is None:\n width = 0.9*(max-min) \n self.imageItem.setWindow(center, width)\n self.imageItem.setDisplay()\n\n def array(self):\n if self.imageItem is None:\n return\n return self.imageItem._array\n\n def lut(self):\n if self.imageItem is None:\n return\n return self.imageItem._lut\n\n def colormap(self):\n if self.imageItem is None:\n return\n return self.imageItem._cmap\n\n def center(self):\n if self.imageItem is None:\n return\n return self.imageItem._center\n \n def width(self):\n if self.imageItem is None:\n return\n return self.imageItem._width\n\n\nclass AnyItem(QGraphicsObject):\n \"\"\"Displays an image.\n \"\"\"\n\n def __init__(self, parent=None): \n super().__init__(parent)\n self.boundingRectangle = QRectF(0, 0, 0, 0) \n\n def addSeparator(self, menu):\n separator = QAction(menu)\n separator.setSeparator(True)\n menu.addAction(separator)\n\n def boundingRect(self): \n \"\"\"Abstract method - must be overridden.\"\"\"\n return self.boundingRectangle\n\n def paint(self, painter, option, widget):\n \"\"\"Abstract method - must be overridden.\"\"\"\n pass\n\n\nclass ImageItem(AnyItem):\n \"\"\"Displays an image.\n \"\"\"\n def __init__(self, array, center, width, lut): \n super().__init__()\n #self.setFlag(QGraphicsItem.ItemIsSelectable)\n self.setOpacity(1.0)\n self.setData(array, center, width, lut)\n self.setDisplay()\n\n def paint(self, painter, option, widget):\n \"\"\"Executed by GraphicsView when calling update()\"\"\"\n if self._qImage is None: # image is corrupted\n return\n painter.drawImage(0, 0, self._qImage)\n\n def setData(self, array, center, width, lut):\n try:\n self.setArray(array)\n self.setWindow(center, width)\n self.setLUT(lut)\n except: # image is corrupted\n self._array = None\n self._width = None\n self._center = None \n self._cmap = None\n self._lut = None \n self._array_scaled = None\n self._BGRA = None\n self._qImage = None \n \n def setArray(self, array):\n self._array = array\n nx, ny = array.shape[0], array.shape[1]\n if nx is None: # image is corrupted\n nx, ny = 0, 0\n self.boundingRectangle = QRectF(0, 0, nx, ny)\n self._BGRA = np.empty((ny, nx, 4), dtype=np.ubyte)\n self._BGRA[:,:,3] = 0 \n # QImage points to self._BGRA in memory - does not need to be updated\n self._qImage = QImage(self._BGRA, self._BGRA.shape[1], self._BGRA.shape[0], QImage.Format_RGB32)\n\n def setWindow(self, center, width):\n self._width = width\n self._center = center\n max = center + width/2\n min = center - width/2\n # Scale pixel array into byte range\n array = np.clip(self._array, min, max)\n array -= min\n if max > min:\n scale = 255/(max-min)\n array *= scale\n # QImage expects the array transposed\n self._array_scaled = array.astype(np.ubyte)\n self._array_scaled = np.transpose(self._array_scaled)\n\n def setLUT(self, lut):\n #LUT is lookup table with values in range [0,1]\n if lut is None:\n self._lut = None\n else:\n # Create RGB array by indexing LUT with pixel array\n lut = 255*lut \n self._lut = lut.astype(np.ubyte) \n\n def setDisplay(self):\n if self._BGRA is None: # image is corrupted\n return\n if self._lut is None:\n # Greyscale image\n for c in range(3):\n self._BGRA[:,:,c] = self._array_scaled\n else:\n # Create RGB array by indexing LUT with pixel array \n for c in range(3):\n self._BGRA[:,:,c] = self._lut[self._array_scaled, 2-c]\n self.update()\n\n def array(self):\n return self._array \n\n\nclass MaskItem(AnyItem):\n \"\"\"Displays a mask as an overlay on an image.\n \"\"\"\n maskChanged = Signal()\n\n def __init__(self, imageItem, mask, opacity=0.75, color=0): \n super().__init__(imageItem)\n self._bin = []\n self._current = None\n self._BGRA = None\n self._qImage = None\n self._BGR = list(reversed(self.RGB(color)))\n self.boundingRectangle = None\n self.setData(mask)\n self.setOpacity(opacity)\n\n def color(self):\n return list(reversed(self._BGR))\n\n def boundingRect(self): \n \"\"\"Abstract method - must be overridden.\"\"\"\n if self.boundingRectangle is None:\n self.boundingRectangle = self.parentItem().boundingRect()\n return self.boundingRectangle\n\n def paint(self, painter, option, widget):\n \"\"\"Executed by GraphicsView when calling update()\"\"\"\n if self._qImage is not None:\n painter.drawImage(0, 0, self._qImage)\n\n def setBin(self, bin):\n self._bin[self._current] = bin\n\n def bin(self):\n if self._current == None:\n return\n return self._bin[self._current]\n\n def setData(self, mask):\n #array = mask.array()\n #self._bin = array != 0\n if mask is None:\n return\n self._bin = [mask != 0]\n self._current = 0\n shape = (self.bin().shape[1], self.bin().shape[0], 4)\n self._BGRA = np.zeros(shape, dtype=np.ubyte)\n self._qImage = QImage(self._BGRA, self._BGRA.shape[1], self._BGRA.shape[0], QImage.Format_ARGB32)\n self.setDisplay()\n self.maskChanged.emit()\n\n def initMask(self):\n rect = self.boundingRect()\n dx, dy = rect.width(), rect.height()\n self._bin = [np.zeros((int(dx), int(dy)), dtype=bool)]\n self._current = 0\n shape = (self.bin().shape[1], self.bin().shape[0], 4)\n self._BGRA = np.zeros(shape, dtype=np.ubyte)\n self._qImage = QImage(self._BGRA, self._BGRA.shape[1], self._BGRA.shape[0], QImage.Format_ARGB32)\n\n def setDisplay(self):\n if self._bin == []:\n return\n LUT = np.array([0,1], dtype=np.ubyte)\n mask = self.bin().astype(np.ubyte)\n mask = np.transpose(mask)\n mask = LUT[mask]\n for c in range(3):\n if self._BGR[c] != 0:\n self._BGRA[:,:,c] = mask*self._BGR[c]\n self._BGRA[:,:,3] = mask*255\n self.update()\n self.maskChanged.emit()\n\n def setPixel(self, x, y, value):\n # if self._bin == []:\n # self.initMask()\n self.bin()[x,y] = value\n if value: \n self._BGRA[y,x,:3] = self._BGR\n self._BGRA[y,x,3] = 255\n else:\n self._BGRA[y,x,:] = 0\n\n def extend(self):\n if self._bin == []:\n self.initMask()\n bin = copy.deepcopy(self.bin())\n self._bin = self._bin[:self._current+1]\n self._bin.append(bin)\n self._current += 1\n self.maskChanged.emit()\n\n def undo(self):\n if self._current == None:\n return\n if self._current != 0:\n self._current -= 1\n self.setDisplay()\n \n def redo(self):\n if self._current == None:\n return\n if self._current != len(self._bin)-1:\n self._current += 1\n self.setDisplay()\n \n def erase(self):\n self.extend()\n self.bin().fill(False)\n self.setDisplay()\n\n def RGB(self, color):\n if isinstance(color, list):\n return color\n if color == 0:\n return [255, 0, 0]\n if color == 1:\n return [0, 255, 0]\n if color == 2:\n return [0, 0, 255]\n if color == 3:\n return [0, 255, 255]\n if color == 4:\n return [255, 0, 255]\n if color == 5:\n return [255, 255, 0]\n if color == 6:\n return [0, 128, 255]\n if color == 7:\n return [255, 0, 128]\n if color == 8:\n return [128, 255, 0]\n return [\n random.randint(0,255), \n random.randint(0,255), \n random.randint(0,255)]\n\n\nclass FilterItem(AnyItem):\n \"\"\"Base class for View events.\n \"\"\"\n\n def __init__(self): \n super().__init__()\n pixMap = QPixmap(icons.hand)\n self.cursor = QCursor(pixMap, hotX=4, hotY=0)\n self.icon = QIcon(pixMap)\n self.toolTip = 'Filter'\n self.text = 'Filter'\n self.boundingRectangle = QRectF(0, 0, 0, 0) \n self.x = None\n self.y = None\n self.setAcceptHoverEvents(True)\n self.setFlag(QGraphicsItem.ItemIsFocusable)\n\n def setActionPick(self):\n self.actionPick = QAction(self.icon, self.text)\n self.actionPick.setCheckable(True)\n #self.actionPick.setEnabled(False)\n self.actionPick.filter = self\n menu = self.menuOptions()\n if menu is not None:\n self.actionPick.setMenu(menu)\n\n def menuOptions(self):\n return\n\n def initialize(self):\n pass\n\n def keyPressEvent(self, event):\n cnvs = self.scene().parent()\n if event.key() == 16777234:\n cnvs.arrowKeyPress.emit('left') \n elif event.key() == 16777235:\n cnvs.arrowKeyPress.emit('up')\n elif event.key() == 16777236:\n cnvs.arrowKeyPress.emit('right')\n elif event.key() == 16777237:\n cnvs.arrowKeyPress.emit('down')\n\n def hoverEnterEvent(self, event):\n self.x = int(event.pos().x())\n self.y = int(event.pos().y())\n self.setCursor(self.cursor)\n self.setFocus()\n cnvs = self.scene().parent()\n cnvs.mousePositionMoved.emit(self.x, self.y)\n\n def hoverLeaveEvent(self, event):\n self.x = int(event.pos().x())\n self.y = int(event.pos().y())\n cnvs = self.scene().parent()\n cnvs.mousePositionMoved.emit(self.x, self.y) \n\n def hoverMoveEvent(self, event):\n self.x = int(event.pos().x())\n self.y = int(event.pos().y())\n self.setFocus()\n cnvs = self.scene().parent()\n cnvs.mousePositionMoved.emit(self.x, self.y) \n\n def wheelEvent(self, event):\n if event.delta() < 0:\n factor = 1.25\n else:\n factor = 1/1.25\n cnvs = self.scene().parent()\n cnvs.scale(factor, factor)\n\n def mousePressEvent(self, event):\n self.x = int(event.pos().x())\n self.y = int(event.pos().y())\n\n def mouseMoveEvent(self, event):\n self.x = int(event.pos().x())\n self.y = int(event.pos().y())\n\n def mouseReleaseEvent(self, event):\n self.x = int(event.pos().x())\n self.y = int(event.pos().y())\n \n def contextMenu(self):\n menu = QMenu()\n canvas = self.scene().parent()\n toolBar = canvas.toolBar\n if toolBar is None:\n return menu\n menu.addAction(toolBar.actionFitItem)\n menu.addAction(toolBar.actionZoomTo)\n menu.addAction(toolBar.actionZoomIn)\n menu.addAction(toolBar.actionZoomOut)\n if canvas.maskItem is not None:\n self.addSeparator(menu)\n menu.addAction(toolBar.actionOpacity)\n return menu\n\n def contextMenuEvent(self, event):\n menu = self.contextMenu()\n menu.exec_(event.screenPos())\n\n def pick(self):\n self.actionPick.setChecked(True)\n self.actionPick.triggered.emit()\n self.update()\n\n\nclass FilterSet():\n def __init__(self):\n self.filters = None\n self.icon = None\n self.text = None\n self.current = None\n\n def pick(self, filter):\n self.current = filter\n self.actionPick.filter = filter\n self.actionPick.setChecked(True)\n self.actionPick.triggered.emit()\n #self.update()\n\n def setActionPick(self):\n self.actionPick = QAction(self.icon, self.text)\n self.actionPick.setCheckable(True)\n #self.actionPick.setEnabled(False)\n self.actionPick.filter = self.current\n self.actionPick.setMenu(self.menu())\n for filter in self.filters:\n filter.contextMenu = self.menu\n\n def menu(self):\n menu = QMenu()\n menu.triggered.connect(lambda action: self.pick(action.filter))\n actionGroup = QActionGroup(menu)\n for filter in self.filters:\n action = QAction(filter.iconInSet, filter.textInSet)\n action.filter = filter\n action.setCheckable(True)\n action.setChecked(action.filter == self.current)\n actionGroup.addAction(action)\n menu.addAction(action)\n return menu\n\n\n\n","sub_path":"src/wezel/canvas/canvas.py","file_name":"canvas.py","file_ext":"py","file_size_in_byte":16810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"250106884","text":"# -*- coding: utf-8 -*-\nimport random\nfrom classes.game import Person, bcolors\nfrom classes.magic import Spell\nfrom classes.inventory import Item\n\n# create black magic\nfire = Spell(\"Fire\", 10, 100, \"black\")\nthunder = Spell(\"Thunder\", 10, 100, \"black\")\nblizzard = Spell(\"Blizzard\", 10, 100, \"black\")\nmeteor = Spell(\"Meteor\", 20, 200, \"black\")\nquake = Spell(\"Quake\", 14, 140, \"black\")\n\n# create white magic\ncure = Spell(\"Cure\", 12, 120, \"white\")\ncura = Spell(\"Cura\", 18, 200, \"white\")\n\n# create some items\npotion = Item(\"Potion\", \"potion\", \"heals 50 HP\", 50)\nhipotion = Item(\"Hipotion\", \"potion\", \"heals 100 HP\", 100)\nsuperpotion = Item(\"Superpotion\", \"potion\", \"heals 200 HP\", 200)\nelixir = Item(\"Elixir\", \"elixir\", \"fully restores HP/MP of one party member\",\n 9999)\nhielixir = Item(\"MegaElixir\", \"elixir\", \"fully restores party's HP/MP\", 9999)\ngrenade = Item(\"Grenade\", \"attack\", \"Deals 500 damage\", 300)\n\n\n# Instantiate characters\nplayer_spells = [fire, thunder, blizzard, meteor, quake, cure, cura]\nplayer_items = [{'item': potion, 'quantity': 5},\n {'item': hipotion, 'quantity': 3},\n {'item': superpotion, 'quantity': 2},\n {'item': elixir, 'quantity': 1},\n {'item': hielixir, 'quantity': 1},\n {'item': grenade, 'quantity': 1}]\nplayer1 = Person(\"Valos \", 326, 65, 70, 34, player_spells, player_items)\nplayer2 = Person(\"Thanos\", 416, 65, 80, 34, player_spells, player_items)\nplayer3 = Person(\"Robot \", 308, 65, 60, 34, player_spells, player_items)\nplayers = [player1, player2, player3]\n\nenemy_spells = [fire, meteor, cure]\nenemy_items = [{'item': potion, 'quantity': 5},\n {'item': superpotion, 'quantity': 2},\n {'item': elixir, 'quantity': 1},\n {'item': grenade, 'quantity': 1}]\nenemy1 = Person(\"Imp1 \", 280, 65, 100, 25, enemy_spells, enemy_items)\nenemy2 = Person(\"Magus \", 920, 145, 200, 35, enemy_spells, enemy_items)\nenemy3 = Person(\"Imp2 \", 280, 65, 200, 25, enemy_spells, enemy_items)\nenemies = [enemy1, enemy2, enemy3]\n\n# battle\nrunning = True\nround = 0\n\nprint(bcolors.FAIL + bcolors.BOLD + \"AN ENEMY ATTACKS!\" + bcolors.ENDC)\nprint(\"Normal Text\")\n\nwhile running:\n\n print(bcolors.HEADER+\"\\n\\nRound \"+str(round)+bcolors.ENDC)\n round += 1\n print(\"=================================================\" +\n \"=========================\")\n print(\"NAME HP MP\")\n for player in players:\n player.get_stats()\n print(\"\\n\")\n for enemy in enemies:\n enemy.get_enemy_stats()\n\n for player in players:\n\n if len(enemies) == 0:\n break\n\n if player.get_hp() > 0:\n\n action = player.choose_action()\n\n if action == 0:\n dmg = player.generate_dmg()\n enemy = player.choose_target(enemies)\n print(bcolors.FAIL+\"You attacked \" +\n enemies[enemy].name.replace(\" \", \"\") +\n \" for \"+str(dmg)+\" points of damage.\"+bcolors.ENDC)\n enemies[enemy].take_dmg(dmg)\n if enemies[enemy].get_hp() == 0:\n print(enemies[enemy].name.replace(\" \", \"\")+\" has died!\")\n del enemies[enemy]\n\n elif action == 1:\n spell = player.choose_magic()\n '''\n magic_dmg = player.generate_spell_dmg(magic_choice)\n spell = player.get_spell_name(magic_choice)\n cost = player.get_spell_mp_cost(magic_choice)\n '''\n magic_dmg = spell.generate_dmg()\n current_mp = player.get_mp()\n if current_mp < spell.cost:\n print(bcolors.FAIL + \"\\nNot enough MP\\n\" + bcolors.ENDC)\n continue\n\n player.reduce_mp(spell.cost)\n if spell.type == 'white':\n player.heal(magic_dmg)\n print(bcolors.OKBLUE + \"\\n\" + spell.name + \" heals for \" +\n str(magic_dmg) + \" HP.\" + bcolors.ENDC)\n elif spell.type == 'black':\n enemy = player.choose_target(enemies)\n print(bcolors.OKBLUE + \"\\n\" + spell.name + \" deals \" +\n str(magic_dmg) + \" points of damage to \" +\n enemies[enemy].name.replace(\" \", \"\")+bcolors.ENDC)\n enemies[enemy].take_dmg(magic_dmg)\n if enemies[enemy].get_hp() == 0:\n print(enemies[enemy].name.replace(\" \", \"\") +\n \" has died!\")\n del enemies[enemy]\n\n elif action == 2:\n player.choose_item()\n item_choice = int(input(\" Choose Item:\"))-1\n item = player.items[item_choice]['item']\n player.items[item_choice]['quantity'] -= 1\n if player.items[item_choice]['quantity'] < 0:\n print(bcolors.WARNING+\"\\nNone left\"+bcolors.ENDC)\n continue\n\n if item.type == 'potion':\n player.heal(item.prop)\n print(bcolors.OKGREEN + \"\\n\" + item.name + \" heals for \" +\n str(item.prop) + \" HP.\" + bcolors.ENDC)\n elif item.type == 'elixir':\n if item.name == 'MegaElixir':\n for i in players:\n i.hp = i.maxhp\n i.mp = i.maxmp\n print(bcolors.OKGREEN+'\\n'+item.name +\n 'fully restores HP/MP of all players' +\n bcolors.ENDC)\n else:\n player.hp = player.maxhp\n player.mp = player.maxmp\n print(bcolors.OKGREEN+'\\n'+item.name +\n 'fully restores HP/MP' + bcolors.ENDC)\n elif item.type == 'attack':\n enemy = player.choose_target(enemies)\n enemies[enemy].take_dmg(item.prop)\n print(bcolors.OKBLUE + '\\n' + item.name + ' deals ' +\n str(item.prop) + \" points of damage to \" +\n enemies[enemy].name.replace(\" \", \"\")+bcolors.ENDC)\n if enemies[enemy].get_hp() == 0:\n print(enemies[enemy].name.replace(\" \", \"\") +\n \" has died!\")\n del enemies[enemy]\n# check if battle is over\n if len(enemies) == 0:\n print(bcolors.OKGREEN + \"YOU WIN!\" + bcolors.ENDC)\n running = False\n elif len(players) == 0:\n print(bcolors.FAIL + \"YOU LOSE!\" + bcolors.ENDC)\n running = False\n# enemies' move\n else:\n for enemy in enemies:\n enemy_choice = random.randrange(0, 2)\n # chose attack\n if enemy_choice == 0:\n if len(players) == 0:\n continue\n target = random.randrange(0, len(players))\n enemy_dmg = enemy.generate_dmg()\n players[target].take_dmg(enemy_dmg)\n print(bcolors.WARNING+\"\\n\"+enemy.name.replace(\" \", \"\") +\n \" attacks \"+players[target].name.replace(\" \", \"\") +\n \" for \"+str(enemy_dmg)+\" points of damage\"+bcolors.ENDC)\n if players[target].get_hp() == 0:\n print(players[target].name+\" has died!\")\n del players[target]\n # chose magic\n elif enemy_choice == 1:\n magic_choice = random.randrange(0, len(enemy.magic))\n spell = enemy.magic[magic_choice]\n magic_dmg = spell.generate_dmg()\n current_mp = enemy.get_mp()\n if current_mp < spell.cost:\n print(bcolors.FAIL + \"\\nNot enough MP\\n\" + bcolors.ENDC)\n continue\n\n enemy.reduce_mp(spell.cost)\n if spell.type == 'white':\n enemy.heal(magic_dmg)\n print(bcolors.OKBLUE + \"\\n\" + spell.name + \" heals for \" +\n str(magic_dmg) + \" HP.\" + bcolors.ENDC)\n elif spell.type == 'black':\n player = random.randrange(0, len(players))\n print(bcolors.OKBLUE + \"\\n\" + spell.name + \" deals \" +\n str(magic_dmg) + \" points of damage to \" +\n players[player].name.replace(\" \", \"\")+bcolors.ENDC)\n players[player].take_dmg(magic_dmg)\n if players[player].get_hp() == 0:\n print(players[player].name.replace(\" \", \"\") +\n \" has died!\")\n del players[player]\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"396804067","text":"#!/usr/bin/env python\n\nimport functools\n\n#lambda args : expression\n\na = lambda x,y : x+y\n\nb = a(2,3)\n\nprint(b)\n\n#find max\n\nf = lambda a, b: a if (a > b) else b\nx = functools.reduce(f, [4, 2, 55, 1, 99, 6])\nprint(x)\n\nf2 = lambda x: x*x\ny = list(map(f2, [1, 2, 3, 4, 5]))\nprint(y)\n\n#true if odd\n\nf3 = lambda x : x % 2\nz = list(filter(f3, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))\nprint(z)\n\ndef func(x):\n return x.isalnum()\n\nseq = ['foo', 'x44', '?!', '***']\n\n#use function for filtering\n\nl = list(filter(func,seq))\n\nprint(l)\n\n#use list comprehension\n\nl = [x for x in seq if x.isalnum()]\nprint(l)\n\n#use lambda with filter\nl = list(filter(lambda x: x.isalnum(), seq))\nprint(l)","sub_path":"lambda.py","file_name":"lambda.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"605555985","text":"import sys\n\nimport os\n\nsys.path.append('..'+os.sep+'..'+os.sep)\nfrom amqpstorm import Message\n\nfrom rmqworkers.rmqWorker import Workers\nfrom rmqworkers.queues import BaseQueue\nfrom os import getenv\nimport logging\n\nLOG = logging.getLogger('rmqworkers')\n\n\nclass InQueue(BaseQueue):\n _instance = None # must be here\n _type = 'in' # a single in queue is permitted per Workers object\n queue_name = getenv('IN_QUE', 'in')\n priority = 1\n\n @staticmethod\n def work(message):\n \"\"\"\n Extract work\n :param message:\n :return:k\n \"\"\"\n try:\n msg = eval(message.body)\n except Exception as err:\n msg = message.body\n LOG.error('Unexpected Error:' + str(err))\n else:\n # change something.\n msg['job_info'] = msg['job_info'] + '!!!!!!!'\n LOG.info(\"Message was consumed: {}\".format(msg))\n # send to out queue\n if InQueue.out_queues['out'].work(msg):\n message.ack()\n\n\nclass OutQueue(BaseQueue):\n \"\"\"\n In queue that needs to be used in your code\n \"\"\"\n _instance = None\n _type = 'out'\n routing = 'normal'\n queue_name = getenv('OUT_QUE', 'out')\n priority = 1\n\n @staticmethod\n def work(message):\n \"\"\"\n Every class that inherits BaseQueue has a work method that will be automatically called\n :param message: message object that needs to be consumed\n :return: None\n \"\"\"\n properties = {\n 'priority': OutQueue.priority,\n 'content_type': 'text/plain',\n 'expiration': '3600',\n 'headers': {'key': 'value'},\n }\n message['add_something'] = 'something'\n message_out = Message.create(channel=OutQueue.channel,\n body=str(message),\n properties={})\n message_out.publish(OutQueue.queue_name)\n LOG.info(\"Message was added in out queue: {}\".format(message))\n return True\n\n\n# publish in queue in { \"job_info\": \"11111\"}\nWorkers(queues=(InQueue, OutQueue), running_type='process', standalone=False)\n# read from out queue that you modified\n","sub_path":"rmqworkers/examples/example_1queue_in_and_1queue_out_process.py","file_name":"example_1queue_in_and_1queue_out_process.py","file_ext":"py","file_size_in_byte":2199,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"251149336","text":"\n# coding: utf-8\n\n# ## Step04_Binarization\n# 画像の読み込み→画像を２値化→保存\n\n# In[ ]:\n\n\nfrom skimage import io\nimport numpy as np\n\n\n# In[ ]:\n\n\nTrainingSampleNum = 2000 # 学習サンプル総数\nTestSampleNum = 10000 # テストサンプル総数\nClassNum = 10 # クラス数（今回は10）\nImageSize = 28 # 画像サイズ（今回は縦横ともに28）\nTrainingDataFile = './Images/TrainingSamples/{0:1d}-{1:04d}.png'\nTestDataFile = './Images/TestSamples/{0:1d}-{1:04d}.png'\nOutFile = './Images/OutSamples/bin_{0:1d}-{1:04d}.png'\n\n\n# In[ ]:\n\n\n# Binarization ルーチン\ndef Binarization ( src, thres ):\n '''\n ここでは画素の操作方法がわかるようにあえて２重ループで書いている．\n '''\n \n dest = np.zeros (src.shape, dtype=np.uint8)\n for y in range (0, src.shape[0] ):\n for x in range ( 0, src.shape[1]):\n if src[y,x] < thres :\n dest[y,x] = 255\n else :\n dest[y,x] = 0\n\n return dest\n\n\n# In[ ]:\n\n\n# main ルーチン\n\nfor label in range (0, ClassNum):\n for sample in range (0, TrainingSampleNum // ClassNum ):\n filename = TrainingDataFile.format(label,sample)\n print (\"Loading the file: \" + filename )\n img = io.imread ( filename )\n \n res = Binarization (img, 200)\n \n filename = OutFile.format(label, sample)\n print (\"Saving the file: \" + filename )\n io.imsave ( filename, res )\n \n\n","sub_path":"kyushu-homework/pattern recognition/Step04_Binarization.py","file_name":"Step04_Binarization.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"212844982","text":"# M이상 N이하의 자연수 중 소수인 것을 모두 골라 소수의 합 / 최솟값 찾기\nM = int(input())\nN = int(input())\n\nsosu = [False, False] + [True]*(N-1)\nList = []\n\nfor i in range(2, N+1):\n if sosu[i]:\n if M <= i <= N:\n List.append(i)\n for j in range(2*i, N+1, i):\n sosu[j] = False\n\nif len(List) != 0: # 문제 꼼꼼하게 읽기\n print(sum(List))\n print(min(List))\nelse:\n print(-1)\n","sub_path":"BAEKJOON/수학2/2581_소수.py","file_name":"2581_소수.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"264149010","text":"class Solution(object):\n def uncommonFromSentences(self, A, B):\n \"\"\"\n :type A: str\n :type B: str\n :rtype: List[str]\n \"\"\"\n map = dict()\n words = (A + ' ' + B).split()\n for w in words:\n map[w] = map.get(w, 0) + 1\n return [k for k in map if map[k] == 1]\n\n","sub_path":"leetcode_884/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"298531161","text":"\"\"\"\nDescription: Search for a field matching and regex expression and replace it with a value\n\"\"\"\nimport re\nfrom mdatapipe.core import PipelinePlugin\nfrom functools import partial\nfrom collections import OrderedDict\n\n\nclass Plugin(PipelinePlugin):\n\n \"\"\":\n Plugin config is transformed to a more efficient runtime structure:\n runtime_config = { field_name: { match_pattern: replace_value ... } ...}\n \"\"\"\n supported_types = [dict]\n\n def on_start(self):\n self.runtime_config = runtime_config = {}\n\n for field_name, replace_dict in self.config.items():\n field_dict = runtime_config.get(field_name, OrderedDict())\n runtime_config[field_name] = field_dict\n for pattern, replace_value in replace_dict.items():\n match_func = partial(re_match, re.compile(pattern))\n field_dict[match_func] = replace_value\n\n def on_input(self, item):\n for field_name, replace_dict in self.runtime_config.items():\n for match_func, replace_value in replace_dict.items():\n if match_func(item[field_name]):\n item[field_name] = replace_value\n self.put(item)\n\n\ndef re_match(regex, value):\n return regex.match(value)\n","sub_path":"mdatapipe-0.1/mdatapipe/plugins/transform/field/regex_replace.py","file_name":"regex_replace.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"460409082","text":"from django.conf.urls import url\n\nfrom smzdm import views\n\nurlpatterns = [\n url(r'^case_set/$', views.view_case_set, name='view_case_set'),\n url(r'^case_set/(?P[0-9]+)/$',views.view_his,name='view_his'),\n url(r'^case_set/(?P[0-9]+)/pages/(?P[0-9]+)/$',views.view_his,name='his_page_with_pages'),\n url(r'^(?P[0-9]+)/add_case/$',views.add_case,name='add_case'),\n url(r'^$',views.home_page,name='home_page'),\n url(r'^pages/(?P[0-9]+)/$',views.home_page,name='home_page_with_pages'),\n url(r'^case_set/case_delete/(?P[0-9]+)/$',views.delete_case,name='delete_case'),\n url(r'^login/$',views.login,name='login'),\n url(r'^logout/$',views.logout,name='logout'),\n url(r'^register/$', views.register, name='register'),\n url(r'^register/active_user/(?P.*)/$', views.active_user, name='active_user'),\n url(r'^user_info/$',views.view_user_info,name='view_user_info'),\n url(r'^forgot/$',views.forget_page,name='forget_page'),\n url(r'^forgot/(?P.*)$',views.forgot_user,name='forgot_user'),\n]\n","sub_path":"smzdm/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"115029375","text":"import json\n\nimport time\n\n\nclass BaseError(Exception):\n\n # Response Messages\n BASE_DATA = \"Unknown error with resource\"\n\n # Response Codes\n BASE_CODE = 0\n PATH_CODE = 1\n DATA_CODE = 2\n RESOURCE_CODE = 3\n BUILD_CODE = 4\n DATA_BASE_CODE = 5\n\n # Response Info\n BASE_INFO = \"Unknown error\"\n ACTION_ERROR = \"error\"\n\n\n def __init__(self, code: int=BASE_CODE, subcode: int=BASE_CODE, data: str=BASE_DATA):\n self.code = code\n self.subcode = subcode\n self.data = data\n\n\n def generate_error(self) -> str:\n return json.dumps({\n \"action\": self.ACTION_ERROR,\n \"code\": self.code,\n \"subcode\": self.subcode,\n \"data\": {\n \"message\": self.data\n },\n \"time\": time.time()\n })\n\n\n @staticmethod\n def generate_base_error(data: str = BASE_DATA) -> str:\n return json.dumps({\n \"action\": BaseError.ACTION_ERROR,\n \"code\": BaseError.BASE_CODE,\n \"subcode\": BaseError.BASE_CODE,\n \"data\": {\n \"message\": data\n },\n \"time\": time.time()\n })\n","sub_path":"std/error/base_error.py","file_name":"base_error.py","file_ext":"py","file_size_in_byte":1162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"368432609","text":"from django.db import models\n\nclass Customer(models.Model):\n \"\"\"A typical class defining a model, derived from the Model class.\"\"\"\n\n # Fields\n name = models.CharField(max_length=50)\n address = models.TextField()\n mobile_number = models.CharField(max_length=13)\n email = models.EmailField(max_length=100, blank=True, default='')\n created = models.DateField(auto_now_add=True)\n\n # Metadata\n class Meta: \n ordering = ['id']\n\n # Methods\n def get_absolute_url(self):\n \"\"\"Returns the url to access a particular instance of MyModelName.\"\"\"\n return reverse('customer_details', args=[str(self.id)])\n \n def __str__(self):\n \"\"\"String for representing the MyModelName object (in Admin site etc.).\"\"\"\n return self.name","sub_path":"travels/customers/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"647113219","text":"from django import forms\nfrom django.conf import settings\nfrom django.utils.safestring import mark_safe\n\nfrom sentry.plugins.bases.notify import NotifyPlugin\nfrom sentry.web.helpers import render_to_string\n\nimport sentry_flowdock\nimport json\nimport logging\nimport urllib2\n\n\nclass FlowdockOptionsForm(forms.Form):\n token = forms.CharField(help_text='Your flow API token.')\n\n\nclass FlowdockMessage(NotifyPlugin):\n author = 'Sentry Team'\n author_url = 'https://github.com/getsentry/sentry-flowdock'\n version = sentry_flowdock.VERSION\n description = 'Event notification to Flowdock.'\n resource_links = [\n ('Bug Tracker', 'https://github.com/getsentry/sentry-flowdock/issues'),\n ('Source', 'https://github.com/getsentry/sentry-flowdock'),\n ]\n slug = 'flowdock'\n title = 'Flowdock'\n conf_title = title\n conf_key = 'flowdock'\n project_conf_form = FlowdockOptionsForm\n\n logger = logging.getLogger('sentry.errors')\n base_url = 'https://api.flowdock.com/v1/messages/team_inbox/{token}'\n\n def is_configured(self, project):\n return all((self.get_option(k, project) for k in ('token',)))\n\n def on_alert(self, alert, **kwargs):\n project = alert.project\n token = self.get_option('token', project)\n\n subject = '[{0}] ALERT: {1}'.format(\n project.name.encode('utf-8'),\n alert.message.encode('utf-8')[:50],\n )\n\n message = render_to_string('sentry_flowdock/alert.html', {\n 'alert': alert,\n })\n\n self.send_payload(\n token=token,\n subject=subject,\n message=message,\n link=alert.get_absolute_url(),\n )\n\n def notify_users(self, group, event, **kwargs):\n project = group.project\n token = self.get_option('token', project)\n\n subject = '[%s] %s: %s' % (\n project.name.encode('utf-8'),\n unicode(group.get_level_display()).upper().encode('utf-8'),\n event.error().encode('utf-8').splitlines()[0])\n\n interface_list = []\n for interface in event.interfaces.itervalues():\n body = interface.to_email_html(event)\n if not body:\n continue\n interface_list.append((interface.get_title(), mark_safe(body)))\n\n message = render_to_string('sentry_flowdock/event.html', {\n 'group': group,\n 'event': event,\n 'link': 'http://example.com/link',\n 'interfaces': interface_list,\n 'tags': event.get_tags(),\n })\n\n self.send_payload(\n token=token,\n subject=subject,\n message=message,\n link=group.get_absolute_url(),\n )\n\n def send_payload(self, token, subject, message, link):\n url = self.base_url.format(token=token)\n\n context = {\n 'source': 'Sentry',\n 'from_address': settings.DEFAULT_FROM_EMAIL,\n 'from_name': \"Sentry\",\n 'subject': subject,\n 'content': message,\n 'link': link,\n }\n\n body = json.dumps(context)\n\n headers = {\n 'Content-Type': 'application/json',\n 'User-Agent': 'sentry-flowdock/%s' % (self.version,),\n }\n\n request = urllib2.Request(url, headers=headers)\n try:\n urllib2.urlopen(request, body)\n except urllib2.HTTPError as e:\n self.logger.exception('Unexpected response from Flowdock: %s', e.read())\n","sub_path":"sentry_flowdock/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"311347981","text":"from djitellopy import tello\nimport keyboard as kp\nfrom time import sleep\nimport numpy as np\nimport cv2\nimport math\n\nkp.init()\n# me = tello.Tello()\n# me.connect()\n# print(me.get_battery())\n\n#############parameters###############\nx, y = 500, 500\n\nfSpeed = 120/10\naSpeed = 360/10\ninterval = 0.25\n\na = 0\nyaw = 0\n\ndInterval = fSpeed*interval\naInterval = aSpeed*interval\n\npoints = []\n\ndef getKeyboardInput():\n lr, fb, ud, yv = 0,0,0,0\n speed = 50\n d = 0\n global x, y, yaw, a\n\n if kp.getKey('LEFT'):\n lr = -speed\n d = dInterval\n a = -180\n\n elif kp.getKey('RIGHT'):\n lr = speed\n d = -dInterval\n a = 180\n\n if kp.getKey('UP'):\n fb = speed\n d = dInterval\n a = 270\n\n elif kp.getKey('DOWN'):\n fb = -speed\n d = -dInterval\n a = -90\n\n if kp.getKey('w'):\n ud = speed\n\n elif kp.getKey('s'):\n ud = -speed\n\n if kp.getKey('a'):\n yv = -speed\n yaw -= aInterval\n\n elif kp.getKey('d'):\n yv = speed\n yaw += aInterval\n\n # if kp.getKey(\"q\") : me.land()\n # if kp.getKey(\"e\") : me.takeoff()\n\n sleep(interval)\n a += yaw\n x += int(d * math.cos(math.radians(a)))\n y += int(d * math.sin(math.radians(a)))\n\n return [lr, fb, ud, yv, x, y]\n\ndef drawCircles(img, points):\n for point in points:\n cv2.circle(img, point ,5,(0,0,255), cv2.FILLED)\n cv2.putText(img, f\"({(points[-1][0]-500)/100}, {(points[-1][1]-500)/100})m\",\n (points[-1][0]+10, points[-1][1]+30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)\n\n\nwhile True:\n vals = getKeyboardInput()\n # me.send_rc_control(vals[0], vals[1], vals[2], vals[3])\n\n points.append((vals[4], vals[5]))\n\n img = np.zeros((1000,1000,3), np.uint8)\n drawCircles(img, points)\n cv2.imshow(\"Output\",img)\n cv2.waitKey(1)\n\n","sub_path":"Mapping.py","file_name":"Mapping.py","file_ext":"py","file_size_in_byte":1844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"595856451","text":"class environment(object):\r\n def __init__(self,num_edge_clouds,lis_edge_clouds,dis_adj_mat,net_env_adj_mat,err_adj_mat,num_grades,lis_dist_ec_grade):\r\n self.num_edge_clouds=num_edge_clouds #环境中的edge clous数量，包含移动设备（坐标位置（0，0））\r\n self.num_total_processors=-1\r\n self.lis_processors=[]\r\n if lis_edge_clouds:\r\n self.lis_edge_clouds=lis_edge_clouds #edge clous对象列表\r\n else:\r\n self.lis_edge_clouds=[]\r\n if dis_adj_mat:\r\n self.dis_adj_mat=dis_adj_mat #edge clouds之间的距离-邻接矩阵\r\n else:\r\n self.dis_adj_mat=None\r\n if net_env_adj_mat:\r\n self.net_env_adj_mat=net_env_adj_mat #edge clouds之间的网络环境-邻接矩阵，上三角矩阵 ###统一将上三角矩阵补全 （用!）已改\r\n else:\r\n self.net_env_adj_mat=None\r\n if err_adj_mat:\r\n self.err_adj_mat=err_adj_mat #edge clouds之间的网络的固有传输出错概率的邻接矩阵，上三角矩阵\r\n else:\r\n self.err_adj_mat=None\r\n\r\n self.lis_err_prob_op=None #edge clous固有执行出错概率的列表\r\n\r\n self.num_grades=num_grades #该执行环境中层级的数量\r\n self.lis_dist_ec_grade=lis_dist_ec_grade #各个层级距离移动设备（坐标0，0）的距离（半径r）\r\n # self.dic_grade_class_ecs=dic_grade_class_ecs #记录各个层级（grade）中不同类型（class）的edge cloud的数量列表的字典\r\n\r\n # self.wall_time=0\r\n\r\n def Get_dis_adj_mat(self):\r\n return self.dis_adj_mat\r\n\r\n def Set_lis_ecs(self,lis_ecs):\r\n self.lis_edge_clouds=lis_ecs\r\n\r\n def Set_dis_adj_mat(self,dis_adj_mat):\r\n self.dis_adj_mat=dis_adj_mat\r\n\r\n def Set_net_env_adj_mat(self,net_env_adj_mat):\r\n self.net_env_adj_mat=net_env_adj_mat\r\n\r\n def Set_err_adj_mat(self,err_adj_mat):\r\n self.err_adj_mat=err_adj_mat\r\n\r\n def Set_lis_err_prob_op(self,lis_err_prob_op):\r\n self.lis_err_prob_op=lis_err_prob_op\r\n\r\n def reset(self):\r\n for ec in self.lis_edge_clouds:\r\n self.num_total_processors+=ec.num_processors\r\n self.lis_processors.extend(ec.lis_processors)\r\n for ec in self.lis_edge_clouds:\r\n ec.reset()\r\n for i in range(self.num_edge_clouds):\r\n for j in range(self.num_edge_clouds):\r\n if i!=j:\r\n self.net_env_adj_mat[i][j].reset()\r\n","sub_path":"edge_cloud_env/environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":2622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"437795952","text":"import sys\nfrom gurobipy import *\n\n\nCourseQuota = { \"cmpe478\": 2, \"cmpe352\": 2, \"cmpe362\": 4 } \n\nNumStudentCourses = { \"ali\":2, \"veli\": 3, \"aliye\":2}\n\nCourseSelections = {\"ali\": [\"cmpe478\",\"cmpe352\"], \"veli\": [\"cmpe478\",\"cmpe352\"], \n \"aliye\": [\"cmpe362\",\"cmpe352\",\"cmpe478\"] } \n\ntry:\n\n # Create a new model\n model = Model(\"mip1\")\n \n\n # Create variables\n x = [] \n c2s = {} \n i = 0 \n for s in CourseSelections: \n cl = CourseSelections[s]\n for c in cl: \n varname = s + c ; \n x.append(model.addVar(vtype=GRB.BINARY, name=varname))\n if c not in c2s:\n c2s[c] = [ i ]\n else:\n c2s[c].append(i) \n i = i + 1 \n\n # Set objective\n obj = LinExpr();\n for v in x:\n obj += v \n model.setObjective(obj, GRB.MAXIMIZE);\n \n # Add constraints for NumStudentCourses \n k = 0 \n for s in CourseSelections: \n cl = CourseSelections[s] \n cvarlist = [x[i] for i in range(k,k+len(cl)) ] \n oneconst = [1 for i in range(0,len(cl))]\n constraintexpr = LinExpr(oneconst,cvarlist)\n model.addConstr(constraintexpr <= NumStudentCourses[s])\n k = k+len(cl) \n \n # Add constraints for CourseQuota \n for c in CourseQuota: \n cl = c2s[c]\n cvarlist = [x[cl[i]] for i in range(0,len(cl)) ] \n oneconst = [1 for i in range(0,len(cl)) ]\n constraintexpr = LinExpr(oneconst,cvarlist)\n model.addConstr(constraintexpr <= CourseQuota[c]) \n \n # Optimize model\n model.optimize()\n\n for v in model.getVars():\n print('%s %g' % (v.varName, v.x))\n\n print('Obj: %g' % model.objVal)\n\nexcept GurobiError as e:\n print('Error code ' + str(e.errno) + \": \" + str(e))\n\nexcept AttributeError:\n print('Encountered an attribute error')\n","sub_path":"gurobi/grb1copy.py","file_name":"grb1copy.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"482738485","text":"import numpy as np\n\nfrom .pipeline_evaluator import PipelineEvaluator\n\nclass TrainedPipeline(object):\n \"\"\"\n Class mostly for storing metadata on pipelines (at least for the moment)\n \"\"\"\n def __init__(self, pipeline_id=None, pipeline=None, scoring_metric=None,\n score_type='median'):\n ############### Initialize fields ###############\n self.id = pipeline_id\n\n self.pipeline = pipeline\n\n self.estimator_type = None\n\n # Whether a median and IQR or mean and std metric are used for\n # statistics\n self.score_type = score_type\n\n self.test_scores = []\n self.train_scores = []\n\n # Metric ('rmse', 'accuracy', 'auc') used to score pipelines\n self.scoring_metric = scoring_metric\n\n # Data related\n self.X_train = None\n self.y_train = None\n self.y_test_pred = None\n\n self.X_test = None\n self.y_test = None\n self.y_train_pred = None\n\n ############### Infer fields ###############\n if self.scoring_metric == 'rmse':\n self.estimator_type = 'regressor'\n else:\n self.estimator_type = 'classification'\n\n def fit(self, X_train, y_train, X_test, y_test):\n \"\"\"\n Simple wrapper for the fit method of the pipeline\n \"\"\"\n ############### Save inputs ###############\n self.X_train = X_train\n self.y_train = y_train\n\n self.X_test = X_test\n self.y_test = y_test\n\n ############### Fit on training data ###############\n # Fit input data to individual pipeline\n self.pipeline.fit(X_train, y_train)\n\n ############### Get predicted targets ###############\n self.y_test_pred = self.predict(X_test)\n\n self.y_train_pred = self.predict(X_train)\n\n ############### Obtain relevant scores ###############\n evaluator = PipelineEvaluator()\n\n # Calculate train/test scores\n self.train_scores.append(evaluator.get_score(self.y_train,\n self.y_train_pred,\n self.scoring_metric))\n\n self.test_scores.append(evaluator.get_score(self.y_test,\n self.y_test_pred,\n self.scoring_metric))\n\n def predict(self, X):\n \"\"\"\n Predicts targets given a feature array\n \"\"\"\n return self.pipeline.predict(X)\n\nclass OuterFoldTrainedPipeline(TrainedPipeline):\n \"\"\"\n Class for pipelines contained in outer loops\n \"\"\"\n def __init__(self, pipeline_id=None, pipeline=None, scoring_metric=None,\n score_type='median'):\n # Tell class it is itself for weird Jupyter notebook %autoload\n # incompatibility\n self.__class__ = OuterFoldTrainedPipeline\n\n super(OuterFoldTrainedPipeline, self).__init__(\n pipeline_id=pipeline_id,\n pipeline=pipeline,\n scoring_metric=scoring_metric,\n score_type=score_type)\n\n ############### Initialize addtional fields ###############\n self.inner_loop_test_scores = None\n self.inner_loop_train_scores = None\n\n def set_inner_loop_scores(self, train_scores, test_scores):\n self.inner_loop_train_scores = train_scores\n self.inner_loop_test_scores = test_scores\n\n def get_inner_loop_score_center(self, score_type=None, fold_type=None):\n \"\"\"\n Returns the measure of centrality of the inner-fold test or train\n scores\n\n score_type : str, {'mean', 'median'}, optional\n Statistical measure to return\n mean : Use the highest mean of the inner-fold test scores to pick\n the best pipeline/model\n median : Use the highest median of the inner-fold test scores to\n pick the best pipeline/model\n\n fold_type : str, {'test', 'train'}\n Type of score to obtain for designated pipeline\n test: Score(s) for test fold\n train: Score(s) for train fold\n\n \"\"\"\n ############### Check inputs ###############\n if not score_type or type(score_type) is not str \\\n or score_type not in ['mean', 'median']:\n\n raise Exception(\"The keyword argument dictating the test \" \\\n \"statistic to return, score_type, must be either\" \\\n \" 'mean' or 'median'\")\n\n if not fold_type or type(fold_type) is not str \\\n or fold_type not in ['test', 'train']:\n\n raise Exception(\"The keyword argument indicating the type of \" \\\n \"fold, fold_type, must be 'test or 'train'\")\n\n # Get scores\n if fold_type == 'test':\n fold_scores = self.inner_loop_test_scores\n elif fold_type == 'train':\n fold_scores = self.inner_loop_train_scores\n\n # Get measure of centrality\n if score_type == 'mean':\n centrality_measure = np.mean(fold_scores)\n elif score_type == 'median':\n centrality_measure = np.median(fold_scores)\n\n return centrality_measure\n","sub_path":"pyplearnr/trained_pipeline.py","file_name":"trained_pipeline.py","file_ext":"py","file_size_in_byte":5233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"89627074","text":"import numpy as np\nimport chaospy as cp\nfrom scipy.integrate import odeint\nimport matplotlib.pyplot as plt\nimport time\n\n\n# to use the odeint function, we need to transform the second order differential equation\n# into a system of two linear equations\ndef model(init_cond, t, params):\n z0, z1 = init_cond\n c, k, f, w = params\n\n f = [z1, f * np.cos(w * t) - k * z0 - c * z1]\n\n return f\n\n\n# discretize the oscillator using the odeint function\ndef discretize_oscillator_odeint(model, init_cond, t, args, atol, rtol):\n sol = odeint(model, init_cond, t, args=(args,), atol=atol, rtol=rtol)\n\n return sol\n\n\nc = 0.5\nk = 2.0\nf = 0.5\nw = 1.0\ny0 = 0.5\ny1 = 0.\n\n# time domain setup\nt_max = 20.\ndt = 0.01\n\n# arguments setup for calling the three discretization functions\nparams = c, k, f, w, y0, y1\ninit_cond = y0, y1\nparams_odeint = c, k, f, w\n\n# relative and absolute tolerances for the ode int solver\natol = 1e-10\nrtol = 1e-10\n\n# ploting\ngrid_size = int(t_max / dt) + 1\nt = np.linspace(0, t_max, grid_size, endpoint=True)\nt_10 = int(10 / dt) + 1\nprint(t_10)\n\nsol_odeint = discretize_oscillator_odeint(model, init_cond, t, params_odeint, atol, rtol)\ny_10_determ = sol_odeint[t_10]\nprint(\"Deterministic solution:\", y_10_determ)\n\n\n##### Generating different trajectories\n\nN = [10, 100, 1000, 10000]\nmu, V = np.zeros((len(N), 2)), np.zeros((len(N), 2))\nmu_quasi, V_quasi = np.zeros((len(N), 2)), np.zeros((len(N), 2))\n\nnp.random.seed(120)\nfor i, n in enumerate(N):\n distr = cp.Uniform(0.95, 1.05)\n w_generated, w_Halton = distr.sample(size=n), distr.sample(size=n, rule =\"H\")\n outputs_y, outputs_y_Halton = [], []\n print(\"Calculating ... \", n)\n for (w_value, w_quasi) in zip(w_generated, w_Halton):\n params_odeint = c, k, f, w_value\n params_odeint_Halton = c, k, f, w_quasi\n sol_odeint = discretize_oscillator_odeint(model, init_cond, t, params_odeint, atol, rtol)\n sol_odeint_Halton = discretize_oscillator_odeint(model, init_cond, t, params_odeint_Halton, atol, rtol)\n outputs_y.append(sol_odeint[t_10])\n outputs_y_Halton.append(sol_odeint_Halton[t_10])\n mu[i] = np.mean(np.array(outputs_y), axis = 0)\n V[i] = np.var(np.array(outputs_y), axis = 0)\n mu_quasi[i] = np.mean(np.array(outputs_y_Halton), axis=0)\n V_quasi[i] = np.var(np.array(outputs_y_Halton), axis=0)\n\nprint(\"Generated mean and variance:\")\nfor i in range(mu.shape[0]):\n print(\"N = %6d\" %N[i],\"mean :\",\"%.3f\\t%.3f\" %(mu[i][0], mu[i][1]))\n print(\"\\t\\t\\tvar :%.6f\\t%.6f\" %(V[i][0], V[i][1]))\n\nprint(\"Generated mean and variance via Halton sequences:\")\nfor i in range(mu.shape[0]):\n print(\"N = %6d\" % N[i], \"mean :\", \"%.3f\\t%.3f\" % (mu[i][0], mu[i][1]))\n print(\"\\t\\t\\tvar :%.6f\\t%.6f\" % (V[i][0], V[i][1]))\n\n\nmu_ref = [-0.43893703, 0.04293818]\nV_ref = [0.00019678, 0.01336294]\n\nrel_err_mu = np.abs(1 - mu/ mu_ref).T\nrel_err_V = np.abs(1 - V / V_ref).T\n\nrel_err_mu_quasi = np.abs(1 - mu_quasi / mu_ref).T\nrel_err_V_quasi = np.abs(1 - V_quasi / V_ref).T\n\n#plotting relative errors\nplt.figure(\"Relative error, mean\")\nplt.loglog(N, rel_err_mu[0], 'r--', label='rel err (mu), y0')\nplt.loglog(N, rel_err_mu[1], 'b--', label='rel err (mu), y1')\nplt.loglog(N, rel_err_mu_quasi[0], 'r-', label='rel err quasi (mu), y0')\nplt.loglog(N, rel_err_mu_quasi[1], 'b-', label='rel err quasi (mu), y1')\nplt.legend(loc='best', fontsize=8)\nplt.ylabel('Error values')\nplt.xlabel('Number of samples (loglog)')\n\n\nplt.figure(\"Relative error, variance\")\nplt.loglog(N, rel_err_V[0], 'r--', label='rel err (Var), y0')\nplt.loglog(N, rel_err_V[1], 'b--', label='rel err (Var), y1')\nplt.loglog(N, rel_err_V_quasi[0], 'r-', label='rel err quasi (Var), y0')\nplt.loglog(N, rel_err_V_quasi[1], 'b-', label='rel err quasi (Var), y1')\nplt.legend(loc='best', fontsize=8)\nplt.ylabel('Error values')\nplt.xlabel('Number of samples (loglog)')\nplt.show()\n\n","sub_path":"week_3_tutorial_3_HW/Programming 1/Programming1_Maryna_Nemyrovska/assignment_4.2.py","file_name":"assignment_4.2.py","file_ext":"py","file_size_in_byte":3860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"652518129","text":"from redis import StrictRedis\nfrom time import sleep\nimport os\n\n\nclass BCNRedis(object):\n \"\"\"\n This class will be used to connect to local redis server running\n on the device. It can be acts as mediator between the device connector and services running\n in the device through the pub/sub mechanism\n \"\"\"\n def __init__(self, host='10.16.86.181', port=6379):\n self.port = port\n self.host = host\n self.redis_obj = StrictRedis(host=self.host, port=self.port)\n self.pub_sub_obj = self.redis_obj.pubsub()\n self.time_to_exit = False\n self.sub_dict = dict()\n self.theard_id = None\n #self.log = log_handle\n # self.sub_dict['/device/fwConfig'] = self.redis_msg_callback\n\n def redis_get_value(self, key_name):\n \"\"\"\n This Function performs the Redis in-memory database look up based on key\n and returns the corresponding value\n :param key_name:\n :return: Value if key exists in the in-memory Database\n None if key not found\n \"\"\"\n return self.redis_obj.get(key_name)\n \n\n def redis_set_value(self, key_name, value):\n \"\"\"\n This function will set the key with the corresponding value in-memroy redis data store\n :param key_name:\n :param value:\n :return: True if the value is set\n \"\"\"\n return self.redis_obj.set(key_name, value)\n\n","sub_path":"rest_framework_testing/rest/redis_con.py","file_name":"redis_con.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"45655466","text":"\"\"\"\nelasticapm.conf\n~~~~~~~~~~\n\n:copyright: (c) 2011-2017 Elasticsearch\n\nLarge portions are\n:copyright: (c) 2010 by the Sentry Team, see AUTHORS for more details.\n:license: BSD, see LICENSE for more details.\n\"\"\"\n\nimport logging\n\n__all__ = ('setup_logging', )\n\n\ndef setup_logging(handler, exclude=['elasticapm',\n 'gunicorn',\n 'south',\n 'elasticapm.errors']):\n \"\"\"\n Configures logging to pipe to Elastic APM.\n\n - ``exclude`` is a list of loggers that shouldn't go to ElasticAPM.\n\n For a typical Python install:\n\n >>> from elasticapm.handlers.logging import LoggingHandler\n >>> client = ElasticAPM(...)\n >>> setup_logging(LoggingHandler(client))\n\n Within Django:\n\n >>> from elasticapm.contrib.django.handlers import LoggingHandler\n >>> setup_logging(LoggingHandler())\n\n Returns a boolean based on if logging was configured or not.\n \"\"\"\n logger = logging.getLogger()\n if handler.__class__ in map(type, logger.handlers):\n return False\n\n logger.addHandler(handler)\n\n # Add StreamHandler to sentry's default so you can catch missed exceptions\n for logger_name in exclude:\n logger = logging.getLogger(logger_name)\n logger.propagate = False\n logger.addHandler(logging.StreamHandler())\n\n return True\n","sub_path":"elasticapm/conf/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"379081055","text":"from typing import Any # noqa: D100\n\nfrom pydantic.error_wrappers import ValidationError\n\nfrom modules.airtable.shared_table import BaseAirtableTable\nfrom modules.models.scheduled_message_models import ScheduledMessageInfo\nfrom modules.utils import snake_case\n\n\nclass ScheduledMessagesTable(BaseAirtableTable): # noqa: D101\n def __init__(self): # noqa: ANN101, ANN204, D107\n super().__init__(\"Scheduled Messages\")\n\n @property\n def all_valid_scheduled_messages(self) -> list[ScheduledMessageInfo]: # noqa: ANN101, D102\n return [self.parse_scheduled_message_row(row) for row in self.all(view=\"Valid\")]\n\n @staticmethod\n def parse_scheduled_message_row(row: dict[str, Any]) -> ScheduledMessageInfo: # noqa: D102\n fields = {snake_case(k): v for k, v in row[\"fields\"].items()}\n try:\n return ScheduledMessageInfo(\n **fields,\n airtable_id=row[\"id\"],\n created_at=row[\"createdTime\"],\n )\n except ValidationError as valid_e:\n raise valid_e # noqa: TRY201\n","sub_path":"modules/airtable/scheduled_message_table.py","file_name":"scheduled_message_table.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"446662740","text":"\"\"\" Pseudo provides static monitor support for file alteration events \"\"\"\n\nimport os\nimport logging\nfrom Bcfg2.Server.FileMonitor import FileMonitor, Event\n\nlogger = logging.getLogger(__name__)\n\nclass Pseudo(FileMonitor):\n __priority__ = 99\n\n def AddMonitor(self, path, obj, handleID=None):\n \"\"\"add a monitor to path, installing a callback to obj.HandleEvent\"\"\"\n if handleID is None:\n handleID = len(list(self.handles.keys()))\n self.events.append(Event(handleID, path, 'exists'))\n if os.path.isdir(path):\n dirList = os.listdir(path)\n for includedFile in dirList:\n self.events.append(Event(handleID, includedFile, 'exists'))\n self.events.append(Event(handleID, path, 'endExist'))\n\n if obj != None:\n self.handles[handleID] = obj\n return handleID\n","sub_path":"src/lib/Bcfg2/Server/FileMonitor/Pseudo.py","file_name":"Pseudo.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"28388916","text":"import time\n\n\nclass BinarySearchTree:\n def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None\n\n def insert(self, value):\n if value < self.value:\n # check left add left if none if less than self.value\n if not self.left:\n self.left = BinarySearchTree(value)\n else:\n self.left.insert(value)\n else:\n # check right go right if none\n if not self.right:\n self.right = BinarySearchTree(value)\n else:\n self.right.insert(value)\n\n def contains(self, target):\n if self.value == target:\n return True\n\n if target < self.value:\n if not self.left:\n return False\n else:\n return self.left.contains(target)\n else:\n if not self.right:\n return False\n else:\n return self.right.contains(target)\n\n\nstart_time = time.time()\n\nf = open('names_1.txt', 'r')\nnames_1 = f.read().split(\"\\n\") # List containing 10000 names\nf.close()\n\nf = open('names_2.txt', 'r')\nnames_2 = f.read().split(\"\\n\") # List containing 10000 names\nf.close()\n\n\n# kind of suprised hash is working. I think there are downsides for a real application.\n# Creating a bst hashing each name and inserting the hash value into tree\n# Hashing names in set 2 and searching with contains for duplicates\n# Appending duplicates to duplicates if contains check passes\n\n# runtime: 0.08380603790283203 seconds\n\nduplicates = []\n\nbst = BinarySearchTree(0)\nfor name_1 in names_1:\n hash_name = hash(name_1)\n # num_name = int(name_1)\n bst.insert(hash_name)\n\nfor name_2 in names_2:\n hash_name = hash(name_2)\n if bst.contains(hash_name):\n # num_name = int(name_2)\n duplicates.append(name_2)\n\n# runtime: 5.326942205429077 seconds\n\n# for name_1 in names_1:\n# for name_2 in names_2:\n# if name_1 == name_2:\n# duplicates.append(name_1)\n\nend_time = time.time()\nprint(f\"{len(duplicates)} duplicates:\\n\\n{', '.join(duplicates)}\\n\\n\")\nprint(f\"runtime: {end_time - start_time} seconds\")\n","sub_path":"names/names.py","file_name":"names.py","file_ext":"py","file_size_in_byte":2185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"546048118","text":"import sys\nimport os\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom scipy.stats import dweibull\nfrom matplotlib import rc\n\nplt.style.use('seaborn-whitegrid')\n\n\nmatplotlib.rcParams.update({'font.size': 14})\n#matplotlib.rcParams.update({'text.latex.unicode': True})\n#rc('font',**{'family':'sans-serif','sans-serif':['Source Sans Pro']})\n\nmatplotlib.rcParams['font.family'] = 'sans-serif'\n\nfig = plt.figure()\nax = plt.axes()\nfilenames = sys.argv\n\nzcs = []\nvalues = []\nmaxPad = []\nminZ = sys.float_info.max\nmaxZ = -sys.float_info.max;\n# 0 is the script name\nfor i in range(1, len(filenames) - 1):\n #read value\n zc, value = np.loadtxt(filenames[i], dtype='float, float', delimiter='\\t', usecols=(4, 5), unpack=True, skiprows=11)\n zcs.append(zc)\n values.append(value)\n maxPad.append(max(value))\n tmpMinZ = min(zc) \n tmpMaxZ = max(zc)\n if tmpMinZ < minZ:\n minZ = tmpMinZ\n\n if tmpMaxZ > maxZ:\n maxZ = tmpMaxZ\n\nmaxOfMax = max(maxPad)\nax.plot(values[0], zcs[0], label= \"scan 0\")\nax.plot(values[1], zcs[1], label= \"scan 1\")\nax.plot(values[2], zcs[2], label= \"scan 2\")\nax.plot(values[3], zcs[3], label= \"scan 3\")\nax.plot(values[4], zcs[4], label= \"scan 4\")\nax.plot(values[5], zcs[5], label= \"Larchitect\")\n\n\n#filename2 = sys.argv[2]\n#filename3 = sys.argv[2]\n\ntitle = filenames[len(filenames) - 1]\nxlabel = \"PAD\"\nylabel = \"z (m)\"\nax.legend()\nax.set_ylim(minZ, maxZ)\nminor_ticks = np.arange(int(minZ), maxZ)\nax.set_yticks(minor_ticks, minor=True)\nax.grid(which='both')\nax.grid(which='minor', alpha=0.2)\nax.grid(which='major', alpha=0.5)\n\n\n#print \"its me\"\n\n\nplt.title(title)\nplt.xlabel(xlabel)\nplt.ylabel(ylabel);\n\nfig.set_size_inches(6, 9)\nbase = os.path.splitext(filenames[1])[0]\noutfile = base + \".pdf\"\nfig.savefig(outfile)\n#plt.show()\n","sub_path":"scan0vs1234.py","file_name":"scan0vs1234.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"342730445","text":"# https://leetcode.com/problems/how-many-numbers-are-smaller-than-the-current-number/\n\nclass Solution:\n def smallerNumbersThanCurrent(self, nums):\n dic = {}\n sorted_list = sorted(nums)\n\n for i,n in enumerate(sorted_list):\n if dic.get(n) == None:\n dic[n] = i\n return [dic[n] for n in nums]\n\nprint(Solution.smallerNumbersThanCurrent(Solution, nums=[7,7,7,7]))","sub_path":"Leetcode/Easy/1365_HowManyNumbersAreSmallerThanTheCurrentNumber.py","file_name":"1365_HowManyNumbersAreSmallerThanTheCurrentNumber.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"151466670","text":"import random\nimport os\ndef main():\n x = random.randrange(1,300)\n y = random.randrange(1,300)\n print(x, y)\nmain()\n\n#This imports the turtle module\nimport turtle\n#This imports the random module\nimport random\n#This will create a light blue screen\nwn = turtle.Screen()\nwn.bgcolor(\"lightblue\")\njeb = turtle.Turtle()\nmitch = turtle.Turtle()\n#This will make Jeb's turtle orange and Mitch's turtle blue.\njeb.color(\"orange\")\nmitch.color(\"blue\")\n#This will make both of their turtles to be shaped like actual turtles.\njeb.shape(\"turtle\")\nmitch.shape(\"turtle\")\n#This will pick up the pen so that they do not make lines.\njeb.up()\nmitch.up()\n#This will make them go to position (-100,20)\njeb.goto(-100,20)\nmitch.goto(-100,-20)\n#Race 1: This will make them go forward one at a time.\njeb.forward(random.randrange(1,300))\nmitch.forward(random.randrange(1,300))\n#This will reset the turtles to their original positions and prepare them for race 2.\njeb.goto(-100,20)\nmitch.goto(-100,-20)\n#Race 2: This will create a for loop which moves the turtles forward by 1. Then, the random function will determine how many times this loops.\nrandomnumbers = random.randrange(1,300)\nfor number in range(1, randomnumbers):\n jeb.forward(1)\n mitch.forward(1)\n#This will reset the turtles to their original positions and prepare them for race 3.\njeb.goto(-100,20)\nmitch.goto(-100,-20)\n#Race 3: This will create a single for loop that will loop 300 times. Inside this loop, each turtle will move between 0 and 3.\nfor i in range(300):\n amount=random.randrange(4)\n jeb.forward(amount)\n amount=random.randrange(4)\n mitch.forward(amount)\n# wn.exitonclick()\n\ndef months():\n\tmonthList = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\",\"December\"]\n\tfor i in monthList:\n\t\tprint(\"One of the months is\", i)\nmonths()\nprint(\"===END MONTH LIST===\")\ndef numbers():\n\tnumberList=[12,10,32,3,99]\n\tfor i in numberList:\n\t\tprint(i, i**2)\nnumbers()\nprint(\"===END NUMBER LIST===\")\n\nimport turtle\ndef drawTriangle(mitch, length):\n\tmitch.left(30)\n\tmitch.forward(length)\n\tfor i in range(2):\n\t\tmitch.left(120)\n\t\tmitch.forward(length)\n\t\tmitch.left(120)\n\t\tmitch.forward(length)\n\ndef drawSquare(mitch, length):\n\tfor i in range(4):\n\t\tmitch.left(90)\n\t\tmitch.forward(length)\n\ndef drawHexagon(mitch, length):\n\tmitch.right(150)\n\tmitch.forward(length)\n\tfor i in range(5):\n\t\tmitch.left(60)\n\t\tmitch.forward(50)\n\ndef drawOctagon(mitch, length):\n\tmitch.right(135)\n\tmitch.forward(50)\n\tfor i in range(7):\n\t\tmitch.left(45)\n\t\tmitch.forward(50)\n\ndef main():\n\twn = turtle.Screen()\n\tjeb = turtle.Turtle()\n\tjeb.shape(\"turtle\")\n\tjeb.color(\"green\")\n\tlength = 50\n\tprint(length)\n\tdrawTriangle(jeb, length)\n\tdrawSquare(jeb, length)\n\tdrawHexagon(jeb,length)\n\tdrawOctagon(jeb, length)\nmain()\nprint(\"===END POLYGON TURTLES===\")\nimport turtle\n\nsides = int(input(\"Please enter the number of sides in your regular polygon:\"))\nmyvar1 = int(sides)\nmyvar2 = 180*myvar1\nmyvar3 = myvar2 - 360\nmyvar4 = myvar3/myvar1\nprint(myvar4)\nsides2 = int(sides-1)\nprint(sides2)\n\nlength = int(input(\"Please enter a length\"))\n\n\ndef drawRectangle(mitch, length):\n for i in range(sides2):\n mitch.fd(length)\n mitch.left(myvar4)\n\ndef main():\n wn = turtle.Screen()\n jeb = turtle.Turtle()\n jeb.shape(\"turtle\")\n jeb.color(input(\"Please enter the desired turtle color\"), input(\"Please enter the desired fill color\"))\n jeb.begin_fill()\n drawRectangle(jeb, length)\n jeb.end_fill()\n wn.exitonclick()\nmain()\n","sub_path":"resources/badcode.py","file_name":"badcode.py","file_ext":"py","file_size_in_byte":3507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"69950932","text":"# -*- coding: utf-8 -*-\n# © <2017> \n# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl.html).\n\nfrom random import randint\nimport logging\n\nfrom odoo import fields, models, api\nfrom odoo.exceptions import Warning\n\n_logger = logging.getLogger(__name__)\n\nclass hr_cfdi_mail_wizard(models.TransientModel):\n _name = 'hr.cfdi.mail.wizard'\n _description = 'HR CFDI MAIL WIZARD'\n\n\n mail_action = fields.Selection(\n string='Acción',\n default='CHECK_TOKEN',\n selection=[\n ('CHECK_TOKEN', 'Validar token'),\n ('SEND_TOKEN', 'Enviar token')\n ]\n )\n employee_id = fields.Many2one(\n string='Empleado',\n comodel_name='hr.employee'\n )\n cfdi_token = fields.Char(\n string='Token'\n )\n\n @api.multi\n def cfdi_mail_action(self):\n\n if self.mail_action == 'SEND_TOKEN':\n\n self.employee_id.cfdi_mail_token = randint(1000, 9999)\n self.employee_id.cfdi_mail_ok = False\n\n template = self.env.ref('hr_paysheet.cfdi_mail_send_token_template')\n\n self.env['mail.template'].browse(template.id).sudo().send_mail(self.employee_id.id, force_send=True)\n\n if self.mail_action == 'CHECK_TOKEN':\n\n if str(self.employee_id.cfdi_mail_token) == str(self.cfdi_token):\n self.employee_id.cfdi_mail_ok = True\n else:\n raise Warning('El token no coincide.')","sub_path":"hr_paysheet/wizards/hr_cfdi_mail_wizard.py","file_name":"hr_cfdi_mail_wizard.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"232266622","text":"from kivy.app import App\nfrom kivy.uix.button import Button\nfrom kivy.uix.textinput import TextInput\nfrom kivy.uix.boxlayout import BoxLayout\n\nclass ClearApp(App):\n def build(self):\n self.box = BoxLayout(orientation='horizontal', spacing=20)\n self.txt = TextInput(hint_text='Write here', size_hint=(.5,.1))\n self.btn = Button(text='Clear All', on_press=self.clearText, size_hint=(.1,.1))\n # self.btn = Button(text='Clear All', on_press=cetakk, size_hint=(.1, .1))\n self.box.add_widget(self.txt)\n self.box.add_widget(self.btn)\n return self.box\n\n def clearText(self, instance):\n print(self.txt.text)\n self.txt.text = ''\n\ndef cetakk():\n print(\"Hello World\")\n\nClearApp().run()","sub_path":"kivy4.py","file_name":"kivy4.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"155285472","text":"#\n# This file is part of do-mpc\n#\n# do-mpc: An environment for the easy, modular and efficient implementation of\n# robust nonlinear model predictive control\n#\n# Copyright (c) 2014-2016 Sergio Lucia, Alexandru Tatulea-Codrean\n# TU Dortmund. All rights reserved\n#\n# do-mpc is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Lesser General Public License as\n# published by the Free Software Foundation, either version 3\n# of the License, or (at your option) any later version.\n#\n# do-mpc 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 Lesser General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with do-mpc. If not, see .\n#\n\nimport matplotlib.pyplot as plt\nfrom casadi import *\nimport numpy as NP\nimport core_do_mpc\nfrom matplotlib.ticker import MaxNLocator\nimport scipy.io\n\n\nclass mpc_data:\n \"A class for the definition of the mpc data that is managed throughout the mpc loop\"\n def __init__(self, configuration):\n # get sizes\n nx = configuration.model.x.size(1)\n nu = configuration.model.u.size(1)\n np = configuration.model.p.size(1)\n if NP.size(configuration.model.z) > 0: # If DAE\n nz = configuration.model.z.size(1)\n else: # Model is ODE\n nz = 0\n t_end = configuration.optimizer.t_end\n t_step = configuration.simulator.t_step_simulator\n # Initialize the data structures\n self.mpc_states = NP.resize(NP.array([]),(1 ,nx))\n self.mpc_control = NP.resize(NP.array([]),(1 ,nu))\n self.mpc_alg = NP.resize(NP.array([]),(1, nz))\n self.mpc_time = NP.resize(NP.array([]),(1, 1))\n self.mpc_cost = NP.resize(NP.array([]),(1, 1))\n self.mpc_ref = NP.resize(NP.array([]),(1, 1))\n self.mpc_cpu = NP.resize(NP.array([]),(1, 1))\n self.mpc_parameters = NP.resize(NP.array([]),(1, np))\n # Initialize with initial conditions\n self.mpc_states[0,:] = configuration.model.ocp.x0 / configuration.model.ocp.x_scaling\n self.mpc_control[0,:] = configuration.model.ocp.u0 / configuration.model.ocp.u_scaling\n self.mpc_time[0] = 0\n\nclass opt_result:\n \"\"\" A class for the definition of the result of an optimization problem containing optimal solution, optimal cost and value of the nonlinear constraints\"\"\"\n def __init__(self,res):\n self.optimal_solution = NP.array(res[\"x\"])\n self.optimal_cost = NP.array(res[\"f\"])\n self.constraints = NP.array(res[\"g\"])\n\n\n\ndef export_to_matlab(configuration):\n if configuration.simulator.export_to_matlab:\n data = configuration.mpc_data\n export_name = configuration.simulator.export_name\n x_scaling = configuration.model.ocp.x_scaling\n u_scaling = configuration.model.ocp.u_scaling\n export_dict = {\n \"mpc_states\":data.mpc_states * x_scaling,\n \"mpc_control\":data.mpc_control * u_scaling,\n \"mpc_alg\": data.mpc_alg,\n \"mpc_time\": data.mpc_time,\n \"mpc_cost\": data.mpc_cost,\n \"mpc_ref\": data.mpc_ref,\n \"mpc_parameters\": data.mpc_parameters,\n }\n scipy.io.savemat(export_name, mdict=export_dict)\n print(\"Exporting to Matlab as ''\" + export_name + \"''\")\n\ndef plot_mpc(configuration):\n \"\"\" This function plots the states and controls chosen in the variables plot_states and plot_control until a certain index (index_mpc) \"\"\"\n mpc_data = configuration.mpc_data\n mpc_states = mpc_data.mpc_states\n mpc_control = mpc_data.mpc_control\n mpc_time = mpc_data.mpc_time\n index_mpc = configuration.simulator.mpc_iteration\n plot_states = configuration.simulator.plot_states\n plot_control = configuration.simulator.plot_control\n x = configuration.model.x\n x_scaling = configuration.model.ocp.x_scaling\n u = configuration.model.u\n u_scaling = configuration.model.ocp.u_scaling\n\n plt.ion()\n fig = plt.figure(1)\n total_subplots = len(plot_states) + len(plot_control)\n # First plot the states\n for index in range(len(plot_states)):\n \tplot = plt.subplot(total_subplots, 1, index + 1)\n \tplt.plot(mpc_time[0:index_mpc], mpc_states[0:index_mpc,plot_states[index]] * x_scaling[plot_states[index]])\n \tplt.ylabel(str(x[plot_states[index]]))\n \tplt.xlabel(\"Time\")\n \tplt.grid()\n \tplot.yaxis.set_major_locator(MaxNLocator(4))\n\n # Plot the control inputs\n for index in range(len(plot_control)):\n \tplot = plt.subplot(total_subplots, 1, len(plot_states) + index + 1)\n \tplt.plot(mpc_time[0:index_mpc], mpc_control[0:index_mpc,plot_control[index]] * u_scaling[plot_control[index]] ,drawstyle='steps')\n \tplt.ylabel(str(u[plot_control[index]]))\n \tplt.xlabel(\"Time\")\n \tplt.grid()\n \tplot.yaxis.set_major_locator(MaxNLocator(4))\n\n\n\ndef plot_state_pred(v,t0,el,lineop, n_scenarios, n_branches, nk, child_scenario, X_offset, x_scaling, t_step):\n # This function plots the prediction of a state\n #plt.clf()\n plt.hold(True)\n # Time grid\n tf = t_step * nk\n tgrid = NP.linspace(t0,t0+tf,nk+1)\n # For all control intervals\n for k in range(nk):\n # For all scenarios\n for s in range(n_scenarios[k]):\n # For all uncertainty realizations\n for b in range(n_branches[k]):\n # Get state trajectory segment\n x_beginning = v[el+X_offset[k][s]]\n s_next = child_scenario[k][s][b]\n x_end = v[el+X_offset[k+1][s_next]]\n x_segment = NP.array([x_beginning,x_end])*x_scaling[el]\n plt.plot(tgrid[k:k+2],x_segment,lineop)\n\n\ndef plot_control_pred(v,t0,el,lineop, n_scenarios, n_branches, nk, parent_scenario, U_offset, u_scaling, t_step, u_last_step):\n\t# This function plots the prediction of a control input\n\tplt.hold(True)\n\t# Time grid\n\ttf = t_step * nk\n\ttgrid = NP.linspace(t0,t0+tf,nk+1)\n\t# For all control intervals\n\tfor k in range(nk):\n\t\t# For all scenarios\n\t\tfor s in range(n_scenarios[k]):\n\t\t\t# Time segment\n\t\t\tt_beginning = tgrid[k]\n\t\t\tt_end = tgrid[k+1]\n\n\t\t\t# Plot state trajectory segment\n\t\t\tu_this = v[el+U_offset[k][s]]*u_scaling[el]\n\t\t\tplt.plot(NP.array([t_beginning,t_end]),NP.array([u_this,u_this]),lineop)\n\n\t\t\t# Plot vertical line connecting the scenarios\n\t\t\tif k == 0:\n\t\t\t\tu_prev = u_last_step\n\t\t\telse:\n\t\t\t\tu_prev = v[el+U_offset[k-1][parent_scenario[k][s]]]*u_scaling[el]\n\t\t\tplt.plot(NP.array([t_beginning,t_beginning]),NP.array([u_prev,u_this]),lineop)\n\n\n\ndef plot_animation(configuration):\n \"\"\"This function plots the current evolution of the system together with the predicted trajectories at the current time \"\"\"\n if configuration.simulator.plot_anim:\n mpc_data = configuration.mpc_data\n mpc_states = mpc_data.mpc_states\n mpc_control = mpc_data.mpc_control\n mpc_time = mpc_data.mpc_time\n index_mpc = configuration.simulator.mpc_iteration\n plot_states = configuration.simulator.plot_states\n plot_control = configuration.simulator.plot_control\n x = configuration.model.x\n x_scaling = configuration.model.ocp.x_scaling\n u = configuration.model.u\n u_scaling = configuration.model.ocp.u_scaling\n X_offset = configuration.optimizer.nlp_dict_out['X_offset']\n U_offset = configuration.optimizer.nlp_dict_out['U_offset']\n n_branches = configuration.optimizer.nlp_dict_out['n_branches']\n n_scenarios = configuration.optimizer.nlp_dict_out['n_scenarios']\n child_scenario = configuration.optimizer.nlp_dict_out['child_scenario']\n parent_scenario = configuration.optimizer.nlp_dict_out['parent_scenario']\n nk = configuration.optimizer.n_horizon\n t0 = configuration.simulator.t0_sim - configuration.simulator.t_step_simulator\n t_step = configuration.simulator.t_step_simulator\n v_opt = configuration.optimizer.opt_result_step.optimal_solution\n plt.ion()\n total_subplots = len(plot_states) + len(plot_control)\n plt.figure(2)\n # Clear the previous animation\n plt.clf()\n # First plot the states\n for index in range(len(plot_states)):\n \tplot = plt.subplot(total_subplots, 1, index + 1)\n \t# First plot the prediction\n \tplot_state_pred(v_opt, t0, plot_states[index], '-b', n_scenarios, n_branches, nk, child_scenario, X_offset, x_scaling, t_step)\n \tplt.plot(mpc_time[0:index_mpc], mpc_states[0:index_mpc,plot_states[index]] * x_scaling[plot_states[index]], '-k', linewidth=2.0)\n \tplt.ylabel(str(x[plot_states[index]]))\n \tplt.xlabel(\"Time\")\n \tplt.grid()\n \tplot.yaxis.set_major_locator(MaxNLocator(4))\n\n # Plot the control inputs\n for index in range(len(plot_control)):\n \tplot = plt.subplot(total_subplots, 1, len(plot_states) + index + 1)\n \t# First plot the prediction\n \tplot_control_pred(v_opt, t0, plot_control[index], '-b', n_scenarios, n_branches, nk, parent_scenario, U_offset, u_scaling, t_step, mpc_control[index_mpc-1,plot_control[index]])\n \tplt.plot(mpc_time[0:index_mpc], mpc_control[0:index_mpc,plot_control[index]] * u_scaling[plot_control[index]],'-k' ,drawstyle='steps', linewidth=2.0)\n \tplt.ylabel(str(u[plot_control[index]]))\n \tplt.xlabel(\"Time\")\n \tplt.grid()\n \tplot.yaxis.set_major_locator(MaxNLocator(4))\n raw_input(\"Press Enter to continue...\")\n\n else:\n # nothing to be done if no animation is chosen\n pass\n","sub_path":"src/code/data_do_mpc.py","file_name":"data_do_mpc.py","file_ext":"py","file_size_in_byte":9609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"164763726","text":"from controller.IEvent import IEvent\nfrom .coord import Coord, getObj\nfrom model.Image import Image\nimport model.pokemonModel as pokemonModel\nfrom controller.fight.fightController import FightController\n\nclass ZoneController(IEvent): # le choix des régions lors d'un combat contre un pokémon sauvage\n def __init__(self):\n self.next_ = self\n self.nextFenetre_ = [Image(\"ressource/images/VueMenuChoixRegion.jpg\", 0, 0)]\n objs = []\n zoneKanto = (Coord(28, 40, 310, 140), FightController(1)) # les régions existantes\n zoneJohto = (Coord(335, 40, 610, 140), FightController(2))\n zoneHoenn = (Coord(28, 165, 310, 260), FightController(3))\n zoneSinnoh = (Coord(335, 165, 610, 260), FightController(4))\n objs.append(zoneKanto)\n objs.append(zoneJohto)\n objs.append(zoneHoenn)\n objs.append(zoneSinnoh)\n self.objs = objs\n\n def onClick(self, pos):\n obj = getObj(self.objs, pos)\n if obj != None:\n if obj[1] != None:\n self.next_ = obj[1]\n\n def next(self):\n return self.next_\n\n def onBackPressed(self):\n from controller.accueilController import AccueilController\n self.next_ = AccueilController()\n\n def nextFenetre(self):\n return self.nextFenetre_\n","sub_path":"controller/ZoneController.py","file_name":"ZoneController.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"2540648","text":"add_library('VideoExport')\nimport os\nfrom pendulum_group import *\nl = 200\nnumber = 4\nrec = False\ndef setup():\n blendMode(MULTIPLY)\n # size(640,480)\n fullScreen()\n global c, videoExport\n if rec:\n dst = str(hour()) + \"_\" + str(minute()) +\"_\" + str(second()) + \"_\" + str(millis())\n videoExport = VideoExport(this,dst+\".mp4\")\n videoExport.startMovie()\n c = []\n c .append(funky(PVector(width / 2, height / 2), number, l))\n colorMode(HSB, 360, 100, 100)\n background(0, 0, 100)\n\ndef draw():\n strokeWeight(2)\n for i in c:\n i.show()\n if rec:\n videoExport.saveFrame()\ndef keyPressed():\n if key == 's':\n save(\"outputs/\" + str(hour()) + \"_\" + str(minute()) +\n \"_\" + str(second()) + \"_\" + str(millis()) + \".jpg\")\n if key == 'n':\n c.remove(c[0])\n c.append(funky(PVector(width / 2, height / 2), number, l))\n if key == 'c':\n background(0, 0, 100)\n c.remove(c[0])\n c.append(funky(PVector(width / 2, height / 2), number, l))\n if key == 'q':\n videoExport.endMovie()\n # dst = str(hour()) + \"_\" + str(minute()) +\"_\" + str(second()) + \"_\" + str(millis())\n # os.rename(r'E:\\Programme outputs\\Processing\\Pycessing\\MultiPendulum\\processing-movie.mp4',\n # r'E:\\Programme outputs\\Processing\\Pycessing\\MultiPendulum\\outputs\\\\'+dst+'.mp4')\n exit()\n","sub_path":"Pycessing/MultiPendulum/MultiPendulum.pyde","file_name":"MultiPendulum.pyde","file_ext":"pyde","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"205482864","text":"#coding=utf-8\n\"\"\"\nGiven a roman numeral, convert it to an integer.\n\nInput is guaranteed to be within the range from 1 to 3999.\n\"\"\"\nclass Solution(object):\n def romanToInt(self, s):\n \"\"\"\n :type s: str\n :rtype: int\n \"\"\"\n d = {'I': 1, 'II': 2, 'III': 3, 'IV': 4, 'V': 5, 'VI': 6, 'VII': 7, 'VIII': 8, 'IX': 9,\n 'X': 10, 'XX': 20, 'XXX': 30, 'XL': 40, 'L': 50, 'LX': 60, 'LXX': 70, 'LXXX': 80, 'XC': 90,\n 'C': 100, 'CC': 200, 'CCC': 300, 'CD': 400, 'D': 500, 'DC': 600, 'DCC': 700, 'DCCC': 800, 'CM': 900,\n 'M': 1000, 'MM': 2000, 'MMM': 3000}\n p = ''\n res = 0\n for c in s:\n p += c\n if p not in d:\n res += int(d[p[:-1]])\n p = c\n if p:\n res += d[p]\n return res\n\n def romanToInt_better(self, s):\n \"\"\"\n :type s: str\n :rtype: int\n \"\"\"\n roman = {'M': 1000, 'D': 500, 'C': 100, 'L': 50, 'X': 10, 'V': 5, 'I': 1}\n res = 0\n for i in range(len(s) - 1):\n if roman[s[i]] < roman[s[i+1]]:\n res -= roman[s[i]]\n else:\n res += roman[s[i]]\n return res + roman[s[-1]]\n\n\nif __name__ == '__main__':\n print(Solution().romanToInt_better(\"DCXXI\"))\n","sub_path":"13_Roman_to_Integer.py","file_name":"13_Roman_to_Integer.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"470230551","text":"#1\n#소문자 대문자 숫자 가능/특수문자(_제외) 불가능/ 첫글자 숫자불가능 _가능 / 예약어 불가능/ 의미있게 == 알아보기 쉽게\n\n#2\ndef square(x): #제곱으로 반환해주는 함수\n return x*x\nprint(square(5)) #5->25\n\n#3\nprint((lambda x : x*x)(5)) #람다를 이용해 제곱반환 함수 사용\n\n#4\nm = [i for i in range(1,101)] #1~100까지 담긴 리스트m\ndef triple(x): #3배로 반환해주는 함수triple\n return x*3\nprint(list(map(triple,m))) #리스트m을 triple함수에 전부 매핑시키고 리스트화 하여 출력\n\n#5\nprint(list(map(lambda i : i*3,m))) #람다를 이용해 3배반환 함수를 만들어 리스트m에 매핑\n\n#6\nimport random \nrnd = set() #set을 만듬(특징: 중복불가)\nwhile len(rnd)<10: #set에 값이 10개가 될때까지\n rnd.add(random.randint(1,100)) #1~100중 랜덤값을 추가\nrnd = list(rnd) #완성된 set을 list로 형변환\nprint(min(rnd),max(rnd)) #내장함수 min과 max를 활용\n\nrnd = [] #빈 리스트를 만듬\nwhile len(rnd)<10: #리스트에 값이 10개가 될때까지\n a = random.randint(1,100) #1~100중 랜덤값을 생성하여\n if a in rnd: #그 값이 리스트에 존재하는지 확인하여\n continue #있다면 다음 회차를 진행하고\n rnd.append(a) #없다면 리스트에 값을 추가\nprint(min(rnd),max(rnd)) #내장함수 min과 max를 활용\n\n#7\nk = list(range(1,101)) #1~100값을 순서대로 가진 리스트 생성\nprint(k) \n\n#8\nk = [i for i in range(1,101)] #리스트 내포를 활용하여 같은 결과값 생성\nprint(k)\n\n#9\nk = [i for i in range(1,101) if i%2==0] #리스트 내포를 활용하여 짝수만 생성\nprint(k)","sub_path":"W3/W3D4/hw.py","file_name":"hw.py","file_ext":"py","file_size_in_byte":2319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"287449071","text":"with open('data.txt', 'r') as file:\n profiles = file.readlines()\n print(profiles)\nslist = []\nfor i, profile in enumerate(profiles):\n date = profile.split()[3].split('.')\n sdate = int(date[2]+date[1]+date[0])\n slist.append((i, sdate))\nslist.sort(key=lambda x: x[1])\nwith open('output.txt', 'w') as file:\n for profile in slist:\n file.write(profiles[profile[0]])\n","sub_path":"12113.py","file_name":"12113.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"172591339","text":"class Solution:\n def maskPII(self, S):\n \"\"\"\n :type S: str\n :rtype: str\n \"\"\"\n if '@' in S: # email\n name, postfix = S.lower().split('@')\n return name[0] + '*****' + name[-1] + '@' + postfix\n else:\n chars = {'+', '-', '(', ')', ' '}\n digits = []\n for c in S:\n if c not in chars:\n digits.append(c)\n prefix = ''\n if len(digits) > 10:\n prefix = '+' + ('*' * (len(digits) - 10)) + '-'\n return prefix + '***-***-' + ''.join(digits[-4:])","sub_path":"leetcode/python/ex_831.py","file_name":"ex_831.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"9165856","text":"#!/usr/bin/python\n# http://sourceforge.net/projects/fonttools/\n\n\"\"\"\nExtract font name??\n\"\"\"\n\nfrom fontTools import ttLib\nfrom sys import argv\n\n\ndef main(filename):\n font = ttLib.TTFont(filename)\n return font['name'].names[17].string.decode(\"utf-16-be\")\n\n\nif __name__ == \"__main__\":\n for fname in argv[1:]:\n print(fname, main(fname) + \".ttf\")\n","sub_path":"snippet/ttf-name.py","file_name":"ttf-name.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"287052323","text":"import json\n\nwith open('output/pods-and-pvcs.json') as f:\n data = json.load(f)\n\n\n# keep track of Pod associations to PVCs\npvc_pod_mapping = []\n\n# for each PVC, need to find the pod (if any) that mounts the data\nfor pvc in data.get(\"pvcs\", {}):\n pvc_name = pvc.get(\"metadata\", {}).get(\"name\", \"\")\n pvc_ns = pvc.get(\"metadata\", {}).get(\"namespace\", \"\")\n\n # look through all pods for the one that mounts this PVC\n for pod in data.get(\"pods\", {}):\n pod_name = pod.get(\"metadata\", {}).get(\"name\", \"\")\n pod_ns = pod.get(\"metadata\", {}).get(\"namespace\", \"\")\n pod_uid = pod.get(\"metadata\", {}).get(\"uid\", \"\")\n\n volumes = pod.get(\"spec\", {}).get(\"volumes\", \"\")\n # skip if no vols\n if volumes == \"\":\n continue\n for volume in volumes:\n claimed_pvc = volume.get(\"persistentVolumeClaim\", {}).get(\"claimName\", \"\")\n # skip if no pvcs\n if claimed_pvc == \"\":\n continue\n if claimed_pvc == pvc_name:\n pvc_pod_mapping.append(\n {\n \"pod_uid\": pod_uid,\n \"pod_ns\": pod_ns,\n \"pod_name\": pod_name,\n \"pvc_name\": claimed_pvc,\n }\n )\n\nwith open('output/pvc-data.json') as f:\n pvc_data = json.load(f)\n\nfor pvc in pvc_data:\n pvc_name = pvc.get(\"pvc_name\", \"\")\n pvc_ns = pvc.get(\"pvc_namespace\", \"\")\n # for each PVC in pvc-data.json, see if there is a matching Pod UID/NS/Name\n for pod_pvc_pair in pvc_pod_mapping:\n if pod_pvc_pair[\"pvc_name\"] == pvc_name and pod_pvc_pair[\"pod_ns\"] == pvc_ns:\n pvc[\"bound_pod_name\"] = pod_pvc_pair[\"pod_name\"]\n pvc[\"bound_pod_uid\"] = pod_pvc_pair[\"pod_uid\"]\n break\n\n# Write the result back out to pvc-data.json\nwith open('output/pvc-data.json', 'w') as f:\n pvc_data = json.dump(pvc_data, f, indent=4)\n\n\n \n\n \n","sub_path":"1_pvc_data_gen/scripts/add_pod_info_to_pvc.py","file_name":"add_pod_info_to_pvc.py","file_ext":"py","file_size_in_byte":1979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"253314929","text":"import pytest\nimport time\n#from conftest import conf\ncount = 0\n\n@pytest.fixture(scope='module')\ndef module_resource(conf):\n print(\"\\n【ModuleSetup...】\")\n yield conf\n print(\"\\n【ModuleTeardown...】\")\n\n@pytest.mark.debug\n@pytest.mark.usefixtures(\"conf\")\nclass TestBoilerplate:\n '''\n 会员营销\n '''\n AUTHOR = 'YT'\n count_class = 0\n count_case = 0\n case_list = [\n {'name':'test_case1','code':'DM-TP-001', 'desc':'模板用例'},\n {'name':'test_case2','code':'DM-TP-002', 'desc':'模板用例2'},\n ]\n case_dict = {'TestBoilerplate':{\n 'author':'YT',\n 'module':'Demo',\n 'cases':{\n 'test_case1':{'code':'DM-TP-001','desc':'模板用例1'},\n 'test_case2':{'code':'DM-TP-002','desc':'模板用例2'},\n }\n }\n }\n\n\n @classmethod\n @pytest.fixture(scope=\"class\", autouse=True)\n def setup_class(self, conf, module_resource):\n print(\"\\n【TestClass setup...】\")\n self.conf = conf\n conf.cases += self.case_list\n conf.case_dict = dict(conf.case_dict, **self.case_dict)\n self.count_class += 1\n print(\"\\nClassSetup called %d time(s)...\" % self.count_class)\n yield\n print(\"\\n【TestClass teardown...】\")\n\n @pytest.fixture(autouse=True)\n def setup_case(self,conf):\n global count\n print(\"\\n【TestCase setup...】\")\n count += 1\n print(\"\\nCaseSetup called %d time(s)...\" % count)\n yield\n print(\"\\n【Testcase teardown...】\")\n\n @pytest.mark.demo\n @pytest.mark.yt\n def test_case1(self):\n #test_case1\n x = 11\n y = 22\n assert x+y == 33\n\n @pytest.mark.demo\n def test_case2(self):\n #test_case2\n a = 'yt'\n b = 'hoo'\n assert a+b == 'ythoo'\n\n@pytest.mark.usefixtures(\"conf\")\nclass TestSimple(object):\n count_class = 0\n count_case = 0\n case_list = [\n {'name':'test_first','code':'DM-001', 'desc':'演示用例1'},\n {'name':'test_second','code':'DM-002', 'desc':'演示用例2'},\n ]\n case_dict = {'TestSimple':{\n 'author':'YT',\n 'module':'Demo',\n 'cases':{\n 'test_first':{'code':'DM-001','desc':'演示用例1'},\n 'test_second':{'code':'DM-002','desc':'演示用例2'},\n }\n }\n }\n\n @classmethod\n @pytest.fixture(scope=\"class\", autouse=True)\n def setup_class(self, conf, module_resource):\n print(\"\\n【TestClass setup...】\")\n self.conf = conf\n conf.cases += self.case_list\n conf.case_dict = dict(conf.case_dict, **self.case_dict)\n self.count_class += 1\n print(\"\\nClassSetup called %d time(s)...\" % self.count_class)\n yield\n print(\"\\n【TestClass teardown...】\")\n\n def test_first(self):\n print(\"test first...\")\n x = 11\n y = 22\n assert x+y == 33\n print(\"Flag: %s\" % self.conf.FLAG)\n\n def test_second(self):\n print(\"second first...\")\n x = 11\n y = 22\n assert x+y == 44\n\nclass TestDemo(object):\n \"\"\"docstring for TestHome\"\"\"\n @pytest.fixture(autouse=True)\n def setup(self, conf):\n \"\"\" 初始化 \"\"\"\n self.db_param = conf.DbParam()\n\n def test_db_param(self):\n \"\"\"从数据库获取参数\"\"\"\n pd_param = self.db_param.get_param('product_price')\n for i in pd_param:\n print(\"Name:%s, Value:%s, GroupName:%s\" % (i['name'],i['value'],i['test_point']))\n\n#\n","sub_path":"Test/pytest_starter/tests/test_temp.py","file_name":"test_temp.py","file_ext":"py","file_size_in_byte":3503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"492773841","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 25 01:07:51 2019\n\n@author: 43739\n\"\"\"\n\nfrom __future__ import (absolute_import, division, print_function,\n unicode_literals)\n\nimport os\nimport pandas as pd\n\n# parameter\nos.chdir('C:\\\\Users\\\\43739\\\\OneDrive\\\\us\\\\2019 spring\\\\paper trading')\ntickers = ['BP','RDS-A']\n# name in variable_name is of no importance, just to nominate some dataframe \nvariable_name=['BP','RDSA']\n#intra_freq = '1min'\nyear='2017'\n\n# Import the backtrader platform\nimport backtrader as bt\n\n\nclass ValueObserver(bt.Observer):\n lines = ('value',)\n\n plotinfo = dict(plot=True, subplot=True, plotlinelabels=True)\n\n plotlines = dict(\n #value=dict(marker='*', markersize=8.0, color='lime', fillstyle='full')\n value=dict(linewidth=1.5)\n ) \n def next(self):\n self.lines.value[0]=self._owner.dataclose_x-self._owner.params.m*self._owner.dataclose_y\n \n \n# Create a Stratey\nclass PairStrategy(bt.Strategy):\n params = (\n ('m', 0.500137892846492),#slope\n ('b', 8.197409139),#intercept\n ('std', 1.531843703),\n ('avg', 0.5),\n ('muti', 1),# muti*std+avg\n ('size', 1000)\n )\n \n\n def log(self, txt, dt=None):\n ''' Logging function fot this strategy'''\n dt = dt or self.datas[0].datetime.datetime(0)\n print('%s, %s' % (dt.strftime('%Y-%m-%d %H:%M:%S'), txt))\n \n \n def __init__(self):\n # Keep a reference to the \"close\" line in the data\n #x is BP y is RDSA\n self.dataclose_x = self.datas[0].close\n self.dataclose_y = self.datas[1].close\n \n # To keep track of pending orders and buy price/commission\n self.order = None\n self.buyprice = None\n self.buycomm = None\n \n \n \n def notify_cashvalue(self, cash, value):\n self.log('Cash %s Value %s' % (cash, value))\n def notify_order(self, order):\n print(type(order), 'Is Buy ', order.isbuy())\n if order.status in [order.Submitted, order.Accepted]:\n # Buy/Sell order submitted/accepted to/by broker - Nothing to do\n return\n \n \n # Check if an order has been completed\n # Attention: broker could reject order if not enough cash\n if order.status in [order.Completed]:\n if order.isbuy():\n self.log(\n 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n (order.executed.price,\n order.executed.value,\n order.executed.comm))\n \n \n self.buyprice = order.executed.price\n self.buycomm = order.executed.comm\n else: # Sell\n self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n (order.executed.price,\n order.executed.value,\n order.executed.comm))\n \n \n self.bar_executed = len(self)\n \n \n elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n self.log('Order Canceled/Margin/Rejected')\n \n \n self.order = None\n \n \n def notify_trade(self, trade):\n if not trade.isclosed:\n return\n \n self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %\n (trade.pnl, trade.pnlcomm))\n \n \n def next(self):\n # Simply log the closing price of the series from the reference\n self.log('Close(%s), %.2f' % (tickers[0],self.dataclose_x[0]))\n self.log('Close(%s), %.2f' % (tickers[1],self.dataclose_y[0]))\n \n \n # Check if an order is pending ... if yes, we cannot send a 2nd one\n if self.order:\n return\n \n # Check if we are in the market\n if not self.position:\n \n if(((self.dataclose_x[0]- self.params.m*self.dataclose_y[0]-self.params.b) <= (2*self.params.std*self.params.muti+self.params.avg))and\n ((self.dataclose_x[0]- self.params.m*self.dataclose_y[0]-self.params.b) >= -(2*self.params.std*self.params.muti+self.params.avg))):\n #limit loss, we will not trade unless -(2*std + 0.5) <= (BP-m*RSDA-b) <= (2*std + 0.5)\n \n # BUY\n # BP-m*RSDA-b >1*std + 0.5\n if ((self.dataclose_x[0]- self.params.m*self.dataclose_y[0]-self.params.b) > (self.params.std*self.params.muti+self.params.avg)):\n self.log('SELL PORTFOLIO,SELL{},BUY{}'.format(self.dataclose_x[0],self.dataclose_y[0]) )\n # Keep track of the created order to avoid a 2nd order\n self.order = self.sell(self.datas[0],size=self.params.size)\n self.order = self.buy(self.datas[1],size=int(self.params.m*self.params.size))\n \n # BP-m*RSDA-b <-(1*std + 0.5)\n elif ((self.dataclose_x[0]- self.params.m*self.dataclose_y[0]-self.params.b) < -(self.params.std*self.params.muti+self.params.avg)):\n self.log('BUY PORTFOLIO,BUY{},SELL{}'.format(self.dataclose_x[0],self.dataclose_y[0]) )\n # Keep track of the created order to avoid a 2nd order\n self.order = self.buy(self.datas[0],size=self.params.size)\n self.order = self.sell(self.datas[1],size=int(self.params.m*self.params.size))\n\n \n else:\n # this part is not exactly what I want but till now it is fine\n \n #if the price is too high we need to buy our portfolio back incase of risk(buy BP sell RDSA)\n if((self.dataclose_x[0]- self.params.m*self.dataclose_y[0]-self.params.b) >= (2*self.params.std*self.params.muti+self.params.avg)):\n self.log('CLOSE THE SELL LIMIT LOSS,BUY{},SELL{}'.format(self.dataclose_x[0],self.dataclose_y[0]) )\n # Keep track of the created order to avoid a 2nd order\n self.order = self.buy(self.datas[0],size=self.params.size)\n self.order = self.sell(self.datas[1],size=int(self.params.m*self.params.size))\n \n #if the price is too low we need to sell our portfolio back incase of risk(sell BP buy RDSA)\n elif ((self.dataclose_x[0]- self.params.m*self.dataclose_y[0]-self.params.b) <= -(2*self.params.std*self.params.muti+self.params.avg)):\n self.log('CLOSE THE BUY LIMIT LOSS,SELL{},BUY{}'.format(self.dataclose_x[0],self.dataclose_y[0]) )\n # Keep track of the created order to avoid a 2nd order\n self.order = self.sell(self.datas[0],size=self.params.size)\n self.order = self.buy(self.datas[1],size=int(self.params.m*self.params.size))\n \n else:\n # SELL\n # -0.5 < BP-m*RSDA-b < 0.5 \n if (((self.dataclose_x[0]- self.params.m*self.dataclose_y[0]-self.params.b) < self.params.avg) and\n ((self.dataclose_x[0]- self.params.m*self.dataclose_y[0]-self.params.b) > -self.params.avg)):\n \n # comes from higher portfolio value\n if ((self.dataclose_x[0]- self.params.m*self.dataclose_y[0]-self.params.b) > 0):\n self.log('CLOSE THE SELL,BUY{},SELL{}'.format(self.dataclose_x[0],self.dataclose_y[0]) )\n # Keep track of the created order to avoid a 2nd order\n self.order = self.buy(self.datas[0],size=self.params.size)\n self.order = self.sell(self.datas[1],size=int(self.params.m*self.params.size))\n \n # comes from lower portfolio value\n else:\n self.log('CLOSE THE BUY,SELL{},BUY{}'.format(self.dataclose_x[0],self.dataclose_y[0]) )\n # Keep track of the created order to avoid a 2nd order\n self.order = self.sell(self.datas[0],size=self.params.size)\n self.order = self.buy(self.datas[1],size=int(self.params.m*self.params.size))\n \n\n\n \n# Run the model \nif __name__ == '__main__':\n # Create a cerebro entity\n cerebro = bt.Cerebro()\n\n # Add a strategy\n cerebro.addstrategy(PairStrategy)\n \n # load the data\n datalist = [\n ('data\\\\yahoo finance\\\\'+tickers[0]+'\\\\'+tickers[0]+'_'+year+'.csv', variable_name[0]), #[0] = Data file, [1] = Data name\n ('data\\\\yahoo finance\\\\'+tickers[1]+'\\\\'+tickers[1]+'_'+year+'.csv', variable_name[1]),\n ]\n\n # Loop through the list adding to cerebro.\n for i in range(len(datalist)):\n data = bt.feeds.GenericCSVData(dataname=datalist[i][0],\n datetime=0,\n open=1,\n high=2,\n low=3,\n close=4,\n openinterest=-1,\n time=-1,\n volume=-1,\n dtformat=\"%m/%d/%Y\",\n timeframe=bt.TimeFrame.Minutes, \n compression=1)\n cerebro.adddata(data, name=datalist[i][1])\n\n\n # Set our desired cash start\n cerebro.broker.setcash(100000.0)\n\n # Add a FixedSize sizer according to the stake\n cerebro.addsizer(bt.sizers.FixedSize, stake=10)\n\n # Set the commission - 0.1% ... divide by 100 to remove the %\n cerebro.broker.setcommission(commission=0.001)\n \n cerebro.addobserver(ValueObserver)\n\n # Print out the starting conditions\n print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n\n # Run over everything\n cerebro.run()\n\n # Print out the final result\n print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())\n \n # Print\n cerebro.plot(volume=False)","sub_path":"old version/python file/backtrader/backtrader_test_v5.0_daily.py","file_name":"backtrader_test_v5.0_daily.py","file_ext":"py","file_size_in_byte":9934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"644745810","text":"import paho.mqtt.client as mqtt\n\n\nstatus = 1\n\ndef on_connect(client, userdata, flags, rc):\n print(\"Connected with result code \" + str(rc))\n\n client.subscribe(\"test/temperature\")\n\n\ndef on_message(client, userdata, msg):\n print(msg.topic + \" \" + str(msg.payload))\n # cast payload to string\n message = msg.payload.decode();\n # cast string to int\n if int(message) > 24 and status == 1:\n off()\n client.publish(\"test/switch\", 0)\n elif int(message) < 24 and status == 0:\n on()\n client.publish(\"test/switch\", 1)\n\ndef off():\n print(\"Turned off\")\n global status\n status = 0\n\ndef on():\n print(\"Turned on\")\n global status\n status = 1\n\n\nclient = mqtt.Client()\nclient.on_connect = on_connect\nclient.on_message = on_message\n\nclient.connect(\"192.168.12.1\", 1883, 60)\n\nclient.loop_forever()","sub_path":"Portfolio/Lab/Exercise03/mqtt_subscriber.py","file_name":"mqtt_subscriber.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"286154489","text":"import numpy as np\n\ndef sigmoid(x):\n return 1. / (1. + np.exp(-x))\n\ndef sigmoid_derivative(x):\n return x * (1.0 - x)\n\n\n\nclass NeuralNetwork:\n def __init__(self, x, y):\n self.input = x\n self.weights1 = np.random.rand(self.input.shape[1], 4)\n self.weights2 = np.random.rand(4,1)\n self.y = y\n self.output = np.zeros(self.y.shape)\n\n\n def feed_forward(self):\n self.layer1 = sigmoid(np.dot(self.input, self.weights1))\n self.output = sigmoid(np.dot(self.layer1, self.weights2))\n\n\n def back_prop(self):\n # Application of the chain rule to find derivative of the loss function with respect to weights1 and weights2\n d_weights2 = np.dot(self.layer1.T, (2 * (self.y - self.output) * sigmoid_derivative(self.output)))\n d_weights1 = np.dot(self.input.T, (np.dot(2 * (self.y - self.output) * sigmoid_derivative(self.output),\n self.weights2.T) * sigmoid_derivative(self.layer1)))\n\n # Update the weights with th derivative of the loss function\n self.weights1 += d_weights1\n self.weights2 += d_weights2\n\n\n def train(self, iterations):\n for i in range(iterations):\n self.feed_forward()\n self.back_prop()\n\nif __name__ == '__main__':\n x = np.array([[0,0,1],[0,1,1],[1,0,1],[1,1,1]])\n y = np.array([[0],[1],[1],[0]])\n n = 1500\n\n nn = NeuralNetwork(x, y)\n nn.train(n)\n\n print(nn.output)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"43424117","text":"import requests\nimport json\nimport ast\nurl_access = \"https://www.zopim.com/oauth2/token\"\ntoken = \"aoaEoJFtHiwbtn2HYNkR4qGRPFhyO4TUJ5kpAWZpxG3utS2K3RU1DCrhuLTkIUnm\"\nh2 = {\n \"Content-Type\": \"application/json; charset=utf-8\",\n \"Authorization\": \"Bearer \" + token\n}\nurl = \"https://www.zopim.com/api/v2/roles\"\nwith open(\"userroles.json\",\"r\") as f:\n e = json.load(f)\n for x in e:\n obj = {\n 'display_name' : (x[\"display_name\"]),\n 'role_id' : ast.literal_eval(x[\"role\"])\n }\n url = \"https://www.zopim.com/api/v2/agents/\" + x[\"id\"]\n print(url)\n print(obj)\n r = requests.get(url, headers=h2,data=json.dumps(obj))\n print(r.json())\n # print(ast.literal_eval(x[\"id\"]))\n\n# r = requests.get(url,headers=h2)\n# print(r.json())\n\n","sub_path":"roles.py","file_name":"roles.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"116473749","text":"from .base import *\nprint('production settings loaded')\nALLOWED_HOSTS = ['smallissue.eba-4uzry5z8.ap-northeast-2.elasticbeanstalk.com',\n 'awseb-e-f-AWSEBLoa-1EUNDVO81EY6P-1847361572.ap-northeast-2.elb.amazonaws.com',\n 'api.smallissue.app',]\n\nJWT_AUTH_SECURE = True\nDEBUG = False\n\nAWS_ACCESS_KEY_ID = os.getenv('AWS_ACCESS_KEY_ID')\nAWS_SECRET_ACCESS_KEY = os.getenv('AWS_SECRET_ACCESS_KEY')\nAWS_STORAGE_BUCKET_NAME = os.getenv('AWS_STORAGE_BUCKET_NAME')\nAWS_S3_SIGNATURE_VERSION = 's3v4'\nAWS_S3_REGION_NAME = os.getenv('AWS_S3_REGION_NAME')\nAWS_S3_FILE_OVERWRITE = False\nAWS_DEFAULT_ACL = None\nAWS_S3_VERIFY = True\nDEFAULT_FILE_STORAGE = 'storages.backends.s3boto3.S3Boto3Storage'\n\n","sub_path":"smallissue/settings/production.py","file_name":"production.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"117195320","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Apr 1 10:48:54 2020\r\n\r\n@author: VBANKS\r\n\"\"\"\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nJPHTarget = 32\r\nAvailability = 95.00\r\nHoursAvailable = 70\r\n\r\nSecondsInHour = 3600\r\nSubCells = ['SubCell 1', 'SubCell 2', 'SubCell 3', 'SubCell 4', 'SubCell 5', 'SubCell 6']\r\nCycleTimes = [90, 102, 113, 104, 107, 103]\r\nGrossOutputPerHour = []\r\nGrossOutputPerWeek = []\r\nNetOutputPerHour = []\r\nNetOutputPerWeek = []\r\n\r\n# Create loops to fill in the blank lists which indicate outputs\r\nj = 0\r\nfor i in SubCells:\r\n GrossSubOutput = SecondsInHour / CycleTimes[j]\r\n GrossOutputPerHour.append(GrossSubOutput)\r\n j = j + 1\r\nprint(GrossOutputPerHour)\r\nk = 0\r\nfor i in SubCells:\r\n GrossSubWeekly = HoursAvailable * GrossOutputPerHour[k]\r\n GrossOutputPerWeek.append(GrossSubWeekly)\r\n k = k + 1\r\nprint(GrossOutputPerWeek)\r\nl = 0\r\nfor i in SubCells:\r\n NetSubOutput = GrossOutputPerHour[l] / 100 * Availability\r\n NetOutputPerHour.append(NetSubOutput)\r\n l = l + 1\r\nprint(NetOutputPerHour)\r\nm = 0\r\nfor i in SubCells:\r\n NetSubWeekly = GrossOutputPerWeek[m] / 100 * Availability\r\n NetOutputPerWeek.append(NetSubWeekly)\r\n m = m + 1\r\nprint(NetOutputPerWeek)\r\n\r\n#Convert data into a DataFrame using pandas\r\n\r\ndf = pd.DataFrame(list(zip(SubCells, CycleTimes, GrossOutputPerHour, GrossOutputPerWeek, NetOutputPerHour, NetOutputPerWeek)),\r\n columns = ['SubCells', 'CycleTimes', 'GrossOutputPerHour', 'GrossOutputPerWeek', 'NetOutputPerHour', 'NetOutputPerWeek'])\r\n\r\nprint(df)\r\n\r\n\r\n\r\n","sub_path":"BodyshopSubCells.py","file_name":"BodyshopSubCells.py","file_ext":"py","file_size_in_byte":1557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"342324279","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport unittest\n\nfrom mock import Mock\nfrom datetime import datetime, timedelta\nfrom time import mktime, time\n\nfrom superset import db\n\nfrom .base_tests import SupersetTestCase\n\nfrom superset.tasks.manager import (\n TaskManager, ManagedTask,\n TaskThread, _run_task, task_manager\n)\nfrom superset.tasks.tasklist import (\n BaseTask, DruidClusterRefreshTask\n)\nfrom superset.tasks.processor import (\n validate_config, execute_task_config\n)\nfrom superset.tasks.utils import (\n is_valid_crontab_str, round_time, is_valid_task_config\n)\nfrom superset.tasks.models import CronTask\nfrom superset.tasks.views import CronTaskModelView\nfrom superset.connectors.druid.models import DruidCluster\n\n\nclass TasksTestCase(unittest.TestCase):\n def test_execute_nonexisting_task(self):\n fake_task_config = {'type': 'does_not_exist'}\n result = execute_task_config(fake_task_config)\n self.assertFalse(result)\n\n def test_execute_task_and_validate_config(self):\n invalid_config = {'badkey': 'badval'}\n with self.assertRaises(ValueError):\n validate_config(invalid_config)\n invalid_config = {'type': 'badtype'}\n with self.assertRaises(ValueError):\n validate_config(invalid_config)\n\n def test_druid_refresh_task_validate_config(self):\n invalid_config = {'type': 'druid_cluster_refresh'}\n with self.assertRaises(ValueError):\n validate_config(invalid_config)\n invalid_config['clusters'] = 'somestring'\n with self.assertRaises(TypeError):\n validate_config(invalid_config)\n invalid_config['clusters'] = []\n with self.assertRaises(ValueError):\n validate_config(invalid_config)\n valid_config = {\n 'type': 'druid_cluster_refresh',\n 'clusters': ['A', 'B'],\n }\n self.assertTrue(validate_config(valid_config))\n\n def test_base_task(self):\n base_task = BaseTask({'type': 'fake_task'})\n self.assertFalse(base_task.execute())\n self.assertFalse(BaseTask.validate_task_config(base_task.config))\n\n def test_is_valid_config_json(self):\n invalid_json_str = '{\"invalid_key: invalid_va'\n self.assertFalse(is_valid_task_config(invalid_json_str))\n valid_json_str = (\n '{\"type\": \"druid_cluster_refresh\", \"clusters\": [\"a\", \"b\"]}'\n )\n self.assertTrue(is_valid_task_config(valid_json_str))\n\n def test_is_valid_crontab_str(self):\n invalid_crontab_str = '5 * *'\n self.assertFalse(is_valid_crontab_str(invalid_crontab_str))\n valid_crontab_str = '5 10 * * *'\n self.assertTrue(is_valid_crontab_str(valid_crontab_str))\n\n def test_round_time(self):\n test_datetime = datetime(2000, 1, 1, 5, 25, 20, 624555)\n to_nearest_second = round_time(test_datetime, roundTo=1)\n to_nearest_halfMinute = round_time(test_datetime, roundTo=30)\n to_nearest_minute = round_time(test_datetime, roundTo=60)\n self.assertEqual(to_nearest_second.microsecond, 0)\n self.assertEqual(to_nearest_second.second, 20)\n self.assertEqual(to_nearest_halfMinute.microsecond, 0)\n self.assertEqual(to_nearest_halfMinute.second, 30)\n self.assertEqual(to_nearest_halfMinute.minute, 25)\n self.assertEqual(to_nearest_minute.microsecond, 0)\n self.assertEqual(to_nearest_minute.second, 0)\n self.assertEqual(to_nearest_minute.minute, 25)\n\n def test_managed_task_class(self):\n test_task = Mock()\n test_task.abs_execution_time = Mock(return_value=10)\n test_task.id = 99999\n test_task.config_json = Mock(return_value={\n 'type': 'druid_cluster_refresh',\n 'clusters': ['__fake_cluster']\n })\n test_task.is_repeating = Mock(return_value=True)\n managed_task = ManagedTask(test_task)\n self.assertEqual('Task id=99999', managed_task.__repr__())\n self.assertEqual(True, managed_task.is_repeating())\n test_task2 = Mock()\n test_task2.abs_execution_time = Mock(return_value=100)\n managed_task2 = ManagedTask(test_task2)\n self.assertEqual(-1, managed_task.__cmp__(managed_task2))\n self.assertEqual(0, managed_task.__cmp__(managed_task))\n self.assertEqual(1, managed_task2.__cmp__(managed_task))\n self.assertTrue(managed_task.__lt__(managed_task2))\n self.assertTrue(managed_task.__eq__(managed_task))\n self.assertFalse(managed_task.__eq__(managed_task2))\n self.assertTrue(managed_task.run())\n managed_task.invalidate()\n self.assertFalse(_run_task(managed_task))\n self.assertFalse(managed_task.is_repeating())\n\n def test_task_thread_class(self):\n watcher = {'val': 0}\n\n def test_target(arg1, arg2):\n watcher['val'] += arg1 * arg2\n\n test_thread = TaskThread(test_target, 5, 6)\n test_thread.run()\n self.assertEqual(30, watcher['val'])\n\n def test_task_manager(self):\n watcher = {\n 'fake_id': 9990,\n 'enqueue': 0\n }\n\n def get_fake_task(abs_time=0, repeat=False, id=None):\n fake_task = Mock()\n if not id:\n fake_task.id = watcher['fake_id']\n watcher['fake_id'] += 1\n else:\n fake_task.id = id\n fake_task.abs_execution_time = Mock(\n return_value=abs_time\n )\n fake_task.config_json = Mock(\n return_value={'type': 'dummytask'}\n )\n fake_task.is_repeating = Mock(return_value=repeat)\n return fake_task, fake_task.id\n\n f_task1, fid1 = get_fake_task()\n f_task2, fid2 = get_fake_task()\n f_task3, fid3 = get_fake_task()\n f_existing_tasks = [f_task1, f_task2, f_task3]\n test_tm = TaskManager(f_existing_tasks, tick_delay=0)\n self.assertEqual(3, len(test_tm.task_queue.queue))\n self.assertEqual(3, len(test_tm.managed_tasks))\n for fid in [fid1, fid2, fid3]:\n self.assertIn(fid, test_tm.managed_tasks)\n test_tm.enqueue_task(f_task1, False)\n test_tm.enqueue_task(f_task2, False)\n self.assertEqual(5, len(test_tm.task_queue.queue))\n self.assertEqual(3, len(test_tm.managed_tasks))\n self.assertFalse(test_tm.task_queue.queue[0].valid)\n self.assertFalse(test_tm.task_queue.queue[1].valid)\n self.assertTrue(test_tm.task_queue.queue[3].valid)\n self.assertTrue(test_tm.task_queue.queue[4].valid)\n self.assertFalse(test_tm.cancel_task(-5))\n test_tm.cancel_task(fid1)\n self.assertEqual(2, len(test_tm.managed_tasks))\n self.assertEqual(5, len(test_tm.task_queue.queue))\n self.assertFalse(test_tm.task_queue.queue[3].valid)\n test_tm.is_ticking = True\n test_tm._tick()\n self.assertEqual(0, len(test_tm.managed_tasks))\n self.assertEqual(0, len(test_tm.task_queue.queue))\n self.assertFalse(test_tm.is_ticking)\n test_tm.is_ticking = True\n test_tm._tick()\n self.assertFalse(test_tm.is_ticking)\n test_tm.is_ticking = True\n test_tm.start_ticking()\n self.assertFalse(test_tm.is_ticking)\n for f_task in [f_task1, f_task2, f_task3]:\n test_tm.enqueue_task(f_task, False)\n for fid in [fid1, fid2, fid3]:\n test_tm.cancel_task(fid)\n self.assertEqual(0, len(test_tm.managed_tasks))\n test_tm.is_ticking = True\n test_tm._tick()\n self.assertEqual(0, len(test_tm.task_queue.queue))\n f_task4, fid4 = get_fake_task(repeat=True)\n test_tm.enqueue_task(f_task4, False)\n\n def enqueue_side_effect(task):\n watcher['enqueue'] += 1\n\n test_tm.enqueue_task = Mock(\n side_effect=enqueue_side_effect\n )\n test_tm.is_ticking = True\n test_tm._tick()\n self.assertEqual(watcher['enqueue'], 1)\n self.assertEqual(0, len(test_tm.task_queue.queue))\n test_tm = TaskManager(tick_delay=1)\n f_task5, fid5 = get_fake_task(repeat=False)\n datetime_now = datetime.now()\n datetime_run = datetime_now + timedelta(0, 3)\n f_runtime_5 = mktime(datetime_run.timetuple())\n f_task5.abs_execution_time = Mock(return_value=f_runtime_5)\n test_tm.enqueue_task(f_task5)\n test_tm.thread.join()\n self.assertEqual(0, len(test_tm.task_queue.queue))\n\n\nclass DBTaskTestCase(SupersetTestCase):\n def __init__(self, *args, **kwargs):\n super(DBTaskTestCase, self).__init__(*args, **kwargs)\n\n def test_cron_task_model(self):\n self.login(username='admin')\n crontask1 = (\n db.session.query(CronTask)\n .filter_by(id=99991)\n .first()\n )\n crontask2 = (\n db.session.query(CronTask)\n .filter_by(id=99992)\n .first()\n )\n if crontask1:\n db.session.delete(crontask1)\n if crontask2:\n db.session.delete(crontask2)\n db.session.commit()\n crontask1 = CronTask(\n id=99991,\n crontab_str='30 * * * *',\n config='{\"type\": \"faketask\"}',\n description='fake test for testing',\n )\n crontask2 = CronTask(\n id=99992,\n crontab_str='45 * * * *',\n config='{\"type\": \"faketask\"}',\n description='another fake test for testing',\n )\n db.session.add(crontask1)\n db.session.add(crontask2)\n db.session.commit()\n expected = '99991: 30 * * * *'\n self.assertEqual(expected, crontask1.__repr__())\n expected = '99992: 45 * * * *'\n self.assertEqual(expected, crontask2.__repr__())\n self.assertTrue(crontask1.is_repeating())\n expected = '[Task].(id:99991)'\n self.assertEqual(expected, crontask1.get_perm())\n expected = '[Task].(id:99992)'\n self.assertEqual(expected, crontask2.get_perm())\n expected = {'type': 'faketask'}\n self.assertEqual(expected, crontask1.config_json())\n self.assertEqual(expected, crontask2.config_json())\n cronobj1 = crontask1.crontab_obj()\n cronobj2 = crontask2.crontab_obj()\n half_hour = datetime(2000, 1, 1, 1, 30)\n three_quarter_hour = datetime(2000, 1, 1, 1, 45)\n self.assertTrue(cronobj1.test(half_hour))\n self.assertTrue(cronobj2.test(three_quarter_hour))\n quarter_hour = datetime(2000, 1, 1, 1, 15)\n self.assertFalse(cronobj1.test(quarter_hour))\n self.assertFalse(cronobj2.test(quarter_hour))\n self.assertTrue(crontask1.time_to_execution() > 0)\n time_to_exec_sec = crontask1.time_to_execution_nearest_sec()\n self.assertEqual(time_to_exec_sec, round(time_to_exec_sec))\n timestamp_now = time()\n self.assertTrue(crontask1.abs_execution_time() >= round(timestamp_now))\n abs_exec_time = crontask1.abs_execution_time()\n self.assertEqual(round(abs_exec_time), abs_exec_time)\n self.assertTrue(crontask1.next_execution_date() >= datetime.now())\n self.logout()\n\n def test_crontask_model_view(self):\n f_modelview = CronTaskModelView()\n fake_task = Mock()\n fake_task.crontab_str = '5 * *'\n with self.assertRaises(ValueError):\n f_modelview.pre_update(fake_task)\n fake_task.crontab_str = '* * * * *'\n fake_task.config = '{\"invalid\"'\n with self.assertRaises(ValueError):\n f_modelview.pre_update(fake_task)\n fake_task.config = '{\"type\": \"faketask\"}'\n f_modelview.post_update(fake_task)\n self.assertTrue(task_manager.is_ticking)\n self.assertEqual(1, len(task_manager.task_queue.queue))\n f_modelview.pre_delete(fake_task)\n self.assertEqual(0, len(task_manager.managed_tasks))\n\n def test_execute_druid_refresh_task(self):\n self.login(username='admin')\n cluster1 = (\n db.session.query(DruidCluster)\n .filter_by(cluster_name='test_cluster1')\n .first()\n )\n if cluster1:\n db.session.delete(cluster1)\n db.session.commit()\n cluster2 = (\n db.session.query(DruidCluster)\n .filter_by(cluster_name='test_cluster2')\n .first()\n )\n if cluster2:\n db.session.delete(cluster2)\n db.session.commit()\n\n cluster1 = DruidCluster(\n cluster_name='test_cluster1',\n coordinator_host='localhost',\n coordinator_port=7979,\n broker_host='localhost',\n broker_port=7980,\n metadata_last_refreshed=datetime.now()\n )\n cluster2 = DruidCluster(\n cluster_name='test_cluster2',\n coordinator_host='localhost',\n coordinator_port=8080,\n broker_host='localhost',\n broker_port=8880,\n metadata_last_refreshed=datetime.now()\n )\n db.session.add(cluster1)\n db.session.add(cluster2)\n refresh_count = {'val': 0}\n\n def refresh_side_effect(refreshAll):\n refresh_count['val'] += 1\n cluster1.refresh_datasources = Mock(\n return_value=True,\n side_effect=refresh_side_effect)\n cluster2.refresh_datasources = Mock(\n return_value=True,\n side_effect=refresh_side_effect)\n db.session.commit()\n task_config = {\n 'type': 'druid_cluster_refresh',\n 'clusters': ['test_cluster1', 'test_cluster2']\n }\n self.assertTrue(execute_task_config(task_config))\n self.assertEqual(refresh_count['val'], 2)\n self.assertEqual('druid_cluster_refresh', DruidClusterRefreshTask({\n 'type': 'druid_cluster_refresh'\n }).__repr__())\n self.logout()\n","sub_path":"tests/task_tests.py","file_name":"task_tests.py","file_ext":"py","file_size_in_byte":13970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"268549919","text":"import os\nimport threading\n\ndef main():\n entries = {}\n curr_key = ''\n\n def update_entries():\n if entries.get(curr_key, None) != None:\n entries[curr_key] += 1\n\n interval(update_entries, 1)\n\n while (1):\n os.system('cls' if os.name == 'nt' else 'clear')\n for k, v in sorted(entries.items(), key=lambda x:x[1], reverse=1):\n print('* ' if k == curr_key else ' ', end='')\n print('{:<32}'.format(k), format_seconds(v))\n u = input('> ').strip()\n if u == 'q':\n break\n if u == '':\n continue\n if u == '-':\n entries = {}\n continue\n if u[0] == '-':\n entries.pop(u[1:], 0)\n continue\n if not entries.get(u, None):\n entries.update({u: 0})\n curr_key = u\n\n\ndef interval(fn, sec):\n def fn_():\n interval(fn, sec)\n fn()\n t = threading.Timer(sec, fn_)\n t.start()\n return t\n\ndef format_seconds(sec):\n m, s = divmod(sec, 60)\n h, m = divmod(m, 60)\n f = lambda n: str(n).zfill(2)\n d = '.'\n return f(h) + d + f(m) + d + f(s)\n\nmain()\n","sub_path":"chronolog.py","file_name":"chronolog.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"81935134","text":"\r\n# =============================================================================\r\n# import:\r\n\r\nimport math\r\n\r\n# =============================================================================\r\n# osztalyok, fuggvenyek:\r\n\r\n# -----------------------------------------------------------------------------\r\n\r\nclass Pont:\r\n def __init__(self, p1=None, p2=None) :\r\n if type(p1) is float and type(p2) is float :\r\n x = p1\r\n y = p2\r\n elif type(p1) is str and p2 is None :\r\n koordinatak = p1.split()\r\n x = float(koordinatak[0])\r\n y = float(koordinatak[1])\r\n elif p1 is None and p2 is None :\r\n x = 0.0\r\n y = 0.0\r\n else :\r\n raise TypeError(\"Nem megfelelo konstruktorhivas!\")\r\n self.x = x\r\n self.y = y\r\n \r\n def __eq__(self, jobb) :\r\n return self.x == jobb.x and self.y == jobb.y\r\n \r\n def __ne__(self, jobb) :\r\n return not __eq__(self, jobb)\r\n \r\n def __sub__(self, jobb) :\r\n return Pont(self.x - jobb.x, self.y - jobb.y)\r\n \r\n def __abs__(self) :\r\n return math.sqrt(self.x ** 2 + self.y ** 2)\r\n \r\n#\r\n\r\ndef tav(p1, p2) :\r\n return math.sqrt((p1.x - p2.x) ** 2 + (p1.y - p2.y) ** 2)\r\n#\r\n\r\ndef egyenlo(p1, p2) :\r\n return p1.x == p2.x and p1.y == p2.y # float: valodi ertekeknel nem sok ertelme van\r\n#\r\n\r\ndef beolvas() :\r\n x = float(input(\"x : \"))\r\n y = float(input(\"y : \"))\r\n return Pont(x, y)\r\n \r\n#\r\n\r\n\r\n\r\n# -----------------------------------------------------------------------------\r\n\r\ndef main():\r\n origo = Pont(0.0, 0.0)\r\n p1 = Pont(3.0, 4.0)\r\n print(tav(p1, origo))\r\n \r\n p2 = beolvas()\r\n \r\n print(egyenlo(p1, p2))\r\n \r\n \r\n # -------------------------------------------------------------------------\r\n # 1. megoldas\r\n print(\"-\" * 79)\r\n \r\n pontok1 = []\r\n print(\"\\nAdd meg a torespont x es y koordinatait!\")\r\n pontok1.append(beolvas())\r\n osszes_husszusag = 0.0\r\n \r\n while True :\r\n print(\"\\nAdd meg a torespont x es y koordinatait!\")\r\n pontok1.append(beolvas())\r\n tavolsag = tav(pontok1[-1], pontok1[-2])\r\n osszes_husszusag += tavolsag\r\n print(\"tavolsag = {}, osszes_husszusag = {}\".format(tavolsag, osszes_husszusag))\r\n if egyenlo(pontok1[0], pontok1[-1]) :\r\n break\r\n \r\n \r\n \r\n \r\n # -------------------------------------------------------------------------\r\n # 2. megoldas\r\n print(\"-\" * 79)\r\n print(\"operator overloadinggal:\")\r\n print()\r\n \r\n pontok2 = []\r\n print(\"\\nAdd meg a torespont x es y koordinatait, szokozzel elvalasztva egy sorban!\")\r\n pontok2.append(Pont(input()))\r\n osszes_husszusag2 = 0.0\r\n \r\n while True :\r\n print(\"\\nAdd meg a torespont x es y koordinatait!\")\r\n pontok2.append(Pont(input()))\r\n tavolsag2 = abs(pontok2[-1] - pontok2[-2])\r\n osszes_husszusag2 += tavolsag2\r\n print(\"tavolsag2 = {}, osszes_husszusag2 = {}\".format(tavolsag2, osszes_husszusag2))\r\n if pontok2[0] == pontok2[-1] :\r\n break\r\n \r\n \r\n \r\n \r\n#\r\n\r\n\r\n# =============================================================================\r\n# futtatas:\r\n\r\n\r\nmain()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"week_07/kerites.py","file_name":"kerites.py","file_ext":"py","file_size_in_byte":3268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"391844556","text":"#!/bin/env python\n# coding:utf-8\nimport sqlite3\nimport MeCab\nimport argparse\n# ネガポジ辞書作成\nimport urllib.request\n#from mpi4py import MPI\nimport pandas as pd\nimport codecs\nimport logging\nimport json\nimport os\nlogger = logging.getLogger(\"logger\") #logger名loggerを取得\nlogger.setLevel(logging.DEBUG) #loggerとしてはDEBUGで\n\n\ndef main():\n parser = argparse.ArgumentParser(description='calculate board score')\n parser.add_argument('--db',type=str, default=os.environ[\"DATA\"]+\"/board/board.db\")\n parser.add_argument('--dump',type=str, default=\"board_emoji_count.dump\")\n parser.add_argument('--logfile',type=str, default=\"03_emoji_count.log\")\n parser.add_argument('--fmdate',type=str,default=\"2017-05-01\")\n parser.add_argument('--todate',type=str,default=\"2017-05-31\")\n args = parser.parse_args()\n\n logging.basicConfig(level=logging.DEBUG,\n filename=args.logfile,\n format=\"%(asctime)s %(levelname)-7s %(message)s\")\n\n\n con=sqlite3.connect(args.db)\n import pandas as pd\n cur = con.cursor()\n data = pd.io.sql.read_sql_query(\"select * from emoji_sentiment_score \", con)\n cur.close()\n con.close()\n\n emoji={}\n keys=data[\"emoji\"].values.flatten()\n vals=data[\"score\"].values.flatten()\n for (k,v) in zip(keys,vals):\n emoji[k]=v\n\n\n #tagger = MeCab.Tagger('-Ochasen')\n ## http://qiita.com/kasajei/items/0805b433f363f1dba785\n #tagger.parse(\"\")\n ## 文をMecabで単語分割\n #def mecab_analysis(sentence):\n # res=[]\n # s = sentence.replace('\\n', ' ')\n # node = tagger.parseToNode(s)\n # while node:\n # if node.surface != \"\": # BOS/EOSを除外\n # ftlist = (node.feature).split(',')\n # if ftlist[6] != \"*\":\n # o = ftlist[6]\n # else:\n # o = node.surface\n # res.append(o+\"\\t\"+ftlist[0]) # word \\t hinshi\n # node = node.next\n # return res\n\n #def emoji_count(word_list,dict):\n # emoji_dict={}\n # score=0\n # emojinum=0\n # for wd in word_list:\n # #print(\"word=\"+str(wd))\n # (word,hinshi)=wd.split(\"\\t\")\n # if word in dict:\n # if word in emoji_dict:\n # emoji_dict[word]+=1\n # else:\n # emoji_dict[word]=1\n # return emoji_dict\n\n def emoji_count(body,emoji):\n emoji_dict={}\n for k in emoji.keys():\n cnt=body.count(k)\n if cnt > 0:\n emoji_dict[k]=cnt\n return emoji_dict\n\n #fmdate=\"2017-05-01\"\n #todate=\"2017-05-31\"\n\n con=sqlite3.connect(args.db)\n import pandas as pd\n cur = con.cursor()\n data = pd.io.sql.read_sql_query(\"select * from board where date between '\" + args.fmdate + \"' and '\" + args.todate + \"'\", con)\n cur.close()\n con.close()\n\n\n mcon=sqlite3.connect(\":memory:\")\n mcur = mcon.cursor()\n with open(\"board.sql\", \"r\") as f:\n lines = \"\".join(f.readlines())\n for sql in lines.split(\";\"):\n mcur.execute(sql)\n mcon.commit()\n\n for code, tno, mno, body in zip(data[\"code\"], data[\"tno\"], data[\"mno\"], data[\"body\"]):\n #emoji_dict = emoji_count(mecab_analysis(body), emoji)\n emoji_dict = emoji_count(body, emoji)\n for word in emoji_dict.keys():\n mcur.execute(\"insert into board_emoji_count values (?,?,?,?,?)\",(code,str(tno),str(mno),word,emoji_dict[word],))\n print(str(code)+\",\"+str(tno)+\",\"+str(mno)+\",\"+str(word)+\",\"+str(emoji_dict[word]))\n\n mcon.commit()\n\n with open(args.dump, 'w') as f:\n for line in mcon.iterdump():\n f.write('%s\\n' % line)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"theme/src/17_emoji_count.py","file_name":"17_emoji_count.py","file_ext":"py","file_size_in_byte":3776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"124678989","text":"import socket\nimport threading\n\n\nclass ChatServer:\n audience_list = []\n latest_msg = \"\"\n\n def __init__(self):\n self.socket_fd = None\n self.listener()\n\n def listener(self):\n self.socket_fd = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n server_ip = '127.0.0.1'\n server_port = 10000\n self.socket_fd.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n self.socket_fd.bind((server_ip, server_port))\n print(\"Listener activated. Awaiting connections..\")\n self.socket_fd.listen(5)\n self.threaded_message()\n\n def receiver_list(self, client):\n if client not in self.audience_list:\n self.audience_list.append(client)\n\n def receive_messages(self, so):\n while True:\n try:\n message_buff = so.recv(256)\n # empty messages will not be delivered to the receiver\n msg_checker = (message_buff.decode(\"utf-8\")).split(':')[-1]\n if not message_buff or len(msg_checker) < 2:\n break\n self.latest_msg = message_buff.decode('utf-8')\n self.show_to_audience(so) # send to all clients\n except (KeyboardInterrupt, ConnectionResetError):\n print(\"Thanks for using my chat server. Bye..\")\n exit(0)\n so.close()\n\n def show_to_audience(self, senders_socket):\n for client in self.audience_list:\n so, (ip, port) = client\n if so is not senders_socket:\n so.sendall(self.latest_msg.encode('utf-8'))\n\n def threaded_message(self):\n while True:\n try:\n client = so, (ip, port) = self.socket_fd.accept()\n self.receiver_list(client)\n print(f'Connection accepted from {ip}:{str(port)}')\n thread = threading.Thread(target=self.receive_messages, args=(so,))\n thread.start()\n except KeyboardInterrupt:\n print(\"Thanks for using my chat server. Bye..\")\n exit(0)\n\n\nif __name__ == \"__main__\":\n ChatServer()\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"145789922","text":"import re\nfrom collections import namedtuple\nfrom datetime import datetime\nimport logging\n\nfrom PyQt4 import QtGui, QtCore\n\nfrom Orange.widgets import widget, gui, settings\nfrom Orange.data.table import Table\n\n\nlog = logging.getLogger()\n\n\nSHEETS_PATTERN = re.compile(\n r'(?:https?://)?(?:www\\.)?'\n 'docs\\.google\\.com/spreadsheets/d/'\n '(?P[-\\w_]+)'\n '(?:/.*?gid=(?P\\d+).*|.*)?',\n re.IGNORECASE\n)\n\n\ndef SHEETS_URL(url):\n match = SHEETS_PATTERN.match(url)\n workbook, sheet = match.group('workbook_id'), match.group('sheet_id')\n if not workbook: raise ValueError\n url = 'https://docs.google.com/spreadsheets/d/{}/export?format=tsv'.format(workbook)\n if sheet: url += '&gid=' + sheet\n return url\n\n\nSheet = namedtuple('Sheet', ('name', 'url'))\n\n\nclass OWGoogleSheets(widget.OWWidget):\n name = \"Google Sheets\"\n description = \"Read data from a Google Sheets spreadsheet.\"\n icon = \"icons/GoogleSheets.svg\"\n priority = 20\n outputs = [(\"Data\", Table)]\n\n want_main_area = False\n resizing_enabled = False\n\n recent = settings.Setting([])\n autocommit = settings.Setting(True)\n\n def __init__(self):\n super().__init__()\n hb = gui.widgetBox(self.controlArea, 'Google Sheets', orientation='horizontal')\n self.combo = combo = QtGui.QComboBox(hb)\n combo.setEditable(True)\n combo.setMinimumWidth(300)\n hb.layout().addWidget(QtGui.QLabel('URL:', hb))\n hb.layout().addWidget(combo)\n hb.layout().setStretch(1, 2)\n box = gui.widgetBox(self.controlArea, \"Info\", addSpace=True)\n info = self.data_info = gui.widgetLabel(box, '')\n info.setWordWrap(True)\n self.controlArea.layout().addStretch(1)\n gui.auto_commit(self.controlArea, self, 'autocommit', label='Commit')\n\n self.set_combo_items()\n self.table = None\n self.set_info()\n self.timer = QtCore.QTimer(self)\n combo.editTextChanged.connect(self.on_combo_textchanged)\n combo.currentIndexChanged.connect(self.on_combo_activated)\n combo.currentIndexChanged.emit(0)\n\n def set_combo_items(self):\n self.combo.clear()\n for sheet in self.recent:\n self.combo.addItem(sheet.name, sheet.url)\n\n def commit(self):\n self.send('Data', self.table)\n\n def on_combo_textchanged(self, text):\n self.timer.stop()\n try: url = SHEETS_URL(text)\n except (ValueError, AttributeError):\n self.error('Unrecognized URL; should be \"docs.google.com/spreadsheets/d/\"')\n return\n self.error()\n self.timer = QtCore.QTimer(self)\n self.timer.setSingleShot(True)\n self.timer.timeout.connect(lambda: self.on_combo_activated(url=url))\n self.timer.start(500)\n\n def on_combo_activated(self, index=float('inf'), url=''):\n self.error()\n # Index from combobox selection\n if 0 <= index < len(self.recent):\n sheet = self.recent.pop(index)\n self.table = self.retrieve(sheet.url)\n self.recent.insert(0, sheet)\n # URL from textchanged event\n elif url:\n table = self.table = self.retrieve(url)\n if not table: return\n sheet = Sheet(table.name, url)\n self.recent = [s for s in self.recent if s.url != url]\n self.recent.insert(0, sheet)\n else: return\n self.set_info()\n self.commit()\n\n self.combo.editTextChanged.disconnect(self.on_combo_textchanged)\n self.combo.currentIndexChanged.disconnect(self.on_combo_activated)\n self.set_combo_items()\n self.combo.editTextChanged.connect(self.on_combo_textchanged)\n self.combo.currentIndexChanged.connect(self.on_combo_activated)\n\n def set_info(self):\n data = self.table\n if not data:\n self.data_info.setText('No spreadsheet loaded.')\n return\n text = \"{} instance(s), {} feature(s), {} meta attribute(s)\".format(\n len(data), len(data.domain.attributes), len(data.domain.metas))\n try: text += '\\nFirst entry: {}\\nLast entry: {}'.format(data[0, 'Timestamp'],\n data[-1, 'Timestamp'])\n except Exception: pass # no Timestamp header\n self.data_info.setText(text)\n\n def retrieve(self, url):\n if not url: return\n progress = gui.ProgressBar(self, 10)\n for i in range(3): progress.advance()\n try: table = Table.from_url(url)\n except Exception as e:\n import traceback\n log.error(traceback.format_exc())\n log.error(\"Couldn't load spreadsheet %s: %s\", url, e)\n self.error(\"Couldn't load spreadsheet. Ensure correct read permissions; rectangular, top-left aligned sheet data ...\")\n return\n else:\n for i in range(7): progress.advance()\n finally:\n progress.finish()\n return table\n\n\nif __name__ == \"__main__\":\n a = QtGui.QApplication([])\n ow = OWGoogleSheets()\n ow.show()\n a.exec_()\n ow.saveSettings()\n","sub_path":"orangecontrib/prototypes/widgets/owgooglesheets.py","file_name":"owgooglesheets.py","file_ext":"py","file_size_in_byte":5125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"4494128","text":"import time, sensor, image, pyb\nfrom image import SEARCH_EX, SEARCH_DS\nfrom pyb import Pin, Timer\nfrom pyb import UART\nuart = UART(3,9600, timeout_char = 1000)\n# Reset sensor\nled = pyb.LED(1)\nled2 = pyb.LED(2)\nled3 = pyb.LED(3)\nsensor.reset()\n\n# Set sensor settings\nsensor.set_contrast(1)\nsensor.set_gainceiling(16)\n# Max resolution for template matching with SEARCH_EX is QQVGA\nsensor.set_framesize(sensor.QQVGA)\n# You can set windowing to reduce the search image.\n#sensor.set_windowing(((640-80)//2, (480-60)//2, 80, 60))\nsensor.set_pixformat(sensor.GRAYSCALE) # Configuramos escala de grises\n\n# Load template.\n# Template should be a small (eg. 32x32 pixels) grayscale image.\ntemplateH = image.Image(\"/exampleH1.pgm\") # Abrimos archivo H\ntemplateS = image.Image(\"/exampleS1.pgm\") # Abrimos archivo S\ntemplateU = image.Image(\"/exampleU1.pgm\") # Abrimos archivo U\ntim = Timer(4, freq=1000) # Frequency in Hz\nclock = time.clock()\n\n# Run template matching\nwhile (True):\n clock.tick()\n img = sensor.snapshot()\n r = img.find_template(templateH, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))\n p = img.find_template(templateS, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))\n q = img.find_template(templateU, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))\n if r:\n img.draw_rectangle(r)\n print('H')\n uart.write(\"%d\\n\"% 5)\n\n elif p:\n img.draw_rectangle(p)\n print('S')\n uart.write(\"%d\\n\"% 4)\n\n elif q:\n img.draw_rectangle(q)\n print('U')\n uart.write(\"%d\\n\"% 3)\n\n else:\n print(\"No_se_reconoce_nada\")\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"532884093","text":"import random\nnumber = random.randint(1,5)\nisGuessRight = False\nwhile isGuessRight != True:\n guess = input(\"Guess a number between 1 and 5: \")\n if int(guess) == number:\n print(\"You guessed {}. That is right! You win!\".format(guess))\n isGuessRight = True\n break\n else:\n print(\"You guessed {}. Sorry, that isn’t it. Try again.\".format(guess))\n","sub_path":"guess_the_number.py","file_name":"guess_the_number.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"638763179","text":"'''\nRemove any system library provided in the application tar ball\n\n:author: sposs\n:since: Jan 26, 2011\n'''\n\nfrom __future__ import print_function\n__RCSID__ = \"$Id$\"\n\nimport os\nfrom DIRAC import gLogger\nLOG = gLogger.getSubLogger(__name__)\nFILES_TO_REMOVE = [\"libc.so\",\"libc-2.5\",\"libm.so\",\"libpthread.so\",\"libdl.so\", \"libstdc++.so\", \"libgcc_s.so.1\"]\n\ndef removeLibc(libraryPath):\n \"\"\" Remove libraries that can be problematic, like libc.so\n\n :param str libraryPath: libraryPath to look for libraries to remove\n :returns: True on Success, False in case of error\n \"\"\"\n\n LOG.debug(\"RemoveLibC: Trying to remove these libraries:\")\n LOG.debug(\"RemoveLibC - \" + \"\\nRemoveLibC - \".join(FILES_TO_REMOVE))\n\n curdir = os.getcwd()\n try:\n os.chdir(libraryPath)\n except OSError:\n return True\n listlibs = os.listdir(os.getcwd())\n for lib in listlibs:\n for lib_to_remove in FILES_TO_REMOVE:\n if lib.count(lib_to_remove):\n try:\n libraryPath = os.getcwd() + os.sep + lib\n LOG.info(\"RemoveLibC: Trying to remove: %s\" % libraryPath)\n os.remove(libraryPath)\n except OSError:\n LOG.error(\"RemoveLibC: Could not remove\", lib)\n os.chdir(curdir)\n return False\n os.chdir(curdir)\n return True\n\ndef getLibsToIgnore():\n \"\"\" :returns: static list of system libraries \"\"\"\n return FILES_TO_REMOVE\n\ndef main():\n \"\"\" Main method, executed when this file is executed as a python script \"\"\"\n import sys\n if not len(sys.argv)>1:\n LOG.error(\"You need to pass the path\")\n return 1\n PATH = sys.argv[1]\n LOG.notice(\"Will remove from %s the files that look like %s\" % (PATH, getLibsToIgnore()))\n \n if not removeLibc(PATH):\n LOG.error(\"Could not clean libs\")\n return 1\n return 0\n\nif __name__ == \"__main__\":\n exit( main() )\n","sub_path":"Core/Utilities/PrepareLibs.py","file_name":"PrepareLibs.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"50541663","text":"import os, pickle, pyaes, sys, random, pycurl\n\nkey = \"This_key_for_demo_purposes_only!\"\naes = pyaes.AESModeOfOperationCTR(key)\n#size of the database\ndbsize=20000\n\n#size of elements\nelesize=10\n\ndef deciph(ciphertext):\n\treturn aes.decrypt(ciphertext)\n\n#size of partition\npartisize = int(sys.argv[1])\nsize_buc =1\nrr=random.randint(0,19999)\nmodus=pickle.load(open('data_encrypted.txt', 'rb'))\nmapp=pickle.load(open('map.txt', 'rb'))\nresults=[]\ncounter=0\n\nPY3 = sys.version_info[0] > 2\n\n\nclass Test:\n def __init__(self):\n self.contents = 'def switch_join(switch):# Repeat Port 1 to Port 2 p1 = {in_port:1} a1 = [forward(2)] install(switch, p1, DEFAULT, a1) # Repeat Port 2 to Port 1 p2 = {in_port:2} a2 = [forward(1)] install(switch, p2, DEFAULT, a2)'\n if PY3:\n self.contents = self.contents.encode('ascii')\n\n def body_callback(self, buf):\n self.contents = self.contents + buf\n\n\nsys.stderr.write(\"Testing %s\\n\" % pycurl.version)\n\n\nt = Test()\nc = pycurl.Curl()\nc.setopt(c.URL, 'http://localhost')\nc.setopt(c.WRITEFUNCTION, t.body_callback)\nc.perform()\nprint(t.contents)\nc.close()\n\nfor j in range(0,dbsize/partisize):\n\tcounter=counter+1\n\t#print modus[i][0]\n\t#results.append(modus[rr][0])\n\tresults.append(deciph(modus[rr][0]))\n\nrr=[]\nstrf='22'\nfor i in range(0, counter):\n\trr.append(results[i].find(strf))\n","sub_path":"insourceIoT/edgeAlone.py","file_name":"edgeAlone.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"400973426","text":"from tools.utils.project import Project\nfrom tools.utils.get_script import get_script\n\n\ndef main():\n # read in data\n data_path = './data/1112 RF(2).xlsx'\n project = Project(data_path)\n\n # start analyze by input int\n analyze_types = input(\n 'Input a int or int list(use space to separate) to choose analyze type ( 1 ~ 13 ): '\n )\n analyze_types = analyze_types.split(' ')\n for analyze_type in analyze_types:\n try:\n analyze_type = int(analyze_type)\n if analyze_type not in range(1, 14):\n raise Exception\n except Exception:\n raise ValueError(\n f'Please input int in 1 ~ 13, but \"{analyze_type}\" is read')\n # use specific script to analyze\n script = get_script(analyze_type)\n script(project)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"local_motors_data_analyze.py","file_name":"local_motors_data_analyze.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"226627596","text":"# -*- coding: utf-8 -*-\na = '杜沛渔是个好姑娘'\nb = a\na = 'Hi, %s, you have $%d.' % ('Michael', 1000000)\nprint(b)\nprint(a)\nprint(b)\n\ns1 = 72\ns2 = 85\nr = (s2-s1)/s1*100\n\nprint ('小明的成绩提高了：%f %%' %r)\n\nn=input('what\\'s your name:')\ns1=input('请输入去年的成绩：')\ns2=input('请输入今年的成绩：')\nt=(float(s2)-float(s1))/float(s1)*100\nif t>0 :\n print('恭喜%s,你的成绩提高了%.3f%%' %(n,t))\nelse :\n t=abs(t)\n print('很遗憾%s,你的成绩降低了%.1f%%' %(n,t))\n\n","sub_path":"no1/hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"430083418","text":"import os\nimport unittest\n\nimport wutu.graphics\n\n\ndef provide_image(relative_path):\n def decorator(test):\n def function(self, *args):\n try:\n path = os.path.join(os.path.dirname(__file__), relative_path)\n args = self, wutu.graphics.Image.load(path)\n except Exception as exception:\n self.skipTest(exception)\n return test(*args)\n return function\n return decorator\n\n\nclass GraphicsTestCase(unittest.TestCase):\n\n def assertImageEqual(self, expected, actual):\n if expected.pixels != actual.pixels:\n actual.source = expected.source.replace('expected', 'actual')\n actual.save(actual.source)\n raise AssertionError('{} != {}'.format(expected.source, actual.source))\n\n\nclass TestFont(GraphicsTestCase):\n\n def test_measure_text(self):\n fira = wutu.graphics.Font()\n fira.load('data/assets/fonts/fira/FiraSans-Regular.ttf', 16)\n width, height = fira.measure_text('Beautiful is better than ugly.')\n self.assertEqual(208, width)\n self.assertEqual(22, height)\n\n def test_measure_text_monospaced(self):\n terminus = wutu.graphics.Font()\n terminus.load('data/assets/fonts/terminus/ter-u12n.pcf.gz', 12)\n width, height = terminus.measure_text('Explicit is better than implicit.')\n self.assertEqual(198, width)\n self.assertEqual(12, height)\n\n def test_measure_multiline_text(self):\n fira = wutu.graphics.Font()\n fira.load('data/assets/fonts/fira/FiraSans-Regular.ttf', 16)\n lines = [\n 'Simple is better than complex.',\n 'Complex is better than complicated.'\n ]\n width, height = fira.measure_text('\\n'.join(lines))\n self.assertEqual(268, width)\n self.assertEqual(44, height)\n\n def test_measure_multiline_text_monospaced(self):\n terminus = wutu.graphics.Font()\n terminus.load('data/assets/fonts/terminus/ter-u12n.pcf.gz', 12)\n lines = [\n 'Now is better than never.',\n 'Although never is often better than *right* now.'\n ]\n width, height = terminus.measure_text('\\n'.join(lines))\n self.assertEqual(288, width)\n self.assertEqual(24, height)\n\n @provide_image('data/expected/test_render_multiline_text.png')\n def test_render_multiline_text(self, expected_image):\n fira = wutu.graphics.Font()\n fira.load('data/assets/fonts/fira/FiraSans-Regular.ttf', 16)\n lines = [\n 'hello',\n 'привет',\n 'γεια σας',\n 'halló',\n 'cześć'\n ]\n self.assertImageEqual(expected_image, fira.render_text('\\n'.join(lines)))\n\n @provide_image('data/expected/test_render_multiline_text_monospaced.png')\n def test_render_multiline_text_monospaced(self, expected_image):\n terminus = wutu.graphics.Font()\n terminus.load('data/assets/fonts/terminus/ter-u12n.pcf.gz', 12)\n lines = [\n 'hello',\n 'привет',\n 'γεια σας',\n 'halló',\n 'cześć'\n ]\n self.assertImageEqual(expected_image, terminus.render_text('\\n'.join(lines)))\n\n\nclass TestRenderer(GraphicsTestCase):\n\n def setUp(self):\n self.window = wutu.graphics.Window(128, 128, visible=False)\n\n @provide_image('data/expected/test_clear.png')\n def test_clear(self, expected):\n renderer = wutu.graphics.Renderer(self.window)\n renderer.clear('#7bc0fd')\n self.assertImageEqual(expected, renderer.present())\n\n @provide_image('data/expected/test_draw_polygon.png')\n def test_draw_polygon(self, expected):\n renderer = wutu.graphics.Renderer(self.window)\n renderer.clear('#777777')\n renderer.set_color('#f0ad4e')\n coordinates = (\n 32, 32,\n 32, 96,\n 64, 112,\n 96, 96,\n 96, 32,\n )\n renderer.draw_polygon(coordinates)\n self.assertImageEqual(expected, renderer.present())\n\n @provide_image('data/expected/test_draw_rectangle.png')\n def test_draw_rectangle(self, expected):\n renderer = wutu.graphics.Renderer(self.window)\n renderer.clear('#f7f7ef')\n renderer.set_color('#d9534f')\n renderer.draw_rectangle(10, 10, 80, 24)\n self.assertImageEqual(expected, renderer.present())\n\n @provide_image('data/expected/test_draw_line_loop.png')\n def test_draw_line_loop(self, expected):\n renderer = wutu.graphics.Renderer(self.window)\n renderer.clear('#777777')\n renderer.set_color('#f0ad4e')\n coordinates = (\n 32, 32,\n 32, 96,\n 64, 112,\n 96, 96,\n 96, 32,\n )\n renderer.draw_line_loop(coordinates)\n self.assertImageEqual(expected, renderer.present())\n\n @provide_image('data/expected/test_draw_texture.png')\n def test_draw_texture(self, expected):\n renderer = wutu.graphics.Renderer(self.window)\n image = wutu.graphics.Image.load('data/assets/images/grid.png')\n texture = renderer.create_texture(image)\n renderer.draw_texture(texture)\n self.assertImageEqual(expected, renderer.present())\n\nif __name__ == '__main__':\n unittest.main()","sub_path":"test/test_graphics.py","file_name":"test_graphics.py","file_ext":"py","file_size_in_byte":5315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"222293359","text":"from tkinter import *\n\n\ndef closer(event):\n root.destroy()\n\n\nroot = Tk()\n\nlabel_1 = Label(root, text=\"Моя перша програма\")\nlabel_1.grid()\nb1 = Button(root, text=\"Закрити\")\nb1.grid(row=1)\nb1.bind(\"\", closer)\nroot.mainloop()\n","sub_path":"1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"20586775","text":"import re\nimport nltk\n\n\ndef cleanStatement(text):\n cleanedText = []\n for each in text:\n #if(each==\"casing\"):\n # print(each)\n cleanText = each\n #print (cleanText)\n cleanText = cleanText.lower() #convert text to lower case\n #print (cleanText)\n cleanText = re.sub(r'[?;!$:+*\"\\']*','',cleanText) #remove punctuations\n #print (cleanText)\n cleanText = re.sub(r'(\\[comma\\])',' ',cleanText) #remove [comma]\n #print (cleanText)\n cleanText = re.sub(r'[./()\\-=_]',' ',cleanText)\n\n #print (cleanText)\n #remove stop words\n from nltk.corpus import stopwords\n stopwordsSet = stopwords.words('english')\n #print(stopwordsSet)\n from nltk.tokenize import word_tokenize\n tokens = word_tokenize(cleanText)\n cleanText=''\n for word in tokens:\n if not word in stopwordsSet:\n cleanText+=word+' '\n #print(tokens)\n\n #print(cleanText)\n\n #lemmatizing the text\n\n from nltk.stem import WordNetLemmatizer\n tokens =word_tokenize(cleanText)\n cleanText =''\n for word in tokens:\n cleanText+= WordNetLemmatizer().lemmatize(word)+' '\n #print(cleanText)\n\n #Stemming - PorterStemmer\n #from nltk.stem.porter import PorterStemmer\n #tokens =word_tokenize(cleanText)\n #cleanText =''\n #for word in tokens:\n\n # cleanText+= PorterStemmer().stem(word)+' '\n\n #if(each==\"casing\"):\n # print(cleanText)\n #print(cleanText)\n\n\n\n cleanedText.append(cleanText)\n return cleanedText\n\ndef find_sub_list(sl,l):\n results=[]\n sll=len(sl)\n for ind in (i for i,e in enumerate(l) if e==sl[0]):\n if l[ind:ind+sll]==sl:\n results.append((ind,ind+sll-1))\n\n return results\n\n\ndef getContextWindow(cleanedText, cleanedAspect):\n j=0\n window = 4\n textContextList = []\n for text, aspect in zip(cleanedText,cleanedAspect):\n j=j+1\n textContext=[]\n\n from nltk.tokenize import word_tokenize\n texttokens = word_tokenize(text)\n aspectToken = word_tokenize(aspect)\n #print(\"Text: \"+text)\n #print(\"Aspect: \"+aspect)\n results = find_sub_list(aspectToken,texttokens)\n if(len(results)==0):\n #print(j)\n #print(\"Text:\"+text)\n #print (\"Aspect:\"+aspect)\n textContext = text\n #print(textContext)\n else:\n #textContext = []\n startIndex =0\n if(results[0][0]len(texttokens)-window):\n endIndex = len(texttokens)\n else:\n endIndex = results[0][1]+window\n for i in range(startIndex,endIndex):\n textContext.append(texttokens[i])\n\n #print(textContext)\n #print(results[0][0])\n textContextList.append(' '.join(textContext))\n\n return textContextList\n\n\n","sub_path":"datapreprocessing.py","file_name":"datapreprocessing.py","file_ext":"py","file_size_in_byte":3126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"536011257","text":"from maskeditor_test_ui import Ui_Dialog\nfrom maskeditor import MaskeditorDialog\nfrom PyQt5 import QtWidgets, QtCore, QtGui\n\n## init\nDEFAUT_APP_PATH = '/home/asquad/workspaces/other/rosher_cv'\n# DEFAUT_APP_PATH = 'C:/Users/VISION/rosher_cv/'\n######\n\n\nclass MaskeditorTestWindow(Ui_Dialog):\n def __init__(self, dialog):\n super().__init__()\n self.setupUi(dialog)\n\n self.pushButton.clicked.connect(self.pushButton_callback)\n\n def pushButton_callback(self):\n dialog = QtWidgets.QDialog()\n maskeditor = MaskeditorDialog(dialog, DEFAUT_APP_PATH)\n dialog.exec_()","sub_path":"src/maskedition/maskeditor_test.py","file_name":"maskeditor_test.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"306602435","text":"#!/usr/bin/env python3\n\n\nclass Solution:\n def uniquePaths(self, m: int, n: int) -> int:\n if m == 0 or n == 0:\n return 0\n\n dp = [1 for _ in range(m)]\n\n for r in range(1, n):\n for c in range(1, m):\n dp[c] += dp[c - 1]\n\n return dp[-1]\n","sub_path":"62-unique-paths/unique_paths.py","file_name":"unique_paths.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"213512848","text":"def chi(G_real_rotated, G_imag_rotated, K_real_rotated, K_imag_rotated, A, omega, LambdaInverse):\n import numpy as np\n\n Niom = len(G_real_rotated)\n Nomega = len(omega)\n\n vector = G_real_rotated - K_real_rotated.dot(A)\n result = np.transpose(vector).dot(LambdaInverse).dot(vector)\n\n vector = G_imag_rotated - K_imag_rotated.dot(A)\n result = result + np.transpose(vector).dot(LambdaInverse).dot(vector)\n return result\n","sub_path":"src/chi.py","file_name":"chi.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"558993950","text":"# Copyright (c) 2019 zfit\n\nfrom zfit.core.testing import setup_function, teardown_function, tester\n\n\nimport pytest\nimport numpy as np\nimport tensorflow as tf\n\n\n\nimport zfit\nfrom zfit.core.sample import extract_extended_pdfs, extended_sampling\nfrom zfit.core.testing import setup_function, teardown_function, tester\n\nobs1 = 'obs1'\n\n\n@pytest.mark.flaky(reruns=3) # poissonian sampling\ndef test_extract_extended_pdfs():\n gauss1 = zfit.pdf.Gauss(obs=obs1, mu=1.3, sigma=5.4)\n gauss2 = zfit.pdf.Gauss(obs=obs1, mu=1.3, sigma=5.4)\n gauss3 = zfit.pdf.Gauss(obs=obs1, mu=1.3, sigma=5.4)\n gauss4 = zfit.pdf.Gauss(obs=obs1, mu=1.3, sigma=5.4)\n gauss5 = zfit.pdf.Gauss(obs=obs1, mu=1.3, sigma=5.4)\n gauss6 = zfit.pdf.Gauss(obs=obs1, mu=1.3, sigma=5.4)\n\n yield1 = zfit.Parameter('yield123' + str(np.random.random()), 200.)\n\n # sum1 = 0.3 * gauss1 + gauss2\n gauss3_ext = 45. * gauss3\n gauss4_ext = 100. * gauss4\n sum2_ext_daughters = gauss3_ext + gauss4_ext\n sum3 = 0.4 * gauss5 + gauss6\n sum3_ext = sum3.create_extended(yield1)\n\n sum_all = zfit.pdf.SumPDF(pdfs=[sum2_ext_daughters, sum3_ext])\n sum_all.set_norm_range((-5, 5))\n\n extracted_pdfs = extract_extended_pdfs(pdfs=sum_all)\n assert frozenset(extracted_pdfs) == {gauss3_ext, gauss4_ext, sum3_ext}\n\n limits = zfit.Space(obs=obs1, limits=(-4, 5))\n limits = limits.with_autofill_axes()\n extended_sample = extended_sampling(pdfs=sum_all, limits=limits)\n extended_sample_np = zfit.run(extended_sample)\n assert np.shape(extended_sample_np)[0] == pytest.approx(expected=(45 + 100 + 200), rel=0.1)\n samples_from_pdf = sum_all.sample(n='extended', limits=limits)\n samples_from_pdf_np = zfit.run(samples_from_pdf)\n assert np.shape(samples_from_pdf_np)[0] == pytest.approx(expected=(45 + 100 + 200), rel=0.1)\n","sub_path":"tests/test_extended.py","file_name":"test_extended.py","file_ext":"py","file_size_in_byte":1819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"231594214","text":"# Refer the Huggingface's Wav2Vec2 finetuning blog at https://huggingface.co/blog/fine-tune-wav2vec2-english\n\n\nclass DataCollatorWav2Vec2(object):\n \"\"\"\n Data collator that will dynamically pad the inputs received.\n \"\"\"\n\n def __init__(\n self,\n processor,\n padding=True,\n max_length=None,\n max_length_labels=None,\n pad_to_multiple_of=None,\n pad_to_multiple_of_labels=None,\n ):\n\n self.processor = processor\n self.padding = padding\n self.max_length = max_length\n self.max_length_labels = max_length_labels\n self.pad_to_multiple_of = pad_to_multiple_of\n self.pad_to_multiple_of_labels = pad_to_multiple_of_labels\n\n def __call__(self, features):\n # split inputs and labels since they have to be of different lengths and need\n # different padding methods\n input_features = [\n {\"input_values\": feature[\"input_values\"]} for feature in features\n ]\n\n label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n\n batch = self.processor.pad(\n input_features,\n padding=self.padding,\n max_length=self.max_length,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=\"pt\",\n )\n\n with self.processor.as_target_processor():\n labels_batch = self.processor.pad(\n label_features,\n padding=self.padding,\n max_length=self.max_length_labels,\n pad_to_multiple_of=self.pad_to_multiple_of_labels,\n return_tensors=\"pt\",\n )\n\n # replace padding with -100 to ignore loss correctly\n labels = labels_batch[\"input_ids\"].masked_fill(\n labels_batch.attention_mask.ne(1), -100\n )\n\n batch[\"labels\"] = labels\n\n return batch\n","sub_path":"imperio/sonorus/experimental/modules/DataCollatorWav2Vec2.py","file_name":"DataCollatorWav2Vec2.py","file_ext":"py","file_size_in_byte":1883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"580725459","text":"import logging\nimport os\nimport json\nfrom flask import Flask\n\nlog = logging.getLogger('werkzeug')\nlog.setLevel(logging.ERROR)\n\napp_root = os.path.dirname(os.path.abspath(__file__))\n\nflask_app = Flask(__name__)\n\n@flask_app.route('/secret', methods=['GET'])\ndef get_secret():\n with open(os.path.join(app_root, \"resources/application.txt\")) as f:\n return(f.read())\n\n@flask_app.route('/healthz', methods=['GET'])\ndef check_healthz():\n return json.dumps({'success':True}), 200, {'ContentType':'application/json'}\n\nif __name__ == \"__main__\":\n flask_app.run(host='0.0.0.0', port='8080')","sub_path":"examples/python3-example/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"66970587","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Feb 14 09:54:21 2021\n\n@author: Henao\n\"\"\"\n''' Según el reto:\ndef calcular_cambio(cambio: int)-> str:\n cantidad_A = cambio // 500\n cambio = cambio - (cantidad_A *500)\n cantidad_B = cambio // 200\n cambio = cambio - (cantidad_B *200)\n cantidad_C = cambio // 100\n cambio = cambio - (cantidad_C *100)\n cantidad_D = cambio // 50\n cantidad_monedas = str(cantidad_A) + \",\" + str(cantidad_B) + \",\" + str(cantidad_C) + \",\" + str(cantidad_D)\n\n return cantidad_monedas\n\n'''\ndef calcular_cambio(cambio: int)-> str:\n cantidad_A = cambio // 500\n cambio = cambio - (cantidad_A *500)\n cantidad_B = cambio // 200\n cambio = cambio - (cantidad_B *200)\n cantidad_C = cambio // 100\n cambio = cambio - (cantidad_C *100)\n cantidad_D = cambio // 50\n cambio_monedas = str(cantidad_A) + \" monedas de 500, \" + str(cantidad_B) + \" monedas de 200, \" + str(cantidad_C) + \" monedas de 100, \" + str(cantidad_D) + \" monedas de 50 \"\n return cambio_monedas\n\ncambio = int(input(\"Ingrese el valor del cambio a entregar: \"))\nprint(\"El cambio para el cliente en la menor cantidad de monedas posible es: \", calcular_cambio(cambio))\n","sub_path":"cantidad monedas.py","file_name":"cantidad monedas.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"202103590","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Dec 04\nCopyright (c) 2018, Vu Hoang Minh. All rights reserved.\n@author: Vu Hoang Minh\n@email: minh.vu@umu.se\n@license: BSD 3-clause.\n\"\"\"\n\nimport os\nimport ntpath\nfrom unet3d.utils.print_utils import print_processing, print_section, print_separator\nimport numpy as np\nfrom unet3d.utils.utils import str2bool\nfrom brats.config import config_dict\n\n\ndef find_is_augment(config):\n augment_flipud = config[\"augment_flipud\"],\n augment_fliplr = config[\"augment_fliplr\"],\n augment_elastic = config[\"augment_elastic\"],\n augment_rotation = config[\"augment_rotation\"],\n augment_shift = config[\"augment_shift\"],\n augment_shear = config[\"augment_shear\"],\n augment_zoom = config[\"augment_zoom\"]\n augment = augment_flipud[0] or augment_fliplr[0] or augment_elastic[0] or augment_rotation[0] or augment_shift[0] or augment_shear[0] or augment_zoom\n if augment:\n is_augment = \"1\"\n else:\n is_augment = \"0\"\n return is_augment\n\n\ndef update_is_augment(args, config):\n config[\"augment_flipud\"] = False\n config[\"augment_elastic\"] = False\n config[\"augment_rotation\"] = False\n config[\"augment_shift\"] = False\n config[\"augment_shear\"] = False\n config[\"augment_zoom\"] = False\n config[\"augment_rotation\"], config[\"augment_fliplr\"]= False, False\n if args.is_augment==\"1\":\n config[\"augment_rotation\"], config[\"augment_fliplr\"]= True, True\n return config\n\n\ndef get_project_dir(path, project_name):\n paths = path.split(project_name)\n return paths[0] + project_name\n\n\ndef split_dos_path_into_components(path):\n folders = []\n while 1:\n path, folder = os.path.split(path)\n\n if folder != \"\":\n folders.append(folder)\n else:\n if path != \"\":\n folders.append(path)\n\n break\n\n folders.reverse()\n return folders\n\n\ndef get_h5_image_path(brats_dir,\n is_normalize_mean_std=False,\n challenge=2018,\n dataset=\"original\"):\n if is_normalize_mean_std:\n dataset_fullname = \"brats{}_{}_normalize_mean_std\".format(\n challenge, dataset)\n else:\n dataset_fullname = \"brats{}_{}_normalize_minh\".format(\n challenge, dataset)\n\n save_to_dir = os.path.join(brats_dir, \"database\", dataset_fullname)\n return save_to_dir\n\n\ndef get_data_dir(brats_dir, data_folder=\"data_train\", dataset=\"test\"):\n return os.path.join(brats_dir, data_folder, dataset)\n\n\ndef get_analysis_dir(dataset_dir, data_folder):\n return os.path.join(dataset_dir, data_folder)\n\n\ndef get_normalize_minh_dir(brats_dir, data_folder=\"data_train\", dataset=\"test\"):\n return os.path.join(brats_dir, data_folder, dataset + \"_minh_normalize\")\n\n\ndef get_normalize_minh_file_path(path, dataset=\"test\"):\n return path.replace(dataset, dataset + \"_minh_normalize\")\n\n\ndef get_parent_dir(path):\n return os.path.abspath(os.path.join(path, os.pardir))\n\n\ndef get_filename(path):\n head, tail = ntpath.split(path)\n return tail or ntpath.basename(head)\n\n\ndef get_filename_without_extension(path):\n filename = get_filename(path)\n return os.path.splitext(filename)[0]\n\n\ndef get_modality(path, ext=\".nii.gz\"):\n filename = get_filename(path)\n modality = filename.replace(ext, \"\")\n return modality\n\n\ndef make_dir(dir):\n if not os.path.exists(dir):\n print_separator()\n print(\"making dir\", dir)\n os.makedirs(dir)\n\n\ndef get_template_path(path, brats_dir, dataset=\"test\", template_data_folder=\"data_train\", template_folder=\"HGG/Brats18_2013_2_1\"):\n filename = get_filename(path)\n template_path = os.path.join(brats_dir, template_data_folder,\n dataset, template_folder,\n filename)\n return template_path\n\n\ndef get_h5_training_dir(brats_dir, datatype=\"data\"):\n return os.path.join(brats_dir, \"database\", datatype)\n\n\ndef get_core_name(args):\n return \"{}_{}_is-{}_crop-{}_bias-{}_denoise-{}_norm-{}_hist-{}\".format(\n args.challenge, args.year, args.image_shape, str(args.crop),\n str(args.is_bias_correction), str(args.is_denoise),\n str(args.is_normalize), str(args.is_hist_match))\n\n\ndef get_model_name(args):\n model_temp = args.model\n model_temp = \"{}{}d\".format(args.model, str(args.model_dim))\n if \"tv\" in args.loss:\n from decimal import Decimal\n args.loss = \"{}-{}\".format(args.loss,\n \"{:.0E}\".format(Decimal(str(args.weight_tv_to_main_loss))))\n # loss = loss + \"-\" + Decimal(str(weight_tv_to_main_loss))\n if any(ext in args.model for ext in config_dict[\"model_depth\"]):\n return \"ps-{}_{}_crf-{}_d-{}_nb-{}_loss-{}_aug-{}\".format(\n args.patch_shape, model_temp, str(args.is_crf),\n str(args.depth_unet), str(args.n_base_filters_unet),\n args.loss, str(args.is_augment))\n else:\n return \"ps-{}_{}_crf-{}_loss-{}_aug-{}\".format(\n args.patch_shape, model_temp, str(args.is_crf),\n args.loss, str(args.is_augment))\n\n\ndef get_short_model_name(args):\n model_temp = args.model\n model_temp = \"{}{}d\".format(args.model, str(args.model_dim))\n if \"unet\" in args.model or \"simple\" in args.model or \"eye\" in args.model:\n return \"ps-{}_{}_crf-{}_d-{}_nb-{}\".format(\n args.patch_shape, model_temp, str(args.is_crf),\n str(args.depth_unet), str(args.n_base_filters_unet))\n else:\n return \"ps-{}_{}_crf-{}\".format(\n args.patch_shape, model_temp, str(args.is_crf))\n\n\ndef get_short_core_name(args):\n return \"{}_{}_is-{}_crop-{}_bias-{}\".format(\n args.challenge, args.year, args.image_shape,\n str(args.crop), str(args.is_bias_correction))\n\n\ndef get_finetune_name(args):\n short_model_name = get_short_model_name(args)\n short_core_name = get_short_core_name(args)\n return short_model_name, short_core_name\n\n\ndef get_model_baseline_path(folder, args):\n import glob\n short_model_name, short_core_name = get_finetune_name(args)\n print(folder, short_model_name, short_core_name)\n model_baseline_path = None\n for filename in glob.glob(folder+\"/*\"):\n # print(filename)\n if short_model_name in filename and short_core_name in filename:\n model_baseline_path = filename\n return model_baseline_path\n\n\ndef get_model_h5_filename(datatype, args):\n core_name = get_core_name(args)\n model_full_name = get_model_name(args)\n if str2bool(args.is_test):\n return \"test_{}_{}_{}.h5\".format(\n core_name, model_full_name, datatype)\n else:\n return \"{}_{}_{}.h5\".format(\n core_name, model_full_name, datatype)\n\n\ndef get_training_h5_filename(datatype, args):\n core_name = get_core_name(args)\n if str2bool(args.is_test):\n return \"test_{}_{}.h5\".format(core_name, datatype)\n else:\n return \"{}_{}.h5\".format(core_name, datatype)\n\n\ndef get_mask_path_from_set_of_files(in_files):\n for file in in_files:\n if \"mask.nii.gz\" in file:\n return file\n\n\ndef get_shape_string(image_shape):\n shape_string = \"\"\n for i in range(len(image_shape)-1):\n shape_string = shape_string + str(image_shape[i]) + \"-\"\n shape_string = shape_string + str(image_shape[-1])\n return shape_string\n\n\ndef get_shape_from_string(shape_string):\n splitted_string = shape_string.split(\"-\")\n splitted_number = list(map(int, splitted_string))\n return tuple(splitted_number)\n\n\ndef get_training_h5_paths(brats_dir, args, is_finetune=False, dir_read_write=\"base\"):\n\n data_dir = get_h5_training_dir(brats_dir, \"data\")\n make_dir(data_dir)\n trainids_dir = get_h5_training_dir(brats_dir, \"train_val_test_ids\")\n make_dir(trainids_dir)\n validids_dir = get_h5_training_dir(brats_dir, \"train_val_test_ids\")\n make_dir(validids_dir)\n testids_dir = get_h5_training_dir(brats_dir, \"train_val_test_ids\")\n make_dir(testids_dir)\n model_dir = get_h5_training_dir(brats_dir, \"model\")\n make_dir(model_dir)\n\n data_filename = get_training_h5_filename(\"data\", args)\n model_filename = get_model_h5_filename(\"model\", args)\n if args.is_test == \"1\":\n trainids_filename = \"test_train_ids.h5\"\n validids_filename = \"test_valid_ids.h5\"\n testids_filename = \"test_test_ids.h5\"\n else:\n trainids_filename = \"train_ids.h5\"\n validids_filename = \"valid_ids.h5\"\n testids_filename = \"test_ids.h5\"\n\n data_path = os.path.join(data_dir, data_filename)\n if is_finetune:\n model_path = os.path.join(model_dir, dir_read_write, model_filename)\n else:\n model_path = os.path.join(model_dir, model_filename)\n trainids_path = os.path.join(trainids_dir, trainids_filename)\n validids_path = os.path.join(validids_dir, validids_filename)\n testids_path = os.path.join(testids_dir, testids_filename)\n\n return data_path, trainids_path, validids_path, testids_path, model_path","sub_path":"unet3d/utils/path_utils.py","file_name":"path_utils.py","file_ext":"py","file_size_in_byte":8978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"527549808","text":"import tensorflow as tf\r\nimport batch_builder as bb\r\nimport prepare_dataset\r\n\r\nimage_height = 300\r\nimage_width = 300\r\n\r\ncategories_count = 12\r\n\r\nlearning_rate = 1e-4\r\nepoch_count = 1\r\ntraining_batch_size = 10\r\nvalidation_batch_size = 20\r\n\r\ntf.reset_default_graph()\r\n\r\n# Load Data\r\ntrain_dataset = prepare_dataset.prepare_training_files()\r\ntest_dataset = prepare_dataset.prepare_testing_files()\r\n\r\ntrain_dataset_length = len(train_dataset)\r\n# train_dataset_length = 0.8 * train_dataset_length\r\n\r\ntraining_data_set = train_dataset[0:int(train_dataset_length)-100]\r\nprint('Training Data size:', len(training_data_set))\r\nvalidation_data_set = train_dataset[int(train_dataset_length)-99: len(train_dataset)-1]\r\nprint('Validation Data size:', len(validation_data_set))\r\n\r\ntrain_data = bb.BatchBuilder(training_data_set, training_batch_size)\r\nvalidation_data = bb.BatchBuilder(validation_data_set, validation_batch_size)\r\ntest_data = bb.BatchBuilder(training_data_set, 1)\r\n\r\n# Output File\r\nfile_name = 'predictions.csv'\r\n\r\n# Placeholders\r\n\r\n# [Batch Size, Width, Height, ColorChannelSize = 3(RGB)]\r\ninput = tf.placeholder(dtype=tf.float32, shape=[None, image_width, image_height, 3])\r\n# [Batch Size, Categories]\r\noutput = tf.placeholder(dtype=tf.float32, shape=[None, categories_count])\r\n\r\n\r\n# Convolution Layers\r\n# [Batch, 300, 300, 3] -> [Batch, 300, 300, 3*16]\r\nconv1 = tf.layers.conv2d(inputs=input, filters=3*16, kernel_size=[7,7], padding=\"same\", activation=tf.nn.relu)\r\n# [Batch, 300, 300, 3*16] -> [Batch, 100, 100, 3*16]\r\npool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=3)\r\n\r\n# [Batch, 100, 100, 3*16] -> [Batch, 100, 100, 3*32]\r\nconv2 = tf.layers.conv2d(inputs=pool1, filters=3*32, kernel_size=[7,7], padding=\"same\", activation=tf.nn.relu)\r\n# [Batch, 100, 100, 3*32] -> [Batch, 50, 50, 3*32]\r\npool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)\r\n\r\n# [Batch, 50, 50, 3*32 -> [Batch, 50, 50, 3*64]\r\nconv3 = tf.layers.conv2d(inputs=pool2, filters=3*64, kernel_size=[7,7], padding=\"same\", activation=tf.nn.relu)\r\n# [Batch, 50, 50, 3*64] -> [Batch, 25, 25, 3*64]\r\npool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=2)\r\n\r\n# # [Batch, 50, 50, 3*64] -> [Batch, 50, 50, 3*128]\r\n# conv4 = tf.layers.conv2d(inputs=pool3, filters=3*128, kernel_size=[5,5], padding=\"same\", activation=tf.nn.relu)\r\n# # [Batch, 50, 50, 3*128] -> [Batch, 25, 25, 3*128]\r\n# pool4 = tf.layers.max_pooling2d(inputs=conv4, pool_size=[2, 2], strides=2)\r\n\r\npool4_flat = tf.reshape(pool3, [-1, 25*25*3*64])\r\n\r\n# Dense Neural Network Layers\r\ndense1 = tf.layers.dense(inputs=pool4_flat, units=1024, activation=tf.nn.relu)\r\ndropout1 = tf.layers.dropout(inputs=dense1, rate=0.4)\r\n\r\ndense2 = tf.layers.dense(inputs=dropout1, units=512, activation=tf.nn.relu)\r\ndropout2 = tf.layers.dropout(inputs=dense2, rate=0.4)\r\n\r\nlogits = tf.layers.dense(inputs=dropout2, units=categories_count)\r\n\r\n# Loss\r\nloss = tf.reduce_mean(tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=output))\r\n\r\n# Adam Optimizer\r\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)\r\n\r\n# Metrics\r\npredictions = tf.equal(tf.arg_max(logits, 1), tf.arg_max(output, 1))\r\naccuracy = tf.reduce_mean(tf.cast(predictions, dtype=tf.float32))\r\n\r\n# Save Model\r\nsaver = tf.train.Saver()\r\n\r\n# Training Loop\r\naccuracy_step_counter = 0\r\nwith tf.Session() as sess:\r\n sess.run(tf.global_variables_initializer())\r\n for epoch_num in range(epoch_count):\r\n\r\n train_data.initialize_batches()\r\n while train_data.data_exist():\r\n\r\n # Optimize\r\n batch_x, batch_y = train_data.get_next_batch()\r\n feed_dict = {input: batch_x, output: batch_y}\r\n _, loss_value = sess.run([optimizer, loss], feed_dict=feed_dict)\r\n\r\n print('Loss=', loss_value)\r\n # Accuracy\r\n if accuracy_step_counter % 20 == 0:\r\n final_accuracy = 0\r\n count = 0\r\n validation_data.initialize_batches()\r\n while validation_data.data_exist():\r\n batch_x, batch_y = validation_data.get_next_batch()\r\n feed_dict = {input: batch_x, output: batch_y}\r\n accu = sess.run(accuracy, feed_dict=feed_dict)\r\n final_accuracy += accu\r\n count += 1\r\n\r\n final_accuracy /= count\r\n print('Accuracy=', final_accuracy)\r\n\r\n accuracy_step_counter += 1\r\n\r\n # Save Model\r\n save_path = saver.save(sess, 'tf_model.ckpt')\r\n print('Model saved in path:', save_path)\r\n\r\n # Generate Output File\r\n file_data = 'file,species'\r\n\r\n for data in test_dataset:\r\n\r\n # Prepare Data\r\n test_data = prepare_dataset.get_testing_data_line(data)\r\n test_image = [test_data['input']]\r\n feed_dict = {input: test_image}\r\n\r\n # Make Prediction\r\n test_data['output'] = sess.run(logits, feed_dict=feed_dict)\r\n actual_prediction = tf.arg_max(test_data['output'], 1)\r\n test_data['category'] = prepare_dataset.inv_categ_dict[actual_prediction]\r\n\r\n # Add line to CSV file\r\n file_data += '\\r\\n' + data + ',' + test_data['category']\r\n\r\n # Prepare CSV File\r\n with open(file_name, 'w') as csv_file:\r\n csv_file.write(file_data)","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"178649213","text":"import numpy as np\nfrom sklearn.svm import LinearSVC\nfrom sklearn.linear_model import Ridge\n\n\nclass reweightEG():\n \n \"\"\"\n Exclusive group Lasso\n \n \n -------------------------\n Solves the following problem with reweighted features and weights\n \n min_w || Xw - y ||_2^2 + alpha * sum_g ||w_g||_1^2\n \n where g \\in G represents a set of indices of features\n\n -------------------------\n Optimisation procedure:\n\n Auxiliary diagonal matrix F: F_ii = sum_{j \\in g} idx_group[j, i] ||w_g||_1 / w[i]\n OR equivalently G_ii = 1 / F_ii\n\n Compute F with w\n Compute X_tilde = X * sqrt(F)^-1\n Compute w_tilde = argmin_w_tilde f(X_tilde * w_tilde, y) + lambda ||w_tilde||_2^2\n Compute w = sqrt(F)^-1 * w_tilde\n \n -------------------------\n Parameters:\n \n :param alpha: float\n regularisation parameter in exclusive group Lasso\n \n :param idx_group: array-like, shape (n_group, n_feature)\n indicator matrix of group allocation\n \n :param n_group: int\n number of groups, must be specified if idx_group is not predefined, when n_group random groups wiil be created\n \n :param crit: float\n criteria to stop optimisation\n \n :param n_iter: int\n maximum number of iterations\n \n :param verbose: binary int\n if verbose = 1, summary of each optimisation iteration will be printed \n \n -------------------------\n Return (as attributes):\n \n :return coef: array-like, shape (n_feature, )\n estimated weights/coefficients of exclusive group Lasso\n\n :return idx: list, length n_selected_feature\n indices of selected features\n\n :return converged: boolean\n boolean variable that indicates whether the optimisation has converged\n \n \"\"\"\n\n def __init__(self, alpha=1, idx_group=None, n_group=None, crit=5*10**-4, n_iter=10**4, verbose=0):\n\n self.alpha = alpha\n self.idx_group = idx_group\n self.n_group = n_group\n self.loss_func = None\n self.crit = crit\n self.n_iter = n_iter\n self.verbose = verbose\n\n self.coef = None\n self.idx = None\n self.converged = False\n\n if n_iter < 1:\n raise ValueError('At lease one iteration is required.')\n\n if idx_group is None and n_group is None:\n raise KeyError('n_group must be specified if idx_group is None.')\n\n def _compute_G(self, w, feat_group):\n \"\"\"\n Compute auxiliary matrix G\n\n :param w: array-like, shape (n_feature, )\n estimated weights/coefficients from the previous iteration\n\n :param feat_group: [??]\n\n :return G_diag: array-like, shape (n_feature, )\n diagonal of auxiliary matrix G\n \"\"\"\n\n w = np.ravel(w)\n\n n_group = len(self.idx_group_new)\n n_feature = w.shape[0]\n\n G_diag = np.zeros(n_feature)\n w_group_norm = np.empty(n_group)\n for group_counter in range(n_group):\n w_group = w[self.idx_group_new[group_counter]]\n w_group_norm[group_counter] = np.linalg.norm(w_group, ord=1)\n\n w_group_norm[np.where(w_group_norm == 0)[0]] = 10 ** -9\n\n w_abs = np.abs(w)\n for feature_counter in range(n_feature):\n G_diag[feature_counter] = np.sqrt(w_abs[feature_counter] / w_group_norm[feat_group[feature_counter]])\n\n return G_diag\n\n def _compute_X_tran(self, X, G_diag):\n \"\"\"\n Compute transformed feature matrix X_tilde\n\n :param X: array-like, shape (n_sample, n_feature)\n input features\n\n :param G_diag: array-like, shape (n_feature, )\n diagonal of auxiliary matrix G\n\n :return: array-like, shape (n_sample, n_feature)\n transformed feature matrix X_tilde\n \"\"\"\n\n return np.dot(X, np.diag(G_diag))\n\n def _compute_w_tran(self, X_tran, y):\n \"\"\"\n Compute transformed weight vector w_tran\n\n :param X_tran: array-like, shape (n_sample, n_feature)\n transformed feature matrix X_tilde\n\n :param y: array-like, shape (n_sample, )\n input labels\n\n :return: array-like, shape (n_feature, )\n transformed weight vector\n \"\"\"\n\n w = 0\n if self.loss_func == 'hinge':\n clf = LinearSVC(fit_intercept=False, C=self.alpha)\n clf.fit(X_tran, y)\n w = clf.coef_\n elif self.loss_func == 'square':\n\n clf = Ridge(alpha=self.alpha, fit_intercept=False, tol=10 ** -9)\n clf.fit(X_tran, y)\n w = clf.coef_\n\n return np.ravel(w)\n\n def _create_rand_group(self, n_feature):\n \"\"\"\n Create randomly allocated groups if idx_group is not specified\n\n :param n_feature: int\n number of features\n\n :return: array-like, shape (n_group, n_feature) [??]\n indicator matrix of random group allocation\n \"\"\"\n\n self.idx_group = np.zeros((self.n_group, n_feature))\n idx = np.random.permutation(n_feature)\n idx = np.array_split(idx, self.n_group)\n\n for sub_counter, sub_idx in enumerate(idx):\n self.idx_group[sub_counter, sub_idx] = 1\n\n def _l12_norm(self, X, y):\n \"\"\"\n Fit exclusive group Lasso\n\n\n Parameters:\n -------------------------\n :param X: array-like, shape (n_sample, n_feature)\n input features\n\n :param y: array-like, shape (n_feature, )\n input labels\n\n Return (as attributes)\n -------------------------\n :return coef: array-like, shape (n_feature, )\n estimated weights/coefficients\n\n :return idx: list, length n_selected_feature\n indices of selected features, a cut-off threshold of 10**-3 is used, can be modified to other thresholds\n\n :return converged: boolean\n boolean variable indicating the convergence of exclusive group Lasso\n \"\"\"\n\n n_sample, n_feature = X.shape\n\n if len(np.unique(y)) == 2:\n self.loss_func = 'hinge'\n else:\n self.loss_func = 'square'\n\n if self.idx_group is None:\n self._create_rand_group(n_feature)\n\n self.idx_group_new = []\n feat_group = {}\n for group_counter in range(self.idx_group.shape[0]):\n temp = np.nonzero(self.idx_group[group_counter, :])[0]\n self.idx_group_new.append(temp)\n for idx_feature in temp:\n feat_group[idx_feature] = group_counter\n\n w = np.ones(n_feature) / n_feature\n G_diag = self._compute_G(w, feat_group)\n X_tran = self._compute_X_tran(X, G_diag)\n w_tran = self._compute_w_tran(X_tran, y)\n\n counter = 0\n while True:\n counter += 1\n\n w_pre = w.copy()\n w = np.multiply(w_tran, G_diag)\n\n G_diag = self._compute_G(w, feat_group)\n X_tran = self._compute_X_tran(X, G_diag)\n w_tran = self._compute_w_tran(X_tran, y)\n\n temp = np.linalg.norm(w_pre - w)\n\n if self.verbose == 1:\n print('iteration: %d, criteria: %.4f.' % (counter, temp))\n\n if temp <= self.crit or counter >= self.n_iter:\n break\n\n self.coef = w\n self.idx = np.where(np.abs(w) > 10 ** -3)[0]\n self.coef[np.where(np.abs(w) <= 10 ** -3)] = 0\n\n if counter < self.n_iter:\n self.converged = True\n\n def fit(self, X, y):\n \"\"\"\n Fit exclusive group Lasso\n\n :param X: array-like, shape (n_sample, n_feature)\n input features\n\n :param y: array-like, shape (n_sample, )\n input labels\n \"\"\"\n\n self._l12_norm(X, y)\n\n def predict(self, X):\n \"\"\"\n Predict with fitted model\n\n :param X: array-like, shape (n_sample, n_feature)\n input features\n\n :return: array-like, shape (n_sample, )\n predicted labels\n \"\"\"\n\n if self.loss_func == 'hinge':\n return np.ravel(np.sign(np.dot(X, self.coef)))\n else:\n return np.ravel(np.dot(X, self.coef))","sub_path":"exclGroupLasso/ExclGroupLasso.py","file_name":"ExclGroupLasso.py","file_ext":"py","file_size_in_byte":8154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"606628229","text":"class confFile(object):\n #confFile information\n testname = ''\n recordingoptions = ''\n generationoptions = ''\n verificationscript = ''\n\n def __setattr__(self, key, value):\n super.__setattr__(self,key.lower().strip(),value.lower().strip()) if hasattr(self, key.lower().strip()) else ''\n\n\n def __repr__(self):\n output = ''\n for var in vars(self):\n output +='\\n'+ var + ' : ' + (str(getattr(self, var))) + '\\n'\n return output\n\n\nclass testConfLegacy(confFile):\n trainerinitscript = ''\n trainerscript = ''\n\nclass testConfErrinj(confFile):\n testanalyzer = ''\n testgroup =''\n testcode = ''\n testdescription = ''\n trainerscript = ''\n\n\nclass testConfLegacySequenceFlow(confFile):\n def __setattr__(self, key, value):\n if isinstance(value, str):\n super.__setattr__(self,key.lower().strip(),value.lower().strip()) if hasattr(self, key.lower().strip()) else ''\n else:\n super.__setattr__(self, key, value)\n\n results = \"\"\n sequancefile = ''\n flowoperations = []\n\n\n\n","sub_path":"OWLcontroller/configControl/confFile.py","file_name":"confFile.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"383344770","text":"import re\n\n#input ip and subnet mask to cal ip segment\ndef ip_cal(ip,subnet_mask):\n a = ''\n a_end = ip.find('.')\n for i in range(0,a_end):\n a = a+ip[i]\n b_end = ip.find('.',a_end+1)\n b=''\n for i in range(a_end+1,b_end):\n b = b+ip[i]\n c_end = ip.find('.',b_end+1)\n c = \"\"\n for i in range(b_end+1,c_end):\n c = c+ip[i]\n d = \"\"\n for i in range(c_end+1,len(ip)):\n d = d+ip[i]\n a = int(a)\n b = int(b)\n c = int(c)\n d = int(d)\n subnet_mask = 32-int(subnet_mask)\n #print subnet_mask #32-subnet_mask\n if subnet_mask>8:\n if subnet_mask>16:\n subnet_mask = subnet_mask-16\n num = 1\n num = num<<(subnet_mask)\n b = b+num-1\n c = c+255\n d = d+255\n #Jinwei\n\n #print(num)\n else:\n subnet_mask = subnet_mask-8\n num = 1\n num = num<<(subnet_mask)\n d = d+255\n c = c+num-1\n #print(num)\n else:\n num = 1\n num = num<<(subnet_mask)\n d = d+num-1\n #print num\n return '%d.%d.%d.%d' % (a,b,c,d)\n\n#get value from ipset's info\ndef get_ipset_info(read_line):\n start_num = read_line.find('\\\"')\n end_num = read_line.find('\\\"',start_num+1)\n info = ''\n for i in range(start_num+1,end_num):\n info = info+read_line[i]\n return info\n\ndef get_subnet_mask(read_line):\n start_num = read_line.find('/')\n subnet_mask = ''\n for i in range(start_num+1,read_line.__len__()):\n subnet_mask = subnet_mask+read_line[i]\n return subnet_mask\n\ndef ip_to_int(s):\n l=[i for i in s.split('.')]\n return ((int(l[0]))<<24)|((int(l[1]))<<16)|((int(l[2]))<<8)\n\ndef get_ip_start(s):\n l=[i for i in s.split('.')]\n return (((int(l[0]))<<24)|((int(l[1]))<<16)|((int(l[2]))<<8))\n\ndef get_ip_end(s):\n l=[i for i in s.split('.')]\n num = l[3].rfind(' ')\n newl = ''\n for i in range(num,len(l[3])):\n newl = newl+l[3][i]\n num2 = l[6].find(' ')\n newl2 = ''\n for i in range(0,num2):\n newl2 = newl2+l[6][i]\n return (((int(newl))<<24)|((int(l[4]))<<16)|((int(l[5]))<<8))|(int(newl2))\n\nreip = re.compile(r'(?= x_min:\r\n return x\r\n else:\r\n return generate_log_normal(x_min,x_max,mu,sigma)\r\n\r\n\r\n# use these values of sigma and mu throughout\r\nsigma = 0.5\r\nmu = 0.25\r\n\r\n# recover the min and max values of the energy from your calculations above\r\nx_min = 0. #GeV\r\nx_max = 6. #GeV\r\n\r\n# set up the run\r\ndistrib_x = []\r\nn_samples = 1000\r\n\r\n# pick values from the distribution\r\nfor idx in range(n_samples):\r\n distrib_x.append(generate_log_normal(x_min,x_max,mu,sigma))\r\n \r\nplt.plot(distrib_x)\r\nplt.show()\r\nplt.close()\r\n\r\nplt.hist(distrib_x, bins = 100)\r\nplt.show()\r\n\r\n\r\n# b) Repeat exercise in 3.1, now drawing the energy of the parent particles from the distribution at each iteration in the loop, and saving the values of `energy_nu_lab` each time. Remember to still keep the value of $\\theta$ random! Finally, plot the histogram of the sum of the energies of the product neutrinos.\r\n\r\n# In[19]:\r\n\r\n\r\nn_events_pi = 100000\r\n\r\nens_1 = []\r\nths_1 = []\r\n\r\nfor idx in range(n_events_pi):\r\n theta = np.pi * np.random.rand() # - np.pi\r\n en_pi_lab = np.sqrt(m_pi**2+1.e6*generate_log_normal(x_min,x_max,mu,sigma)**2)\r\n b_lab_1 = -Beta(m_pi,en_pi_lab)\r\n en_nu_rf_1 = Energy_c(m_pi, m_mu, m_nu)\r\n p_nu_rf_1 = Momentum_c(m_pi, m_mu, m_nu, theta, 0.)\r\n fmomentum_nu_1_lab = Boost_along_z(FMomentum_c(en_nu_rf_1, p_nu_rf_1), b_lab_1)\r\n ens_1.append(fmomentum_nu_1_lab[0])\r\n ths_1.append(theta)\r\n \r\nens_2 = []\r\nths_2 = []\r\n\r\nn_events_K = int(n_events_pi / 10)\r\n\r\nfor idx in range(n_events_K):\r\n theta = np.pi * np.random.rand() # - np.pi\r\n en_K_lab = np.sqrt(m_K**2+1.e6*generate_log_normal(x_min,x_max,mu,sigma)**2)\r\n b_lab_2 = -Beta(m_pi,en_K_lab)\r\n en_nu_rf_2 = Energy_c(m_K, m_mu, m_nu)\r\n p_nu_rf_2 = Momentum_c(m_K, m_mu, m_nu, theta, 0.)\r\n fmomentum_nu_2_lab = Boost_along_z(FMomentum_c(en_nu_rf_2, p_nu_rf_2), b_lab_2)\r\n ens_2.append(fmomentum_nu_2_lab[0])\r\n ths_2.append(theta)\r\n\r\nens_all = ens_1 + ens_2\r\n\r\n#plt.hist(ens_2, bins = 100, label = 'nus from Kaon')\r\n#plt.hist(ens_1, bins = 100, label = 'nus from Pion')\r\nplt.hist(ens_all, bins = 100, label = 'product nus energy')\r\nplt.yscale('log')\r\nplt.xlim(0, 10000)\r\nplt.ylabel('N events')\r\nplt.xlabel('Energy (MeV)')\r\nplt.show()\r\n\r\n\r\n# c) You run the experiment and you get the histogram in b), which includes all the product neutrinos. How could you differentiate between the $\\pi$ neutrinos and the $K$ neutrinos? Replot the histogram for b) separating the $K$ and $\\pi$ neutrinos explicitly, and point out the differences.\r\n\r\n# In[20]:\r\n\r\n\r\nplt.hist(ens_all, bins = 250, label = 'product nus energy', alpha = 0.5)\r\nplt.hist(ens_2, bins = 250, label = 'nus from Kaon', histtype = 'step', linewidth=2)\r\nplt.hist(ens_1, bins = 25, label = 'nus from Pion', histtype = 'step', linewidth=2)\r\nplt.yscale('log')\r\nplt.xlim(0, 10000)\r\nplt.ylabel('N events')\r\nplt.xlabel('Energy (MeV)')\r\nplt.legend()\r\nplt.show()\r\n\r\n### or\r\n\r\nrange_hist = [min(ens_all), max(ens_all)] \r\n\r\nplt.hist(ens_all, bins = 100, range = range_hist, label = 'product nus energy', alpha = 0.5)\r\nplt.hist(ens_2, bins = 100, range = range_hist, label = 'nus from Kaon', histtype = 'step', linewidth=2)\r\nplt.hist(ens_1, bins = 100, range = range_hist, label = 'nus from Pion', histtype = 'step', linewidth=2)\r\nplt.yscale('log')\r\nplt.xlim(0, 10000)\r\nplt.ylabel('N events')\r\nplt.xlabel('Energy (MeV)')\r\nplt.legend()\r\nplt.show()\r\n\r\n\r\n# In[ ]:\r\n\r\n\r\n\r\n\r\n","sub_path":"_build/jupyter_execute/rel.py","file_name":"rel.py","file_ext":"py","file_size_in_byte":12689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"560836908","text":"from .enums import *\nimport typing\n\nclass ChessData:\n r\"\"\"\n The chess payload. This contains the board theme and the black and white chess piece theme.\n\n Paramaters\n ----------\n black: :class:`ChessPieceTheme`\n The theme for the black chess piece.\n white: :class:`ChessPieceTheme`\n The theme for the white chess piece.\n board: :class:`BoardTheme`\n The theme for the board.\n \"\"\"\n def __init__(self, black: ChessPieceTheme, white: ChessPieceTheme, board: BoardTheme):\n self.black = black\n self.white = white\n self.board = board\n\n def to_payload(self):\n return {\n \"white-theme\": str(self.white),\n \"black-theme\": str(self.black),\n \"board-theme\": str(self.board)\n }\n\nclass ChessRender:\n r\"\"\"\n The chess payload for rendering. This contains the arrow and the board.\n\n Paramaters\n ----------\n board: :class:`dict`\n The board returned in chess endpoint.\n arrow: Any\n The arrows to be shown in the image.\n \"\"\"\n def __init__(self, board: dict, arrow: typing.Any):\n self.board = board\n self.arrow = arrow\n\n def to_payload(self):\n return {\n \"board\": self.board,\n \"arrow\": self.arrow\n }\n\nclass ChessTurn:\n r\"\"\"\n The chess payload for turn endpoint.\n\n Paramaters\n ----------\n board: :class:`dict`\n The board returned in chess endpoint.\n turn: :class:`str`\n The turn of the chess. For example, 'a2-a4'\n player: :class:`ChessPiece`\n The player that is turning.\n \"\"\"\n def __init__(self, board: dict, turn: str, player: ChessPiece):\n self.board = board\n self.turn = turn\n self.player = player\n\n def to_payload(self):\n return {\n \"board\": self.board,\n \"turn\": self.turn,\n \"move-turn\": str(self.player)\n }\n\nclass ChessTranscript:\n r\"\"\"\n The chess transcript.\n\n Paramaters\n ----------\n board: :class:`dict`\n The board returned in chess endpoint.\n \"\"\"\n def __init__(self, board: dict):\n self.board = board\n\n def to_payload(self):\n return {\n \"board\": self.board\n }\n","sub_path":"aiodevision/baseclasses.py","file_name":"baseclasses.py","file_ext":"py","file_size_in_byte":2233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"473808916","text":"import pandas as pd\nimport numpy as np\nimport tensorflow as tf\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation\nfrom keras.optimizers import SGD\n\n\n# //// DATOS DE LA RED NEURONAL ///////\nn = 17 # Numero de característias de entrada\nm = 1 # Numero de Salidas\nh1 = 300 # Numero de neuronas en la capa oculta\nh2 = 0\ndef read_test_data(filepath):\n # ////////// LEEMOS LOS DATOS DE PRUEBA /////////////\n df = pd.read_csv(\n filepath_or_buffer=filepath,\n header = None,\n sep=',')\n df.dropna(how=\"all\", inplace=True) # drops the empty line at file-end\n ancho = len(df.columns)\n Xte = df.ix[:,0:ancho - 2].values\n Yte = df.ix[:, ancho - 1 ].values\n return Xte, Yte\n\ndef read_training_data(filepath):\n # //////////// LEEMOS TODOS LOS ELEMENTOS TRANSFORMADOS POR EL PCA /////////////\n df = pd.read_csv(\n filepath_or_buffer=filepath,\n header = None,\n sep=',')\n\n df.dropna(how=\"all\", inplace=True) # drops the empty line at file-end\n ancho = len(df.columns)\n Xtr = df.ix[:,0:ancho - 2].values\n Ytr = df.ix[:, ancho - 1 ].values\n return Xtr, Ytr\n\ndef transform_tags_to_0_1(Y):\n k = 0\n while (k < len(Y)) :\n if Y[k].strip() == 'M':\n Y[k] = 0\n else :\n Y[k] = 1\n k+=1\n Y = Y.reshape((len(Y), m))\n return Y\n\ndef round_results(Y):\n k = 0\n while (k < len(Y)) :\n Y[k] = round(Y[k])\n k+=1\n return Y\n\ndef round(n):\n dec = n - int(n)\n rst = int(n) if dec < 0.5 else int(n)+1\n return rst\n\n# /////////////// TRAINING ////////////////////\ndef training_model(Xtr):\n x_ = tf.placeholder(tf.float32, shape=[len(Xtr), n], name=\"x-input\")\n y_ = tf.placeholder(tf.float32, shape=[len(Xtr), m], name=\"y-input\")\n\n W1 = tf.Variable(tf.random_uniform([n,h1], -1, 1), name=\"W1\")\n B1 = tf.Variable(tf.zeros([h1]), name=\"Bias1\")\n output_1 = tf.sigmoid(tf.matmul(x_, W1) + B1)\n\n # 1 Capa oculta\n W2 = tf.Variable(tf.random_uniform([h1, m], -1, 1), name=\"W2\")\n B2 = tf.Variable(tf.zeros([m]), name=\"Bias2\")\n PRED = tf.sigmoid(tf.matmul(output_1, W2) + B2)\n return PRED, W1, B1, W2, B2, y_, x_\n\n # 2 Capas ocultas\n '''\n W2 = tf.Variable(tf.random_uniform([h1,h2], -1, 1), name=\"W2\")\n B2 = tf.Variable(tf.zeros([h2]), name=\"Bias2\")\n output_2 = tf.sigmoid(tf.matmul(output_1, W2) + B2)\n\n W3 = tf.Variable(tf.random_uniform([h2, m], -1, 1), name=\"W3\")\n B3 = tf.Variable(tf.zeros([m]), name=\"Bias3\")\n PRED = tf.sigmoid(tf.matmul(output_2, W3) + B3)\n return PRED, W1, B1, W2, B2, W3, B3, y_, x_\n '''\n\n\n\n\n# /////////////// TEST MODEL ////////////////////\ndef test_model(W1, B1, W2, B2):\n xt_ = tf.placeholder(tf.float32, shape=[1 , n], name=\"x-input\")\n output_1 = tf.sigmoid(tf.matmul(xt_, W1) + B1)\n\n # 1 Capa oculta\n PRED = tf.sigmoid(tf.matmul(output_1, W2) + B2)\n '''\n # 2 Capas ocultas\n output_2 = tf.sigmoid(tf.matmul(output_1, W2) + B2)\n PRED = tf.sigmoid(tf.matmul(output_2, W3) + B3)\n '''\n return PRED, xt_\n\n# //////// LEEMOS LA MATRIZ DE PESOS DEL PCA /////////////\ndef get_pca_w():\n df = pd.read_csv(\n filepath_or_buffer='/Users/alancruz/Desktop/PYTHON/CORE/data/w_pca.csv',\n header = None,\n sep=',')\n\n df.dropna(how=\"all\", inplace=True) # drops the empty line at file-end\n ancho = len(df.columns)\n W = df.ix[:,0:ancho - 1].values\n return W, ancho\n\n# //////// KERAS MODEL /////////////\ndef config_keras_model(input_shape, n_classes, epochs, lrate = 0.01):\n # define the architecture of the network\n model = Sequential()\n model.add(Dense(int(input_shape/4), input_dim=input_shape, init=\"uniform\", activation=\"relu\"))\n model.add(Dense(int(input_shape/8), init=\"uniform\", activation=\"relu\"))\n model.add(Dense(n_classes))\n ## 2 --> Number of final claseses\n model.add(Activation(\"softmax\"))\n decay = lrate / epochs\n sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)\n ## lr = 0.01, learning rate\n model.compile(loss=\"categorical_crossentropy\", optimizer=sgd, metrics=[\"accuracy\"])\n return model\n","sub_path":"GENERO_RED_NEURONAL/CORE.py","file_name":"CORE.py","file_ext":"py","file_size_in_byte":4136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"273711934","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Dec 19 14:14:46 2015\n\n@author: Kapythone\n\"\"\"\n\nclass Solution(object):\n def maxProfit(self, prices):\n \"\"\"\n :type prices: List[int]\n :rtype: int\n \"\"\"\n if len(prices) == 0:\n return 0\n result = 0\n low = high = prices[0]\n for i in prices:\n if i >= low and i <= high:\n continue\n elif i > high:\n high = i\n temp = high - low\n if temp > result:\n result = temp\n elif i < low:\n low = high = i\n \n return result","sub_path":"Best Time to Buy and Sell Stock.py","file_name":"Best Time to Buy and Sell Stock.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"325418424","text":"from RPLCD import CharLCD\nfrom RPi import GPIO\nimport time\nimport RPi.GPIO as a\n\nGPIO.setwarnings(False)\nsensor=16\n\nlcd = CharLCD(numbering_mode=GPIO.BOARD, cols=16, rows=2, pin_rs=37, pin_e=35, pins_data=[33, 31, 29, 23])\ntime.sleep(2)\nlcd.clear()\n\na.setwarnings(False)\na.setup(sensor,a.IN)\n\nlcd.write_string(\"Initializing Soil Sensor...\")\ntime.sleep(12)\nlcd.clear()\nlcd.write_string(\"Soil sensor READY\")\nlcd.write_string(\" \")\n\ntry:\n while 1:\n if a.input(sensor):\n a.output(led,True)\n lcd.clear()\n lcd.write_string(\"Motion detected\")\n time.sleep(0.2)\n lcd.clear()\n else:\n a.output(led,False)\n\nexcept keyboardInterrupt:\n a.cleanup()\n\n\n","sub_path":"New/Soil_sensor.py","file_name":"Soil_sensor.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"346434548","text":"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n# vim: set fileencoding=UTF-8 :\n\nfrom django import forms\nfrom .models import *\nimport re\n\n\nclass MemberCreationForm(forms.ModelForm):\n\n\t\"\"\"\n\tForm zur Mitgliedserstellung\n\t\"\"\"\n\t\n\t\n\tclass Meta:\n\t\tmodel = Member\n\t\tfields = ('name', 'short', 'team')\n\t\t\n\tdef clean(self):\n\t\tcleaned_data = super(MemberCreationForm, self).clean()\n\t\t\n\t\tshort = cleaned_data.get('short')\n\t\t\n\t\tr = re.compile('[a-z]{2}\\d{3}')\n\t\tif not r.match(short):\n\t\t\tself._errors['short'] = self.error_class(['Please enter your acronym (e.g. mm001)'])\n\t\t\n\t\treturn cleaned_data\n","sub_path":"members/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"6416488","text":"from random import randint\n\ndef quicksort(unsorted, bottom, top):\n if bottom < top:\n # Partition (Write functions, one for smaller and one for bigger)\n newPivot = partition(unsorted, bottom, top)\n quicksort(unsorted, bottom, newPivot-1)\n quicksort(unsorted, newPivot, top)\n\ndef partition(unsorted, bottom, top):\n pivot = randint(bottom, top)\n\n swap(unsorted, top, pivot)\n\n pivot = top\n finalPivotIndex = bottom\n for i in range(bottom, top):\n if (unsorted[i] < unsorted[pivot]):\n swap(unsorted, finalPivotIndex, i)\n finalPivotIndex += 1\n\n swap(unsorted, finalPivotIndex, pivot)\n\n return finalPivotIndex\n\ndef swap(arr, a, b):\n temp = arr[a]\n arr[a] = arr[b]\n arr[b] = temp \n\n\ntestArrays = [[], [1], [2,4], [4,2], [5,2,3,1,4], [1,2,3,4,5]]\nfor arr in testArrays:\n print(arr)\n quicksort(arr, 0, len(arr)-1)\n print(arr)\n print()\n","sub_path":"sorting/quicksort.py","file_name":"quicksort.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"565117273","text":"import random\r\n\r\n#set randomly named values to variables\r\nschools = [\"SchoolName.ocklynge\", \"SchoolName.parklands\"]\r\ngender = [\"Gender.male\", \"Gender.female\"]\r\ncourseType = [\"CourseType.alevel\", \"CourseType.vocational\", \"CourseType.appgeneral\"]\r\ncollegeAttending = [\"College.eastbourne\", \"College.hastings\", \"College.lewes\"]\r\n\r\n#opens a .ts file and then writes to it\r\ndataFile = open(\"./src/RandomData.ts\", \"w\")\r\ndataFile.write('import { CourseType, Gender, SchoolName, College } from \"./enums\";\\nimport { Student } from \"./student\";\\n\\nexport const studentInfo:\\nStudent[] = [\\n')\r\n\r\n#loops until condition is met and prints random data\r\nfor currentNumber in range(0,1000):\r\n schoolsRandom = random.randint(1,len(schools))\r\n genderRandom = random.randint(1,len(gender))\r\n courseTypeGender = random.randint(1,len(courseType))\r\n collegeAttendingRandom = random.randint(1,len(collegeAttending))\r\n dataFile.write(\" {\\n school: \" + schools[schoolsRandom - 1] + \",\" + \"\\n college: \" + collegeAttending[collegeAttendingRandom - 1] + \",\" + \"\\n gender: \" + \r\n gender[genderRandom - 1] + \",\"+ \" \\n course: \" + courseType[courseTypeGender - 1] + \",\" + \"\\n },\\n\");dataFile.write(\"\\n\")\r\n\r\ndataFile.write(\"]\")\r\n#closes file\r\n\r\ndataFile.close()\r\n","sub_path":"client/src/scripts/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"282712055","text":"# https://nanti.jisuanke.com/t/T1556\n\nimport sys\ninput=sys.stdin.readline\n\n\nn, m = list(map(int, input().strip().split()))\na = list(map(int, input().strip().split()))\na.sort()\n\n\ndef find(x):\n l, r = 0, n - 1\n while l < r - 1:\n mid = (l + r) // 2\n if a[mid] < x:\n l = mid\n else:\n r = mid - 1\n\n if l == r:\n if a[l] < x:\n return a[l]\n else:\n return -1\n elif l == r - 1:\n if a[l] >= x:\n return -1\n elif a[r] < x:\n return a[r]\n else:\n return a[l]\n\n\nfor i in range(m):\n x = int(input())\n res = find(x)\n print(res)","sub_path":"binary_search/二分查找_6.py","file_name":"二分查找_6.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"38703319","text":"import requests\nimport json\nimport time\nfrom pprint import pprint\nfrom urllib.parse import urlencode\nfrom pymongo import MongoClient\n\n\ndef api_request(URL, params):\n try:\n repeat = True\n while repeat:\n response = requests.get(URL, params=params).json()\n if 'error' in response and 'error_code' in response['error'] and response['error']['error_code'] == 6:\n time.sleep(1)\n else:\n repeat = False\n return response\n except requests.exceptions.ReadTimeout:\n n = 1\n while n < 3:\n print('\\n Reconnecting to server. \\n')\n try:\n return requests.get(URL, params=params).json()\n except requests.exceptions.ReadTimeout:\n print('\\n Reconnecting to server. \\n')\n n+=1 \n else:\n print('Failed, please check your Internet connection.')\n\n\ndef get_token():\n app_id = 7412922\n oauth_url = 'https://oauth.vk.com/authorize'\n oauth_params = {\n 'client_id': app_id,\n 'display': 'page',\n 'scope': 'friends, groups, stats, offline',\n 'response_type': 'token',\n 'v': '5.52'\n }\n print('?'.join((oauth_url, urlencode(oauth_params))))\n\n\ndef welcome():\n with open('welcome.txt') as welcome:\n print(welcome.read())\n\n\ndef get_people(access_token, sex, age_from, age_to, city_id, country_id):\n URL = 'https://api.vk.com/method/users.search'\n params = {\n 'v': '5.89',\n 'access_token': access_token,\n 'sex': sex,\n 'age_from': age_from,\n 'age_to': age_to,\n 'status': 6,\n 'has_photo': 1,\n 'city': city_id,\n 'country': country_id,\n 'is_closed': False,\n 'can_access_closed': False\n }\n result = api_request(URL, params)\n return result\n\n\ndef get_country_code():\n user_country = input('Введите страну для поиска: ').capitalize()\n with open('countries.json', 'r') as countries_file:\n countries = json.load(countries_file)\n if user_country not in countries.keys():\n print('Страна введена неверно, попробуйте ещё раз.')\n get_country_code()\n else:\n for country, code in countries.items():\n if country == user_country:\n country_code = code\n return country_code\n\n\ndef get_country_id():\n country_code = get_country_code()\n URL = 'https://api.vk.com/method/database.getCountries'\n params = {'v': '5.80', 'access_token': access_token, 'code': country_code}\n result = api_request(URL, params)\n return result['response']['items'][0]['id']\n \n\ndef get_city_id(country_id):\n city = input('Введите желаемый город для поиска: ').capitalize()\n URL = 'https://api.vk.com/method/database.getCities'\n params = {'v': '5.80', 'access_token': access_token, 'country_id': country_id, 'q': city}\n result = api_request(URL, params)\n if result['response']['count'] == 0:\n print('Город введен неверно, попробуйте ещё раз.')\n get_city_id(country_id)\n else:\n return result['response']['items'][0]['id']\n\n\ndef find_photos(owner_id):\n URL = 'https://api.vk.com/method/photos.get'\n params = {'v': '5.80', 'access_token': access_token, 'owner_id': owner_id, 'album_id': 'profile', 'extended': 1, 'count': 1000}\n result = api_request(URL, params)\n photos = {}\n try:\n for items in result['response']['items']:\n for size in items['sizes']:\n if size['type'] == 'x':\n photos[size['url']] = items['likes']['count']\n except KeyError:\n if result['error']['error_code'] == 15:\n print('Не удается загрузить фото, приватный профиль.')\n else:\n print(result)\n return sorted(photos.items(), key=lambda kv: kv[1], reverse=True)[0:3]\n\n\ndef write_json(ten_users):\n people_list = []\n for user in ten_users:\n user_dict = {}\n user_dict['photos'] = find_photos(user['id'])\n user_dict['first name'] = user['first_name']\n user_dict['second name'] = user['last_name']\n user_dict['link'] = f\"https://vk.com/id{user['id']}\"\n people_list.append(user_dict)\n\n with open('people.json', 'w') as people_file:\n json.dump(people_list, people_file, ensure_ascii=False, indent=4)\n\n\ndef write_result(people):\n client = MongoClient()\n vk_db = client['VK']\n users = vk_db['users']\n for each in people['response']['items']:\n users.insert_one(each)\n return list(users.find())\n\n\ndef get_ten_users(people_db, n1, n2):\n ten_users = ckeck_is_empty(people_db, n1, n2)\n if ten_users != None: \n write_json(ten_users)\n print('Результаты поиска записаны в json-файл.')\n\n if input('Найти следующих 10 человек? (да/нет): ') == \"да\":\n print('Поиск в процессе...')\n n1 += 10\n n2 += 10\n get_ten_users(people_db, n1, n2)\n\n\ndef check_age():\n age = input('Введите диапазон возраста в формате \"18-35\": ')\n age_from = age[:2]\n age_to = age[-2:]\n try:\n int(age_from) >= int(age_to)\n return age_from, age_to\n except ValueError:\n print('Введите чила')\n check_age()\n except TypeError:\n print('Укажите диапазон возраста от меньшего к большему')\n check_age() \n\n\ndef check_sex():\n sex = input('Введите пол (1 - жен., 2 - муж., 0 - любой): ')\n possible_vars = [1, 2, 0]\n try:\n if int(sex) in possible_vars:\n return sex\n else:\n print('Укажите индекс одного из доступных вариантов (1, 2 или 0)')\n check_sex()\n except ValueError:\n print('Укажите индекс одного из доступных вариантов (1, 2 или 0)')\n check_sex()\n\n\ndef clear_my_db():\n client = MongoClient()\n vk_db = client['VK']\n users = vk_db['users']\n vk_db.users.drop()\n return list(users.find())\n\n\ndef ckeck_is_empty(people_db, n1, n2):\n if not people_db[n1:n2]:\n if input('По вашему запросу ничего не найдено, хотите изменить параметры поиска? ') == 'да':\n main()\n else: \n return people_db[n1:n2]\n\n\ndef main():\n country_id = get_country_id()\n city_id = get_city_id(country_id)\n sex = check_sex()\n age_from, age_to = check_age()\n print('Поиск в процессе...')\n people = get_people(access_token, sex, age_from, age_to, city_id, country_id)\n people_db = write_result(people)\n n1, n2 = 0, 10\n get_ten_users(people_db, n1, n2)\n\n\nif __name__ == \"__main__\":\n welcome()\n\n access_token = input('Введите токен для ВК (если у Вас нет токена,\\nнапечатайте \"нет\" и пройдите по ссылке): ')\n if access_token == \"нет\":\n get_token()\n access_token = input('Введите полученный токен для ВК: ')\n\n main()\n\n # print(clear_my_db())","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"329039673","text":"from tkinter import *\r\nfrom tkinter import ttk\r\nfrom tkinter import filedialog\r\nimport time\r\nfrom pytube import YouTube ##pip install pytube\r\n\r\n\r\nFileSize = 0\r\nFolder_Name = \"\"\r\n\r\n\r\n#To set the folder for saving the downloaded video\r\ndef openLocation():\r\n global Folder_Name\r\n Folder_Name = filedialog.askdirectory()\r\n if(len(Folder_Name) > 1):#To check if folder is selected properly\r\n PathErrormsg.config(text=Folder_Name,fg=\"green\")\r\n\r\n else:\r\n PathErrormsg.config(text=\"Please Choose Folder!!\",fg=\"red\")\r\n\r\n\r\n#To download the video from the provied youtube link\r\ndef downloadVideo():\r\n global FileSize\r\n Choice = ChoiceMenu.get()\r\n url = LinkBox.get()\r\n\r\n if(len(url)>1):#To check if the link is valid\r\n yt = YouTube(url,on_progress_callback=show_progress_bar)\r\n if(Choice == Choices[0]):\r\n select = yt.streams.filter(res=\"1080p\").first()\r\n \r\n elif(Choice ==Choices[1]):\r\n select = yt.streams.filter(res=\"720p\").first()\r\n\r\n elif(Choice ==Choices[2]):\r\n select = yt.streams.filter(res=\"480p\").first()\r\n \r\n elif(Choice == Choices[3]):\r\n LinkErrormsg.config(text=\"Paste Link Again!!\",fg=\"red\")\r\n\r\n else:\r\n LinkErrormsg.config(text=\"Please Slect the qulity!!\",fg=\"red\")\r\n FileSize = select.filesize\r\n select.download(Folder_Name)\r\n LinkErrormsg.config(text=\"Download Completed!!\",fg=\"green\")\r\n else:\r\n LinkErrormsg.config(text=\"Please Paste the proper link again\")\r\n \r\n\r\n#To download the audio file only\r\ndef downloadAudio():\r\n global FileSize\r\n url =LinkBox.get()\r\n\r\n if(len(url)>1):\r\n yt =YouTube(url,on_progress_callback=show_progress_bar)\r\n select = yt.streams.filter(only_audio=True).first()\r\n else:\r\n LinkErrormsg.config(text=\"Paste your link properly!!\",fg=\"green\")\r\n FileSize = select.filesize\r\n select.download(Folder_Name)\r\n LinkErrormsg.config(text=\"Download Completed!!\")\r\n\r\n\r\n#To update the progessbar while downloading video\r\ndef show_progress_bar(chunk,file_handler, bytes_remaining):\r\n #print(bytes_remaining)\r\n global FileSize\r\n FileDownloaded = FileSize - bytes_remaining\r\n #print(FileDownloaded)\r\n percent = (FileDownloaded/FileSize)*100\r\n #print(percent)\r\n ProgressVar.set(percent)\r\n time.sleep(0.02)\r\n root.update_idletasks()\r\n\r\n\r\n\r\n#initialization\r\nroot=Tk()\r\nroot.title(\"youtube Downloader\")\r\nroot.geometry(\"800x500\") #window size\r\n\r\n#adding an image \r\nfile = PhotoImage(file='logo1.png')\r\nheaderIcon = Label(root, image=file,bg=\"lightgreen\")\r\nheaderIcon.place(x=100,y=0)\r\n\r\nroot.config(bg=\"lightgreen\")\r\n\r\n#UI design\r\nTextLabel = Label(root,text=\"Youtube Video and Audio Downloader\", font=(\"Maiandra GD\",16,\"bold\"),bg=\"lightgreen\")\r\nTextLabel.place(x=200,y=20)\r\n\r\nLinkLabel = Label(root,text=\"Enter the video link : \", font=(\"Comic Sans MS\",15),bg=\"lightgreen\")\r\nLinkLabel.place(x=30,y=80)\r\n\r\nLinkBoxVar = StringVar()\r\nLinkBox = Entry(root,width=80,textvariable = LinkBoxVar,bg=\"pink\")\r\nLinkBox.place(x=240, y=86)\r\n\r\nLinkErrormsg = Label(root, text = \"\",fg=\"red\",font=(\"Comic Sans MS\",12),bg=\"lightgreen\")\r\nLinkErrormsg.place(x=300, y=130)\r\n\r\nPathLabel = Label(root,text=\"Select the folder where you want save the video : \", font=(\"Comic Sans MS\",15),bg=\"lightgreen\")\r\nPathLabel.place(x=30,y=180)\r\n\r\nSaveButton = Button(root, width =15, bg=\"red\",fg=\"white\",text=\"Choose Path\",font=(\"Arial Rounded MT Bold\",12,\"bold\"),command=openLocation)\r\nSaveButton.place(x=520,y=180)\r\n\r\nPathErrormsg = Label(root, text = \"\",fg=\"red\",font=(\"Comic Sans MS\",12),bg=\"lightgreen\")\r\nPathErrormsg.place(x=300, y=240)\r\n\r\nChoiceLabel = Label(root,text=\"Select the Quality of the video : \", font=(\"Comic Sans MS\",15),bg=\"lightgreen\")\r\nChoiceLabel.place(x=100,y=300)\r\n\r\nChoices = [\"1080p\",\"720p\",\"480p\"]\r\nChoiceMenu = ttk.Combobox(root, values = Choices)\r\nChoiceMenu.place(x=450,y=305,)\r\n\r\nQualityErrormsg = Label(root, text = \"\",fg=\"red\",font=(\"Comic Sans MS\",12),bg=\"lightgreen\")\r\nQualityErrormsg.place(x=280, y=360)\r\n\r\nProgressVar = DoubleVar()\r\nProgressBar = ttk.Progressbar(root,orient=HORIZONTAL,variable=ProgressVar,length=500,mode='determinate') #Label(root,text=\"progress\",fg=\"red\",font=(\"Comic Sans MS\",12),bg=\"lightgreen\")\r\nProgressBar.place(x=150,y=390)\r\n\r\nVideoButton = Button(root, width =20, bg=\"red\",fg=\"white\",text=\"Download Video\",font=(\"Arial Rounded MT Bold\",12,\"bold\"),command=downloadVideo)\r\nVideoButton.place(x=80,y=450)\r\n\r\nAudioButton = Button(root, width =20, bg=\"red\",fg=\"white\",text=\"Download Audio\",font=(\"Arial Rounded MT Bold\",12,\"bold\"),command=downloadAudio)\r\nAudioButton.place(x=500,y=450)\r\n\r\nroot.mainloop()\r\n\r\n","sub_path":"YoutubeDownloader.py","file_name":"YoutubeDownloader.py","file_ext":"py","file_size_in_byte":4701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"575528225","text":"\nimport time\nimport sys\n\nimport av\n\n\nstart_time = last_time = time.monotonic()\ndef lap(name):\n global last_time\n now = time.monotonic()\n print(f'[{now - start_time:5.3f} (+{now - last_time:5.3f})] {name}')\n last_time = now\n\n\ndef v1(path):\n\n keyframes = []\n fh = av.open(path)\n stream = fh.streams.video[0]\n # stream.thread_type = 'AUTO'\n\n for pi, packet in enumerate(fh.demux(video=0)):\n if not packet.is_keyframe:\n continue\n\n lap(f'found keyframe {len(keyframes)} packet at {pi}')\n for frame in packet.decode():\n keyframes.append(frame)\n lap(f'decoded {len(keyframes)} frames')\n if len(keyframes) >= 3:\n break\n\n\ndef v2(path):\n\n keyframes = []\n fh = av.open(path)\n\n pcount = 0\n stream = fh.streams.video[0]\n # stream.thread_type = 'AUTO'\n\n demuxer = fh.demux(stream)\n\n frame_pts = (1 / stream.rate) / stream.time_base\n seek_pts = frame_pts // 2\n\n while True:\n\n for packet in demuxer:\n\n pcount += 1\n if not packet.is_keyframe:\n continue\n\n lap(f'found keyframe {len(keyframes)} packet at {pcount}')\n for frame in packet.decode():\n keyframes.append(frame)\n lap(f'decoded {len(keyframes)} frames')\n\n if len(keyframes) >= 3:\n return\n\n stream.seek(packet.pts + seek_pts, backward=False)\n\n\n\n\nfor path in sys.argv[1:]:\n lap(f'starting {path}')\n v1(path)\n\nlap('done')\n","sub_path":"scratchpad/email-2018-09-07.py","file_name":"email-2018-09-07.py","file_ext":"py","file_size_in_byte":1516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"155908015","text":"import pandas as pd\nimport seaborn as sns\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ntrain=pd.read_csv(\"C:\\\\Users\\\\lenovo\\\\Desktop\\\\Dataset\\\\all\\\\train.csv\")\ntest=pd.read_csv(\"C:\\\\Users\\\\lenovo\\\\Desktop\\\\Dataset\\\\all\\\\test.csv\")\ngender=pd.read_csv(\"C:\\\\Users\\\\lenovo\\\\Desktop\\\\Dataset\\\\all\\\\gender_submission.csv\")\n\n# fig=plt.Figure((30,30))\n# fig.subplots(10,10)\n# sns.countplot(x='Pclass',hue='Survived',data=train)\n\ntrain['Age']=train['Age'].fillna(train['Age'].mean())\n#print(train[train['Age'].isna()])\n# fig.subplots(10,10)\n# sns.distplot(train['Age'])\n# plt.show()\ngr=train.groupby('Sex')\nprint(gr.sum())\ndata_corr=train.iloc[:,1:].corr()\ncol=['Survived','Pclass','Age','SibSp','Parch','Fare']\nsns.heatmap(data_corr,xticklabels=col,yticklabels=col)\nplt.show()\n\n\n# from sklearn.linear_model import LogisticRegression\n# from sklearn.cross_validation import train_test_split\n# pd.get_dummies(data)\n\n\n# train_x, test_x, train_y, test_y = train_test_split(X,y,split=0.2,randome_state=10)\n\n# reg=LogisticRegression()\n# model=reg.fit(X,y)\n# y_pred=model.predict(test_x)\n\n# model.score(test_x,test_y)","sub_path":"analysis1.py","file_name":"analysis1.py","file_ext":"py","file_size_in_byte":1109,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"575020422","text":"import numpy as np\nimport math\n\nfrom constants import *\n\n\"\"\"\nTwo tire model\nImagine a point mass with two tires, that's this model!\n\"\"\"\n\ndef derate_curvature(curv, raddl):\n return curv/(1.0 + raddl*curv)\n\ndef floor_sqrt(x):\n \"\"\"\n Like sqrt but with a floor. If x <= 0, return 0.\n \"\"\"\n if x > 0:\n return math.sqrt(x)\n return 0\n\nclass sim_fourtires:\n def __init__(self):\n pass\n\n def step(self, vehicle, prior_result, segment, segment_next, brake, shifting, gear):\n \"\"\"\n Takes a vehicle step. Picks the aerodynamic strategy that works out to be the best.\n See substep for return value. If no aero strategy is valid, returns None, else returns the best.\n \"\"\"\n # return self.substep(vehicle, prior_result, segment, segment_next, brake, shifting, gear, AERO_FULL)\n if brake:\n if abs(vehicle.downforce(prior_result[O_VELOCITY],AERO_BRK)-vehicle.downforce(prior_result[O_VELOCITY],AERO_FULL)) < 1e-3 and abs(vehicle.drag(prior_result[O_VELOCITY],AERO_BRK)-vehicle.drag(prior_result[O_VELOCITY],AERO_FULL)) < 1e-3: \n return self.substep(vehicle, prior_result, segment, segment_next, brake, shifting, gear, AERO_FULL)\n out_brk = self.substep(vehicle, prior_result, segment, segment_next, brake, shifting, gear, AERO_BRK)\n out_nor = self.substep(vehicle, prior_result, segment, segment_next, brake, shifting, gear, AERO_FULL)\n if out_nor is not None:\n if out_brk is not None:\n if out_brk[O_VELOCITY] < out_nor[O_VELOCITY]:\n return out_brk\n else:\n return out_nor\n else:\n return out_nor\n elif out_brk is not None:\n return out_brk\n else:\n return None\n else:\n if abs(vehicle.downforce(prior_result[O_VELOCITY],AERO_DRS)-vehicle.downforce(prior_result[O_VELOCITY],AERO_FULL)) < 1e-3 and abs(vehicle.drag(prior_result[O_VELOCITY],AERO_DRS)-vehicle.drag(prior_result[O_VELOCITY],AERO_FULL)) < 1e-3: \n return self.substep(vehicle, prior_result, segment, segment_next, brake, shifting, gear, AERO_FULL)\n out_drs = self.substep(vehicle, prior_result, segment, segment_next, brake, shifting, gear, AERO_DRS)\n out_nor = self.substep(vehicle, prior_result, segment, segment_next, brake, shifting, gear, AERO_FULL)\n if out_nor is not None:\n if out_drs is not None:\n if out_drs[O_VELOCITY] > out_nor[O_VELOCITY]:\n return out_drs\n else:\n return out_nor\n else:\n return out_nor\n elif out_drs is not None:\n return out_drs\n else:\n return None\n\n def compute_Ff_Fr(self, N, vehicle, v, a_long, segment, prior_curvature):\n alpha = v**2*(derate_curvature(segment.curvature, vehicle.r_add)-derate_curvature(prior_curvature, vehicle.r_add))/segment.length + segment.curvature*a_long\n a_lat = derate_curvature(segment.curvature, vehicle.r_add)*v**2\n kf = vehicle.k_roll_front\n kr = vehicle.k_roll_rear\n kcf = vehicle.k_chassis/(vehicle.weight_bias)\n kcr = vehicle.k_chassis/(1-vehicle.weight_bias)\n\n Nf1 = N[0]/2\n Nf2 = N[0]/2\n Nr1 = N[1]/2\n Nr2 = N[1]/2\n Mint = a_lat*vehicle.mass*vehicle.cg_height\n if vehicle.lltd == 'compute':\n Mf = Mint*kcf*kf*(kcr+kr)/(kcf*kcr*kf+kcf*kcr*kr+kcf*kf*kr+kcr*kf*kr)\n Mr = Mint*kcr*kr*(kcf+kf)/(kcf*kcr*kf+kcf*kcr*kr+kcf*kf*kr+kcr*kf*kr)\n\n Nf1 = N[0]/2 - Mf/vehicle.track_front\n Nf2 = N[0]/2 + Mf/vehicle.track_front\n Nr1 = N[1]/2 - Mr/vehicle.track_rear\n Nr2 = N[1]/2 + Mr/vehicle.track_rear\n elif vehicle.lltd == 'perfect':\n Mf = Mint*(vehicle.weight_bias-0.05) # 0.05 is 'magic'\n Mr = Mint*(1.0-vehicle.weight_bias+0.05)\n\n Nf1 = N[0]/2 - Mf/vehicle.track_front\n Nf2 = N[0]/2 + Mf/vehicle.track_front\n Nr1 = N[1]/2 - Mr/vehicle.track_rear\n Nr2 = N[1]/2 + Mr/vehicle.track_rear\n else:\n Mf = Mint*vehicle.lltd\n Mr = Mint*(1.0-vehicle.lltd)\n\n Nf1 = N[0]/2 - Mf/vehicle.track_front\n Nf2 = N[0]/2 + Mf/vehicle.track_front\n Nr1 = N[1]/2 - Mr/vehicle.track_rear\n Nr2 = N[1]/2 + Mr/vehicle.track_rear\n\n Ff_lat = (vehicle.weight_bias)*a_lat*vehicle.mass - alpha*vehicle.moi_yaw/vehicle.wheelbase_length\n Fr_lat = (1-vehicle.weight_bias)*a_lat*vehicle.mass + alpha*vehicle.moi_yaw/vehicle.wheelbase_length\n\n return (Nf1, Nf2, Nr1, Nr2, Ff_lat, Fr_lat)\n \n def substep(self, vehicle, prior_result, segment, segment_next, brake, shifting, gear, aero_mode):\n \"\"\"\n Takes a vehicle step. Returns (see last line) if successful, returns None if vehicle skids off into a wall.\n @param v0 the initial vehicle speed for this step\n @param segment the Segment of the track the vehicle is on\n @param brake a boolean value specifying whether or not to apply the brakes (with full available force)\n @param shifting a shifting status code\n \"\"\"\n\n # Initialize values to those from the previous step\n Nf1 = prior_result[O_NF];\n Nf2 = prior_result[O_NF2];\n Nr1 = prior_result[O_NR];\n Nr2 = prior_result[O_NR2];\n v0 = prior_result[O_VELOCITY];\n x0 = prior_result[O_DISTANCE];\n t0 = prior_result[O_TIME];\n a_long = prior_result[O_LONG_ACC]*vehicle.g\n co2_elapsed = prior_result[O_CO2];\n status = S_TOPPED_OUT \n\n Nf = Nf1+Nf2\n Nr = Nr1+Nr2\n\n # Determine how much grip is used keeping the car from skidding away\n Nf1, Nf2, Nr1, Nr2, Ff_lat, Fr_lat = self.compute_Ff_Fr([Nf,Nr],vehicle, v0, a_long, segment, prior_result[O_CURVATURE])\n\n # Determine the remaining longitudinal grip. If there isn't any, then we're out of luck.\n Ff_remaining,_ = vehicle.f_long_remain_pair([Nf1,Nf2], Ff_lat, True)\n Fr_remaining,_ = vehicle.f_long_remain_pair([Nr1,Nr2], Fr_lat, False)\n if min(Ff_remaining+Fr_remaining) < 0:\n # print('failpt A')\n # print([Nf1, Nf2, Nr1, Nr2])\n # print(Ff_remaining+Fr_remaining)\n return None\n\n # Determine how much force the engine can produce.\n Fr_engine_limit, eng_rpm = vehicle.eng_force(v0, int(gear))\n\n # Initialize these values which will be overriden later asneedbe.\n Ff_long = 0\n Fr_long = 0\n F_longitudinal = 0\n\n if brake:\n # Two different brake strategies: Perfect biasing and static bias\n status = S_BRAKING\n if vehicle.perfect_brake_bias:\n Fr_long = -min(Fr_remaining)*2\n Ff_long = -min(Ff_remaining)*2\n else:\n # Find which tire limits the force, and base force off of it\n F_brake = min(min(Ff_remaining)*2/vehicle.front_brake_bias(), min(Fr_remaining)*2/vehicle.rear_brake_bias())\n Fr_long = -F_brake*vehicle.rear_brake_bias()\n Ff_long = -F_brake*vehicle.front_brake_bias()\n # Gear is undefined when shifting\n gear = np.nan\n elif shifting:\n # No force (or gear) when you're shifting\n status = S_SHIFTING\n Fr_long = 0\n Ff_long = 0\n gear = np.nan\n else:\n # @FIXME NOT PLAYED AROUND WITH ENOUGH WITH TWO TIRE MODEL!!!!\n # This logic helps absorb simulation oscillations (brake-accel oscillation on corners)\n # If there's curvature, and we were braking before (we are not anymore) or we were sustaining before with negligible curvature change, continue sustaining\n if segment.curvature > 0 and (prior_result[O_STATUS] == S_BRAKING or ((segment.curvature - prior_result[O_CURVATURE])>=0 and prior_result[O_STATUS] == S_SUSTAINING)):\n status = S_SUSTAINING\n Fr_long = vehicle.drag(v0, aero_mode)\n # If not sustaining, jammalam that throttle\n else:\n status = S_ENG_LIM_ACC\n Fr_long = Fr_engine_limit\n if Fr_long <= vehicle.drag(v0, aero_mode):\n status = S_DRAG_LIM\n\n # Still not allowed to use more force than your tire can provide\n if Fr_long > min(Fr_remaining)*2:\n status = S_TIRE_LIM_ACC\n Fr_long = min(Fr_remaining)*2\n\n if eng_rpm > vehicle.engine_rpms[-1]:\n status = S_TOPPED_OUT \n\n # Determine the longitudinal force and resulting acceleration\n F_longitudinal = Ff_long + Fr_long - vehicle.drag(v0, aero_mode)\n a_long = F_longitudinal / vehicle.mass\n\n # Determine the vehicle velocity after said acceleration\n vf = floor_sqrt(v0**2 + 2*a_long*segment.length)\n\n # Also determine limits for top and low speeds if grip were reassigned\n vfu = floor_sqrt(v0**2 + 2*(Fr_engine_limit - vehicle.drag(v0, aero_mode))/vehicle.mass*segment.length)\n vfl = floor_sqrt(v0**2 + 2*(-min(Ff_remaining)*2 -min(Fr_remaining)*2 - vehicle.drag(v0, aero_mode))/vehicle.mass*segment.length)\n\n \n # Calculate normal force on each tire\n Nf = ( (vehicle.weight_bias)*vehicle.g*vehicle.mass\n + (vehicle.cp_bias[aero_mode])*vehicle.downforce(vf,aero_mode)\n - vehicle.mass*a_long*vehicle.cg_height/vehicle.wheelbase_length\n - vehicle.drag(vf,aero_mode)*vehicle.cp_height[aero_mode]/vehicle.wheelbase_length )\n\n Nr = ( (1-vehicle.weight_bias)*vehicle.g*vehicle.mass\n + vehicle.downforce(vf,aero_mode)*(1-vehicle.cp_bias[aero_mode])\n + vehicle.mass*a_long*vehicle.cg_height/vehicle.wheelbase_length\n + vehicle.drag(vf,aero_mode)*vehicle.cp_height[aero_mode]/vehicle.wheelbase_length )\n\n # Determine how much grip is used keeping the car from skidding away\n Nf1, Nf2, Nr1, Nr2, Ff_lat, Fr_lat = self.compute_Ff_Fr([Nf,Nr],vehicle, vf, a_long, segment_next, segment.curvature)\n\n # Determine the remaining longitudinal grip. If there isn't any, then we're out of luck.\n Ff_remaining,_ = vehicle.f_long_remain_pair([Nf1,Nf2], Ff_lat, True)\n Fr_remaining,_ = vehicle.f_long_remain_pair([Nr1,Nr2], Fr_lat, False)\n\n # Figure out how much longitudinal grip remains\n remaining_long_grip = Ff_remaining+Fr_remaining\n # # print('remain', remaining_long_grip)\n\n # Loop through a range of acceleration possiblities if the result from this step was invalid (try to fix this step)\n # This was bisection in single tire (for sustaining usage) but I couldn't get that to work here. Thus dumb iteration.\n vf_working = None\n N_ITERS = 25\n if min(remaining_long_grip) < 0:\n # If we were scheduled to coast, we're not using our tires anyways, so we're kinda screwed anyways. \n # @FIXME: THIS MIGHT BE THE PROBLEM!!!!!!! If you are midway through a shift when you hit a corner, there's no recourse. Not sure how to solve.\n if shifting == IN_PROGRESS and not brake:\n # print('failpt B (recovery attempted)')\n vfu = floor_sqrt(v0**2 + 2*(- vehicle.drag(v0, aero_mode))/vehicle.mass*segment.length)\n # return None\n valid_entries = []\n for n in range(N_ITERS):\n vf = (vfu+vfl)/2\n\n # Prescribe an acceleration\n a_long = (vf**2-v0**2)/2/segment.length\n\n # Calculate normal force on each tire\n Nf = ( (vehicle.weight_bias)*vehicle.g*vehicle.mass\n + (vehicle.cp_bias[aero_mode])*vehicle.downforce(vf,aero_mode)\n - vehicle.mass*a_long*vehicle.cg_height/vehicle.wheelbase_length\n - vehicle.drag(vf,aero_mode)*vehicle.cp_height[aero_mode]/vehicle.wheelbase_length )\n\n Nr = ( (1-vehicle.weight_bias)*vehicle.g*vehicle.mass\n + vehicle.downforce(vf,aero_mode)*(1-vehicle.cp_bias[aero_mode])\n + vehicle.mass*a_long*vehicle.cg_height/vehicle.wheelbase_length\n + vehicle.drag(vf,aero_mode)*vehicle.cp_height[aero_mode]/vehicle.wheelbase_length )\n\n # Determine how much grip is used keeping the car from skidding away\n Nf1, Nf2, Nr1, Nr2, Ff_lat, Fr_lat = self.compute_Ff_Fr([Nf,Nr],vehicle, vf, a_long, segment_next, segment.curvature)\n\n # Determine the remaining longitudinal grip. If there isn't any, then we're out of luck.\n Ff_remaining,_ = vehicle.f_long_remain_pair([Nf1,Nf2], Ff_lat, True)\n Fr_remaining,_ = vehicle.f_long_remain_pair([Nr1,Nr2], Fr_lat, False)\n\n # Calculate how much grip there is left\n remaining_long_grip = Ff_remaining+Fr_remaining\n # Calculate how much grip is needed\n F_req_long = a_long*vehicle.mass+vehicle.drag(vf,aero_mode)\n\n # Calculate how grip has to be distributed\n if F_req_long < 0:\n status = S_BRAKING\n if vehicle.perfect_brake_bias:\n order = np.argsort(remaining_long_grip)\n for o in order[:-1]:\n if remaining_long_grip[o] < F_req_long:\n F_req_long -= remaining_long_grip[o]\n remaining_long_grip[o] = 0\n else:\n remaining_long_grip[o] -= F_req_long\n F_req_long = 0\n\n remaining_long_grip[order[-1]] -= F_req_long\n \n else:\n F_brake = -F_req_long\n remaining_long_grip[0] -= F_brake*vehicle.front_brake_bias()/2\n remaining_long_grip[1] -= F_brake*vehicle.front_brake_bias()/2\n remaining_long_grip[2] -= F_brake*vehicle.rear_brake_bias()/2\n remaining_long_grip[3] -= F_brake*vehicle.rear_brake_bias()/2\n else:\n # If this requires more force than the engine will allow (for some reason) then move along, it's not working out.\n status = S_TIRE_LIM_ACC\n remaining_long_grip[2]-=F_req_long/2\n remaining_long_grip[3]-=F_req_long/2\n\n if brake:\n if min(remaining_long_grip) >= 0:\n vfu = vf # if you can, brake harder\n vf_working = vf\n elif min(remaining_long_grip[:2]) > min(remaining_long_grip[2:]): # grip problem is worse on the rear, maybe try less braking\n vfl = vf\n else:\n vfu = vf\n else:\n if F_req_long > Fr_engine_limit or shifting == IN_PROGRESS:\n vfu = vf # force bisect down; you can't be here\n elif min(remaining_long_grip) > 0:\n vfl = vf # try to go faster\n vf_working = vf\n elif min(remaining_long_grip[:2]) > min(remaining_long_grip[2:]): # grip problem is worse on the rear, maybe try more acceleration\n vfl = vf\n else:\n vfu = vf\n \n if abs(vfu - vfl) < 1e-5:\n break\n # If nothing was valid then nothing will work on this step. Gotta brake earlier.\n if min(remaining_long_grip) < 0:\n if vf_working is None:\n # print('failpt C')\n return None\n vf = vf_working\n\n\n # we have found the range of workable solutions, let's hone in on those now\n # by hone in I mean pick the best one\n # Then go through and do the same set of calculations...\n \n\n a_long = (vf**2-v0**2)/2/segment.length\n\n # Calculate normal force on each tire\n Nf = ( (vehicle.weight_bias)*vehicle.g*vehicle.mass\n + (vehicle.cp_bias[aero_mode])*vehicle.downforce(vf,aero_mode)\n - vehicle.mass*a_long*vehicle.cg_height/vehicle.wheelbase_length\n - vehicle.drag(vf,aero_mode)*vehicle.cp_height[aero_mode]/vehicle.wheelbase_length )\n\n Nr = ( (1-vehicle.weight_bias)*vehicle.g*vehicle.mass\n + vehicle.downforce(vf,aero_mode)*(1-vehicle.cp_bias[aero_mode])\n + vehicle.mass*a_long*vehicle.cg_height/vehicle.wheelbase_length\n + vehicle.drag(vf,aero_mode)*vehicle.cp_height[aero_mode]/vehicle.wheelbase_length )\n\n # Determine how much grip is used keeping the car from skidding away\n Nf1, Nf2, Nr1, Nr2, Ff_lat, Fr_lat = self.compute_Ff_Fr([Nf,Nr],vehicle, vf, a_long, segment_next, segment.curvature)\n\n # Determine the remaining longitudinal grip. If there isn't any, then we're out of luck.\n Ff_remaining,_ = vehicle.f_long_remain_pair([Nf1,Nf2], Ff_lat, True)\n Fr_remaining,_ = vehicle.f_long_remain_pair([Nr1,Nr2], Fr_lat, False)\n\n # Calculate how much grip there is left\n remaining_long_grip = Ff_remaining+Fr_remaining\n F_req_long = a_long*vehicle.mass+vehicle.drag(vf,aero_mode)\n if F_req_long < 0:\n status = S_BRAKING\n if vehicle.perfect_brake_bias:\n order = np.argsort(remaining_long_grip)\n for o in order[:-1]:\n if remaining_long_grip[o] < F_req_long:\n F_req_long -= remaining_long_grip[o]\n remaining_long_grip[o] = 0\n else:\n remaining_long_grip[o] -= F_req_long\n F_req_long = 0\n\n remaining_long_grip[order[-1]] -= F_req_long\n \n else:\n F_brake = -F_req_long\n remaining_long_grip[0] -= F_brake*vehicle.front_brake_bias()/2\n remaining_long_grip[1] -= F_brake*vehicle.front_brake_bias()/2\n remaining_long_grip[2] -= F_brake*vehicle.rear_brake_bias()/2\n remaining_long_grip[3] -= F_brake*vehicle.rear_brake_bias()/2\n Fr_long = -1\n else:\n status = S_TIRE_LIM_ACC\n remaining_long_grip[2]-=F_req_long/2\n remaining_long_grip[3]-=F_req_long/2\n Fr_long = F_req_long\n\n if v0+vf > 0:\n tf = t0 + segment.length/((v0+vf)/2)\n else:\n tf = t0\n xf = x0 + segment.length\n\n if Fr_long > 0:\n co2_elapsed += segment.length*Fr_long*vehicle.co2_factor/vehicle.e_factor\n\n\n # Nf = (Nf1+Nf2)/2\n # Nr = (Nr1+Nr2)/2\n\n # # Determine how much grip is used keeping the car from skidding away\n # Nf1, Nf2, Nr1, Nr2, Ff_lat, Fr_lat = self.compute_Ff_Fr([Nf,Nr],vehicle, vf, segment_next, segment.curvature)\n\n # # Determine the remaining longitudinal grip. If there isn't any, then we're out of luck.\n # Ff_remaining,_ = vehicle.f_long_remain_pair([Nf1,Nf2], Ff_lat, True)\n # Fr_remaining,_ = vehicle.f_long_remain_pair([Nr1,Nr2], Fr_lat, False)\n # remaining_long_grip = Ff_remaining+Fr_remaining\n # # print([Nf1, Nf2, Nr1, Nr2])\n # # print(remaining_long_grip)\n # if min(remaining_long_grip) < 0:\n # print('failpt WTF???')\n\n output = np.array([\n tf,\n xf,\n vf,\n Nf1,\n Nf2,\n Nr1,\n Nr2, \n segment.sector,\n status,\n gear,\n a_long / vehicle.g, \n (v0 ** 2) * derate_curvature(segment.curvature, vehicle.r_add) / vehicle.g, \n 0,\n 0,\n remaining_long_grip[0], \n remaining_long_grip[1],\n remaining_long_grip[2],\n remaining_long_grip[3], \n segment.curvature,\n eng_rpm,\n\n co2_elapsed,\n aero_mode\n ])\n\n return output\n\n def solve(self, vehicle, segments, output_0 = None):\n \"\"\"\n The solver! The big, bad boy that does all the dirty work.\n \"\"\"\n\n # set up initial stuctures\n output = np.zeros((len(segments), O_MATRIX_COLS))\n precrash_output = np.zeros((len(segments), O_MATRIX_COLS))\n shifting = NOT_SHIFTING\n STALLED_SPEED = 2\n launched = False\n brake = False\n shiftpt = -1\n shift_v_req = 0\n \n # Initialize the output matrix appropriately\n if output_0 is None:\n output[0,O_NF] = vehicle.mass*(vehicle.weight_bias)*vehicle.g/2\n output[0,O_NF2] = vehicle.mass*(vehicle.weight_bias)*vehicle.g/2\n output[0,O_NR] = vehicle.mass*(1-vehicle.weight_bias)*vehicle.g/2\n output[0,O_NR2] = vehicle.mass*(1-vehicle.weight_bias)*vehicle.g/2\n gear = vehicle.best_gear(output[0,O_VELOCITY], np.inf)\n else:\n output[0,:] = output_0\n output[0,O_TIME] = 0\n output[0,O_DISTANCE] = 0\n gear = vehicle.best_gear(output_0[O_VELOCITY], output_0[O_FR_REMAINING]+output_0[O_FR2_REMAINING])\n launched = True\n\n # Take the first step...\n step_result = self.step(vehicle, output[0], segments[0], segments[1], brake, shiftpt>=0, gear)\n\n # OK, the first step shouldn't fail, so put it into the output matrix\n output[0] = step_result\n\n # step loop set up\n i = 1\n backup_amount = int(6.0/segments[0].length)\n bounds_found = False\n failpt = -1\n precrash_i = -1\n middle_brake_bound = -1\n lower_brake_bound = -1\n upper_brake_bound = -1\n\n while i=0, gear)\n if step_result is None:\n # Vehicle crashed. Initiate braking algorithm!\n if not brake:\n # print(\"%d,%.2f: start braking\" % (i,output[i-1,O_DISTANCE]))\n # Start braking\n\n # Make a backup (deep) copy of the output matrix prior to crash. (enables bisection algorithm)\n precrash_output = np.copy(output)\n brake = True\n bounds_found = False\n failpt = i-1\n precrash_i = i\n # while segments[failpt-1].curvature < segments[failpt].curvature and failpt=0 and not bounds_found:\n # print('%d,%.2f: nailed it at %d' % (i, step_result[O_VELOCITY], lower_brake_bound))\n bounds_found = True\n #upper_brake_bound = precrash_i-1 #lower_brake_bound+backup_amount\n middle_brake_bound = int(float(upper_brake_bound+lower_brake_bound)/2)\n i = middle_brake_bound\n output = np.copy(precrash_output)\n elif failpt>=0 and bounds_found and abs(lower_brake_bound - upper_brake_bound) > 1:\n # print(\"%d,%.2f: converged (%d,%d,%d)\" % (i,output[i,O_DISTANCE],lower_brake_bound,middle_brake_bound,upper_brake_bound))\n # If past the point of crashing and we've not yet successfully bisected to convergence\n lower_brake_bound = middle_brake_bound\n middle_brake_bound = int(float(upper_brake_bound+lower_brake_bound)/2)\n i = middle_brake_bound\n output = np.copy(precrash_output)\n else:\n # print(\"%d,%.2f: normal op\" % (i,output[i-1,O_DISTANCE]))\n # normal operation\n\n # quit braking\n brake = False # problematic??\n failpt = -1\n lower_brake_bound = -1\n upper_brake_bound = -1\n bounds_found = False\n\n output[i] = step_result\n\n better_gear = vehicle.best_gear(output[i,O_VELOCITY], output[i,O_FR_REMAINING])\n\n if shiftpt < 0 and gear != better_gear and output[i,O_STATUS]==S_ENG_LIM_ACC and output[i,O_VELOCITY]>shift_v_req:\n gear += int((better_gear-gear)/abs(better_gear-gear))\n shiftpt = i\n shift_v_req = output[i,O_VELOCITY]*1.01\n elif shiftpt < 0 and output[i,O_STATUS]==S_TOPPED_OUT and gear= 0 and output[i,O_TIME] > output[shiftpt,O_TIME]+vehicle.shift_time:\n shiftpt = -1\n i-=1\n \n i+=1\n\n #np.savetxt('dump.csv', output, delimiter=\",\")\n return output\n\n def steady_solve(self, vehicle,segments):\n output = self.solve(vehicle,segments)\n output[-1,O_VELOCITY] = output[-1,O_VELOCITY]*0.95\n return self.solve(vehicle,segments,output[-1, :])\n\n def colorgen(num_colors, idx):\n color_norm = colors.Normalize(vmin=0, vmax=num_colors-1)\n scalar_map = cmx.ScalarMappable(norm=color_norm, cmap='hsv') \n def map_index_to_rgb_color(index):\n return scalar_map.to_rgba(index)\n return map_index_to_rgb_color(idx)\n","sub_path":"py/RoseLapCore/sims/sim_fourtires.py","file_name":"sim_fourtires.py","file_ext":"py","file_size_in_byte":25473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"565092593","text":"#encoding: utf-8\n#description: 将txt加工成json格式\nfrom __future__ import print_function\nimport os\nimport re\nimport json\nimport csv\n\n\ndef produce_filename(targetdir):\n targetnames = os.listdir(targetdir)\n\n for name in targetnames:\n if '.txt' == name[-4:]:\n print(\"//\"*20,name,\"//\"*20)\n print(name,'OK')\n attr_get(targetdir+\"\\\\\"+name)\n\ndef attr_get(filename):\n f = open(filename,'r',encoding='utf-8')\n newname =\"clean_for_js_\"+filename[-59:]\n print(newname)\n save =open(newname,'w',encoding='utf-8')\n\n #print(s)\n i = 0\n li = []\n context = []\n tmp =[]\n for line in f:\n line = line.strip('\\n')\n save.write(line+' ')\n\n f.close()\n save.close()\n\n jsname = \"js_\"+filename[-59:]\n f_again = open(newname,'r',encoding='utf-8')\n save_again = open(jsname,'w',encoding='utf-8')\n s=f_again.read()\n #print(s)\n print('{',file=save_again)\n print('\"filename\":\"',filename[-50:-4],'\",',file=save_again)\n stt = s\n while(i<30):\n\n try:\n p_begin_attr = \"ROUNDBEGIN \" +\"[\\u4e00-\\u9fa5]+\"\n pattern_sub = \"ROUNDEND \"\n match_attr = re.search(p_begin_attr, stt, re.M)\n match_sub =re.search(pattern_sub,stt,re.M)\n entity = stt[match_attr.start()+11:match_attr.end()]\n print('\"',entity,'\":\"',stt[match_attr.end()+1:match_sub.start()-1],'\",',file=save_again)\n stt = stt[match_sub.end():]\n\n except:\n pass\n i+=1\n print(\"}\", file=save_again)\n\nproduce_filename('D:\\\\KG\\\\attr_get_new_yiliao')\n","sub_path":"txt2json.py","file_name":"txt2json.py","file_ext":"py","file_size_in_byte":1597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"37044343","text":"#******************************************************\r\n#Program Name: tempConvert.py\r\n#Programmer: Gabriela Tolosa Ramirez\r\n#CSC - 119: Fall 2018 - 002\r\n#Date: Oct 15, 2018\r\n#Purpose: Convert temperature form Celsius\r\n# to Fahrenheit and vise versa\r\n#Modules used: None\r\n#Input Variable(s): onceMore(str),tempType(str),\r\n# cValue(float),fValue(float)\r\n#Output(s): cValue(float),fValue(float)\r\n#******************************************************\r\n\r\ndef cToF (temp):\r\n f = ((9.0/5.0)*temp)+32\r\n return f\r\n\r\ndef fToC (temp):\r\n c = (temp-32)*5.0/9.0\r\n return c\r\n\r\ndef main():\r\n onceMore = 'y'\r\n while onceMore.lower() == 'y':\r\n tempType = input(\"What temperature are you converting into (f or c)? \")\r\n\r\n if tempType.lower() == \"f\":\r\n cValue = float(input(\"What is the temperature in Celsius? \"))\r\n fValue = cToF (cValue)\r\n print(\"The Fahrenheit value is:\",fValue,\"and the Celsius calue is: \",cValue)\r\n elif tempType.lower() == \"c\":\r\n fValue = float(input(\"What is the temperature in Fahrenheit? \"))\r\n cValue = fToC (fValue)\r\n print(\"The Celsius value is:\",cValue,\"and the Fahrenheit calue is:\",fValue)\r\n else:\r\n print(\"You did not anser with a 'f' or 'c'\")\r\n onceMore = input(\"Do you want to continue(y/n)? \")\r\n\r\nmain()\r\ninput()\r\n","sub_path":"In-Class work/Day 8/tempConvert.py","file_name":"tempConvert.py","file_ext":"py","file_size_in_byte":1473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"575184806","text":"from hashlib import md5\nfrom os import makedirs\nfrom os.path import exists, split\n\nfrom gdrive.model import DriveNode, MimeType\n\nclass FileSystemService(object):\n def __init__(self):\n pass\n\n def create_file(self, path, content):\n parent_path = split(path)[0]\n\n if not exists(parent_path):\n makedirs(parent_path)\n\n with open(path, \"wb\") as f:\n f.write(content)\n\n def file_already_exists(self, node):\n if node.file_type == MimeType.FOLDER or node.file_type == MimeType.GOOGLE_APPS_DOC:\n return False\n\n file_path = node.get_file_path()\n\n if exists(file_path):\n with open(file_path, \"rb\") as f:\n file_contents = f.read()\n\n checksum = md5(file_contents).hexdigest()\n return checksum == node.checksum\n\n return False\n","sub_path":"gdrive/fs_service.py","file_name":"fs_service.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"428524834","text":"import pandas as pd\r\nimport numpy as np\r\nimport warnings\r\nimport plac\r\nimport time\r\nimport os\r\n\r\nfrom glob import glob\r\nfrom tqdm import tqdm\r\n\r\nfrom src.timing import timeit\r\n\r\nwarnings.simplefilter(action='ignore', category=FutureWarning)\r\ntqdm.pandas()\r\n\r\n\r\ndef euclidean(x, y):\r\n '''Compute the euclidean distance \r\n between two vectors or 2d arrays\r\n '''\r\n return np.sum(np.square(x-y), axis=-1)\r\n\r\n\r\ndef brute_force_1nn(x, y, return_distance=False):\r\n '''K-nearest neighbors algorithm for k=1\r\n using brute-force\r\n '''\r\n distances = [None]*len(y)\r\n indices = [None]*len(y)\r\n for i, y_ in enumerate(y):\r\n dist = euclidean(x, y_)\r\n # add the smallest distance\r\n indices[i] = np.argmin(dist)\r\n distances[i] = dist[indices[i]]\r\n\r\n if return_distance:\r\n return indices, distances\r\n # else return only indices\r\n return indices\r\n\r\n\r\ndef match_hits_to_mc_points(event_id, \r\n event_data, \r\n event_mc,\r\n verbose=False):\r\n '''For each hit finds the most closest Monte-Carlo\r\n point and assigns to it's track_id to this hit.\r\n \r\n Note: \r\n one hit may contain two mc_points, \r\n such hit will be duplicated\r\n\r\n # Arguments\r\n event_id: int, identifier of the event\r\n event_data: pd.DataFrame, hits\r\n event_mc: pd.DataFrame, Monte-Carlo points\r\n\r\n # Returns\r\n array with shape=(N, 5), where columns are:\r\n event_id, x, y, z, track_id\r\n '''\r\n # Faster than KD-Tree and avoids dynamic allocations\r\n usecols = ['x', 'y', 'z']\r\n event_hits = event_data[usecols].values\r\n event_mc_points = event_mc[usecols].values\r\n\r\n # TODO: drop events, where distance is too high\r\n ii, dist = brute_force_1nn(event_hits, event_mc_points, return_distance=True)\r\n\r\n if np.max(dist) > 1:\r\n if verbose:\r\n print('\\nEvent id: %d, max distance: %.2f' % (event_id, np.max(dist)))\r\n print('Drop this event')\r\n raise ValueError\r\n\r\n # indices of hits without track_id\r\n ii_ = set(range(len(event_hits))) - set(ii)\r\n ii_ = list(ii_)\r\n # event_id, x, y, z, station, track_id\r\n # track_id = -1, if fake hit\r\n result_array = np.full((len(ii)+len(ii_), 6), -1, dtype=np.float32)\r\n # add event_id\r\n result_array[:, 0] = event_id\r\n # get hits with track_ids\r\n result_array[:len(ii), 1:5] = event_data[usecols+['station']].values[ii]\r\n result_array[:len(ii), -1] = event_mc.track\r\n # get the remaining hits\r\n result_array[len(ii):, 1:5] = event_data[usecols+['station']].values[ii_]\r\n return result_array\r\n\r\n\r\n@timeit\r\ndef read_mc_file(mc_fpath, sep='\\t', index_col=None, encoding='utf-8'):\r\n '''Reads file 'mc_fpath'\r\n \r\n # Return\r\n pandas.DataFrame{event, track, x, y, z, station}\r\n '''\r\n usecols = ['event', 'track', 'x_in', 'y_in', 'z_in', 'station']\r\n dtypes = [np.int32, np.int32, np.float32, np.float32, np.float32, np.int32]\r\n # read dataframe\r\n df = pd.read_csv(mc_fpath, sep=sep, encoding=encoding, index_col=index_col, \r\n usecols=usecols, dtype=dict(zip(usecols, dtypes)))\r\n # rename columns\r\n df = df.rename(columns={'x_in': 'x', 'y_in': 'y', 'z_in': 'z'})\r\n return df\r\n\r\n\r\n@timeit\r\ndef read_hits_file(hits_fpath, sep='\\t', index_col=0, encoding='utf-8'):\r\n '''Reads file 'mc_fpath'\r\n \r\n # Return\r\n pandas.DataFrame{event, x, y, z, station}\r\n '''\r\n return pd.read_csv(hits_fpath, \r\n sep=sep, \r\n encoding=encoding, \r\n index_col=index_col, \r\n dtype={'event': np.int32, \r\n 'x': np.float32, \r\n 'y': np.float32, \r\n 'z': np.float32, \r\n 'station': np.int32})\r\n\r\n\r\n@timeit\r\ndef drop_short_tracks(mc_df, hits_df, n_points=3):\r\n gp_size = mc_df.groupby(['event', 'track']).size()\r\n # extract groups with size more than 2\r\n gp = gp_size[gp_size >= n_points]\r\n # create multiindex\r\n mc_df = mc_df.set_index(['event', 'track'])\r\n # mask dataframe\r\n mc_df = mc_df.loc[gp.index].reset_index()\r\n # after cleaning some events may be fully removed, \r\n # so remove also in hits_df\r\n removed_events = set(hits_df.event) - set(mc_df.event)\r\n hits_df = hits_df[~hits_df.event.isin(removed_events)]\r\n return mc_df, hits_df\r\n \r\n\r\n@timeit\r\ndef drop_spinning_tracks(mc_df, n_points=1):\r\n gp_size = mc_df.groupby(['event', 'track']).station.value_counts()\r\n # exclude tracks with more than n_points per station\r\n gp = gp_size[gp_size > n_points]\r\n # create multiindex\r\n mc_df = mc_df.set_index(['event', 'track'])\r\n # mask dataframe\r\n idx = gp.index.droplevel('station').unique()\r\n mask = mc_df.index.isin(idx)\r\n return mc_df[~mask].reset_index()\r\n\r\n\r\n@timeit\r\ndef drop_events_by_hits_number(mc_df, hits_df, n_hits=10):\r\n gp_size = hits_df.groupby(['event', 'station']).size()\r\n # exclude events with more than n_hits per station \r\n gp = gp_size[gp_size > n_hits]\r\n event_ids = gp.index.get_level_values('event')\r\n # exclude selected events\r\n mc_df = mc_df[~mc_df.event.isin(event_ids)]\r\n hits_df = hits_df[~hits_df.event.isin(event_ids)]\r\n return mc_df, hits_df\r\n\r\n\r\n\r\n@timeit\r\ndef label_hits(mc_df, hits_df):\r\n # TODO: add station to the file\r\n hits_with_track_id = []\r\n\r\n for event_id, event_data in tqdm(hits_df.groupby('event')):\r\n # extract event_mc_points\r\n event_mc_points = mc_df[mc_df.event==event_id]\r\n\r\n try:\r\n # match points to hits\r\n matched = match_hits_to_mc_points(\r\n event_id, event_data, event_mc_points)\r\n except ValueError:\r\n continue\r\n\r\n hits_with_track_id.extend(matched)\r\n\r\n # create dataframe\r\n hits_with_track_id_df = pd.DataFrame(hits_with_track_id,\r\n columns=['event', 'x', 'y', 'z', 'station', 'track'])\r\n\r\n # data types conversion\r\n hits_with_track_id_df = hits_with_track_id_df.astype({\r\n 'event': np.int32,\r\n 'x': np.float32,\r\n 'y': np.float32,\r\n 'z': np.float32,\r\n 'station': np.int32,\r\n 'track': np.int32})\r\n\r\n return hits_with_track_id_df\r\n\r\n\r\n@timeit\r\ndef merge_mc_with_hits(mc_fpath, hits_fpath):\r\n print(\"1. Read data...\")\r\n mc_df = read_mc_file(mc_fpath)\r\n hits_df = read_hits_file(hits_fpath)\r\n print(\"Event number: %d\" % hits_df.event.nunique())\r\n\r\n #print(\"2. Remove events with anomalous number of hits\")\r\n #mc_df, hits_df = drop_events_by_hits_number(mc_df, hits_df)\r\n\r\n print(\"2. Remove tracks containing less than 3 points\")\r\n mc_df, hits_df = drop_short_tracks(mc_df, hits_df)\r\n print(\"Event number: %d\" % hits_df.event.nunique())\r\n\r\n print(\"3. Drop spinning tracks\")\r\n mc_df = drop_spinning_tracks(mc_df)\r\n print(\"Event number: %d\" % hits_df.event.nunique())\r\n\r\n print(\"4. Set labels to hits\")\r\n hits_with_track_id_df = label_hits(mc_df, hits_df)\r\n print(\"Event number: %d\" % hits_with_track_id_df.event.nunique())\r\n \r\n return hits_with_track_id_df\r\n\r\n\r\n@timeit\r\n@plac.annotations(\r\n datapath=(\"Path to the directory with root files\", \"positional\", None, str))\r\ndef main(datapath):\r\n mc_files = sorted(glob(os.path.join(datapath, 'evetest*')))\r\n hits_files = sorted(glob(os.path.join(datapath, 'bmndst*')))\r\n\r\n for mc_fpath, hits_fpath in zip(mc_files, hits_files):\r\n print(\"Files:\\n\\t%s\\n\\t%s\" % (mc_fpath, hits_fpath))\r\n df = merge_mc_with_hits(mc_fpath, hits_fpath)\r\n # create name of the file to save\r\n save_fname = os.path.split(mc_fpath)[1]\r\n save_fname = save_fname.split(\"evetest_\")[1]\r\n save_fname = os.path.join(datapath, save_fname)\r\n # save to the same location with different name\r\n print(\"Save data into `%s`\" % save_fname)\r\n df.to_csv(save_fname, encoding='utf-8', index=None, sep='\\t')\r\n print(\"--- OK ---\\n\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n plac.call(main)","sub_path":"src/label_mc_hits.py","file_name":"label_mc_hits.py","file_ext":"py","file_size_in_byte":8153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"363940181","text":"import pytest\nimport markdown\nfrom markdown.extensions import tables\n\n\n@pytest.mark.parametrize('fn', [\n ('simple',)])\ndef test_markdown_works(fn):\n extensions = [tables.TableExtension()]\n\n with open('tests/input/%s.md' % fn) as md:\n out = markdown.markdown(md.read(), extensions=extensions)\n\n with open('tests/expected/%s.html' % fn) as expected:\n assert out == expected.read().strip('\\n')\n","sub_path":"tests/test_project.py","file_name":"test_project.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"52530592","text":"from rest_framework import serializers\nfrom rest_framework_simplejwt.serializers import TokenObtainPairSerializer\n\nfrom .models import Category, Comment, CustomUser, Genre, Review, Title\n\n\nclass CategorySerializer(serializers.ModelSerializer):\n class Meta:\n exclude = ('id',)\n model = Category\n\n\nclass GenreSerializer(serializers.ModelSerializer):\n class Meta:\n exclude = ('id',)\n model = Genre\n\n\nclass TitleSerializer(serializers.ModelSerializer):\n genre = serializers.SlugRelatedField(slug_field='slug',\n queryset=Genre.objects.all(),\n many=True)\n category = serializers.SlugRelatedField(slug_field='slug',\n queryset=Category.objects.all())\n rating = serializers.FloatField(read_only=True)\n\n class Meta:\n model = Title\n fields = '__all__'\n\n def to_representation(self, instance):\n data = super(TitleSerializer, self).to_representation(instance)\n data['genre'] = GenreSerializer(\n instance=instance.genre,\n many=True).data\n data['category'] = CategorySerializer(instance=instance.category).data\n return data\n\n\nclass UserSerializer(serializers.ModelSerializer):\n\n class Meta:\n fields = [\n 'first_name', 'last_name', 'username', 'bio', 'email', 'role',\n ]\n model = CustomUser\n\n\nclass AdminUserSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = CustomUser\n fields = ('first_name', 'last_name',\n 'username', 'bio', 'email', 'role')\n\n\nclass ReviewSerializer(serializers.ModelSerializer):\n author = serializers.SlugRelatedField(read_only=True,\n slug_field='username')\n title = serializers.PrimaryKeyRelatedField(read_only=True)\n\n class Meta:\n model = Review\n fields = '__all__'\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n author = serializers.SlugRelatedField(read_only=True,\n slug_field='username')\n review = serializers.PrimaryKeyRelatedField(read_only=True)\n\n class Meta:\n model = Comment\n fields = '__all__'\n\n\nclass MyTokenObtainPairSerializer(TokenObtainPairSerializer):\n username_field = CustomUser.USERNAME_FIELD\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields['password'].required = False\n\n def validate(self, attrs):\n attrs['password'] = self.context['request'].data.get(\n 'confirmation_code')\n return super().validate(attrs)\n\n\nclass GetOTPSerializer(serializers.Serializer):\n email = serializers.EmailField()\n","sub_path":"api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"618904813","text":"import glob\nimport sys\nimport time\nimport datetime\nimport json\nfrom influxdb import InfluxDBClient\n\nbase_dir = '/sys/bus/w1/devices/'\ndevice_folder = glob.glob(base_dir + '28*')[0]\ndevice_file = device_folder + '/w1_slave'\ndbClient = InfluxDBClient('localhost', 8086, 'root', 'root', 'temp')\n\n\ndef read_temp_raw():\n f = open(device_file, 'r')\n lines = f.readlines()\n f.close()\n return lines\n\ndef read_tempC():\n lines = read_temp_raw()\n while lines[0].strip()[-3:] != 'YES':\n time.sleep(0.2)\n lines = read_temp_raw()\n equals_pos = lines[1].find('t=')\n if equals_pos != -1:\n temp_string = lines[1][equals_pos+2:]\n temp_c = float(temp_string) / 1000.0\n temp_f = temp_c * 9.0 / 5.0 + 32.0\n return temp_c\n\ndef read_tempF():\n lines = read_temp_raw()\n while lines[0].strip()[-3:] != 'YES':\n time.sleep(0.2)\n lines = read_temp_raw()\n equals_pos = lines[1].find('t=')\n if equals_pos != -1:\n temp_string = lines[1][equals_pos+2:]\n temp_c = float(temp_string) / 1000.0\n temp_f = temp_c * 9.0 / 5.0 + 32.0\n return temp_f\ntry:\n while True:\n now = time.ctime()\n celcius_temp = read_tempC()\n print(read_tempC(), 'C, ')\n with open(\"temp_data.json\", \"w\") as write_file:\n json.dump(read_tempC(), write_file)\n json_body = [{\n \"measurement\": \"Celcius\",\n \"tags\": {\n \"location\": \"weather-station\",\n },\n \"fields\": {\n \"temperature\" : celcius_temp,\n }\n }]\n\n dbClient.write_points(json_body)\n time.sleep(1)\n\nexcept KeyboardInterrupt:\n pass\n\n","sub_path":"temp-check-db.py","file_name":"temp-check-db.py","file_ext":"py","file_size_in_byte":1705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"631186399","text":"from django.conf.urls import url, include\nfrom django.views.generic import TemplateView\nfrom rest_framework import routers\nfrom frontend import views\n\n\n\n\nrouter = routers.DefaultRouter()\nrouter.register(r'users', views.UserViewSet)\nrouter.register(r'groups', views.GroupViewSet)\nrouter.register(r'books', views.BookViewSet)\nrouter.register(r'brands', views.BrandViewSet)\nrouter.register(r'pieces', views.PieceViewSet)\nrouter.register(r'clothTypes', views.ClothTypeViewSet)\nrouter.register(r'pieceImages', views.PieceImageViewSet)\n\n# Wire up our API using automatic URL routing.\n# Additionally, we include login URLs for the browsable API.\n\npageView = views.PageView()\n\nurlpatterns = [\n url(r'^api/', include(router.urls)),\n url(r'^api-auth/', include('rest_framework.urls', namespace='rest_framework')),\n url(r'^$',pageView.home,name='home'),\n url(r'^api/books/(?P[0-9]*)/views$',pageView.book,name='book'),\n url(r'^books/$',pageView.books,name='books'),\n url(r'^contactus/$',pageView.contactus,name='contactus'),\n url(r'^shopingcart/$',pageView.shoppingCart,name='shopingCart'),\n url(r'^createorder/$', pageView.createOrder, name='createOrder'),\n]\n","sub_path":"frontend/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"455894574","text":"'''\n Date: 22 January 2019\n Author: Anis Sarker\n Cropping satellite images in 1024x1024 for Kutupalong area\n'''\n\nimport cv2\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndirectory = '/home/void/Documents/Project/Newly_cropped/432/'\n\nimageName = []\nimgListName = [i for i in os.listdir(directory)]\n\nx, y = 5150, 700\n\nfor file in os.listdir(directory):\n file_name, file_ext = os.path.splitext(file)\n print(file_name, '-', file_ext)\n n_img = cv2.imread(os.path.join(directory, file))\n # n_img = np.asarray(n_img)\n # cv2.rectangle(n_img, (x, y), (x+1024, y+1024), (255, 0, 0), 5)\n crop_img = n_img[x:x+1024, y:y+1024]\n # n_img = cv2.resize(n_img, dsize=(512, 512), interpolation=cv2.INTER_CUBIC)\n cv2.imwrite('New/' + file_name + file_ext, crop_img)\n # cv2.imshow(file_name, crop_img)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows()","sub_path":"image_crop.py","file_name":"image_crop.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"90740375","text":"import pandas as pd \nfrom scipy import sparse as ss\nfrom lightfm import LightFM\nimport numpy as np \n\n\ntraining[['session_id', 'cate_0']]\ntraining.set_index('session_id')['cate_0'].apply(pd.Series).stack().reset_index(level=0).rename(columns={0:'cate_0'}).reset_index(drop = True)\n## Unnest data \n#def unnest_data(df, index_col, val_col):\n# return df.set_index(index_col)[val_col].apply(pd.Series).stack().reset_index(level=0).rename(columns={0:val_col}).reset_index(drop = True)\n\n## generate matrix \ndef matrix_generating(df, index_col, val_col, item_set):\n def get_item_frequency(input_list):\n output = []\n for item in item_set:\n output.append(input_list.count(item))\n return ss.csr_matrix(output)\n \n sparse_matrix = ss.vstack(df[val_col].apply(get_item_frequency))\n return sparse_matrix\n \n## generate feature vectors \ndef item_vector_generating(interaction_matrix, item_set, feature_num):\n lfmmodel = LightFM(no_components = feature_num, loss = 'warp')\n lfmmodel.fit(ss.csr_matrix(interaction_matrix), epochs = 100, num_threads = 4)\n return lfmmodel.item_embeddings\n\ndef matrix_multiplication(interaction_matrix, item_matrix):\n result_matrix = ss.csr_matrix.dot(interaction_matrix, item_matrix)\n result_matrix_weighted = []\n weights = [i[0] for i in matrix.sum(axis=1).tolist()]\n for row, weight in zip(result_matrix, weights):\n result_matrix_weighted.append(row/weight)\n return np.array(result_matrix_weighted)\n \n## session vectors\n\n## predict\n\nmatrix = matrix_generating(training, index_col = 'session_id', val_col = 'cate_0', item_set=cate_0)\n\nitem_matrix = item_vector_generating(matrix,item_set=cate_0, feature_num = 10)\n#X = ss.csr_matrix.dot(matrix, item_matrix)\nX = matrix_multiplication(matrix, item_matrix)\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, f1_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder\n\n\nlabel_encoder = LabelEncoder()\ncate_0_num = label_encoder.fit_transform(training['cate_0'].apply(lambda x:x[0]))\none_hot_encoder = OneHotEncoder()\nX = one_hot_encoder.fit_transform(cate_0_num.reshape(-1, 1))\n\n\ny = training.is_female\n\nX_train, X_test, y_train, y_test = train_test_split(X, y)\n\nrfc = RandomForestClassifier()\nrfc.fit(X_train, y_train)\ny_pred = rfc.predict(X_test)\naccuracy_score(y_test, y_pred)\nprecision_score(y_test, y_pred)\nrecall_score(y_test, y_pred)\nconfusion_matrix(y_test, y_pred)\nf1_score(y_test, y_pred)\n\n","sub_path":"miscellaneous/modelling.py","file_name":"modelling.py","file_ext":"py","file_size_in_byte":2605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"469489340","text":"import datetime\nfrom django.db import models\nfrom django.db.models.fields import BooleanField, TextField\nfrom users.models import UserProfile\nfrom ckeditor.fields import RichTextField\nfrom django.db.models.fields.related import ManyToManyField\nfrom multiselectfield import MultiSelectField\nfrom phone_field import PhoneField\nfrom django.core.validators import MaxValueValidator, MinValueValidator\nfrom django_countries.fields import CountryField\nfrom django.db.models import Q\nfrom django.urls import reverse\nfrom django.contrib.contenttypes.models import ContentType\nimport re\nfrom django.template.defaultfilters import slugify\nfrom PIL import Image, ImageOps, ImageDraw\nfrom io import BytesIO\nimport numpy as np\nfrom django.core.files import File\nimport random\nimport string\nfrom django.utils.timezone import make_aware\nfrom users.models import *\nfrom django.core.files.uploadedfile import InMemoryUploadedFile\nimport sys\n\ndef random_string_generator(size=10, chars=string.ascii_lowercase + string.digits):\n return ''.join(random.choice(chars) for _ in range(size))\n\n# Accommodation Models\ndef unique_slug_generator(instance, new_slug=None):\n \"\"\"\n This is for a Django project and it assumes your instance \n has a model with a slug field and a title character (char) field.\n \"\"\"\n if new_slug is not None:\n slug = new_slug\n else:\n slug = slugify(instance.title)\n\n Klass = instance.__class__\n qs_exists = Klass.objects.filter(slug=slug).exists()\n if qs_exists:\n new_slug = \"{slug}-{randstr}\".format(\n slug=slug,\n randstr=random_string_generator(size=4)\n )\n return unique_slug_generator(instance, new_slug=new_slug)\n return slug\n\n\n\n\nclass Staff(models.Model):\n GENDER_CHOICES = (\n (1, 'Male'),\n (2, 'Female')\n )\n is_verified = models.BooleanField(default=False)\n first_name = models.CharField(max_length=100, blank=False, null=False)\n last_name = models.CharField(max_length=100, blank=False, null=False)\n email = models.EmailField(max_length=254, blank=True, null=True)\n gender = models.IntegerField(choices=GENDER_CHOICES, default=1)\n phone = PhoneField(blank=True, null=True)\n skype = models.CharField(max_length=254, blank=True, null=True)\n bio = models.TextField(blank=True, null=True)\n portrait = models.ImageField(upload_to='media/staff/')\n featured_portrait = models.ImageField(upload_to='media/staff/')\n is_host = models.BooleanField(default=False)\n is_instructor = models.BooleanField(default=False)\n added_date = models.DateTimeField(auto_now_add=True)\n host_organization = models.ForeignKey(UserProfile, blank=True, null=True, on_delete=models.CASCADE, related_name='staff')\n viewed_by_admin = models.BooleanField(default=False)\n masked = models.BooleanField(default=False)\n staff_notes = models.TextField(blank=True, null=True)\n\n def delete(self):\n self.portrait.delete(save=False)\n self.featured_portrait.delete(save=False)\n super().delete()\n\n\n def get_admin_url(self):\n content_type = ContentType.objects.get_for_model(self.__class__)\n return reverse(\"admin:%s_%s_change\" % (content_type.app_label, content_type.model), args=(self.id,))\n\n def get_progress(self):\n progress = 0\n if self.first_name:\n progress += 1\n if self.last_name:\n progress += 1\n if self.email:\n progress += 1\n if self.gender:\n progress += 1\n if self.phone:\n progress += 1\n if self.skype:\n progress += 1\n if self.bio:\n progress += 1\n if self.portrait:\n progress += 1\n if self.featured_portrait:\n progress += 1\n total = int((progress/9) * 100)\n return total\n\n def count_skills(self):\n return self.skills.count()\n\n def get_listings(self):\n instructors = self.instructors.all()\n hosts = self.hosts.all()\n return instructors.union(hosts)\n\n def __str__(self):\n return self.first_name + \" \" + self.last_name\n \n def save(self, *args, **kwargs):\n if not self.masked:\n dirty_image = self.portrait\n dirty_portrait = self.featured_portrait\n # load image\n img = Image.open(dirty_image)\n img2 = Image.open(dirty_portrait)\n # crop image \n width, height = img.size\n x = (width - height)//2\n img_cropped = img.crop((x, 0, x+height, height))\n\n width2, height2 = img2.size\n x2 = (width2 - height2)//2\n img2_cropped = img2.crop((x2, 0, x2+height2, height2))\n\n # create grayscale image with white circle (255) on black background (0)\n mask = Image.new('L', img_cropped.size)\n mask_draw = ImageDraw.Draw(mask)\n width, height = img_cropped.size\n mask_draw.ellipse((0, 0, width, height), fill=255)\n #mask.show()\n\n # add mask as alpha channel\n img_cropped.putalpha(mask)\n img_cropped = img_cropped.convert(mode='P', palette=Image.ADAPTIVE)\n blob = BytesIO()\n blob2 = BytesIO()\n img_cropped.save(blob, 'PNG', optimize=True, quality=30)\n img2_cropped.save(blob2, 'JPEG', optimize=True, quality=30)\n self.portrait.save(self.first_name + '_imagenew_round.png', File(blob), save=False)\n self.featured_portrait.save(self.first_name + '_imagenew_portrait.jpg', File(blob2), save=False)\n self.masked = False\n super().save(*args, **kwargs)\n \n\n def get_full_name(self):\n return self.first_name + ' ' + self.last_name\n \n def portrait_filename(self):\n if self.portrait:\n return os.path.basename(self.portrait.url)\n else:\n return 'emptyfilename'\n \n def featured_filename(self):\n return os.path.basename(self.featured_portrait.url)\n\nclass PageHits(models.Model):\n page = models.CharField(max_length=50, blank=False)\n hits = models.IntegerField(default=0)\n date = models.DateField(null=True, blank=True)\n\nclass SalesGoal(models.Model):\n monthly_goal = models.IntegerField(default=0)\n yearly_goal = models.IntegerField(default=0)\n\n def get_admin_url(self):\n content_type = ContentType.objects.get_for_model(self.__class__)\n return reverse(\"admin:%s_%s_change\" % (content_type.app_label, content_type.model), args=(self.id,))\n\n\n\nclass Listing(models.Model):\n SAFETY_CHOICES = (\n (1, 'Use of cleaning chemicals that are effective against coronavirus.'),\n (2, 'Linens, towels and laundry washed in accordance with local authority guidelines.'),\n (3, 'Guest accommodation is disinfected between stays.'),\n (4, 'The accommodation partner we work with follows the guidelines of the local authoritie'),\n (5, 'Equipment for activities is disinfected before and/or after use.'),\n (6, 'Cashless payment available.'),\n (7, 'Physical distancing is maintained.'),\n (8, 'Instructors maintain a distance from the client at all times possible.'),\n (9, 'Activities take place outside where possible.'),\n (10, 'Staff follow all safety protocols as directed by the local government.'),\n (11, 'Hand sanitizer available in guest rooms and key areas.'),\n (12, 'Process in place to check the health of guests.'),\n (13, 'First aid kit available.'),\n (14, 'A room is available to isolate suspected or infected COVID-19 patients.'),\n (15, 'Protective masks are available for all staff.'),\n (16, 'Protective masks available for clients.')\n )\n\n SHUTTLE_CHOICES = (\n (1, 'Included in the price / Free of Charge'),\n (2, 'Available for additional cost'),\n (3, 'Not provided')\n )\n\n MEAL_CHOICES = (\n (1, 'Breakfast'),\n (2, 'Brunch'),\n (3, 'Lunch'),\n (4, 'Dinner'),\n (5, 'Snacks'),\n (6, 'Drinks')\n )\n\n FOOD_CHOICES = (\n (1, 'Ayurvedic Food'),\n (2, 'Fruitarian Food'),\n (3, 'Gluten Free Food'),\n (4, 'Halal Food'),\n (5, 'Lacto-Ovo Vegetarian Food'),\n (6, 'Lactose-Free Food'),\n (7, 'Naturopathic Diet Food'),\n (8, 'Organic Food'),\n (9, 'Other Dietary Requirements'),\n (10, 'Paleo Diet Food'),\n (11, 'Pescatarian Food'),\n (12, 'Raw Food'),\n (13, 'Regular Food (Meat, Poultry, Fish)'),\n (14, 'Seafood'),\n (15, 'Vegan Food'),\n (16, 'Vegetarian Food'),\n (17, 'Whole Food'),\n (18, 'Yogic Diet Food')\n )\n\n DRINK_CHOICES = (\n (1, 'Alcoholic Beverages'),\n (2, 'Coffee'),\n (3, 'Detox Juices'),\n (4, 'Soda'),\n (5, 'Tea'),\n (6, 'Water')\n )\n\n LANGUAGE_CHOICES = (\n (1, \"Afrikaans\"), (2, \"Arabic\"), (3, \"Armenian\"), (4, \"Bulgarian\"), (5, \"Catalan; Valencian\"), (6, \"Chinese\"), (7, \"Croatian\"), (8, \"Czech\"), (9, \"Danish\"), (10, \"Dutch\"), (11, \"English\"), (12, \"Estonian\"), (13, \"Fijian\"), (14, \"Finnish\"), (15, \"French\"), (16, \"Georgian\"), (17, \"German\"), (18, \"Greek, Modern\"), (19, \"Hebrew, Modern\"), (20, \"Hindi\"), (21, \"Hungarian\"), (22, \"Indonesian\"), (23, \"Italian\"), (24, \"Japanese\"), (25, \"Kyrgyz\"), (26, \"Korean\"), (27, \"Lao\"), (28, \"Lithuanian\"), (29, \"Latvian\"), (30, \"Macedonian\"), (31, \"Malay\"), (32, \"Malayalam\"), (33, \"Mongolian\"), (34, \"Nepali\"), (35, \"Norwegian\"), (36, \"Polish\"), (37, \"Portuguese\"), (38, \"Romanian\"), (39, \"Russian\"), (40, \"Sanskrit\"), (41, \"Serbian\"), (42, \"Sinhala, (Sinhalese)\"), (43, \"Slovak\"), (44, \"Spanish; Castilian\"), (45, \"Swedish\"), (46, \"Tamil\"), (47, \"Telugu\"), (48, \" Thai\"), (49, \"Tagalog\"), (50, \"Turkish\"), (51, \"Ukrainian\"), (52, \"Vietnamese\")\n )\n\n SKILL_CHOICES = (\n (1, 'Beginner'),\n (2, 'Intermediate'),\n (3, 'Advanced')\n )\n\n REMAINDER_CHOICES = (\n (1, 'On Arrival'),\n (2, 'On Depature'),\n (3, 'Specified Days Before Arrival')\n )\n\n YOGA_CHOICES = (\n (1, 'Ashtanga Yoga'),\n (2, 'Vinyasa Yoga'),\n (3, 'Hatha Yoga'),\n (4, 'Kundalini Yoga'),\n (5, 'Yin Yoga'),\n (6, 'Integral Yoga'),\n (7, 'Nidra Yoga'),\n (8, 'Bikram / Hot Yoga'),\n (9, 'AcroYoga'),\n (10, 'Baptiste Yoga'),\n (11, 'Forrest Yoga'),\n (12, 'Jivamukti Yoga'),\n (13, 'Power Yoga'),\n (14, 'Rocket Yoga'),\n (15, 'Tibetan Yoga'),\n (16, 'Zen Yoga'),\n (17, 'Iyengar Yoga'),\n (18, 'Tantra Yoga'),\n (19, 'Alignment Yoga'),\n (20, 'Ananda Yoga'),\n (21, 'Chair Yoga'),\n (22, 'Chakra Yoga'),\n (23, 'Critical Alignment Yoga'),\n (24, 'Dru Yoga'),\n (25, 'Ganja Yoga'),\n (26, 'Japa Yoga'),\n (27, 'Kashmir Yoga'),\n (28, 'Kripalu Yoga'),\n (29, 'Laughter Yoga'),\n (30, 'Laya Yoga'),\n (31, 'Nada Yoga'),\n (32, 'Nidra Yoga'),\n (33, 'Para Yoga'),\n (34, 'Partnet Yoga'),\n (35, 'Restorative Yoga'),\n (36, 'Satyananda Yoga'),\n (37, 'Sivananda Yoga'),\n (38, 'Somatic Yoga'),\n (39, 'Thai Yoga'),\n (40, 'Therapeutic Yoga'),\n (41, 'Tibetan Yoga'),\n (42, 'Transformational Yoga'),\n (43, 'Viniyoga'),\n (44, 'Aerial Yoga'),\n (45, 'Chakra Yoga'),\n (46, 'Vipassana Yoga'),\n (47, 'Anusara Yoga'),\n )\n\n CAT_CHOICES = (\n (1, 'Budget Retreats'),\n (2, 'Luxury Holidays'),\n (3, 'All-Inclusive Yoga Retreats'),\n (4, 'Online Experiences'),\n (5, 'Travel Experiences')\n )\n \n user_profile = models.ForeignKey(UserProfile, null=True, blank=True, on_delete=models.CASCADE, related_name='listings')\n # Admin Approval\n is_verified = models.BooleanField(default=False)\n viewed_by_admin = models.BooleanField(default=False)\n admin_approval_notes = models.TextField(null=True, blank=True)\n created_at = models.DateTimeField(auto_now_add=True)\n progress = models.IntegerField(default=0)\n instant_booking = models.BooleanField(default=False)\n # Title & Intro\n\n url = models.URLField(max_length=200, blank=True, null=True)\n brochure = models.FileField(upload_to='media/listings/brochures/', blank=True, null=True)\n title = models.CharField(max_length=100, blank=True, null=True)\n tagline = models.CharField(max_length=47, blank=True, null=True)\n header = models.CharField(max_length=100, blank=True, null=True)\n introduction = RichTextField(blank=True, null=True)\n highlights = RichTextField(blank=True, null=True)\n private_groups = models.BooleanField(default=False)\n clean_and_safe = models.BooleanField(default=False)\n safety_checklist = MultiSelectField(choices=SAFETY_CHOICES, blank=True, null=True)\n health_hygiene = RichTextField(blank=True, null=True)\n\n # Location & Accomodation\n country = CountryField(blank=True, null=True)\n country_name = models.CharField(max_length=250, blank=True, null=True)\n address = models.CharField(max_length=250, blank=True, null=True)\n location_info = RichTextField(blank=True, null=True)\n accomodation_info = RichTextField(blank=True, null=True)\n category = models.IntegerField(choices=CAT_CHOICES, null=True, blank=True)\n\n # Arrival Information\n checkin_time = models.TimeField(blank=True, null=True)\n checkout_time = models.TimeField(blank=True, null=True)\n spoken_languages = MultiSelectField(default=11, choices=LANGUAGE_CHOICES, max_length=250)\n airport_code = models.CharField(max_length=5, blank=True, null=True)\n airport_shuttle = models.IntegerField(choices=SHUTTLE_CHOICES, null=True, blank=True)\n airport_shuttle_cost = models.PositiveIntegerField(null=True, blank=True)\n airport_info = RichTextField(blank=True, null=True)\n\n # Food and Drinks\n meals = MultiSelectField(choices=MEAL_CHOICES, blank=True, null=True)\n foods = MultiSelectField(choices=FOOD_CHOICES, blank=True, null=True)\n drinks = MultiSelectField(choices=DRINK_CHOICES, blank=True, null=True)\n food_info = RichTextField(blank=True, null=True)\n\n # Guest Requirements\n instruction_language = models.IntegerField(default=11, choices=LANGUAGE_CHOICES)\n skill_level = MultiSelectField(choices=SKILL_CHOICES, blank=True, null=True)\n min_age = models.PositiveIntegerField(default=0)\n max_age = models.PositiveIntegerField(default=0)\n min_child_age = models.PositiveIntegerField(default=0)\n child_allowed = models.BooleanField(default=False)\n\n # Program & Itinerary\n yoga_style = MultiSelectField(choices=YOGA_CHOICES, blank=True, null=True)\n program_duration = models.PositiveIntegerField(default=14)\n instruction_duration = models.PositiveIntegerField(default=14)\n max_group_size = models.PositiveIntegerField(default=0)\n min_group_size = models.PositiveIntegerField(default=0)\n itinerary_info = RichTextField(blank=True, null=True)\n excursion_info = RichTextField(blank=True, null=True)\n \n # Pricing & Inclusions\n display_currency = models.CharField(max_length=3, default=\"USD\") # Verify the use of this\n included_info = RichTextField(blank=True, null=True)\n not_included_info = RichTextField(blank=True, null=True)\n availability_info = RichTextField(blank=True, null=True)\n\n # Extras\n extra_payment_todo_info = RichTextField(blank=True, null=True)\n spa_treament_info = RichTextField(blank=True, null=True)\n know_before_you_go = RichTextField(blank=True, null=True)\n\n # Packages & Availability\n # ListingPackage Model\n\n # Booking Conditions\n deposit_policy = models.PositiveIntegerField(default=14, validators=[MinValueValidator(14), MaxValueValidator(100)], null=True, blank=True)\n remainder_due = models.IntegerField(choices=REMAINDER_CHOICES, default=1, null=True, blank=True)\n #remainder_due = models.IntegerField(default=0) # -1 On Arrival, -2 On Departure, and any positive integer for days before arrival\n days_to_pay = models.PositiveIntegerField(default=0, null=True, blank=True)\n\n # Ranking\n ranking = models.DecimalField(null=True, blank=True, max_digits=3, decimal_places=2)\n commission_percent = models.IntegerField(default=14, null=True, blank=True)\n\n hosts = ManyToManyField(Staff, blank=True, related_name='hosts')\n\n instructors = ManyToManyField(Staff, blank=True, related_name='instructors')\n\n available_all_year = models.BooleanField(default=False)\n value_for_money = models.IntegerField(validators=[MinValueValidator(1), MaxValueValidator(5)], blank=True, null=True)\n accommodation_and_facilities = models.IntegerField(validators=[MinValueValidator(1), MaxValueValidator(5)], blank=True, null=True)\n food = models.IntegerField(validators=[MinValueValidator(1), MaxValueValidator(5)], blank=True, null=True)\n location = models.IntegerField(validators=[MinValueValidator(1), MaxValueValidator(5)], blank=True, null=True)\n quality_of_activity = models.IntegerField(validators=[MinValueValidator(1), MaxValueValidator(5)], blank=True, null=True)\n overall_rating = models.DecimalField(max_digits=2, decimal_places=1, blank=True, null=True)\n lowest_price = models.IntegerField(default=0)\n # Slug\n slug = models.SlugField(blank=True)\n updated_after_approval = models.BooleanField(default=False)\n updated_fields = models.TextField(null=True, blank=True)\n\n \n def get_inquiry_count(self):\n today = datetime.date.today()\n month_ago = today - datetime.timedelta(days=30)\n count = Inquiry.objects.filter(Q(listing__pk=self.pk) & Q(created_at__gte=month_ago)).count()\n return count\n\n def get_admin_url(self):\n content_type = ContentType.objects.get_for_model(self.__class__)\n return reverse(\"admin:%s_%s_change\" % (content_type.app_label, content_type.model), args=(self.id,))\n \n def get_progress(self):\n progress = 0\n if self.url:\n progress += 1\n if self.title:\n progress += 1\n if self.tagline:\n progress += 1\n if self.header:\n progress += 1\n if self.introduction:\n progress += 1\n if self.highlights:\n progress += 1\n if self.health_hygiene:\n progress += 1\n if self.country:\n progress += 1\n if self.address:\n progress += 1\n if self.location_info:\n progress += 1\n if self.accomodation_info:\n progress += 1\n if self.checkin_time:\n progress += 1\n if self.checkout_time:\n progress += 1\n if self.airport_code:\n progress += 1\n if self.airport_shuttle:\n progress += 1\n if self.airport_info:\n progress += 1\n if self.meals:\n progress += 1\n if self.foods:\n progress += 1\n if self.drinks:\n progress += 1\n if self.food_info:\n progress += 1\n if self.skill_level:\n progress += 1\n if self.yoga_style:\n progress += 1\n if self.program_duration:\n progress += 1\n if self.itinerary_info:\n progress += 1\n if self.excursion_info:\n progress += 1\n if self.included_info:\n progress += 1\n if self.not_included_info:\n progress += 1\n if self.availability_info:\n progress += 1\n if self.extra_payment_todo_info:\n progress += 1\n if self.spa_treament_info:\n progress += 1\n if self.know_before_you_go:\n progress += 1\n progress_percent = int((progress / 31) * 100)\n return progress_percent\n\n def get_lowest_package_price(self):\n min_price = 0\n for package in self.packages.all():\n if min_price == 0:\n min_price = package.default_price\n elif min_price > package.default_price:\n min_price = package.default_price\n \n return min_price\n\n def get_organization(self):\n return self.user_profile.business_name\n\n def check_vegetarian(self):\n foods = str(self.foods).lower()\n if 'veg' in foods:\n return True\n else:\n return False\n\n def __str__(self):\n if self.title:\n return self.title\n else:\n return self.slug\n\n def get_absolute_url(self):\n return reverse('listings:listing_detail', kwargs={'pk':self.pk, 'slug': self.slug})\n\n def save(self, *args, **kwargs):\n if self.get_lowest_package_price():\n self.lowest_price = self.get_lowest_package_price()\n if self.get_progress():\n self.progress = self.get_progress()\n if not self.id: # new listing - no title\n if not self.title:\n self.slug = unique_slug_generator(self)\n else:\n # if 'request' in kwargs:\n # request = kwargs.pop('request') \n # print(\"inside request\")\n # if not request.user.is_superuser:\n # print('in here')\n # self.updated_after_approval = True\n # cls = self.__class__\n # old = cls.objects.get(pk=self.pk)\n # new = self\n # changed_fields = []\n # for field in cls._meta.get_fields():\n # field_name = field.name\n # try:\n # if getattr(old, field_name) != getattr(new, field_name):\n # changed_fields.append(field_name)\n \n # except Exception as ex:\n # pass\n # kwargs['updated_fields'] = changed_fields\n # self.updated_fields = ', '.join(changed_fields)\n if not self.title:\n old_slug = self.slug\n new_slug = unique_slug_generator(self, old_slug)\n short_slug = new_slug[:48]\n self.slug = unique_slug_generator(self, old_slug)\n else:\n full_slug = slugify(self.title)\n short_slug = full_slug[:48]\n self.slug = short_slug\n if self.country:\n self.country_name = self.country.name\n\n super(Listing, self).save(*args, **kwargs)\n\n def get_first_image(self):\n try:\n image = self.images.first().image.url\n except:\n image = ''\n\n return image\n \n def get_first_video(self):\n try:\n video = self.videos.first().video.url\n except:\n video = ''\n return video\n \n\nclass Favorite(models.Model):\n user = models.ForeignKey(User, on_delete=models.CASCADE, related_name='favorites')\n listing = models.ForeignKey(Listing, on_delete=models.CASCADE, related_name='favorites')\n\nclass Videos(models.Model):\n profile = models.ForeignKey(UserProfile, on_delete=models.CASCADE, related_name='videos')\n video = models.FileField(upload_to='media/listings/videos/', null=True, blank=True)\n approved = models.BooleanField(default=False)\n listings = ManyToManyField(Listing, blank=True, related_name='videos')\n viewed_by_admin = models.BooleanField(default=False)\n created_at = models.DateTimeField(auto_now_add=True)\n size = models.FloatField(null=True, blank=True)\n staff_notes = models.TextField(blank=True, null=True)\n\n def save(self, *args, **kwargs):\n if self.video:\n self.size = self.video.size\n super(Videos, self).save(*args, **kwargs)\n\n\n def filename(self):\n return os.path.basename(self.video.name)\n\n def get_admin_url(self):\n content_type = ContentType.objects.get_for_model(self.__class__)\n return reverse(\"admin:%s_%s_change\" % (content_type.app_label, content_type.model), args=(self.id,))\n \n\nclass Accommodation(models.Model):\n ACC_TYPES = (\n (1, 'Guests travelling together will have the entire unit for themselves'),\n (2, 'It has multiple rooms, can accomodate many groups of guests')\n )\n\n FACILITY_TYPES = (\n (1, 'Air-conditioned public areas'),\n (2, 'Air-conditioned rooms'),\n (3, 'Environmentally friendly'),\n (4, 'Free bicycle'),\n (5, 'Free parking'),\n (6, 'Free Wi-Fi'),\n (7, 'Internet access'),\n (8, 'Multilingual staff'),\n (9, 'Parking lot'),\n (10, 'Smoke-free property'),\n (11, 'Wireless internet'),\n (12, 'Baby sitting'),\n (13, 'Child care'),\n (14, 'Concierge desk'),\n (15, 'Dry cleaning'),\n (16, 'Ironing / Iron board'),\n (17, 'Laundry'),\n (18, 'Luggage Room / Storage'),\n (19, 'Medical assistance'),\n (20, 'Newspaper'),\n (21, 'Pet care / Grooming'),\n (22, 'Valet'),\n (23, 'Wedding'),\n (24, 'Conference Room'),\n (25, 'Dining Area'),\n (26, 'Fireplace'),\n (27, 'Kitchen'),\n (28, 'Library'),\n (29, 'Lobby'),\n (30, 'Lounge'),\n (31, 'Meeting Room'),\n (32, 'Garden'),\n (33, 'Hot Spring'),\n (34, 'Ironing / Ironing Board'),\n (35, 'Labyrinth'),\n (36, 'Meditation Garden'),\n (37, 'Outdoor Shower'),\n (38, 'Picnic Area'),\n (39, 'Terrace'),\n (40, 'ATM / Banking'),\n (41, 'Bar'),\n (42, 'Barbeque Facilities'),\n (43, 'Cafe'),\n (44, 'Convenience / Grocery Store'),\n (45, 'Currency Exchange'),\n (46, 'Honesty Bar'),\n (47, 'Poolside Bar'),\n (48, 'Restaurant'),\n (49, 'Shopping'),\n (50, 'Special Menu Request'),\n (51, 'Tour Assistance'),\n (52, 'Golf Course'),\n (53, 'Gym'),\n (54, 'Health Club'),\n (55, 'Swimming Pool (indoor)'),\n (56, 'Swimming Pool (outdoor)'),\n (57, 'Table Tennis'),\n (58, 'Tennis Court'),\n (59, 'Volleyball Court'),\n (60, 'Yoga Deck'),\n (61, 'Yoga Shala'),\n (62, 'Yoga Studio'),\n (63, 'Beauty Salon'),\n (64, 'Hair Salon'),\n (65, 'Hot Tub / Jacuzzi'),\n (66, 'Sauna'),\n (67, 'Spa'),\n (68, 'Steam Room'),\n (69, 'Temazcal'),\n (70, 'Bicycle Rental'),\n (71, 'Board Rental'),\n (72, 'Car Rental'),\n (73, 'Cell Phone Rental'),\n (74, 'Laptop Rental')\n )\n\n CATEGORY_TOGETHER_CHOICES = (\n (1, 'House'),\n (2, 'Apartment'),\n (3, 'Cabin'),\n (4, 'Lodge'),\n (5, 'Tent'),\n (6, 'Bungalow'),\n (7, 'Chalet'),\n (8, 'Villa'),\n (9, 'Cabin(boat)'),\n (10, 'Studio'),\n (11, 'Recreational Vehicle'),\n (12, 'Yurt'),\n (13, 'Cottage'),\n (14, 'Various Accommodations'),\n (15, 'Barn'),\n (16, 'Ranch'),\n (17, 'Boat'),\n )\n\n CATEGORY_MULTIPLE_CHOICES = (\n (1, 'Hotel'),\n (2, 'Hostel'),\n (3, 'House'),\n (4, 'Apartment'),\n (5, 'Ashram'),\n (6, 'Academy'),\n (7, 'Cabin'),\n (8, 'Resort'),\n (9, 'School'),\n (10, 'Lodge'),\n (11, 'Tent'),\n (12, 'Bungalow'),\n (13, 'Chalet'),\n (14, 'Villa'),\n (15, 'Cabin(boat)'),\n (16, 'Motel'),\n (17, 'Inn'),\n (18, 'Studio'),\n (19, 'Campus'),\n (20, 'Recreational Vehicle'),\n (21, 'Yurt'),\n (22, 'Retreat Center'),\n (23, 'Temple'),\n (24, 'Monastery'),\n (25, 'Cottage'),\n (26, 'Campsite'),\n (27, 'Various Accommodations'),\n (28, 'Castle'),\n (29, 'Barn'),\n (30, 'Ranch'),\n (31, 'Bed and Breakfast'),\n (32, 'Treehouse'),\n (33, 'Boat'),\n (34, 'Farm')\n )\n user_profile = models.ForeignKey(UserProfile, null=True, blank=True, on_delete=models.CASCADE)\n type = models.IntegerField(blank=True, null=True, choices=ACC_TYPES)\n category_private_unit = models.IntegerField(blank=True, null=True, choices=CATEGORY_TOGETHER_CHOICES)\n category_multiple_units = models.IntegerField(null=True, blank=True, choices=CATEGORY_MULTIPLE_CHOICES)\n name = models.CharField(max_length=150, blank=True, null=True)\n facilities = MultiSelectField(choices=FACILITY_TYPES, blank=True, null=True)\n description = TextField(blank=True, null=True)\n max_occupancy = models.PositiveIntegerField(null=True, blank=True)\n listing = models.ManyToManyField(Listing, blank=True, related_name=\"listings\")\n\n def has_rooms(self):\n return self.rooms.all()\n\n def __str__(self):\n return self.name\n \n def image_count(self):\n num_images = self.images.count()\n return num_images\n\n def get_acc_type(self):\n if self.type == 1:\n return self.get_category_private_unit_display()\n else:\n return self.get_category_multiple_units_display()\n\nclass AccommodationImages(models.Model):\n accommodation = models.ForeignKey(Accommodation, on_delete=models.CASCADE, related_name=\"images\")\n image = models.ImageField(upload_to='media/accommodations/images/', null=True, blank=True)\n\n def __str__(self):\n return self.accommodation.name\n\n def delete(self):\n self.image.delete(save=False)\n super().delete()\n\n\n# Rooms Models\n\nclass Room(models.Model):\n ROOM_TYPES = (\n (1, 'Single Room'),\n (2, 'Double Room'),\n (3, 'Twin Room'),\n (4, 'Triple Room'),\n (5, 'Quadruple Room'),\n (6, 'Dorm')\n )\n\n SHARED_STATUS = (\n (1, 'Private'),\n (2, 'Shared')\n )\n\n ROOM_FACILITIES = (\n (1, 'Bunk beds'),\n (2, 'Double bed'),\n (3, 'King bed'),\n (4, 'Queen bed'),\n (5, 'Single bed'),\n (6, 'Twin beds'),\n (7, 'Bathrobe'),\n (8, 'Bathtub'),\n (9, 'Hair Dryer'),\n (10, 'Outdoor Shower'),\n (11, 'Private Bathroom'),\n (12, 'Shared Bathroom'),\n (13, 'Shower'),\n (14, 'Toiletries'),\n (15, 'Towels'),\n (16, 'Air-conditioned Rooms'),\n (17, 'Coffee / Tea'),\n (18, 'Desk'),\n (19, 'Free bottled water'),\n (20, 'Free Wi-Fi / Computer'),\n (21, 'In-room Safe'),\n (22, 'Internet Access'),\n (23, 'Ironing / Ironing Board'),\n (24, 'Kitchen'),\n (25, 'Mini bar'),\n (26, 'Mosquito Net'),\n (27, 'Refrigerator'),\n (28, 'Room Cleaning'),\n (29, 'Standing / Ceiling Fan'),\n (30, 'TV'),\n (31, 'Wardrobe / Closet'),\n (32, 'Balcony'),\n (33, 'Fireplace'),\n (34, 'Hammock'),\n (35, 'Hot Tub / Jacuzzi'),\n (36, 'In-Room Dining'),\n (37, 'Patio'),\n (38, 'Sauna'),\n (39, 'Steam Room'),\n (40, 'Terrace')\n )\n\n accommodation = models.ForeignKey(Accommodation, on_delete=models.CASCADE, related_name='rooms')\n type = models.IntegerField(blank=True, null=True, choices=ROOM_TYPES)\n name = models.CharField(max_length=150, blank=True, null=True)\n shared = models.IntegerField(default=1, choices=SHARED_STATUS)\n max_occupancy = models.PositiveIntegerField(default=0)\n facilities = MultiSelectField(choices=ROOM_FACILITIES, blank=True, null=True)\n description = TextField(blank=True, null=True)\n\n def get_type(self):\n return self.get_type_display()\n\n def get_acc(self):\n return self.accommodation.name\n\n def __str__(self):\n return self.accommodation.name + \" - \" + self.name\n \n def get_image_count(self):\n return self.images.count()\n\n\nclass RoomImages(models.Model):\n room = models.ForeignKey(Room, on_delete=models.CASCADE, related_name='images')\n image = models.ImageField(upload_to='media/rooms/images/', null=True, blank=True)\n\n def __str__(self):\n return self.room.accommodation.name + ' - ' + self.room.name + ' - Image #' + str(self.pk)\n \n def delete(self):\n self.image.delete(save=False)\n super().delete()\n\nclass BedConfig(models.Model):\n\n room = models.ForeignKey(Room, on_delete=models.CASCADE, null=True, blank=True, related_name='room_configs')\n def __str__(self):\n return str(self.room.name) + \" - Configuration Set\" \n\n\nclass BedCombo(models.Model):\n BED_CHOICES = (\n (1, 'Single Bed (1 people)'),\n (2, 'Double Bed (2 people)'),\n (3, 'Queen Size Bed (2 people)'),\n (4, 'King size bed (2 people)'),\n (5, 'Platform bed (2 people)'),\n (6, 'Folding bed (1 people)'),\n (7, 'Murphy bed (2 people)'),\n (8, 'Canopy bed (2 people)'),\n (9, 'Crib (1 people)'),\n (10, 'Twin bed (2 people)'),\n (11, 'Cot bed (1 people)'),\n (12, 'Bunk bed (2 people)'),\n (13, 'Mid sleeper bed (1 people)'),\n (14, 'Antique style bed (2 people)'),\n (15, 'Trundle bed (2 people)'),\n (16, 'L-shaped bunk bed (3 people)'),\n (17, 'Sofa bed (2 people)'),\n (18, 'Water bed (2 people)'),\n (19, 'Hanging bed (1 people)'),\n (20, 'Hammock (1 people)'),\n (21, 'Ottoman bed (1 people)'),\n )\n bed_type = models.IntegerField(choices=BED_CHOICES, blank=True, null=True)\n num_beds = models.PositiveIntegerField(blank=True, null=True)\n bed_config = models.ForeignKey(BedConfig, on_delete=models.CASCADE, blank=True, null=True, related_name='combos')\n\n def __str__(self):\n BED_CHOICES = (\n (1, 'Single Bed (1 people)'),\n (2, 'Double Bed (2 people)'),\n (3, 'Queen Size Bed (2 people)'),\n (4, 'King size bed (2 people)'),\n (5, 'Platform bed (2 people)'),\n (6, 'Folding bed (1 people)'),\n (7, 'Murphy bed (2 people)'),\n (8, 'Canopy bed (2 people)'),\n (9, 'Crib (1 people)'),\n (10, 'Twin bed (2 people)'),\n (11, 'Cot bed (1 people)'),\n (12, 'Bunk bed (2 people)'),\n (13, 'Mid sleeper bed (1 people)'),\n (14, 'Antique style bed (2 people)'),\n (15, 'Trundle bed (2 people)'),\n (16, 'L-shaped bunk bed (3 people)'),\n (17, 'Sofa bed (2 people)'),\n (18, 'Water bed (2 people)'),\n (19, 'Hanging bed (1 people)'),\n (20, 'Hammock (1 people)'),\n (21, 'Ottoman bed (1 people)'),\n )\n bed_choices = dict(BED_CHOICES)\n if self.bed_type in bed_choices:\n bed_choice = bed_choices[self.bed_type]\n\n return str(self.num_beds) + \" \" + bed_choice + \" - \" + str(self.bed_config.room.name) + ' - Set ID: ' + str(self.bed_config.pk)\n\n\n\n\nclass ListingPackage(models.Model):\n listing = models.ForeignKey(Listing, on_delete=models.CASCADE, related_name='packages')\n number_of_people = models.PositiveIntegerField(default=1)\n room = models.ForeignKey(Room, null=True, blank=True, on_delete=models.CASCADE)\n note = models.CharField(max_length=150, null=True, blank=True)\n default_price = models.PositiveIntegerField(blank=True, null=True)\n price_calculation = RichTextField(blank=True, null=True)\n instant_booking = models.BooleanField(default=False)\n \n\n \n def get_images(self):\n images = self.room.images.all()\n image_urls = []\n for image in images:\n image_urls.append(image.image.url)\n image_text_urls = \", \".join(image_urls)\n image_urls = image_text_urls\n return image_urls\n\n def __str__(self):\n return self.room.name + ' - ' + self.listing.title \n\n\nclass CustomPriceRequest(models.Model):\n package = models.ForeignKey(ListingPackage, on_delete=models.CASCADE, related_name='reqs', null=True, blank=True)\n start_date = models.DateField(null=True, blank=True)\n end_date = models.DateField(null=True, blank=True)\n used_slots = models.IntegerField(default=0, null=True, blank=True)\n max_slots = models.IntegerField(null=True, blank=True)\n price = models.IntegerField(null=True, blank=True)\n is_available = models.BooleanField(default=False)\n\n\ndef increment_invoice_number():\n last_invoice = Inquiry.objects.all().order_by('id').last()\n if not last_invoice:\n return 'AYA0001'\n invoice_no = last_invoice.invoice_no\n invoice_int = int(invoice_no.split('AYA')[-1])\n width = 4\n new_invoice_int = invoice_int + 1\n formatted = (width - len(str(new_invoice_int))) * \"0\" + str(new_invoice_int)\n new_invoice_no = 'AYA' + str(formatted)\n return new_invoice_no \n\nclass Inquiry(models.Model):\n DUE_TYPES = (\n (1, 'On Arrival'),\n (2, 'On Departure'),\n (3, 'Before Arrival')\n )\n staff_notes = models.TextField(blank=True, null=True)\n host_name = models.CharField(max_length=250, blank=True, null=True)\n phone_number = PhoneField(blank=True, null=True)\n customer = models.ForeignKey(Customer, on_delete=models.CASCADE, related_name='inquiry', null=True, blank=True)\n listing = models.ForeignKey(Listing, on_delete=models.CASCADE, related_name='inquiry', null=True, blank=True)\n package = models.ForeignKey(ListingPackage, on_delete=models.CASCADE, related_name='inquiry', null=True, blank=True)\n arrival_date = models.DateField(null=True, blank=True)\n departure_date = models.DateField(null=True, blank=True)\n total_price = models.DecimalField(max_digits=7, decimal_places=2, null=True, blank=True)\n is_approved = models.BooleanField(default=False)\n req = models.ForeignKey(CustomPriceRequest, on_delete=models.CASCADE, null=True, blank=True)\n number_of_people = models.IntegerField(default=1)\n is_conditional = models.BooleanField(default=False)\n created_at = models.DateTimeField(auto_now_add=True)\n duration = models.IntegerField(null=True, blank=True)\n viewed = models.BooleanField(default=False)\n is_cancelled = models.BooleanField(default=False)\n host_declined = models.BooleanField(default=False)\n commission_amount = models.DecimalField(max_digits=7, decimal_places=2, null=True, blank=True)\n invoice_no = models.CharField(max_length = 500, default = increment_invoice_number, null = True, blank = True)\n to_be_paid = models.IntegerField(choices=DUE_TYPES, default=1, null=True, blank=True) # (0, on-arrival) (1, on-departure), \n number_of_days_before_arrival = models.IntegerField(null=True, blank=True)\n required_deposit = models.IntegerField(default=14) \n remaining_amount = models.IntegerField(default=86)\n\n deposit_amount = models.DecimalField(max_digits=7, decimal_places=2, null=True, blank=True)\n deposit_due_date = models.DateField(auto_now_add=False, null=True, blank=True)\n \n \n deposit_paid = models.BooleanField(default=False)\n deposit_paid_date = models.DateTimeField(auto_now_add=False, null=True, blank=True)\n # Deposit payout due date\n deposit_payout_date = models.DateTimeField(auto_now_add=False, null=True, blank=True)\n deposit_payout_amount = models.DecimalField(max_digits=7, decimal_places=2, null=True, blank=True)\n deposit_payout_amount_confirmed = models.BooleanField(default=False)\n depsoit_payout_paid_amount = models.DecimalField(max_digits=7, decimal_places=2, default=0)\n deposit_payout_paid_date = models.DateTimeField(auto_now_add=False, null=True, blank=True)\n deposit_payout_complete = models.BooleanField(default=False)\n net_deposit_payout = models.DecimalField(max_digits=7, decimal_places=2, null=True, blank=True)\n paid_amount = models.DecimalField(max_digits=7, decimal_places=2, null=True, blank=True) # Amount the customer paid\n commission_percent = models.IntegerField(default=14, null=True, blank=True)\n status = models.IntegerField(default=0)\n\n\n def get_admin_url(self):\n content_type = ContentType.objects.get_for_model(self.__class__)\n return reverse(\"admin:%s_%s_change\" % (content_type.app_label, content_type.model), args=(self.id,))\n \n def is_past(self):\n today = datetime.date.today()\n if self.departure_date >= today:\n return False\n else:\n return True\n\n def save(self, *args, **kwargs):\n time = (self.departure_date - self.arrival_date).days\n self.duration = time\n self.host_name = self.listing.user_profile.business_name\n\n\n if self._state.adding: \n # print('ONLY WHEN SAVING A NEW INQUIRY')\n self.commission_percent = self.listing.commission_percent\n # if self.commission_percent < self.listing.deposit_policy:\n # original_deposit = self.listing.deposit_policy\n # else:\n # original_deposit = self.commission_percent\n # self.required_deposit = original_deposit\n # self.remaining_amount = 100 - original_deposit\n remainder_due_date = self.listing.remainder_due\n self.to_be_paid = remainder_due_date\n #REMAINDER_CHOICES = (\n # (1, 'On Arrival'),\n # (2, 'On Depature'),\n # (3, 'Specified Days Before Arrival')\n # )\n if remainder_due_date == 3:\n num_days = self.listing.days_to_pay\n self.number_of_days_before_arrival = num_days\n remaining_deposit_date = self.arrival_date - datetime.timedelta(num_days)\n self.deposit_due_date = remaining_deposit_date\n elif remainder_due_date == 2:\n remaining_deposit_date = self.departure_date\n self.deposit_due_date = remaining_deposit_date\n elif remainder_due_date == 1:\n remaining_deposit_date = self.arrival_date\n self.deposit_due_date = remaining_deposit_date\n \n self.deposit_payout_date = self.arrival_date\n # self.deposit_amount = float(self.total_price) * (self.required_deposit / 100)\n # We removed this as Vincent said match deposit and commission\n self.deposit_amount = float(self.total_price) * (self.commission_percent / 100)\n self.commission_amount = float(self.total_price) * (self.commission_percent / 100)\n self.deposit_payout_date = self.arrival_date\n self.deposit_payout_amount = self.deposit_amount - self.commission_amount\n self.net_deposit_payout = self.deposit_payout_amount - float(self.depsoit_payout_paid_amount)\n if self.deposit_paid:\n self.status = 3\n elif self.host_declined:\n self.status = 4\n elif self.is_approved and not self.deposit_paid:\n self.status = 2\n else:\n self.status = 1\n super(Inquiry, self).save(*args, **kwargs)\n\n\nclass Review(models.Model):\n listing = models.ForeignKey(Listing, on_delete=models.CASCADE, related_name='reviews')\n inquiry = models.OneToOneField(Inquiry, on_delete=models.CASCADE, related_name='review')\n customer = models.ForeignKey(Customer, on_delete=models.CASCADE, related_name='reviews')\n positive_info = models.TextField(null=True, blank=True)\n negative_info = models.TextField(null=True, blank=True)\n host_response = models.TextField(blank=True, null=True)\n value_for_money = models.IntegerField(validators=[MinValueValidator(1), MaxValueValidator(5)])\n accommodation_and_facilities = models.IntegerField(validators=[MinValueValidator(1), MaxValueValidator(5)])\n food = models.IntegerField(validators=[MinValueValidator(1), MaxValueValidator(5)])\n location = models.IntegerField(validators=[MinValueValidator(1), MaxValueValidator(5)])\n quality_of_activity = models.IntegerField(validators=[MinValueValidator(1), MaxValueValidator(5)])\n overall_rating = models.DecimalField(max_digits=2, decimal_places=1)\n approved = models.BooleanField(default=False)\n viewed_by_admin = models.BooleanField(default=False)\n staff_notes = models.TextField(blank=True, null=True)\n created_at = models.DateTimeField(auto_now_add=True)\n\n def get_admin_url(self):\n content_type = ContentType.objects.get_for_model(self.__class__)\n return reverse(\"admin:%s_%s_change\" % (content_type.app_label, content_type.model), args=(self.id,))\n \n def get_progress(self):\n if self.host_response:\n return 100\n else:\n return 50\n \n\nclass Conversation(models.Model):\n host = models.ForeignKey(UserProfile, on_delete=models.CASCADE, related_name='conversations', null=True, blank=True)\n customer = models.ForeignKey(Customer, on_delete=models.CASCADE, related_name='conversations', null=True, blank=True)\n created_at = models.DateTimeField(auto_now_add=True)\n inquiry = models.OneToOneField(Inquiry, related_name='conversation', on_delete=models.CASCADE, null=True, blank=True)\n staff_notes = models.TextField(blank=True, null=True)\n is_unread_customer = models.BooleanField(default=True)\n is_unread_host = models.BooleanField(default=True)\n\n def _get_last_msg_time(self):\n return self.messages.all().last().created_at\n last_message = property(_get_last_msg_time)\n\n def check_customer_unread_messages(self):\n for message in self.messages.all():\n if message.is_unread_customer == True:\n return True\n return False\n \n def check_host_unread_messages(self):\n for message in self.messages.all():\n if message.is_unread_host == True:\n return True\n return False\n \n def get_message_snippet(self):\n message = self.messages.all().last().content\n return message\n \n def get_last_message_time(self):\n time = self.messages.all().last().created_at\n return time\n\n def get_admin_url(self):\n content_type = ContentType.objects.get_for_model(self.__class__)\n return reverse(\"admin:%s_%s_change\" % (content_type.app_label, content_type.model), args=(self.id,))\n\nclass BlogPhotos(models.Model):\n image = models.ImageField(upload_to='media/blog/images/', null=True, blank=True)\n created_at = models.DateTimeField(auto_now_add=True)\n\n class Meta:\n ordering = ['-id']\n\n def get_photo_url(self):\n request = self.context.get('request')\n return request.build_absolute_uri(self.image.url)\n\n def delete(self):\n self.image.delete(save=False)\n super().delete()\n\nclass Messages(models.Model):\n from_host = models.BooleanField(default=False)\n from_owner = models.BooleanField(default=False)\n content = models.TextField(null=True, blank=True)\n created_at = models.DateTimeField(auto_now_add=True)\n conversation = models.ForeignKey(Conversation, related_name='messages', blank=False, null=False, on_delete=models.CASCADE)\n ordering = ['created_at']\n is_flagged = models.BooleanField(default=False)\n staff_notes = models.TextField(blank=True, null=True)\n is_unread_customer = models.BooleanField(default=True)\n is_unread_host = models.BooleanField(default=True)\n original_context = models.TextField(null=True, blank=True)\n \n def save(self, *args, **kwargs):\n if not self.id: # new message\n if self.from_host:\n self.conversation.is_unread_customer = True\n self.conversation.save()\n elif self.from_owner:\n self.conversation.is_unread_customer = True\n self.conversation.is_unread_host = True\n self.conversation.save()\n else:\n self.conversation.is_unread_host = True\n self.conversation.save()\n super(Messages, self).save(*args, **kwargs)\n\n def get_admin_url(self):\n content_type = ContentType.objects.get_for_model(self.__class__)\n return reverse(\"admin:%s_%s_change\" % (content_type.app_label, content_type.model), args=(self.id,))\n\n\nclass Attachment(models.Model):\n file = models.FileField(upload_to='media/messages/attachments/', null=True, blank=True)\n message = models.ForeignKey(Messages, related_name='files', on_delete=models.CASCADE, null=True, blank=True)\n\n\n\n\n\nclass ListingImage(models.Model):\n listing = models.ForeignKey(Listing, on_delete=models.CASCADE, related_name='images')\n image = models.ImageField(upload_to='media/listings/images/', null=True, blank=True)\n\n def __str__(self):\n return self.listing.title\n\n def delete(self):\n self.image.delete(save=False)\n super().delete()\n\n def save(self, *args, **kwargs):\n # Opening the uploaded image\n im = Image.open(self.image)\n\n output = BytesIO()\n\n # after modifications, save it to the output\n im.save(output, format='JPEG', quality=30)\n output.seek(0)\n\n # change the imagefield value to be the newley modifed image value\n self.image = InMemoryUploadedFile(output, 'ImageField', \"%s.jpg\" % self.image.name.split('.')[0], 'image/jpeg',\n sys.getsizeof(output), None)\n\n super(ListingImage, self).save()\n\n\n\ndef current_year():\n return datetime.date.today().year\n\ndef max_value_current_year(value):\n return MaxValueValidator(current_year())(value)\n\nclass Skill(models.Model):\n name = models.CharField(max_length=100, blank=False, null=False)\n training_year = models.PositiveIntegerField(default=current_year(), validators=[MinValueValidator(1932), max_value_current_year])\n teacher = models.CharField(max_length=100, blank=True, null=True)\n school = models.CharField(max_length=200, blank=True, null=True)\n staff_member = models.ForeignKey(Staff, on_delete=models.CASCADE, related_name='skills')\n\n def __str__(self):\n return self.staff_member.first_name + \" \" + self.staff_member.last_name + ' - ' + self.name\n\n\n\n\nclass StripeOrder(models.Model):\n customer = models.ForeignKey(Customer, on_delete=models.SET_NULL, null=True)\n customer_email = models.EmailField(verbose_name='Customer Email')\n host = models.ForeignKey(UserProfile, on_delete=models.SET_NULL, null=True)\n host_email = models.EmailField(verbose_name='Host Email')\n listing = models.ForeignKey(Listing, on_delete=models.SET_NULL, null=True)\n listing_title = models.CharField(max_length=250)\n amount = models.IntegerField(verbose_name='Amount')\n stripe_payment_intent = models.CharField(max_length=200)\n has_paid = models.BooleanField(default=False, verbose_name='Payment Status')\n created_on = models.DateTimeField(auto_now_add=True)\n updated_on = models.DateTimeField(auto_now_add=True)\n\nclass Author(models.Model):\n first_name = models.CharField(max_length=150, blank=True, null=True)\n last_name = models.CharField(max_length=150, null=True, blank=True)\n image = models.ImageField(upload_to='media/blog/authors')\n bio = models.TextField(blank=True, null=True)\n \n def get_full_name(self):\n return self.first_name + ' ' + self.last_name\n\n def delete(self):\n self.image.delete(save=False)\n super().delete() \n\nclass PostCategory(models.Model):\n title = models.CharField(max_length=255, blank=True, null=True)\n \n def __str__(self):\n if self.title:\n return self.title\n\nclass Post(models.Model):\n title = models.CharField(max_length=255, blank=True, null=True)\n subtitle = models.CharField(max_length=255, blank=True, null=True)\n author = models.ForeignKey(Author, on_delete=models.CASCADE, null=True, blank=True, related_name='posts')\n category = models.ForeignKey(PostCategory, on_delete=models.CASCADE, null=True, blank=True, related_name='posts')\n content = RichTextField(blank=True, null=True, config_name=\"toolbar_custom\")\n image = models.ImageField(upload_to='media/blog/posts')\n created_at = models.DateField(auto_now_add=True)\n is_featured = models.BooleanField(default=False)\n slug = models.SlugField(blank=True)\n\n def delete(self):\n self.image.delete(save=False)\n super().delete()\n\n def __str__(self):\n if self.title:\n return self.title\n else:\n return self.slug\n\n def save(self, *args, **kwargs):\n if not self.id: # new listing - no title\n if not self.title:\n self.slug = unique_slug_generator(self)\n else:\n if not self.title:\n old_slug = self.slug\n new_slug = unique_slug_generator(self, old_slug)\n short_slug = new_slug[:48]\n self.slug = unique_slug_generator(self, old_slug)\n else:\n full_slug = slugify(self.title)\n short_slug = full_slug[:48]\n self.slug = short_slug\n super(Post, self).save(*args, **kwargs)\n\n def get_absolute_url(self):\n return reverse('listings:blog_detail', kwargs={'pk':self.pk, 'slug': self.slug})\n","sub_path":"listings/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":52902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"75227024","text":"import sys\nimport os\nimport csv\nimport random\n\nimport pickle\nimport scipy\nimport pandas as pd\nimport numpy as np\n\nseed = 10\n\nimport dynet_config\n\n# Declare GPU as the default device type\ndynet_config.set_gpu()\n# Set some parameters manualy\ndynet_config.set(mem=400, random_seed=seed)\n# Initialize dynet import using above configuration in the current scope\nimport dynet as dy\n\n\nfrom utils.io_utils import IOUtils\nfrom utils.nlp_utils import NLPUtils\n\nfrom sklearn.metrics import roc_auc_score, average_precision_score\nfrom sklearn.model_selection import KFold\n\nrandom.seed(seed)\nnp.random.seed(seed)\n\n\nclass Data:\n def __init__(self):\n self.w2i = None\n self.entries = None\n self.train_entries = None\n self.test_entries = None\n self.ext_embedding = None\n self.reviews = None\n self.predicted_reviews = None\n\n def to(self, device):\n if self.entries:\n for entry in self.entries:\n entry.index_tensor = entry.index_tensor.to(device=device)\n if self.reviews:\n for doc_id in self.reviews:\n for review in self.reviews[doc_id]:\n review.index_tensor = review.index_tensor.to(device=device)\n if self.predicted_reviews:\n for doc_id in self.predicted_reviews:\n for review in self.predicted_reviews[doc_id]:\n review.index_tensor = review.index_tensor.to(device=device)\n\n def load(self, infile):\n with open(infile, \"rb\") as target:\n self.ext_embeddings, self.entries, self.w2i = pickle.load(target)\n\n def save_data(self, infile):\n with open(infile, \"rb\") as target:\n self.ext_embeddings, self.entries, self.w2i = pickle.dump(target)\n\n def load_predicted_reviews(self, infile):\n with open(infile, \"rb\") as target:\n self.predicted_reviews = pickle.load(target)\n for app_id in self.predicted_reviews.keys():\n self.predicted_reviews[app_id].sort(\n key=lambda x: x.prediction_result.item(), reverse=True\n )\n\n def load_reviews(self, infile):\n with open(infile, \"rb\") as target:\n self.reviews = pickle.load(target)\n\n\nclass Model:\n def __init__(self, data, opt):\n self.opt = opt\n self.model = dy.ParameterCollection()\n self.trainer = dy.MomentumSGDTrainer(self.model)\n self.w2i = data.w2i\n self.wdims = opt.embedding_size\n self.ldims = opt.hidden_size\n self.attsize = opt.attention_size\n\n self.ext_embeddings = data.ext_embeddings\n # Model Parameters\n self.wlookup = self.model.add_lookup_parameters((len(self.w2i), self.wdims))\n\n self.__load_external_embeddings()\n\n if self.opt.encoder_dir == \"single\":\n if self.opt.encoder_type == \"lstm\":\n self.sentence_rnn = [\n dy.VanillaLSTMBuilder(1, self.wdims, self.ldims, self.model)\n ]\n elif self.opt.encoder_type == \"gru\":\n self.sentence_rnn = [\n dy.GRUBuilder(1, self.wdims, self.ldims, self.model)\n ]\n self.attention_w = self.model.add_parameters((self.attsize, self.ldims))\n self.attention_b = self.model.add_parameters(self.attsize)\n self.att_context = self.model.add_parameters(self.attsize)\n self.mlp_w = self.model.add_parameters((1, self.ldims + 2 * self.ldims))\n self.mlp_b = self.model.add_parameters(1)\n elif self.opt.encoder_dir == \"bidirectional\":\n if self.opt.encoder_type == \"lstm\":\n self.sentence_rnn = [\n dy.VanillaLSTMBuilder(1, self.wdims, self.ldims, self.model),\n dy.VanillaLSTMBuilder(1, self.wdims, self.ldims, self.model),\n ]\n elif self.opt.encoder_type == \"gru\":\n self.sentence_rnn = [\n dy.GRUBuilder(1, self.wdims, self.ldims, self.model),\n dy.GRUBuilder(1, self.wdims, self.ldims, self.model),\n ]\n\n self.attention_w = self.model.add_parameters((self.attsize, 2 * self.ldims))\n self.attention_b = self.model.add_parameters(self.attsize)\n self.att_context = self.model.add_parameters(self.attsize)\n self.mlp_w = self.model.add_parameters((1, 2 * self.ldims + 4 * self.ldims))\n self.mlp_b = self.model.add_parameters(1)\n\n def __load_external_embeddings(self):\n print(\"Initializing word embeddings by pre-trained vectors\")\n count = 0\n for word in self.w2i:\n if word in self.ext_embeddings:\n count += 1\n self.wlookup.init_row(self.w2i[word], self.ext_embeddings[word])\n print(\n \"Vocab size: %d; #words having pretrained vectors: %d\"\n % (len(self.w2i), count)\n )\n\n def save(self):\n self.model.save(self.opt.model_checkpoint)\n\n def load(self):\n self.model.populate(self.opt.model_checkpoint)\n\ndef write_file(filename, string):\n with open(filename, \"a\") as target:\n target.write(\"{}\\n\".format(string))\n target.flush()\n\n\ndef encode_sequence(model, seq, rnn_builder):\n def predict_sequence(builder, inputs):\n s_init = builder.initial_state()\n return s_init.transduce(inputs)\n\n if model.opt.encoder_dir == \"bidirectional\":\n f_in = [entry for entry in seq]\n b_in = [rentry for rentry in reversed(seq)]\n forward_sequence = predict_sequence(rnn_builder[0], f_in)\n backward_sequence = predict_sequence(rnn_builder[1], b_in)\n return [\n dy.concatenate([s1, s2])\n for s1, s2 in zip(forward_sequence, backward_sequence)\n ]\n elif model.opt.encoder_dir == \"single\":\n f_in = [entry for entry in seq]\n state = rnn_builder[0].initial_state()\n states = []\n for entry in seq:\n state = state.add_input(entry)\n states.append(state.output())\n return states\n\n\ndef max_pooling(encoded_sequence):\n values = np.array([encoding.value() for encoding in encoded_sequence])\n min_indexes = np.argmax(values, axis=0)\n pooled_context = dy.concatenate(\n [encoded_sequence[row][col] for col, row in enumerate(min_indexes)]\n )\n return pooled_context\n\n\ndef min_pooling(encoded_sequence):\n values = np.array([encoding.value() for encoding in encoded_sequence])\n min_indexes = np.argmin(values, axis=0)\n pooled_context = dy.concatenate(\n [encoded_sequence[row][col] for col, row in enumerate(min_indexes)]\n )\n return pooled_context\n\n\ndef average_pooling(encoded_sequence):\n averages = []\n for col in range(encoded_sequence[0].dim()[0][0]):\n avg = []\n for row in range(len(encoded_sequence)):\n avg.append(encoded_sequence[row][col])\n averages.append(dy.average(avg))\n return dy.concatenate(averages)\n\n\ndef train_item(args, model, sentence):\n loss = None\n seq = [\n model.wlookup[int(model.w2i.get(entry, 0))]\n for entry in sentence.preprocessed_sentence\n ]\n if len(seq) > 0:\n encoded_sequence = encode_sequence(model, seq, model.sentence_rnn)\n global_max = max_pooling(encoded_sequence)\n global_min = average_pooling(encoded_sequence)\n if len(encoded_sequence) > 0:\n att_mlp_outputs = []\n for e in encoded_sequence:\n mlp_out = (model.attention_w * e) + model.attention_b\n att_mlp_outputs.append(mlp_out)\n\n lst = []\n for o in att_mlp_outputs:\n lst.append(dy.exp(dy.sum_elems(dy.cmult(o, model.att_context))))\n\n sum_all = dy.esum(lst)\n\n probs = [dy.cdiv(e, sum_all) for e in lst]\n att_context = dy.esum(\n [dy.cmult(p, h) for p, h in zip(probs, encoded_sequence)]\n )\n context = dy.concatenate([att_context, global_max, global_min])\n y_pred = dy.logistic((model.mlp_w * context) + model.mlp_b)\n\n if sentence.permissions[args.permission_type]:\n loss = dy.binary_log_loss(y_pred, dy.scalarInput(1))\n else:\n loss = dy.binary_log_loss(y_pred, dy.scalarInput(0))\n\n loss.backward()\n model.trainer.update()\n loss_val = loss.scalar_value()\n dy.renew_cg()\n return loss_val\n return 0\n\n\ndef test_item(model, sentence):\n seq = [\n model.wlookup[int(model.w2i.get(entry, 0))]\n for entry in sentence.preprocessed_sentence\n ]\n if len(seq) > 0:\n encoded_sequence = encode_sequence(model, seq, model.sentence_rnn)\n global_max = max_pooling(encoded_sequence)\n global_min = average_pooling(encoded_sequence)\n if len(encoded_sequence) > 0:\n att_mlp_outputs = []\n for e in encoded_sequence:\n mlp_out = (model.attention_w * e) + model.attention_b\n att_mlp_outputs.append(mlp_out)\n\n lst = []\n for o in att_mlp_outputs:\n lst.append(dy.exp(dy.sum_elems(dy.cmult(o, model.att_context))))\n\n sum_all = dy.esum(lst)\n\n probs = [dy.cdiv(e, sum_all) for e in lst]\n att_context = dy.esum(\n [dy.cmult(p, h) for p, h in zip(probs, encoded_sequence)]\n )\n context = dy.concatenate([att_context, global_max, global_min])\n y_pred = dy.logistic((model.mlp_w * context) + model.mlp_b)\n sentence.prediction_result = y_pred.scalar_value()\n dy.renew_cg()\n return sentence.prediction_result\n return 0\n\n\ndef train_all(args, model, data):\n write_file(args.outdir, \"Training...\")\n losses = []\n for index, sentence in enumerate(data.train_entries):\n loss = train_item(args, model, sentence)\n if index != 0:\n if index % model.opt.print_every == 0:\n write_file(\n args.outdir,\n \"Index {} Loss {}\".format(\n index, np.mean(losses[index - model.opt.print_every :])\n ),\n )\n losses.append(loss)\n\n\ndef test_all(args, model, data):\n def pr_roc_auc(predictions, gold):\n y_true = np.array(gold)\n y_scores = np.array(predictions)\n roc_auc = roc_auc_score(y_true, y_scores)\n pr_auc = average_precision_score(y_true, y_scores)\n return roc_auc, pr_auc\n\n write_file(args.outdir, \"Predicting..\")\n\n predictions, gold = [], []\n for index, sentence in enumerate(data.test_entries):\n pred = test_item(model, sentence)\n predictions.append(pred)\n gold.append(sentence.permissions[args.permission_type])\n return pr_roc_auc(predictions, gold)\n\n\ndef kfold_validation(args, data):\n data.entries = np.array(data.entries)\n random.shuffle(data.entries)\n\n kfold = KFold(n_splits=10, shuffle=True, random_state=seed)\n roc_l, pr_l = [], []\n for foldid, (train, test) in enumerate(kfold.split(data.entries)):\n write_file(args.outdir, \"Fold {}\".format(foldid + 1))\n\n model = Model(data, args)\n data.train_entries = data.entries[train]\n data.test_entries = data.entries[test]\n max_roc_auc, max_pr_auc = 0, 0\n for epoch in range(args.num_epoch):\n train_all(args, model, data)\n roc_auc, pr_auc = test_all(args, model, data)\n if pr_auc > max_pr_auc:\n max_pr_auc = pr_auc\n max_roc_auc = roc_auc\n write_file(\n args.outdir, \"Epoch {} ROC {} PR {}\".format(epoch + 1, roc_auc, pr_auc)\n )\n #model.save()\n write_file(args.outdir, \"ROC {} PR {}\".format(max_roc_auc, max_pr_auc))\n roc_l.append(max_roc_auc)\n pr_l.append(max_pr_auc)\n write_file(\n args.outdir, \"Summary : ROC {} PR {}\".format(np.mean(roc_l), np.mean(pr_l))\n )\n\n\ndef run(args):\n data = Data()\n data.load(args.saved_data)\n\n kfold_validation(args, data)\n","sub_path":"SentenceOnlyAttentionDynet/models/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":12102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"424600957","text":"#======================================#\n# deeper systems neural networks test\n# @ claudio\n#=====================================#\n\n# import packages\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport cv2\nimport random\nimport pickle \nimport tensorflow as tf \n\n#==========================#\n# PREPROCESSING\n#=========================#\n\n#==================== LOAD AND SAVE TRAIN AND TEST DATA\n\n# load label\nlb = pd.read_csv('data/train.truth.csv')\n\n# directory and files\ntdir = 'data/train/'\n\n# label to index\nindlab = list(lb['label'].unique())\ndict_labels = {}\nfor label in indlab:\n dict_labels[label] = indlab.index(label)\n\n# make data\ndata = []\nfor i in range(len(lb)):\n img_array = cv2.imread(os.path.join(tdir,lb['fn'][i]), cv2.IMREAD_GRAYSCALE)\n img_array = tf.keras.utils.normalize(img_array, axis=1)\n img_array = np.array(img_array).reshape(-1, 64*64)\n label = lb['label'][i]\n label_ind = dict_labels[label]\n data.append([img_array, label_ind])\n\n# split in train and test\nrandom.shuffle(data)\ntrain = data[:(round(len(data)*0.8))]\ntest = data[(round(len(data)*0.8)):]\n\n# divide features and labels\ndef generateFeatureLabel(data):\n features = []\n labels = []\n for x, y in data:\n features.append(x)\n labels.append(y)\n return np.array(features), np.array(labels)\n\n# generate train and test \ntrain_feature, train_label = generateFeatureLabel(train)\ntest_feature, test_label = generateFeatureLabel(test)\n\n# save as pickle\ndef pickleSave(data, filename):\n picout = open(('data/generated/'+filename), 'wb')\n pickle.dump(data, picout)\n picout.close()\n\npickleSave(train_feature,'train_feature.pickle')\npickleSave(train_label,'train_label.pickle')\npickleSave(test_feature,'test_feature.pickle')\npickleSave(test_label,'test_label.pickle')\n\n#==================== LOAD AND SAVE EVALUATION\n\n# directory and files\nevaldir = 'data/test/'\nlist_eval_images = os.listdir(evaldir)\n\n# make data\neval_data = []\nfor i in range(len(list_eval_images)):\n img_array = cv2.imread(os.path.join(evaldir,list_eval_images[i]), cv2.IMREAD_GRAYSCALE)\n img_array = tf.keras.utils.normalize(img_array, axis=1)\n img_array = np.array(img_array).reshape(-1, 64*64)\n eval_data.append([img_array])\n\n# save as pickle\npickleSave(eval_data,'eval_data.pickle')","sub_path":"codes/build_dataset.py","file_name":"build_dataset.py","file_ext":"py","file_size_in_byte":2326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"96823111","text":"from django.contrib import messages\nfrom django.shortcuts import render, redirect\nfrom .forms import CreateContactForm\nfrom .amazon_ses import send_email\n\nimport os\nimport requests\n\ndef submit(request):\n\n # reCAPTCHA v3\n r = requests.post(\n 'https://www.google.com/recaptcha/api/siteverify', \n params={\n 'response': request.POST[\"g-recaptcha-response\"],\n 'secret': os.environ[\"RECAPTCHA_SECRET_KEY\"],\n }\n )\n\n # Return 'error' message if reCAPTCHA fails\n if r.json()['success'] == False:\n messages.add_message(request, messages.INFO, 'There was an error with your request. We appologize for the inconvenience.')\n return redirect('contact')\n\n # Continue with normal POST request if reCAPTCHA succeeds\n if request.method == \"POST\":\n\n # Send email via Amazon SES\n send_email(request.POST)\n\n # Create a contact form using the CreateContactForm model\n form = CreateContactForm(request.POST)\n\n # Save form to database if data is valid, create a messages{} \n # object with a success message, and redirect to contact.html\n if form.is_valid():\n\n # Here, we want to add the user's IP address to the database. So far we've\n # gathered the user's contact form submission in an instance of the\n # ModelForm class, and named the instance 'form'.\n #\n # An instance of the ModelForm class looks like a string of HTML tags, so \n # trying to add information to that would be difficult. \n #\n # Instead, use *.save(commit=False) to return a Model object, and adding \n # data to objects is a lot easier than trying to append a string that\n # resembles HTML to the output of an instance of the ModelForm class.\n form_submission = form.save(commit=False)\n\n # Add the IP address to an instance of the Model object\n form_submission.ip_address = request.META['REMOTE_ADDR']\n\n # Save the complete submission\n form.save()\n # messages.add_message(request, messages.INFO, 'Your Message Has Been Sent!')\n return redirect('thanks')\n\n # Django stores error messages in form.error. Create & send a new\n # blank empty form when there's an error.\n else: \n form = CreateContactForm() \n return render(request, 'contact', { 'form': form })","sub_path":"apps/contact_form/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"418359881","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date : 2017-04-21 17:16:37\n# @Author : lzg (wb-lzg228465@autonavi.com)\n# @Link : ${link}\n# @Version : $Id$\n\nimport os\n\na = 1.3 * (10**9)\nb = 0\nfor i in range(0, 1300000000):\n a = a / 2\n b = b + 1\n if a < 2:\n break\nprint(a, b)\n","sub_path":"python/calc3.py","file_name":"calc3.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"626329315","text":"#!/usr/bin/env python\n# -*- coding: utf8 -*-\n\nimport nysensors\nimport nytumblr\nimport nydb\n\nimport logging\nimport threading\nimport json\nimport time\nimport datetime\nimport plotly.plotly as ply\nfrom plotly.graph_objs import *\n\nwith open('/home/pi/NichoirConnecte/config/plotly.json') as config_file:\n plotly_user_config = json.load(config_file)\n\nlogger = logging.getLogger(__name__)\n\nply.sign_in(plotly_user_config[\"plotly_username\"], plotly_user_config[\"plotly_api_key\"])\n\nurl = ply.plot([\n {\n 'x': [], 'y': [], 'type': 'scatter',\n 'stream': {\n 'token': plotly_user_config['plotly_streaming_tokens'][0],\n 'maxpoints': 5000\n }\n }], filename='Nichoir du CIV')\n\nlogger.debug(\"Le graphe est disponible ici : %s\" % url)\nnytumblr.message(\"graphe\", \"Courbe de température\", \"Vous pouvez consulter la courbe des températures ici : %s\" % url.encode('utf8'))\n\nstream = ply.Stream(plotly_user_config['plotly_streaming_tokens'][0])\nstream.open()\n\ndef publie_graphe_poids_heure():\n # relance pour 1 heure\n threading.Timer(3600.0, publie_graphe_poids_heure).start()\n # construction d'un graphe\n resultat = nydb.liste_poids_heure()\n abscisse = [item[0] for item in resultat]\n poids0 = Scatter(x=abscisse, y=[item[1] for item in resultat])\n poids1 = Scatter(x=abscisse, y=[item[2] for item in resultat])\n data = Data([poids0, poids1])\n url = ply.plot(data, filename='Derniers poids')\n\n # génération d'une image\n nytumblr.message(\"poids\", \"Les derniers poids\", \"Dispo ici : %s\" % url.encode('utf8'))\n\n # envoi de l'image sur Tumblr\n\n\n\ndef publie_graphe_temp_live():\n # relance pour 10 seconde\n threading.Timer(10.0, publie_graphe_temp_live).start()\n #\n sensor_data = nysensors.sensor_temp()\n stream.write({'x': datetime.datetime.now(),'y': sensor_data})\n\n# lancement comme un module du Cube\ndef init(cube, params):\n publie_graphe_poids_heure()\n publie_graphe_temp_live()\n\t\n\n","sub_path":"src/lecube/nichoir/graphiques.py","file_name":"graphiques.py","file_ext":"py","file_size_in_byte":1950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"83496446","text":"import os\nimport sys\nimport ffmpeg_streaming\n\n\ndef progress(percentage, ffmpeg, media):\n # You can update a field in your database\n # You can also create a socket connection and show a progress bar to users\n sys.stdout.write(\"\\r Transcoding... (%s%%)[%s%s]\" % (percentage, '#' * percentage, '-' * (100 - percentage)))\n sys.stdout.flush()\n\n\ndef create_dash_files(_input, _output, __progress=None):\n (\n ffmpeg_streaming\n .dash(_input, adaption='\"id=0,streams=v id=1,streams=a\"')\n .format('libx265')\n .auto_rep()\n .package(_output, __progress)\n )\n\n\nif __name__ == \"__main__\":\n name = os.path.basename(__file__).split('.')[0]\n current_dir = os.path.dirname(os.path.abspath(__file__))\n\n _input = os.path.join(current_dir, '_example.mp4')\n _output = os.path.join(current_dir, name, 'output')\n\n _progress = progress\n\n create_dash_files(_input, _output, _progress)\n","sub_path":"examples/dash.py","file_name":"dash.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"381246522","text":"from django.db import models\nfrom django.conf import settings\n\nfrom django.contrib.auth import get_user_model\nfrom django.dispatch import receiver\nfrom django.db.models.signals import post_save\nfrom simmarket.models import SimPackage\nfrom django_encrypted_filefield.fields import EncryptedImageField\nfrom . utils import GENDER_CHOICES, DEFAULT_USER_GENDER, NOTIFICATION_TYPES, AREA_CHOICES, SPECIALIZATION_CHOICES\n\nUser=get_user_model()\n\n\nclass Profile(models.Model):\n user = models.OneToOneField(settings.AUTH_USER_MODEL,\n on_delete=models.CASCADE)\n photo = models.ImageField(upload_to='users/%Y/%m/%d', blank=True, null=True)\n airline_image = models.ImageField(upload_to='users/%Y/%m/%d', blank=True, null=True)\n travel_image = models.ImageField(upload_to='users/%Y/%m/%d', blank=True, null=True)\n passport_image = models.ImageField(upload_to='users/%Y/%m/%d', blank=True, null=True)\n medical_image = models.ImageField(upload_to='users/%Y/%m/%d', blank=True, null=True)\n\n gender = models.CharField(max_length=50, choices=GENDER_CHOICES, default=DEFAULT_USER_GENDER)\n points = models.IntegerField(default=0, blank=True, null=True)\n rank = models.IntegerField(default=0, blank=True, null=True)\n payment_method = models.CharField(max_length=50, blank=True)\n subscribe = models.BooleanField(default=False)\n phone = models.CharField(max_length=16,blank=True,null=True)\n age = models.IntegerField(default=None, blank=True, null=True)\n notifications = models.CharField(max_length=100, choices=NOTIFICATION_TYPES,default=NOTIFICATION_TYPES[0][0])\n date_birth = models.DateField(blank=True, null=True)\n address = models.CharField(max_length=200,null=True,blank=True)\n call_points = models.IntegerField(default=0)\n document_id = models.CharField(blank=True,null=True,max_length=100)\n doctor_license = models.IntegerField(blank=True,null=True)\n pbx_domain = models.CharField(max_length=100,blank=True,null=True)\n extension_number = models.CharField(max_length=100,blank=True,null=True)\n extension_password = models.CharField(max_length=100,blank=True,null=True)\n\n\n\n\n provider = models.CharField(max_length=200, blank=True, null=True)\n specialization = models.CharField(max_length=200, choices=SPECIALIZATION_CHOICES,blank=True, null=True)\n sim_number = models.CharField(max_length=16, blank=True, null=True)\n area = models.CharField(max_length=40,choices=AREA_CHOICES,blank=True,null=True)\n\n\n def __str__(self):\n return 'Profile for user {}'.format(self.user.username)\n\n\n@receiver(post_save, sender=User)\ndef create_user_profile(sender, instance, created, **kwargs):\n if created:\n Profile.objects.create(user=instance)\n\n\n\nclass UserSimPackage(models.Model):\n user = models.ForeignKey(User,on_delete=models.CASCADE,related_name='user_sim_package')\n created = models.DateTimeField(auto_now=True)\n package = models.ForeignKey(SimPackage,on_delete=models.CASCADE,related_name='package_users')\n","sub_path":"backend/simapp/account/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"301362711","text":"import json\n\n# For creating randomized problem sets\nimport random\n\nOPERATOR_END_STRING = \"--------\"\n\n\n\ndef make_new_problem_file(path, operand_type, num_problems,\n num_vars, var_len_min, var_len_max):\n problem_list = \\\n generate_problem_list_as_json(operand_type, num_problems,\n num_vars, var_len_min, var_len_max)\n\n print(\"PROBLEM_LIST:\")\n print(problem_list)\n print(\"END PROBLEM_LIST:\")\n\n\n # Make all the text we will write to file\n problem_list_as_string = \",\".join(problem_list)\n return '{\\n \\\"problem_set\\\": [\\n' + problem_list_as_string + '\\n ]\\n}'\n\ndef generate_problem_list_as_json(operand_type, num_problems,\n num_vars, var_len_min, var_len_max):\n json_problem_list = []\n idx = 0\n while idx < num_problems:\n print(\"Creating randomized problem\", idx)\n json_problem_list.append(make_json_problem_item(\n num_vars, operand_type, var_len_min, var_len_max))\n idx += 1\n\n print(\">>>>>>>>>>>>>>>>>\")\n print(json_problem_list)\n print(type(json_problem_list))\n print(\"<<<<<<<<<<<<<<<<<\")\n\n return json_problem_list\n\n#\n#\n#\n#\n#\ndef make_json_problem_item(num_vars, operand_type, var_len_min, var_len_max):\n vars = []\n operator = []\n var_val_min = 1 * 10 ** var_len_min\n var_val_max = 1 * (10 ** var_len_max + 1) - 1\n\n jdx = 0\n while jdx < (num_vars - 1):\n vars.append(random.randint(var_val_min, var_val_max))\n operator.append(operand_type)\n jdx += 1\n\n vars.append(random.randint(var_val_min, var_val_max))\n operator.append(OPERATOR_END_STRING)\n\n # Make Python json object\n pre_json_obj = {\n \"vars\": vars,\n \"operator\": operator,\n \"result\": 57,\n \"numTries\": 0,\n \"numCorrect\": 0,\n \"numWrong\": 0,\n \"tryAgain\": \"True\"\n }\n\n # Convert to json\n json_object = json.dumps(pre_json_obj)\n return json_object\n\n#\n#\ndef assemble_problem_as_line(prob_dict_item):\n question_string = \"\"\n idx = 0\n varlist = prob_dict_item['vars']\n num_elements = len(varlist)\n while idx < (num_elements - 1): # wish to avoid the \"---\" at end of operators\n question_string += str(varlist[idx])\n question_string += prob_dict_item['operator'][idx]\n idx += 1\n question_string += str(varlist[idx])\n\n return question_string\n\n# assemble_problem()\n# Returns tuple with a problem string and an answer\n#\n# Args: Single problem object\n# Rets: Tuple, string (problem string) and answer (int)\ndef assemble_problem(prob_dict_item):\n question_string = \"\\n \"\n\n idx = 0\n for var in prob_dict_item['vars']:\n question_string += str(var)\n question_string += \"\\n \"\n question_string += prob_dict_item['operator'][idx]\n question_string += \" \"\n idx += 1\n\n return question_string, prob_dict_item['result']\n\n\n","sub_path":"kidsMathQuiz/problem_builder.py","file_name":"problem_builder.py","file_ext":"py","file_size_in_byte":2929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"48833041","text":"# tab:4\n\n# Class: Connection\n\n# TODO: each type in its own class\n\nimport os, socket, time, binascii, copy\n\ndef Connection ( node, address ):\n\tif address['type'] in ['IPv4', 'IPv6']:\n\t\treturn IPConnection ( node, address )\n\telif address['type'] == 'WLsim':\n\t\treturn WLsimConnection ( node, address )\n\telif address['type'] == 'serial':\n\t\treturn SerialConnection ( node, address )\n\ndef compare_address ( a1, a2 ):\n\tif a1['type'] != a2['type']:\n\t\treturn False\n\tif a1['type'] in ['IPv4', 'IPv6']:\n\t\treturn IPConnection.compare ( a1, a2 )\n\telif a1['type'] == 'WLsim':\n\t\treturn WLsimConnection.compare ( a1, a2 )\n\telif a1['type'] == 'serial':\n\t\treturn SerialConnection.compare ( a1, a2 )\n\telse:\n\t\treturn ConnectionClass.compare ( a1, a2 )\n\ndef addr_to_bytes ( addr ):\n\tif addr['type'] in ['IPv4', 'IPv6']:\n\t\treturn IPConnection.addr_to_bytes ( addr )\n\telif addr['type'] == 'WLsim':\n\t\treturn WLsimConnection.addr_to_bytes ( addr )\n\telif addr['type'] == 'serial':\n\t\treturn SerialConnection.addr_to_bytes ( addr )\n\telse:\n\t\treturn ConnectionClass.addr_to_bytes ( addr )\n\nclass ConnectionClass:\n\tdef __init__ ( self, node, address ):\n\t\t# logger\n\t\tself.node = node\n\t\t( self.debug, self.info, self.warning, self.error, self.critical ) = \\\n\t\t( node.debug, node.info, node.warning, node.error, node.critical )\n\t\tself.address = address # record from \"address\" table\n\tdef conn_type (self):\n\t\treturn self.address['type']\n\t@staticmethod\n\tdef compare ( a1, a2 ):\n\t\treturn a1 == a2\n\tdef send ( self, address, packet ):\n\t\t'''Send packet to address'''\n\t\tif self.address['type'] != address['type']:\n\t\t\tself.error ( \"Mismatched address types\" )\n\t\t\treturn False\n\t\treturn False\n\tdef recv ( self ):\n\t\t'''Read packet if any; don't block if no packet'''\n\t\t#return ( packet, src_address, bcast )\n\t\treturn ( None, None, False )\n\t@staticmethod\n\tdef addr_to_bytes ( addr ):\n\t\treturn None\n\nclass IPConnection(ConnectionClass):\n\tMAX_MSG_SIZE = 1500\n\tdef __init__ ( self, node, address ):\n\t\tsuper().__init__( node, address )\n\t\t# IP address example:\n\t\t# { 'id':'1111', 'prio':5, 'type':'IPv6', \"bcast\": false,\n\t\t# 'addr':{'ip':'localhost', 'port':12345} }\n\t\ta = self.__get_my_ip_addr ( self.address, socket.SOCK_DGRAM )\n\t\ttry:\n\t\t\tself.sock = socket.socket ( a['family'], a['type'], a['proto'] )\n\t\t\tself.sock.setblocking ( False )\n\t\t\tself.sock.bind ( a['addr'] )\n\t\t\tself.address[\"addr\"][\"ip\"] = self.sock.getsockname()[0]\n\t\t\tself.address[\"addr\"][\"port\"] = self.sock.getsockname()[1]\n\t\t\tself.up = True\n\t\texcept:\n\t\t\tself.up = False\n\n\tdef __del__ (self):\n\t\ttry:\n\t\t\tif self.sock:\n\t\t\t\tself.sock.close()\n\t\texcept AttributeError:\n\t\t\tpass\n\n\tdef __get_ip_addr (self, address, protocol = socket.SOCK_DGRAM ):\n\t\tx = socket.getaddrinfo( address['addr']['ip'],\n\t\t\t\t\t\t\t\taddress['addr']['port'], 0, protocol)\n\t\ta = {}\n\t\t(a['family'],a['type'],a['proto'],a['canonname'],a['addr'])=x[0]\n\t\treturn a\n\n\tdef __get_my_ip_addr (self, address, protocol = socket.SOCK_DGRAM ):\n\t\t\"\"\" Get IP address for .socket and .bind \"\"\"\n\t\t# if not given completely or at all try to get it automatically\n\t\tport = 0\n\t\tip = None\n\t\tif \"addr\" in address:\n\t\t\tif \"port\" in address['addr']:\n\t\t\t\tport = address['addr']['port']\n\t\t\tif \"ip\" in address['addr']:\n\t\t\t\tip = address['addr']['ip']\n\n\t\tif not ip and address['type']==\"IPv6\":\n\t\t\t# get IPv6; needs IPv6 Internet access (google IPv6 DNS)\n\t\t\ttry:\n\t\t\t\ts = socket.socket(socket.AF_INET6, socket.SOCK_DGRAM)\n\t\t\t\ts.connect(('2001:4860:4860::8888', 0, 0, 0))\n\t\t\t\tip = s.getsockname()[0]\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\tif not ip:\n\t\t\t\tip = \"::1\"\n\t\tif not ip and address['type']==\"IPv4\":\n\t\t\t# try to get \"public\" IPv4 address\n\t\t\ttry:\n\t\t\t\ts = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\t\t\t\ts.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)\n\t\t\t\ts.connect(('', 0))\n\t\t\t\tip = s.getsockname()[0]\n\t\t\texcept:\n\t\t\t\tpass\n\n\t\tif not ip: #use localhost address\n\t\t\tip = socket.gethostbyname(socket.gethostname())\n\t\t\tif not ip:\n\t\t\t\treturn None\n\n\t\tx = socket.getaddrinfo ( ip, port, 0, protocol )\n\t\ta = {}\n\t\t(a['family'], a['type'], a['proto'], a['canonname'], a['addr']) = x[0]\n\t\treturn a\n\n\t@staticmethod\n\tdef compare ( a1, a2 ):\n\t\taddr1 = socket.getaddrinfo ( a1['addr']['ip'], a1['addr']['port'] )\n\t\taddr2 = socket.getaddrinfo ( a2['addr']['ip'], a2['addr']['port'] )\n\t\treturn addr1 == addr2\n\n\t@staticmethod\n\tdef addr_to_bytes ( addr ):\n\t\treturn addr['addr']['ip'].encode('utf-8') + \\\n\t\t\taddr['addr']['port'].to_bytes(2, byteorder='little')\n\n\tdef send ( self, address, packet ):\n\t\tif self.address['type'] != address['type']:\n\t\t\tself.error ( \"Mismatched address types\" )\n\t\t\treturn False\n\t\tdestaddr = self.__get_ip_addr ( address )\n\t\tsent_size = self.sock.sendto( packet, destaddr['addr'] )\n\t\treturn sent_size == len(packet)\n\n\tdef recv ( self ):\n\t\tpacket = None\n\t\tsrc_address = None\n\t\tbcast = False\n\t\ttry:\n\t\t\tpacket, src_addr = self.sock.recvfrom (IPConnection.MAX_MSG_SIZE)\n\t\t\tif packet:\n\t\t\t\tsrc_address = {\n\t\t\t\t\t'type': self.address['type'],\n\t\t\t\t\t'addr': {\n\t\t\t\t\t\t\"ip\":src_addr[0],\n\t\t\t\t\t\t\"port\":src_addr[1]\n\t\t\t\t\t},\n\t\t\t\t\t'bcast': self.address['bcast']\n\t\t\t\t}\n\t\texcept socket.error:\n\t\t\tpass\n\t\treturn ( packet, src_address, self.address, self.address['bcast'] )\n\nclass WLsimConnection(ConnectionClass):\n\twl = {} # simulate wireless packet exchange\n\t# format: {\n\t#\t'name': { # group name\n\t#\t\t'nodes'=['id1', ...], # nodes in group (for debug only)\n\t#\t\t'packets': [\n\t#\t\t\t{\n\t#\t\t\t\t'packet': packet, # bytes\n\t#\t\t\t\t'src_addr': source_address,\n\t#\t\t\t\t'added': timestamp # created by time.time()\n\t#\t\t\t}\n\t#\t\t]\n\t#\t}, ...\n\t# }\n\tT_SEND = 0.2\n\tT_RETRY = 0.1\n\tADDR_LEN = 6\n\tBCAST_ADDR = \"FF:FF:FF:FF:FF:FF\"\n\tdef __init__ ( self, node, address ):\n\t\tsuper().__init__( node, address )\n\t\t# Simulated wireless address example:\n\t\t# {'id':'1111', 'prio':5, 'type':'WLsim', \"bcast\": true,\n\t\t# 'addr':{'range':['wg1','wg2'], 'hwaddr':'cd:ef:01:23:45:67'}}\n\t\tfor grp in self.address['addr']['range']:\n\t\t\tif grp not in WLsimConnection.wl:\n\t\t\t\tWLsimConnection.wl[grp] = { 'packets':[], 'nodes':[] }\n\t\t\tWLsimConnection.wl[grp]['nodes'].append ( self.address[\"id\"] )\n\t\tself.up = True\n\t\tself.recent = [] # recently received messages (don't process them again)\n\n\tdef __del__ (self):\n\t\tfor grp in self.address['addr']['range']:\n\t\t\tWLsimConnection.wl[grp]['nodes'].remove ( self.address[\"id\"] )\n\n\t@staticmethod\n\tdef compare ( a1, a2 ):\n\t\treturn a1['addr']['hwaddr'].lower() == a2['addr']['hwaddr'].lower()\n\t\t#if set(a1['addr']['range']).intersection(set(a2['addr']['range'])):\n\t\t#\treturn True\n\n\t@staticmethod\n\tdef addr_to_bytes ( addr ):\n\t\treturn addr['id']\n\n\tdef remove_expired (self):\n\t\t# remove expired messages from all groups\n\t\tfor grp in WLsimConnection.wl:\n\t\t\tfor packet in WLsimConnection.wl[grp]['packets'][:]:\n\t\t\t\tif packet['added'] - time.time() > WLsimConnection.T_SEND:\n\t\t\t\t\tWLsimConnection.wl[grp].remove(packet)\n\t\t# remove expired messages this node already sent or received\n\t\tfor packet in self.recent[:]:\n\t\t\tif packet['added'] - time.time() > WLsimConnection.T_SEND:\n\t\t\t\tself.recent.remove(packet)\n\n\tdef send ( self, dest_address, packet, src_addr=None ):\n\t\tself.remove_expired()\n\t\tif not src_addr:\n\t\t\tsrc_addr = self.address\n\t\tsrc_addr = copy.deepcopy(src_addr)\n\t\tdest_addr = copy.deepcopy(dest_address)\n\t\tmsg = { 'packet':packet, 'src_addr':src_addr,\n\t\t\t'dest_addr':dest_addr, 'added':time.time() }\n\t\tself.recent.append ( msg )\n\t\tsent = False\n\t\tfor grp in dest_address['addr']['range']:\n\t\t\tmsg[\"src_addr\"][\"addr\"][\"range\"] = [grp]\n\t\t\tmsg[\"dest_addr\"][\"addr\"][\"range\"] = [grp]\n\t\t\tWLsimConnection.wl[grp]['packets'].append ( msg )\n\t\t\tsent = True\n\t\treturn sent\n\n\tdef recv ( self ):\n\t\tself.remove_expired()\n\t\tfor grp in self.address['addr']['range']:\n\t\t\tfor packet in WLsimConnection.wl[grp]['packets']:\n\t\t\t\tif packet in self.recent:\n\t\t\t\t\tcontinue\n\t\t\t\tself.recent.append ( packet )\n\t\t\t\thwaddr = packet['dest_addr']['addr']['hwaddr']\n\t\t\t\tif hwaddr.lower() == self.address['addr']['hwaddr'].lower() \\\n\t\t\t\tor hwaddr.lower() == WLsimConnection.BCAST_ADDR.lower():\n\t\t\t\t\treturn ( packet['packet'], packet['src_addr'], \\\n\t\t\t\t\t\tpacket['dest_addr'], True )\n\t\treturn ( None, None, None, False )\n\nclass SerialConnection(ConnectionClass):\n\tADDR_LEN = 4\n\tSTX = b'\\x02'\n\tETX = b'\\x03'\n\tDLE = b'\\x10'\n\tESCAPES = { STX, ETX, DLE }\n\tMIN_SIZE = 1+4+4+(4+4+4)+4+1\n\tBCAST_ADDR = b'\\xff' * ADDR_LEN\n\tSERIAL_CHUNK_SIZE = 200\n\t# packet format: STXETX\n\n\tdef __init__ ( self, node, address ):\n\t\tsuper().__init__( node, address )\n\t\t# serial (easyRadio and other?): this node address\n\t\t# {'id':'1111', 'prio':5, 'type':'serial', \"bcast\": true,\n\t\t# 'addr':{\n\t\t# 'serial_id':'1122334455667788',\n\t\t# 'port='/dev/ttyUSB0' or 'COM3', 'baudrate'= 19200,\n\t\t#\t 'parity'=serial.PARITY_NONE, 'stopbits'=serial.STOPBITS_ONE,\n\t\t#\t 'bytesize'=serial.EIGHTBITS, 'timeout' = 0\n\t\t# }\n\t\t# }\n\t\t# serial (easyRadio and other?): other node address\n\t\t# {'id':'2222', 'prio':5, 'type':'serial', \"bcast\": true,\n\t\t# 'addr':{\n\t\t# 'serial_id':'aabbccddeeff0011'\n\t\t# }\n\t\t# }\n\t\t# serial API: https://pythonhosted.org/pyserial/pyserial_api.html\n\t\ttry:\n\t\t\timport serial\n\t\t\tself.serial = serial.Serial (\n\t\t\t\tport = address['addr']['port'],\n\t\t\t\tbaudrate = address['addr']['baudrate'],\n\t\t\t\tparity = address['addr']['parity'],\n\t\t\t\tstopbits = address['addr']['stopbits'],\n\t\t\t\tbytesize = address['addr']['bytesize'],\n\t\t\t\ttimeout = address['addr']['timeout']\n\t\t\t)\n\t\t\tself.in_sw_buf = b''\n\t\t\tself.up = True\n\t\t\t#waif for arduino to \"boot\" after reset\n\t\t\tself.debug ( \"Waiting 3 sec for remote to sync\" )\n\t\t\ttime.sleep(3)\n\t\t\tself.debug ( \"Done\" )\n\t\texcept:\n\t\t\tself.up = False\n\n\tdef __del__ (self):\n\t\ttry:\n\t\t\tif self.serial:\n\t\t\t\tself.serial.close()\n\t\texcept:\n\t\t\tpass\n\n\t@staticmethod\n\tdef compare ( a1, a2 ):\n\t\treturn a1['addr']['serial_id'][-SerialConnection.ADDR_LEN:] == \\\n\t\ta2['addr']['serial_id'][-SerialConnection.ADDR_LEN:]\n\n\t@staticmethod\n\tdef addr_to_bytes ( addr ):\n\t\treturn addr['addr']['serial_id'][-SerialConnection.ADDR_LEN:]\n\n\tdef send ( self, dest_address, packet, src_address=None ):\n\t\t# create packet\n\t\tif not src_address:\n\t\t\tsrc_address = self.address\n\t\traw_packet = src_address['addr']['serial_id'][-SerialConnection.ADDR_LEN:] + \\\n\t\t\tdest_address['addr']['serial_id'][-SerialConnection.ADDR_LEN:] + packet\n\t\traw_packet += binascii.crc32(raw_packet).to_bytes(4,byteorder='little')\n\n\t\t_packet = SerialConnection.STX\n\t\tfor b in raw_packet:\n\t\t\tb = bytes([b])\n\t\t\tif b in SerialConnection.ESCAPES:\n\t\t\t\t_packet += SerialConnection.DLE\n\t\t\t_packet += b\n\t\t_packet += SerialConnection.ETX\n\n\t\t# send and flush\n\t\tself.serial.write ( _packet )\n\t\t# from doc.: write() is blocking by default, unless write_timeout is set.\n\t\tself.serial.flush ()\n\t\treturn True\n\n\tdef recv ( self ):\n\t\t'''Read packet if any; don't block if no packet'''\n\t\tpacket = None\n\t\tsrc_address = None\n\t\tbcast = False # message received via broadcast or unicast ?\n\n\t\t# get data into software buffer\n\t\t#while self.serial.inWaiting() > 0: #old version\n\t\twhile self.serial.in_waiting > 0:\n\t\t\tself.in_sw_buf += self.serial.read(SerialConnection.MIN_SIZE)\n\n\t\tif len(self.in_sw_buf) < SerialConnection.MIN_SIZE:\n\t\t\treturn ( packet, src_address, self.address, bcast )\n\n\t\t# parse self.in_sw_buf for packet: STX .... ETX; remove DLEs\n\t\tDLE_on = False\n\t\tstx = -1\n\t\tetx = -1\n\t\t_packet = b''\n\t\tfor i in range(len(self.in_sw_buf)):\n\t\t\tb = bytes ( [ self.in_sw_buf[i] ] )\n\t\t\tif DLE_on:\n\t\t\t\tif stx != -1: # in packet\n\t\t\t\t\t_packet += b\n\t\t\t\telse: #ignore bytes prior to STX\n\t\t\t\t\tself.error(\"ignore bytes prior to STX\")\n\t\t\t\tDLE_on = False\n\t\t\telif b == SerialConnection.DLE:\n\t\t\t\tDLE_on = True\n\t\t\telif b == SerialConnection.STX:\n\t\t\t\tif stx != -1: # already have start byte?\n\t\t\t\t\t# error in _packet: restart\n\t\t\t\t\tself.error(\"error in _packet: restart\")\n\t\t\t\t\t_packet = b''\n\t\t\t\tstx = i\n\t\t\telif b == SerialConnection.ETX:\n\t\t\t\tif stx == -1: # have start byte?\n\t\t\t\t\t# ignore\n\t\t\t\t\tself.error(\"don't have start byte\")\n\t\t\t\t\tcontinue\n\t\t\t\tetx = i\n\t\t\t\tbreak\n\t\t\telse: # regular byte (not STX, ETX nor DLE)\n\t\t\t\t_packet += b\n\n\t\tif stx == -1: # no start byte: drop all\n\t\t\tself.in_sw_buf = b''\n\t\t\treturn ( packet, src_address, self.address, bcast )\n\n\t\tif etx == -1: # no end byte yet\n\t\t\tself.in_sw_buf = self.in_sw_buf[stx:]\n\t\t\treturn ( packet, src_address, self.address, bcast )\n\n\t\tif len(_packet) < SerialConnection.MIN_SIZE: # malformed packet\n\t\t\tself.in_sw_buf = self.in_sw_buf[etx:]\n\t\t\treturn ( packet, src_address, self.address, bcast )\n\n\t\tself.in_sw_buf = self.in_sw_buf[etx+1:]\n\n\t\t# test crc\n\t\tcrc = binascii.crc32(_packet[:-4]).to_bytes(4, byteorder='little')\n\t\tif _packet[-4:] != crc:\n\t\t\tself.error ( \"CRC failed for: \" + \\\n\t\t\t\tbinascii.hexlify ( _packet ).decode('utf-8') )\n\t\t\treturn ( packet, src_address, self.address, bcast )\n\n\t\t# parse packet\n\t\tsrc_id = _packet[0:SerialConnection.ADDR_LEN]\n\t\tdest_id = _packet[SerialConnection.ADDR_LEN:SerialConnection.ADDR_LEN*2]\n\t\tiot_packet = _packet[SerialConnection.ADDR_LEN*2:-4]\n\n\t\t# process only packets for this node and broadcasted ones\n\t\tif dest_id != self.address['addr']['serial_id'] and \\\n\t\tdest_id != SerialConnection.BCAST_ADDR:\n\t\t\tself.error ( \"Packet not for this node \" + \\\n\t\t\t\tbinascii.hexlify ( _packet ).decode('utf-8'))\n\t\t\treturn ( packet, src_address, self.address, bcast )\n\n\t\tpacket = iot_packet\n\t\tbcast = self.address['bcast'] or dest_id == SerialConnection.BCAST_ADDR\n\t\tsrc_address = {\n\t\t\t'type': self.address['type'],\n\t\t\t'bcast': self.address['bcast'],\n\t\t\t'addr': { 'serial_id':src_id }\n\t\t}\n\t\tdest_address = {\n\t\t\t'type': self.address['type'],\n\t\t\t'bcast': self.address['bcast'],\n\t\t\t'addr': { 'serial_id':dest_id }\n\t\t}\n\n\t\treturn ( packet, src_address, dest_address, bcast )\n","sub_path":"src/Connection.py","file_name":"Connection.py","file_ext":"py","file_size_in_byte":13565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"510200908","text":"import psycopg2\nimport argparse\n\nparser = argparse.ArgumentParser(description='Create a mate record for table transgenic_mouse_breeding.')\nparser.add_argument('--mateid', required=True, help='the original mateid you want to keep mating')\nparser.add_argument('--server', default='localhost', help='Server host')\nparser.add_argument('--database', default='levy_lab_data', help='database name')\nparser.add_argument('--user', default='postgres', help='username')\nparser.add_argument('--password', default = None, help='password')\n\nargs = parser.parse_args()\n\ndef matestyle(num):\n style = 'M' + '%04d' % (num)\n return(style)\n\ndef main(mateid = args.mateid, server = args.server, \n database = args.database, user = args.user, password = args.password):\n conncommend = \"host={} dbname={} user={}\".format(server, database, user)\n # print(conncommend)\n conn = psycopg2.connect(conncommend)\n\n cur = conn.cursor()\n cur.execute(\n \"\"\"\n SELECT mateid FROM {}\n \"\"\".format('transgenic_mouse_breeding')\n )\n mateids = cur.fetchall()\n conn.commit()\n\n mateids = [x[0] for x in mateids]\n mateids.sort()\n\n newmate = matestyle(int(mateids[-1][1:])+1)\n print(newmate)\n\n # get the record\n cur = conn.cursor()\n cur.execute(\n \"\"\"\n SELECT * FROM transgenic_mouse_breeding where mateid = '{}';\n \"\"\".format(mateid))\n record = cur.fetchall()[0]\n conn.commit()\n print(record)\n \n # set old record inprocess to false\n cur = conn.cursor()\n cur.execute(\n \"\"\"\n UPDATE transgenic_mouse_breeding SET inprocess = false WHERE mateid = '{}';\n \"\"\".format(mateid))\n conn.commit()\n\n\n # insert a new record using same information as old record\n cur = conn.cursor()\n cur.execute(\n \"\"\"\n INSERT INTO transgenic_mouse_breeding (mateid, cageid, father_id, mother_id, pair_date, inprocess) \n VALUES ('{}', '{}', '{}', '{}', '{}', '{}');\n \"\"\".format(newmate, record[1], record[2], record[3], record[4], 'true')\n )\n conn.commit()\n \n print('input done')\n\n cur = conn.cursor()\n cur.execute(\n \"\"\"\n SELECT * FROM transgenic_mouse_breeding\n WHERE mateid = '{}'\n \n \"\"\".format(newmate)\n )\n tmp = cur.fetchall()\n conn.commit()\n print(tmp)\n\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"transgenic/keep_mating.py","file_name":"keep_mating.py","file_ext":"py","file_size_in_byte":2333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"245756662","text":"from __future__ import print_function\nimport shutil\nimport h5py\nimport fnmatch\nimport os\nimport argparse\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\nimport torch.optim as optim\nfrom torch.nn import functional as F\nfrom torch.distributions import one_hot_categorical\nfrom torch.utils.data import DataLoader, WeightedRandomSampler\nimport torchvision.transforms as transforms\nimport torchvision.utils as vutils\nimport numpy as np\nfrom torch.autograd import Variable\nfrom datetime import datetime\nimport itertools\nimport shutil\nfrom scipy.stats import t as t_dist\nfrom nets_16col_layernorm import _netG, _netE, _netD, weights_init, GANLoss\n#from nets_16col_v3_nonorm import _netG, _netE, _netD, weights_init, GANLoss\nimport pickle\nfrom song_training_dataloader import *\nfrom utils import *\nimport pdb\nimport joblib\n\n \nopts_dict = {'input_path': '/media/songbird/datapartition/mdgan_training_input_with_age_HDF/',\n 'outf': '/media/songbird/datapartition/mdgan_output/',\n 'distance_fun': 'L1', \n 'subset_age_weights' : [0., 1.],\n 'workers': 6,\n 'batchSize': 128,\n 'imageH': 129,\n 'imageW': 16,\n 'nz': 16,\n 'nc': 1,\n 'ngf': 256,\n 'ndf': 128,\n 'niter': 50,\n 'lr': 1e-5,\n 'lambdaa': 150,\n 'zreg_weight': 1,\n 'schedule_lr':False,\n 'd_noise': 0.1,\n 'beta1': 0.5,\n 'cuda': 1,\n 'ngpu': 1,\n 'netG': '',\n 'netE': '',\n 'netD1': '',\n 'netD2': '',\n 'netD3': '',\n 'log_every': 300,\n 'sample_rate': 16000.,\n 'noise_dist': 'normal',\n 'z_var': 1.,\n 'nfft': 256,\n 'manualSeed': []}\n\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--training_path', required=True, help='path to training dataset')\nparser.add_argument('--test_path', required=True, help='path to test dataset')\nparser.add_argument('--outf', required=True, help='folder to output images and model checkpoints')\nparser.add_argument('--external_file_path', required=True, help='path to folder containing bird hdf files')\nparser.add_argument('--age_weights_path', help='path to file containin age_weight for resampling')\nparser.add_argument('--subset_age_weights', nargs = '+', help = 'number between 0 and 1 which selects an age range (1 is younger, 0 is older)')\nparser.add_argument('--batchSize', type=int, default=128, help='input batch size')\nparser.add_argument('--imageH',type=int, default=129, help='the height of the input image to network')\nparser.add_argument('--imageW',type=int, default=8, help='the width of the input image to network')\nparser.add_argument('--nz', type=int, default=32, help='size of the latent z vector')\nparser.add_argument('--noisevar', type=float, default=1., help='variance of P(z)')\nparser.add_argument('--noise_dist', type=str, default = 'normal', help='noise distribution: {normal, uniform, t}')\nparser.add_argument('--lambdaa', type = float, default = 100., help = 'weighting for recon loss')\nparser.add_argument('--ngf', type=int, default=256)\nparser.add_argument('--ndf', type=int, default=256)\nparser.add_argument('--niter', type=int, default=50, help='number of epochs to train for')\nparser.add_argument('--lr', type=float, default = 0.00001, help='learning rate')\nparser.add_argument('--z_reg', action=\"store_true\", help='whether to regularize the posterior')\nparser.add_argument('--zreg_weight', type = float, default = 1, help = 'weight for z regularization')\nparser.add_argument('--manualSeed', type=int, default=-1, help='random number generator seed')\nparser.add_argument('--schedule_lr', action = 'store_true', help='change learning rate')\nparser.add_argument('--log_every', type=int, default=300, help='make images and print loss every X batches')\n\ndef make_output_folder(path):\n \n if not os.path.exists(path):\n os.makedirs(path)\n dirs = os.listdir(path)\n for d in dirs:\n if len(os.listdir(os.path.join(path, d)))<=3:\n try:\n os.rmdir(os.path.join(path, d))\n except:\n shutil.rmtree(os.path.join(path,d))\n path += str(datetime.now()).replace(':', '-')\n if not os.path.exists(path):\n os.makedirs(path)\n os.makedirs(os.path.join(path,'losses'))\n os.makedirs(os.path.join(path, 'hist'))\n return path\n \ntorch.backends.cudnn.deterministic = True\n\n\ndef main():\n args = parser.parse_args()\n outf = make_output_folder(args.outf)\n \n if args.manualSeed==-1:\n opts_dict['manualSeed'] = random.randint(1, 10000) # fix seed\n else:\n opts_dict['manualSeed'] = args.manualSeed\n \n V = vars(args)\n for k in V.keys():\n opts_dict[k] = V[k]\n opts_dict['outf'] = outf\n opts_dict['subset_age_weights'] = [float(w) for w in args.subset_age_weights]\n \n print(opts_dict)\n joblib.dump(opts_dict,os.path.join(opts_dict['outf'],'opts_dict.pkl'))\n \n print(\"Random Seed: \", opts_dict['manualSeed'])\n random.seed(opts_dict['manualSeed'])\n torch.manual_seed(opts_dict['manualSeed'])\n\n cudnn.benchmark = True\n\n if torch.cuda.is_available() and not opts_dict['cuda']:\n print(\"WARNING: You have a CUDA device, so you should probably run with --cuda\")\n \n # load age_weights\n #with open(args.age_weights_path, 'rb') as f:\n # age_weights = pickle.load(f)\n \n # initialize the dataset and dataloader objects\n #train_sampler = WeightedRandomSampler(age_weights, num_samples = len(age_weights), replacement=True)\n\n # initialize the dataset and dataloader objects\n train_dataset = songbird_dataset(args.training_path, opts_dict['imageW'], \n args.external_file_path, opts_dict['subset_age_weights'])\n \n train_dataloader = DataLoader(train_dataset, batch_size=opts_dict['batchSize'], #sampler = train_sampler,\n shuffle=True, num_workers=int(opts_dict['workers']))\n \n test_dataset = songbird_dataset(args.test_path, opts_dict['imageW'], args.external_file_path,\n opts_dict['subset_age_weights'])\n \n test_dataloader = DataLoader(test_dataset, batch_size= opts_dict['batchSize'],\n shuffle=True, num_workers=int(opts_dict['workers']))\n \n sample_dataset = songbird_random_sample(args.training_path, args.external_file_path)\n \n # useful renaming\n ngpu = opts_dict['ngpu']\n nz = opts_dict['nz']\n ngf = opts_dict['ngf']\n ndf = opts_dict['ndf']\n nc = opts_dict['nc']\n logpt = opts_dict['log_every']\n \n # custom weights initialization called on netG and netD\n netG = _netG(ngpu,nz,ngf,nc)\n netG.apply(weights_init)\n if opts_dict['netG'] != '':\n netG.load_state_dict(torch.load(opts_dict['netG']))\n print(netG)\n\n netD1 = _netD(ngpu,ndf,nc)\n netD1.apply(weights_init)\n if opts_dict['netD1'] != '':\n netD1.load_state_dict(torch.load(opts_dict['netD1']))\n print(netD1)\n\n netD2 = _netD(ngpu,ndf,nc)\n netD2.apply(weights_init)\n if opts_dict['netD2'] != '':\n netD2.load_state_dict(torch.load(opts_dict['netD2']))\n print(netD2)\n\n netD3 = _netD(ngpu,ndf,nc)\n netD3.apply(weights_init)\n if opts_dict['netD3'] != '':\n netD3.load_state_dict(torch.load(opts_dict['netD3']))\n print(netD3)\n \n netE = _netE(ngpu,nz,ngf,nc)\n print(netE)\n netE.apply(weights_init)\n if opts_dict['netE'] != '':\n netE.load_state_dict(torch.load(opts_dict['netE']))\n\n if opts_dict['cuda']:\n criterion_gan = GANLoss(tensor=torch.cuda.FloatTensor,use_lsgan=False)\n else:\n criterion_gan = GANLoss()\n\n if opts_dict['distance_fun']=='L1':\n print('Using L1 loss')\n criterion_dist = nn.L1Loss()\n else:\n print('Using L2 loss')\n criterion_dist = nn.MSELoss()\n\n # downsample function for reconstruction error \n # what if no downsample?\n #downsample_pth = torch.nn.\n downsample_pth = torch.nn.AvgPool2d(3, stride=4)\n\n input = torch.FloatTensor(opts_dict['batchSize'], nc, opts_dict['imageH'], opts_dict['imageW'])\n if opts_dict['cuda']:\n netD1.cuda()\n netD2.cuda()\n netD3.cuda()\n netG.cuda()\n criterion_dist.cuda()\n netE.cuda()\n\n noise = Variable(torch.cuda.FloatTensor(opts_dict['batchSize'],nz,1,1))\n \n def noise_t():\n ''' t distributed noise '''\n random_sample = t_dist.rvs(10,size=(opts_dict['batchSize'],nz,1,1))\n out = Variable(torch.from_numpy(random_sample.astype(np.float32)))\n if opts_dict['cuda']:\n return out.cuda()\n else:\n return out\n\n # setup optimizer\n optimizerD1 = optim.Adam(netD1.parameters(), lr = opts_dict['lr'], betas = (opts_dict['beta1'], 0.999))\n optimizerD2 = optim.Adam(netD2.parameters(), lr = opts_dict['lr'], betas = (opts_dict['beta1'], 0.999))\n optimizerD3 = optim.Adam(netD3.parameters(), lr = opts_dict['lr'], betas = (opts_dict['beta1'], 0.999))\n optimizerG = optim.Adam(itertools.chain(netG.parameters(),\n netE.parameters()), lr = opts_dict['lr'], betas = (opts_dict['beta1'], 0.999))\n #optimizerE = optim.Adam(netE.parameters(), lr = opts_dict['lr'], betas = (opts_dict['beta1'], 0.999))\n \n # learning rate scheduler\n if args.schedule_lr:\n lambda1 = lambda epoch: epoch+1 // 5\n schedulerG = torch.optim.lr_scheduler.LambdaLR(optimizerG, lr_lambda = lambda1)\n schedulerD1 = torch.optim.lr_scheduler.LambdaLR(optimizerD1, lr_lambda = lambda1)\n schedulerD2 = torch.optim.lr_scheduler.LambdaLR(optimizerD2, lr_lambda = lambda1)\n \n #losses\n minibatchLossD1 = []\n minibatchLossG1_rec = []\n minibatchLossG1_gan = []\n minibatchLossD2 = []\n minibatchLossG2 = []\n minibatchLossD3 = []\n minibatchLossG_d3 = []\n \n # label noise for discriminator\n d_prob = 1.0-opts_dict['d_noise']\n def true_wp(prob):\n if np.random.random() < prob:\n return True\n else:\n return False\n \n per_epoch_avg_loss_recon = np.zeros(opts_dict['niter'])\n per_epoch_avg_loss_gan = np.zeros(opts_dict['niter'])\n per_epoch_std_loss_recon = np.zeros(opts_dict['niter'])\n per_epoch_std_loss_gan = np.zeros(opts_dict['niter'])\n \n for epoch in range(opts_dict['niter']):\n for i, (data, age) in enumerate(train_dataloader):\n \n data = data.view(data.size(0),nc,data.size(1),data.size(2))\n if opts_dict['cuda']:\n data = data.cuda()\n \n # map data X -> Z latent\n encoding = netE(data)\n netG.mode(reconstruction=True)\n \n # map Z -> Xhat\n reconstruction = netG(encoding)\n netD1.zero_grad()\n # map Xhat -> class [0: Fake, 1: Real] (Make discriminator give it value 1)\n pred_rec_d1 = netD1(reconstruction.detach())\n # map X -> class (maximize D)\n pred_real_d1 = netD1(data)\n # For discriminator, the Pr(class=1|X) = 0.9, true_wp = label with that probability\n err_real_d1 = criterion_gan(pred_real_d1, true_wp(d_prob))\n # For disc, the Pr(class=1| Xhat) = 0.1 = d_noise\n err_fake_d1 = criterion_gan(pred_rec_d1, true_wp(1-d_prob))\n err_d1 = err_real_d1 + err_fake_d1\n err_d1.backward()\n # minimize -logD(X) and maximize -log(D(Xhat)) only w.r.t Discriminator params!\n optimizerD1.step()\n\n netG.zero_grad()\n netE.zero_grad()\n netD1.zero_grad()\n # map Xhat -> class\n pred_rec_d1 = netD1(reconstruction)\n errG_discrim = criterion_gan(pred_rec_d1, True)\n errG_recon = (criterion_dist(reconstruction, data) + \\\n criterion_dist(downsample_pth(reconstruction),downsample_pth(data))) * opts_dict['lambdaa']\n #errG_recon = criterion_dist(downsample_pth(reconstruction),downsample_pth(input)) * opts_dict['lambdaa']\n err_g_d1 = errG_discrim + errG_recon\n err_g_d1.backward()\n # maximize log D(Xhat) or minimize -log D(Xhat) + MSE for encoder and generator\n optimizerG.step()\n\n # ------------- Diffusion step ---------------\n netE.zero_grad()\n netG.zero_grad()\n encoding = netE(data)\n \n netG.mode(reconstruction=True)\n reconstruction = netG(encoding)\n netD2.zero_grad()\n pred_rec_d2 = netD2(reconstruction.detach())\n err_real_d2 = criterion_gan(pred_rec_d2, true_wp(d_prob))\n \n if args.noise_dist == 't':\n noise = noise_t()\n elif args.noise_dist == 'normal':\n noise.data.normal_(0,opts_dict['z_var'])\n else:\n noise.data.uniform_(-opts_dict['z_var'],opts_dict['z_var'])\n \n netG.mode(reconstruction=False)\n netG.zero_grad()\n fake = netG(noise)\n \n pred_fake_d2 = netD2(fake.detach())\n err_fake_d2 = criterion_gan(pred_fake_d2, true_wp(1-d_prob))\n err_d2 = err_real_d2 + err_fake_d2\n err_d2.backward()\n optimizerD2.step()\n \n #------ extra regularization for z------#\n if args.z_reg:\n err_E = opts_dict['zreg_weight'] * criterion_dist(netE(fake), noise.squeeze())\n err_E.backward(retain_graph=True)\n optimizerG.step()\n \n netE.zero_grad()\n netG.zero_grad()\n netD2.zero_grad()\n pred_fake_d2 = netD2(fake)\n err_g_d2 = criterion_gan(pred_fake_d2, True)\n err_g_d2.backward()\n optimizerG.step()\n\n netD3.zero_grad()\n netG.mode(reconstruction=False)\n fake = netG(noise)\n pred_fake_d3 = netD3(fake.detach())\n pred_real_d3 = netD3(data)\n err_fake_d3 = criterion_gan(pred_fake_d3,False)\n err_real_d3 = criterion_gan(pred_real_d3,True)\n err_d3 = err_fake_d3+err_real_d3\n err_d3.backward()\n optimizerD3.step()\n\n err_g_d3 = criterion_gan(pred_fake_d3,True)\n\n # SAVE LOSSES\n minibatchLossD1.append(err_d1.item())\n minibatchLossD2.append(err_d2.item())\n minibatchLossD3.append(err_d3.item())\n minibatchLossG1_gan.append(errG_discrim.item())\n minibatchLossG1_rec.append(errG_recon.item())\n minibatchLossG_d3.append(err_g_d3.item())\n minibatchLossG2.append(err_g_d2.item())\n\n\n ### SHOW LOSS AFTER SOME BATCHES ####\n if (i % logpt == 0) & (i > 0):\n print('[%d/%d][%d/%d] D1: %.2f (%.2f) D2: %.2f (%.2f) G1_gan: %.2f (%.2f) G1_rec: %.2f (%.2f) G2: %.2f (%.2f) D3: %.2f (%.2f) G_D3: %.2f (%.2f)'\n % (epoch, opts_dict['niter'], i, len(train_dataloader),\n np.mean(minibatchLossD1[-logpt:]), np.std(minibatchLossD1[-logpt:]),\n np.mean(minibatchLossD2[-logpt:]), np.std(minibatchLossD2[-logpt:]),\n np.mean(minibatchLossG1_gan[-logpt:]), np.std(minibatchLossG1_gan[-logpt:]),\n np.mean(minibatchLossG1_rec[-logpt:]), np.std(minibatchLossG1_rec[-logpt:]),\n np.mean(minibatchLossG2[-logpt:]), np.std(minibatchLossG2[-logpt:]),\n np.mean(minibatchLossD3[-logpt:]), np.std(minibatchLossD3[-logpt:]),\n np.mean(minibatchLossG_d3[-logpt:]), np.std(minibatchLossG_d3[-logpt:])\n )\n )\n \n # sample and reconstruct\n # put netG and netE in eval mode\n #netG.eval()\n #netE.eval()\n #with torch.no_grad():\n fixed_noise = torch.cuda.FloatTensor(opts_dict['batchSize'],nz,1,1)\n if args.noise_dist == 't':\n fixed_noise = noise_t()\n elif args.noise_dist == 'normal':\n fixed_noise.data.normal_(0,opts_dict['z_var'])\n else:\n fixed_noise.data.uniform_(-opts_dict['z_var'],opts_dict['z_var'])\n\n netG.mode(reconstruction=False)\n fake = netG(fixed_noise)\n out_shape = [opts_dict['imageH'], opts_dict['imageW']]\n fake_spectrograms =[fake.data[k,:,:,:].cpu().numpy().reshape(out_shape) for k in range(8)]\n fake_spectrograms = np.concatenate(fake_spectrograms,axis=1)\n gagan_save_spect('%s/fake_samples_epoch_%03d_batchnumb_%d.png' \n % (opts_dict['outf'], epoch, i),rescale_spectrogram(fake_spectrograms))\n gagan_save_spect('%s/fake_samples_epoch_%03d_batchnumb_%d.eps' \n % (opts_dict['outf'], epoch, i),rescale_spectrogram(fake_spectrograms))\n\n\n # randomly sample a file and save audio sample\n netG.mode(reconstruction=True)\n sample = sample_dataset.get(nsamps=1)[0] # first element of list output \n sample = sample[0]\n try:\n save_audio_sample(lc.istft(inverse_transform(transform(sample))), \\\n '%s/input_audio_epoch_%03d_batchnumb_%d.wav' % \n (opts_dict['outf'], epoch, i), opts_dict['sample_rate'])\n except:\n print('..audio buffer error, skipped audio file generation')\n\n # save original spectrogram\n gagan_save_spect('%s/input_spect_epoch_%03d_batchnumb_%d.eps'\n % (opts_dict['outf'], epoch, i), \n rescale_spectrogram(transform(sample)))\n # save reconstruction\n _, spect,_ = encode_and_decode(sample, netE, netG, opts_dict['batchSize'], 1, \n opts_dict['imageH'], opts_dict['imageW'], opts_dict['cuda'], \n transform_sample=True, return_tensor=False)\n \n try:\n audio = lc.istft(inverse_transform(spect))\n save_audio_sample(audio,'%s/rec_audio_epoch_%03d_batchnumb_%d.wav' % \n (opts_dict['outf'], epoch, i),opts_dict['sample_rate'])\n except:\n print('..audio buffer error, skipped audio file generation')\n\n # save reconstructed spectrogram\n spect = rescale_spectrogram(spect)\n gagan_save_spect('%s/rec_spect_epoch_%03d_batchnumb_%d.eps' % (opts_dict['outf'], epoch, i), spect)\n \n # document losses \n losspath = os.path.join(opts_dict['outf'], 'losses/')\n np.save(losspath+'D1',np.array(minibatchLossD1))\n np.save(losspath+'D2',np.array(minibatchLossD2))\n np.save(losspath+'D3',np.array(minibatchLossD3))\n np.save(losspath+'G1rec',np.array(minibatchLossG1_rec))\n np.save(losspath+'G1gan',np.array(minibatchLossG1_gan))\n np.save(losspath+'G2',np.array(minibatchLossG2))\n np.save(losspath+'G3',np.array(minibatchLossG_d3))\n \n #netG.train()\n #netE.train()\n # if schedule for learning rate, update lr\n if args.schedule_lr:\n schedulerG.step()\n schedulerD1.step()\n schedulerD2.step()\n \n \n # do checkpointing\n torch.save(netG.state_dict(), '%s/netG_epoch_%d.pth' % (opts_dict['outf'], epoch))\n torch.save(netD1.state_dict(), '%s/netD1_epoch_%d.pth' % (opts_dict['outf'], epoch))\n torch.save(netD2.state_dict(), '%s/netD2_epoch_%d.pth' % (opts_dict['outf'], epoch))\n torch.save(netD3.state_dict(), '%s/netD3_epoch_%d.pth' % (opts_dict['outf'], epoch))\n torch.save(netE.state_dict(), '%s/netE_epoch_%d.pth' % (opts_dict['outf'], epoch))\n \n # evaluate test error \n print('\\n .... evaluating test loss .... ')\n #netG.eval()\n #netE.eval()\n test_loss_recon = []\n test_loss_gan = []\n with torch.no_grad():\n for k,(data,age) in enumerate(test_dataloader):\n data = data.view(data.size(0),nc,data.size(1),data.size(2))\n if opts_dict['cuda']:\n data = data.cuda()\n # map X -> Z\n encoding = netE(data)\n netG.mode(reconstruction=True)\n # map Z -> Xhat\n reconstruction = netG(encoding)\n # reconstruction error\n errG_recon = (criterion_dist(reconstruction, data) + \\\n criterion_dist(downsample_pth(reconstruction),downsample_pth(data))) * opts_dict['lambdaa']\n #errG_recon = criterion_dist(downsample_pth(reconstruction),downsample_pth(input)) * opts_dict['lambdaa']\n # generate fake image\n noise.data.normal_(0,opts_dict['z_var'])\n netG.mode(reconstruction=False)\n fake = netG(noise)\n # classify it with D3\n pred_fake_d2 = netD2(fake)\n err_g_d2 = criterion_gan(pred_fake_d2, True)\n\n test_loss_recon.append(errG_recon.item())\n test_loss_gan.append(err_g_d2.item())\n #netG.train()\n #netE.train()\n per_epoch_avg_loss_recon[epoch] = np.mean(np.array(test_loss_recon))\n per_epoch_std_loss_recon[epoch] = np.std(np.array(test_loss_recon))\n per_epoch_avg_loss_gan[epoch] = np.mean(np.array(test_loss_gan))\n per_epoch_std_loss_gan[epoch] = np.std(np.array(test_loss_gan))\n print('[%d/%d] test loss recon: %.2f +/- %.2f , test loss gan: %.2f +/- %.2f'%(epoch, opts_dict['niter'], \\\n per_epoch_avg_loss_recon[epoch], \\\n per_epoch_std_loss_recon[epoch], \\\n per_epoch_avg_loss_gan[epoch], \\\n per_epoch_std_loss_gan[epoch]))\n \n joblib.dump( {'avg_recon': per_epoch_avg_loss_recon, 'std_recon': per_epoch_std_loss_recon, \n 'avg_gan': per_epoch_avg_loss_gan, 'std_gan': per_epoch_std_loss_gan}, losspath+'testloss.pkl')\n \nif __name__ == '__main__':\n main()","sub_path":"train/train_without_age.py","file_name":"train_without_age.py","file_ext":"py","file_size_in_byte":23087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"394355527","text":"from functools import wraps\nfrom iso8601 import parse_date\nfrom munch import munchify\nfrom restkit import BasicAuth, errors, request, Resource\nfrom retrying import retry\nfrom simplejson import dumps, loads\nfrom urlparse import parse_qs, urlparse\nimport logging\n\nlogger = logging.getLogger(__name__)\n\nIGNORE_PARAMS = ('uri', 'path')\n\n\ndef verify_file(fn):\n @wraps(fn)\n def wrapper(self, file_, *args, **kwargs):\n if isinstance(file_, str):\n file_ = open(file_, 'rb')\n if hasattr(file_, 'read'):\n # A file-like object must have 'read' method\n return fn(self, file_, *args, **kwargs)\n else:\n raise TypeError('Expected either a string '\n 'containing a path to file or a '\n 'file-like object, got {}'.format(type(file_)))\n return wrapper\n\n\nclass InvalidResponse(Exception):\n pass\n\n\nclass NoToken(Exception):\n pass\n\n\nclass Client(Resource):\n \"\"\"docstring for API\"\"\"\n def __init__(self, key,\n host_url=\"https://api-sandbox.openprocurement.org\",\n api_version='0.8',\n resource='tenders',\n params=None,\n **kwargs):\n super(Client, self).__init__(\n host_url,\n filters=[BasicAuth(key, \"\")],\n **kwargs\n )\n self.prefix_path = '/api/{}/{}'.format(api_version, resource)\n if not isinstance(params, dict):\n params = {\"mode\": \"_all_\"}\n self.params = params\n self.headers = {\"Content-Type\": \"application/json\"}\n # To perform some operations (e.g. create a tender)\n # we first need to obtain a cookie. For that reason,\n # here we send a HEAD request to a neutral URL.\n self.head('/api/{}/spore'.format(api_version))\n\n def request(self, method, path=None, payload=None, headers=None,\n params_dict=None, **params):\n _headers = dict(self.headers)\n _headers.update(headers or {})\n try:\n response = super(Client, self).request(\n method, path=path, payload=payload, headers=_headers,\n params_dict=params_dict, **params\n )\n if 'Set-Cookie' in response.headers:\n self.headers['Cookie'] = response.headers['Set-Cookie']\n return response\n except errors.ResourceNotFound as e:\n if 'Set-Cookie' in e.response.headers:\n self.headers['Cookie'] = e.response.headers['Set-Cookie']\n raise e\n\n def patch(self, path=None, payload=None, headers=None,\n params_dict=None, **params):\n \"\"\" HTTP PATCH\n\n - payload: string passed to the body of the request\n - path: string additionnal path to the uri\n - headers: dict, optionnal headers that will\n be added to HTTP request.\n - params: Optionnal parameterss added to the request\n \"\"\"\n\n return self.request(\"PATCH\", path=path, payload=payload,\n headers=headers, params_dict=params_dict, **params)\n\n def delete(self, path=None, headers=None):\n \"\"\" HTTP DELETE\n - path: string additionnal path to the uri\n - headers: dict, optionnal headers that will\n be added to HTTP request.\n - params: Optionnal parameterss added to the request\n \"\"\"\n return self.request(\"DELETE\", path=path, headers=headers)\n\n def _update_params(self, params):\n for key in params:\n if key not in IGNORE_PARAMS:\n self.params[key] = params[key]\n\n ###########################################################################\n # GET ITEMS LIST API METHODS\n ###########################################################################\n\n @retry(stop_max_attempt_number=5)\n def get_tenders(self, params={}, feed='changes'):\n params['feed'] = feed\n try:\n self._update_params(params)\n response = self.get(\n self.prefix_path,\n params_dict=self.params)\n if response.status_int == 200:\n tender_list = munchify(loads(response.body_string()))\n self._update_params(tender_list.next_page)\n return tender_list.data\n\n except errors.ResourceNotFound:\n del self.params['offset']\n raise\n\n raise InvalidResponse\n\n def get_latest_tenders(self, date, tender_id):\n iso_dt = parse_date(date)\n dt = iso_dt.strftime(\"%Y-%m-%d\")\n tm = iso_dt.strftime(\"%H:%M:%S\")\n response = self._get_resource_item(\n '{}?offset={}T{}&opt_fields=tender_id&mode=test'.format(\n self.prefix_path,\n dt,\n tm\n )\n )\n if response.status_int == 200:\n tender_list = munchify(loads(response.body_string()))\n self._update_params(tender_list.next_page)\n return tender_list.data\n raise InvalidResponse\n\n def _get_tender_resource_list(self, tender_id, items_name, access_token=None):\n if not access_token:\n access_token = \"\"\n return self._get_resource_item(\n '{}/{}/{}'.format(self.prefix_path, tender_id, items_name),\n headers={'X-Access-Token':access_token}\n )\n\n def get_questions(self, tender_id, params={}, access_token=None):\n return self._get_tender_resource_list(tender_id, \"questions\", access_token)\n\n def get_documents(self, tender_id, params={}, access_token=None):\n return self._get_tender_resource_list(tender_id, \"documents\", access_token)\n\n def get_awards(self, tender_id, params={}, access_token=None):\n return self._get_tender_resource_list(tender_id, \"awards\", access_token)\n\n def get_lots(self, tender_id, params={}, access_token=None):\n return self._get_tender_resource_list(tender_id, \"lots\", access_token)\n\n ###########################################################################\n # CREATE ITEM API METHODS\n ###########################################################################\n def _create_resource_item(self, url, payload, headers={}):\n headers.update(self.headers)\n response_item = self.post(\n url, headers=headers, payload=dumps(payload)\n )\n if response_item.status_int == 201:\n return munchify(loads(response_item.body_string()))\n raise InvalidResponse\n\n def _create_tender_resource_item(self, tender_id, item_obj, items_name, access_token=None):\n if not access_token:\n access_token = \"\"\n return self._create_resource_item(\n '{}/{}/{}'.format(self.prefix_path, tender_id, items_name),\n item_obj,\n headers={'X-Access-Token':access_token}\n )\n\n def create_tender(self, tender_id):\n return self._create_resource_item(self.prefix_path, tender)\n\n def create_question(self, tender_id, question, access_token=None):\n return self._create_tender_resource_item(tender_id, question, \"questions\", access_token)\n\n def create_bid(self, tender_id, bid, access_token=None):\n return self._create_tender_resource_item(tender_id, bid, \"bids\", access_token)\n\n def create_lot(self, tender_id, lot, access_token=None):\n return self._create_tender_resource_item(tender_id, lot, \"lots\", access_token)\n\n def create_award(self, tender_id, award, access_token=None):\n return self._create_tender_resource_item(tender_id, award, \"awards\", access_token)\n\n def create_cancellation(self, tender_id, cancellation, access_token=None):\n return self._create_tender_resource_item(tender_id, cancellation, \"cancellations\", access_token)\n\n ###########################################################################\n # GET ITEM API METHODS\n ###########################################################################\n\n def _get_resource_item(self, url, headers={}):\n headers.update(self.headers)\n response_item = self.get(url, headers=headers)\n if response_item.status_int == 200:\n return munchify(loads(response_item.body_string()))\n raise InvalidResponse\n\n def get_tender(self, id):\n return self._get_resource_item('{}/{}'.format(self.prefix_path, id))\n\n def _get_tender_resource_item(self, tender_id, item_id, items_name,\n access_token=None):\n if not access_token:\n access_token = \"\"\n return self._get_resource_item(\n '{}/{}/{}/{}'.format(self.prefix_path,\n tender_id,\n items_name,\n item_id),\n headers={'X-Access-Token': access_token}\n )\n\n def get_question(self, tender_id, question_id, access_token=None):\n return self._get_tender_resource_item(tender_id, question_id, \"questions\", access_token)\n\n def get_bid(self, tender_id, bid_id, access_token=None):\n return self._get_tender_resource_item(tender_id, bid_id, \"bids\", access_token)\n\n def get_lot(self, tender_id, lot_id, access_token=None):\n return self._get_tender_resource_item(tender_id, lot_id, \"lots\", access_token)\n\n def get_file(self, tender, url, access_token):\n logger.info(\"get_file is deprecated. In next update this function will no takes tender.\")\n parsed_url = urlparse(url)\n if access_token:\n headers = {'X-Access-Token': access_token}\n else:\n raise NoToken\n\n headers.update(self.headers)\n response_item = self.get(parsed_url.path,\n headers=headers,\n params_dict=parse_qs(parsed_url.query))\n\n if response_item.status_int == 302:\n response_obj = request(response_item.headers['location'])\n if response_obj.status_int == 200:\n return response_obj.body_string(), \\\n response_obj.headers['Content-Disposition'] \\\n .split(\";\")[1].split('\"')[1]\n raise InvalidResponse\n\n ###########################################################################\n # PATCH ITEM API METHODS\n ###########################################################################\n\n def _patch_resource_item(self, url, payload, headers={}):\n headers.update(self.headers)\n response_item = self.patch(\n url, headers=headers, payload=dumps(payload)\n )\n if response_item.status_int == 200:\n return munchify(loads(response_item.body_string()))\n raise InvalidResponse\n\n def _patch_tender_resource_item(self, tender_id, item_obj, items_name, access_token):\n return self._patch_resource_item(\n '{}/{}/{}/{}'.format(\n self.prefix_path, tender_id, items_name, item_obj['data']['id']\n ),\n payload=item_obj,\n headers={'X-Access-Token':access_token}\n )\n\n def patch_tender(self, tender):\n return self._patch_resource_item(\n '{}/{}'.format(self.prefix_path, tender[\"data\"][\"id\"]),\n payload=tender,\n headers={'X-Access-Token':\n getattr(getattr(tender, 'access', ''), 'token', '')}\n )\n\n def patch_question(self, tender_id, question, access_token):\n return self._patch_tender_resource_item(tender_id, question, \"questions\", access_token)\n\n def patch_bid(self, tender_id, bid, access_token):\n return self._patch_tender_resource_item(tender_id, bid, \"bids\", access_token)\n\n def patch_qualification(self, tender_id, qualification, access_token):\n return self._patch_tender_resource_item(tender_id, qualification, \"qualifications\", access_token)\n\n def patch_award(self, tender_id, award, access_token):\n return self._patch_tender_resource_item(tender_id, award, \"awards\", access_token)\n\n def patch_cancellation(self, tender_id, cancellation, access_token):\n return self._patch_tender_resource_item(tender_id, cancellation, \"cancellations\", access_token)\n\n def patch_cancellation_document(self, tender, cancellation_data, cancel_num, doc_num):\n cancel_num = int(cancel_num)\n doc_num = int(doc_num)\n return self._patch_resource_item(\n '{}/{}/{}/{}/documents/{}'.format(\n self.prefix_path, tender.data.id, \"cancellations\", tender['data']['cancellations'][cancel_num]['id'], tender['data']['cancellations'][cancel_num]['documents'][doc_num]['id']\n ),\n payload=cancellation_data,\n headers={'X-Access-Token':\n getattr(getattr(tender, 'access', ''), 'token', '')}\n )\n\n def patch_lot(self, tender_id, lot, access_token):\n return self._patch_tender_resource_item(tender_id, lot, \"lots\", access_token)\n\n def patch_document(self, tender_id, document, access_token):\n return self._patch_tender_resource_item(tender_id, document, \"documents\", access_token)\n\n def patch_contract(self, tender_id, contract, access_token):\n return self._patch_tender_resource_item(tender_id, contract, \"contracts\", access_token)\n\n ###########################################################################\n # UPLOAD FILE API METHODS\n ###########################################################################\n def _upload_resource_file(self, url, data, headers={}, method='post'):\n file_headers = {}\n file_headers.update(self.headers)\n file_headers.update(headers)\n file_headers['Content-Type'] = \"multipart/form-data\"\n response_item = getattr(self, method)(\n url, headers=file_headers, payload=data\n )\n if response_item.status_int in (201, 200):\n return munchify(loads(response_item.body_string()))\n raise InvalidResponse\n\n @verify_file\n def upload_document(self, file_, tender_id, access_token):\n return self._upload_resource_file(\n '{}/{}/documents'.format(\n self.prefix_path,\n tender_id\n ),\n data={\"file\": file_},\n headers={'X-Access-Token':access_token}\n )\n\n @verify_file\n def upload_bid_document(self, file_, tender_id, bid_id, access_token):\n return self._upload_resource_file(\n '{}/{}/bids/{}/documents'.format(\n self.prefix_path,\n tender_id,\n bid_id\n ),\n data={\"file\": file_},\n headers={'X-Access-Token':access_token}\n )\n\n @verify_file\n def update_bid_document(self, file_, tender_id, bid_id, document_id, access_token):\n return self._upload_resource_file(\n '{}/{}/bids/{}/documents/{}'.format(\n self.prefix_path,\n tender_id,\n bid_id,\n document_id\n ),\n data={\"file\": file_},\n headers={'X-Access-Token':access_token},\n method='put'\n )\n\n @verify_file\n def upload_cancellation_document(self, file_, tender_id, cancellation_id, access_token):\n return self._upload_resource_file(\n '{}/{}/cancellations/{}/documents'.format(\n self.prefix_path,\n tender_id,\n cancellation_id\n ),\n data={\"file\": file_},\n headers={'X-Access-Token':access_token}\n )\n\n @verify_file\n def update_cancellation_document(self, file_, tender_id, cancellation_id, document_id, access_token):\n return self._upload_resource_file(\n '{}/{}/cancellations/{}/documents/{}'.format(\n self.prefix_path,\n tender_id,\n cancellation_id,\n document_id\n ),\n data={\"file\": file_},\n headers={'X-Access-Token':access_token},\n method='put'\n )\n\n ###########################################################################\n # DELETE ITEMS LIST API METHODS\n ###########################################################################\n\n def _delete_resource_item(self, url, headers={}):\n response_item = self.delete(url, headers=headers)\n if response_item.status_int == 200:\n return munchify(loads(response_item.body_string()))\n raise InvalidResponse\n\n def delete_bid(self, tender_id, bid_id, access_token):\n return self._delete_resource_item(\n '{}/{}/bids/{}'.format(\n self.prefix_path,\n tender_id,\n bid_id\n ),\n headers={'X-Access-Token': access_token}\n )\n\n def delete_lot(self, tender_id, lot_id, access_token):\n return self._delete_resource_item(\n '{}/{}/lots/{}'.format(\n self.prefix_path,\n tender_id,\n lot_id\n ),\n headers={'X-Access-Token':access_token}\n )\n ###########################################################################\n","sub_path":"openprocurement_client/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":17177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"134189021","text":"# Configuration file for the Sphinx documentation builder.\n# This file only contains a selection of the most common options.\n# For a full list see the documentation: https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n\nfrom datetime import date\nimport json\nimport os\n\n\nPACKAGE_NAMESPACE = 'h1st'\nMETADATA_FILE_NAME = 'metadata.json'\n\n\n_metadata = json.load(\n open(os.path.join(\n os.path.dirname(\n os.path.dirname(\n os.path.dirname(__file__))),\n PACKAGE_NAMESPACE,\n METADATA_FILE_NAME)))\n\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory, add these directories to sys.path here.\n# If the directory is relative to the documentation root, use os.path.abspath to make it absolute, like shown here.\n\n# import sys\n# sys.path.insert(0, os.path.abspath('.'))\n\n\n# -- Project information -----------------------------------------------------\n\nproject = 'Human-First AI (H1ST)'\nauthor = \"H1st AI\"\ncopyright = f'{date.today().year}, {author}'\n\n# The full version, including alpha/beta/rc tags\nrelease = _metadata['VERSION']\n\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings.\n# They can be extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones.\nextensions = (\n 'recommonmark', # Markdown parser\n\n 'sphinx.ext.autodoc', # Include documentation from docstrings\n 'sphinx.ext.autodoc.typehints',\n\n 'sphinx.ext.autosectionlabel', # Allow reference sections using its title\n 'sphinx.ext.autosummary', # Generate autodoc summaries\n 'sphinx.ext.coverage', # Collect doc coverage stats\n 'sphinx.ext.doctest', # Test snippets in the documentation\n 'sphinx.ext.duration', # Measure durations of Sphinx processing\n 'sphinx.ext.extlinks', # Markup to shorten external links\n 'sphinx.ext.githubpages', # Publish HTML docs in GitHub Pages\n 'sphinx.ext.graphviz', # Add Graphviz graphs\n 'sphinx.ext.ifconfig', # Include content based on configuration\n 'sphinx.ext.imgconverter', # A reference image converter using Imagemagick\n 'sphinx.ext.inheritance_diagram', # Include inheritance diagrams\n 'sphinx.ext.intersphinx', # Link to other projects' documentation\n # 'sphinx.ext.linkcode', # Add external links to source code\n\n 'sphinx.ext.napoleon', # Support for NumPy and Google style docstrings\n 'sphinx.ext.todo', # Support for todo items\n 'sphinx.ext.viewcode', # Add links to highlighted source code\n\n 'sphinx_rtd_theme',\n)\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# List of patterns, relative to source directory,\n# that match files and directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = []\n\n# source parsers\nsource_suffix = {\n '.rst': 'restructuredtext',\n '.md': 'markdown',\n}\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for a list of builtin themes.\nhtml_theme = 'sphinx_rtd_theme'\n\n# Add any paths that contain custom static files (such as style sheets) here, relative to this directory.\n# They are copied after the builtin static files, so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n\nmaster_doc = 'index'\n\n\n# AutoDoc\nautodoc_default_options = {\n # 'members': ...,\n 'member-order': 'bysource',\n\n 'exclude-members': '__weakref__',\n\n # 'imported-members': False,\n\n # 'show-inheritance': True,\n # 'inherited-members': False,\n\n # 'private-members': False,\n 'special-members': '__init__',\n\n 'undoc-members': False, # *** HAVE TO MANUALLY REMOVE FROM GENERATED .RST FILES ***\n\n # 'ignore-module-all': False\n}\n\nautodoc_typehints = 'signature'\n","sub_path":"docs/old/old-conf.py","file_name":"old-conf.py","file_ext":"py","file_size_in_byte":4138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"432348144","text":"import os\nimport sys\nimport cv2\nimport net\nimport util\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom scipy.io import mmread\n\ndef run_pretraining(args=None):\n \n eps = 1e-10\n base_lr = 0.001 # learning rate\n \n # GPU\n if args.gpu:\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n print(\"GPU 0 will be used\")\n else:\n sys.exit(\"Require GPU to run the code\")\n \n # Model\n if args.model is not None:\n model_dir = args.model\n else:\n model_dir = os.path.join(os.getcwd(), \"model\")\n if not os.path.exists(model_dir):\n os.makedirs(model_dir)\n \n print(\"Start pre-training on MNIST data...\")\n \n # Load MNIST dataset\n (mnist_train_data, mnist_train_labels), (mnist_test_data, mnist_test_labels) = tf.keras.datasets.mnist.load_data()\n mnist_train_data = np.expand_dims(mnist_train_data, axis=-1)\n mnist_test_data = np.expand_dims(mnist_test_data, axis=-1)\n\n mnist_data = np.concatenate([mnist_train_data, mnist_test_data], axis=0)\n mnist_labels = np.concatenate([mnist_train_labels, mnist_test_labels], axis=0)\n \n n_sample, w, h, _ = np.shape(mnist_data)\n \n # Normalization\n mnist_data_norm = mnist_data.reshape(mnist_data.shape[0], -1)\n mnist_data_norm = mnist_data_norm/np.amax(mnist_data_norm, axis=1)[:, None]\n mnist_data_norm = mnist_data_norm.reshape(mnist_data.shape)\n\n imgs = tf.placeholder(shape=[None, w, h, 1], dtype=tf.float32, name='images')\n \n u_thres = tf.placeholder(shape=[], dtype=tf.float32, name='u_thres')\n l_thres = tf.placeholder(shape=[], dtype=tf.float32, name='l_thres')\n lr = tf.placeholder(shape=[], dtype=tf.float32, name='learning_rate')\n \n label_feat = net.mnistNetwork(imgs, args.cluster, name=\"mnistNetwork\", reuse=False)\n label_feat_norm = tf.nn.l2_normalize(label_feat, dim=1)\n # Compute similarity matrix based on embeddings from CNN encoder\n sim_mat = tf.matmul(label_feat_norm, label_feat_norm, transpose_b=True)\n \n pos_loc = tf.greater(sim_mat, u_thres, name='greater')\n neg_loc = tf.less(sim_mat, l_thres, name='less')\n pos_loc_mask = tf.cast(pos_loc, dtype=tf.float32)\n neg_loc_mask = tf.cast(neg_loc, dtype=tf.float32)\n \n pos_entropy = tf.multiply(-tf.log(tf.clip_by_value(sim_mat, eps, 1.0)), pos_loc_mask)\n neg_entropy = tf.multiply(-tf.log(tf.clip_by_value(1-sim_mat, eps, 1.0)), neg_loc_mask)\n \n # Construct loss function based on similarity matrix\n loss_sum = tf.reduce_mean(pos_entropy) + tf.reduce_mean(neg_entropy)\n\n train_op = tf.train.RMSPropOptimizer(lr).minimize(loss_sum)\n\n saver = tf.train.Saver()\n \n with tf.Session() as sess:\n \n sess.run(tf.global_variables_initializer())\n\n eta = 0 # step size\n epoch = 1\n u = 0.95 # upper threshold\n l = 0.455 # lower threshold\n \n while u > l:\n\n print(\"Epoch %d\" % epoch)\n \n # Update upper and lower thresholds\n u = 0.95 - eta\n l = 0.455 + 0.1*eta\n \n for i in range(1, int(args.epoch + 1)):\n mnist_batch, batch_index = util.get_mnist_batch(args.batch_size, n_sample, w, h, mnist_data_norm)\n feed_dict={imgs: mnist_batch,\n u_thres: u,\n l_thres: l,\n lr: base_lr}\n\n train_loss, _ = sess.run([loss_sum, train_op], feed_dict=feed_dict)\n if i % 5 == 0:\n print('training loss at iter %d is %f' % (i, train_loss))\n \n # Update step size\n eta += 1.1 * 0.009\n \n # Create checkpoint every 5 epochs \n if epoch % 5 == 0: \n model_name = 'CNN_MNIST_ep_' + str(epoch) + '.ckpt'\n save_path = saver.save(sess, os.path.join(model_dir, model_name))\n print(\"Checkpoint created in file: %s\" % save_path)\n\n epoch += 1\n\ndef run_gene_clustering(args=None):\n \n eps = 1e-10\n base_lr = 0.001 # learning rate \n \n # Extract tissue name\n tn = args.gene_maps.split('/')[-2] if args.gene_maps[-1] is \"/\" else args.gene_maps.split('/')[-1]\n \n # GPU\n if args.gpu:\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n print(\"GPU 0 will be used\")\n else:\n sys.exit(\"Require GPU to run the code\")\n \n # Checkpoint\n if args.checkpoint is not None:\n checkpoint = args.checkpoint\n else:\n checkpoint = os.path.join(os.getcwd(), \"model\", \"CNN_MNIST_ep_45.ckpt\")\n if not os.path.exists(checkpoint):\n sys.exit(\"Pre-trained model does not exist\")\n \n # Model \n if args.model is not None:\n model_dir = args.model\n else:\n model_dir = os.path.join(os.getcwd(), \"model\")\n if not os.path.exists(model_dir):\n os.makedirs(model_dir)\n \n # Clustering results\n if args.gene_clusters is not None:\n gene_cluster_dir = args.gene_clusters\n else:\n gene_cluster_dir = os.path.join(os.getcwd(), \"clustering\")\n if not os.path.exists(gene_cluster_dir):\n os.makedirs(gene_cluster_dir)\n \n # Load Mouse or Human PPI graph\n if args.sp:\n print(\"Load mus musculus PPI network\")\n gene_ids = np.loadtxt(args.PPI + '/Mmusculus_gene_list_80.txt', dtype=np.str)\n A = mmread(args.PPI + '/Mmusculus_PPI_80.mtx').toarray()\n else:\n print(\"Load homo sapiens PPI network\")\n gene_ids = np.loadtxt(args.PPI + '/Hsapiens_gene_list_80.txt', dtype=np.str)\n A = mmread(args.PPI + '/Hsapiens_PPI_80.mtx').toarray()\n \n # Load gene activity maps\n gene_data = np.stack([np.load(os.path.join(args.gene_maps, gene_id + '.npy')) for gene_id in gene_ids], axis=0)\n gene_data = np.expand_dims(gene_data, axis=-1)\n # Remove lowly expressed genes \n gene_filter = np.where(gene_data.reshape(gene_data.shape[0], -1).sum(axis=1) > args.expr_thres)[0]\n gene_ids = gene_ids[gene_filter]\n A = A[np.ix_(gene_filter, gene_filter)]\n \n # Load spatially variable gene list if provided\n if args.svgs is not None:\n print(\"Start clustering on spatially variable genes for %s ...\" % tn)\n svgs = np.loadtxt(args.svgs, dtype=np.str)\n gene_filter = np.where([gene_id in svgs for gene_id in gene_ids])[0]\n gene_ids = gene_ids[gene_filter]\n A = A[np.ix_(gene_filter, gene_filter)]\n else:\n print(\"Start clustering on all genes for %s ...\" % tn)\n \n # Reload gene activity maps\n gene_data = np.stack([np.load(os.path.join(args.gene_maps, 'activity_maps', gene_id + '.npy')) for gene_id in gene_ids], axis=0)\n gene_data_norm = gene_data.reshape(gene_data.shape[0], -1)\n gene_data_norm = gene_data_norm/np.amax(gene_data_norm, axis=1)[:, None]\n gene_data_norm = gene_data_norm.reshape(gene_data.shape)\n \n # Process gene activity maps (padding or resize)\n if args.prep:\n gene_data_norm = np.stack([cv2.resize(gene_data_norm[i, ...], dsize=(28,28)) \n for i in range(gene_data_norm.shape[0])],axis=0)\n else:\n gene_data_norm = np.stack([np.pad(gene_data_norm[i, ...], pad_width=((3, 3), (10, 10))) \n for i in range(gene_data_norm.shape[0])],axis=0)\n \n gene_data_norm = np.expand_dims(gene_data_norm, axis=-1)\n\n n_gene, w, h, _ = np.shape(gene_data_norm)\n\n gene_maps = tf.placeholder(shape=[None, w, h, 1], dtype=tf.float32, name='gene_maps')\n lap_mat = tf.placeholder(shape=None, dtype=tf.float32, name='lap_mat')\n \n u_thres = tf.placeholder(shape=[], dtype=tf.float32, name='u_thres')\n l_thres = tf.placeholder(shape=[], dtype=tf.float32, name='l_thres')\n lr = tf.placeholder(shape=[], dtype=tf.float32, name='learning_rate')\n alpha = tf.placeholder(shape=[], dtype=tf.float32, name='alpha')\n \n # Prepend additional convolutional to CNN encoder according to gene activity maps processing \n if args.prep:\n gene_embs = net.mnistNetwork(gene_maps, args.cluster, name=\"mnistNetwork\", reuse=False)\n else:\n gene_embs = net.VisiumNetwork(gene_maps, args.cluster)\n \n gene_embs_norm = tf.nn.l2_normalize(gene_embs, dim = 1)\n \n # Use exact or approximated PPI graph regularization based on the number of genes involved in the clustering\n if args.svgs is not None:\n # PPI graph regularization\n gene_embs_norm_rest = tf.placeholder(shape=[None, args.cluster], dtype=tf.float32)\n gene_embs_norm_mat = tf.concat([gene_embs_norm, gene_embs_norm_rest], 0)\n # Compute similarity matrix and PPI graph regularization based on all gene embeddings\n sim_mat = tf.matmul(gene_embs_norm_mat, gene_embs_norm_mat, transpose_b=True)\n graph_reg = tf.linalg.trace(tf.matmul(tf.matmul(tf.transpose(gene_embs_norm_mat), lap_mat), gene_embs_norm_mat))\n \n else:\n # Approximated PPI graph regularization\n # Compute similarity matrix and PPI graph regularization based on gene embeddings in the batch\n sim_mat = tf.matmul(gene_embs_norm, gene_embs_norm, transpose_b=True)\n graph_reg = tf.linalg.trace(tf.matmul(tf.matmul(tf.transpose(gene_embs_norm), lap_mat), gene_embs_norm))\n \n pos_loc = tf.greater(sim_mat, u_thres, name='greater')\n neg_loc = tf.less(sim_mat, l_thres, name='less')\n pos_loc_mask = tf.cast(pos_loc, dtype=tf.float32)\n neg_loc_mask = tf.cast(neg_loc, dtype=tf.float32)\n \n pos_entropy = tf.multiply(-tf.log(tf.clip_by_value(sim_mat, eps, 1.0)), pos_loc_mask)\n neg_entropy = tf.multiply(-tf.log(tf.clip_by_value(1-sim_mat, eps, 1.0)), neg_loc_mask)\n \n graph_reg = tf.math.divide(graph_reg, args.cluster)\n \n # Construct combined loss (clustering loss and PPI graph regularization)\n loss_sum = tf.reduce_mean(pos_entropy) + tf.reduce_mean(neg_entropy) + tf.multiply(graph_reg, alpha)\n\n train_op = tf.train.RMSPropOptimizer(lr).minimize(loss_sum)\n \n # Infer gene cluster membership based on gene embeddings\n gene_clusters = tf.argmax(gene_embs, axis=1)\n \n saver = tf.train.Saver()\n \n with tf.Session() as sess:\n \n # Load pre-trained CNN\n if args.prep:\n util.complete_restore(sess, checkpoint)\n else:\n util.partial_restore(sess, checkpoint)\n \n print('Pre-trained model restored!')\n\n eta = 0 # step size\n epoch = 1\n u = 0.95 # threshold for similar gene selection\n l = 0.455 # threshold for dissimilar gene selection\n \n # Create gene embedding matrix when fewer genes involved in the clustering\n if args.svgs is not None:\n \n F = np.zeros((n_gene, args.cluster))\n for j in range(int(np.ceil(n_gene/args.batch_size))):\n gene_batch = np.copy(gene_data_norm[args.batch_size*j:args.batch_size*(j+1), ...])\n feed_dict={gene_maps: gene_batch}\n F[j*args.batch_size:(j+1)*args.batch_size, ...] = sess.run(gene_embs_norm, feed_dict=feed_dict)\n\n while u > l:\n\n print(\"Epoch %d\" % epoch)\n \n # Update thresholds for both similar and dissimilar gene selection\n u = 0.95 - eta\n l = 0.455 + 0.1*eta\n\n for i in range(1, int(args.epoch + 1)):\n \n if args.svgs is not None:\n \n gene_batch, ppi_lap_mat, batch_index, rest_index = util.get_gene_batch_graph_reg(args.batch_size, n_gene,\n w, h, gene_data_norm, A)\n\n feed_dict={gene_maps: gene_batch,\n gene_embs_norm_rest: F[rest_index, ...],\n lap_mat: ppi_lap_mat,\n alpha: args.alpha,\n u_thres: u,\n l_thres: l,\n lr: base_lr}\n \n else:\n \n gene_batch, ppi_lap_mat, batch_index = util.get_gene_batch_approx_reg(args.batch_size, n_gene, \n w, h, gene_data_norm, A)\n\n feed_dict={gene_maps: gene_batch,\n lap_mat: ppi_lap_mat,\n alpha: args.alpha,\n u_thres: u,\n l_thres: l,\n lr: base_lr}\n\n train_loss, _ = sess.run([loss_sum, train_op], feed_dict=feed_dict)\n \n # Update gene embedding matrix when fewer genes involved in the clustering\n if args.svgs is not None:\n \n for j in range(int(np.ceil(n_gene/args.batch_size))):\n gene_batch = np.copy(gene_data_norm[args.batch_size*j:args.batch_size*(j+1), ...])\n feed_dict={gene_maps: gene_batch}\n F[j*args.batch_size:(j+1)*args.batch_size, ...] = sess.run(gene_embs_norm, feed_dict=feed_dict)\n\n if i % 20 == 0:\n print('training loss at iter %d is %f' % (i, train_loss))\n \n # Update step size\n eta += 1.1 * 0.009\n \n # Create checkpoint every 5 epochs\n if epoch % 5 == 0: # save model at every 5 epochs\n model_name = 'CNN_PReg_ep_' + str(epoch) + '.ckpt'\n save_path = saver.save(sess, os.path.join(model_dir, model_name))\n print(\"Checkpoint created in file: %s\" % save_path)\n\n epoch += 1\n\n with tf.Session() as sess:\n \n # Load the most recent checkpoint\n saver.restore(sess, os.path.join(model_dir, 'CNN_PReg_ep_45.ckpt'))\n \n # Infer gene memberships\n all_gene_clusters = np.zeros([n_gene], dtype=np.float32)\n for j in range(int(np.ceil(n_gene/args.batch_size))):\n gene_batch = np.copy(gene_data_norm[args.batch_size*j:args.batch_size*(j+1), ...])\n feed_dict={gene_maps: gene_batch}\n all_gene_clusters[j*args.batch_size:(j+1)*args.batch_size] = sess.run(gene_clusters, feed_dict=feed_dict)\n\n data = pd.DataFrame({'gene_id': gene_ids, 'cluster_id': all_gene_clusters}, columns=['gene_id', 'cluster_id'])\n data.to_csv(os.path.join(gene_cluster_dir, tn + '_gene_clusters.csv'), index=False)","sub_path":"training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":14612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"404432669","text":"#!/usr/bin/python\n#\n# Copyright 2018-2022 Polyaxon, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom coredb.query_managers import callback_conditions\nfrom coredb.query_managers.manager import BaseQueryManager\nfrom polyaxon.pql.builder import (\n ArrayCondition,\n BoolCondition,\n CallbackCondition,\n ComparisonCondition,\n DateTimeCondition,\n SearchCondition,\n ValueCondition,\n)\nfrom polyaxon.pql.parser import (\n parse_cpu_operation,\n parse_datetime_operation,\n parse_memory_operation,\n parse_scalar_operation,\n parse_search_operation,\n parse_value_operation,\n)\n\n\nclass RunQueryManager(BaseQueryManager):\n NAME = \"run\"\n FIELDS_USE_NAME = {\n \"project\",\n \"agent\",\n \"queue\",\n \"artifacts_store\",\n \"connections\",\n }\n FIELDS_USE_STATE = {\"artifacts\"}\n FIELDS_USE_UUID = {\n \"original\",\n \"upstream\",\n \"downstream\",\n \"pipeline\",\n \"controller\",\n \"upstream_runs\",\n \"downstream_runs\",\n }\n FIELDS_PROXY = {\n \"params\": \"inputs\",\n \"in\": \"inputs\",\n \"out\": \"outputs\",\n \"metrics\": \"outputs\",\n \"meta_values\": \"meta_info\",\n \"meta_flags\": \"meta_info\",\n \"id\": \"uuid\",\n \"uid\": \"uuid\",\n \"upstream\": \"upstream_runs\",\n \"downstream\": \"downstream_runs\",\n \"user\": \"user__username\",\n }\n FIELDS_ORDERING = (\n \"created_at\",\n \"updated_at\",\n \"started_at\",\n \"finished_at\",\n \"schedule_at\",\n \"name\",\n \"kind\",\n \"namespace\",\n \"runtime\",\n \"user\",\n \"uuid\",\n \"duration\",\n \"wait_time\",\n \"status\",\n \"cost\",\n \"cpu\",\n \"memory\",\n \"gpu\",\n \"custom\",\n \"state\",\n \"component_state\",\n )\n FIELDS_ORDERING_PROXY = {\n \"metrics\": {\"field\": \"outputs\", \"annotate\": True},\n \"params\": {\"field\": \"inputs\", \"annotate\": True},\n \"inputs\": {\"field\": \"inputs\", \"annotate\": True},\n \"in\": {\"field\": \"inputs\", \"annotate\": True},\n \"outputs\": {\"field\": \"outputs\", \"annotate\": True},\n \"out\": {\"field\": \"outputs\", \"annotate\": True},\n \"meta_flags\": {\"field\": \"meta_info\", \"annotate\": True},\n \"meta_info\": {\"field\": \"meta_info\", \"annotate\": True},\n \"meta_values\": {\"field\": \"meta_info\", \"annotate\": True},\n }\n FIELDS_DEFAULT_ORDERING = (\"-updated_at\",)\n CHECK_ALIVE = True\n PARSERS_BY_FIELD = {\n # Uuid\n \"id\": parse_search_operation,\n \"uid\": parse_search_operation,\n \"uuid\": parse_search_operation,\n # Dates\n \"created_at\": parse_datetime_operation,\n \"updated_at\": parse_datetime_operation,\n \"started_at\": parse_datetime_operation,\n \"finished_at\": parse_datetime_operation,\n \"schedule_at\": parse_datetime_operation,\n # Name\n \"name\": parse_search_operation,\n # Description\n \"description\": parse_search_operation,\n # User\n \"user\": parse_value_operation,\n # Status\n \"status\": parse_value_operation,\n # Project\n \"project\": parse_value_operation,\n # Original\n \"original\": parse_value_operation,\n # Pipeline\n \"pipeline\": parse_value_operation,\n # Controller\n \"controller\": parse_value_operation,\n # Upstream\n \"upstream\": parse_value_operation,\n # Downstream\n \"downstream\": parse_value_operation,\n # Cloning kind\n \"cloning_kind\": parse_value_operation,\n # Artifact\n \"in_artifact_kind\": parse_value_operation,\n \"out_artifact_kind\": parse_value_operation,\n # Backend\n \"backend\": parse_value_operation,\n # Framework\n \"framework\": parse_value_operation,\n # Commit\n \"commit\": parse_value_operation,\n # Kind\n \"kind\": parse_value_operation,\n # Meta Kind\n \"runtime\": parse_value_operation,\n # Namespace\n \"namespace\": parse_value_operation,\n # Params\n \"params\": parse_value_operation,\n \"inputs\": parse_value_operation,\n \"in\": parse_value_operation,\n # Results\n \"outputs\": parse_value_operation,\n \"out\": parse_value_operation,\n # Metrics\n \"metrics\": parse_scalar_operation,\n # Meta\n \"meta_flags\": parse_value_operation,\n \"meta_info\": parse_value_operation,\n \"meta_values\": parse_scalar_operation,\n # Tags\n \"tags\": parse_value_operation,\n # Live state\n \"live_state\": parse_value_operation,\n # Duration\n \"duration\": parse_scalar_operation,\n # Wait time\n \"wait_time\": parse_scalar_operation,\n # Agent\n \"agent\": parse_value_operation,\n \"queue\": parse_value_operation,\n # Artifacts store\n \"artifacts_store\": parse_value_operation,\n # States\n \"state\": parse_value_operation,\n \"component_state\": parse_value_operation,\n # Flags\n \"is_managed\": parse_value_operation,\n \"pending\": parse_value_operation,\n # Resources\n \"cost\": parse_scalar_operation,\n \"cpu\": parse_cpu_operation,\n \"memory\": parse_memory_operation,\n \"gpu\": parse_scalar_operation,\n \"custom\": parse_scalar_operation,\n # Artifacts\n \"artifacts\": parse_value_operation,\n # Connections\n \"connections\": parse_value_operation,\n }\n CONDITIONS_BY_FIELD = {\n # Uuid\n \"id\": SearchCondition,\n \"uid\": SearchCondition,\n \"uuid\": SearchCondition,\n # Dates\n \"created_at\": DateTimeCondition,\n \"updated_at\": DateTimeCondition,\n \"started_at\": DateTimeCondition,\n \"finished_at\": DateTimeCondition,\n \"schedule_at\": DateTimeCondition,\n # Name\n \"name\": SearchCondition,\n # Description\n \"description\": SearchCondition,\n # User\n \"user\": ValueCondition,\n # Status\n \"status\": ValueCondition,\n # Project\n \"project\": ValueCondition,\n # Original\n \"original\": ValueCondition,\n # Pipeline\n \"pipeline\": ValueCondition,\n # Controller\n \"controller\": ValueCondition,\n # Upstream\n \"upstream\": ValueCondition,\n # Downstream\n \"downstream\": ValueCondition,\n # Cloning kind\n \"cloning_kind\": ValueCondition,\n # Artifact\n \"in_artifact_kind\": CallbackCondition(\n callback_conditions.in_artifact_kind_condition\n ),\n \"out_artifact_kind\": CallbackCondition(\n callback_conditions.in_artifact_kind_condition\n ),\n # Backend\n \"backend\": ValueCondition,\n # Framework\n \"framework\": ValueCondition,\n # Commit\n \"commit\": CallbackCondition(callback_conditions.commit_condition),\n # Kind\n \"kind\": ValueCondition,\n # Meta Kind\n \"runtime\": ValueCondition,\n # Namespace\n \"namespace\": ValueCondition,\n # Params\n \"params\": ComparisonCondition,\n \"inputs\": ComparisonCondition,\n \"in\": ComparisonCondition,\n # Results\n \"outputs\": ComparisonCondition,\n \"out\": ComparisonCondition,\n # Metrics\n \"metrics\": ComparisonCondition,\n # Meta\n \"meta_flags\": BoolCondition,\n \"meta_info\": ValueCondition,\n \"meta_values\": ValueCondition,\n # Tags\n \"tags\": ArrayCondition,\n # Live state\n \"live_state\": ValueCondition,\n # Duration\n \"duration\": ComparisonCondition,\n # Wait time\n \"wait_time\": ComparisonCondition,\n # Agent\n \"agent\": ValueCondition,\n \"queue\": ValueCondition,\n # Artifacts store\n \"artifacts_store\": ValueCondition,\n # States\n \"state\": ValueCondition,\n \"component_state\": ValueCondition,\n # Flags\n \"is_managed\": BoolCondition,\n \"pending\": ValueCondition,\n # Resources\n \"cost\": ComparisonCondition,\n \"cpu\": ComparisonCondition,\n \"memory\": ComparisonCondition,\n \"gpu\": ComparisonCondition,\n \"custom\": ComparisonCondition,\n # Artifacts\n \"artifacts\": ValueCondition,\n # Connections\n \"connections\": ValueCondition,\n }\n","sub_path":"platform/coredb/coredb/query_managers/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":8856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"435925616","text":"#Introdução à Programaçãol de Computadores\r\n#Professor: Jucimar Junior\r\n#Any Mendes Carvalho - 1615310044\r\n#Calebe Roberto Chaves da Silva Rebello - 1615310043\r\n#Luiz Gustavo Rocha Melo - 1615310015\r\n#Igor Menezes Sales Vieira - 1615310007\r\n\r\ndef potencia(k, n):\r\n if n == 0:\r\n return 1\r\n if n == 1:\r\n return k\r\n if k == 0:\r\n return 0\r\n else:\r\n return k*potencia(k,n-1)\r\nk = int(input(\"Digite o valor de k: \"))\r\nn = int(input(\"Digite o valor de n: \"))\r\nprint(potencia(k,n))\r\n\r\n","sub_path":"lista6/Equipe2/ipc_lista06.06.py","file_name":"ipc_lista06.06.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"592714928","text":"#-*- coding:utf-8 -*-\n#-------------------------------------------------------------------------------\n# Name: 模块1\n# Purpose:\n#\n# Author: liushuqing506\n#\n# Created: 08/05/2018\n# Copyright: (c) liushuqing506 2018\n# Licence: \n#-------------------------------------------------------------------------------\n## -*- coding: utf-8 -*-\n## __author__ = Next\nimport xlrd\nimport win32com.client\nimport easygui as g\nfrom os import getcwd\nfrom tkinter import Tk, Text, Button, Label\n\n'''\n此处定义一个Function类，用来放需要用到的一些函数\n'''\nclass Function():\n\n\n def __init__(self):\n self.path = getcwd()\n\n ## 使用说明\n def info(self):\n pass\n\n ## ----- 选择数据文件\n def choosedata(self):\n return g.fileopenbox(msg='请打开数据文件', title=None, default='*', filetypes=None)\n\n ## ----- 选择模板文件\n def choosemodel(self):\n return g.fileopenbox(msg='请打开模板文件', title=None, default='*', filetypes=None)\n\n def ready(self, place):\n return g.msgbox(msg='生成的文件将保存在'+str(place)+'文件中，现在开始吗', title='Are you ready??!!', ok_button='Ready Go...!!!')\n\n\n ## ----- 自定义保存的名称，定义一个CCbox\n def savename(self, c):\n choice = g.choicebox(msg='请选择一列内容作为保存Word文件的名称', title='文件名称选择', choices=c)\n return c.index(choice)\n\n ## ----- 自定义生成文件保存的位置\n def saveplace(self):\n place = g.buttonbox(msg='请选择保存结果文件的文件夹，默认文件夹为根目录下的User文件夹',\n title='选择文件夹', choices=('选择文件夹','使用默认文件夹','退出程序'))\n if place == '退出程序':\n return\n elif place == '使用默认文件夹':\n return self.path+'\\\\User\\\\'\n return g.diropenbox(msg='请选择文件夹保存结果文件', title='选择文件夹保存结果', default=self.path+'\\\\User')+'\\\\'\n\n\n ## ----- 打开主页面\n def index(self):\n database = ''\n model = ''\n while 1:\n databasetype = ('xls','xlsx',)\n modeltype = ('doc', 'docx',)\n choose = g.buttonbox(msg='轻松Word',title='智能Word填写软件__ByNext',\n choices=('打开数据文件', '打开模板文件','下一步', '退出'))\n if choose == '打开数据文件':\n database = self.choosedata() #database 数据文件名称\n continue\n elif choose == '打开模板文件':\n model = self.choosemodel() #mode 模板文件名称\n continue\n elif choose == '下一步':\n if database.endswith(databasetype) and model.endswith(modeltype):\n break\n elif not (database.endswith(databasetype) or model.endswith(modeltype)):\n c = g.boolbox(msg='数据和模板文件错误',choices=('Yes', 'No'))\n elif not database.endswith(databasetype):\n c = g.boolbox(msg='数据文件错误',choices=('Yes', 'No'))\n elif not model.endswith(modeltype):\n c = g.boolbox(msg='模板文件错误',choices=('Yes', 'No'))\n if c:\n continue\n else:\n return ## 退出\n elif choose == '退出':\n return\n\n return database, model # 放回2个值，分别是 数据文件名称、模板名称\n\n ## ------ 写入word的函数\n def makeword(self, name, title, lenth, info, filename,saveplace):\n w = win32com.client.Dispatch(\"Word.Application\")\n w.Visible = True\n while 1:\n try:\n doc = w.Documents.Open(str(name)) # 载入模板\n break\n except:\n if g.boolbox(msg='模板文件错误，请重新选择！',choices=('Yes', 'No，不选了，麻烦！')):\n name = Func.index()[1] # 重新调用index函数，重新选择\n continue\n else:\n return False\n w.Selection.Find.ClearFormatting()\n w.Selection.Find.Replacement.ClearFormatting()\n for i in range(1,lenth):\n OldStr = title[i]\n NewStr = info[i]\n w.Selection.Find.Execute(OldStr, False, False, False, False, False, True, 1, True, NewStr, 2)\n while 1:\n try:\n #if saveplace:\n doc.SaveAs(saveplace+info[filename]+'.doc')\n #else:\n #doc.SaveAs(saveplace+info[filename]+'.doc')\n break\n except:\n if g.boolbox(msg='文件名称含有特殊字符或其他错误，请重新选择文件名称',choices=('Yes', 'No，不选了，麻烦！')):\n filename = self.savename(title)\n continue\n else:\n break\n doc.Close()\n return True\n\n\n\n\n'''\n数据表中，第一行必须是标题，且标题的数据必须采用标准格式\n第一列必须是序号，从1开始，一直到最后一组数据\n'''\ndef run():\n Func = Function() #函数类的实例化\n try:\n database, model = Func.index() #调用打开页面,返回 database 和 model 的文件名称\n except:\n return\n\n ## ----- 载入数据文件\n while 1:\n try:\n data = xlrd.open_workbook(str(database))\n break\n except:\n if g.boolbox(msg='数据文件错误，请重新选择！',choices=('Yes', 'No,我要退出！')):\n database, model = Func.index() # 重新调用index函数，重新选择\n else:\n return\n table = data.sheets()[0]\n title = table.row_values(0)\n count = len(table.col_values(0)[1:]) # 数据总个数\n num = 1 # 计数\n filename = Func.savename(title) # 自定义保存的文件名称\n saveplace = Func.saveplace() # 自定义保存的文件位置\n if not saveplace:\n return\n if not Func.ready(saveplace): # 准备开始\n return\n window = Tk()\n window.title('欢迎使用智能Word填写软件__ByNext')\n window.geometry('600x400')\n label = Label(window)\n label.pack()\n b = Button(window,text='退出', command = window.quit)\n b.pack()\n text = Text(label, font='宋体 -18')\n text.pack()\n\n\n\n # ------ 开始循环生成文件\n while num <= count:\n info = table.row_values(num)\n try:\n text.insert('1.0', ('正在处理第'+str(num)+'份文件......\\n'))\n except:\n return\n window.update()\n flag = Func.makeword(model, title, len(title), info, filename, saveplace)\n if flag:\n num += 1\n else:\n if g.boolbox(msg='第'+ str(num)+'文件出错，是否继续生成剩余文件？',choices=('Yes', 'No，坚决退出！')):\n continue\n else:\n return\n window.destroy()\n if num == count:\n g.msgbox(msg='文件已全部处理完成',ok_button = 'OK,good job!')\n elif num > 1:\n g.msgbox(msg='完成了'+str(num)+'份文件',ok_button='OK')\n window.mainloop()\n\n\nif __name__ == '__main__':\n run()\n\n","sub_path":"excel2word/excel2word.py","file_name":"excel2word.py","file_ext":"py","file_size_in_byte":7476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"359605511","text":"class Player:\n def __init__(self, role):\n self.role = role\n self.hp = 100\n self.gun = None\n\n def attack(self, enemy, count=10):\n if self.gun is None:\n print(\"%s have no gun!\" % self.role)\n elif self.gun.bullet_count == 0:\n self.gun.add_bullet(count)\n else:\n self.gun.shoot(enemy)\n\n def hurt(self, damage):\n self.hp -= damage\n if self.hp <= 0:\n print(\"%s died!\" % self.role)\n else:\n print(\"%s is injured! hp is %d\" % (self.role, self.hp))\n\n def __str__(self):\n if self.hp <= 0:\n return \"%s died!\" % self.role\n else:\n if self.gun is None:\n return \"you are %s, hp: %d, you have no gun\" % (self.role, self.hp)\n else:\n return \"you are %s, hp: %d, gun: %s\" % (self.role, self.hp, self.gun)\n\n\nclass Gun:\n def __init__(self, modal, damage):\n self.type = modal\n self.damage = damage\n self.bullet_count = 30\n\n def add_bullet(self, count):\n self.bullet_count += count\n\n def shoot(self, enemy):\n if self.bullet_count == 0:\n print(\"has no bullet\")\n else:\n self.bullet_count -= 3\n enemy.hurt(self.damage)\n\n def __str__(self):\n return \"type: %s, damage: %d, bullet_count: %d\" % (self.type, self.damage, self.bullet_count)\n\n\nak47 = Gun(\"AK47\", 30)\nxm = Player(\"JC\")\nlw = Player(\"TF\")\nlw.gun = ak47\nfor i in range(4):\n lw.attack(xm)\nprint(lw)\nprint(xm)\n","sub_path":"days/d11_03_cs.py","file_name":"d11_03_cs.py","file_ext":"py","file_size_in_byte":1540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"58215941","text":"from __future__ import print_function\nfrom keras.datasets import cifar10\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Dropout, Activation, Flatten\nfrom keras.layers.convolutional import Convolution2D, MaxPooling2D\nfrom keras.optimizers import SGD, Adadelta, Adagrad\nfrom keras.utils import np_utils, generic_utils\nfrom six.moves import range\nimport numpy as np\nimport scipy as sp\nfrom keras import backend as K \nimport scipy.io\nimport sklearn\nfrom sklearn.metrics import accuracy_score\nimport numpy as np\nimport math\n\nbatch_size = 32\nnb_classes = 10\nnb_epoch = 1\ndata_augmentation = False\n\n# input image dimensions\nimg_rows, img_cols = 32, 32\n# the CIFAR10 images are RGB\nimg_channels = 3\n\n# the data, shuffled and split between train and test sets\n(X_train_All, y_train_All), (X_test, y_test) = cifar10.load_data()\n\nprint('Original size of the cifar10 dataset')\nprint('X_train shape:', X_train_All.shape)\nprint('y_train shape:', y_train_All.shape)\nprint('X_test shape:', X_test.shape)\nprint('y_test shape:', y_test.shape)\n\ntrain_points = 100\ntest_points = 50\n\nX_train = X_train_All[0:train_points, 0:3,0:32,0:32]\ny_train = y_train_All[0:train_points, :]\n\nX_test = X_test[0:test_points,0:3,0:32,0:32]\ny_test = y_test[0:test_points,:]\n#pool of training data points\n\npool_count = 1500\nX_Pool = X_train_All[1001:pool_count, 0:3, 0:32, 0:32]\nY_oracle = y_train_All[1001:pool_count, :]\n\n# no labels available for the Pool Training Points\n\nTrain_After_Acquisition_X = X_train\nTrain_After_Acquisition_Y = y_train\nTrain_After_Acquisition_Y = np_utils.to_categorical(Train_After_Acquisition_Y, nb_classes)\n\nscore=0\naccuracy=0\nsklearn_accuracy=0\nall_accuracy = 0\niterations = 3\n\n\nfor i in range(iterations):\t\t\t# for each round t\n\n\tprint('ITERATION NUMBER', i)\n\n\tY_train = np_utils.to_categorical(y_train, nb_classes)\n\tY_test = np_utils.to_categorical(y_test, nb_classes)\n\n\tfor pool_point in range(X_Pool.shape[0]):# X_Pool.shape[0]\t\t\t# for each unlabelled image\n\t\t\n\t\tfor pool_label in range(2): # nb_classes\t\t\t# for each possible class label\n\t\t\t\n\t\t\t#estimate P(Y_i =j | L)\n\t\t\t# L - labelled points\n\t\t\tLabelled_data_X = X_train\n\t\t\tLabelled_data_Y = Y_train\n\n\t\t\tmodel = Sequential()\n\t\t\tmodel.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=(img_channels, img_rows, img_cols)))\n\t\t\tmodel.add(Activation('relu')) #using relu activation function\n\t\t\tmodel.add(Convolution2D(32, 3, 3))\n\t\t\tmodel.add(Activation('relu'))\n\t\t\tmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\t\t\tmodel.add(Dropout(0.25))\n\n\t\t\tmodel.add(Convolution2D(64, 3, 3, border_mode='same'))\n\t\t\tmodel.add(Activation('relu'))\n\t\t\tmodel.add(Convolution2D(64, 3, 3))\n\t\t\tmodel.add(Activation('relu'))\n\t\t\tmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\t\t\tmodel.add(Dropout(0.25))\n\n\t\t\tmodel.add(Flatten())\n\t\t\tmodel.add(Dense(512))\n\t\t\tmodel.add(Activation('relu'))\n\t\t\tmodel.add(Dropout(0.5))\n\t\t\tmodel.add(Dense(nb_classes))\n\t\t\tmodel.add(Activation('softmax'))\n\n\t\t\tsgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n\t\t\tmodel.compile(loss='categorical_crossentropy', optimizer=sgd)\n\n\t\t\tLabelled_data_X = Labelled_data_X.astype('float32')\n\t\t\tX_Pool = X_Pool.astype('float32')\n\t\t\tLabelled_data_X /= 255\n\t\t\tX_Pool /= 255\n\n\t\t\tmodel.fit(Labelled_data_X, Labelled_data_Y, batch_size=batch_size, nb_epoch=nb_epoch)\n\n\t\t\tClass_Probability = model.predict_proba(X_Pool, batch_size=batch_size, verbose=1)\n\n\t\t\tindex = np.array([pool_point])\n\t\t\ti_pool_x = X_Pool[index, 0:3, 0:32, 0:32]\n\t\t\ti_pool_y = Y_oracle[index, :]\n\n\t\t\t#all other pool points other than current point\n\t\t\tRemaining_Pool_Points = np.delete(X_Pool, pool_point, 0)\n\t\t\ti_pool_y = np_utils.to_categorical(i_pool_y, nb_classes)\n\n\n\t\t\tfor rm_pool_point in range(Remaining_Pool_Points.shape[0]):\t#Remaining_Pool_Points.shape[0]\n\t\t\t\tfor pool_class in range(2):\t#nb_classes\n\n\t\t\t\t\tNew_Labelled_data_X = np.concatenate((Labelled_data_X, i_pool_x), axis=0)\n\t\t\t\t\tNew_Labelled_data_Y = np.concatenate((Labelled_data_Y, i_pool_y), axis=0)\n\n\t\t\t\t\tNew_Labelled_data_Y\t= np_utils.to_categorical(New_Labelled_data_Y, nb_classes)\n\n\t\t\t\t\t#estimate P(Y_k =j | L U (x_i, j))\n\t\t\t\t\t# fit a model with the new concatenated labelled data - using the unlabelled image and queried point\n\n\t\t\t\t\tmodel = Sequential()\n\t\t\t\t\tmodel.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=(img_channels, img_rows, img_cols)))\n\t\t\t\t\tmodel.add(Activation('relu')) #using relu activation function\n\t\t\t\t\tmodel.add(Convolution2D(32, 3, 3))\n\t\t\t\t\tmodel.add(Activation('relu'))\n\t\t\t\t\tmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\t\t\t\t\tmodel.add(Dropout(0.25))\n\n\t\t\t\t\tmodel.add(Convolution2D(64, 3, 3, border_mode='same'))\n\t\t\t\t\tmodel.add(Activation('relu'))\n\t\t\t\t\tmodel.add(Convolution2D(64, 3, 3))\n\t\t\t\t\tmodel.add(Activation('relu'))\n\t\t\t\t\tmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\t\t\t\t\tmodel.add(Dropout(0.25))\n\n\t\t\t\t\tmodel.add(Flatten())\n\t\t\t\t\tmodel.add(Dense(512))\n\t\t\t\t\tmodel.add(Activation('relu'))\n\t\t\t\t\tmodel.add(Dropout(0.5))\n\t\t\t\t\tmodel.add(Dense(nb_classes))\n\t\t\t\t\tmodel.add(Activation('softmax'))\n\n\t\t\t\t\tsgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n\t\t\t\t\tmodel.compile(loss='categorical_crossentropy', optimizer=sgd)\n\n\t\t\t\t\tNew_Labelled_data_X = New_Labelled_data_X.astype('float32')\n\t\t\t\t\tRemaining_Pool_Points = Remaining_Pool_Points.astype('float32')\n\t\t\t\t\tNew_Labelled_data_X /= 255\n\t\t\t\t\tRemaining_Pool_Points /= 255\n\n\t\t\t\t\tmodel.fit(New_Labelled_data_X, New_Labelled_data_Y, batch_size=batch_size, nb_epoch=nb_epoch)\n\n\t\t\t\t\t# set of P(Y_k = l | L U (x_i, j)) values0\n\t\t\t\t\tP_Y_k = model.predict_proba(Remaining_Pool_Points, batch_size=batch_size, verbose=1)\n\n\t\t\t\t\tlog_P_Y_k = np.log2(P_Y_k)\n\t\t\t\t\tEntropy_Y_k = - np.multiply(P_Y_k, log_P_Y_k)\n\n\t\t\t\t\t#Entropy_Y_k = np.sum(Entropy_Y_k_Each_Cell, axis=1)\t# summing across rows of the array\n\n\t\t# Computing H_x - the expected Entropy\t\t\t\n\t\tH_x = np.array([[ np.dot( Class_Probability[pool_point , :], Entropy_Y_k[pool_point, :] ) ]])\n\t\tH_x = np.append(H_x, H_x, axis=0)\n\n\n\t# acquise the point with argmin (H_x) and its index value\n\tacquised_point = np.amin(H_x)\n\tacquised_point_index = np.where(H_x==H_x.min())[0]\n\t#acquised_point_index = H_x.argsort()[:1]\n\n\tPooled_X = X_Pool[acquised_point_index, 0:3,0:32,0:32]\n\tPooled_Y = Y_oracle[acquised_point_index, :]\n\tPooled_Y = np_utils.to_categorical(Pooled_Y, nb_classes)\n\n\t# accumulate all the acquised points from X_Pool - accumulate all indices of the acquised points\n\t# concatenate all the acquised points with the training data\n\tTrain_After_Acquisition_X = np.concatenate( ( Train_After_Acquisition_X, Pooled_X ), axis=0 )\n\tTrain_After_Acquisition_Y = np.concatenate( ( Train_After_Acquisition_Y, Pooled_Y ), axis=0 )\n\n\n\n\n# Fit the model with all the training data\nTrain_After_Acquisition_Y = np_utils.to_categorical(Train_After_Acquisition_Y, nb_classes)\nY_test = np_utils.to_categorical(y_test, nb_classes)\n\nmodel = Sequential()\n\nmodel.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=(img_channels, img_rows, img_cols)))\nmodel.add(Activation('relu')) #using relu activation function\nmodel.add(Convolution2D(32, 3, 3))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Convolution2D(64, 3, 3, border_mode='same'))\nmodel.add(Activation('relu'))\nmodel.add(Convolution2D(64, 3, 3))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Flatten())\nmodel.add(Dense(512))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(nb_classes))\nmodel.add(Activation('softmax'))\n\n\nsgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n\nmodel.compile(loss='categorical_crossentropy', optimizer=sgd)\n\nTrain_After_Acquisition_X = Train_After_Acquisition_X.astype('float32')\nX_test = X_test.astype('float32')\nTrain_After_Acquisition_X /= 255\nX_test /= 255\n\nmodel.fit(Train_After_Acquisition_X, Train_After_Acquisition_Y, batch_size=batch_size, nb_epoch=nb_epoch)\n\n# Test the model\nprint('TEST THE MODEL ACCURACY')\n# Compute the test error and accuracy \nscore, acc = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)\n\nprint('Test score:', score)\nprint('Test accuracy:', acc)\n\nall_accuracy = np.append(all_accuracy, acc)\n\nnp.savetxt(\"Minimum Expected Error Accuracy Values.csv\", all_accuracy, delimiter=\",\")\n\n\n\n\n\n\nprint ('DONE')\n\n\n\n\n\n\n","sub_path":"ConvNets/active_learning/Acquisition_Functions/Minimum_Expected_Entropy/acquisition_minimum_expected_entropy.py","file_name":"acquisition_minimum_expected_entropy.py","file_ext":"py","file_size_in_byte":8347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"58453372","text":"\"\"\"\nREVISIT: Need to do this problem, and annotate it as well\nQuestion: https://leetcode.com/problems/android-unlock-patterns/\nGiven an Android 3x3 key lock screen and two integers m and n, where 1 ≤ m ≤ n ≤ 9,\ncount the total number of unlock patterns of the Android lock screen, which consist of minimum of m keys and maximum n keys.\n\n\n\nRules for a valid pattern:\n\nEach pattern must connect at least m keys and at most n keys.\nAll the keys must be distinct.\nIf the line connecting two consecutive keys in the pattern passes through any other keys,\nthe other keys must have previously selected in the pattern. No jumps through non selected key is allowed.\nThe order of keys used matters.\n\n\n\n\n\nExplanation:\n\n| 1 | 2 | 3 |\n| 4 | 5 | 6 |\n| 7 | 8 | 9 |\nInvalid move: 4 - 1 - 3 - 6\nLine 1 - 3 passes through key 2 which had not been selected in the pattern.\n\nInvalid move: 4 - 1 - 9 - 2\nLine 1 - 9 passes through key 5 which had not been selected in the pattern.\n\nValid move: 2 - 4 - 1 - 3 - 6\nLine 1 - 3 is valid because it passes through key 2, which had been selected in the pattern\n\nValid move: 6 - 5 - 4 - 1 - 9 - 2\nLine 1 - 9 is valid because it passes through key 5, which had been selected in the pattern.\n\n\n\nExample:\n\nInput: m = 1, n = 1\nOutput: 9\n\"\"\"\n\n\nclass Solution(object):\n def numberOfPatterns(self, m, n):\n \"\"\"\n :type m: int\n :type n: int\n :rtype: int\n \"\"\"\n skip = {}\n\n skip[(1, 7)] = 4\n skip[(1, 3)] = 2\n skip[(1, 9)] = 5\n skip[(2, 8)] = 5\n skip[(3, 7)] = 5\n skip[(3, 9)] = 6\n skip[(4, 6)] = 5\n skip[(7, 9)] = 8\n res = 0\n\n def bfs(used, last):\n nonlocal res\n if len(used) >= m:\n res += 1\n if len(used) == n:\n return\n for j in range(1, 10):\n if j not in used: # if j is not used\n # Sort the vertices of the edge to search in skip\n edge = (min(last, j), max(last, j))\n if edge not in skip or skip[edge] in used:\n bfs(used + [j], j)\n\n for i in range(1, 10):\n bfs([i], i)\n return res\n","sub_path":"python/coding_challenges/leet_code/android_unlock_patterns.py","file_name":"android_unlock_patterns.py","file_ext":"py","file_size_in_byte":2209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"501348513","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport codecs\n\nfull_ontology_filepath = u'ontology/gastrointestinal.owl'\ndisease_name_filepath = 'ontology/diseaseName.txt'\n\ndef read_file(filepath):\n results = []\n\n file_obj = codecs.open(filepath,'r','utf-8')\n while True:\n line = file_obj.readline()\n line=line.strip('\\r\\n')\n if not line:\n break\n results.append(line)\n file_obj.close()\n return results\n\ndef read_ontology(filepath):\n results = []\n\n file_obj = codecs.open(filepath,'r','utf-8')\n while True:\n line = file_obj.readline()\n line=line.strip('\\r\\n')\n if 'wdswdshcyhcy' in line:\n break\n results.append(line)\n file_obj.close()\n return results\n\ndef extract_name(ontology):\n results = []\n i = 0\n while i < len(ontology):\n line = ontology[i]\n\n if '' in line:\n left = line.index('>') + 1\n right = line.index(r' 1:\n size = left(x, y, temp_length, grid)\n size_list.append(size)\n temp_length += 1\n for y in range(10):\n for x in range(20):\n temp_length = 1\n while x + temp_length-1 < len(grid_right[0]) and grid_right[y][x + temp_length-1] == 1:\n if temp_length > 1:\n rsize = left(x, y, temp_length, grid_right)\n size_list.append(rsize)\n temp_length += 1\n for y in range(10):\n for x in range(20):\n temp_length = 1\n while x + temp_length-1 < len(grid_right[0]) and grid_left[y][x + temp_length-1] == 1:\n if temp_length > 1:\n lsize = left(x, y, temp_length, grid_left)\n size_list.append(lsize)\n temp_length += 1\n\n if size_list:\n return max(size_list)\n return 0\n\n\ndef left(x, y, length, grid):\n temp_y = y\n while temp_y <= 10:\n for _x in range(x, x + length):\n if temp_y == 10:\n return (length) * (10 - y)\n if temp_y < 10 and x < len(grid[0]) and grid[temp_y][_x] == 0:\n if temp_y - y > 1:\n return (length) * (temp_y - y)\n else:\n return 0\n temp_y += 1\n return 0\n\n\n\n# POSSIBLY DEFINE OTHER FUNCTIONS\n\ntry:\n\n for_seed, density = (int(x) for x in input('Enter two integers, the second '\n 'one being strictly positive: '\n ).split()\n )\n if density <= 0:\n raise ValueError\nexcept ValueError:\n print('Incorrect input, giving up.')\n sys.exit()\n\nseed(for_seed)\ngrid = [[int(randrange(density) != 0) for _ in range(dim)]\n for _ in range(dim)\n ]\nprint('Here is the grid that has been generated:')\ndisplay_grid()\nsize = size_of_largest_parallelogram()\nif size:\n print('The largest parallelogram with horizontal sides '\n f'has a size of {size}.'\n )\nelse:\n print('There is no parallelogram with horizontal sides.')\n","sub_path":"Quiz/Quiz_6/WZZ.py","file_name":"WZZ.py","file_ext":"py","file_size_in_byte":3504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"588992223","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Aug 28 07:51:08 2019\nCreated on Fri Aug 30 09:55:03 2019\n\n@author: thevexis\n\nWe make the assumptions of knowing some constant values\nthe original equation in the text is equal to N(t) = X*e^(-k_I*t)+(N_V0 - X)*e^(-k_v*t)\nwhere X was a constant value equal to beta/(k_V-k_I), where k_I is the clearence rate constant\nfor infected T cells, k_V is the clearence rate for virions, beta is equal to gamma*N_I0, where gamma \nis the rate constant for virion production per infected T cell and N_I0 is the intial population of \ninfected T cells.\n\nWe also made some assumptions in the derivation of our differential equation.\nInstead of solving equation 1.3 intuitions were made based on if k_I >> k_V and \nk_V >> k_I in which it was assumed that the long term equation is proportional either\nto e^(-k_V*t) or e^(-k_I*t) in which the trial function N(t) was formed.\n\nIf we did not make these assumptions then we would most likely have a partial differential\nequation that we would have to solve with an infinite sum so our script could not have used\nan easy exponential function instead we would have to do many summations of linear fucntions with many constants \nand use that as our equation for python to solve which would take more time and computing power\n\n\n\"\"\"\n\n\n\n\nimport math as math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nHIV_data = np.loadtxt(\"C:/Users/theve/Documents/GitHub/PHYS-3210/Week 01/data/HIVseries.csv\")\nplt.plot(HIV_data)\n \ntime = np.linspace(1,10,101)\n\n\"\"\"\n\nA = float(input(\"Enter a value for constant A: \", ))\nB = float(input(\"Enter a value for constant B: \", ))\nalpha = float(input(\"Enter a value for constant alpha: \", ))\nbeta = float(input(\"Enter a value for constant beta: \", ))\n\n\"\"\"\n\nfig = plt.figure()\nax = fig.add_subplot(111)\n\nfor A in range(0,5):\n \n B = 1\n alpha = 1\n beta = 1\n \n viral_load = A*np.exp((-1)*alpha*time)+ B*np.exp((-1)*beta*time)\n x_points = viral_load\n y_points = time\n p = ax.plot(x_points, y_points, 'o', c='r')\n \np = ax.plot(x_points, y_points, 'o', c='r', label=\"A\")\n \n \n \nfor B in range(0,5):\n \n A = 1\n alpha = 1\n beta = 1\n \n viral_load = A*np.exp((-1)*alpha*time)+ B*np.exp((-1)*beta*time)\n x_points = viral_load\n y_points = time\n p = ax.plot(x_points, y_points, '-', c='g')\n \np = ax.plot(x_points, y_points, '-', c='g', label=\"B\")\n \n \n\nfor alpha in range(0,5):\n \n A = 1\n B = 1\n beta = 1\n \n viral_load = A*np.exp((-1)*alpha*time)+ B*np.exp((-1)*beta*time)\n x_points = viral_load\n y_points = time\n p = ax.plot(x_points, y_points, 'o', c='c')\n \np = ax.plot(x_points, y_points, 'o', c='c', label=\"alpha\")\n \n \n\nfor beta in range(0,5):\n \n A = 1\n B = 1\n alpha = 1\n \n viral_load = A*np.exp((-1)*alpha*time)+ B*np.exp((-1)*beta*time)\n x_points = viral_load\n y_points = time\n p = ax.plot(x_points, y_points, '-', c='b')\n \np = ax.plot(x_points, y_points, '-', c='b', label=\"beta\")\n \n \nax.set_xlabel('Viral Load')\nax.set_ylabel('Time')\nax.set_title('HIV Graph')\nax.legend(loc='upper right')\nfig.show()\nplt.savefig(\"HIV.pdf\")\n\n\n#plt.plot(time, viral_load)\n\n","sub_path":"Labs/HIV.py","file_name":"HIV.py","file_ext":"py","file_size_in_byte":3195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"585791131","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n\"\"\"\nfrom requests_html import HTMLSession\nfrom requests_html import MaxRetries\nfrom bs4 import BeautifulSoup\nimport json\nimport re\nimport datetime\nimport settings\nimport scrape_tools\nimport utils\nimport data_sources\n\n\ndef target_loop(target, file_name=''):\n '''\n\n '''\n session = HTMLSession()\n categorias = target['categorias']\n productos = []\n\n for categoria, url in categorias.items():\n\n print('Procesando categoria ' + categoria)\n print('url : ' + url)\n\n category_page = None\n try:\n category_page = scrape_tools.render_html(session, url)\n\n except MaxRetries:\n print('no se puede renderizar la pagina, compruebe la url proporcionada e intente en unos minutos')\n return None\n print('recuperando')\n\n if category_page is not None:\n n_prod = 0\n for page in category_page.html:\n bs = BeautifulSoup(page.html, 'html.parser')\n raw_products = bs.find_all(target['html-container'], {target['selector-type']: target['css-selector']})\n marcas = bs.find_all(target['html-container'], {target['selector-type']: target['css-brand-selector']})\n articulos = bs.find_all(target['html-container'], {target['selector-type']: target['css-item-selector']})\n precios = bs.find_all(target['html-container'], {target['selector-type']: target['css-precio-selector']})\n\n i = 0\n max_index = len(articulos) -1\n for i in range(0, max_index):\n dp = {}\n dp['marca'] = marcas[i].text\n dp['articulo'] = articulos[i].text\n\n valores = []\n for precio in precios[i]:\n valores.append(re.sub('[^0-9]','', precio.text))\n\n dp['precios'] = valores\n\n productos.append(dp)\n i += 1\n n_prod += 1\n\n print('Cantidad de productos procesados: ', n_prod)\n\n\n if len( productos) > 0:\n print('guardando productos target : ' + target['nombre'])\n if file_name.strip:\n now = datetime.datetime.now()\n file_name = 'target_' + target['nombre'] + '_' + now.isoformat()\n\n print(file_name)\n json_dump = json.dumps(productos)\n with open(file_name, \"w\") as f:\n f.write(json_dump)\n print('exito al guardar')\n\n '''\n print(\"recuperando...\")\n productos_html = product_site.html.find('div.' + data_sources.targets['falabella']['css-selector'])\n\n print(\"Construyendo listas\")\n productos = []\n for p in productos_html:\n pp = p.html\n bs = BeautifulSoup(pp, 'html.parser')\n producto = {}\n producto['marca'] = bs.find(\"div\", {\"class\": \"section__pod-top-brand\"}).text\n producto['articulo'] = bs.find(\"div\", {\"class\": \"section__pod-top-title\"}).text\n raw_values = bs.find_all(\"p\", {\"class\": \"fb-price\"})\n\n\n precios = []\n for raw_value in raw_values:\n\n precios.append(re.sub('[^0-9]','', raw_value.text))\n\n producto['valores'] = precios\n\n productos.append(producto)\n\n\n print(productos)\n json = json.dumps(productos)\n f = open(\"dict.json\",\"w\")\n f.write(json)\n f.close()\n\nexcept MaxRetries:\n print('no se puede renderizar la pagina, compruebe la url proporcionada e intente en unos minutos')\n '''\n","sub_path":"0.1.1/page_behaviours.py","file_name":"page_behaviours.py","file_ext":"py","file_size_in_byte":3644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"60043811","text":"# Copyright 2013, Big Switch Networks, Inc.\n#\n# LoxiGen is licensed under the Eclipse Public License, version 1.0 (EPL), with\n# the following special exception:\n#\n# LOXI Exception\n#\n# As a special exception to the terms of the EPL, you may distribute libraries\n# generated by LoxiGen (LoxiGen Libraries) under the terms of your choice, provided\n# that copyright and licensing notices generated by LoxiGen are not altered or removed\n# from the LoxiGen Libraries and the notice provided below is (i) included in\n# the LoxiGen Libraries, if distributed in source code form and (ii) included in any\n# documentation for the LoxiGen Libraries, if distributed in binary form.\n#\n# Notice: \"Copyright 2013, Big Switch Networks, Inc. This library was generated by the LoxiGen Compiler.\"\n#\n# You may not use this file except in compliance with the EPL or LOXI Exception. You may obtain\n# a copy of the EPL at:\n#\n# http://www.eclipse.org/legal/epl-v10.html\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# EPL for the specific language governing permissions and limitations\n# under the EPL.\n\nfrom collections import namedtuple\nimport struct\nimport of_g\nimport loxi_front_end.type_maps as type_maps\nimport loxi_utils.loxi_utils as utils\nimport util\nimport oftype\nfrom loxi_ir import *\n\nPyOFClass = namedtuple('PyOFClass', ['name', 'pyname', 'members', 'type_members',\n 'min_length', 'is_fixed_length',\n 'has_internal_alignment', 'has_external_alignment'])\n\n# Return the name for the generated Python class\ndef generate_pyname(cls):\n if utils.class_is_action(cls):\n return cls[10:]\n elif utils.class_is_oxm(cls):\n return cls[7:]\n elif utils.class_is_meter_band(cls):\n return cls[14:]\n elif utils.class_is_instruction(cls):\n return cls[15:]\n else:\n return cls[3:]\n\n# Create intermediate representation, extended from the LOXI IR\n# HACK the oftype member attribute is replaced with an OFType instance\ndef build_ofclasses(version):\n ofclasses = []\n for ofclass in of_g.ir[version].classes:\n cls = ofclass.name\n if ofclass.virtual:\n continue\n\n members = []\n type_members = []\n\n for m in ofclass.members:\n if type(m) == OFTypeMember:\n members.append(m)\n type_members.append(members[-1])\n elif type(m) == OFLengthMember:\n members.append(m)\n elif type(m) == OFFieldLengthMember:\n members.append(m)\n elif type(m) == OFPadMember:\n members.append(m)\n elif type(m) == OFDataMember:\n if utils.class_is_message(ofclass.name) and m.name == 'version':\n # HACK move to frontend\n members.append(OFTypeMember(\n name=m.name,\n oftype=m.oftype,\n value=version))\n type_members.append(members[-1])\n else:\n members.append(m)\n\n ofclasses.append(\n PyOFClass(name=cls,\n pyname=generate_pyname(cls),\n members=members,\n type_members=type_members,\n min_length=of_g.base_length[(cls, version)],\n is_fixed_length=(cls, version) in of_g.is_fixed_length,\n has_internal_alignment=cls == 'of_action_set_field',\n has_external_alignment=cls == 'of_match_v3'))\n return ofclasses\n\ndef generate_init(out, name, version):\n util.render_template(out, 'init.py', version=version)\n\ndef generate_action(out, name, version):\n ofclasses = [x for x in build_ofclasses(version)\n if utils.class_is_action(x.name)]\n util.render_template(out, 'action.py', ofclasses=ofclasses, version=version)\n\ndef generate_oxm(out, name, version):\n ofclasses = [x for x in build_ofclasses(version)\n if utils.class_is_oxm(x.name)]\n util.render_template(out, 'oxm.py', ofclasses=ofclasses, version=version)\n\ndef generate_common(out, name, version):\n ofclasses = [x for x in build_ofclasses(version)\n if not utils.class_is_message(x.name)\n and not utils.class_is_action(x.name)\n and not utils.class_is_instruction(x.name)\n and not utils.class_is_meter_band(x.name)\n and not utils.class_is_oxm(x.name)\n and not utils.class_is_list(x.name)]\n util.render_template(out, 'common.py', ofclasses=ofclasses, version=version)\n\ndef generate_const(out, name, version):\n util.render_template(out, 'const.py', version=version,\n enums=of_g.ir[version].enums)\n\ndef generate_instruction(out, name, version):\n ofclasses = [x for x in build_ofclasses(version)\n if utils.class_is_instruction(x.name)]\n util.render_template(out, 'instruction.py', ofclasses=ofclasses, version=version)\n\ndef generate_message(out, name, version):\n ofclasses = [x for x in build_ofclasses(version)\n if utils.class_is_message(x.name)]\n util.render_template(out, 'message.py', ofclasses=ofclasses, version=version)\n\ndef generate_meter_band(out, name, version):\n ofclasses = [x for x in build_ofclasses(version)\n if utils.class_is_meter_band(x.name)]\n util.render_template(out, 'meter_band.py', ofclasses=ofclasses, version=version)\n\ndef generate_pp(out, name, version):\n util.render_template(out, 'pp.py')\n\ndef generate_util(out, name, version):\n util.render_template(out, 'util.py', version=version)\n","sub_path":"py_gen/codegen.py","file_name":"codegen.py","file_ext":"py","file_size_in_byte":5859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"528907793","text":"#!/usr/bin/env python3\n\nwith open('by-precinct.csv', 'rt') as f:\n with open('nm-results.csv', 'wt') as out:\n for i, line in enumerate(f):\n if len(line) == 0: next\n if i == 0:\n out.write('NAME10,color\\n')\n continue\n print(line.strip())\n name, trump_s, clinton_s = line.strip().split(',')\n trump = int(trump_s)\n clinton = int(clinton_s)\n\n color = None\n if trump >= 2 * clinton: color = '#f66'\n elif clinton >= 2 * trump: color = '#66f'\n elif trump > clinton: color = '#faa'\n elif clinton > trump: color = '#aaf'\n else: color = '#ddd'\n\n # Fix names that the Shapefile gets wrong\n name = name.replace('Dona Ana', 'Doa Ana')\n\n out.write('%s,%s\\n' % (name, color))\n","sub_path":"data/nm/render-colors.py","file_name":"render-colors.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"116251665","text":"def countLetters(line):\n \"\"\"\n recieves a line of string and prints the number of alphabet on answer.txt.\n answers are NOT appended when this function is called multiple times.\n \"\"\"\n # iterate over 'line' and check if each character is an alphabet(a letter)\n count = 0\n for ch in line:\n if ch.isalpha():\n count += 1\n # print result on output file\n output = open('answer.txt', 'w')\n output.write(str(count) + '\\n')\n output.close()","sub_path":"CSI2100_01 lab assignment/lab10/lab10_p1.py","file_name":"lab10_p1.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"251593446","text":"# python 3.7.4\n# coding = utf-8\n# filename api.py\n# author 463714869@qq.com/www.cdzcit.com,\n# create by VIM at 2019/12/30\n\nimport ctypes\nfrom gdapi.data_paser.parser_functions import FromMdarec\nfrom gdapi.data_paser.const import *\nfrom pprint import pprint\nimport time\nimport os\nimport log\n\nLOGGER = log.init_logging('.\\\\gdlog.txt', 'debug')\n\n\ndef set_OnReceiveData(pUserParam, nDate, nMarketId, sCode, sName, uType, nServiceId, pData, nLen):\n \"\"\"\n 光大接口数据回调函数\n void __stdcall OnReceiveData(\n void* pUserParam, # [in]用户自定义参数,由用户调用TDR_Create时传入\n T_I32 nDate, # [in]日期\n T_I32 nMarketId, # [in]市场代码，参见tdr.h中对市场进行的宏定义\n const char* sCode, # [in]证券代码\n const char* sName, # [in]证券名称\n T_U32 uType, # [in]证券类型\n T_I32 nServiceId, # [in]服务数据ID，比如行情、逐笔成交等，参见tdr.h文件定义\n void* pData, # [in]数据内容\n T_I32 nLen # [in]数据长度\n )\n \"\"\"\n LOGGER.debug('TRIGGERED with param(pUserParam:%s, nDate:%s, nMarketId:%s, sCode:%s, sName:%s, uType:%s, '\n 'nServiceId:%s, pData:%s, nLen:%s)' % (\n pUserParam, nDate, nMarketId, sCode, sName, uType, nServiceId, pData, nLen))\n g = (ctypes.c_char * nLen).from_address(pData)\n ret_mds = FromMdarec(nMarketId, nServiceId, g[0:nLen], nLen)\n LOGGER.info('get all %d datas' % len(ret_mds))\n # if len(ret_mds) > 0: pprint(ret_mds[0])\n for data in ret_mds:\n LOGGER.info('{}'.format(data))\n\n\n# 注册数据回调C函数\nONRECEIVEDATAFUNC = ctypes.CFUNCTYPE(None,\n ctypes.c_void_p,\n ctypes.c_int,\n ctypes.c_int,\n ctypes.c_char_p,\n ctypes.c_char_p,\n ctypes.c_uint,\n ctypes.c_int,\n ctypes.c_void_p,\n ctypes.c_int)\ncallback_OnReceiveData = ONRECEIVEDATAFUNC(set_OnReceiveData)\n\n\ndef set_OnErrorMsg(pUserParam, nError, nErrSource, uData):\n \"\"\"\n 光大接口错误回调函数\n :param LOGGER: 日志对象\n :param pUserParam: see set_OnReceiveData\n :param nError: see set_OnReceiveData\n :param nErrSource: see set_OnReceiveData\n :param uData: see set_OnReceiveData\n :return:\n \"\"\"\n LOGGER.debug('TRIGGERED with param(pUserParam:%s, nError:%s, nErrSource:%s, uData:%s)' % (\n pUserParam, nError, nErrSource, uData))\n if nError == 0 and nErrSource == ERRMSGSRC_LOGIN:\n LOGGER.debug('%d-%s' % (nError, errorStringList[nError]))\n else:\n if nErrSource == ERRMSGSRC_MARKETSTATE and nError in (\n HQD_MARKET_SH, HQD_MARKET_SZ) and uData == MARKET_STATE_CLOSE:\n LOGGER.error('Market closed %d' % nError)\n else:\n LOGGER.error('Unkown error: %d' % nError)\n\n\n# 注册错误回调C函数\nONERRORMSGFUNC = ctypes.CFUNCTYPE(None,\n ctypes.c_void_p,\n ctypes.c_int,\n ctypes.c_int,\n ctypes.c_uint)\ncallback_OnErrorMsg = ONERRORMSGFUNC(set_OnErrorMsg)\n\n\ndef init_inst(dlldir, dllpath):\n \"\"\"\n 打开库文件并创建可执行句柄\n :param dlldir: 光大库文件目录\n :param dllpath: 要打开的库文件路径\n :return: dll + 句柄\n \"\"\"\n if not os.path.exists(os.path.expanduser(dllpath)) or \\\n not os.path.exists(os.path.expanduser(dlldir)):\n LOGGER.error('%s or %s not exist' % (dlldir, dllpath))\n return None, None\n os.environ['PATH'] = os.path.expanduser(dlldir) + ';' + os.environ['PATH']\n inst = ctypes.windll.LoadLibrary(os.path.expanduser(dllpath))\n inst.TDR_Create.restype = ctypes.c_int64\n inst.TDR_Create.argtypes = [ctypes.c_char_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p,\n ctypes.c_bool, ctypes.c_bool]\n handle = inst.TDR_Create(ctypes.c_char_p(0),\n callback_OnReceiveData,\n ctypes.c_void_p(0),\n callback_OnErrorMsg,\n ctypes.c_void_p(0),\n ctypes.c_bool(True),\n ctypes.c_bool(True))\n return inst, handle\n\n\ndef gd_login(inst, handle, uesrname, password, serverip, port, nettype, netoperator, loginmode, timeout):\n \"\"\"\n 登录到光大接口\n :param inst: init_inst()返回的inst\n :param handle: init_inst()返回的handle\n :param uesrname: 光大接口用户名\n :param password: 光大接口用户密码\n :param serverip: 服务器IP\n :param port: 服务器端口\n :param nettype: 网络模式：\n SIP_SVR_WAN = 0 # SIP_SVR的外网\n IPSIP_SVR_LAN = 1 # SIP_SVR的内网IP\n :param netoperator: 网络运营商：\n WAN_TC = 0 # 电信\n WAN_NC = 1 # 网通\n WAN_UC = 2 # 联通\n WAN_MC = 3 # 移动\n WAN_CC = 4 # 广电\n :param loginmode: 登录模式：\n UI_LOGIN_NORMAL = 0x81 # 普通模式，登陆后用户需主动订阅，触发注册的回调函数\n UI_LOGIN_UPLINK = 1 # 级联模式，则无需订阅，登陆后自动获取全部行情 并触发注册的回调函数\n UI_LOGIN_PGM = 2 # PGM模式\n :param timeout: 超市时间（秒）\n :return:\n \"\"\"\n inst.TDR_ConnectByDynamic.restype = ctypes.c_int64\n inst.TDR_ConnectByDynamic.argtypes = [ctypes.c_int64, ctypes.c_char_p, ctypes.c_int, ctypes.c_int, ctypes.c_int,\n ctypes.c_char_p, ctypes.c_char_p, ctypes.c_int, ctypes.c_int]\n ret = inst.TDR_ConnectByDynamic(handle,\n ctypes.c_char_p(serverip.encode('utf-8')),\n ctypes.c_int(port),\n ctypes.c_int(nettype),\n ctypes.c_int(netoperator),\n ctypes.c_char_p(uesrname.encode('utf-8')),\n ctypes.c_char_p(password.encode('utf-8')),\n ctypes.c_int(loginmode),\n ctypes.c_int(timeout))\n LOGGER.info('ret=%s，信息：%s' % (ret, errorStringList[ret]))\n return ret\n\n\ndef gd_subscribe_by_market(inst, handle, mcode, mode, serviceid):\n \"\"\"\n 通过证券代码订阅实时行情\n :param inst: dll句柄\n :param handle: 光大库句柄\n :param mcode: 市场代码\n HQD_MARKET_SH = 1 # 上海交易所\n HQD_MARKET_SZ = 2 # 深圳交易所\n HQD_MARKET_CFFEX = 3 # 中金所\n HQD_MARKET_CZCE = 4 # 郑商所\n HQD_MARKET_DCE = 5 # 大商所\n HQD_MARKET_SHFE = 6 # 上期所\n HQD_MARKET_ZZZS = 7 # 中证指数行情市场\n HQD_MARKET_SHOP = 8 # 上交所期权\n HQD_MARKET_HK = 9 # 香港市场\n HQD_MARKET_SZOP = 11 # 深交所期权\n HQD_MARKET_SGE = 12 # 上海黄金市场\n HQD_MARKET_SHHK = 13 # 香港市场？\n HQD_MARKET_NEEQ = 15 # 新三板市场\n HQD_MARKET_JTZX = 16 # 静态咨询\n :param mode: 订阅模式\n RSS_MODE_NEW = 0 # 最新订阅\n RSS_MODE_INC = 1 # 增量订阅\n :param serviceid: 服务编码\n HQD_MARKET_SH = 1 # 上海交易所\n ID_SH_INDEXDATA = 0x00 # 指数(Stock_IndexData)\n ID_SH_TRANSACTION = 0x01 # 成交(Stock_Transaction)\n ID_SH_ORDERQUEUE = 0x02 # 委托队列(Stock_OrderQueue_Head+Stock_OrderQueue)\n ID_SH_MARKETDATA = 0x04 # 行情数据(Stock_MarketData)\n ID_SH_MARKETDATA_L1 = 0x05 # 用于L1行情 上海(Stock_MarketData_L1)\n ID_SH_KLINEDATA = 0x07 # 上交所个股分钟K线数据(T_SH_Kline)\n HQD_MARKET_SZ = 2\n ID_SZ_INDEXDATA = 0x00 # 指数(Stock_IndexData)\n ID_SZ_TRANSACTION = 0x01 # 成交(Stock_TransactionEx)\n ID_SZ_ORDERQUEUE = 0x02 # 委托队列(Stock_OrderQueue_Head+Stock_OrderQueue)\n ID_SZ_STEPORDER = 0x03 # 逐笔委托(Stock_StepOrder)\n ID_SZ_MARKETDATA = 0x04 # 行情数据(Stock_MarketData)\n ID_SZ_MARKETDATA_L1 = 0x06 # 用于V5 L1行情 深圳(Stock_MarketData_L1)\n ID_SZ_KLINEDATA = 0x07 # 深交所个股分钟K线数据(T_SZ_Kline)\n ID_SZ_QDHQDATA = 0x08 # 深交所千档行情数据(t_SZ_QDHQData)\n :return: 0 - 成功，其他 - 失败\n \"\"\"\n inst.TDR_SubscribeByMarket.restypes = ctypes.c_int\n inst.TDR_SubscribeByMarket.argtypes = [ctypes.c_int64,\n ctypes.c_char_p,\n ctypes.c_int,\n ctypes.c_int]\n ret = inst.TDR_SubscribeByMarket(handle, mcode, mode, serviceid)\n LOGGER.info('ret=%s，信息：%s' % (ret, errorStringList[ret]))\n return ret\n\n\ndef gd_subscribe_by_code(inst, handle, mcode, scode, mode, serviceid):\n \"\"\"\n 通过证券代码订阅实时行情\n :param inst: dll句柄\n :param handle: 光大库句柄\n :param mcode: 市场代码\n HQD_MARKET_SH = 1 # 上海交易所\n HQD_MARKET_SZ = 2 # 深圳交易所\n HQD_MARKET_CFFEX = 3 # 中金所\n HQD_MARKET_CZCE = 4 # 郑商所\n HQD_MARKET_DCE = 5 # 大商所\n HQD_MARKET_SHFE = 6 # 上期所\n HQD_MARKET_ZZZS = 7 # 中证指数行情市场\n HQD_MARKET_SHOP = 8 # 上交所期权\n HQD_MARKET_HK = 9 # 香港市场\n HQD_MARKET_SZOP = 11 # 深交所期权\n HQD_MARKET_SGE = 12 # 上海黄金市场\n HQD_MARKET_SHHK = 13 # 香港市场？\n HQD_MARKET_NEEQ = 15 # 新三板市场\n HQD_MARKET_JTZX = 16 # 静态咨询\n :param scode: 证券代码\n :param mode: 订阅模式\n RSS_MODE_NEW = 0 # 最新订阅\n RSS_MODE_INC = 1 # 增量订阅\n :param serviceid: 服务编码\n HQD_MARKET_SH = 1 # 上海交易所\n ID_SH_INDEXDATA = 0x00 # 指数(Stock_IndexData)\n ID_SH_TRANSACTION = 0x01 # 成交(Stock_Transaction)\n ID_SH_ORDERQUEUE = 0x02 # 委托队列(Stock_OrderQueue_Head+Stock_OrderQueue)\n ID_SH_MARKETDATA = 0x04 # 行情数据(Stock_MarketData)\n ID_SH_MARKETDATA_L1 = 0x05 # 用于L1行情 上海(Stock_MarketData_L1)\n ID_SH_KLINEDATA = 0x07 # 上交所个股分钟K线数据(T_SH_Kline)\n HQD_MARKET_SZ = 2\n ID_SZ_INDEXDATA = 0x00 # 指数(Stock_IndexData)\n ID_SZ_TRANSACTION = 0x01 # 成交(Stock_TransactionEx)\n ID_SZ_ORDERQUEUE = 0x02 # 委托队列(Stock_OrderQueue_Head+Stock_OrderQueue)\n ID_SZ_STEPORDER = 0x03 # 逐笔委托(Stock_StepOrder)\n ID_SZ_MARKETDATA = 0x04 # 行情数据(Stock_MarketData)\n ID_SZ_MARKETDATA_L1 = 0x06 # 用于V5 L1行情 深圳(Stock_MarketData_L1)\n ID_SZ_KLINEDATA = 0x07 # 深交所个股分钟K线数据(T_SZ_Kline)\n ID_SZ_QDHQDATA = 0x08 # 深交所千档行情数据(t_SZ_QDHQData)\n :return: 0 - 成功，其他 - 失败\n \"\"\"\n inst.TDR_SubscribeByCode.restypes = ctypes.c_int\n inst.TDR_SubscribeByCode.argtypes = [ctypes.c_int64,\n ctypes.c_char_p,\n ctypes.c_char_p,\n ctypes.c_int,\n ctypes.c_int]\n ret = inst.TDR_SubscribeByCode(handle, mcode, scode, mode, serviceid)\n LOGGER.info('ret=%s，信息：%s' % (ret, errorStringList[ret]))\n return ret\n\n\ndef gd_subscribe_by_group(inst, handle, mcode, scode, mode, serviceid):\n \"\"\"\n 通过证券代码订阅实时行情\n :param inst: dll句柄\n :param handle: 光大库句柄\n :param mcode: 市场代码\n HQD_MARKET_SH = 1 # 上海交易所\n HQD_MARKET_SZ = 2 # 深圳交易所\n HQD_MARKET_CFFEX = 3 # 中金所\n HQD_MARKET_CZCE = 4 # 郑商所\n HQD_MARKET_DCE = 5 # 大商所\n HQD_MARKET_SHFE = 6 # 上期所\n HQD_MARKET_ZZZS = 7 # 中证指数行情市场\n HQD_MARKET_SHOP = 8 # 上交所期权\n HQD_MARKET_HK = 9 # 香港市场\n HQD_MARKET_SZOP = 11 # 深交所期权\n HQD_MARKET_SGE = 12 # 上海黄金市场\n HQD_MARKET_SHHK = 13 # 香港市场？\n HQD_MARKET_NEEQ = 15 # 新三板市场\n HQD_MARKET_JTZX = 16 # 静态咨询\n :param scode: 证券代码\n :param mode: 订阅模式\n RSS_MODE_NEW = 0 # 最新订阅\n RSS_MODE_INC = 1 # 增量订阅\n :param serviceid: 服务编码\n HQD_MARKET_SH = 1 # 上海交易所\n ID_SH_INDEXDATA = 0x00 # 指数(Stock_IndexData)\n ID_SH_TRANSACTION = 0x01 # 成交(Stock_Transaction)\n ID_SH_ORDERQUEUE = 0x02 # 委托队列(Stock_OrderQueue_Head+Stock_OrderQueue)\n ID_SH_MARKETDATA = 0x04 # 行情数据(Stock_MarketData)\n ID_SH_MARKETDATA_L1 = 0x05 # 用于L1行情 上海(Stock_MarketData_L1)\n ID_SH_KLINEDATA = 0x07 # 上交所个股分钟K线数据(T_SH_Kline)\n HQD_MARKET_SZ = 2\n ID_SZ_INDEXDATA = 0x00 # 指数(Stock_IndexData)\n ID_SZ_TRANSACTION = 0x01 # 成交(Stock_TransactionEx)\n ID_SZ_ORDERQUEUE = 0x02 # 委托队列(Stock_OrderQueue_Head+Stock_OrderQueue)\n ID_SZ_STEPORDER = 0x03 # 逐笔委托(Stock_StepOrder)\n ID_SZ_MARKETDATA = 0x04 # 行情数据(Stock_MarketData)\n ID_SZ_MARKETDATA_L1 = 0x06 # 用于V5 L1行情 深圳(Stock_MarketData_L1)\n ID_SZ_KLINEDATA = 0x07 # 深交所个股分钟K线数据(T_SZ_Kline)\n ID_SZ_QDHQDATA = 0x08 # 深交所千档行情数据(t_SZ_QDHQData)\n :return: 0 - 成功，其他 - 失败\n \"\"\"\n inst.TDR_SubscribeByGroup.restypes = ctypes.c_int\n inst.TDR_SubscribeByGroup.argtypes = [ctypes.c_int64,\n ctypes.c_char_p,\n ctypes.c_char_p,\n ctypes.c_int,\n ctypes.c_int]\n ret = inst.TDR_SubscribeByGroup(handle, mcode, scode, mode, serviceid)\n LOGGER.info('ret=%s，信息：%s' % (ret, errorStringList[ret]))\n return ret\n\n\ndef gd_getdata(inst, handle, mcode, scode, serviceid, pdata, datalen):\n \"\"\"\n 订阅模式，主动在内存中取数据\n :param inst: DLL实例\n :param handle: 光大库句柄\n :param mcode: 市场代码，见gd_subscribe\n :param scode: 证券代码\n :param serviceid: 服务ID，见gd_subscribe\n :param pdata: 数据缓存区\n :param datalen: 数据长度\n :return: 获取到的数据长度\n \"\"\"\n inst.TDR_GetMarketData.restypes = ctypes.c_int\n inst.TDR_GetMarketData.argtypes = [ctypes.c_int64,\n ctypes.c_char_p,\n ctypes.c_char_p,\n ctypes.c_int,\n ctypes.c_void_p,\n ctypes.c_int]\n ret = inst.TDR_GetMarketData(handle, mcode, scode, serviceid, pdata, datalen)\n LOGGER.info('ret=%s，信息：%s' % (ret, errorStringList[ret]))\n if ret == 0:\n print(pdata)\n return ret\n\n\ndef gd_unsubscribe(inst, handle):\n inst.TDR_UnsubscribeAll.restypes = ctypes.c_int\n inst.TDR_UnsubscribeAll.argtypes = [ctypes.c_int64]\n ret = inst.TDR_UnsubscribeAll(handle)\n LOGGER.info('ret=%s，信息：%s' % (ret, errorStringList[ret]))\n return ret\n\n\ndef gd_isconnected(inst, handle):\n inst.TDR_IsConnected.restype = ctypes.c_bool\n inst.TDR_IsConnected.argtypes = [ctypes.c_int64]\n ret = inst.TDR_IsConnected(handle)\n LOGGER.info('Isconnencted? ret=%d' % ret)\n return ret\n\n\ndef gd_disconnect(inst, handle):\n inst.TDR_DisConnect.restype = ctypes.c_int\n inst.TDR_DisConnect.argtypes = [ctypes.c_int64]\n ret = inst.TDR_DisConnect(handle)\n LOGGER.info('ret=%s，信息：%s' % (ret, errorStringList[ret]))\n return ret\n\n\ndef gd_destroy(inst, handle):\n inst.TDR_Destroy.restypes = ctypes.c_int\n inst.TDR_Destroy.argtypes = [ctypes.c_int64]\n ret = inst.TDR_Destroy(handle)\n LOGGER.info('ret=%s，信息：%s' % (ret, errorStringList[ret]))\n return ret\n\n\nDLLDIR = 'C:\\\\Users\\\\Admin\\\\Documents\\\\ZCIT-Projects\\\\PythonProj\\\\Quantification\\\\gdapi\\\\dll'\nDLLPATH = 'C:\\\\Users\\\\Admin\\\\Documents\\\\ZCIT-Projects\\\\PythonProj\\\\Quantification\\\\gdapi\\\\dll\\\\sipuicom64.dll'\nSERVER = '116.236.247.183'\nPORT = 9888\nUSER = 'gdzq_jinshi'\nPWD = 'test123456'\n\nif __name__ == '__main__':\n dll, handle = init_inst(DLLDIR, DLLPATH)\n if dll is None or handle is None:\n print('Cannot load dll from: %s' % DLLPATH)\n\n ret = gd_login(dll, handle, USER, PWD, SERVER, PORT,\n SIP_SVR_WAN, WAN_TC, UI_LOGIN_NORMAL, 120)\n print('Login: %d-%s' % (ret, errorStringList[ret]))\n\n ret = gd_subscribe_by_group(dll, handle, 'SZ'.encode('utf-8'), '128053,113555,123034,113510'.encode('utf-8'), RSS_MODE_INC, ID_SZ_MARKETDATA)\n print('Subscribe: %d-%s' % (ret, errorStringList[ret]))\n\n \"\"\"\n while True:\n if gd_isconnected(dll, handle):\n print('Connected, wait TRANSACTIONS...')\n time.sleep(120)\n else:\n break\n\n ret = gd_unsubscribe(dll, handle)\n print('Unsubscribe: %d-%s' % (ret, errorStringList[ret]))\n\n ret = gd_disconnect(dll, handle)\n print('Disconnect: %d-%s' % (ret, errorStringList[ret]))\n\n ret = gd_destroy(dll, handle)\n print('Distroy: %d-%s' % (ret, errorStringList[ret]))\n \"\"\"\n","sub_path":"gdapi/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":19338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"181850047","text":"# -*- coding: utf-8 -*-\n\"\"\" Test configuration and fixtures. \"\"\"\n\nimport pytest\nimport sqlalchemy\n\nfrom alembic import command\nfrom alembic.config import Config as AlembicConfig\nfrom decouple import config\nfrom flask_migrate import Migrate\n\nfrom versions_service.app import create_app\nfrom versions_service.extensions import db as _db\nfrom versions_service.settings import Config\n\n\nclass TestConfig(Config):\n \"\"\"TestConfig\n\n A configuration object specific to the testing context.\n \"\"\"\n\n # Database connection parameters\n SQLALCHEMY_DATABASE_URI = config('TEST_DATABASE_URI', 'postgresql://postgres@postgres:5432/test_versions_service') # noqa\n\n # When the FLASK_DEBUG flag is on, preserving context causes\n # \"Popped wrong request context\" assertion errors when running tests.\n PRESERVE_CONTEXT_ON_EXCEPTION = False\n\n\n@pytest.fixture(scope=\"session\")\ndef app(request):\n \"\"\"app\n\n Returns session-wide application.\n \"\"\"\n app = create_app(config_object=TestConfig)\n ctx = app.app_context()\n ctx.push()\n\n yield app\n\n ctx.pop()\n\n\n@pytest.fixture(scope='session')\ndef db(app, request):\n \"\"\"db\n\n Returns session-wide initialized database.\n \"\"\"\n\n database_uri = sqlalchemy.engine.url.make_url(TestConfig.SQLALCHEMY_DATABASE_URI)\n host_uri = sqlalchemy.engine.url.URL(\n database_uri.drivername,\n username=database_uri.username,\n password=database_uri.password,\n host=database_uri.host,\n port=database_uri.port)\n database_name = database_uri.database\n template_engine = sqlalchemy.create_engine(host_uri, echo=False)\n conn = template_engine.connect()\n conn = conn.execution_options(\n autocommit=True, isolation_level='AUTOCOMMIT')\n\n try:\n conn.execute(f'DROP DATABASE IF EXISTS {database_name};')\n conn.execute(f'CREATE DATABASE {database_name};')\n except:\n pass\n finally:\n conn.close()\n template_engine.dispose()\n\n Migrate(_db.app, _db)\n alembic_config = AlembicConfig('/srv/migrations/alembic.ini')\n alembic_config.set_main_option('script_location', 'migrations')\n command.upgrade(alembic_config, 'head')\n\n yield _db\n\n\n@pytest.fixture(scope='function')\ndef session(app, db, request):\n \"\"\"session\n\n Returns function-scoped session, ensuring that tests run in an isolated context.\n\n This includes database cleaning and any other operations that clean the application state\n so that the tests run consistently.\n \"\"\"\n for table in reversed(db.metadata.sorted_tables):\n db.session.execute(f'TRUNCATE TABLE {table} CASCADE;')\n\n db.session.commit()\n\n yield db.session\n\n db.session.close()\n","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":2686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"632592177","text":"import torch\nimport torch.nn as nn\n\noperation_canditates = {\n '00': lambda C, stride: Zero(stride),\n '01': lambda C, stride: Inception(C, C, 3, stride, 1),\n '10': lambda C, stride: Identity() if stride == 1 else FactorizedReduce(C, C),\n '11': lambda C, stride: ResSepConv(C, C, 3, stride, 1),\n}\n\n\nclass Conv2d(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, padding, stride, bias):\n super(Conv2d, self).__init__()\n self.features = nn.Sequential(\n nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias),\n nn.GroupNorm(6, out_channels),\n nn.ReLU(inplace=True)\n )\n\n def forward(self, x):\n return self.features(x)\n\n\nclass Inception(nn.Module):\n def __init__(self, C_in, C_out, ksize, stride, padding):\n super(Inception, self).__init__()\n self.branch1 = Conv2d(C_in, C_out // 2, kernel_size=ksize, stride=stride,\n padding=padding, bias=False)\n self.branch2 = Conv2d(C_in, C_out // 2, kernel_size=ksize, stride=stride,\n padding=padding, bias=False)\n\n def forward(self, x):\n x1 = self.branch1(x)\n x2 = self.branch2(x)\n out = torch.cat([x1, x2], dim=1)\n return out\n\n\nclass SepConv(nn.Module):\n def __init__(self, C_in, C_out, ksize, stride, padding):\n super(SepConv, self).__init__()\n self.features = nn.Sequential(\n nn.ReLU(inplace=False),\n nn.Conv2d(C_in, C_in, kernel_size=ksize, stride=stride, padding=padding, groups=C_in, bias=False),\n nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False),\n nn.GroupNorm(6, C_in, affine=False),\n nn.ReLU(inplace=False),\n nn.Conv2d(C_in, C_in, kernel_size=ksize, stride=1, padding=padding, groups=C_in, bias=False),\n nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),\n nn.GroupNorm(6, C_out, affine=False)\n )\n\n def forward(self, x):\n return self.features(x)\n\n\nclass Identity(nn.Module):\n def __init__(self):\n super(Identity, self).__init__()\n\n def forward(self, x):\n return x\n\n\nclass ResSepConv(nn.Module):\n def __init__(self, C_in, C_out, kernel_size, stride, padding):\n super(ResSepConv, self).__init__()\n self.conv = SepConv(C_in, C_out, kernel_size, stride, padding)\n self.res = Identity() if stride == 1 else FactorizedReduce(C_in, C_out)\n\n def forward(self, x):\n return sum([self.conv(x), self.res(x)])\n\n\nclass ReLUConvBN(nn.Module):\n def __init__(self, C_in, C_out, kernel_size, stride, padding):\n super(ReLUConvBN, self).__init__()\n self.op = nn.Sequential(\n nn.ReLU(inplace=False),\n nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False),\n nn.GroupNorm(6, C_out, affine=False)\n )\n\n def forward(self, x):\n return self.op(x)\n\n\nclass FactorizedReduce(nn.Module):\n\n def __init__(self, C_in, C_out):\n super(FactorizedReduce, self).__init__()\n assert C_out % 2 == 0\n self.relu = nn.ReLU(inplace=False)\n self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)\n self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)\n self.bn = nn.GroupNorm(6, C_out, affine=False)\n\n def forward(self, x):\n x = self.relu(x)\n out = torch.cat([self.conv_1(x), self.conv_2(x[:, :, 1:, 1:])], dim=1)\n out = self.bn(out)\n return out\n","sub_path":"nas/basic_operation_gn.py","file_name":"basic_operation_gn.py","file_ext":"py","file_size_in_byte":3603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"241533545","text":"import numpy as np\n\nfrom models.rich_vrp.agent import Agent\nfrom models.rich_vrp.agent_type import AgentType\nfrom models.rich_vrp.costs import AgentCosts\nfrom models.rich_vrp.geometry import DistanceMatrixGeometry\nfrom models.rich_vrp.job import Job\nfrom models.rich_vrp.problem import RichVRPProblem\n\n\ndef get_test_problem():\n \"\"\"\n Vroom tests\n \"\"\"\n np_dist_matrix = np.array([[0, 3, 5], [3, 0, 2], [5, 2, 0]])\n dist_matrix = DistanceMatrixGeometry(np.array([]), np_dist_matrix, 10)\n\n costs = {\"fixed\": 100, \"time\": 3, \"distance\": 5}\n value = [10000]\n start = '0'\n end = '10000'\n start_place = 0\n end_place = 0\n agent_cost = AgentCosts(5, 100, 2, 3, 5)\n agent_type = AgentType(100, 0, [50, 20], agent_cost, [2, 3])\n agent = Agent(0, costs, value, start,end, start_place, end_place, agent_type)\n j1 = Job(0, 0, 0, 0, 0, [(10, 20)], 5, np.array([10]), [2], 5)\n j2 = Job(1, 0, 3, 0, 3, [(30, 50)], 5, np.array([10]), [3], 5)\n j3 = Job(2, 0, 5, 0, 5, [(60, 80)], 5, np.array([10]), [3], 5)\n res = RichVRPProblem(dist_matrix, [agent], [j1, j2, j3], [''])\n return res\n","sub_path":"tests/core/data_generation.py","file_name":"data_generation.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"290480162","text":"import codecs\nimport pandas as pd\nfrom nltk.corpus import wordnet as wn\nimport nltk\nfrom nltk.stem.wordnet import WordNetLemmatizer\nimport string\nimport psycopg2 as pg2\nfrom sqlalchemy import create_engine\n\nwith codecs.open('../data/speeches.csv', 'r', encoding='utf-8', errors='ignore') as fdata:\n df = pd.read_csv(fdata)\n\nlemmatizer = WordNetLemmatizer() # Create Lemmatizer\n\ndef create_dataset(df, db, table):\n inaug_df = pd.DataFrame(columns=['word', 'pos', 'lemma', 'year', 'president']) #Create empty DF\n counter = 0\n for idx, doc in df['text'].iteritems(): # For every inauguration speech\n doc = doc.replace('xa0', '')\n doc = doc.replace('(Applause)', '')\n doc = doc.replace('(applause)', '')\n doc = doc.replace('(Laughter)', '')\n doc = doc.replace('(laughter)', '')\n doc = doc.replace('(Laughter and applause)','')\n text = nltk.word_tokenize(doc) # Tokenize all words in current speech\n pos_tags = nltk.pos_tag(text) # Create Part of Speech Tag for every word\n year = int(df.loc[idx]['date'][-4:]) # Pull Year From Date Column, for new DF\n president = df.loc[idx]['president']\n print(idx, ' / ', len(df)) # Status Check\n for item in pos_tags:\n if item[0] in string.punctuation: # Remove Punctuation\n pass\n else:\n word = item[0].lower() # Seperate Word and Part of Speech into different variables\n part_of_speech = item[1] # To be placed in new DF\n\n # Lemmatize all possible words, adj, verbs, nouns, adverbs\n if str(item[1][0]) == 'J':\n lemma_tag = 'a'\n lemma = lemmatizer.lemmatize(item[0], lemma_tag)\n elif item[1][0] == 'V' or item[1][0] == 'N' or item[1][0] == 'R':\n lemma_tag = str(item[1][0]).lower()\n lemma = lemmatizer.lemmatize(item[0], lemma_tag)\n else:\n lemma = item[0]\n \n inaug_df.loc[counter] = [word, part_of_speech, lemma, year, president] # Add row to DF\n counter += 1 # Move to the next row\n \n sql_upload(inaug_df, db, table)\n\ndef sql_upload(df, db, table): # Upload a DF to SQL\n conn = pg2.connect(dbname = 'postgres', host = \"localhost\")\n conn.autocommit = True\n engine = create_engine('postgresql+psycopg2://owner:Fulfyll@localhost/' + db)\n df.to_sql(table, con = engine, if_exists= \"append\", index=False)\n conn.close()\n\ndef dataset_query(): # Create Pandas DF from entire SQL Table\n conn = pg2.connect(dbname=\"rhetoric_capstone\" , host = \"localhost\")\n # SQL Pull\n query = \"\"\"SELECT *\n FROM inaug_speeches\n \"\"\"\n \n\n inaug_df = pd.read_sql(query, con=conn)\n\n conn.close()\n\n return inaug_df\n\ndef create_lemmaless_dataset(df, db, table):\n inaug_df = pd.DataFrame(columns=['word', 'pos', 'year', 'president']) #Create empty DF\n counter = 0\n for idx, doc in df['text'].iteritems(): # For every inauguration speech\n doc = doc.replace('xa0', '')\n doc = doc.replace('(Applause)', '')\n doc = doc.replace('(applause)', '')\n doc = doc.replace('(Laughter)', '')\n doc = doc.replace('(laughter)', '')\n doc = doc.replace('(Laughter and applause)','')\n text = nltk.word_tokenize(doc) # Tokenize all words in current speech\n pos_tags = nltk.pos_tag(text) # Create Part of Speech Tag for every word\n year = int(df.loc[idx]['date'][-4:]) # Pull Year From Date Column, for new DF\n president = df.loc[idx]['president']\n print(idx, ' / ', len(df)) # Status Check\n for item in pos_tags:\n if item[0] in string.punctuation: # Remove Punctuation\n pass\n else:\n word = item[0].lower() # Seperate Word and Part of Speech into different variables\n part_of_speech = item[1] # To be placed in new DF\n \n inaug_df.loc[counter] = [word, part_of_speech, year, president] # Add row to DF\n counter += 1 # Move to the next row\n \n sql_upload(inaug_df, db, table)\n\n#create_lemmaless_dataset(df, 'rhetoric_capstone', 'all_speeches')","sub_path":"american_values/rhetoric/rhetoric_dataset.py","file_name":"rhetoric_dataset.py","file_ext":"py","file_size_in_byte":4288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"531934514","text":"# -*- coding: utf-8 -*-\n\n# Количество байтов в одном гигабайте\nBYTES = 1073741824\n# Имена файлов содержащих url-адреса\nNAME_URL_FILES = ['../files/url1.txt',\n '../files/url2.txt',\n '../files/url3.txt']\n# Количество рабочей памяти в гигабайтах\nCOUNT_MEM = 1\n\n","sub_path":"hard/urls_filter/src/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"337664750","text":"import pyaudio\nimport numpy as np\nimport wave\n\nCHUNK = 1024\nWIDTH = 2\nCHANNELS = 1\nRATE = 44100\nRECORD_SECONDS = 5\n\np = pyaudio.PyAudio()\n\nstream = p.open(format=pyaudio.paInt16,\n channels=CHANNELS,\n rate=RATE,\n input=True,\n output=True,\n frames_per_buffer=CHUNK)\n\nprint(\"* recording\")\n\nfor i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):\n data = stream.read(CHUNK)\n\n data = np.array(wave.struct.unpack(\"%dh\" % (len(data) / WIDTH), data)) * 2 #Unpack the bytes to numpy array\n\n data = np.fft.rfft(data) #apply fast fourier transformation\n\n #manupulation phase\n\n data = np.fft.irfft(data) #apply reverse fast fourier transformation\n\n data_out = np.array(data * 0.5, dtype='int16') # undo the *2 that was done at reading\n chunk_out = wave.struct.pack(\"%dh\" % (len(data_out)), *list(data_out)) # convert back to 16-bit data\n stream.write(chunk_out)\n\n\nprint(data)\nprint(\"* done\")\n\nstream.stop_stream()\nstream.close()\n\np.terminate()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"474716167","text":"import json\nimport re\nimport requests\nfrom requests.exceptions import ProxyError\nimport time\nfrom scrapy import Selector\nfrom model import Goods, GoodsEvaluate, GoodsEvaluateSummary\nfrom fake_useragent import UserAgent\nimport get_cookie\n\ncookie = get_cookie.get_cookies('https://passport.jd.com/new/login.aspx')\nua = UserAgent()\n\n\ndef get_headers():\n headers = {\n 'User-Agent': ua.random}\n return headers\n\n\ndef get_goods_id():\n search = input('输入要搜索的商品：')\n goods_id = []\n for i in range(1, 2):\n url_temp = 'https://search.jd.com/Search?keyword={}&enc=utf-8&page={}'.format(search, i)\n html = requests.get(url_temp, headers=get_headers(), cookies=cookie).text\n sel = Selector(text=html)\n\n goods_temp = sel.xpath('//*[@id=\"J_goodsList\"]/ul/li/div/div[1]/a/@href').extract()\n '''\n //item.jd.com/68827454951.html\n '''\n for i in goods_temp:\n pattern = re.compile(r'\\d+')\n goods_id.append(re.findall(pattern, i)[0])\n return list(set(goods_id))\n\n\ndef get_proxy():\n temp = requests.get(\n 'http://api.tianqiip.com/getip?secret=732kzp0t7w0rf8ii&type=json&num=1&time=3&port=2').text.strip()\n proxy_json = json.loads(temp)\n proxyHost = proxy_json['data'][0]['ip']\n proxyPort = proxy_json['data'][0]['port']\n proxyMeta = \"http://%(host)s:%(port)s\" % {\n \"host\": proxyHost,\n \"port\": proxyPort,\n }\n proxies = {\n \"http\": proxyMeta,\n \"https\": proxyMeta\n }\n return proxies\n\n\ndef get_comment_data(i, tag_name):\n try:\n temp = i[tag_name]\n return temp\n except KeyError:\n return ''\n\n\ndef pares_goods(goods_id):\n global id\n flag = True\n statistics = {}\n max_page = 1\n summary = {}\n comments = {}\n comments_save = []\n\n # 获取商品信息\n url_temp = 'https://item.jd.com/{}.html'.format(goods_id)\n html = requests.get(url_temp, headers=get_headers(), cookies=cookie).text\n sel = Selector(text=html)\n\n goods.id = goods_id\n # 删去字符串中的空格和换行符\n goods.name = ''.join(sel.xpath('//*[@class=\"sku-name\"]/text()').extract()[0]).strip()\n goods.url = url_temp\n # 轮播图\n image_list = []\n for i in sel.xpath('//*[@id=\"spec-list\"]/ul/li/img/@src').extract():\n image_list.append('https:' + i)\n goods.image_list = image_list\n\n # 获取价格\n goods_info_temp = 'https://item-soa.jd.com/getWareBusiness?skuId={}'.format(goods_id)\n goods_info_text = requests.get(goods_info_temp, headers=get_headers(), cookies=cookie).text.strip()\n goods_info_json = json.loads(goods_info_text)\n if goods_info_json:\n # 价格\n goods.price = float(goods_info_json['price']['p'])\n # 供应商\n supplier = goods_info_json['stockInfo']['serviceInfo']\n if supplier:\n pattern = re.compile(r'(.*?)(.*?)(.*?)\\.')\n goods.supplier = ''.join(list(re.findall(pattern, supplier)[0]))\n\n # 获取商品评价\n evaluate_url = 'https://club.jd.com/comment/productPageComments.action?productId={}&score=0&sortType=5&page={}&pageSize=10&isShadowSku=0&fold=1'.format(\n goods_id, 0)\n proxy = get_proxy()\n temp = ''\n while flag:\n try:\n temp = requests.get(evaluate_url, headers=get_headers(), proxies=proxy, timeout=3).text\n if len(temp) != 0:\n flag = False\n else:\n time.sleep(1)\n print('temp is [] sleep 1s')\n proxy = get_proxy()\n except ProxyError as e:\n print(e)\n time.sleep(1)\n print('sleep 1s')\n proxy = get_proxy()\n continue\n if temp:\n evaluate_json = json.loads(temp)\n max_page = evaluate_json['maxPage']\n summary = evaluate_json['productCommentSummary']\n # 性能统计\n statistics = evaluate_json['hotCommentTagStatistics']\n comments = evaluate_json['comments']\n\n goods.comment_num = summary['commentCountStr']\n goods.image_comment_num = summary['showCountStr']\n goods.video_comment_num = summary['videoCountStr']\n goods.add_comment_num = summary['afterCountStr']\n goods.good_comment_num = summary['goodCountStr']\n goods.mid_comment_num = summary['generalCountStr']\n goods.bad_comment_num = summary['poorCountStr']\n exist = goods.select().where(goods.id == Goods.id)\n if exist:\n goods.save()\n else:\n goods.save(force_insert=True)\n\n # 获取商品标签\n\n for i in statistics:\n goods_evaluate_summary.tag_id = i['id'] + '_' + str(goods_id)\n goods_evaluate_summary.goods = goods\n goods_evaluate_summary.tag = i['name']\n goods_evaluate_summary.num = i['count']\n exist = goods_evaluate_summary.select().where(\n goods_evaluate_summary.tag_id == GoodsEvaluateSummary.tag_id)\n if exist:\n goods_evaluate_summary.save()\n else:\n goods_evaluate_summary.save(force_insert=True)\n\n # 获取评价\n if temp:\n for num in range(0, int(max_page)):\n for i in comments:\n goods_evaluate = {}\n goods_evaluate['goods'] = goods\n goods_evaluate['user_head_url'] = (\n 'https://' + get_comment_data(i, 'userImageUrl')) if get_comment_data(i,\n 'userImageUrl') else ''\n goods_evaluate['user_name'] = get_comment_data(i, 'nickname')\n goods_evaluate['good_info'] = get_comment_data(i, 'productColor') + get_comment_data(i, 'productSize')\n goods_evaluate['evaluate_time'] = get_comment_data(i, 'creationTime')\n goods_evaluate['content'] = get_comment_data(i, 'content')\n goods_evaluate['star'] = get_comment_data(i, 'score')\n goods_evaluate['comment_num'] = get_comment_data(i, 'replyCount')\n goods_evaluate['press_num'] = get_comment_data(i, 'usefulVoteCount')\n\n image_temp = []\n for k in get_comment_data(i, 'images'):\n image_temp.append(\n ('https:' + get_comment_data(k, 'imgUrl')) if len(get_comment_data(k, 'imgUrl')) else '')\n goods_evaluate['image_list'] = image_temp\n\n video_temp = []\n for k in get_comment_data(i, 'videos'):\n video_temp.append(get_comment_data(k, 'mainUrl'))\n goods_evaluate['video_list'] = video_temp\n comments_save.append(goods_evaluate)\n\n if num == 0:\n continue\n evaluate_url = 'https://club.jd.com/comment/productPageComments.action?productId={}&score=0&sortType=5&page={}&pageSize=10&isShadowSku=0&fold=1'.format(\n goods_id, num)\n time.sleep(0.1)\n temp = requests.get(evaluate_url, headers=get_headers(), timeout=3).text.encode(\n 'utf-8')\n if temp:\n evaluate_json = json.loads(temp)\n comments = evaluate_json['comments']\n GoodsEvaluate.bulk_create(comments_save)\n else:\n print(temp)\n print('complete {}'.format(goods_id))\n\n\nif __name__ == '__main__':\n goods = Goods()\n goods_evaluate_summary = GoodsEvaluateSummary()\n goods_id = get_goods_id()\n for i in goods_id:\n pares_goods(i)\n","sub_path":"jd_spider/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":7529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"392203081","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 22 16:14:46 2017\n\n@author: lagerwer\n\"\"\"\n\nimport odl\nimport numpy as np\n\npart = odl.nonuniform_partition([0,1,4], min_pt=0, max_pt=4)\nweight = np.ravel(np.multiply.reduce(np.meshgrid(*part.cell_sizes_vecs)))\n\nfspace = odl.FunctionSpace(part.set)\nspace = odl.DiscreteLp(fspace, part, odl.rn(part.size, weighting=weight), interp='nearest')\n\nx1 = space.element(np.ones(3))\nx2 = space.element(np.ones(3))\n\nx3 = space.element(np.ones(3))\nx4 = space.element(np.arange(3))\ninner1 = space.inner(x1, x2)\ninner2 = space.inner(x3, x4)","sub_path":"issue_non_uniform_space.py","file_name":"issue_non_uniform_space.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"289666763","text":"import pygame\n\ncharWalkOld = [\n\"charlg00.png\",\"charlg01.png\",\"charlg02.png\",\"charlg03.png\",\"charlg04.png\",\n\"charlg05.png\",\"charlg06.png\",\"charlg07.png\",\"charlg08.png\",\"charlg09.png\",\n\"charlg10.png\",\"charlg11.png\",\"charlg12.png\",\"charlg13.png\",\"charlg14.png\",\n\"charlg15.png\",\"charlg16.png\",\"charlg17.png\",\"charlg18.png\",\"charlg19.png\",\n\"charlg20.png\",\"charlg21.png\",\"charlg22.png\",\"charlg23.png\",\"charlg24.png\",\n\"charlg25.png\",\"charlg26.png\",\"charlg27.png\",\"charlg28.png\",\"charlg29.png\",\n\"charlg30.png\",\"charlg31.png\",\"charlg32.png\",\"charlg33.png\"\n]\n\ndef convertImageList(path, oldlist):\n\tnewlist = []\n\tfor each in oldlist:\n\t\tconverted = pygame.image.load(path + each).convert_alpha()\n\t\tnewlist.append(converted)\n\treturn newlist\n\nclass ImageManager(object):\n\t\n\tcharWalk = convertImageList('walkpng/', charWalkOld)\n\n\tdef __init__(self, host):\n\t\tself.animation = ImageManager.charWalk\n\t\tself.currentFrame = 0\n\t\tself.host = host\n\t\tself.position = None\n\t\tself.image = self.makeImage()\n\n\tdef animate(self):\n\t\tself.currentFrame += 1\n\t\tif self.currentFrame >= len(self.animation):\n\t\t\tself.currentFrame = 0\n\n\tdef makeImage(self):\n\t\timage = self.animation[self.currentFrame]\n\t\timage = pygame.transform.rotate(image, self.host.angle)\n\t\treturn image\n\n\tdef update(self):\n\t\t#figure out if we should animate the image\n\t\tif not self.position is self.host.rect.center:\n\t\t\tself.animate()\n\t\t\tself.position = self.host.rect.center\n\n\t\tself.image = self.makeImage()\n\n\tdef getImage(self):\n\t\treturn self.image\n\n\n\n\t\t\n\n\n\t\t\t\n\n\t\t","sub_path":"imagemanager.py","file_name":"imagemanager.py","file_ext":"py","file_size_in_byte":1501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"561683093","text":"import bpy\n# Clear scene\nbpy.ops.object.mode_set(mode='OBJECT')\nbpy.ops.object.select_all(action='SELECT')\nbpy.ops.object.delete()\n\n## подставка+\n#bpy.context.scene.cursor_location = (0.0, 0.0, 0.0)\nbpy.context.scene.cursor_location = (0.0, 0.0, 0.25)\n#bpy.ops.mesh.primitive_cylinder_add(depth=0.5, radius=2.5, \nbpy.ops.mesh.primitive_cylinder_add(depth=0.5, radius=2.4, \n vertices=360, end_fill_type='NOTHING')\n \nbpy.ops.object.modifier_add(type='SOLIDIFY')\nbpy.context.object.modifiers[\"Solidify\"].thickness = 0.1\nbpy.ops.object.modifier_apply(apply_as='DATA', \n modifier=\"Solidify\")\n\n## дно +\nbpy.context.scene.cursor_location = (0.0, 0.0, 0.5)\nbpy.ops.mesh.primitive_circle_add(radius= 2.5, \n fill_type='TRIFAN') \n\n## стенки\n#bpy.context.scene.cursor_location = (0.0, 0.0, 4.5)\nbpy.context.scene.cursor_location = (0.0, 0.0, 4.75)\nbpy.ops.mesh.primitive_cone_add(radius2=3.65, radius1=2.5, \nvertices=360, depth=9, end_fill_type='NOTHING')\nbpy.ops.object.modifier_add(type='SOLIDIFY')\nbpy.context.object.modifiers[\"Solidify\"].thickness = 0.1\nbpy.ops.object.modifier_apply(apply_as='DATA', \n modifier=\"Solidify\")\n\n\n## закрутка # 0.3 радиус тора 3.65 внутренний радиус \n#bpy.context.scene.cursor_location = (0.0, 0.0, 9.0)\nbpy.context.scene.cursor_location = (0.0, 0.0, 9.25)\nbpy.ops.mesh.primitive_torus_add(major_radius=3.7,\n major_segments=320, minor_radius=0.15)\n #, abso_major_rad=3.8)\n\n\n## add camera\nfrom math import pi\n#bpy.ops.object.camera_add(view_align=False, location=[0, 10, 30], rotation=[0.436, 0, pi])\nbpy.ops.object.camera_add(view_align=False, \n location=[0, 30, 5], rotation=[1.6, 0, pi])\n## add lamp\nbpy.ops.object.lamp_add(location=[0,10,18])\nbpy.ops.object.lamp_add(location=[0,-10,18])\nbpy.ops.object.lamp_add(location=[-10,10,18])\nbpy.ops.object.lamp_add(location=[10,10,18])\n\n################################################################\n\nmaterial_obj = bpy.data.materials.new('number_1_material')\n\nimgpath = \"/Users/pike/blender/logo.png\" \nimage_obj = bpy.data.images.load(imgpath)\n\ntexture_obj = bpy.data.textures.new('number_1_texture', \n type='IMAGE')\ntexture_obj.image = image_obj\ntexture_obj.extension = 'CLIP'\n\n\ntexture_slot = material_obj.texture_slots.add()\n\ntexture_slot.texture = texture_obj\ntexture_slot.texture_coords = 'OBJECT'\ntexture_slot.object = bpy.data.objects['Cone']\ntexture_slot.mapping = 'CUBE'\n\ncone = bpy.data.objects['Cone']\nbpy.context.scene.objects.active = cone\nbpy.context.scene.objects.active.data.materials.append(material_obj)\n\n#bpy.data.textures[\"number_1_tex\"].use_flip_axis = True\n#bpy.data.textures[\"number_1_tex\"].extension = 'CLIP'\n\nbpy.data.objects['Cone'].select = True\n\n#bpy.data.materials['number_1_material'].diffuse_color","sub_path":"cup2.py","file_name":"cup2.py","file_ext":"py","file_size_in_byte":2777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"438859595","text":"# so much faster with yield instead of list\ndef partitionGenerator(n, offset=1):\n yield n,\n for i in range(offset, n // 2 + 1):\n for p in partitionGenerator(n - i, i):\n yield p+(i,)\n\n\n\npartitions = sorted(partitionGenerator(20), reverse=True)\nfor p in partitions:\n print(p)","sub_path":"Partitions.py","file_name":"Partitions.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"38253642","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport ase.db\n\ncon = ase.db.connect('cubic_perovskites.db')\n\ndf = pd.read_csv('perovskites.csv')\n\nbgap_dict = {}\n\nfor row in con.select('combination'):\n formula = row.A_ion + row.B_ion + row.anion\n bgap_dict[formula] = row.gllbsc_dir_gap\n\nn_r_dict = {}\nfor i, rowdf in df.iterrows():\n for ion in ['A', 'B', 'X']:\n ion_name = rowdf[ion]\n if ion not in n_r_dict:\n n_r_dict[ion_name] = [rowdf['n'+ion], rowdf['r'+ion+' (Ang)']]\n\nk = 0\ngaps = []\nfor i, rowdf in df.iterrows():\n name = rowdf['ABX3']\n if name in bgap_dict:\n gap = bgap_dict[name]\n gaps.append(gap)\n else:\n gaps.append('?')\n\ndf['Bandgap [eV]'] = gaps\n\nfor row in con.select('combination'):\n A = row.A_ion\n B = row.B_ion\n X = row.anion.replace('3','')\n gap = row.gllbsc_dir_gap\n if (A in n_r_dict) and (B in n_r_dict) and (X in n_r_dict) and (gap > 0):\n formula = A + B + X + '3'\n newrow = pd.DataFrame([[formula, A, B, X, n_r_dict[A][0], n_r_dict[B][0], n_r_dict[X][0], n_r_dict[A][1], n_r_dict[B][1], n_r_dict[X][1],'?', '?', '?', gap]], columns = df.columns)\n df = df.append(newrow)\n\n\n\ndf.to_csv('perovskites_gaps.csv')\n","sub_path":"projects/perovskite/data/perovskite_data.py","file_name":"perovskite_data.py","file_ext":"py","file_size_in_byte":1234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"351124282","text":"s = \"WEHAVEADREAM\"\r\nnumRows = 4\r\n# The above two lines are only for testing\r\n\r\ndef convert():\r\n if numRows == 1 or numRows >= len(s):\r\n return s\r\n\r\n test = [[] for i in range(numRows)]\r\n row = 0\r\n step = 1\r\n for c in s:\r\n test[row] += c,\r\n if(row == 0):\r\n step = 1\r\n elif(row == numRows - 1):\r\n step = -1\r\n row += step\r\n\r\n result = \"\"\r\n for i in range(len(test)):\r\n result += \"\".join(test[i]) ## join can turn list to str\r\n return result\r\n\r\nif __name__ == \"__main__\":\r\n convert()\r\n","sub_path":"String/006 Zig Zag Conversion/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"265201796","text":"import random\nimport math\n# Reading the data file without using pandas\na=open(\"/home/deeksha/Downloads/iris.data\")\nlist2=[]\nfor i1 in a:\n we=i1.split(',')\n we[0]=float(we[0])\n we[1]=float(we[1])\n we[2]=float(we[2])\n we[3]=float(we[3])\n we[4]=we[4][:-2]\n list2.append(we)\n# Splitting into training and test data\ndef train_test_split(data,training_data,test_data,split=0.75):\n for i1 in data:\n if (random.random()parse.out',\r\n\t\t# shellで実行\r\n\t\tshell=True,\r\n\t\t# エラーチェックあり\r\n\t\tcheck=True\r\n\t)\r\n\r\n# 解析結果のxmlをパース\r\nroot = ET.parse(fname_parsed)\r\n\r\n# wordのみ取り出し\r\nfor word in root.iter('word'):\r\n\tprint(word.text)\r\n","sub_path":"NaturalLanguageProcessing/ex53.py","file_name":"ex53.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"332762622","text":"#!/usr/bin/env python\n'''\nRename histograms for 8TeV datacards \nAuthor: T.M.Perry\n'''\nimport ROOT\nfrom ROOT import *\n\ninFilename = raw_input ('Name of File to be Renamed (no .root):\\n')\n\n\n#inFilename = 'Datacard_2j2525_2bt_highestJetPt'\ninFile = TFile(inFilename+'.root')\n\noutFile=gROOT.FindObject('Renamed_'+inFilename+'.root')\nif outFile : outFile.Close()\noutFile = TFile('Renamed_'+inFilename+'.root','RECREATE','renamed histograms')\nlog = open(inFilename+'.log','a')\n\n# Unshifted\ndata_obs_INFILE = inFile.Get(\"dataih\")\ndata_obs = data_obs_INFILE.Clone()\ndata_obs.SetName(\"data_obs\")\n\nWbb_INFILE = inFile.Get(\"wbbihNo\")\nWbb = Wbb_INFILE.Clone()\nWbb.SetName(\"Wbb\")\nTTbar_INFILE = inFile.Get(\"ttbihNo\")\nTTbar = TTbar_INFILE.Clone()\nTTbar.SetName(\"TTbar\")\nWcc_INFILE = inFile.Get(\"wccihNo\")\nWcc = Wcc_INFILE.Clone()\nWcc.SetName(\"Wcc\")\nT_INFILE = inFile.Get(\"tihNo\")\nT = T_INFILE.Clone()\nT.SetName(\"T\")\nTbar_INFILE = inFile.Get(\"tbihNo\")\nTbar = Tbar_INFILE.Clone()\nTbar.SetName(\"Tbar\")\ntW_INFILE = inFile.Get(\"t_twihNo\")\ntW = tW_INFILE.Clone()\ntW.SetName(\"tW\")\nDrell_INFILE = inFile.Get(\"zihNo\")\nDrell = Drell_INFILE.Clone()\nDrell.SetName(\"Drell\")\nVV_INFILE = inFile.Get(\"vvihNo\")\nVV = VV_INFILE.Clone()\nVV.SetName(\"VV\")\nQCD_INFILE = inFile.Get(\"qhNo\")\nQCD = QCD_INFILE.Clone()\nQCD.SetName(\"QCD\")\n\nlog.write('\\n\\n')\nlog.write(\"data: \"+str(data_obs.Integral())+'\\n')\nlog.write(\"Wbb: \"+str(Wbb.Integral()) +'\\n')\nlog.write(\"TTbar: \"+str(TTbar.Integral()) +'\\n')\nlog.write(\"Wcc: \"+str(Wcc.Integral()) +'\\n')\nlog.write(\"T: \"+str(T.Integral()) +'\\n')\nlog.write(\"Tbar: \"+str(Tbar.Integral()) +'\\n')\nlog.write(\"tW: \"+str(tW.Integral()) +'\\n')\nlog.write(\"Drell: \"+str(Drell.Integral()) +'\\n')\nlog.write(\"VV: \"+str(VV.Integral()) +'\\n')\nlog.write(\"QCD: \"+str(QCD.Integral()) +'\\n')\nlog.close()\n\n# TTbar Up\nWbb_INFILE_TTUp = inFile.Get(\"wbbihTup\")\nWbb_TTUp = Wbb_INFILE_TTUp.Clone()\nWbb_TTUp.SetName(\"Wbb_TTUp\")\nWcc_INFILE_TTUp = inFile.Get(\"wccihTup\")\nWcc_TTUp = Wcc_INFILE_TTUp.Clone()\nWcc_TTUp.SetName(\"Wcc_TTUp\")\nTTbar_INFILE_TTUp = inFile.Get(\"ttbihTup\")\nTTbar_TTUp = TTbar_INFILE_TTUp.Clone()\nTTbar_TTUp.SetName(\"TTbar_TTUp\")\nT_INFILE_TTUp = inFile.Get(\"tihTup\")\nT_TTUp = T_INFILE_TTUp.Clone()\nT_TTUp.SetName(\"T_TTUp\")\nTbar_INFILE_TTUp = inFile.Get(\"tbihTup\")\nTbar_TTUp = Tbar_INFILE_TTUp.Clone()\nTbar_TTUp.SetName(\"Tbar_TTUp\")\ntW_INFILE_TTUp = inFile.Get(\"t_twihTup\")\ntW_TTUp = tW_INFILE_TTUp.Clone()\ntW_TTUp.SetName(\"tW_TTUp\")\nDrell_INFILE_TTUp = inFile.Get(\"zihTup\")\nDrell_TTUp = Drell_INFILE_TTUp.Clone()\nDrell_TTUp.SetName(\"Drell_TTUp\")\nVV_INFILE_TTUp = inFile.Get(\"vvihTup\")\nVV_TTUp = VV_INFILE_TTUp.Clone()\nVV_TTUp.SetName(\"VV_TTUp\")\n#QCD_INFILE_TTUp = inFile.Get(\"qhTup\")\n#QCD_TTUp = QCD_INFILE_TTUp.Clone()\n#QCD_TTUp.SetName(\"QCD_TTUp\")\n\n# TTbar Down\nWbb_INFILE_TTDown = inFile.Get(\"wbbihTdn\")\nWbb_TTDown = Wbb_INFILE_TTDown.Clone()\nWbb_TTDown.SetName(\"Wbb_TTDown\")\nWcc_INFILE_TTDown = inFile.Get(\"wccihTdn\")\nWcc_TTDown = Wcc_INFILE_TTDown.Clone()\nWcc_TTDown.SetName(\"Wcc_TTDown\")\nTTbar_INFILE_TTDown = inFile.Get(\"ttbihTdn\")\nTTbar_TTDown = TTbar_INFILE_TTDown.Clone()\nTTbar_TTDown.SetName(\"TTbar_TTDown\")\nT_INFILE_TTDown = inFile.Get(\"tihTdn\")\nT_TTDown = T_INFILE_TTDown.Clone()\nT_TTDown.SetName(\"T_TTDown\")\nTbar_INFILE_TTDown = inFile.Get(\"tbihTdn\")\nTbar_TTDown = Tbar_INFILE_TTDown.Clone()\nTbar_TTDown.SetName(\"Tbar_TTDown\")\ntW_INFILE_TTDown = inFile.Get(\"t_twihTdn\")\ntW_TTDown = tW_INFILE_TTDown.Clone()\ntW_TTDown.SetName(\"tW_TTDown\")\nDrell_INFILE_TTDown = inFile.Get(\"zihTdn\")\nDrell_TTDown = Drell_INFILE_TTDown.Clone()\nDrell_TTDown.SetName(\"Drell_TTDown\")\nVV_INFILE_TTDown = inFile.Get(\"vvihTdn\")\nVV_TTDown = VV_INFILE_TTDown.Clone()\nVV_TTDown.SetName(\"VV_TTDown\")\n#QCD_INFILE_TTDown = inFile.Get(\"qhTdn\")\n#QCD_TTDown = QCD_INFILE_TTDown.Clone()\n#QCD_TTDown.SetName(\"QCD_TTDown\")\n\n# Muon Up\nWbb_INFILE_muonUp = inFile.Get(\"wbbihUp\")\nWbb_muonUp = Wbb_INFILE_muonUp.Clone()\nWbb_muonUp.SetName(\"Wbb_muonUp\")\nWcc_INFILE_muonUp = inFile.Get(\"wccihUp\")\nWcc_muonUp = Wcc_INFILE_muonUp.Clone()\nWcc_muonUp.SetName(\"Wcc_muonUp\")\nTTbar_INFILE_muonUp = inFile.Get(\"ttbihUp\")\nTTbar_muonUp = TTbar_INFILE_muonUp.Clone()\nTTbar_muonUp.SetName(\"TTbar_muonUp\")\nT_INFILE_muonUp = inFile.Get(\"tihUp\")\nT_muonUp = T_INFILE_muonUp.Clone()\nT_muonUp.SetName(\"T_muonUp\")\nTbar_INFILE_muonUp = inFile.Get(\"tbihUp\")\nTbar_muonUp = Tbar_INFILE_muonUp.Clone()\nTbar_muonUp.SetName(\"Tbar_muonUp\")\ntW_INFILE_muonUp = inFile.Get(\"t_twihUp\")\ntW_muonUp = tW_INFILE_muonUp.Clone()\ntW_muonUp.SetName(\"tW_muonUp\")\nDrell_INFILE_muonUp = inFile.Get(\"zihUp\")\nDrell_muonUp = Drell_INFILE_muonUp.Clone()\nDrell_muonUp.SetName(\"Drell_muonUp\")\nVV_INFILE_muonUp = inFile.Get(\"vvihUp\")\nVV_muonUp = VV_INFILE_muonUp.Clone()\nVV_muonUp.SetName(\"VV_muonUp\")\nQCD_INFILE_muonUp = inFile.Get(\"qhUp\")\nQCD_muonUp = QCD_INFILE_muonUp.Clone()\nQCD_muonUp.SetName(\"QCD_muonUp\")\n\n# Muon Down\nWbb_INFILE_muonDown = inFile.Get(\"wbbihDn\")\nWbb_muonDown = Wbb_INFILE_muonDown.Clone()\nWbb_muonDown.SetName(\"Wbb_muonDown\")\nWcc_INFILE_muonDown = inFile.Get(\"wccihDn\")\nWcc_muonDown = Wcc_INFILE_muonDown.Clone()\nWcc_muonDown.SetName(\"Wcc_muonDown\")\nTTbar_INFILE_muonDown = inFile.Get(\"ttbihDn\")\nTTbar_muonDown = TTbar_INFILE_muonDown.Clone()\nTTbar_muonDown.SetName(\"TTbar_muonDown\")\nT_INFILE_muonDown = inFile.Get(\"tihDn\")\nT_muonDown = T_INFILE_muonDown.Clone()\nT_muonDown.SetName(\"T_muonDown\")\nTbar_INFILE_muonDown = inFile.Get(\"tbihDn\")\nTbar_muonDown = Tbar_INFILE_muonDown.Clone()\nTbar_muonDown.SetName(\"Tbar_muonDown\")\ntW_INFILE_muonDown = inFile.Get(\"t_twihDn\")\ntW_muonDown = tW_INFILE_muonDown.Clone()\ntW_muonDown.SetName(\"tW_muonDown\")\nDrell_INFILE_muonDown = inFile.Get(\"zihDn\")\nDrell_muonDown = Drell_INFILE_muonDown.Clone()\nDrell_muonDown.SetName(\"Drell_muonDown\")\nVV_INFILE_muonDown = inFile.Get(\"vvihDn\")\nVV_muonDown = VV_INFILE_muonDown.Clone()\nVV_muonDown.SetName(\"VV_muonDown\")\nQCD_INFILE_muonDown = inFile.Get(\"qhDn\")\nQCD_muonDown = QCD_INFILE_muonDown.Clone()\nQCD_muonDown.SetName(\"QCD_muonDown\")\n\n# Jet Up\nWbb_INFILE_jetUp = inFile.Get(\"wbbihJUp\")\nWbb_jetUp = Wbb_INFILE_jetUp.Clone()\nWbb_jetUp.SetName(\"Wbb_jetUp\")\nWcc_INFILE_jetUp = inFile.Get(\"wccihJUp\")\nWcc_jetUp = Wcc_INFILE_jetUp.Clone()\nWcc_jetUp.SetName(\"Wcc_jetUp\")\nTTbar_INFILE_jetUp = inFile.Get(\"ttbihJUp\")\nTTbar_jetUp = TTbar_INFILE_jetUp.Clone()\nTTbar_jetUp.SetName(\"TTbar_jetUp\")\nT_INFILE_jetUp = inFile.Get(\"tihJUp\")\nT_jetUp = T_INFILE_jetUp.Clone()\nT_jetUp.SetName(\"T_jetUp\")\nTbar_INFILE_jetUp = inFile.Get(\"tbihJUp\")\nTbar_jetUp = Tbar_INFILE_jetUp.Clone()\nTbar_jetUp.SetName(\"Tbar_jetUp\")\ntW_INFILE_jetUp = inFile.Get(\"t_twihJUp\")\ntW_jetUp = tW_INFILE_jetUp.Clone()\ntW_jetUp.SetName(\"tW_jetUp\")\nDrell_INFILE_jetUp = inFile.Get(\"zihJUp\")\nDrell_jetUp = Drell_INFILE_jetUp.Clone()\nDrell_jetUp.SetName(\"Drell_jetUp\")\nVV_INFILE_jetUp = inFile.Get(\"vvihJUp\")\nVV_jetUp = VV_INFILE_jetUp.Clone()\nVV_jetUp.SetName(\"VV_jetUp\")\nQCD_INFILE_jetUp = inFile.Get(\"qhJUp\")\nQCD_jetUp = QCD_INFILE_jetUp.Clone()\nQCD_jetUp.SetName(\"QCD_jetUp\")\n\n# Jet Down\nWbb_INFILE_jetDown = inFile.Get(\"wbbihJDn\")\nWbb_jetDown = Wbb_INFILE_jetDown.Clone()\nWbb_jetDown.SetName(\"Wbb_jetDown\")\nWcc_INFILE_jetDown = inFile.Get(\"wccihJDn\")\nWcc_jetDown = Wcc_INFILE_jetDown.Clone()\nWcc_jetDown.SetName(\"Wcc_jetDown\")\nTTbar_INFILE_jetDown = inFile.Get(\"ttbihJDn\")\nTTbar_jetDown = TTbar_INFILE_jetDown.Clone()\nTTbar_jetDown.SetName(\"TTbar_jetDown\")\nT_INFILE_jetDown = inFile.Get(\"tihJDn\")\nT_jetDown = T_INFILE_jetDown.Clone()\nT_jetDown.SetName(\"T_jetDown\")\nTbar_INFILE_jetDown = inFile.Get(\"tbihJDn\")\nTbar_jetDown = Tbar_INFILE_jetDown.Clone()\nTbar_jetDown.SetName(\"Tbar_jetDown\")\ntW_INFILE_jetDown = inFile.Get(\"t_twihJDn\")\ntW_jetDown = tW_INFILE_jetDown.Clone()\ntW_jetDown.SetName(\"tW_jetDown\")\nDrell_INFILE_jetDown = inFile.Get(\"zihJDn\")\nDrell_jetDown = Drell_INFILE_jetDown.Clone()\nDrell_jetDown.SetName(\"Drell_jetDown\")\nVV_INFILE_jetDown = inFile.Get(\"vvihJDn\")\nVV_jetDown = VV_INFILE_jetDown.Clone()\nVV_jetDown.SetName(\"VV_jetDown\")\nQCD_INFILE_jetDown = inFile.Get(\"qhJDn\")\nQCD_jetDown = QCD_INFILE_jetDown.Clone()\nQCD_jetDown.SetName(\"QCD_jetDown\")\n\n# UCE Up\nWbb_INFILE_jetUCEUp = inFile.Get(\"wbbihUUp\")\nWbb_jetUCEUp = Wbb_INFILE_jetUCEUp.Clone()\nWbb_jetUCEUp.SetName(\"Wbb_jetUCEUp\")\nWcc_INFILE_jetUCEUp = inFile.Get(\"wccihUUp\")\nWcc_jetUCEUp = Wcc_INFILE_jetUCEUp.Clone()\nWcc_jetUCEUp.SetName(\"Wcc_jetUCEUp\")\nTTbar_INFILE_jetUCEUp = inFile.Get(\"ttbihUUp\")\nTTbar_jetUCEUp = TTbar_INFILE_jetUCEUp.Clone()\nTTbar_jetUCEUp.SetName(\"TTbar_jetUCEUp\")\nT_INFILE_jetUCEUp = inFile.Get(\"tihUUp\")\nT_jetUCEUp = T_INFILE_jetUCEUp.Clone()\nT_jetUCEUp.SetName(\"T_jetUCEUp\")\nTbar_INFILE_jetUCEUp = inFile.Get(\"tbihUUp\")\nTbar_jetUCEUp = Tbar_INFILE_jetUCEUp.Clone()\nTbar_jetUCEUp.SetName(\"Tbar_jetUCEUp\")\ntW_INFILE_jetUCEUp = inFile.Get(\"t_twihUUp\")\ntW_jetUCEUp = tW_INFILE_jetUCEUp.Clone()\ntW_jetUCEUp.SetName(\"tW_jetUCEUp\")\nDrell_INFILE_jetUCEUp = inFile.Get(\"zihUUp\")\nDrell_jetUCEUp = Drell_INFILE_jetUCEUp.Clone()\nDrell_jetUCEUp.SetName(\"Drell_jetUCEUp\")\nVV_INFILE_jetUCEUp = inFile.Get(\"vvihUUp\")\nVV_jetUCEUp = VV_INFILE_jetUCEUp.Clone()\nVV_jetUCEUp.SetName(\"VV_jetUCEUp\")\nQCD_INFILE_jetUCEUp = inFile.Get(\"qhUUp\")\nQCD_jetUCEUp = QCD_INFILE_jetUCEUp.Clone()\nQCD_jetUCEUp.SetName(\"QCD_jetUCEUp\")\n\n# UCE Down\nWbb_INFILE_jetUCEDown = inFile.Get(\"wbbihUDn\")\nWbb_jetUCEDown = Wbb_INFILE_jetUCEDown.Clone()\nWbb_jetUCEDown.SetName(\"Wbb_jetUCEDown\")\nWcc_INFILE_jetUCEDown = inFile.Get(\"wccihUDn\")\nWcc_jetUCEDown = Wcc_INFILE_jetUCEDown.Clone()\nWcc_jetUCEDown.SetName(\"Wcc_jetUCEDown\")\nTTbar_INFILE_jetUCEDown = inFile.Get(\"ttbihUDn\")\nTTbar_jetUCEDown = TTbar_INFILE_jetUCEDown.Clone()\nTTbar_jetUCEDown.SetName(\"TTbar_jetUCEDown\")\nT_INFILE_jetUCEDown = inFile.Get(\"tihUDn\")\nT_jetUCEDown = T_INFILE_jetUCEDown.Clone()\nT_jetUCEDown.SetName(\"T_jetUCEDown\")\nTbar_INFILE_jetUCEDown = inFile.Get(\"tbihUDn\")\nTbar_jetUCEDown = Tbar_INFILE_jetUCEDown.Clone()\nTbar_jetUCEDown.SetName(\"Tbar_jetUCEDown\")\ntW_INFILE_jetUCEDown = inFile.Get(\"t_twihUDn\")\ntW_jetUCEDown = tW_INFILE_jetUCEDown.Clone()\ntW_jetUCEDown.SetName(\"tW_jetUCEDown\")\nDrell_INFILE_jetUCEDown = inFile.Get(\"zihUDn\")\nDrell_jetUCEDown = Drell_INFILE_jetUCEDown.Clone()\nDrell_jetUCEDown.SetName(\"Drell_jetUCEDown\")\nVV_INFILE_jetUCEDown = inFile.Get(\"vvihUDn\")\nVV_jetUCEDown = VV_INFILE_jetUCEDown.Clone()\nVV_jetUCEDown.SetName(\"VV_jetUCEDown\")\nQCD_INFILE_jetUCEDown = inFile.Get(\"qhUDn\")\nQCD_jetUCEDown = QCD_INFILE_jetUCEDown.Clone()\nQCD_jetUCEDown.SetName(\"QCD_jetUCEDown\")\n\noutFile.Write()\n","sub_path":"test/wbb/eight/renameHistosTwoD.py","file_name":"renameHistosTwoD.py","file_ext":"py","file_size_in_byte":10462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"287066850","text":"#!D:\\Python\\python.exe\n#-*- coding:utf-8 -*-\n# datatime : 2018/4/27 11:47\n# author : badbugu17\n# file : Aries\nimport getopt\nimport time\n\nimport sys\n\nfrom mp4Crawler.entity.DownloadHtml import DownloadHtml\nfrom mp4Crawler.scheduler.DBOperation import DBOperation\nfrom mp4Crawler.scheduler.WebPageDownloader import WebPageDownloader\nfrom mp4Crawler.scheduler.WebPageParser import WebPageParser\n\n\nclass Aries:\n\n _dbo = DBOperation(); # 数据库操作类\n _webPageDownloader = WebPageDownloader(); # 网页下载器\n _webPageParser = WebPageParser(); # 网页解析器\n\n def mainFunction(self):\n\n \"\"\"<白羊座>调度器\"\"\"\n\n # 方法调用，使用linux脚本定时执行\n # 格式 python Aries.py -ild -t\n try:\n opts, args = getopt.getopt(sys.argv[1:],\"ildt:\", [\"init\", \"list\", \"detail\", \"type=\"]);\n\n if len(opts) == 0 :\n print(\"[错误]：请输入参数\\n\");\n return;\n\n\n typeid = 0; # 爬取的电影id 爬取不同类型的电影需要修改这里的属性 PS:注意\n # 1、国产电影 2、港台电影 3、欧美电影 4、日韩电影 5、海外电影 6、动画电影\n crawlStatus = None;\n startUrl = None;\n for name, value in opts:\n if value != \"\":\n typeid = int(value);\n crawlStatus = self._dbo.getStatusById(typeid);\n startUrl = crawlStatus.endUrl;\n break;\n\n moveTypeName = \"\";\n if typeid == 1:\n moveTypeName = \"国产电影\";\n elif typeid == 2:\n moveTypeName = \"港台电影\";\n elif typeid == 3:\n moveTypeName = \"欧美电影\";\n elif typeid == 4:\n moveTypeName = \"日韩电影\";\n elif typeid == 5:\n moveTypeName = \"海外电影\";\n elif typeid == 6:\n moveTypeName = \"动画电影\";\n\n nowDateTime = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n print(\"[!!!]开始时间：\", nowDateTime)\n print(\"目前爬取的电影类型为：\", moveTypeName, \"\\n\");\n\n # typeid = 5; # 爬取的电影id 爬取不同类型的电影需要修改这里的属性 PS:注意\n # # 1、国产电影 2、港台电影 3、欧美电影 4、日韩电影 5、海外电影 6、动画电影\n #\n # crawlStatus = self._dbo.getStatusById(typeid); # 获取数据库中的爬取状态实体\n # startUrl = crawlStatus.endUrl;\n\n for opt in opts:\n if \"-i\" in opt:\n # 进行初始化操作\n print(\"[提示]：进行初始化PC_Status表\\n\");\n\n # step 1 解析status状态，之后的页面爬取都是从PC_Status表获取相关参数的\n self.initStatus();\n\n break;\n if \"-l\" in opt:\n # 进行列表页的解析\n print(\"[提示]：进行列表页的解析\\n\");\n # step 2 解析列表页\n self.listInfoParser(crawlStatus);\n\n break;\n if \"-d\" in opt:\n # 进行详情页的解析\n print(\"[提示]：进行详情页的解析\\n\");\n # step 3 每次从PC_WaitForCrawl表中取出一条数据，进行下载解析，解析出详情页面的URL和相关信息\n # crawlUrl = dbo.getWaitByTop1();\n self.detailInfoParser(crawlStatus); # step3 step4 在方法内执行\n\n break;\n\n except getopt.GetoptError as ge:\n print(\"[错误]：输入的参数有误，请重新数据!\\n\");\n # raise ge;\n nowDateTime = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime());\n print(\"[!!!]结束时间：\", nowDateTime, \"\\n\");\n\n\n def initStatus(self):\n\n startTime = time.time();\n\n \"\"\"初始化status表，逻辑还欠考虑\"\"\"\n new_url = \"http://www.mp4ba.net/\";\n indexHtmlDoc = self._webPageDownloader.htmlDownload(new_url);\n indexDownloadHtml = DownloadHtml(); # 下载后的页面实体\n indexDownloadHtml.url = new_url;\n indexDownloadHtml.htmlDoc = indexHtmlDoc;\n\n self._webPageParser.parserStatus(indexDownloadHtml) # 初始化PC_Status表信息\n\n endTime = time.time();\n diffTime = endTime - startTime;\n\n print(\"[提示]：PC_Status表初始化成功!耗时%.2f秒\\n\" % (diffTime));\n\n return;\n\n def listInfoParser(self,crawlStatus):\n\n listPageRowCount = 0; # 爬取的列表页电影数\n insertToTableCount = 0; # 实际插入数据库中的列表页记录数\n repeatCount = 0; # 重复的列表页记录数\n\n startTime = time.time(); # 获取开始时间\n\n startUrl = crawlStatus.endUrl; # 获取开始爬取的路径\n\n for i in range(crawlStatus.step):\n # step 2 解析列表页，获取详细页的相关数据和URL放入数据库PC_WaitForCrawl表中\n\n # pageNumber = startUrl[-6:-5]; # pageNumber修改取值方式\n pointIndex = startUrl.rfind(\".\");\n lastIndex = startUrl.rfind(\"-\");\n pageNumber = startUrl[lastIndex+1:pointIndex];\n\n pageNumber = int(pageNumber);\n if pageNumber > crawlStatus.pageNum:\n print(\"[]:该类型的电影已经爬取完毕！\")\n break;\n\n # startUrl = crawlStatus.endUrl;\n listHtmlDoc = self._webPageDownloader.htmlDownload(startUrl);\n listDownloadHtml = DownloadHtml(); # 列表页下载后页面实体\n listDownloadHtml.url = startUrl;\n listDownloadHtml.htmlDoc = listHtmlDoc;\n\n succCount, passCount, sumCount = self._webPageParser.parserListPage(listDownloadHtml);\n insertToTableCount += succCount;\n repeatCount += passCount;\n listPageRowCount += sumCount;\n\n # startUrl = startUrl[:-6] + str(pageNumber + 1) + startUrl[-5:];\n startUrl = startUrl[:lastIndex+1] + str(pageNumber + 1) + startUrl[pointIndex:];\n\n print(\"[提示]:第%d次循环结束，共解析出%d条列表页数据，成功插入%d条，重复%d条！\" % (i+1,sumCount,succCount,passCount));\n\n # step 5 更新PC_Status表中的相关信息\n crawlStatus.endUrl = startUrl;\n self._dbo.updateStatusByStatus(crawlStatus);\n\n stopTime = time.time();\n diffTime = stopTime - startTime;\n\n print(\"┌-----------列表页爬取成功-------------\");\n print(\"│ 状态报告 \");\n print(\"│ 总记录数：%d条 \" % (listPageRowCount));\n print(\"│ 成功：%d条 \" % (insertToTableCount));\n print(\"│ 重���：%d条 \" % (repeatCount));\n print(\"│ 耗时：%.2f秒 \" % (diffTime));\n print(\"└-------------------------------------\\n\");\n\n return;\n\n def detailInfoParser(self,crawlStatus):\n loopNum = 5;\n\n infoCountSum = 0; # 详情页解析数\n\n startTime = time.time();\n\n # 添加的已爬列表的SQL集合\n insertToCompSqlList = [];\n\n\n for i in range(loopNum):\n # cuList = self._dbo.getWaitByTopNum(crawlStatus.step);\n cuList = self._dbo.getWaitByTopNum(5);\n if len(cuList) != 0:\n for crawlUrl in cuList:\n infoPageHtmlDoc = self._webPageDownloader.htmlDownload(crawlUrl.url);\n infoDownloadHtml = DownloadHtml(); # 下载后的详情页实体\n infoDownloadHtml.url = crawlUrl.url;\n infoDownloadHtml.htmlDoc = infoPageHtmlDoc;\n\n # 解析详情页面，并把数据保存到数据库中\n infoCount = self._webPageParser.parserInfoPage(infoDownloadHtml, crawlUrl);\n infoCountSum += infoCount;\n\n # step 4 收尾工作，将PC_WaitForCrawl表中取出的并成功下载解析的记录删除，存入PC_CompleteCrawl表中\n self._dbo.addCompleteTableNew(crawlUrl, insertToCompSqlList); # 方法中已经加入了删除PC_WaitForCrawl表数据的SQL\n # self._dbo.deleteWaitFor(crawlUrl.id);\n\n # print(\"本次循环成功解析%d条记录\\n\" % (infoCount));\n connt = self._dbo.batchExecSql(insertToCompSqlList);\n print(\"%d条数据已从PC_WaitForCrawl表插入到PC_CompleteCrawl表中,PC_WaitForCrawl表数据已删除\" % (connt / 2) )\n\n\n else:\n print(\"[提示]:PC_WaitForCrawl表已爬空\\n\");\n break;\n\n\n endTime = time.time();\n diffTime = endTime - startTime;\n\n print(\"┌-----------详情页爬取成功-------------\");\n print(\"│ 状态报告 \");\n print(\"│ 总记录数：%d条 \" % (infoCountSum));\n print(\"│ 耗时：%.2f秒 \" % (diffTime));\n print(\"└-------------------------------------\\n\");\n\n return;\n\naries = Aries();\n# aries.initStatus();\ncrawlStatus = aries._dbo.getStatusById(1);\n# aries.listInfoParser(crawlStatus);\n\naries.detailInfoParser(crawlStatus);\n\n# for i in range(6):\n # aries.initStatus();\n\n\n\n\n","sub_path":"mp4Crawler/scheduler/Aries.py","file_name":"Aries.py","file_ext":"py","file_size_in_byte":9691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"382937048","text":"#\n# @lc app=leetcode.cn id=589 lang=python\n#\n# [589] N-ary Tree Preorder Traversal\n#\n# https://leetcode-cn.com/problems/n-ary-tree-preorder-traversal/description/\n#\n# algorithms\n# Easy (65.78%)\n# Total Accepted: 4.7K\n# Total Submissions: 7K\n# Testcase Example: '{\"$id\":\"1\",\"children\":[{\"$id\":\"2\",\"children\":[{\"$id\":\"5\",\"children\":[],\"val\":5},{\"$id\":\"6\",\"children\":[],\"val\":6}],\"val\":3},{\"$id\":\"3\",\"children\":[],\"val\":2},{\"$id\":\"4\",\"children\":[],\"val\":4}],\"val\":1}'\n#\n# 给定一个 N 叉树，返回其节点值的前序遍历。\n# \n# 例如，给定一个 3叉树 :\n# \n# \n# \n# \n# \n# \n# \n# 返回其前序遍历: [1,3,5,6,2,4]。\n# \n# \n# \n# 说明: 递归法很简单，你可以使用迭代法完成此题吗?\n#\n\"\"\"\n# Definition for a Node.\nclass Node(object):\n def __init__(self, val, children):\n self.val = val\n self.children = children\n\"\"\"\nclass Solution(object):\n def preorder(self, root):\n \"\"\"\n :type root: Node\n :rtype: List[int]\n \"\"\"\n # 递归\n # ret = []\n # if not root: return ret\n # def pre(node, arr):\n # if node:\n # arr.append(node.val)\n # for child in node.children:\n # pre(child, arr)\n # return arr\n # return pre(root, ret)\n\n # 迭代\n if not root: return []\n stack = [root]\n ret = []\n while stack:\n node = stack.pop()\n if node:\n ret.append(node.val)\n for child in reversed(node.children):\n stack.append(child)\n return ret\n\n","sub_path":"Unknown/589.n-ary-tree-preorder-traversal.py","file_name":"589.n-ary-tree-preorder-traversal.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"409411885","text":"from webium.driver import get_driver\r\nfrom webium.driver import close_driver\r\nfrom Login import loginpage\r\nfrom selenium.webdriver.support.ui import Select\r\nfrom activity_hub_page import ActivityHubPage\r\nfrom activity_page import AddEditActivityPage, switcher_OFF\r\nfrom event_calendar import EventCalendarPage\r\nfrom admin_booking import AdminBookingPage\r\nimport time\r\nfrom event_schelduler import EventScheldulerPage\r\nfrom creds import admin_login, admin_password,server, database, username, password\r\nimport random\r\nfrom random import choice\r\nfrom string import digits\r\nimport datetime\r\nfrom dateutil.relativedelta import relativedelta\r\nfrom selenium.webdriver.support.wait import WebDriverWait\r\nimport pyodbc\r\n\r\nActivityNameList=[]\r\nDateList=[]\r\nTimeList=[]\r\n\r\nclass BaseTest(object):\r\n def teardown_class(self):\r\n close_driver()\r\n\r\nclass Test_GODO859_985(BaseTest):\r\n\r\n def test_859(self):\r\n get_driver().maximize_window()\r\n page = loginpage()\r\n page.open()\r\n page.login_field.send_keys(admin_login)\r\n page.password_field.send_keys(admin_password)\r\n page.button.click()\r\n page=ActivityHubPage()#STEP1\r\n page.open()\r\n page.add_activity_button.click()\r\n page=AddEditActivityPage()#STEP2\r\n time.sleep(15)\r\n NewActivityName = (\"PrivatePartyAutoTest859_\"+''.join(choice(digits) for i in range(4)))\r\n ActivityNameList.append(NewActivityName)\r\n page.activity_name.send_keys(NewActivityName)\r\n select = Select(page.activity_status)\r\n NewActivityStatus = \"Private Party\"\r\n select.select_by_visible_text(NewActivityStatus)\r\n select = Select(page.branch)\r\n NewActivityBranch = \"AlexeyBranch\"\r\n select.select_by_visible_text(NewActivityBranch)\r\n select = Select(page.starting_location)\r\n NewActivityLocation = \"Hotel California\"\r\n select.select_by_visible_text(NewActivityLocation)\r\n select = Select(page.time_zone)\r\n NewActivityTimezone = \"Pacific\"\r\n select.select_by_visible_text(NewActivityTimezone)\r\n NewActivityCancellationPolicy = 'We can cancel an event any time we want.'\r\n page.cancellation_policy.send_keys(NewActivityCancellationPolicy)\r\n NewActivityDurationMinutes = '15'\r\n page.activity_duration_minutes.send_keys(NewActivityDurationMinutes)\r\n page.ticket_maximum.clear()\r\n NewActivityMaxTickets = '100'\r\n page.ticket_maximum.send_keys(NewActivityMaxTickets)\r\n NewActivityFirstTicketType = \"Adult\"\r\n page.first_ticket_type.send_keys(NewActivityFirstTicketType)\r\n NewActivityFirstTicketPrice = '9.99'\r\n page.first_ticket_price.send_keys(NewActivityFirstTicketPrice)\r\n page.stop_booking_sold.click()\r\n select = Select(page.stop_booking_sold)\r\n NewActivityStopbookingSold = \"15 m\"\r\n select.select_by_visible_text(NewActivityStopbookingSold)\r\n NewActivityMinTickets = '4' #MIN TICKETS #STEP3\r\n page.ticket_minimum.send_keys(NewActivityMinTickets)\r\n page.switchers1[0].click()\r\n page.switchers2[0].click()\r\n page.switcher_minimum_enforce.click()\r\n assert page.switchers1[0].get_attribute(\"outerHTML\") != switcher_OFF#Stop Booking Midnight Before\r\n assert page.switchers2[0].get_attribute(\"outerHTML\")!= switcher_OFF#First Sale Closes Event\r\n assert page.switcher_minimum_enforce.get_attribute(\"outerHTML\") != switcher_OFF#Tickets Minimum Enforce\r\n page.save_button.click() #STEP5\r\n time.sleep(5)\r\n page = ActivityHubPage()\r\n page.search_activity_field.send_keys(NewActivityName)\r\n time.sleep(5)\r\n page.activity_actions.click()#STEP6\r\n text = page.activity_title.get_attribute(\"textContent\")\r\n assert text == NewActivityName\r\n page.edit_activity.click()#STEP7\r\n page = AddEditActivityPage()\r\n time.sleep(15)\r\n assert page.activity_name.get_attribute('value') == NewActivityName\r\n select = Select(page.activity_status)\r\n assert select.first_selected_option.text == NewActivityStatus\r\n select = Select(page.branch)\r\n assert select.first_selected_option.text == NewActivityBranch\r\n select = Select(page.starting_location)\r\n assert select.first_selected_option.text == NewActivityLocation\r\n select = Select(page.time_zone)\r\n assert select.first_selected_option.text == NewActivityTimezone\r\n assert page.cancellation_policy.get_attribute('value') == NewActivityCancellationPolicy\r\n assert page.activity_duration_minutes.get_attribute('value') == NewActivityDurationMinutes\r\n assert page.ticket_maximum.get_attribute('value') == NewActivityMaxTickets\r\n assert page.first_ticket_type.get_attribute('value') == NewActivityFirstTicketType\r\n assert page.first_ticket_price.get_attribute('value') == NewActivityFirstTicketPrice\r\n assert page.ticket_minimum.get_attribute('value') == NewActivityMinTickets\r\n assert page.switchers1[0].get_attribute(\"outerHTML\") != switcher_OFF\r\n assert page.switchers2[0].get_attribute(\"outerHTML\") != switcher_OFF\r\n assert page.switcher_minimum_enforce.get_attribute(\"outerHTML\") != switcher_OFF\r\n select = Select(page.stop_booking_sold)\r\n assert select.first_selected_option.text == NewActivityStopbookingSold\r\n cnxn = pyodbc.connect(\r\n 'DRIVER={ODBC Driver 17 for SQL Server};SERVER=' + server + ';DATABASE=' + database + ';UID=' + username + ';PWD=' + password)#STEP8\r\n cursor = cnxn.cursor()\r\n cursor.execute(\"SELECT TOP 1 * FROM activity ORDER BY activity_id DESC\")\r\n row = cursor.fetchone()\r\n assert row[1] == 68#company id\r\n assert row[2] == 47#location_id\r\n assert row[3] == 386#branch_id\r\n assert row[4] == 9 #timezone_id\r\n assert row[6] == NewActivityName\r\n assert row[11] == NewActivityCancellationPolicy\r\n assert row[14] == 1#Firstsalecloseevent\r\n assert row[15] == 0 # StopBookingNoSales\r\n assert row[16] == 15 # StopBookingSold\r\n assert row[17] == 1 # StopBookingMidBefore\r\n assert row[21] == 15 #Duration\r\n assert row[32] == True #GuideUponSellout\r\n assert row[33]==2 #ActivityStatus\r\n assert row[36] == 1 #Tickets Minimum Enforce\r\n assert row[37] == 0 # Viator\r\n assert row[39] == 0 # 2-step Check In\r\n page.cancel_button.click()#STEP9\r\n page = ActivityHubPage()\r\n time.sleep(5)\r\n page.search_activity_field.send_keys(NewActivityName)#STEP10\r\n time.sleep(5)\r\n page.activity_actions.click()#STEP11\r\n page.add_events.click()#STEP12\r\n page = EventScheldulerPage()\r\n wait = WebDriverWait(get_driver(), 15)\r\n wait.until(lambda driver: page.is_element_present('scheldule_type'))\r\n select = Select(page.scheldule_type)\r\n select.select_by_visible_text('Repeating Event (Throughout Day)')#STEP13\r\n page.rep_mult_begin_field.click()#STEP14\r\n page.next_button_calendar_begin.click()\r\n NewDateBegin = random.randint(8, 18)\r\n nextMonthDate = datetime.date.today() + relativedelta(months=1)\r\n NewFullDateBegin = (nextMonthDate.strftime(\"%B\") + ' ' + ''.join(str(NewDateBegin)))\r\n DateList.append(NewDateBegin)\r\n nextMonthDate = datetime.date.today() + relativedelta(months=1)\r\n for i in range(0, len(page.date_calendar)):\r\n if i + 1 == NewDateBegin:\r\n page.date_calendar[i].click()\r\n else:\r\n continue\r\n break\r\n time.sleep(5)\r\n NewDateEnd = NewDateBegin + 10\r\n DateList.append(NewDateEnd)\r\n NewFullDateEnd = (nextMonthDate.strftime(\"%B\") + ' ' + ''.join(str(NewDateEnd)))\r\n page.rep_mult_end_field.click()\r\n page.next_button_calendar_enddate_repmult.click()\r\n time.sleep(5)\r\n end_date_list = []\r\n for i in range(0, len(page.date_calendar_end)):\r\n if page.date_calendar_end[i].is_displayed():\r\n end_date_list.append(page.date_calendar_end[i])\r\n for i in range(0, len(end_date_list)):\r\n if i + 1 == NewDateEnd:\r\n end_date_list[i].click()\r\n else:\r\n continue\r\n break\r\n NewTimeHoursBegin = str(random.randint(1, 10))\r\n select = Select(page.rep_mult_hours_begin)\r\n select.select_by_visible_text(NewTimeHoursBegin )\r\n minutes_values = ('00', '05', '10', '15', '20', '25', '30', '35', '40', '45', '50', '55')\r\n NewTimeMinutesBegin = random.choice(minutes_values)\r\n select = Select(page.rep_mult_min_begin)\r\n select.select_by_visible_text(NewTimeMinutesBegin )\r\n NewTimeAMPM = random.choice(('AM','PM'))\r\n timeEvent = (NewTimeHoursBegin + ':' + ''.join(NewTimeMinutesBegin ) + ' ' + ''.join(NewTimeAMPM))\r\n TimeList.append(timeEvent)\r\n select = Select(page.rep_mult_appm_begin)\r\n select.select_by_visible_text(NewTimeAMPM)\r\n NewTimeHoursEnd = str(int(NewTimeHoursBegin) + 2)\r\n select = Select(page.rep_mult_hours_end)\r\n select.select_by_visible_text(NewTimeHoursEnd)\r\n NewTimeMinutesEnd = NewTimeMinutesBegin\r\n select = Select(page.rep_mult_min_end)\r\n select.select_by_visible_text(NewTimeMinutesEnd)\r\n select = Select(page.rep_mult_appm_end)\r\n select.select_by_visible_text(NewTimeAMPM)\r\n time.sleep(6)\r\n EveryMinutesRuns = '60'\r\n page.rep_every_min.send_keys(EveryMinutesRuns)\r\n page.rep_add_mult.click()#STEP15\r\n time.sleep(5)\r\n assert page.is_element_present('popup_OK') == True\r\n page.popup_OK.click()#STEP16\r\n time.sleep(5)\r\n page=ActivityHubPage()\r\n assert get_driver().current_url==page.url\r\n\r\n\r\n def test_985(self):\r\n page = EventCalendarPage()\r\n page.open()\r\n time.sleep(2)\r\n select = Select(page.activity_name)\r\n select.select_by_visible_text(ActivityNameList[0])\r\n time.sleep(2)\r\n page.hide_events.click()\r\n time.sleep(5)\r\n page.date_picker.click()\r\n time.sleep(2)\r\n page.date_picker_next.click()\r\n EventDate = str(random.choice(DateList))\r\n for i in range(0, len(page.days_date_picker)):\r\n if page.days_date_picker[i].get_attribute(\"textContent\") == EventDate:\r\n page.days_date_picker[i].click()\r\n else:\r\n continue\r\n break\r\n page.day_button.click()\r\n time.sleep(6)\r\n nextMonthDate = datetime.date.today() + relativedelta(months=1)\r\n FullEventDate = (nextMonthDate.strftime(\"%B\") + ' ' + ''.join(EventDate))\r\n assert str(FullEventDate) in page.date_header.get_attribute(\"textContent\")\r\n for ticket in page.day_slots: # STEP25\r\n for i in range(0, len(page.day_slots)):\r\n if TimeList[0] in ticket.day_slot_time[i].get_attribute('textContent'):\r\n page.day_slots[i].click()\r\n else:\r\n continue\r\n break\r\n time.sleep(6)\r\n assert FullEventDate in page.date_time_title.get_attribute('textContent')\r\n assert ActivityNameList[0] == page.activity_name_title.get_attribute('textContent')\r\n assert TimeList[0] in page.date_time_title.get_attribute('textContent')\r\n assert page.event_status.get_attribute(\"textContent\") =='Pending' #STEP4\r\n assert 'Ticket Sold: 0' in page.manifest_title.get_attribute(\"innerText\")\r\n page.add_booking_button.click()#STEP5\r\n time.sleep(5)\r\n page = AdminBookingPage()\r\n time.sleep(5)\r\n page.first_tickets_type.send_keys('3') #STEP6\r\n time.sleep(5)\r\n assert page.final_alert.get_attribute(\"textContent\") == 'Minimum number of tickets (4 tickets) for the event has not been met yet. Do you want to continue?'\r\n page.alert_cancel_button.click() # STEP7\r\n time.sleep(5)\r\n assert page.is_element_present('enter_customer_information_button')==False\r\n page.first_tickets_type.clear()\r\n page.first_tickets_type.send_keys('4')# STEP8\r\n time.sleep(5)\r\n page.enter_customer_information_button.click()#STEP9\r\n time.sleep(5)\r\n NewFirstName = 'James'\r\n page.first_name.send_keys(NewFirstName) # STEP10\r\n NewLastName = 'James'\r\n NewFullName = NewFirstName + ' ' + ''.join(NewLastName)\r\n page.last_name.send_keys(NewLastName)\r\n NewEmail = ('James@mailinator.com')\r\n page.email_address.send_keys(NewEmail)\r\n time.sleep(10)\r\n page.complete_booking_button.click()\r\n time.sleep(5)\r\n wait = WebDriverWait(get_driver(), 15)\r\n wait.until(lambda driver: page.is_element_present('payment_type_list'))\r\n select = Select(page.payment_type_list)\r\n PaymentType = \"Cash\" # STEP11\r\n select.select_by_visible_text(PaymentType)\r\n page.cash_recieved.click()\r\n page.submit_booking_button.click()\r\n time.sleep(5)\r\n assert page.final_alert.get_attribute(\"textContent\") =='Booking Successful!'\r\n page.final_alert_ok_button.click()# STEP12\r\n page = EventCalendarPage() #STEP13\r\n page.open()\r\n time.sleep(2)\r\n select = Select(page.activity_name)\r\n select.select_by_visible_text(ActivityNameList[0])\r\n time.sleep(2)\r\n page.hide_events.click()\r\n time.sleep(5)\r\n page.date_picker.click()\r\n time.sleep(2)\r\n page.date_picker_next.click()\r\n for i in range(0, len(page.days_date_picker)):\r\n if page.days_date_picker[i].get_attribute(\"textContent\") == EventDate:\r\n page.days_date_picker[i].click()\r\n else:\r\n continue\r\n break\r\n page.day_button.click()\r\n time.sleep(6)\r\n assert str(FullEventDate) in page.date_header.get_attribute(\"textContent\")\r\n for ticket in page.day_slots:\r\n for i in range(0, len(page.day_slots)):\r\n if TimeList[0] in ticket.day_slot_time[i].get_attribute('textContent'):\r\n page.day_slots[i].click()\r\n else:\r\n continue\r\n break\r\n time.sleep(6)\r\n assert FullEventDate in page.date_time_title.get_attribute('textContent')\r\n assert ActivityNameList[0] == page.activity_name_title.get_attribute('textContent')\r\n assert TimeList[0] in page.date_time_title.get_attribute('textContent')\r\n assert page.event_status.get_attribute(\"textContent\") == 'Closed'\r\n assert 'Tickets Sold: 4' in page.manifest_title.get_attribute(\"innerText\")\r\n assert page.customer_name_link.get_attribute('textContent') == NewFullName\r\n assert page.email_link.get_attribute('textContent') == NewEmail\r\n assert page.add_booking_button.is_enabled() == False #STEP14\r\n","sub_path":"Tests_Activity Hub- Activities/test_GODO-859-985 Add Private Party-Admin booking set YES.py","file_name":"test_GODO-859-985 Add Private Party-Admin booking set YES.py","file_ext":"py","file_size_in_byte":15054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"606630720","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Dec 7 18:20:43 2019\n\n@author: viniciussaurin\n\"\"\"\n\nfrom selenium import webdriver\nfrom time import sleep\nimport pandas as pd\n\npath = '/chromedriver'\npath_excel = '/RN.xls'\n\n\nclass ChromeAuto:\n def __init__(self):\n self.driver_path = path\n self.options = webdriver.ChromeOptions()\n self.options.add_argument('user-data-dir=Perfil')\n self.chrome = webdriver.Chrome(\n self.driver_path,\n options=self.options\n )\n def acessa(self, site):\n self.chrome.get(site)\n \n def scrapping_head(self):\n tr_elements = self.chrome.find_elements_by_xpath('//tr/th')\n col=[]\n i=0 \n for t in tr_elements:\n i += 1\n name=t.text\n print(f'{i}:{name}')\n col.append(name)\n return col\n \n \n \n def scrapping_body(self):\n td_elements = self.chrome.find_elements_by_xpath('//tr/td')\n tr_elements = self.chrome.find_elements_by_xpath('//tbody/tr')\n qtd_td_por_tr = int(len(td_elements)/len(tr_elements))\n col = []\n temp = []\n for k in range(len(tr_elements)):\n for j in range(qtd_td_por_tr):\n data = td_elements[k * qtd_td_por_tr + j].text\n temp.append(data)\n col.append(temp)\n temp = []\n \n return col\n \n def clicar_proxima_pagina(self):\n try:\n btn_proxima_pg = self.chrome.find_element_by_link_text('próxima')\n btn_proxima_pg.click()\n return True\n except Exception as e:\n print('Erro ao clicar em Próxima página:', e)\n return False\n \n \n def sair(self):\n self.chrome.quit()\n\n\nif __name__ == '__main__':\n chrome = ChromeAuto()\n acessa = chrome.acessa('http://servicos.searh.rn.gov.br/searh/Remuneracao/RemuneracaoPorId/17301247?MesAno=10%2F2019')\n head = chrome.scrapping_head()\n retorno_px_pg = True\n i = 0\n body=pd.DataFrame(columns=head)\n while retorno_px_pg:\n temp = pd.DataFrame.from_records(data=chrome.scrapping_body(), columns=head)\n body = body.append(temp)\n print(i)\n i+=1\n retorno_px_pg = chrome.clicar_proxima_pagina()\n sleep(2)\n chrome.sair()\n body.to_excel(path_excel, index=False)\n","sub_path":"webscrapping_selenium.py","file_name":"webscrapping_selenium.py","file_ext":"py","file_size_in_byte":2399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"268971433","text":"import pyttsx3\nimport PyPDF2\nbook = open('OOP.pdf', 'rb')\npdfReader = PyPDF2.PdfFileReader(book)\npages = pdfReader.numPages\nprint(pages)\nengine = pyttsx3.init()\nfor num in range(7,pages):\n page = pdfReader.getPage(num)\n text = page.extractText()\n engine.say(text)\n engine.runAndWait()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"259327375","text":"from chat.chatter import Chatter, Two, One, Any, Compare, Join\n\nchatter = Chatter()\n\nhello = \"привет\", \"приветики\", \"привет!\", \"ку\", \"привет\"\n\nonly_hello = Any(hello, Compare.equals, 0)\nhas_hello = Any(hello, Compare.inside, 0)\nhas_hello_last = Any(hello, Compare.inside, 2)\n\nchatter.add(Two(has_hello_last, has_hello, Join.everything), \"ты уже здоровался!\")\nchatter.add(only_hello, \"я приветствую тебя!\")\nchatter.add(has_hello, \"я приветствую тебя, но я не понял остальной части предложения!\")\n\nchatter.add(One(\"\", Compare.inside, 0), \"Я не понял!\", \"Не понимаю!\", \"Что?\")\n\n'''\n** Важные пункты:\n1. Всё взаимодействие происходит через chatter, его нельзя переназначать и т.д.\n2. Добавлять реплики надо через chatter.add(*условие*, *ответ1*, *ответ2*, *ответ3*...).\n Выбирается случайный из возможных ответов\n3. Строки рассматриваются, независимо от регистра!\n4. В истории сообщений сохраняются только сообще��ия, на которые ответил и какими ответил бот.\n\n** Сравнения:\nСравнение - это функция, которая принимает две строки и возвращет булевое значение - равны ли эти строки.\n- Compare.close - True, если строки похожи на 80% (с помощью difflib)\n- Compare.inside - True, если вторая строка есть в первой (строка в сообщении пользователя)\n- Compare.equals - True, если строки идентичны\n- Compare.not_close - True, если строки похожи менее, чем на 80% (с помощью difflib)\n- Compare.not_inside - True, если вторая строка отсутствует в первой (строка в сообщении пользователя)\n- Compare.not_equals - True, если строки не идентичны\n\n** Реплика поиска:\nДиалог с пользователем хранится в формате:\n0: Текущее сообщение пользователя\n1: Последнее сообщение бота\n2: Прошлое сообщение пользователя\n3: Ответ бота\nи т.д.\nРеплика поиска - номер строки, которая рассматривается в условии\n\n** Условия:\nУсловие - это то, что определяет, будет отвечать на сообщение бот или нет. Условия бывают нескольких видов и\nобрабатываются в порядке от первого добавленного до последнего(сверху вниз).\n\nAny(*строка или список строк*, *сравнение*, *реплика поиска*)\nСрабатывает, когда *сравнение* вернёт True, на любую из *строк* в *реплика поиска*\n\nAll(*стока или список строк*, *сравнение*, *реплика поиска*)\nСрабатывает, когда *сравнение* вернёт True, на все *строки* в *реплика поиска*\n\nOne(*стока*, *сравнение*, *реплика поиска*)\nСрабатывает, когда *сравнение* вернёт True, на *строку* в *реплика поиска*\n\nTwo(*условие1*, *условие2*, *объединение условий*)\nСрабатывает, когда *объединение условий* вернёт True, на результаты *условий*.\n\n** Объединение условий:\nОбъединение условий - это функция, которая принимает два булевых значения и возвращет булевое\nзначение - объединение условий.\n- Join.anything - True, если выполнено хотя бы одно условие\n- Join.everything - True, если вторая все условия выполнены\n- Join.nothing - True, если ни одно из условий не выполнено\n'''\n","sub_path":"chat/chat.py","file_name":"chat.py","file_ext":"py","file_size_in_byte":4490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"533465020","text":"#!/usr/bin/env python3\n\"\"\"A tool for managing Essential Contacts.\nRequirements in 'requirements.txt'.\n\"\"\"\n# Copyright 2020 Google LLC. This software is provided as-is, without warranty or representation\n# for any use or purpose. Your use of it is subject to your agreement with Google.\n\nimport google.auth\nimport googleapiclient.errors as errors\nfrom googleapiclient.discovery import build\nfrom google.oauth2 import service_account\nimport argparse\nimport pprint\nimport os\nimport sys\n\nAPI_SERVICE_NAME = 'essentialcontacts'\nAPI_VERSION = 'v1alpha1'\nURI = 'https://essentialcontacts.googleapis.com/$discovery/rest?key='\npp = pprint.PrettyPrinter(indent=2)\n\nparser = argparse.ArgumentParser(\n description='Essential Contacts API example tool.')\nparser.add_argument('--api-key',\n default=os.getenv('EC_API_KEY'),\n help='Service account key file.')\nparser.add_argument('--service-account-file',\n default=os.getenv('GOOGLE_APPLICATION_CREDENTIALS'),\n help='API key to call Essential Contacts API with.')\nparser.add_argument('project_id',\n help='Project to set the essential contacts for.')\nparser.add_argument('--categories',\n nargs='+',\n default='ALL',\n choices=[\n 'NOTIFICATION_CATEGORY_UNSPECIFIED', 'OTHER', 'ALL',\n 'SUSPENSION', 'PRIVACY', 'SECURITY', 'TECHNICAL',\n 'BILLING', 'LEGAL', 'PRODUCT_UPDATES'\n ],\n help='Set the category for notifications.')\nparser.add_argument('--contacts', help='Set the contacts for the category.')\nparser.add_argument('--quota-project-id',\n default=None,\n help='Quota project ID (optional).')\n\n# Check validity of command line flags\nargs = parser.parse_args()\nif not args.api_key:\n raise RuntimeError('Must provide API key.')\n\nif args.service_account_file:\n credentials = service_account.Credentials.from_service_account_file(\n args.service_account_file,\n scopes=[\"https://www.googleapis.com/auth/cloud-platform\"],\n quota_project_id=args.quota_project_id)\nelse:\n credentials, project = google.auth.default(\n quota_project_id=args.quota_project_id)\n\nservice = build(API_SERVICE_NAME,\n API_VERSION,\n developerKey=args.api_key,\n credentials=credentials,\n discoveryServiceUrl=URI + args.api_key)\n\nproject_id = 'projects/%s' % args.project_id\ncontacts = service.projects().contacts().list(parent=project_id).execute()\ncontacts_to_set = args.contacts.split(',')\n# List and remove existing contacts that don't match the list\nif 'contacts' in contacts:\n for contact in contacts['contacts']:\n if contact['email'] not in contacts_to_set:\n print('Removing %s from essential contacts.' % (contact['email']),\n file=sys.stderr)\n delete_contact = service.projects().contacts().delete(\n name=contact['name']).execute()\n else:\n print('Updating %s in essential contacts to categories: %s' %\n (contact['email'], ','.join(args.categories)),\n file=sys.stderr)\n update_contact = service.projects().contacts().patch(\n name=contact['name'],\n updateMask='notificationCategorySubscriptions',\n body={\n 'notificationCategorySubscriptions': args.categories\n }).execute()\n contacts_to_set.remove(contact['email'])\n\nfor email in contacts_to_set:\n print('Adding %s to essential contacts for categories: %s' %\n (email, ','.join(args.categories)),\n file=sys.stderr)\n new_contact = service.projects().contacts().create(\n parent=project_id,\n body={\n 'email': email,\n 'languageTag': 'en',\n 'notificationCategorySubscriptions': args.categories\n }).execute()\n","sub_path":"scripts/essential-contacts.py","file_name":"essential-contacts.py","file_ext":"py","file_size_in_byte":4043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"267837277","text":"from flask import Flask\nfrom flask_cors import CORS\nimport json\nfrom flask_mysqldb import MySQL\n \napp = Flask(__name__)\nCORS(app)\n\napp.config['MYSQL_HOST'] = 'localhost'\napp.config['MYSQL_USER'] = 'root'\napp.config['MYSQL_PASSWORD'] = '402@flatAB'\napp.config['MYSQL_DB'] = 'practice_db'\n\n# app.config['MYSQL_PASSWORD'] = '12345'\n# app.config['MYSQL_DB'] = 'mobile_store'\n\nmysql = MySQL(app)\n\n\n@app.route('/hello')\ndef hello_world():\n\n cursor = mysql.connection.cursor()\n\n rawquery = f\"select * from flatmate where id = 1\"\n # rawquery = f\"select id, name, email from users where id = 1\"\n\n cursor.execute(rawquery)\n result = cursor.fetchone()\n print(result)\n\n data = {}\n data['success'] = True\n # data['msg'] = \"Hello Mr. Mahipal Singh, Bhai balcony ka gate open rakha kr\"\n data['msg'] = result\n return json.dumps(data), 200, {'content-type': 'application/json'}\n\nif __name__ == '__main__':\n app.debug = True\n app.run(host='0.0.0.0', port=5000, debug=True)","sub_path":"hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"626438995","text":"import nipype.algorithms.modelgen as model # model generation\nfrom nipype.interfaces import fsl, ants \nfrom nipype.interfaces.io import SelectFiles, DataSink\nfrom nipype.interfaces.utility import IdentityInterface\nimport nipype.pipeline.engine as pe\nfrom nipype.pipeline.engine import Workflow, Node\nfrom nipype import Function\nfrom nipype import MapNode\n\nimport nibabel\nimport nilearn.plotting\n\nimport os,json,glob,sys\nfrom os.path import join as opj\n\nimport numpy\nimport pandas as pd\n\nsubject_list = ['15','16']\n\nsession_list = []\nfor i in range(1,9):\n if i == 2 or i == 8:\n task = 'obj'\n else:\n task = 'scene'\n session_list.append('task-{}_run-0{}'.format(task,i))\n\ntrial_list = []\nfor letter in ['A','B']:\n for j in range(18):\n trial_list.append(letter+str(j))\n\nbase_dir = '/projects/kuhl_lab/wanjiag/NEUDIF/bids_data/derivatives'\nfirst_level_dir = opj(base_dir, '1st_level')\n\nTR=2.0\nhigh_pass_filter_cutoff=128.\n\n# Setup any package specific configuration. The output file format for FSL routines is being set to compressed NIFTI.\nfsl.FSLCommand.set_default_output_type('NIFTI_GZ')\n\nprint(first_level_dir)\nprint(session_list)\nprint(trial_list)\n\nfirst_level_wf = Workflow(name=\"first_level_dir_wf\", base_dir=first_level_dir)\n\n# Infosource - a function free node to iterate over the list of subject names\ninfosource = Node(IdentityInterface(fields=['subject_id', 'session_id', 'trial_type']),\n name=\"infosource\")\ninfosource.iterables = [('subject_id', subject_list),\n ('session_id', session_list),\n ('trial_type', trial_list)]\n\n# SelectFiles - to grab the data (alternativ to DataGrabber)\n# T1 space\nfunc_file = 'prepro_all/sub-{subject_id}/fwhm-2_sub-{subject_id}_{session_id}_medcent.nii.gz'\n\ntemplates = {'func': func_file}\n\nselectfiles = Node(SelectFiles(templates,\n base_directory=base_dir),\n name=\"selectfiles\")\n\ndef create_info(subject_id, session_id, trial_type):\n \n import numpy\n import pandas as pd\n from nipype.interfaces.base import Bunch\n \n confound_file = '/projects/kuhl_lab/wanjiag/NEUDIF/bids_data/derivatives/fmriprep/sub-{}/func/sub-{}_{}_desc-confounds_regressors.tsv'.format(subject_id, subject_id, session_id) \n events_file = '/projects/kuhl_lab/wanjiag/NEUDIF/bids_data/derivatives/prepro_all/sub-{}/{}_events.tsv'.format(subject_id, session_id)\n \n events = pd.read_csv(events_file,sep=\"\\t\", na_values=\"n/a\")\n events['trial_type'] = events['trial_type'].apply(lambda x: x.split('_')[-1])\n confounds = pd.read_csv(confound_file,sep=\"\\t\", na_values=\"n/a\")\n \n indexs = events[events['trial_type']==trial_type].index.values\n print(indexs)\n events_trial = events.copy()\n events_trial['trial_type'] = 'other'\n for index in indexs:\n events_trial.loc[index, 'trial_type'] = trial_type\n \n info = [Bunch(conditions=[trial_type,\n 'other'],\n onsets=[list(events_trial[events_trial.trial_type == trial_type].onset),\n list(events_trial[events_trial.trial_type == 'other'].onset)],\n durations=[list(events_trial[events_trial.trial_type == trial_type].duration),\n list(events_trial[events_trial.trial_type == 'other'].duration)],\n regressors=[list(confounds.framewise_displacement.fillna(0)[5:]),\n list(confounds.a_comp_cor_00[5:]),\n list(confounds.a_comp_cor_01[5:]),\n list(confounds.a_comp_cor_02[5:]),\n list(confounds.a_comp_cor_03[5:]),\n list(confounds.a_comp_cor_04[5:]),\n list(confounds.a_comp_cor_05[5:]),\n ],\n regressor_names=['FramewiseDisplacement',\n 'aCompCor0',\n 'aCompCor1',\n 'aCompCor2',\n 'aCompCor3',\n 'aCompCor4',\n 'aCompCor5',])\n ]\n \n return info\n\nmake_info = Node(Function(input_names=[\"subject_id\", \"session_id\", \"trial_type\"],\n output_names=[\"info\"],\n function=create_info),\n name='make_info')\n\nspecify_model = Node(model.SpecifyModel(input_units = 'secs',\n time_repetition = TR,\n high_pass_filter_cutoff = high_pass_filter_cutoff), \n name=\"specify_model\")\n\ndef make_contrast(trial_type):\n trial_cond = [trial_type,'T', [trial_type],[1]]\n contrasts=[trial_cond]\n \n return contrasts\n\n\nlevel1design = Node(fsl.model.Level1Design(interscan_interval = TR,\n bases = {'dgamma':{'derivs': True}},\n model_serial_correlations=True), \n name=\"level1design\")\n\nmodelgen = Node(fsl.model.FEATModel(),name=\"modelgen\")\nfilmgls= Node(fsl.FILMGLS(autocorr_noestimate = True),name=\"filmgls\")\ndatasink = Node(DataSink(base_directory = first_level_dir),name=\"datasink\")\n\nsubstitutions = [('tstat_map/_session_id_{}_subject_id_{}_trial_type_{}/tstat1.nii.gz'.format(session, sub, trial), 'sub-{}/{}/combine_repeats/{}.nii.gz'.format(sub, session, trial))\n for sub in subject_list\n for session in session_list\n for trial in trial_list]\ndatasink.inputs.substitutions = substitutions\n\nfirst_level_wf.connect([(infosource, selectfiles, [('subject_id', 'subject_id'),\n ('session_id', 'session_id')]),\n (infosource, make_info, [('subject_id', 'subject_id'),\n ('session_id', 'session_id'),\n ('trial_type', 'trial_type')]),\n (selectfiles, specify_model, [('func', 'functional_runs')]),\n (make_info, specify_model, [('info', 'subject_info')]),\n (specify_model, level1design, [('session_info','session_info')]),\n (infosource, level1design, [(('trial_type', make_contrast), 'contrasts')]),\n (level1design, modelgen, [('fsf_files', 'fsf_file'),\n ('ev_files', 'ev_files')]),\n (selectfiles, filmgls, [('func', 'in_file')]),\n (modelgen, filmgls, [('design_file', 'design_file'),\n ('con_file', 'tcon_file')]),\n (filmgls, datasink, [('tstats', 'tstat_map')])\n \n ])\n\n# Create preproc output graph\nfirst_level_wf.write_graph(graph2use='colored', format='png', simple_form=True)\n\nfirst_level_wf.run('MultiProc', plugin_args={'n_procs': 8})\n","sub_path":"jupyter/.ipynb_checkpoints/1st_level_combine_repeats-checkpoint.py","file_name":"1st_level_combine_repeats-checkpoint.py","file_ext":"py","file_size_in_byte":7135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"364613799","text":"import os\n\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\",\"vjproject.settings\")\nimport django\ndjango.setup()\nfrom testapp.models import *\nfrom faker import Faker\n\nfrom random import *\nf=Faker()\ndef phonenumbergen():\n d1=randint(7,9)\n num=''+str(d1)\n for i in range(9):\n num=num+str(randint(0,9))\n return int(num)\n\ndef populate(n):\n for i in range(n):\n fdate=f.date()\n fcompany=f.company()\n ftitle=f.random_element(elements=('Project Manger','Team Lead','Software Engg'))\n feligibility=f.random_element(elements=('Btech','Mtech','MCA','Phd'))\n faddress=f.address()\n femail=f.email()\n fphonenumber=phonenumbergen()\n chennaijobs_record=chennaijobs.objects.get_or_create(date=fdate,company=fcompany,title=ftitle,eligibility=feligibility,address=faddress,email=femail,phonenumber=fphonenumber)\npopulate(25)\n","sub_path":"vjproject/populatechennai.py","file_name":"populatechennai.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"439047010","text":"from tkinter import *\nimport tkinter as tk\n\nroot = Tk()\n\nscrollbar = Scrollbar(root)\nscrollbar.pack(side=RIGHT, fill=Y)\n\nlistbox = Listbox(root)\nlistbox.pack()\nfor i in range(100):\n listbox.insert(END, i)\ntext = Text(root, wrap=NONE,\n xscrollcommand=scrollbar.set,\n yscrollcommand=scrollbar.set)\n#text.pack()\nframe = Frame(root, bd=2, relief=SUNKEN)\n\nframe.grid_rowconfigure(0, weight=1)\nframe.grid_columnconfigure(0, weight=1)\n\nxscrollbar = Scrollbar(frame, orient=HORIZONTAL)\nxscrollbar.grid(row=1, column=0, sticky=E+W)\n\nyscrollbar = Scrollbar(frame)\nyscrollbar.grid(row=0, column=1, sticky=N+S)\n\ntext = Text(frame, wrap=NONE, bd=0,\n xscrollcommand=xscrollbar.set,\n yscrollcommand=yscrollbar.set)\n\ntext.grid(row=0, column=0, sticky=N+S+E+W)\n\nxscrollbar.config(command=text.xview)\nyscrollbar.config(command=text.yview)\n\nframe.pack()\n# attach listbox to scrollbar\nlistbox.config(yscrollcommand=scrollbar.set)\nscrollbar.config(command=listbox.yview)\n\nmainloop()","sub_path":"EXAMPLES/scroll_bar_test3.py","file_name":"scroll_bar_test3.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"157163235","text":"from collections import defaultdict\nclass Solution:\n def calcEquation(self, equations: List[List[str]], values: List[float], queries: List[List[str]]) -> List[float]:\n graph = defaultdict(list)\n for i in range(len(equations)):\n eq = equations[i]\n graph[eq[0]].append((eq[1], values[i]))\n graph[eq[1]].append((eq[0], 1/values[i]))\n soln = []\n for q in queries:\n value = self.findPath(q[0], q[1], graph, set(), 1.0)\n soln.append(value)\n return soln\n \n def findPath(self, start, end, graph, visited, value):\n # Ignores start if visited already.\n if start not in graph or end not in graph or start in visited:\n return -1.0\n # takes care care of end of recursion and the edge to itself case.\n if start == end:\n return value\n visited.add(start)\n for i in range(len(graph[start])):\n tmp = self.findPath(graph[start][i][0], end, graph, visited, value*graph[start][i][1])\n if tmp != -1.0:\n return tmp\n return -1.0\n \n \n \n \n","sub_path":"leetcode/399.Evaluate_division/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"275744848","text":"\"\"\"\nName: Basil\nClass: ICS201\nDate: 04/0214\nAssignment 5\n\"\"\"\n\nimport pygame\nimport random\n\npygame.init()\n\ndef draw_background():\n screen.fill((77, 255, 255))\n pygame.draw.rect(screen, (0, 153, 0), ((0, 400), (900, 600)))\n for a in range(0, random.randint(0, 2)):\n draw_cloud(random.randint(0, 900), random.randint(0, 100))\n\ndef randcolors():\n r = random.randint(0, 255)\n g = random.randint(0, 255)\n b = random.randint(0, 255)\n color = (r, g, b)\n return color\n\ndef draw_house(x, y):\n # Find coordinates for house based on x and y\n \n # House body\n house_box_tl = (x, y + 50)\n house_box_br = (200, 175)\n house_roof_t = (x + 100, y)\n house_roof_l = (x, y + 50)\n house_roof_r = (x + 200, y + 50)\n \n # Door\n door_tl = (x + 30, y + 130)\n door_br = (45, 95)\n knob = (x + 70, y + 172)\n \n # Window\n window_outer_tl = (x + 115, y + 130)\n window_outer_br = (50, 50)\n window_inner_tl = (x + 120, y + 135)\n window_inner_br = (40, 40)\n window_cross_xx = (x + 115, y + 155)\n window_cross_xy = (x + 163, y + 155)\n window_cross_yx = (x + 140, y + 135)\n window_cross_yy = (x + 140, y + 177)\n \n # Draw house\n color = randcolors()\n pygame.draw.rect(screen, color, (house_box_tl, house_box_br))\n color = randcolors()\n pygame.draw.polygon(screen, color, (house_roof_l, house_roof_t, house_roof_r))\n color = randcolors()\n pygame.draw.rect(screen, color, (door_tl, door_br))\n pygame.draw.rect(screen, (0, 0, 0), (window_outer_tl, window_outer_br))\n pygame.draw.rect(screen, (255, 255, 255), (window_inner_tl, window_inner_br))\n pygame.draw.line(screen, (0, 0, 0), window_cross_xx, window_cross_xy, 5)\n pygame.draw.line(screen, (0, 0, 0), window_cross_yx, window_cross_yy, 5)\n pygame.draw.circle(screen, (0, 0, 0), knob, 5)\n\ndef draw_tree(x, y, r):\n tree_leaves_color = (random.randint(20, 150), 255, 0)\n tree_leaves_x = x + r\n tree_leaves_y = y + r\n tree_leaves_r = r\n \n tree_trunk_tl = (x + r - 20, y + r)\n tree_trunk_br = (40, 200)\n \n pygame.draw.rect(screen, (128, 96, 3), (tree_trunk_tl, tree_trunk_br))\n pygame.draw.circle(screen, tree_leaves_color, (tree_leaves_x, tree_leaves_y), tree_leaves_r)\n\ndef draw_cloud(x, y):\n for i in range(0, 10):\n cloud_pos_x = random.randint(x, x + 100)\n cloud_pos_y = random.randint(y, y + 100)\n cloud_r = random.randint(20, 50)\n pygame.draw.circle(screen, (255, 255, 255), (cloud_pos_x, cloud_pos_y), cloud_r)\n \ndef draw_onscreen():\n draw_background()\n draw_house(random.randint(0, 50), 175)\n draw_house(random.randint(275, 325), 175)\n draw_house(random.randint(550, 600), 175)\n draw_tree(random.randint(150, 200), 150, random.randint(50, 100))\n draw_tree(random.randint(425, 450), 150, random.randint(50, 100))\n draw_tree(random.randint(750, 850), 150, random.randint(50, 100))\n\n# Set screen size\nsize = [900, 600]\nscreen = pygame.display.set_mode(size)\npygame.display.set_caption(\"Bp_U2_Assignment5\")\n\n\n\n\ndone = False\nclock = pygame.time.Clock()\n\n# Call draw methods under this comment\ndraw_onscreen()\n# Call draw methods above this comment\n\nwhile not done:\n pygame.display.flip()\n for event in pygame.event.get(): # User did something\n if event.type == pygame.QUIT: # If user clicked close\n done = True # Redefines done as True to exit loop\n pygame.quit()\n elif event.type == pygame.KEYDOWN:\n if event.key == pygame.K_r:\n draw_onscreen()","sub_path":"Grade 10/Bp_U2_Assignment6/Bp_U2_Assignment6_V1.py","file_name":"Bp_U2_Assignment6_V1.py","file_ext":"py","file_size_in_byte":3546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"246561198","text":"# Direct Translation (for Machine Translation)\n\n# COMP 150: Natural Language Processing\n# Jason Krone & Nicholas Yan\n# 5/11/16\n\n##################################################################################################\n# #\n# DIRECT TRANSLATION #\n# #\n##################################################################################################\n\nimport sys\nfrom collections import defaultdict\nimport csv\nimport string\nfrom utilities import get_word_translations, tokenize, get_datasets\n\nclass DirectTrans:\n\n\t# init\n #\n # args: translation_table dict, takes a first key (the word) and return a list of\n # all possible translations for that first key\n def __init__(self, translation_table):\n\n self.all_translations = translation_table\n \n # translate\n #\n # args: source_sent the source (foreign) sentence\n #\n # returns: the directly translated version of the source_sent; that is, the most probable\n # translation of each word in the sentence, in their original order\n #\n # notes: if a translation doesn't exist for a given word in the source_sent, the function\n # skips the word and moves onto the next word in the source_sent\n\n def translate(self, source_sent):\n\n \ttrans_sent = []\n\n \tfor word in source_sent:\n\n \t\t# (word, prob)\n \t\tbest_trans = (None, None)\n\n \t\tfor trans in self.all_translations[word]:\n \t\t\tprob = self.all_translations[word][trans]\n \t\t\tif best_trans[1] is None or prob > best_trans[1]:\n \t\t\t\tbest_trans = (trans, prob)\n\n \t\tif best_trans[0] is not None:\n \t\t\ttrans_sent.append(best_trans[0])\n\n \treturn trans_sent\n\ndef main():\n\n\ttranslation_table = get_word_translations(\"100kword_trans.csv\")\n\ttranslator = DirectTrans(translation_table)\n\n\tenglish = tokenize(\"data/100ktok.low.en\")\n\tspanish = tokenize(\"data/100ktok.low.es\")\n\n\ttraining_set, test_set, translated_set = get_datasets(english, spanish)\n\n\ttest_output = open('trans_direct.txt','w')\n\n\tfor i in range(len(test_set)):\n\t\ttest_output.write(' '.join(translator.translate(test_set[i])) + \"\\n\")\n\n\ttest_output.close()\n\nif __name__ == \"__main__\": \n main()","sub_path":"to_submit/direct_trans.py","file_name":"direct_trans.py","file_ext":"py","file_size_in_byte":2461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"417994063","text":"import datetime\nimport re\n\nfrom bs4 import BeautifulSoup\n\nfrom sqlalchemy import (\n Table,\n Column,\n Index,\n Integer,\n Unicode,\n UnicodeText,\n DateTime,\n ForeignKey,\n )\n\nfrom sqlalchemy.orm import relationship, backref\n\nfrom .meta import Base\n\nblog_entry_tag_assoc_table = Table(\n 'blog_entry_tag_assocs', \n Base.metadata,\n Column('blog_entry_id', Integer, ForeignKey('blog_entries.id')),\n Column('tag_id', Integer, ForeignKey('tags.id')))\n\nclass BlogEntry(Base):\n __tablename__ = 'blog_entries'\n id = Column(Integer, primary_key=True)\n title = Column(Unicode(255), nullable=False)\n body = Column(UnicodeText, default='')\n views = Column(Integer, default=0)\n excerpt = Column(UnicodeText, default='')\n create_time = Column(DateTime, nullable=False, default=datetime.datetime.utcnow)\n edited_time = Column(DateTime, nullable=False, default=datetime.datetime.utcnow)\n tags = relationship('Tag', secondary=blog_entry_tag_assoc_table)\n\n def tagnames(self):\n return list(map(lambda t: t.name, self.tags))\n\n @classmethod\n def generate_excerpt(cls, body):\n preview_threshold = 100\n\n soup = BeautifulSoup(body, 'html.parser')\n psoup = soup.find_all('p')\n\n excerpt = ''\n\n remaining_words = preview_threshold\n\n while min(remaining_words, len(psoup)) > 0:\n p = psoup.pop(0)\n ptext = p.get_text()\n pwords = [m.end() for m in re.finditer(r'\\S+', ptext)]\n if len(pwords) > remaining_words:\n last_index = pwords[remaining_words]\n remaining_lookback = 3\n while last_index > 0 and remaining_lookback > 0:\n if ptext[last_index - 1] not in \".,?!\":\n break;\n last_index -= 1\n remaining_lookback -= 1\n ptext = ptext[0:last_index]\n remaining_words = 0\n if ptext[len(ptext) - 1] == '.':\n ptext += '..'\n else:\n ptext += '...'\n excerpt += \"

{shortened}

\".format(shortened=ptext)\n else:\n remaining_words -= len(p.get_text().split())\n excerpt += p.prettify()\n\n return excerpt\n\n\nclass Tag(Base):\n __tablename__ = 'tags'\n id = Column(Integer, primary_key=True)\n name = Column(Unicode(255), nullable=False)\n","sub_path":"models/blog_entry.py","file_name":"blog_entry.py","file_ext":"py","file_size_in_byte":2444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"637474059","text":"import gambit\nimport csv\nimport pandas as pd\nimport numpy as np\n\nsolver = gambit.nash.ExternalEnumPureSolver()\nresults = []\n\nwith open(\"hidden_weapon_input3.csv\") as csvfile:\n\treader = csv.reader(csvfile)\n\ti = 1\n\tfor row in reader:\n\t\tif i != 1:\n\t\t\tchance_high = int(row[0])\n\t\t\tchance_high_denom = int(row[1])\n\t\t\tblue_start = int(row[2])\n\t\t\tred_start = int(row[3])\n\t\t\tblue_change_weapon = int(row[4])\n\t\t\tblue_change_noweapon = int(row[5])\n\t\t\tred_dev_cost = int(row[6])\n\t\t\tblue_signal_cost = int(row[7])\n\t\t\tblue_chance_weapon = int(row[8])\n\t\t\tblue_chance_weapon_denom = int(row[9])\n\t\t\tblue_chance_noweapon = int(row[10])\n\t\t\tblue_chance_noweapon_denom = int(row[11])\n\t\t\tconflict_cost = int(row[12])\n\t\t\twin_value = int(row[13])\n\n\t\t\tgame = gambit.Game.new_tree()\n\t\t\tgame.title = \"Hidden Weapon\"\n\n\t\t\tBlue = game.players.add(\"Blue\")\n\t\t\tRed = game.players.add(\"Red\")\n\n\t\t\tweapon_signal_accept = game.outcomes.add(\"Weapon Signal Accept\")\n\t\t\tweapon_signal_accept[0] = blue_start \n\t\t\tweapon_signal_accept[1] = red_start \n\n\t\t\tweapon_nosignal_accept = game.outcomes.add(\"Weapon No Signal Accept\")\n\t\t\tweapon_nosignal_accept[0] = blue_start \n\t\t\tweapon_nosignal_accept[1] = red_start\n\n\t\t\tnoweapon_signal_accept = game.outcomes.add(\"No Weapon Signal Accept\")\n\t\t\tnoweapon_signal_accept[0] = blue_start \n\t\t\tnoweapon_signal_accept[1] = red_start \n\n\t\t\tnoweapon_nosignal_accept = game.outcomes.add(\"No Weapon No Signal Accept\")\n\t\t\tnoweapon_nosignal_accept[0] = blue_start \n\t\t\tnoweapon_nosignal_accept[1] = red_start \n\n\t\t\tblue_wins_weapon_signal = game.outcomes.add(\"Blue Wins Weapon Signal\")\n\t\t\tblue_wins_weapon_signal[0] = win_value - blue_signal_cost/ 2 - conflict_cost\n\t\t\tblue_wins_weapon_signal[1] = 0 - conflict_cost\n\n\t\t\tblue_wins_weapon_nosignal = game.outcomes.add(\"Blue Wins Weapon No Signal\")\n\t\t\tblue_wins_weapon_nosignal[0] = win_value - conflict_cost\n\t\t\tblue_wins_weapon_nosignal[1] = 0 - conflict_cost\n\n\t\t\tblue_wins_noweapon_signal = game.outcomes.add(\"Blue Wins No Weapon Signal\")\n\t\t\tblue_wins_noweapon_signal[0] = win_value - blue_signal_cost - conflict_cost\n\t\t\tblue_wins_noweapon_signal[1] = 0 - conflict_cost\n\n\t\t\tblue_wins_noweapon_nosignal = game.outcomes.add(\"Blue Wins No Weapon No Signal\")\n\t\t\tblue_wins_noweapon_nosignal[0] = win_value - conflict_cost\n\t\t\tblue_wins_noweapon_nosignal[1] = 0 - conflict_cost\n\n\t\t\tred_wins_weapon_signal = game.outcomes.add(\"Red Wins Weapon Signal\")\n\t\t\tred_wins_weapon_signal[0] = 0 - conflict_cost - blue_signal_cost/ 2\n\t\t\tred_wins_weapon_signal[1] = win_value - conflict_cost\n\n\t\t\tred_wins_weapon_nosignal = game.outcomes.add(\"Red Wins Weapon No Signal\")\n\t\t\tred_wins_weapon_nosignal[0] = 0 - conflict_cost\n\t\t\tred_wins_weapon_nosignal[1] = win_value - conflict_cost\n\n\t\t\tred_wins_noweapon_signal = game.outcomes.add(\"Red Wins No Weapon Signal\")\n\t\t\tred_wins_noweapon_signal[0] = 0 - conflict_cost - blue_signal_cost \n\t\t\tred_wins_noweapon_signal[1] = win_value - conflict_cost\n\n\t\t\tred_wins_noweapon_nosignal = game.outcomes.add(\"Red Wins No Weapon No Signal\")\n\t\t\tred_wins_noweapon_nosignal[0] = 0 - conflict_cost\n\t\t\tred_wins_noweapon_nosignal[1] = win_value - conflict_cost\n\n\t\t\tmove = game.root.append_move(game.players.chance, 2)\n\t\t\tmove.actions[0].label = \"weapon\"\n\t\t\tmove.actions[0].prob = gambit.Rational(chance_high, chance_high_denom)\n\t\t\tmove.actions[1].label = \"no weapon\"\n\t\t\tmove.actions[1].prob = gambit.Rational(chance_high_denom - chance_high, chance_high_denom)\n\n\t\t\t#weapon branch\n\t\t\twmove = game.root.children[0].append_move(Blue, 2)\n\t\t\twmove.label = \"send signal?\"\n\t\t\twmove.actions[0].label = \"signal\"\n\t\t\twmove.actions[1].label = \"no signal\"\n\n\t\t\t#no weapon branch\n\t\t\tnmove = game.root.children[1].append_move(Blue, 2)\n\t\t\tnmove.label = \"send signal?\"\n\t\t\tnmove.actions[0].label = \"signal\"\n\t\t\tnmove.actions[1].label = \"no signal\"\n\n\t\t\t#sees signal\n\t\t\tmove = game.root.children[0].children[0].append_move(Red, 2)\n\t\t\tmove.actions[0].label = \"accept\"\n\t\t\tmove.actions[1].label = \"aggresive posture\"\n\t\t\tgame.root.children[1].children[0].append_move(move)\n\n\t\t\t#doesn't see signal\n\t\t\tmove = game.root.children[0].children[1].append_move(Red, 2)\n\t\t\tmove.actions[0].label = \"accept\"\n\t\t\tmove.actions[1].label = \"aggressive posture\"\n\t\t\tgame.root.children[1].children[1].append_move(move)\n\n\t\t\t#signal and weapon\n\t\t\tmove = game.root.children[0].children[0].children[1].append_move(game.players.chance, 2)\n\t\t\tmove.actions[0].label = \"blue wins\"\n\t\t\tmove.actions[0].prob = gambit.Rational(blue_chance_weapon, blue_chance_weapon_denom)\n\t\t\tmove.actions[0].label = \"red wins\"\n\t\t\tmove.actions[0].prob = gambit.Rational(blue_chance_weapon_denom - blue_chance_weapon, blue_chance_weapon_denom)\n\n\t\t\t#no signal and weapon\n\t\t\tmove = game.root.children[0].children[1].children[1].append_move(game.players.chance, 2)\n\t\t\tmove.actions[0].label = \"blue wins\"\n\t\t\tmove.actions[0].prob = gambit.Rational(blue_chance_weapon, blue_chance_weapon_denom)\n\t\t\tmove.actions[0].label = \"red wins\"\n\t\t\tmove.actions[0].prob = gambit.Rational(blue_chance_weapon_denom - blue_chance_weapon, blue_chance_weapon_denom)\n\n\t\t\t#signal and no weapon\n\t\t\tmove = game.root.children[1].children[0].children[1].append_move(game.players.chance, 2)\n\t\t\tmove.actions[0].label = \"blue wins\"\n\t\t\tmove.actions[0].prob = gambit.Rational(blue_chance_noweapon, blue_chance_noweapon_denom)\n\t\t\tmove.actions[0].label = \"red wins\"\n\t\t\tmove.actions[0].prob = gambit.Rational(blue_chance_noweapon_denom - blue_chance_noweapon, blue_chance_noweapon_denom)\n\n\t\t\t#no signal and no weapon\n\t\t\tmove = game.root.children[1].children[1].children[1].append_move(game.players.chance, 2)\n\t\t\tmove.actions[0].label = \"blue wins\"\n\t\t\tmove.actions[0].prob = gambit.Rational(blue_chance_noweapon, blue_chance_noweapon_denom)\n\t\t\tmove.actions[0].label = \"red wins\"\n\t\t\tmove.actions[0].prob = gambit.Rational(blue_chance_noweapon_denom - blue_chance_noweapon, blue_chance_noweapon_denom)\n\n\n\t\t\tgame.root.children[0].children[0].children[0].outcome = weapon_signal_accept\n\t\t\tgame.root.children[0].children[1].children[0].outcome = weapon_nosignal_accept\n\t\t\tgame.root.children[1].children[0].children[0].outcome = noweapon_signal_accept\n\t\t\tgame.root.children[1].children[1].children[0].outcome = noweapon_nosignal_accept\n\t\t\tgame.root.children[0].children[0].children[1].children[0].outcome = blue_wins_weapon_signal\n\t\t\tgame.root.children[0].children[0].children[1].children[1].outcome = red_wins_weapon_signal\n\t\t\tgame.root.children[0].children[1].children[1].children[0].outcome = blue_wins_weapon_nosignal\n\t\t\tgame.root.children[0].children[1].children[1].children[1].outcome = red_wins_weapon_nosignal\n\t\t\tgame.root.children[1].children[0].children[1].children[0].outcome = blue_wins_noweapon_signal\n\t\t\tgame.root.children[1].children[0].children[1].children[1].outcome = red_wins_noweapon_signal\n\t\t\tgame.root.children[1].children[1].children[1].children[0].outcome = blue_wins_noweapon_nosignal\n\t\t\tgame.root.children[1].children[1].children[1].children[1].outcome = red_wins_noweapon_nosignal\n\t\t\t\n\n\t\t\tprint(game.write())\n\t\t\t#solver = gambit.nash.ExternalEnumMixedSolver()\n\t\t\t#solution = solver.solve(game)\n\t\t\tsolution = gambit.nash.lcp_solve(game)\n\t\t\t#print(len(solution))\n\t\t\tprint(blue_chance_weapon)\n\t\t\tprint(blue_signal_cost)\n\t\t\tfor el in solution:\n\t\t\t\tfound = False\n\t\t\t\tif not found: #and el[game.players[\"Blue\"].infosets[0].actions[0]] != el[game.players[\"Blue\"].infosets[1].actions[0]]:\n\t\t\t\t\tprint(el)\n\t\t\t\t\tprint(el[game.players[\"Red\"]])\n\t\t\t\t\tprint(el.payoff(game.players[\"Red\"]))\n\t\t\t\t\tprint(el[game.players[\"Blue\"]])\n\t\t\t\t\tprint(el.payoff(game.players[\"Blue\"]))\n\t\t\t\t\tresults.append({\"Signal Cost\":blue_signal_cost,\n\t\t\t\t\t\t\"Weapon Value\":blue_chance_weapon,\n\t\t\t\t\t\t\"Prob Red Aggressively postures\":float(el[game.players[\"Red\"].infosets[0].actions[1]])# + el[game.players[\"Red\"].infosets[1].actions[0]] \n\t\t\t\t\t\t})\n\t\t\t\t\tfound = True\n\t\t\t\telif found:\n\t\t\t\t\tprint(\"multiple semi-pooling equilibrium ignored\")\n\t\t\t\telse:\n\t\t\t\t\tprint(\"pass\")\n\t\t\t\t\t#results.append({\"Signal Cost\":blue_signal_cost,\n\t\t\t\t\t#\t\"Weapon Value\":blue_change_weapon,\n\t\t\t\t\t#\t\"Prob Red Develops\":0\n\t\t\t\t\t#\t})\n\t\t\tprint(\"-------------------------------------------------\\n\")\n\t\ti = i + 1\n\ndf = pd.DataFrame(results)\nprint(df)\ndf.to_csv(\"results_game2.csv\")\n","sub_path":"Hidden_Weapon_game2.py","file_name":"Hidden_Weapon_game2.py","file_ext":"py","file_size_in_byte":8145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"378970196","text":"\n\n#calss header\nclass _WIDOW():\n\tdef __init__(self,): \n\t\tself.name = \"WIDOW\"\n\t\tself.definitions = [u'If someone has been widowed, their husband or wife has died: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'verbs'\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/verbs/_widow.py","file_name":"_widow.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"115260141","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom django.conf.urls import patterns, include, url\n\nurlpatterns = patterns('eleitor.views',\n\n\turl(r'^$','eleitores', name='eleitores'),\t\n\turl(r'^novo/$', 'eleitor_novo', name='eleitor_novo'),\n\turl(r'^editar/(?P\\d+)/$', 'eleitor_editar', name='eleitor_editar'),\n\turl(r'^alterar_status/(?P\\d+)/$', 'eleitor_alterar_status', name='eleitor_alterar_status'),\n\turl(r'^busca/$', 'eleitor_busca', name='eleitor_busca'),\n)","sub_path":"votacaoeletronica_app/eleitor/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"316787140","text":"birthdays = {\n 'Albert Einstein': '03/14/1879',\n 'Benjamin Franklin': '01/17/1706',\n 'Ada Lovelace': '12/10/1815'\n}\n\nprint(\"Welcome to the birthday dictionary. We know the birthdays of:\")\nfor name in birthdays:\n print(name)\n\nname = input(\"\\nWhose birthday do you want to look up?\\n\").title()\n\nif name in birthdays:\n print(\"{}'s birthday is: {}\".format(name, birthdays[name]))\nelse:\n print(\"Sorry, {}'s birthday is not in the database.\".format(name))\n","sub_path":"33_BirthdayDictionaries.py","file_name":"33_BirthdayDictionaries.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"491189133","text":"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Integration test for sequence feature columns with SequenceExamples.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport string\nimport tempfile\n\nfrom google.protobuf import text_format\n\nfrom tensorflow.core.example import example_pb2\nfrom tensorflow.core.example import feature_pb2\nfrom tensorflow.python.data.ops import dataset_ops\nfrom tensorflow.python.feature_column import dense_features\nfrom tensorflow.python.feature_column import feature_column_v2 as fc\nfrom tensorflow.python.feature_column import sequence_feature_column as sfc\nfrom tensorflow.python.keras.layers import recurrent\nfrom tensorflow.python.ops import parsing_ops\nfrom tensorflow.python.ops import variables\nfrom tensorflow.python.platform import test\nfrom tensorflow.python.util import compat\n\n\nclass SequenceFeatureColumnIntegrationTest(test.TestCase):\n\n def _make_sequence_example(self):\n example = example_pb2.SequenceExample()\n example.context.feature['int_ctx'].int64_list.value.extend([5])\n example.context.feature['float_ctx'].float_list.value.extend([123.6])\n for val in range(0, 10, 2):\n feat = feature_pb2.Feature()\n feat.int64_list.value.extend([val] * val)\n example.feature_lists.feature_list['int_list'].feature.extend([feat])\n for val in range(1, 11, 2):\n feat = feature_pb2.Feature()\n feat.bytes_list.value.extend([compat.as_bytes(str(val))] * val)\n example.feature_lists.feature_list['str_list'].feature.extend([feat])\n\n return example\n\n def _build_feature_columns(self):\n col = fc.categorical_column_with_identity('int_ctx', num_buckets=100)\n ctx_cols = [\n fc.embedding_column(col, dimension=10),\n fc.numeric_column('float_ctx')\n ]\n\n identity_col = sfc.sequence_categorical_column_with_identity(\n 'int_list', num_buckets=10)\n bucket_col = sfc.sequence_categorical_column_with_hash_bucket(\n 'bytes_list', hash_bucket_size=100)\n seq_cols = [\n fc.embedding_column(identity_col, dimension=10),\n fc.embedding_column(bucket_col, dimension=20)\n ]\n\n return ctx_cols, seq_cols\n\n def test_sequence_example_into_input_layer(self):\n examples = [_make_sequence_example().SerializeToString()] * 100\n ctx_cols, seq_cols = self._build_feature_columns()\n\n def _parse_example(example):\n ctx, seq = parsing_ops.parse_single_sequence_example(\n example,\n context_features=fc.make_parse_example_spec_v2(ctx_cols),\n sequence_features=fc.make_parse_example_spec_v2(seq_cols))\n ctx.update(seq)\n return ctx\n\n ds = dataset_ops.Dataset.from_tensor_slices(examples)\n ds = ds.map(_parse_example)\n ds = ds.batch(20)\n\n # Test on a single batch\n features = ds.make_one_shot_iterator().get_next()\n\n # Tile the context features across the sequence features\n sequence_input_layer = sfc.SequenceFeatures(seq_cols)\n seq_layer, _ = sequence_input_layer(features)\n input_layer = dense_features.DenseFeatures(ctx_cols)\n ctx_layer = input_layer(features)\n input_layer = sfc.concatenate_context_input(ctx_layer, seq_layer)\n\n rnn_layer = recurrent.RNN(recurrent.SimpleRNNCell(10))\n output = rnn_layer(input_layer)\n\n with self.cached_session() as sess:\n sess.run(variables.global_variables_initializer())\n features_r = sess.run(features)\n self.assertAllEqual(features_r['int_list'].dense_shape, [20, 3, 6])\n\n output_r = sess.run(output)\n self.assertAllEqual(output_r.shape, [20, 10])\n\n\nclass SequenceExampleParsingTest(test.TestCase):\n\n def test_seq_ex_in_sequence_categorical_column_with_identity(self):\n self._test_parsed_sequence_example(\n 'int_list', sfc.sequence_categorical_column_with_identity,\n 10, [3, 6], [2, 4, 6])\n\n def test_seq_ex_in_sequence_categorical_column_with_hash_bucket(self):\n self._test_parsed_sequence_example(\n 'bytes_list', sfc.sequence_categorical_column_with_hash_bucket,\n 10, [3, 4], [compat.as_bytes(x) for x in 'acg'])\n\n def test_seq_ex_in_sequence_categorical_column_with_vocabulary_list(self):\n self._test_parsed_sequence_example(\n 'bytes_list', sfc.sequence_categorical_column_with_vocabulary_list,\n list(string.ascii_lowercase), [3, 4],\n [compat.as_bytes(x) for x in 'acg'])\n\n def test_seq_ex_in_sequence_categorical_column_with_vocabulary_file(self):\n _, fname = tempfile.mkstemp()\n with open(fname, 'w') as f:\n f.write(string.ascii_lowercase)\n self._test_parsed_sequence_example(\n 'bytes_list', sfc.sequence_categorical_column_with_vocabulary_file,\n fname, [3, 4], [compat.as_bytes(x) for x in 'acg'])\n\n def _test_parsed_sequence_example(\n self, col_name, col_fn, col_arg, shape, values):\n \"\"\"Helper function to check that each FeatureColumn parses correctly.\n\n Args:\n col_name: string, name to give to the feature column. Should match\n the name that the column will parse out of the features dict.\n col_fn: function used to create the feature column. For example,\n sequence_numeric_column.\n col_arg: second arg that the target feature column is expecting.\n shape: the expected dense_shape of the feature after parsing into\n a SparseTensor.\n values: the expected values at index [0, 2, 6] of the feature\n after parsing into a SparseTensor.\n \"\"\"\n example = _make_sequence_example()\n columns = [\n fc.categorical_column_with_identity('int_ctx', num_buckets=100),\n fc.numeric_column('float_ctx'),\n col_fn(col_name, col_arg)\n ]\n context, seq_features = parsing_ops.parse_single_sequence_example(\n example.SerializeToString(),\n context_features=fc.make_parse_example_spec_v2(columns[:2]),\n sequence_features=fc.make_parse_example_spec_v2(columns[2:]))\n\n with self.cached_session() as sess:\n ctx_result, seq_result = sess.run([context, seq_features])\n self.assertEqual(list(seq_result[col_name].dense_shape), shape)\n self.assertEqual(\n list(seq_result[col_name].values[[0, 2, 6]]), values)\n self.assertEqual(list(ctx_result['int_ctx'].dense_shape), [1])\n self.assertEqual(ctx_result['int_ctx'].values[0], 5)\n self.assertEqual(list(ctx_result['float_ctx'].shape), [1])\n self.assertAlmostEqual(ctx_result['float_ctx'][0], 123.6, places=1)\n\n\n_SEQ_EX_PROTO = \"\"\"\ncontext {\n feature {\n key: \"float_ctx\"\n value {\n float_list {\n value: 123.6\n }\n }\n }\n feature {\n key: \"int_ctx\"\n value {\n int64_list {\n value: 5\n }\n }\n }\n}\nfeature_lists {\n feature_list {\n key: \"bytes_list\"\n value {\n feature {\n bytes_list {\n value: \"a\"\n }\n }\n feature {\n bytes_list {\n value: \"b\"\n value: \"c\"\n }\n }\n feature {\n bytes_list {\n value: \"d\"\n value: \"e\"\n value: \"f\"\n value: \"g\"\n }\n }\n }\n }\n feature_list {\n key: \"float_list\"\n value {\n feature {\n float_list {\n value: 1.0\n }\n }\n feature {\n float_list {\n value: 3.0\n value: 3.0\n value: 3.0\n }\n }\n feature {\n float_list {\n value: 5.0\n value: 5.0\n value: 5.0\n value: 5.0\n value: 5.0\n }\n }\n }\n }\n feature_list {\n key: \"int_list\"\n value {\n feature {\n int64_list {\n value: 2\n value: 2\n }\n }\n feature {\n int64_list {\n value: 4\n value: 4\n value: 4\n value: 4\n }\n }\n feature {\n int64_list {\n value: 6\n value: 6\n value: 6\n value: 6\n value: 6\n value: 6\n }\n }\n }\n }\n}\n\"\"\"\n\n\ndef _make_sequence_example():\n example = example_pb2.SequenceExample()\n return text_format.Parse(_SEQ_EX_PROTO, example)\n\n\nif __name__ == '__main__':\n test.main()\n","sub_path":"tensorflow/python/feature_column/sequence_feature_column_integration_test.py","file_name":"sequence_feature_column_integration_test.py","file_ext":"py","file_size_in_byte":8733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"200683898","text":"from django.urls import path\nfrom rest_framework.routers import DefaultRouter\nfrom . import views\n\napp_name = 'api-users'\n\nrouter = DefaultRouter()\nrouter.register(r'employees', views.EmployeeViewSet)\n\nurlpatterns = [\n path('login/', views.LoginView.as_view(), name='login'),\n path('logout/', views.LogoutView.as_view(), name='logout'),\n path('employee_create/', views.EmployeeCreateView.as_view(), name='employee_create'),\n path('docs/', views.APIDocsView.as_view(), name='docs'),\n path('excel/', views.EmployeeExcel.as_view(), name='excel')\n]\n\nurlpatterns += router.urls\n","sub_path":"users/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"428497406","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Users/bee/Dev/piu/django/testSite/bee_django_crm/migrations/0041_auto_20191210_1346.py\n# Compiled at: 2019-12-10 00:46:54\nfrom __future__ import unicode_literals\nfrom django.db import migrations\n\nclass Migration(migrations.Migration):\n dependencies = [\n ('bee_django_crm', '0040_auto_20191206_1705')]\n operations = [\n migrations.AlterUniqueTogether(name=b'bargainrecord', unique_together=set([('campaign_record', 'op_wxuser')])),\n migrations.AlterUniqueTogether(name=b'campaignrecord', unique_together=set([('wxuser', 'reward')]))]","sub_path":"pycfiles/bee-django-crm-auth-1.4.64.tar/0041_auto_20191210_1346.py","file_name":"0041_auto_20191210_1346.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"61991470","text":"from .string_display_util import *\nfrom colorama import Fore\n\n\ndef get_constrained_input(prompt: str, constraint) -> str:\n \"\"\"Prompts the user for input, continues to prompt the user for input until the lambda expression passed in for\n constraint returns True.\"\"\"\n\n temp = input(Fore.GREEN + prompt + Fore.RESET)\n while not constraint(temp):\n print_warning(\"Invalid Response!\")\n temp = input(Fore.GREEN + prompt + Fore.RESET)\n\n return temp\n\n\ndef yes_no_prompt(prompt: str = \"\") -> bool:\n \"\"\"Prompts the user for a yes/no answer, automatically appends '(y/n) ' to your prompt.\n Returns True if the user answered yes, and False if they answered no.\"\"\"\n\n choice = input(prompt + \"(y/n) \")\n while choice != 'y' and choice != 'Y' and choice != 'n' and choice != 'N':\n print_warning(\"Invalid choice entered, please enter either 'y' or 'n'.\")\n choice = input(prompt + \"(y/n) \")\n\n return choice == 'y' or choice == 'Y'\n\n\ndef console_dash_menu(options: dict, list_format: str = \"{}: {}\", title: str = \"\", centered_title: bool = True,\n dash: str = '#', tab_width: int = 4):\n \"\"\"Creates a console style menu with the given options as choices to choose from\n Returns a key that was chosen from the options dict.\n Keys must be either a str or have a __str__() defined\n\n Arguments:\n\n options: a dict containing keys that are choices for users, and values that are descriptions for each key,\n the chosen key will be returned.\n\n list_format: uses similar syntax to str.format(), the default is \\\"{}: {}\\\" where the first {} is the key, and the\n second {} is the value.\n\n title: the title string displayed at the top of the menu.\n\n centered_title: True if the title string should be centered in the console.\n\n dash: character or string used as the duplicated dash sequence for the bars at the top and bottom of the menu.\n\n tab_width: width, in number of spaces, considered to be a single tab.\"\"\"\n\n # Determines the longest string entry of the set of options\n entries = [list_format.format(k, options[k]) for k in options]\n max_length = max([len(x) for x in entries])\n if len(title) > max_length:\n max_length = len(title)\n\n # Creates the menu\n print_dashes(max_length, dash)\n print((centered_text(title, max_length) if centered_title else title) + \"\\n\")\n print(\"Options: \\n\")\n for e in entries:\n print(hanging_indent(e, tab_width))\n\n print_dashes(max_length, dash)\n\n # Prompts the user for input and ensures validity\n valid = [str(k) for k in options.keys()]\n\n choice = get_constrained_input(\"Choice? \", lambda x: x in valid)\n\n # Finds the said choice and returns it\n index = valid.index(choice)\n return list(options.keys())[index]\n","sub_path":"PyCharm/pmd_implementation/Depth_Displayer/dependencies/display_util/menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":2803,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"632028284","text":"\"\"\"Classes and functions for working with the Echo extension.\"\"\"\n#\n# (C) Pywikibot team, 2014-2020\n#\n# Distributed under the terms of the MIT license.\n#\nimport pywikibot\n\nfrom pywikibot.tools import deprecated\n\n\nclass Notification:\n\n \"\"\"A notification issued by the Echo extension.\"\"\"\n\n def __init__(self, site):\n \"\"\"Initialize an empty Notification object.\"\"\"\n self.site = site\n\n @classmethod\n def fromJSON(cls, site, data): # noqa: N802\n \"\"\"\n Construct a Notification object from JSON data returned by the API.\n\n @rtype: Notification\n \"\"\"\n notif = cls(site)\n\n notif.event_id = int(data['id'])\n notif.type = data['type']\n notif.category = data['category']\n notif.timestamp = pywikibot.Timestamp.fromtimestampformat(\n data['timestamp']['mw'])\n\n try:\n notif.page = pywikibot.Page(site, data['title']['full'])\n except KeyError:\n notif.page = None\n\n try:\n notif.agent = pywikibot.User(site, data['agent']['name'])\n except KeyError:\n notif.agent = None\n\n try:\n notif.read = pywikibot.Timestamp.fromtimestampformat(data['read'])\n except KeyError:\n notif.read = False\n\n notif.content = data.get('*', None)\n notif.revid = data.get('revid', None)\n return notif\n\n @property\n @deprecated('event_id', since='20190106')\n def id(self):\n \"\"\"\n DEPRECATED: Return notification id as unicode.\n\n @rtype: str\n \"\"\"\n return str(self.event_id)\n\n def mark_as_read(self):\n \"\"\"Mark the notification as read.\"\"\"\n return self.site.notifications_mark_read(list=self.id)\n","sub_path":"pywikibot/echo.py","file_name":"echo.py","file_ext":"py","file_size_in_byte":1733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"232515594","text":"\nfrom .adapter import get_adapter\n\ndef get_next_redirect_url(request, redirect_field_name=\"next\"):\n \"\"\"\n Returns the next URL to redirect to, if it was explicitly passed\n via the request.\n \"\"\"\n redirect_to = request.REQUEST.get(redirect_field_name)\n # light security check -- make sure redirect_to isn't garabage.\n if not redirect_to or \"://\" in redirect_to or \" \" in redirect_to:\n redirect_to = None\n return redirect_to\n\n\ndef get_login_redirect_url(request, url=None, redirect_field_name=\"next\"):\n redirect_url \\\n = (url\n or get_next_redirect_url(request,\n redirect_field_name=redirect_field_name)\n or get_adapter().get_login_redirect_url(request))\n return redirect_url\n\n","sub_path":"allauth/socialaccount/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"99949893","text":"# -*- coding: utf-8 -*-\n'''\nCreated on 2017年12月10日\n\n@author: spring8743\n'''\n\n# below fields are used in the GetKeywordRank.py\nDOMAINS = {\n 'CA':'ca',\n 'DE':'de',\n 'ES':'es',\n 'FR':'fr',\n 'IN':'in',\n 'IT':'it',\n 'JP':'co.jp',\n 'UK':'co.uk',\n 'US':'com'\n }\n\n#the max page you want to find\nmax_page = 21\n\n#Next Page Button\nnextPage = '#pagnNextString'\n\n#chrome driver location\nchrome_driver = '/Users/spring8743/Documents/workspace/chromedriver'\n\n#below field are used in the KeywordRankRobot.py\n#define the empty threads list\nthreads = []\n\n#used for keyword rank thread\nrank_thread_num = 1\n\n#used for keyword click and add to cart, wish list thread\nclick_thread_num = 1\n#used for keyword click and add to cart, wish list, how many times want to click for each keyword\nkeyword_click_number = 1\n\n#used for PPC keyword click thread\nppc_click_thread_num = 1\n#used for PPC keyword click, how many times want to click for each keyword\nkeyword_ppc_click_number = 1\n\n#proxy_url from taiyang proxy server\nproxy_url = 'http://http-api.taiyangruanjian.com/getip?num=1&type=1&pro=&city=0&yys=0&port=11&pack=8955&ts=0&ys=0&cs=0&lb=1&sb=0&pb=4&mr=1®ions='\n\n#define database connection\nuri = r'mysql://root:zaq12wsX@127.0.0.1/Amazon_DB?charset=utf8'\n\n#add to cart and wish list possibility\npossibility = 0.9","sub_path":"utility/Config.py","file_name":"Config.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"64378666","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndata = np.genfromtxt(\"KP.csv\", delimiter = \",\")\n\nxs = [5, 20, 28, 39, 50, 59, 67, 72, 80]\nys = [3, 25, 15, 13, 11, 8, 5.1, 3, 2]\nkps = [\"$\\infty$\", \"1\", \"10\", \"20\", \"50\", \"100\", \"1000\", \"10000\", \"100000\"]\n\ntime = data[:,0]\nerror = data[:,3]\nplt.plot(time, error)\nplt.xlabel(\"Tiempo (min)\")\nplt.ylabel(\"Error ($^\\circ$C)\")\nplt.grid()\n\nfor i in range(len(xs)):\n plt.text(xs[i], ys[i], kps[i])\n\n\nplt.savefig(\"KP.pdf\")\n","sub_path":"horno_pid/parte2/KP.py","file_name":"KP.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"264574196","text":"from PIL import Image, ImageDraw, ImageFont\nfrom models import EventManager\nfrom io import BytesIO\n\nclass ParameterImage(object):\n\n __PADDING__ = 10\n __HEADERFONT__ = '/Library/Fonts/Arial Bold.ttf'\n __BODYFONT__ = '/Library/Fonts/Arial Bold.ttf'\n\n def get_image(self, tank_id, as_of):\n parameters = EventManager().get_latest_measurements(tank_id, as_of)\n header = 'Parameters as of {}:'.format(as_of.strftime('%Y-%m-%d'))\n text = []\n for parameter in parameters:\n text.append('{}: {} {}'.format(parameter.label, parameter.value, parameter.units))\n \n headerfont = ImageFont.truetype(ParameterImage.__HEADERFONT__, 18)\n font = ImageFont.truetype(ParameterImage.__BODYFONT__, 16)\n \n maxwidth, maxheight = (x + ParameterImage.__PADDING__ for x in headerfont.getsize(header))\n totalheight = maxheight\n\n for line in text:\n width, height = (x + ParameterImage.__PADDING__ for x in font.getsize(line))\n maxwidth = (width if width > maxwidth else maxwidth)\n maxheight = (height if height > maxheight else maxheight)\n totalheight += height\n\n totalheight += ParameterImage.__PADDING__\n\n img = Image.new('RGBA', (maxwidth, totalheight))\n draw = ImageDraw.Draw(img)\n draw.rectangle([0, 0, maxwidth - 1, totalheight - 1], outline='#000000')\n \n draw.text((5, 2), header, fill='#000000', font=headerfont)\n y = maxheight + 2\n for line in text:\n draw.text((15, y), line, fill='#000000', font=font)\n y += maxheight\n\n return img\n\n def get_image_stream(self, tank_id, as_of):\n stream = BytesIO()\n self.get_image(tank_id, as_of).save(stream, 'PNG')\n stream.seek(0)\n return stream\n","sub_path":"reef/resources/ParameterImage.py","file_name":"ParameterImage.py","file_ext":"py","file_size_in_byte":1819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"407830956","text":"# -*- coding: utf-8 -*-\n\nfrom django.shortcuts import render\nfrom django.http import HttpResponse, JsonResponse, QueryDict\nfrom django.views.decorators.csrf import csrf_exempt\nfrom listener.models import Issue, Transition, Group\nfrom listener.serializers import IssueSerializer\nfrom listener.business import *\nfrom listener.metricas import *\nimport json\n\n@csrf_exempt\ndef dashboards(request):\n quarter = 'Q22019'\n objectives = get_objectives_in_quarter(quarter)\n tag_html = ''\n\n for objective in objectives:\n tag_html = tag_html + '' + objective.projeto + '' + objective.descricao + ' '\n key_results = get_key_results_by_objective(objective)\n\n for key_result in key_results:\n tag_html = tag_html + ' ' + key_result.descricao + '
' + str(key_result.completude) + '%
'\n\n return render(request, './dashboards.html', {'table_content': tag_html})\n\n@csrf_exempt\ndef update_status(request):\n print('entrou no update_status') \n json_str = request.body.decode('utf-8')\n data = json.loads(json_str)\n processa_evento(data)\n return HttpResponse('Ok')\n\n@csrf_exempt\ndef versions(request):\n print('entrou no versions')\n json_str = request.body.decode('utf-8')\n data = json.loads(json_str)\n processa_versao(data)\n return HttpResponse('Ok')\n\n@csrf_exempt\ndef retorna_arquivo(request): \n try:\n fluxo = request.GET['fluxo']\n except KeyError:\n fluxo = 'Lead_Time'\n\n try:\n issue_type = request.GET['issue_type']\n except KeyError:\n issue_type = 'Generic' \n\n arquivo = gera_csv_por_fluxo(fluxo, issue_type) \n\n response = HttpResponse(arquivo.read())\n\n response['Pragma'] = 'public'\n response['Expires'] = '0'\n response['Cache-Control'] = 'must-revalidate, post-check=0, pre-check=0'\n response['Content-Disposition'] = 'attachment; filename=metricas.csv'\n return response \n\n@csrf_exempt\ndef metricas_mensal(request):\n\n\tprint('######## Projeto #########: ' + str(request.POST.getlist('projeto', 'all')))\n\ttry:\n\t\tstr_de = request.POST.get('de', '2018-10')\n\texcept KeyError:\n\t\tstr_de = '2018-10'\n\n\ttry:\n\t\tstr_ate = request.POST.get('ate', '2018-12')\n\texcept KeyError:\n\t\tstr_ate = '2018-11'\n\n\ttry:\n\t\tpar_projeto = request.POST.getlist('projeto', 'all')\n\texcept KeyError:\n par_projeto = 'all'\n\n\ttry:\n\t\tissue_type = request.GET['issue_type']\n\texcept KeyError:\n\t\tissue_type = 'all'\n\n #monta dados para grafico de vazao\n\tvazao_mensal, projetos = vazao(str_de, str_ate, par_projeto, issue_type)\n\tprint('### issue type' + issue_type + '###')\n\tprint('###' + str(vazao_mensal) + '###')\n\tprint('### projetos do gera_numeros' + str(projetos) + '###')\n\tstr_colunas_vazao = '[0'\n\tstr_projetos = '' + projeto.projeto + '
'\n\t\t#str_projetos = str_projetos + '
  • ' + projeto.projeto + '
    • '\n\n\tstr_projetos = str_projetos + ''\n\tstr_issue_types = ''\n\t#issue_types = get_issue_types()\n\tstr_issue_types = str_issue_types + '
  • Tudo
    • '\n\tstr_issue_types = str_issue_types + '
  • Story
    • '\n\tstr_issue_types = str_issue_types + '
  • Bug
    • '\n\tstr_issue_types = str_issue_types + '
  • Spike
    • '\n\tstr_issue_types = str_issue_types + '
  • Tech Story
    • '\n\n\t#for it in issue_types:\n\t\t#str_issue_types = str_issue_types + '
  • ' + str(it) + '
    • '\n\n\tfor projeto in projetos:\n\t\ti = i + 1\n\t\tstr_colunas_vazao = str_colunas_vazao + ', ' + str(i)\n\t\tstr_colunas_vazao = str_colunas_vazao + ', ' + '{calc: \"stringify\",sourceColumn: ' + str(i) + ',type: \"string\",role: \"annotation\"}'\n\tstr_colunas_vazao = str_colunas_vazao + ']'\n\n #monta dados para grafico de cycle time\n\tct_mensal, projetos = cycle_time(str_de, str_ate, par_projeto, issue_type)\n\n\tct_mensal[0].append('Throughput Médio')\n\n\ti = 1\n\twhile i < len(ct_mensal):\n\t\tct_mensal[i].append(medias_vazao[i-1])\t\n\t\ti = i + 1\n\ti = 0\n\tstr_colunas_ct = '[' + str(i)\n\tfor projeto in projetos:\n\t\ti=i+1\n\t\tstr_colunas_ct = str_colunas_ct + ', ' + str(i)\n\t\tstr_colunas_ct = str_colunas_ct + ', ' + '{calc: \"stringify\",sourceColumn: ' + str(i) + ',type: \"string\",role: \"annotation\"}'\n\tstr_colunas_ct = str_colunas_ct + ', ' + str(i + 1) + ', {calc: \"stringify\",sourceColumn: ' + str(i + 1) + ',type: \"string\",role: \"annotation\"}]'\n\n\t#monta grafico de horas/mes\n\thoras_mes = get_horas(str_de, str_ate, par_projeto)\n\ti=0\n\tstr_colunas_horas = '[' + str(i)\n\tprojetos_e_frentes = ['EA Assinaturas', 'EA Atendimento', 'EA Cobrança', 'EA Backoffice', 'Battlefield', 'Burnout', 'Dragon Age', 'Medal of Honor']\n\tfor projeto in projetos_e_frentes:\n\t\ti=i+1\n\t\tstr_colunas_horas = str_colunas_horas + ', ' + str(i)\n\t\tstr_colunas_horas = str_colunas_horas + ', ' + '{calc: \"stringify\",sourceColumn: ' + str(i) + ',type: \"string\",role: \"annotation\"}'\n\tstr_colunas_horas = str_colunas_horas + ']'\n\t\n\t#monta grafico de desperdicio\n\tdesperdicio = get_desperdicio(str_de, str_ate, par_projeto)\n\n\t#monta grafico de issue types\n\tperc_issue_types = get_perc_issue_types(str_de, str_ate, par_projeto)\n\n\t#monta grafico de custo medio por demanda\n\tcusto_medio_demanda = get_custo_demanda(str_de, str_ate, par_projeto, 'Story')\n\n\t#monta grafico dispercao issues\n\tdisp_issues = get_cycle_time_dispersao(str_de, str_ate, par_projeto, issue_type)\n\n\t#monta grafico de custo por tipo de demanda\t\n\tcusto_tipo_demanda = get_custo_issue_type(str_de, str_ate, par_projeto)\n\n\t#monta grafico cfd\n\tcfd = get_cfd(str_de, str_ate, par_projeto)\n\n\treturn render(request, './metricas.html', {'dados_vazao': vazao_mensal, 'colunas_vazao': str_colunas_vazao, 'dados_ct': ct_mensal, 'colunas_ct': str_colunas_ct, 'str_projetos': str_projetos, 'par_de': str_de, 'par_ate': str_ate, 'total_projetos': len(projetos), 'str_issue_types': str_issue_types, 'horas_mes': horas_mes, 'colunas_horas': str_colunas_horas, 'desperdicio': desperdicio, 'issue_types': perc_issue_types, 'custo_demanda': custo_medio_demanda, 'disp_issues': disp_issues, 'custo_tipo_demanda': custo_tipo_demanda, 'cfd': cfd})\n\n@csrf_exempt\ndef metricas_cards(request):\n\ttry:\n\t\tpar_projeto = request.GET['projeto']\n\texcept KeyError:\n\t\tpar_projeto = 'all'\n\ttry:\n\t\ttime = request.GET['time']\n\texcept KeyError:\n\t\ttime = 'all'\n\t#monta postits\n\thtml_postits = get_issues_doing_html(par_projeto, time)\n\n\treturn render(request, './cards.html', {'postits': html_postits, 'str_projetos': time})\n\n@csrf_exempt\ndef metricas_cards2(request):\n try:\n par_projeto = request.GET['projeto']\n except KeyError:\n par_projeto = 'all'\n\n #monta postits\n html_postits = get_issues_doing_html2(par_projeto)\n\n return render(request, './cards2.html', {'postits': html_postits, 'str_projetos': par_projeto})\n\n\n@csrf_exempt\ndef metricas_performance(request):\n\ttry:\n\t\tstr_de = request.GET['de']\n\texcept KeyError:\n\t\tstr_de = '2017-01'\n\n\ttry:\n\t\tstr_ate = request.GET['ate']\n\texcept KeyError:\n\t\tstr_ate = '2017-12'\n\n\ttry:\n\t\tpar_projeto = [request.GET['projeto']]\n\texcept KeyError:\n\t\tpar_projeto = 'all'\n\n\ttry:\n\t\tissue_type = request.GET['issue_type']\n\texcept KeyError:\n\t\tissue_type = 'all'\n\n\ttry:\n\t\ttipo_fluxo = request.GET['tipo_fluxo']\n\texcept KeyError:\n\t\ttipo_fluxo = 'dev'\n\n\ttry:\n\t\ttime = request.GET['time']\n\texcept KeyError:\n\t\ttime = 'all'\n\n #monta dados para grafico de vazao\n\tvazao_mensal, projetos = vazao(str_de, str_ate, par_projeto, issue_type, tipo_fluxo, time)\n\tstr_colunas_vazao = '[0'\n\tstr_projetos = ''\n\n\ttodos_projetos = get_projetos()\n\n\ti = 1\n\tmedias_vazao = []\n\n\twhile i < len(vazao_mensal):\n\t\tsoma_projeto=0\n\t\tj = 1\n\t\twhile j < len(vazao_mensal[i]):\n\t\t\tsoma_projeto = soma_projeto + vazao_mensal[i][j]\n\t\t\tj = j + 1\n\t\tmedia = soma_projeto/len(projetos)\n\t\tmedias_vazao.append(media)\n\t\ti = i + 1\n\n\ti = 0\n\n\tfor projeto in projetos:\n\t\ti = i + 1\n\t\tstr_colunas_vazao = str_colunas_vazao + ', ' + str(i)\n\t\tstr_colunas_vazao = str_colunas_vazao + ', ' + '{calc: \"stringify\",sourceColumn: ' + str(i) + ',type: \"string\",role: \"annotation\"}'\n\tstr_colunas_vazao = str_colunas_vazao + ']'\n\n #monta dados para grafico de cycle time\n\tct_mensal, projetos = cycle_time(str_de, str_ate, par_projeto, issue_type, tipo_fluxo, time)\n\n\tct_mensal[0].append('Throughput Medio')\n\n\ti = 1\n\twhile i < len(ct_mensal):\n\t\tct_mensal[i].append(medias_vazao[i-1])\n\t\ti = i + 1\n\ti = 0\n\tstr_colunas_ct = '[' + str(i)\n\tfor projeto in projetos:\n\t\ti=i+1\n\t\tstr_colunas_ct = str_colunas_ct + ', ' + str(i)\n\t\tstr_colunas_ct = str_colunas_ct + ', ' + '{calc: \"stringify\",sourceColumn: ' + str(i) + ',type: \"string\",role: \"annotation\"}'\n\tstr_colunas_ct = str_colunas_ct + ', ' + str(i + 1) + ', {calc: \"stringify\",sourceColumn: ' + str(i + 1) + ',type: \"string\",role: \"annotation\"}]'\n\n #monta grafico dispercao issues\n\tdisp_issues = get_cycle_time_dispersao(str_de, str_ate, par_projeto, issue_type, tipo_fluxo, time)\n\n #monta grafico cfd\n\tcfd = get_cfd(str_de, str_ate, par_projeto, time)\n\n\treturn render(request, './performance.html', {'dados_vazao': vazao_mensal, 'colunas_vazao': str_colunas_vazao, 'dados_ct': ct_mensal, 'colunas_ct': str_colunas_ct, 'total_projetos': len(projetos), 'frente': par_projeto, 'frente': time, 'disp_issues': disp_issues, 'cfd': cfd})\n","sub_path":"jira/listener/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"158953533","text":"from __future__ import absolute_import, division, print_function, unicode_literals\nimport re\nfrom collections import defaultdict\nimport utils\nfrom const.dataset import DataSetIter, DataSetFactory\nfrom const import consts\nimport math\nimport datetime\n\n# This is designed to test the speed of the algorithm with rule generation on the fly\n\ndef print(*args, **kwargs):\n return __builtins__.print(*tuple(['[%s]' % str(datetime.datetime.now())] + list(args)), **kwargs)\n\nSPLITTER = re.compile(\"[\" + r'''\"#$%&*+,:<=>?@[\\]^`{|}~ \\-''' + \"]\")\n\nclass ContextsTree:\n def __init__(self):\n class Node:\n def __init__(self, item, clss, score):\n self.i, self.clss, self.score, self.child = item, clss, score, {}\n\n def add(self, item, clss, score):\n self.child[item] = Node(item, clss, score)\n\n self.root = Node(None, None, 0)\n\n def add_rule(self, rule):\n n = self.root\n r, clss, score = rule\n for i, w in enumerate(r):\n if w not in n.child:\n if i == len(r) - 1:\n n.add(w, clss, score)\n else:\n n.add(w, None, 0)\n n = n.child[w]\n\n def search(self, ws):\n rst = consts.NULLPrediction\n for i in range(len(ws)):\n n = self.root\n for j, w in enumerate(ws[i:]):\n if w in n.child:\n n = n.child[w]\n if n.score > rst[1]:\n rst = consts.Prediction(n.clss, n.score, ws[i:i + j + 1])\n else:\n break\n return rst\n\ndef context_quality(s, e):\n return 2 * s * e / (s + e)\n\nRules = defaultdict(list)\nINFO = {}\n\nclass AgentBoundary():\n def __init__(self, support):\n self.rules = {}\n self.support_t = support\n self.conf_t = 0.2\n self.K = 1\n self.HDB = []\n print('Support', self.support_t, 'Score', self.conf_t, 'K', self.K)\n\n def train(self, trainSet):\n counter = defaultdict(set); totalApps = set()\n for tbl, pkg in DataSetIter.iter_pkg(trainSet):\n if pkg.agent == 'None':\n continue\n map(lambda w: counter[w].add(pkg.app), filter(None, SPLITTER.split(utils.process_agent(pkg.agent))))\n segAgent = tuple(['^'] + filter(None, SPLITTER.split(utils.process_agent(pkg.agent))) + ['$'])\n self.HDB.append((segAgent, pkg.app, len(segAgent)))\n totalApps.add(pkg.app)\n\n self.omega = len(totalApps) * self.support_t\n self.totalApp = len(totalApps) * 1.0\n\n\n print(\"Data Size\", len(self.HDB))\n\n for (t, c, l) in self.HDB:\n map(lambda w: counter[w].add(c), t)\n self.IDF = utils.cal_idf(counter)\n self.mine_context()\n\n\n def mine_context(self):\n print('[%s] Start Mining Context' % str(datetime.datetime.now()))\n occurs = defaultdict(list)\n support = defaultdict(set)\n for i in range(len(self.HDB)):\n seq, app, length = self.HDB[i]\n for j in xrange(length):\n occurs[seq[j]].append((i, j))\n support[seq[j]].add(app)\n\n for item, apps in support.items():\n if len(apps) > self.omega:\n self.mine_head([item], occurs[item])\n\n def mine_head(self, prefix, mdb):\n occurs = defaultdict(list)\n support = defaultdict(set)\n for (i, startpos) in mdb:\n if startpos + 1 < self.HDB[i][2]:\n e = self.HDB[i][0][startpos + 1]\n occurs[e].append((i, startpos + 1))\n support[e].add(self.HDB[i][1])\n self.mine_tail(mdb, prefix)\n for e, newmdb in occurs.items():\n if len(support[e]) > self.omega:\n self.mine_head(prefix + [e], newmdb)\n\n def mine_tail(self, mdb, prefix):\n occurs = defaultdict(list)\n support = defaultdict(set)\n for (i, startpos) in mdb:\n if startpos + 2 < len(self.HDB[i][0]):\n for j in xrange(startpos + 2, self.HDB[i][2]):\n e = self.HDB[i][0][j]\n occurs[e].append((i, startpos, j))\n support[e].add(self.HDB[i][1])\n for item, apps in support.items():\n if len(apps) > self.omega:\n self.mine_tail_rec([item], occurs[item], prefix)\n\n def mine_tail_rec(self, tail, mdb, head):\n occurs = defaultdict(list)\n itemSupport = defaultdict(set); support = set()\n SC = defaultdict(set); appSigMap = defaultdict(set); seqAppMap = defaultdict(set)\n for (i, hEnd, startpos) in mdb:\n support.add(self.HDB[i][1])\n seqStr = ' '.join(self.HDB[i][0][hEnd + 1 : startpos - len(tail) + 1])\n # print('Find a sequence', seqStr, 'HEAD:', head, 'TAIL:', tail, 'Origin:', self.HDB[i][0])\n SC[seqStr].add(i); seqAppMap[seqStr].add(self.HDB[i][1]); appSigMap[self.HDB[i][1]].add(seqStr)\n if startpos + 1 < self.HDB[i][2]:\n e = self.HDB[i][0][startpos + 1]\n occurs[e].append((i, hEnd, startpos + 1))\n itemSupport[e].add(self.HDB[i][1])\n\n print('Find a Context', head, tail)\n effective = 0\n seqQuality = {}\n\n for seq, apps in seqAppMap.items():\n if len(apps) == 1:\n if seq not in seqQuality:\n inf = self.idf(seq)\n seqQuality[seq] = inf * self.rel(seq, appSigMap, seqAppMap)\n effective += seqQuality[seq]\n\n\n contextQuality = context_quality(len(support) / self.totalApp, effective * 1.0 / len(seqAppMap))\n print('Context Quality:', contextQuality)\n if contextQuality > self.conf_t:\n for seqStr in SC.keys():\n if len(seqAppMap[seqStr]) == 1:\n for i in SC[seqStr]:\n sigQuality = seqQuality[seqStr]\n currentLen = len(head) + len(tail) + len(seqStr.split(' '))\n Rules[i].append((head, tail, seqStr, contextQuality, sigQuality, currentLen, self.HDB[i][1]))\n Rules[i] = sorted(Rules[i], key=lambda x: (x[3], x[4], 10000 - x[5]), reverse=True)[:self.K]\n\n for e, newmdb in occurs.items():\n if len(itemSupport[e]) > self.omega:\n self.mine_tail_rec(tail + [e], newmdb, head)\n\n\n def idf(self, signature):\n if signature not in INFO:\n sigSeg = signature.split(' ')\n INFO[signature] = sum([self.IDF[i] for i in sigSeg]) / len(sigSeg)\n return INFO[signature]\n\n def rel(self, signature, appSigMap, sigAppMap):\n return math.sqrt(1 / len(appSigMap[list(sigAppMap[signature])[0]]))\n\n\n\n\n\nif __name__ == '__main__':\n tbls = ['ios_packages_2015_08_04', 'ios_packages_2015_10_16','ios_packages_2015_10_21',\n 'ca_ios_packages_2015_12_10', 'ca_ios_packages_2015_05_29', 'ca_ios_packages_2016_02_22',\n 'chi_ios_packages_2015_07_20','chi_ios_packages_2015_09_24','chi_ios_packages_2015_12_15']\n trainSet = DataSetFactory.get_traindata(tbls=tbls, appType=consts.IOS)\n for support in [0.2, 0.4, 0.6, 0.8]:\n a = AgentBoundary(support)\n a.train(trainSet)\n print('Finish Rule Mining')","sub_path":"classifiers/agent_pattern_gen.py","file_name":"agent_pattern_gen.py","file_ext":"py","file_size_in_byte":7305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"206270853","text":"__author__ = 'Vitaha'\nfrom livewires import games, color\n\n#screen initiation\ngames.init(screen_width = 640, screen_height = 640, fps = 50)\nsound = games.load_sound('ping_pong_8bit_plop.wav')\n\nclass Rocket(games.Sprite):\n \"\"\"ping-pong rocket\"\"\"\n image = games.load_image('rocket.bmp')\n score = 0\n\n #score number when need to change difficulty\n cycle = 20\n\n def __init__(self, ball):\n \"\"\"rocket constructor\"\"\"\n super(Rocket, self).__init__(image = Rocket.image,\n x = games.mouse.x,\n bottom = games.screen.height)\n\n #game score initiation\n self.score = games.Text(value = Rocket.score,\n size = 25,\n color = color.black,\n top = 5,\n right = games.screen.width - 10)\n games.screen.add(self.score)\n self.ball = ball\n\n def update(self):\n \"\"\"gorizontal moving of rocket\"\"\"\n self.x = games.mouse.x\n if self.left < 0:\n self.left = 0\n if self.right > games.screen.width:\n self.right = games.screen.width\n\n #checking for ball catch\n self.check_catch()\n\n #change difficulty\n self.difficulty()\n\n def check_catch(self):\n \"\"\"checking catch\"\"\"\n for ball in self.overlapping_sprites:\n if ball.bottom <= self.top + 1:\n Rocket.score += 10\n self.score.value = Rocket.score\n self.score.right = games.screen.width - 10\n ball.handle_catch()\n\n def difficulty(self):\n \"\"\"difficulty change\"\"\"\n #creatre second ball\n if Rocket.cycle == 120 and Rocket.score == 120:\n Ball.speed = 0.5\n self.ball.speed_up()\n the_ball1 = Ball(x = 20, y = 20)\n games.screen.add(the_ball1)\n Rocket.cycle += 20\n\n #just speedUp\n elif Rocket.score/Rocket.cycle == 1:\n self.ball.speed_up()\n Rocket.cycle += 20\n\nclass Ball(games.Sprite):\n \"\"\"flying ball\"\"\"\n image = games.load_image('ball.bmp')\n speed = 1\n\n def __init__(self, x = games.screen.width/2, y = games.screen.height/2):\n super(Ball, self).__init__(image = Ball.image,\n x = x, y = y,\n dx = Ball.speed,\n dy = Ball.speed)\n\n def update(self):\n \"\"\"check for ball position and change speed\"\"\"\n if self.right > games.screen.width:\n self.dx = -self.dx\n sound.play()\n if self.left < 0:\n self.dx = -self.dx\n sound.play()\n if self.top < 0:\n self.dy = -self.dy\n sound.play()\n if self.bottom > games.screen.height:\n self.end_game()\n self.destroy()\n\n def handle_catch(self):\n \"\"\"redirect handeled ball\"\"\"\n self.dy = - self.dy\n sound.play()\n\n def speed_up(self):\n \"\"\"ball speed up\"\"\"\n Ball.speed += 0.5\n if self.dx > 0:\n self.dx = Ball.speed\n else:\n self.dx = - Ball.speed\n if self.dy > 0:\n self.dy = Ball.speed\n else:\n self.dy = - Ball.speed\n\n def end_game(self):\n \"\"\"ending game\"\"\"\n end_message = games.Message(value = 'GAME OVER',\n size = 90,\n color = color.black,\n x = games.screen.width/2,\n y = games.screen.height/2,\n lifetime = 5 * games.screen.fps,\n after_death = games.screen.quit)\n score_message = games.Message(value = 'Score: ' + str(Rocket.score),\n size = 50,\n color = color.black,\n x = games.screen.width/2,\n y = games.screen.height/2 + 100,\n lifetime = 5 * games.screen.fps)\n games.screen.add(end_message)\n games.screen.add(score_message)\n\n\n\n\n\n\ndef main():\n \"\"\"main thred\"\"\"\n #init background\n wall_image = games.load_image('background1.jpg', transparent = False)\n games.screen.background = wall_image\n\n #init ball and rocket\n the_ball = Ball()\n games.screen.add(the_ball)\n the_rocket = Rocket(the_ball)\n games.screen.add(the_rocket)\n\n games.mouse.is_visible = False\n games.screen.event_grab = True\n games.screen.mainloop()\n\nmain()","sub_path":"ping_pong.py","file_name":"ping_pong.py","file_ext":"py","file_size_in_byte":4692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"328652041","text":"import random\r\n\r\njb = random.randint(1, 10)\r\ntries = 1\r\n\r\ncoba = 5\r\n\r\ndef mulaaigemku(cba):\r\n cba = cba - 1\r\n print(\"Anda memiliki\", coba, \"Kesempatan Lagi....\")\r\n if cba == 0:\r\n print(\"maaf, kesempatan anda habis... jawaban yang sebenarnya adalah\", jb)\r\n mulai_gem(cba)\r\n\r\ndef mulai_gem(cba):\r\n print(\"Aku memikirkan salah satu nomor dari 1 sampai 10... bisakah kamu menebaknya?\")\r\n jawaban = int(input(\"Jawabanku adalah : \"))\r\n if jawaban == jb:\r\n print(\"Benar!!\")\r\n else:\r\n print(\"maaf, jawaban anda salah...\")\r\n mulaaigemku(cba)\r\n\r\nprint(\"Hai, Selamat datang di gem Pikirkan nomornya! (by Ihsan)\")\r\nnama = str(input(\"Siapa namamu? \"))\r\nprint(\"Halo,\", nama + \"!\")\r\nyno = str(input(\"apakah kamu(\" + nama +\") Ingin Memainkan gem ini?[Y/T]\"))\r\nif yno == \"Y\":\r\n mulai_gem(5)\r\nelif yno == \"y\":\r\n mulai_gem(5)\r\nelif yno == \"T\":\r\n print(\"ohh.. yaudah kalo gitu\")\r\nelif yno == \"t\":\r\n print(\"ohh.. yaudah kalo gitu\")\r\nelse:\r\n print(\"error\")\r\n","sub_path":"old-codes/Gemku/pikirkan nomornya.py","file_name":"pikirkan nomornya.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"404435242","text":"import numpy as np\n\"\"\"\nDefinit une convolution faisant une moyenne des voisins d'un pixel donne\n( stencil de 3x3 )\n\"\"\"\n\n\ndef convolve_mean(image):\n return 0.25*(image[:-2, 1:-1] + image[2:, 1:-1] +\n image[1:-1, :-2] + image[1:-1, 2:])\n\n\ndef convolve_laplacien(image):\n \"\"\"\n Definie l'operateur laplacien comme convolution :\n permet de detecter les bords dans une image\n \"\"\"\n out_image = np.abs(4*image[1:-1, 1:-1] - image[:-2, 1:-1] -\n image[2:, 1:-1] - image[1:-1, :-2] - image[1:-1, 2:])\n # On renormalise l'image :\n valmax = np.max(out_image)\n valmax = max(1., valmax)+1.E-9\n out_image *= 1./valmax\n return out_image\n\n\ndef convolve_matrix(image, convolution_array):\n \"\"\"\n Convolution generale avec une taille de stencil quelconque.\n Permet de definir tous les stencils que l'on souhaite !\n \"\"\"\n height = image.shape[0]\n width = image.shape[1]\n nx = convolution_array.shape[0]\n ny = convolution_array.shape[1]\n h = height - nx + 1\n w = width - ny + 1\n\n out_image = np.zeros_like(image[:h, :w])\n\n for jw in range(0, ny):\n for iw in range(0, nx):\n out_image += convolution_array[jw, iw] * image[jw:jw+h, iw:iw+w]\n\n # On renormalise l'image en ramenant les valeurs des couleurs entre 0 et 1\n out_image = np.abs(out_image)\n valmax = np.max(out_image)\n valmax = max(1., valmax) + 1.E-9\n out_image *= 1./valmax\n return out_image\n","sub_path":"convolution/numpy_opt/kernels.py","file_name":"kernels.py","file_ext":"py","file_size_in_byte":1472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"353156153","text":"from tkinter import Tk, Canvas\nimport time\n\nclass Car:\n def __init__(self, brand, model, color, canvas, positionX, positionY):\n self.brandname = brand\n self.modelname = model\n self.colorname = color\n\n self.canvas = canvas\n self.car_rectangle = self.canvas.create_rectangle(positionX, positionY, positionX+50, positionY+50, fill=color)\n\n def __str__(self):\n return self.brandname + \" \" + self.modelname + \" is op positie: \" + positionX + \",\" + positionY\n\n def Name(self):\n return self.brandname + \" \" + self.modelname\n\n def Move(self,x,y):\n self.canvas.move(self.car_rectangle,x,y)\n\n\n\n\nif __name__ == '__main__':\n root = Tk()\n\n #achtergrond\n canvas = Canvas(root, width=800, height=800, bg='black')\n canvas.pack()\n\n #gras\n canvas.create_rectangle(0 ,0 ,200,200, fill='green') #lb\n canvas.create_rectangle(600,0 ,800,200, fill='green') #rb\n canvas.create_rectangle(0 ,600,200,800, fill='green') #lo\n canvas.create_rectangle(600,600,800,800, fill='green') #ro\n\n #strepen\n canvas.create_line(400, 0, 400, 800, fill='white', dash=(20, 20)) # vertikaal\n canvas.create_line(0, 400, 800, 400, fill='white', dash=(20, 20)) # horizontaal\n\n\n\n car = Car('Fiat','Punto', 'blue', canvas, 0,500)\n\n\n for i in range(75):\n canvas.update()\n time.sleep(0.05)\n car.Move(10,0)\n\n\n root.mainloop()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"428665964","text":"from .models import Task\nfrom django import forms\nfrom django.http import request\nimport datetime, pytz\n\nclass TaskForm(forms.ModelForm):\n due_date = forms.DateField(widget=forms.DateInput(attrs={'type': 'date'}))\n due_time = forms.TimeField(widget=forms.TimeInput(attrs={'type': 'time'}))\n\n class Meta:\n model = Task\n fields = ('text', 'priority')\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields['text'].widget.attrs['placeholder'] = 'Add Task Here'\n\n def clean(self):\n due_date = self.cleaned_data['due_date']\n due_time = self.cleaned_data['due_time']\n due_datetime = datetime.datetime(year=due_date.year, month=due_date.month, day=due_date.day) + datetime.timedelta(hours=due_time.hour, minutes=due_time.minute) #? add both date and time\n due_datetime = due_datetime.replace(tzinfo=datetime.timezone.utc)\n #? if due datetime is before current date then raise an error\n if due_datetime < datetime.datetime.today().replace(tzinfo=datetime.timezone.utc):\n raise forms.ValidationError(\n 'due date and time cannot occur before current date',\n 'backdating'\n )","sub_path":"apps/tasks/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"85340318","text":"ENEMY = '!!'\nDEFENDER = '{}'\nKING = '**'\nTHRONE = '*'\nEXIT = '>>'\nEMPTY = '-'\n\n\nclass Field:\n def __init__(self, i, j, figure=None):\n self.i = i\n self.j = j\n self.figure = figure\n\n\nclass PlainField(Field):\n def __str__(self):\n if self.figure:\n return str(self.figure)\n else:\n return '-'\n\n\nclass ThroneField(Field):\n def __str__(self):\n if self.figure:\n return str(self.figure)\n else:\n return '**'\n\n\nclass ExitField(Field):\n def __str__(self):\n if self.figure:\n return str(self.figure)\n else:\n return '>>'\n\n\nclass Figure:\n def __init__(self, move_limit=None):\n self.possible_fields = []\n self.possible_moves = []\n self.move_limit = move_limit\n\n def get_possible_moves(self, board, field):\n self.possible_moves = []\n for modifier in ((-1, 0), (1, 0), (0, 1), (0, -1)):\n i, j = field.i, field.j\n while True:\n i += modifier[0]\n j += modifier[1]\n\n is_i_in_board = 0 <= i + modifier[0] < 9\n is_j_in_board = 0 <= j + modifier[1] < 9\n if board[i][j].figure or not is_i_in_board or not is_j_in_board:\n break\n elif self.move_limit and self.move_limit == len(self.possible_moves):\n break\n elif type(board[i][j]) not in self.possible_fields:\n continue\n else:\n self.possible_moves.append((i, j))\n\n\nclass AttackerFigure(Figure):\n def __init__(self):\n super(AttackerFigure).__init__()\n self.possible_fields.extend([PlainField])\n\n def __str__(self):\n return '!!'\n\n\nclass DefenderFigure(Figure):\n def __init__(self):\n super(DefenderFigure).__init__()\n self.possible_fields.extend([PlainField])\n\n def __str__(self):\n return '{}'\n\n\nclass KingFigure(Figure):\n def __init__(self):\n super(KingFigure).__init__()\n self.possible_fields.extend([PlainField, ThroneField, ExitField])\n\n def __str__(self):\n return '**'\n\n\n\nclass King:\n def __init__(self, i, j, move_limit):\n self.i = i\n self.j = j\n self.possible_moves = []\n self.move_limit = move_limit\n\n def __str__(self):\n return KING\n\n def get_possible_moves(self, board):\n self.possible_moves = []\n for modifier in ((-1, 0), (1, 0), (0, 1), (0, -1)):\n move_possible = True\n moves_counter = 0\n i, j = self.i, self.j\n while move_possible and 0 <= i + modifier[0] < 9 and 0 <= j + modifier[1] < 9:\n i += modifier[0]\n j += modifier[1]\n if board[i][j] in (EMPTY, THRONE, EXIT) and moves_counter <= self.move_limit:\n self.possible_moves.append((i, j))\n else:\n move_possible = False\n\n\nboard = [\n [ExitField(0, 0), PlainField(0, 1), PlainField(0, 2), PlainField(0, 3, Enemy)],\n [],\n [],\n [],\n [],\n [],\n [],\n [],\n [],\n]\n\nboard = [\n [EXIT, EMPTY, EMPTY, Enemy(0, 3), Enemy(0, 4), Enemy(0, 5), EMPTY, EMPTY, EXIT],\n [EMPTY, EMPTY, EMPTY, EMPTY, EMPTY, Enemy(1, 4), EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY, EMPTY, Defender(2, 4), EMPTY, EMPTY, EMPTY, EMPTY],\n [Enemy(3, 0), EMPTY, EMPTY, EMPTY, Defender(3, 4), EMPTY, EMPTY, EMPTY, Enemy(3, 8)],\n [Enemy(4, 0), Enemy(4, 1), Defender(4, 2), Defender(4, 3), King(4, 4, move_limit=3), Defender(4, 5), Defender(4, 6), Enemy(4, 7), Enemy(4, 8)],\n [Enemy(5, 0), EMPTY, EMPTY, EMPTY, Defender(5, 4), EMPTY, EMPTY, EMPTY, Enemy(5, 8)],\n [EMPTY, EMPTY, EMPTY, EMPTY, Defender(6, 4), EMPTY, EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY, EMPTY, EMPTY, Enemy(7, 4), EMPTY, EMPTY, EMPTY],\n [EXIT, EMPTY, EMPTY, Enemy(8, 3), Enemy(8, 4), Enemy(8, 5), EMPTY, EMPTY, EXIT],\n]\n\n\n\ndef fill_board(soldiers, board):\n for soldier in soldiers:\n board[soldier.i][soldier.j] = soldier\n\n\ndef get_possible_moves(soldiers):\n for soldier in soldiers:\n soldier.get_possible_moves(board)\n\n\ndef print_board(board):\n for line in board:\n print('\\t'.join(str(field) for field in line))\n\n\n\n\n\n# 1 Расставить фигуры\nfill_board(defenders + enemies, board)\n\nprint_board(board)\n\n# 2 Рассчитать возможные ходы\nget_possible_moves(defenders + enemies)\n\n\n# 3 Сделать ход\ndef make_move():\n print('Human turn')\n from_i, from_j = [int(coord) for coord in input('input from coordinates: ').split(' ')]\n\n field_on_board = 0 <= from_i < 9 and 0 <= from_j < 9\n field_has_figure = type(board[from_i][from_j]) in (Defender, King, Enemy)\n figure_has_moves = board[from_i][from_j].possible_moves\n while not field_on_board and not field_has_figure and not figure_has_moves:\n print('Repeat input..')\n from_i, from_j = (int(coord) for coord in input('input from coordinates: ').split())\n\n to_i, to_j = (int(coord) for coord in input('input to coordinates: ').split())\n field_on_board = 0 <= to_i < 9 and 0 <= to_j < 9\n while not field_on_board and (to_i, to_j) not in board[from_i][from_j].possible_moves:\n print('Repeat input..')\n to_i, to_j = (int(coord) for coord in input('input to coordinates: ').split())\n\n\n board[to_i][to_j] = board[from_i][from_j]\n board[from_i][from_j] = 0\n\n\nmake_move()\n\nprint('Defenders turn')\nfrom_i, from_j = (int(coord) for coord in input('input from coordinates: '))\nto_i, to_j = (int(coord) for coord in input('input to coordinates: '))\n\n# 4 Съесть фигуры\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"567526967","text":"from typing import *\nimport itertools\nimport pandas as pd\nLambdaParameters = List[str]\nLayoutPolicy = List[int]\nKernelPolicy = List[int]\nAccessArguments = List[str]\nStrideOrder = List[int]\nAccessIndices = List[int]\n\n#The input data is the access arguments for each access\n#The input transformations (also sort of data) are the view's layout, the lambda paramaters, and the kernel policy\ndef apply_lambda(params: LambdaParameters, args: AccessArguments) -> AccessIndices:\n\t#apply_lambda args access = foldr (++) [] [elemIndices acc args | acc <- access]\n\treturn [params.index(arg) for arg in args]\n\n\ndef apply_kpol(kpol: KernelPolicy, indices: AccessIndices) -> StrideOrder:\n\treturn [kpol.index(index) for index in indices]\n\n\ndef apply_lpol(lpol: LayoutPolicy, l: List[Any]) -> List[Any]:\n\tassert len(lpol) == len(l)\n\treturn [l[i] for i in lpol]\n\ndef normalized_access(params: LambdaParameters, args: AccessArguments, kpol: KernelPolicy, lpol: LayoutPolicy):\n\treturn apply_kpol(kpol, apply_lpol(lpol, apply_lambda(params, args)))\n\n#\n# Tests\n#\n\nlambda_params = [\"nm\", \"d\", \"g\", \"z\"]\n\nphi_access = [\"nm\", \"g\", \"z\"]\nell_access = [\"nm\", \"d\"]\npsi_access = [\"d\", \"g\", \"z\"]\n\npolicy0 = [0,1,2,3]\npolicy1 = [0,2,3,1]\npolicy2 = [3,2,1,0]\n\nphi_layout1 = [0,2,1]\nell_layout1 = [1,0]\npsi_layout1 = [2,0,1]\n\nphi_layout2 = [0,1,2]\nell_layout2 = [0,1]\npsi_layout2 = [0,1,2]\n\naccesses = [phi_access, ell_access, psi_access]\nlayouts1 = [phi_layout1, ell_layout1, psi_layout1]\n\nprint(\"apply_lambda tests\")\nassert([0,2,3] == apply_lambda(lambda_params, phi_access))\nassert([0,1] == apply_lambda(lambda_params, ell_access))\nassert([1,2,3] == apply_lambda(lambda_params, psi_access))\n\nprint(\"apply_kpol tests\")\nassert([0,1,2] == apply_kpol(policy1, apply_lambda(lambda_params, phi_access)))\nassert([3,1,0] == apply_kpol(policy2, apply_lambda(lambda_params, phi_access)))\nassert([0,3] == apply_kpol(policy1, apply_lambda(lambda_params, ell_access)))\nassert([3,2] == apply_kpol(policy2, apply_lambda(lambda_params, ell_access)))\nassert([3,1,2] == apply_kpol(policy1, apply_lambda(lambda_params, psi_access)))\nassert([2,1,0] == apply_kpol(policy2, apply_lambda(lambda_params, psi_access)))\n\nprint(\"apply_lpol tests\")\nassert [0,2,1] == apply_lpol(phi_layout1, apply_kpol(policy1, apply_lambda(lambda_params, phi_access)))\n","sub_path":"accesses.py","file_name":"accesses.py","file_ext":"py","file_size_in_byte":2295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"506367461","text":"'''\npg 111- 112, in python the and, or & not operations are very literal\n\nnot #in java means ! \nand #in java means &&\nor #in java means ||\n'''\n# Receive an input\nnumber = eval(input(\"Enter an integer: \"))\n\nif number % 2 == 0 and number % 3 == 0:\n print(number, \"is divisible by 2 and 3\")\n\nif number % 2 == 0 or number % 3 == 0:\n print(number, \"is divisible by 2 or 3\")\n\n#if you want to separate a if statements boolean on two lines you need to use the '\\' at the end of each line\nif (number % 2 == 0 or number % 3 == 0) and \\\n not (number % 2 == 0 and number % 3 == 0):\n print(number, \"is divisible by 2 or 3, but not both\")","sub_path":"PythonLearn/ch_4/If_AndOrNot.py","file_name":"If_AndOrNot.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"497395056","text":"''' IoT Greenhouse - Introduction to Webservices\n Introduction to Webservices activity for Connecting the Raspberry Pi workshop\n\n Keith E. Kelly\n K2 Creatives, LLC\n 9/15/19\n'''\nfrom time import sleep\nfrom iot_gh.IoTGreenhouseService import IoTGreenhouseService\nfrom iot_gh.GHTextingService import GHTextingService\n\nprint(\"\\nGroupMe SMS Texting for IoT Greenhouse.\\n\")\nprint(\"\\nOpen your dev.groupme.com page. Access your token and copy here.\")\ntoken = (input(\"GroupMe token: \")).strip()\nprint()\n\nlast_message_id = None\n\nghs = IoTGreenhouseService()\nts = GHTextingService(token, ghs)\n\nwhile True:\n message = ts.last_message\n if message.id != last_message_id:\n print(message.name + \" \" + message.text)\n print()\n\n last_message_id = message.id\n sleep(.5)\n","sub_path":"iot_gh_ws_intro.py","file_name":"iot_gh_ws_intro.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"630466519","text":"from django.urls import path\nfrom .views import weather, clear, cloudy, rain\n\napp_name = \"spring\"\nurlpatterns = [\n path('weather/', weather, name=\"weather\"),\n path('clear/', clear, name=\"clear\"),\n path('cloudy/', cloudy, name=\"cloudy\"),\n path('rain/', rain, name=\"rain\"),\n]\n","sub_path":"spring/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"491806925","text":"import subprocess\n\nwith open(\"../Sample_OS_Folder/sample2.txt\", \"w+\") as fout:\n # with open(\"../Sample_OS_Folder/sample2.txt\", \"w+\") as ferr:\n out = subprocess.call(\n [\"ls\", \"-a\"],\n stdout=fout\n # , stderr=ferr\n )\n # reset file to read from it\n fout.seek(0)\n # save output (if any) in variable\n output = fout.read()\n\n # reset file to read from it\n # ferr.seek(0)\n # save errors (if any) in variable\n # errors = ferr.read()\nprint(output)\nsubprocess.call(\"clear\", shell=True)\n\n\n#########################\n\nimport subprocess\nfrom time import sleep\nfrom os import *\n\nprocess = subprocess.Popen(\n \"ls\", cwd=\"../files\", shell=True, stdout=subprocess.PIPE, text=True\n)\n\nout, err = process.communicate(timeout=2)\nfor i in out.split():\n ext = i.split(\".\")[-1]\n if ext == \"json\":\n print(f\"json: {i}\")\n else:\n print(f\"other: {i}\")\n","sub_path":"activities/subprocess_act.py","file_name":"subprocess_act.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"341590465","text":"import numpy as np\nimport cv2\nimport stereo\n\n\"\"\"\nThis script is used for finding and drawing epolar lines by using fundamental matrix.\nControl point pairs are used for calculate the F matrix, \nwhile the test point pairs are used to test the epolar lines \n\"\"\"\n\n# load the images and point pairs\nimg1, img2, ctrl_l, ctrl_r, test_l, test_r = stereo.load()\n\n# calculate the fundamental matrix\nF, mask = cv2.findFundamentalMat(ctrl_l,ctrl_r,cv2.FM_LMEDS)\n# gF = stereo.getF()\n# F = gF.findF()\n\n[ctrl_l_homo, ctrl_r_homo, test_l_homo, test_r_homo] = stereo.homo([ctrl_l, ctrl_r, test_l, test_r])\n\n# find epolar lines\nlines_r = stereo.getEpLine(0, F, test_l_homo)\nlines_l = stereo.getEpLine(1, F, test_r_homo)\n\n# draw the epolar lines\nimg_l = stereo.drawlines(img1,img2,lines_l,test_l,test_r)\nimg_r = stereo.drawlines(img2,img1,lines_r,test_r,test_l)\n\ncv2.imshow('Campus_left', img_l)\ncv2.imshow('Campus_right', img_r)\ncv2.waitKey(0)","sub_path":"CUNY_ComputerVision_CSC74030/Stereo/epLine.py","file_name":"epLine.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"65062387","text":"#!/usr/bin/env python3\n\nimport concurrent.futures\nimport time\n\n'''\nhttps://docs.python.org/3/library/concurrent.futures.html#module-concurrent.futures\n'''\n\nif 'Raise exception from thread':\n\n # https://stackoverflow.com/questions/2829329/catch-a-threads-exception-in-the-caller-thread-in-python/12808634#12808634\n\n def func_that_raises(do_raise):\n # Very likely that the main thread will set i before we go past this point.\n time.sleep(0.1)\n if do_raise:\n raise Exception()\n else:\n return 42 + i\n\n with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:\n # Ensure that it is asynchronous.\n i = 0\n future = executor.submit(func_that_raises, False)\n i = 1\n assert future.result() == 43\n\n # Check that exceptions are raised up.\n future = executor.submit(func_that_raises, True)\n try:\n future.result()\n except Exception:\n pass\n else:\n assert False\n","sub_path":"concurrent_cheat.py","file_name":"concurrent_cheat.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"100253868","text":"import sys\nN = int(input())\narray_value = list(map(int,input().split()))\narray_cost = list(map(int,input().split()))\n\nif not ( 1 <= N <= 20 ): sys.exit()\nfor I in array_value:\n if not ( 1 <= I <= 50 ): sys.exit()\nfor J in array_cost:\n if not ( 1 <= J <= 50 ): sys.exit()\n\nvalue_check = 0\nfor K in range(len(array_value)):\n value = array_value[K] - array_cost[K]\n value_check += value if value >= 1 else 0\n\nprint(value_check)","sub_path":"ABC/python/125/B.py","file_name":"B.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"266854871","text":"import numpy as np\nimport os\nfrom Visualization import AOI\nfrom DataLib.GetData import Data\n\n# TODO: Idea: It will likely prove useful to have an interface to read dataframes when you know the technology is\n# eyetracking model. Consider implementing it for improving future results.\n\nstrategy = 3\n\n\ndef getTime(minutes, seconds, minutes2, seconds2):\n return (minutes*60 + seconds, minutes2*60 + seconds2)\n\n\ndef getNextMin(minutes, seconds):\n return (minutes*60 + seconds, (minutes+1)*60 + seconds)\n\n\ndef getStrategy(subject, qst_idx, strategyidx):\n error = None\n if subject == '1000':\n if qst_idx == 1:\n if strategyidx == 1:\n return getTime(0, 55, 1, 53)\n elif strategyidx == 2:\n return getTime(1, 53, 2, 41)\n elif strategyidx == 3:\n return getTime(2, 41, 2, 55)\n else:\n return error\n elif qst_idx == 2:\n return error\n elif qst_idx == 3:\n if strategyidx == 1:\n return getTime(5, 15, 5, 30)\n elif strategyidx == 2:\n return getTime(5, 30, 6, 24)\n elif strategyidx == 3:\n return getTime(6, 24, 6, 55)\n else:\n return error\n elif qst_idx == 4:\n if strategyidx == 1:\n return getTime(7, 11, 7, 29)\n elif strategyidx == 2:\n return getTime(7, 29, 7, 58)\n elif strategyidx == 3:\n return getTime(7, 58, 8, 11)\n else:\n return error\n else:\n return error\n elif subject == '009':\n if qst_idx == 1:\n if strategyidx == 1:\n return getTime(1, 17, 1, 44)\n elif strategyidx == 2:\n return getTime(1, 44, 2, 45)\n elif strategyidx == 3:\n return getTime(2, 45, 5, 32)\n else:\n return error\n elif qst_idx == 2:\n return error\n elif qst_idx == 3:\n if strategyidx == 1:\n return getTime(9, 22, 9, 41)\n elif strategyidx == 2:\n return getTime(9, 41, 10, 26)\n elif strategyidx == 3:\n return getTime(10, 26, 10, 54)\n elif strategyidx == 4:\n return getTime(10, 54, 11, 59)\n else:\n return error\n elif qst_idx == 4:\n if strategyidx == 1:\n return getTime(12, 11, 13, 24)\n elif strategyidx == 2:\n return getTime(13, 24, 15, 55)\n elif strategyidx ==3:\n return getTime(15, 55, 16, 40)\n else:\n return error\n else:\n return error\n elif subject == '001':\n if qst_idx == 1:\n if strategyidx == 1:\n return getTime(5, 36, 7, 57)\n elif strategyidx == 2:\n return getTime(7, 57, 11, 12)\n else:\n return error\n elif qst_idx == 2:\n return error\n elif qst_idx == 3:\n return error\n elif qst_idx == 4:\n return error\n else:\n return error\n elif subject == '007':\n if qst_idx == 1:\n if strategyidx == 1:\n return getTime(0, 59, 2, 5)\n elif strategyidx == 2:\n return getTime(2, 5, 5, 7)\n else:\n return error\n elif qst_idx == 2:\n return error\n elif qst_idx == 3:\n return error\n elif qst_idx == 4:\n return error\n else:\n return error\n\n return error\n\n\ninput_fixations_directory = os.path.join('Subjects', 'fixations') # CSV files\ninput_blinks_directory = os.path.join('Subjects', 'blinks') # CSV files\ninput_pupil_directory = os.path.join('Subjects', 'pupil') # CSV files\n\nbackground_images = ['Question1.jpg', 'Question2.jpg', 'Question3.jpg', 'Question4.jpg']\n\nsubjects_dict = {\n # 'None' value means we skip the analysis of that question, and tuple (start time, end time) means we partake\n # these specific times (in seconds) to include in question\n # For example: given this data: '000': [None, (12, 43), (50, 90), None]\n # then we will skip the first question, include seconds 12 through 43 in the second\n # question, include seconds 50 through 90 in the third question and skip the fourth question\n # Note: If you would like to exclude a subject entirely -\n # simply fill them with 'None' values, example to exclude subject 9:\n # '009': [None, None, None, None]\n # ##########################################################\n\n # subject 001 times\n 'whole_001': [getTime(5, 36, 11, 12), getTime(11, 32, 13, 8), getTime(13, 20, 16, 36), getTime(17, 2, 18, 34)],\n 'min_001': [getNextMin(5, 36), getNextMin(11, 32), getNextMin(13, 20), getNextMin(17, 2)],\n 'strategy_001': [getStrategy('001', 1, strategy), getStrategy('001', 2, strategy), getStrategy('001', 3, strategy), getStrategy('001', 4, strategy)],\n\n # subject 007 times\n 'min_007': [getNextMin(0, 59), getNextMin(6, 1), getNextMin(7, 48), getNextMin(10, 33)],\n 'whole_007': [getTime(0, 59, 5, 50), getTime(6, 1, 7, 36), getTime(7, 48, 10, 23), getTime(10, 33, 13, 44)],\n 'strategy_007': [getStrategy('007', 1, strategy), getStrategy('007', 2, strategy), getStrategy('007', 3, strategy), getStrategy('007', 4, strategy)],\n\n # subject 009 times\n 'min_009': [getNextMin(0, 47), getNextMin(7, 36), getNextMin(9, 22), getNextMin(12, 11)],\n 'whole_009': [getTime(1, 17, 6, 51), getTime(7, 36, 9, 12), getTime(9, 22, 11, 59), getTime(12, 11, 16, 40)],\n 'strategy_009': [getStrategy('009', 1, strategy), getStrategy('009', 2, strategy), getStrategy('009', 3, strategy), getStrategy('009', 4, strategy)],\n\n # subject 1000 times\n 'whole_1000': [getTime(0, 55, 2, 55), getTime(3, 22, 5, 2), getTime(5, 16, 6, 56), getTime(7, 11, 8, 11)],\n 'min_1000': [getNextMin(0, 55), getNextMin(3, 22), getNextMin(5, 16), getNextMin(7, 11)],\n 'strategy_1000': [getStrategy('1000', 1, strategy), getStrategy('1000', 2, strategy), getStrategy('1000', 3, strategy), getStrategy('1000', 4, strategy)],\n}\n\n\n# TODO: After Renovating Scanpath and MeanShift.\n# TODO: Rename to QuestionImageWidths\nWIDTHS = [1808, 2046, 1810, 1518] # The width of image for each question (here 2046 width for Question 2)\n# TODO: Rename to QuestionImageHeights\nHEIGHTS = [1013, 1155, 1014, 847] # The height of image for each question (here 1014 width for Question 3)\n\n\n# TODO: Make a class describing what you are outputting, this will help encapsulate what is being output\n# question selection\nQUESTION_IDX = 1\nimg_path = os.path.join('..', 'Heatmap', background_images[QUESTION_IDX])\nheight = HEIGHTS[QUESTION_IDX]\nwidth = WIDTHS[QUESTION_IDX]\n\n# subject selection\nSUBJECT_ID = '1000'\nTIMESTAMPINFO = 'min' # TODO: Turn this to a list selection\nSUBJECT_KEY = TIMESTAMPINFO + '_' + SUBJECT_ID # take the key from subjects_dict (imported above :) )\nsubject_path = os.path.join('..', input_fixations_directory, SUBJECT_ID + \"_fixations.csv\")\nsubject_times = subjects_dict[SUBJECT_KEY][QUESTION_IDX]\n\n\n# Offset all points\n# TODO: It might be a good idea to allow any custom offsets to the point by creating a class which allows any kind of\n# alterations to the points, but it is deemed unnecessary for now. I might do it if I have the time.\nOFFSET_X, OFFSET_Y = 0, 0\n\n\ndef get_random_array_with_range(shape, min_range, max_range):\n return np.random.rand(shape) * (max_range - min_range) + min_range\n\n\ndef match_fixation_to_aoi():\n \"\"\"\n iterate over fixations df, check if the fixations match one of the AOI's and assign if needed\n \"\"\"\n fixations_df = Data.read_only_fixation_data(get_normalized=False)\n fixations_df[\"AOI\"] = None\n\n # Insert fixations to the matching AOI\n for i, row in fixations_df.iterrows():\n for aoi_num, bound in enumerate(AOI.AOI_dict[\"1\"]):\n if bound[0][0] <= row['X'] <= bound[3][0] and bound[3][1] <= row['Y'] <= bound[0][1]:\n # print(\"AOI Detected\")\n fixations_df['AOI'].iloc[i] = aoi_num\n break\n\n # for i, row in fixations_df.iterrows():\n # if row['AOI'] is not None:\n # print(row['AOI'])\n","sub_path":"Utils.py","file_name":"Utils.py","file_ext":"py","file_size_in_byte":8361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"414652953","text":"import datetime\nimport os\nimport pickle\nimport sys\n\nfrom yahoo_finance import Share\n\nimport functions\nfrom functions import *\n\n\nclass TickerDetail(object):\n def __init__(self, ticker):\n self.ticker = ticker\n self.share = Share(ticker)\n self.dtg_collected = None\n self.data_completion = 0\n self.data = {'industry': None, 'sector': None, 'avg_daily_volume': None, 'stock_exchange': None,\n 'market_cap(M)': None,\n 'book_value': None, 'ebitda(M)': None, 'dividend_share': None, 'dividend_yield': None,\n 'earnings_share': None, 'year_high': None, 'year_low': None, 'day50_moving_avg': None,\n 'day200_moving_avg': None, 'price_earnings_ratio': None, 'price_earnings_growth_ratio': None,\n 'price_sales': None, 'price_book': None, 'short_ratio': None, 'start': None, 'end': None}\n self.gather_external_data()\n\n def gather_external_data(self):\n try:\n self.data['avg_daily_volume'] = self.share.get_avg_daily_volume()\n self.data['stock_exchange'] = self.share.get_stock_exchange()\n self.data['book_value'] = self.share.get_book_value()\n s = self.share.get_market_cap()\n if s is not None:\n if 'B' in s:\n s = s.replace('B', '')\n s = float(s) * 1000\n s = str(s)\n elif 'M' in s:\n s = s.replace('M', '')\n self.data['market_cap(M)'] = s\n\n s = self.share.get_ebitda()\n if s is not None:\n if 'B' in s:\n s = s.replace('B', '')\n s = float(s) * 1000\n s = str(s)\n elif 'M' in s:\n s = s.replace('M', '')\n self.data['ebitda(M)'] = s\n\n self.data['dividend_share'] = self.share.get_dividend_share()\n self.data['dividend_yield'] = self.share.get_dividend_yield()\n self.data['earnings_share'] = self.share.get_earnings_share()\n self.data['year_high'] = self.share.get_year_high()\n self.data['year_low'] = self.share.get_year_low()\n self.data['day50_moving_avg'] = self.share.get_50day_moving_avg()\n self.data['day200_moving_avg'] = self.share.get_200day_moving_avg()\n self.data['price_earnings_ratio'] = self.share.get_price_earnings_ratio()\n self.data['price_earnings_growth_ratio'] = self.share.get_price_earnings_growth_ratio()\n self.data['price_sales'] = self.share.get_price_sales()\n self.data['price_book'] = self.share.get_price_book()\n self.data['short_ratio'] = self.share.get_short_ratio()\n info = self.share.get_info()\n try:\n s = info['start'].split('-')\n e = info['end'].split('-')\n for i in range(0, 3):\n if s[i] == 'NaN':\n s[i] = '12'\n s[i] = int(s[i])\n if e[i] == 'NaN':\n e[i] = '12'\n e[i] = int(e[i])\n self.data['start'] = datetime.date(int(s[0]), int(s[1]), int(s[2]))\n self.data['end'] = datetime.date(int(e[0]), int(e[1]), int(e[2]))\n except KeyError:\n pass\n self.dtg_collected = datetime.datetime.today()\n except Exception as e:\n print(\"External Data Collection Failed (\" + self.ticker + ')')\n print(\"Exception: \" + str(e))\n print(\"\")\n raise e\n self.check_data()\n\n def add_industry_data(self, industry, sector):\n self.data['industry'] = industry\n self.data['sector'] = sector\n\n def check_data(self):\n error_string = \"\"\n total = 0\n fail = 0\n for i in self.data.keys():\n if self.data[i] == \"\" or self.data[i] is None:\n error_string += (i + ' not found' + '\\n')\n fail += 1\n total += 1\n self.data_completion = round(1 - (fail / total), 2)\n if error_string != \"\":\n error_string = (str(self.ticker) + ': ' + str(self.data_completion) + '\\n') + error_string\n return error_string\n\n def __str__(self):\n return_string = \"\"\n return_string += \"Ticker detail for : \" + str(self.ticker) + \" collected on \" + str(self.dtg_collected) + '\\n'\n return_string += str(self.data) + '\\n'\n return return_string + '\\n'\n\n def file_name(self):\n return str(self.ticker) + \".TickerDetail\"\n\n\nclass TickerDay(object):\n def __init__(self, ticker, year, month, day):\n self.ticker = ticker\n self.date = datetime.date(year, month, day)\n self.data = {'open': -1, 'close': -1, 'high': -1, 'low': -1, 'adj_close': -1, 'volume': -1}\n\n self.gather_external_data()\n self.data['q1'] = (self.data['low'] + (min(self.data['open'], self.data['close']))) / 2 # Arbitrary\n self.data['q2'] = (self.data['low'] + self.data['high'] + self.data['open'] + self.data[\n 'close']) / 4 # Arbitrary\n self.data['q3'] = (self.data['high'] + (max(self.data['open'], self.data['close']))) / 2 # Arbitrary\n self.data['change'] = self.data['close'] - self.data['open']\n self.data['spread'] = self.data['high'] - self.data['low']\n\n def gather_external_data(self):\n share = Share(self.ticker)\n try:\n raw = share.get_historical(str(self.date), str(self.date))[0]\n self.data['open'] = float(raw['Open'])\n self.data['close'] = float(raw['Close'])\n self.data['high'] = float(raw['High'])\n self.data['low'] = float(raw['Low'])\n self.data['adj_close'] = float(raw['Adj_Close'])\n self.data['volume'] = int(raw['Volume'])\n except Exception as e:\n tb = sys.exc_info()[2]\n f = (\"External Data Collection Failed (\" + self.ticker + ' on date ' + str(self.date) + ')\\n')\n f += (\"Exception: \" + str(e.with_traceback(tb)) + '\\n')\n raise ConnectionError(f)\n\n def __str__(self):\n return_string = \"%s on %s:\\n\" % (self.ticker, self.date)\n return_string += \"[open, high, low, close, adj_close, volume]\\n\"\n return_string += \"[%f, %f, %f, %f, %f, %f]\\n\" % (self.data['open'], self.data['high'],\n self.data['low'], self.data['close'],\n self.data['adj_close'], self.data['volume'],)\n return return_string + '\\n'\n\n def file_name(self):\n return str(self.ticker) + \"_\" + str(self.date) + \".TickerDay\"\n\n\nclass ControlDay(object):\n def __init__(self, year, month, day):\n self.date = datetime.date(year, month, day)\n # [open, high, low, close, volume]\n default = [-1.0, -1.0, -1.0, -1.0, -1.0]\n self.data = {'DOWI': default, 'DSRE': default, 'DXY': default, 'IDX': default,\n 'IGX': default, 'INX': default, 'IUX': default, 'NASX': default}\n self.collect_data()\n\n def collect_data(self):\n for i in self.data.keys():\n f = open('indicies/' + i + '.csv', 'r')\n for line in f:\n if line[:10] == str(self.date):\n self.data[i] = []\n raw = line[11:].split(',')\n for v in raw:\n self.data[i].append(float(v))\n break\n\n def check_data(self): # Call after creation to verify data is correct\n error_string = \"\"\n total = 0\n fail = 0\n for i in self.data.keys():\n if self.data[i] == [-1.0, -1.0, -1.0, -1.0, -1.0]:\n error_string += (str(self.date) + ' not found for index ' + str(i) + '\\n')\n fail += 1\n total += 1\n if error_string != \"\":\n error_string = \"Percent failing: \" + str(round(fail / total, 2)) + '\\n' + error_string\n return error_string\n\n def __str__(self):\n return_string = \"Control data for \" + str(self.date) + ':\\n'\n return_string += self.check_data()\n return_string += \"Index: [open, high, low, close, volume]\\n\"\n return_string += (\"INX: \" + str(self.data['INX']) + '\\n')\n return_string += (\"DOWI: \" + str(self.data['DOWI']) + '\\n')\n return_string += (\"NASX: \" + str(self.data['NASX']) + '\\n')\n return return_string + '\\n'\n\n def file_name(self):\n return str(self.date) + \".ControlDay\"\n\n\nclass ControlledTickerDay(object):\n def __init__(self, ticker, year, month, day):\n self.ticker = ticker\n self.date = datetime.date(year, month, day)\n self.control_day = None\n self.add_control_day()\n self.ticker_day = TickerDay(self.ticker, self.date.year, self.date.month, self.date.day)\n\n def add_control_day(self):\n file_name = str(self.date) + \".ControlDay\"\n directory = 'serialized-objects/ControlDay'\n try:\n f = open(directory + '/' + file_name, 'rb')\n c_d = pickle.load(f)\n assert isinstance(c_d, ControlDay)\n self.control_day = c_d\n f.close()\n except FileNotFoundError as e:\n raise e\n\n def composition_dump(self):\n self.control_day = None\n\n def file_name(self):\n return str(self.ticker) + \"_\" + str(self.date) + \".ControlledTickerDay\"\n\n def __str__(self):\n return_string = \"CTD for \" + str(self.date) + ':\\n'\n return_string += str(self.control_day)\n return_string += str(self.ticker_day)\n return return_string\n\n\nclass ControlledDividendDay(object):\n def __init__(self, ticker, div_amt, year, month, day, n=0):\n self.ticker = ticker\n self.ticker_detail = None\n self.add_ticker_detail()\n self.ex_date = datetime.date(year, month, day)\n self.check_dates()\n try:\n self.ex_d_ctd = ControlledTickerDay(ticker, year, month, day)\n except FileNotFoundError:\n raise AssertionError(\"The ex-dividend date isn't a trading day\")\n self.div_amt = div_amt\n self.padding_days = -1\n self.previous_days = []\n self.subsequent_days = []\n self.add_padding_days(n)\n\n def add_ticker_detail(self):\n file_name = str(self.ticker) + \".TickerDetail\"\n directory = 'serialized-objects/TickerDetail'\n try:\n f = open(directory + '/' + file_name, 'rb')\n t_d = pickle.load(f)\n assert isinstance(t_d, TickerDetail)\n self.ticker_detail = t_d\n f.close()\n except FileNotFoundError as e:\n raise e\n\n def check_dates(self):\n if self.ticker_detail is None:\n self.add_ticker_detail()\n start = self.ticker_detail.data['start']\n end = self.ticker_detail.data['end']\n if start is None or end is None:\n raise ValueError(\"Data not available for this ticker\")\n if not (start < self.ex_date < end):\n raise ValueError(\"Data not available for this date\")\n\n def add_padding_days(self, n=0):\n directory = 'serialized-objects/ControlledTickerDay'\n self.previous_days = []\n i = 1\n while len(self.previous_days) < (n + 1):\n td = datetime.timedelta(days=i)\n nd = self.ex_date - td\n file_name = self.ticker + \"_\" + str(nd) + \".ControlledTickerDay\"\n i += 1\n try:\n if file_name in os.listdir(directory):\n f = open(directory + '/' + file_name, 'rb')\n c_t_d = pickle.load(f)\n assert isinstance(c_t_d, ControlledTickerDay)\n f.close()\n else:\n c_t_d = ControlledTickerDay(self.ticker, nd.year, nd.month, nd.day)\n self.previous_days.append(c_t_d)\n except FileNotFoundError:\n continue\n\n self.subsequent_days = []\n while len(self.subsequent_days) < n:\n td = datetime.timedelta(days=i)\n nd = self.ex_date + td\n file_name = self.ticker + \"_\" + str(nd) + \".ControlledTickerDay\"\n i += 1\n try:\n if file_name in os.listdir(directory):\n f = open(directory + '/' + file_name, 'rb')\n c_t_d = pickle.load(f)\n assert isinstance(c_t_d, ControlledTickerDay)\n f.close()\n else:\n c_t_d = ControlledTickerDay(self.ticker, nd.year, nd.month, nd.day)\n self.subsequent_days.append(c_t_d)\n except FileNotFoundError:\n continue\n\n self.padding_days = n\n\n def dump_ticker_days(self):\n directory = 'serialized-objects/ControlledTickerDay'\n for c in self.previous_days + self.subsequent_days:\n c.composition_dump()\n f = open(directory + '/' + c.file_name(), 'wb')\n pickle.dump(c, f, pickle.HIGHEST_PROTOCOL)\n f.close()\n f = open(directory + '/' + self.ex_d_ctd.file_name(), 'wb')\n pickle.dump(self.ex_d_ctd, f, pickle.HIGHEST_PROTOCOL)\n f.close()\n self.padding_days = -1\n\n def file_name(self):\n return str(self.ticker) + \"_\" + str(self.ex_date) + \".ControlledDividendDay\"\n\n def __str__(self):\n return_string = \"%s paid %f on %s, incl %d padding days\\n\" % \\\n (self.ticker, self.div_amt, self.ex_date, self.padding_days)\n return_string += str(self.ticker_detail)\n return_string += str(self.ex_d_ctd)\n for d in self.previous_days + self.subsequent_days:\n return_string += str(d)\n return return_string + '\\n'\n\n\ndef dump_cont_div_day(to_dump):\n assert isinstance(to_dump, ControlledDividendDay)\n to_dump.dump_ticker_days()\n to_dump.ticker_detail = None\n directory = 'serialized-objects/ControlledDividendDay'\n file_name = to_dump.file_name()\n f = open(directory + '/' + file_name, 'wb')\n pickle.dump(to_dump, f, pickle.HIGHEST_PROTOCOL)\n f.close()\n\n\nclass VectorizedDividendDay(object):\n def __init__(self, cdd):\n assert (isinstance(cdd, ControlledDividendDay))\n self.cdd = cdd\n self.price = self.cdd.ex_d_ctd.ticker_day.data['close']\n\n def x_vector(self):\n x_vector = []\n x_vector += self.div_vector()\n x_vector += self.ticker_vector()\n x_vector += self.control_vector()\n return x_vector\n\n def y_value(self, tier):\n return functions.multi_ror(self.cdd)[tier]\n\n def y_values(self):\n return functions.multi_ror(self.cdd)\n\n def div_vector(self):\n div_amt = self.cdd.div_amt\n div_yield = div_amt / self.price\n return [div_amt, div_yield]\n\n def ticker_vector(self):\n ticker_detail = self.cdd.ticker_detail\n assert (isinstance(ticker_detail, TickerDetail))\n data = ticker_detail.data\n try:\n return_vector = [float(data['avg_daily_volume']), self.price / float(data['day200_moving_avg']),\n self.price / float(data['day50_moving_avg']), float(data['earnings_share']),\n float(data['market_cap(M)']), float(data['price_book']), float(data['short_ratio'])]\n\n low52 = float(data['year_low'])\n high52 = float(data['year_high'])\n return_vector.append((self.price - low52) / (high52 - low52))\n\n exchange = data['stock_exchange']\n if exchange == 'ASE':\n return_vector.append(float(1))\n else:\n return_vector.append(float(0))\n if exchange == 'NCM':\n return_vector.append(float(1))\n else:\n return_vector.append(float(0))\n if exchange == 'NGM':\n return_vector.append(float(1))\n else:\n return_vector.append(float(0))\n if exchange == 'NMS':\n return_vector.append(float(1))\n else:\n return_vector.append(float(0))\n if exchange == 'NYQ':\n return_vector.append(float(1))\n else:\n return_vector.append(float(0))\n if exchange == 'PCX':\n return_vector.append(float(1))\n else:\n return_vector.append(float(0))\n if exchange == 'PNK':\n return_vector.append(float(1))\n else:\n return_vector.append(float(0))\n except TypeError:\n return_vector = [None]\n\n return return_vector\n\n def control_vector(self):\n # Key order: DOWI, DSRE, DXY, IDX, IGX, INX, IUX, NASX\n # Ignore DXY\n self.cdd.previous_days[0].add_control_day()\n cd0 = self.cdd.previous_days[0].control_day\n assert (isinstance(cd0, ControlDay))\n self.cdd.ex_d_ctd.add_control_day()\n cd1 = self.cdd.ex_d_ctd.control_day\n assert (isinstance(cd1, ControlDay))\n return_vector = []\n\n for k in sorted(cd0.data.keys()):\n if k == 'DXY':\n continue\n change = functions.ind_ror(cd0.data[k][0], cd1.data[k][3])\n spread = (max(cd1.data[k][1], cd0.data[k][1]) - min(cd1.data[k][2], cd0.data[k][2])) \\\n / ((cd1.data[k][1] + cd0.data[k][1] + cd1.data[k][2] + cd0.data[k][2]) / 4)\n return_vector.append(change)\n return_vector.append(spread)\n return return_vector\n\n\nclass VectorizedCTickerDay(object):\n def __init__(self, controlled_ticker_day):\n self.feature_vector = [] # Feature vector,\n self.y = 0 # Actual value\n self.vectorize(controlled_ticker_day)\n\n @staticmethod\n def vectorize(c_t_d):\n vector = []\n # TODO convert a ControlledTickerDay to a vector\n return vector\n\n\nclass VectorizedTickerDay(object):\n pass\n\n\nclass VectorizedTickerDetail(object):\n def __init__(self, ticker_detail):\n self.vector = []\n\n\nclass VectorizedControlDay(object):\n def __init__(self, control_day):\n self.vector = []\n\n\nclass DataStats(object):\n def __init__(self, saved=''):\n if saved != '':\n f = open(saved, 'rb')\n old_data_list = pickle.load(f)\n self.monthly = old_data_list[0]\n self.quarterly = old_data_list[1]\n self.yearly = old_data_list[2]\n self.total = old_data_list[3]\n self.last_calculated = old_data_list[4]\n self.last_saved = old_data_list[5]\n else:\n self.monthly = {}\n self.quarterly = {}\n self.yearly = {}\n self.total = {}\n self.last_calculated = None\n self.last_saved = None\n\n def calc_stats(self):\n for folder in ['by-month', 'by-quarter', 'by-year', 'total']:\n for file in os.listdir(folder):\n if file == '.DS_Store':\n break\n size = os.path.getsize(folder + '/' + file)\n size /= 1024\n size /= 1024\n print(folder + '/' + file)\n f = open(folder + '/' + file, 'r')\n n = -1\n for line in f:\n n += 1\n if folder == 'by-month':\n self.monthly[file] = [n, size]\n elif folder == 'by-quarter':\n self.quarterly[file] = [n, size]\n elif folder == 'by-year':\n self.yearly[file] = [n, size]\n elif folder == 'total':\n self.total[file] = [n, size]\n f.close()\n self.last_calculated = datetime.datetime.today()\n\n def save(self):\n f = open('data_stats', 'wb')\n self.last_saved = datetime.datetime.today()\n data_list = [self.monthly, self.quarterly, self.yearly, self.total, self.last_calculated, self.last_saved]\n pickle.dump(data_list, f, pickle.HIGHEST_PROTOCOL)\n\n def __str__(self):\n return_string = \"\"\n for k in sorted(self.yearly.keys()):\n return_string += (str(k) + ': ' + str(self.yearly[k][0]) + ' entries, ' + str(self.yearly[k][1]) + ' MB\\n')\n for k in self.total.keys():\n return_string += (str(k) + ': ' + str(self.total[k][0]) + ' entries, ' + str(self.total[k][1]) + ' MB\\n')\n return return_string\n","sub_path":"objects.py","file_name":"objects.py","file_ext":"py","file_size_in_byte":20542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"1230495","text":"# 第1节 线性拟合（高级接口）\n\nimport tensorflow as tf\nimport numpy as np\nimport random\n\n# 创建训练和测试数据集，y = x * 3 + 1 + noise\ntrain_data = []\ntest_data = []\nprediction_data = []\n\nfor i in range(100000):\n x = random.random()\n noise = random.random() * 0.0001\n y = x * 3 + 1 + noise\n train_data.append({ 'x': x, 'y': y })\n\nfor i in range(500):\n x = random.random()\n noise = random.random() * 0.0001\n y = x * 3 + 1 + noise\n test_data.append({ 'x': x, 'y': y })\n\nfor i in range(100):\n x = random.random()\n noise = random.random() * 0.0001\n y = x * 3 + 1 + noise\n prediction_data.append({ 'x': x, 'y': y })\n\ntrain_x = [ i['x'] for i in train_data ]\ntrain_y = [ i['y'] for i in train_data ]\n\ntest_x = [ i['x'] for i in test_data ]\ntest_y = [ i['y'] for i in test_data ]\n\nprediction_x = { 'x': np.array([ i['x'] for i in prediction_data]) }\nprediction_y = [ i['y'] for i in prediction_data ]\n\n# from_tensor_slices参数是一个元组，第一个元素是features，第二个元素是labels\ntrain = tf.data.Dataset.from_tensor_slices(( { 'x': train_x }, train_y ))\ntest = tf.data.Dataset.from_tensor_slices(( { 'x': test_x }, test_y ))\n\n# 创建线性回归模型\nfeature_columns = [\n tf.feature_column.numeric_column(key = 'x')\n]\nestimator = tf.estimator.LinearRegressor(\n feature_columns = feature_columns,\n model_dir = 'tmp/model'\n )\n\ndef input_train(): # 训练输入函数，打乱，然后取100个，可以循环取，生成一个迭代器\n return train.shuffle(1000).batch(100).repeat().make_one_shot_iterator().get_next()\n\ndef input_test(): # 评估输入函数\n return test.shuffle(1000).batch(100).make_one_shot_iterator().get_next()\n\ndef main(argv):\n # 使用数据训练模型，训练2000次\n estimator.train(input_fn = input_train, steps = 2000)\n\n # 评估模型\n eval_result = estimator.evaluate(input_fn = input_test)\n print(eval_result)\n\n # 使用模型进行预测\n predict_input_fn = tf.estimator.inputs.numpy_input_fn(prediction_x, shuffle = False)\n predict_results = estimator.predict(input_fn = predict_input_fn)\n for i, prediction in enumerate(predict_results):\n print('x=%s, y=%s, prediction=%s, error=%s'%(\n prediction_x['x'][i], \n prediction_y[i], \n prediction['predictions'][0], \n str((prediction_y[i] - prediction['predictions'][0]) / prediction_y[i] * 100) + '%'\n ))\n\nif __name__ == \"__main__\":\n tf.logging.set_verbosity(tf.logging.INFO)\n tf.app.run(main = main)\n","sub_path":"第三章 Tensorflow实例/第1节 线性拟合（高级接口）/linear_regression.py","file_name":"linear_regression.py","file_ext":"py","file_size_in_byte":2475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"430998012","text":"#!/usr/bin/env python\n\n# Copyright (C) 2010-2011 Association of Universities for Research in Astronomy (AURA)\n\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n\n# 1. Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n\n# 2. Redistributions in binary form must reproduce the above\n# copyright notice, this list of conditions and the following\n# disclaimer in the documentation and/or other materials provided\n# with the distribution.\n\n# 3. The name of AURA and its representatives may not be used to\n# endorse or promote products derived from this software without\n# specific prior written permission.\n\n# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED\n# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF\n# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT,\n# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS\n# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\n# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR\n# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE\n# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH\n# DAMAGE.\n\n\"\"\"\nThis script reads UTC times from columns in the INT_TIMES table in the FITS\nfiles provided to it on the command line, converts those times to TDB, and\nsaves the TDB times in another set of columns in the INT_TIMES table. The\ninput FITS files will be modified in-place.\n\"\"\"\nimport logging\nimport sys\n\nfrom jwst.lib.utc_to_tdb import utc_tdb\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\nhandler = logging.StreamHandler()\nhandler.setLevel(logging.DEBUG)\nlogger.addHandler(handler)\n\n\nif __name__ == '__main__':\n if len(sys.argv) <= 1:\n raise ValueError('missing filename argument(s)')\n for filename in sys.argv[1:]:\n logger.info('Populating TDB columns in INT_TIMES table in {}'\n .format(filename))\n (tdb_start_times, tdb_mid_times, tdb_end_times) = utc_tdb(filename)\n","sub_path":"scripts/utc_to_bary.py","file_name":"utc_to_bary.py","file_ext":"py","file_size_in_byte":2376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"464491692","text":"import cv2\nfrom matplotlib import pyplot as plt\n\ndef on_press(event):\n print('you pressed', event.button, int(event.xdata), int(event.ydata))\n\nfig = plt.figure()\ncid = fig.canvas.mpl_connect('button_press_event', on_press)\n\nimageFile = './data/lena.jpg'\nimgGray = cv2.imread(imageFile, cv2.IMREAD_GRAYSCALE)\nplt.axis('off')\n\nplt.imshow(imgGray, cmap = \"gray\", interpolation='bicubic')\nplt.show()\n","sub_path":"opencv/mouseEvent.py","file_name":"mouseEvent.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"127595563","text":"# ------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for\n# license information.\n# -------------------------------------------------------------------------\n\nimport sys\nimport datetime\nimport logging\nimport functools\nimport platform\nfrom typing import Optional, Dict\ntry:\n from urlparse import urlparse\nexcept ImportError:\n from urllib.parse import urlparse\n\nfrom uamqp import authentication, types\n\nfrom ..exceptions import ServiceBusError\nfrom .._version import VERSION\nfrom .constants import (\n JWT_TOKEN_SCOPE,\n TOKEN_TYPE_JWT,\n TOKEN_TYPE_SASTOKEN,\n DEAD_LETTER_QUEUE_SUFFIX,\n TRANSFER_DEAD_LETTER_QUEUE_SUFFIX,\n USER_AGENT_PREFIX\n)\n\n_log = logging.getLogger(__name__)\n\n\nclass UTC(datetime.tzinfo):\n \"\"\"Time Zone info for handling UTC\"\"\"\n\n def utcoffset(self, dt):\n \"\"\"UTF offset for UTC is 0.\"\"\"\n return datetime.timedelta(0)\n\n def tzname(self, dt):\n \"\"\"Timestamp representation.\"\"\"\n return \"Z\"\n\n def dst(self, dt):\n \"\"\"No daylight saving for UTC.\"\"\"\n return datetime.timedelta(hours=1)\n\n\ntry:\n from datetime import timezone # pylint: disable=ungrouped-imports\n\n TZ_UTC = timezone.utc # type: ignore\nexcept ImportError:\n TZ_UTC = UTC() # type: ignore\n\n\ndef utc_from_timestamp(timestamp):\n return datetime.datetime.fromtimestamp(timestamp, tz=TZ_UTC)\n\n\ndef utc_now():\n return datetime.datetime.now(tz=TZ_UTC)\n\n\ndef parse_conn_str(conn_str):\n endpoint = None\n shared_access_key_name = None\n shared_access_key = None\n entity_path = None\n for element in conn_str.split(';'):\n key, _, value = element.partition('=')\n if key.lower() == 'endpoint':\n endpoint = value.rstrip('/')\n elif key.lower() == 'sharedaccesskeyname':\n shared_access_key_name = value\n elif key.lower() == 'sharedaccesskey':\n shared_access_key = value\n elif key.lower() == 'entitypath':\n entity_path = value\n if not all([endpoint, shared_access_key_name, shared_access_key]):\n raise ValueError(\"Invalid connection string\")\n return endpoint, shared_access_key_name, shared_access_key, entity_path\n\n\ndef build_uri(address, entity):\n parsed = urlparse(address)\n if parsed.path:\n return address\n if not entity:\n raise ValueError(\"No Service Bus entity specified\")\n address += \"/\" + str(entity)\n return address\n\n\ndef create_properties(user_agent=None):\n # type: (Optional[str]) -> Dict[types.AMQPSymbol, str]\n \"\"\"\n Format the properties with which to instantiate the connection.\n This acts like a user agent over HTTP.\n\n :param str user_agent: If specified, this will be added in front of the built-in user agent string.\n\n :rtype: dict\n \"\"\"\n properties = {}\n properties[types.AMQPSymbol(\"product\")] = USER_AGENT_PREFIX\n properties[types.AMQPSymbol(\"version\")] = VERSION\n framework = \"Python/{}.{}.{}\".format(\n sys.version_info[0], sys.version_info[1], sys.version_info[2]\n )\n properties[types.AMQPSymbol(\"framework\")] = framework\n platform_str = platform.platform()\n properties[types.AMQPSymbol(\"platform\")] = platform_str\n\n final_user_agent = \"{}/{} {} ({})\".format(\n USER_AGENT_PREFIX, VERSION, framework, platform_str\n )\n if user_agent:\n final_user_agent = \"{} {}\".format(user_agent, final_user_agent)\n\n properties[types.AMQPSymbol(\"user-agent\")] = final_user_agent\n return properties\n\n\ndef renewable_start_time(renewable):\n try:\n return renewable._received_timestamp_utc # pylint: disable=protected-access\n except AttributeError:\n pass\n try:\n return renewable._session_start # pylint: disable=protected-access\n except AttributeError:\n raise TypeError(\"Registered object is not renewable.\")\n\n\ndef create_authentication(client):\n # pylint: disable=protected-access\n try:\n # ignore mypy's warning because token_type is Optional\n token_type = client._credential.token_type # type: ignore\n except AttributeError:\n token_type = TOKEN_TYPE_JWT\n if token_type == TOKEN_TYPE_SASTOKEN:\n auth = authentication.JWTTokenAuth(\n client._auth_uri,\n client._auth_uri,\n functools.partial(client._credential.get_token, client._auth_uri),\n token_type=token_type,\n timeout=client._config.auth_timeout,\n http_proxy=client._config.http_proxy,\n transport_type=client._config.transport_type,\n )\n auth.update_token()\n return auth\n return authentication.JWTTokenAuth(\n client._auth_uri,\n client._auth_uri,\n functools.partial(client._credential.get_token, JWT_TOKEN_SCOPE),\n token_type=token_type,\n timeout=client._config.auth_timeout,\n http_proxy=client._config.http_proxy,\n transport_type=client._config.transport_type,\n )\n\n\ndef generate_dead_letter_entity_name(\n queue_name=None,\n topic_name=None,\n subscription_name=None,\n transfer_deadletter=False\n):\n entity_name = queue_name if queue_name else (topic_name + \"/Subscriptions/\" + subscription_name)\n entity_name = \"{}{}\".format(\n entity_name,\n TRANSFER_DEAD_LETTER_QUEUE_SUFFIX if transfer_deadletter else DEAD_LETTER_QUEUE_SUFFIX\n )\n\n return entity_name\n\n\ndef transform_messages_to_sendable_if_needed(messages):\n \"\"\"\n This method is to convert single/multiple received messages to sendable messages to enable message resending.\n \"\"\"\n # pylint: disable=protected-access\n try:\n msgs_to_return = []\n for each in messages:\n try:\n msgs_to_return.append(each._to_outgoing_message())\n except AttributeError:\n msgs_to_return.append(each)\n return msgs_to_return\n except TypeError:\n try:\n return messages._to_outgoing_message()\n except AttributeError:\n return messages\n","sub_path":"sdk/servicebus/azure-servicebus/azure/servicebus/_common/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"411443245","text":"import json\nimport time\nimport requests\nimport os.path\nimport hashlib\nimport pprint\n\nurl = 'https://raw.githubusercontent.com/mledoze/countries/master/countries.json'\n\ncou_list = []\nresponse = requests.get(url).json()\nfor countries in response:\n cou_list.append(countries[\"name\"][\"common\"])\n\n\nclass Iterator:\n\n def __init__(self, c_list):\n self.c_list = c_list\n self.start = 0\n\n def __iter__(self):\n return self\n\n def __next__(self):\n try:\n c_name = self.c_list[self.start]\n c_url = f'https://wikipedia.org/wiki/{self.c_list[self.start].replace(\" \", \"_\")}'\n except IndexError:\n raise StopIteration\n self.start += 1\n return f'Country: {c_name}, url: {c_url}'\n\n\nif __name__ == '__main__':\n with open('countries.json', 'w', encoding='utf-8-sig') as c:\n final_list = []\n for item in Iterator(cou_list):\n final_list.append(item)\n print(item)\n json.dump(final_list, c, ensure_ascii=False, indent=2)\n\n def gen(path):\n with open(path, encoding='utf-8-sig') as p:\n for i in p:\n hash_object = hashlib.md5(i.encode())\n yield hash_object.hexdigest()\n\n file_path = os.path.abspath('countries.json')\n generator = gen(file_path)\n\n for item in generator:\n print(item)","sub_path":"iter_countries.py","file_name":"iter_countries.py","file_ext":"py","file_size_in_byte":1358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"331838702","text":"from PyQt4.QtGui import * #These two lines import all of the built in PyQt code\r\nfrom PyQt4.QtCore import *\r\n\r\nfrom derived_lesson_menus import * #This contains all of the lesson menus which the buttons in the class connect to\r\n\r\n#This is the template for the lesson topic menu (the one before the menus for each topic)\r\nclass LessonMenuWidget(QMainWindow):\r\n #Constructor\r\n def __init__(self):\r\n #Return a proxy object that delegates method calls to a parent or sibling class of type.\r\n super().__init__()\r\n\r\n #Maximises the screen when it is displayed\r\n self.showMaximized()\r\n\r\n #Sets the background colour of the window to white\r\n pal = QPalette()\r\n pal.setColor(QPalette.Background, Qt.white)\r\n self.setAutoFillBackground(True)\r\n self.setPalette(pal)\r\n\r\n #These are the buttons which each connect to a different child menu\r\n self.t1 = QPushButton(\"Trigonometry 1\")\r\n #Sets the size of hte buttons - having a minimum\r\n # and maximum size helps prevent overlapping on different sizes of screen\r\n self.t1.setMinimumWidth(90)\r\n self.t1.setMinimumHeight(110)\r\n #This sets the font size and house style of the text in the QPushButton\r\n self.t1.setFont(QFont(\"Courier\", 40))\r\n \r\n self.t1_pic = QLabel()\r\n #Imports the image from the file below\r\n self.t1_pic.setPixmap(QPixmap(\"t1_pic\"))\r\n #Aligns the image in the centre of its designated space\r\n self.t1_pic.setAlignment(Qt.AlignCenter)\r\n \r\n self.t2 = QPushButton(\"Trigonometry 2\")\r\n self.t2.setMinimumWidth(90)\r\n self.t2.setMinimumHeight(110)\r\n self.t2.setFont(QFont(\"Courier\", 40))\r\n \r\n self.t2_pic = QLabel()\r\n self.t2_pic.setPixmap(QPixmap(\"t2_pic\"))\r\n self.t2_pic.setAlignment(Qt.AlignCenter)\r\n \r\n self.pyt = QPushButton(\"Pythagoras\")\r\n self.pyt.setMinimumWidth(90)\r\n self.pyt.setMinimumHeight(110)\r\n self.pyt.setFont(QFont(\"Courier\", 40))\r\n \r\n self.pyt_pic = QLabel()\r\n self.pyt_pic.setPixmap(QPixmap(\"pyt_pic\"))\r\n self.pyt_pic.setAlignment(Qt.AlignCenter)\r\n \r\n self.pytrig = QPushButton(\"Vectors\")\r\n self.pytrig.setMinimumWidth(90)\r\n self.pytrig.setMinimumHeight(110)\r\n self.pytrig.setFont(QFont(\"Courier\", 40))\r\n \r\n self.pytrig_pic = QLabel()\r\n self.pytrig_pic.setPixmap(QPixmap(\"pytrig_pic\"))\r\n self.pytrig_pic.setAlignment(Qt.AlignCenter)\r\n \r\n self.sum = QPushButton(\"Summary\")\r\n self.sum.setMinimumWidth(90)\r\n self.sum.setMinimumHeight(110)\r\n self.sum.setFont(QFont(\"Courier\", 40))\r\n \r\n self.sum_pic = QLabel()\r\n self.sum_pic.setPixmap(QPixmap(\"sum_pic\"))\r\n self.sum_pic.setAlignment(Qt.AlignCenter)\r\n\r\n #This button returns to the previous window, unlike the other buttons,\r\n #so it is a different colour to make it clear that it serves a different\r\n #purpose\r\n self.back = QPushButton(\"Return\")\r\n self.back.setMinimumWidth(90)\r\n self.back.setMinimumHeight(110)\r\n self.back.setFont(QFont(\"Courier\", 40))\r\n #This overrides the style of the other buttons\r\n self.back.setStyleSheet(\"QPushButton {background-color: red; color: white; font-size: 20;}\")\r\n \r\n self.lesson_label = QLabel(\"Lessons\")\r\n self.lesson_label.setFont(QFont(\"Courier\", 40))\r\n \r\n self.select = QLabel(\"Please select a topic: \")\r\n self.select.setFont(QFont(\"Courier\", 25))\r\n \r\n self.title_pic = QLabel()\r\n self.title_pic.setPixmap(QPixmap(\"title_lessons\"))\r\n\r\n #This sets the background colour and font colour of all the QPushButtons\r\n self.setStyleSheet(\"QPushButton {background-color: #A3C1DA; color: blue; font-size: 20;}\")\r\n\r\n #Sets the layout as a QGridLayout so all the widgets can be positioned easily\r\n self.layout = QGridLayout()\r\n\r\n #Adds all of the widgets to the layout\r\n self.layout.addWidget(self.title_pic, 0, 0) #These numbers position the widgets\r\n self.layout.addWidget(self.t1_pic, 1, 0)\r\n self.layout.addWidget(self.t1, 1, 1)\r\n self.layout.addWidget(self.t2, 2, 0)\r\n self.layout.addWidget(self.t2_pic, 2, 1)\r\n self.layout.addWidget(self.pyt_pic, 3, 0)\r\n self.layout.addWidget(self.pyt, 3, 1)\r\n self.layout.addWidget(self.pytrig, 4, 0)\r\n self.layout.addWidget(self.pytrig_pic, 4, 1)\r\n self.layout.addWidget(self.sum_pic, 5, 0)\r\n self.layout.addWidget(self.sum, 5, 1)\r\n self.layout.addWidget(self.back, 6, 0)\r\n\r\n #These 3 lines set _centralwidget as the layout to be used\r\n #It needs to be declared as a QWidget because the class it's in is a QMainWindow\r\n self._centralwidget = QWidget()\r\n self._centralwidget.setLayout(self.layout)\r\n self.setCentralWidget(self._centralwidget)\r\n\r\n #The connections for the buttons\r\n self.t1.clicked.connect(self.selected_t1) #These are the methods executed when the button is clicked\r\n self.t2.clicked.connect(self.selected_t2)\r\n self.pyt.clicked.connect(self.selected_pyt)\r\n self.pytrig.clicked.connect(self.selected_pytrig)\r\n self.sum.clicked.connect(self.selected_sum)\r\n self.back.clicked.connect(self.selected_back)\r\n\r\n #These open the selected menus and display them\r\n def selected_t1(self):\r\n trig_1_widget = Trigonometry1()\r\n trig_1_widget.show()\r\n trig_1_widget._raise()\r\n trig_1_widget.showMaximized()\r\n\r\n def selected_t2(self):\r\n trig_2_widget = Trigonometry2()\r\n trig_2_widget.show()\r\n trig_2_widget._raise()\r\n trig_2_widget.showMaximized()\r\n\r\n def selected_pyt(self):\r\n pythagoras_widget = Pythagoras()\r\n pythagoras_widget.show()\r\n pythagoras_widget._raise()\r\n pythagoras_widget.showMaximized()\r\n\r\n def selected_pytrig(self):\r\n pyth_trig_widget = PythagTrig()\r\n pyth_trig_widget.show()\r\n pyth_trig_widget._raise()\r\n pyth_trig_widget.showMaximized()\r\n\r\n def selected_sum(self):\r\n summary_widget = Summary()\r\n summary_widget.show()\r\n summary_widget._raise()\r\n summary_widget.showMaximized()\r\n\r\n #This closes the window and returns the user to the previous window\r\n def selected_back(self):\r\n self.close()\r\n","sub_path":"Implementation/lesson_menu_widget.py","file_name":"lesson_menu_widget.py","file_ext":"py","file_size_in_byte":6510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"571665598","text":"import pymysql\r\ndb = pymysql.connect('127.0.0.1', 'root', 'root', 'python')\r\ncursor = db.cursor()\r\n# sql = \"\"\"\r\n# CREATE TABLE city(\r\n# id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,\r\n# city VARCHAR(255) NOT NULL,\r\n# park VARCHAR(255) NOT NULL,\r\n# location_lat FLOAT,\r\n# location_lng FLOAT,\r\n# address VARCHAR(255),\r\n# stree_id VARCHAR(255),\r\n# uid VARCHAR(255),\r\n# create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP\r\n# )\r\n# \"\"\"\r\n\r\nclass Sql():\r\n @classmethod\r\n def insert_city(cls, city,park, location_lat, location_lng, address, street_id, uid):\r\n sql = 'INSERT INTO city(city, park, location_lat, location_lng, address, street_id, uid) VALUES (%(city)s,%(park)s,%(location_lat)s,%(location_lng)s,%(address)s,%(street_id)s,%(uid)s)'\r\n value = {\r\n 'city': city,\r\n 'park': park,\r\n 'location_lat': location_lat,\r\n 'location_lng': location_lng,\r\n 'address': address,\r\n 'street_id': street_id,\r\n 'uid': uid,\r\n }\r\n try:\r\n cursor.execute(sql, value)\r\n db.commit()\r\n print(\"插入成功\")\r\n except Exception as e:\r\n print(e)\r\n print(\"插入失败\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"other/mysqlAPI.py","file_name":"mysqlAPI.py","file_ext":"py","file_size_in_byte":1235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"153664570","text":"import numpy as np \r\nfrom math import log, exp\r\nfrom matplotlib import pyplot as plt\r\nfrom generator import nro_random\r\nfrom graph_decoration import *\r\n\r\ndef exponencial(alfa):\r\n r = nro_random() # Genera nro pseudoaleatorio\r\n x = ti_exponencial(alfa, r)\r\n return x\r\n\r\ndef ti_exponencial(alfa, r):\r\n e = 1 / alfa # Esperanza\r\n x = - e * log( r ) # Transformada inversa\r\n return x\r\n\r\ndef generate_exponencial(alfa, n):\r\n corrida = []\r\n\r\n for i in range(n): \r\n x = exponencial(alfa) # Genero nro con dist. Exponencial\r\n corrida.append( x ) # Secuencia de nros con dist. Exponencial \r\n\r\n return corrida\r\n\r\ndef graph_exponencial(corrida, alfa):\r\n fig1 = plt.figure(\"Distribucion exponencial\")\r\n fig1.subplots_adjust(hspace=0.46, top = 0.78, bottom = 0.27, wspace=0.30, left=0.05, right=0.98)\r\n\r\n # =================== Histograma ===================\r\n fig1.add_subplot(1,3,1)\r\n plt.hist(corrida, color = exp_hist, bins = int(np.sqrt(len(corrida))), edgecolor = exp_borde_hist, linewidth=1)\r\n plt.xlabel('Valor de x')\r\n plt.ylabel('Frecuencia absoluta')\r\n poner_fondo_color()\r\n\r\n # =================== Funcion de densidad\r\n corrida.sort()\r\n f_den = []\r\n for x in corrida:\r\n f = alfa * exp(- alfa * x) # Funcion densidad exponencial\r\n f_den.append(f)\r\n\r\n fig1.add_subplot(1,3,2)\r\n plt.plot(corrida, f_den, color = exp_curva, label = 'f(x) Dist. Exponencial', linewidth=2.5)\r\n poner_fondo_color()\r\n plt.xlabel('Valor de x')\r\n plt.ylabel('f(x) - funcion de densidad')\r\n plt.legend()\r\n\r\n # =================== Frecuencia acumulada\r\n f_acum = []\r\n for x in corrida:\r\n r = 1 - exp( - alfa * x ) # F(x)\r\n f_acum.append(r)\r\n\r\n fig1.add_subplot(1,3,3)\r\n plt.plot(corrida, f_acum, color = exp_curva, label = 'F(x) Dist. Exponencial', linewidth=2.5)\r\n plt.hlines(y= 1, xmin=-0.3, xmax= int(corrida[-1]), color = exp_puntos, linestyle = '--')\r\n plt.xlabel('Valor de x')\r\n plt.ylabel('F(x) - Frecuencia acumulada')\r\n plt.legend(loc = 'best')\r\n poner_fondo_color()\r\n plt.show()\r\n\r\n\r\n\r\n","sub_path":"TP 2.2 - Generadores VA/Codigo/b_exponencial.py","file_name":"b_exponencial.py","file_ext":"py","file_size_in_byte":2169,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"498191485","text":"from django.shortcuts import render, redirect\n\nfrom ResumeProject import settings\nfrom .models import User\n\n\ndef index(req):\n lang = req.COOKIES.get('lang')\n if not lang:\n lang = req.LANGUAGE_CODE\n redirect_url = f'/{lang}/' if lang != settings.LANGUAGE_CODE else '/'\n if req.path != redirect_url:\n response = redirect(to=redirect_url)\n else:\n user = User.objects.filter(id=1)[0]\n if user:\n context = {\n 'req': req,\n 'browse_lang': req.LANGUAGE_CODE,\n 'profile_image': user.avatar.url,\n 'fio': f'{user.first_name} {user.last_name}',\n 'specs': user.specialization,\n 'phone': user.phone,\n 'email': user.email,\n 'tg': user.tg,\n 'inst': user.inst,\n 'linkedin': user.linkedin,\n 'git': user.git,\n 'about': user.about,\n 'projects': user.project,\n 'work_exp': user.experience,\n 'skills': user.skill,\n 'edus': user.education,\n 'langs': user.language,\n 'musics': user.music,\n }\n else:\n context = {}\n response = render(req, 'main_page/templates/index.html', context)\n response.set_cookie(key='lang', value=lang, samesite='Lax', max_age=60*60*24*30, path='/', )\n return response\n","sub_path":"main_page/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"87"} +{"seq_id":"137762027","text":"import math, collections\n\n\ndef answer():\n perims = []\n for a in range(1, 500):\n for b in range(1, a):\n c = math.sqrt(a**2 + b**2)\n if c == int(c):\n perims.append(a+b+c)\n \n return int(collections.Counter(\n p for p in perims if p <= 1000).most_common(1)[0][0])\n\nif __name__=='__main__':\n print(answer())","sub_path":"pe039.py","file_name":"pe039.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"283868056","text":"\"\"\"\nClass container for all call graph operations\n\"\"\"\n\nfrom os import path\nfrom pprint import pformat\nfrom functools import reduce\nimport llvm_wrapper\n\n\nclass CallGraph:\n \"\"\"\n All information about the callgraph from a specific bitcode file\n \"\"\"\n\n def __init__(self, bitcodefile, errortargets):\n assert path.isfile(bitcodefile)\n self.graph = llvm_wrapper.extract_callgraph(bitcodefile, errortargets)\n self.topology = llvm_wrapper.list_all_funcs_topological(bitcodefile)\n\n def __contains__(self, item):\n return item in self.graph\n\n def __str__(self):\n return pformat(self.graph)\n\n def __getitem__(self, key):\n return self.graph[key]\n\n def isSymbolicEncapsulable(self, function):\n \"\"\"\n Checks, if a function can be encapsulated symbolically\n \"\"\"\n return (not self[function]['hasdoubleptrarg'] and\n not self[function]['hasfuncptrarg'] and\n not self[function]['isexternal'])\n\n def getFlattenedInvertedTopology(self):\n \"\"\"\n Returns a sort of inverted topologically ordered list of all functions\n \"\"\"\n # Nested lists of circles and SCCs are simply flattened\n flattened = []\n for topo in self.topology:\n if isinstance(topo, str):\n flattened.append(topo)\n else:\n flattened.extend(topo)\n return flattened\n\n def listSymbolicEncapsulable(self, removemain=True):\n \"\"\"\n Returns a sort of inverted topologically ordered list of all function\n names, that can be symbolically encapsulated by MACKE\n \"\"\"\n flattened = self.getFlattenedInvertedTopology()\n return [t for t in flattened if (self.isSymbolicEncapsulable(t) or (\n not removemain and t == \"main\"))]\n\n def findFunctionsWithAssertions(self, removemain=True):\n flattened = self.getFlattenedInvertedTopology()\n return [t for t in flattened if ((self.isSymbolicEncapsulable(t) \\\n and self[t]['hasassertion']) \\\n or (not removemain and t == 'main' and self[t]['hasassertion']))]\n\n def groupIndependentCalls(self, removemain=True):\n \"\"\"\n Returns a topologically ordered list of (caller, callee)-tuples\n nested in sublists, that can be analyzed in parallel processes\n \"\"\"\n # Probably the result of this method is not the optimal solution\n # considering the number parallel executable pairs. But I don't\n # know a better algorithm to generate them. Maybe later ...\n\n units = self.groupIndependentCallees()\n\n # Convert the unit list of functions to a list of callers\n result = []\n for unit in units:\n pairs = []\n for callee in unit:\n for caller in self[callee]['calledby']:\n if ((not removemain and caller == \"main\") or\n (self.isSymbolicEncapsulable(caller))):\n pairs.append((caller, callee))\n if pairs:\n result.append(sorted(pairs))\n\n # (partially) assert correctness of the result\n for res in result:\n assert res\n callers, callees = set(), set()\n for (caller, callee) in res:\n if caller != callee:\n callers.add(caller)\n callees.add(callee)\n assert callers.isdisjoint(callees)\n\n return result\n\n def groupIndependentCallees(self):\n \"\"\"\n Group the topological ordered function list in independent units\n \"\"\"\n units = []\n independent = set()\n earlier_calls = set()\n\n for topo in self.topology:\n if isinstance(topo, str):\n if topo in earlier_calls:\n # Add all function, that are called earlier\n if independent:\n units.append(sorted(list(independent)))\n # And restart the search\n independent = set()\n earlier_calls = set()\n\n # Mark this function as indepent\n independent.add(topo)\n # Mark all function called by now\n earlier_calls |= set(self[topo]['calledby'])\n\n else:\n # Add all previous independent functions\n if independent:\n units.append(sorted(list(independent)))\n independent = set()\n\n # Split each part of a scc in a separate run\n for arc in sorted(topo):\n units.append([arc])\n\n # Add all remaining elements\n if independent:\n units.append(list(independent))\n\n return units\n\n def getFunctionsWithNoCaller(self, removemain=True):\n \"\"\"\n Returns a set with all functions, that do not have any caller\n \"\"\"\n zeroCalls = [func for func in self.getFlattenedInvertedTopology()\n if (not self[func][\"calledby\"]) and ((not func == \"main\" and removemain) or removemain==False)]\n oneCalls = [func for func in self.getFlattenedInvertedTopology() if removemain and len(self[func][\"calledby\"]) == 1 and self[func][\"calledby\"][0] == \"main\"]\n return zeroCalls + oneCalls\n\n def findTargetReachableNodes(self, targets, removemain):\n \"\"\"\n Return the set of nodes including the targets which calls directly or indirectly\n one of the target functions\n \"\"\"\n\n result = set(targets)\n pairs = self.groupIndependentCalls(removemain)\n pairs = reduce (lambda x, y: x+y, pairs, [])\n # do not add those already in result such that the recursive calls may cause the following code non-stop\n newSet = [caller for caller, callee in pairs if callee in result and (not caller in result)]\n while len(newSet) > 0:\n result.update(newSet)\n newSet = [caller for caller, callee in pairs if callee in result and (not caller in result)]\n return result\n\n def findSubgraphEdges(self, nodes, removemain):\n \"\"\"\n Return edges whose nodes (starting, ending) are in the nodes.\n Thus only extract the subgraph of the original graph\n \"\"\"\n pairs = self.groupIndependentCalls(removemain)\n pairs = reduce(lambda x, y: x+y, pairs, [])\n result = [(caller, callee) for caller, callee in pairs if caller in nodes and callee in nodes]\n return result;\n","sub_path":"py/call_graph.py","file_name":"call_graph.py","file_ext":"py","file_size_in_byte":6569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"434246602","text":"class Contact:\n contacts = []\n next_id = 1\n \n def __str__(self):\n return f\"First name: {self.first_name}, Last name: {self.last_name}, Email: {self.email}, Note: {self.note}\"\n\n def __init__(self, given_name, surname, user_email, user_note):\n \"\"\"This method should initialize the contact's attributes\"\"\"\n self.first_name = given_name\n self.last_name = surname\n self.email = user_email\n self.note = user_note\n self.id = Contact.next_id\n Contact.next_id += 1\n\n @classmethod\n def convert_string(cls, self, attribute_type):\n attributes = {'first_name': self.first_name, 'last_name': 'last_name', 'email': 'email', 'note': 'note'}\n\n @classmethod\n def create(cls, given_name, surname, user_email, user_note):\n \"\"\"This method should call the initializer,\n store the newly created contact, and then return it\n \"\"\"\n new_contact = Contact(given_name, surname, user_email, user_note)\n cls.contacts.append(new_contact)\n return new_contact\n\n @classmethod\n def all(cls):\n \"\"\"This method should return all of the existing contacts\"\"\"\n for contact in Contact.contacts:\n print(contact)\n\n @classmethod\n def find(cls, search_id):\n \"\"\" This method should accept an id as an argument\n and return the contact who has that id\n \"\"\"\n for contact in Contact.contacts:\n if search_id == contact.id:\n print(contact)\n return contact\n else:\n return False\n\n def update(self, attribute_type, attribute_update = ''):\n \"\"\" This method should allow you to specify\n 1. which of the contact's attributes you want to update\n 2. the new value for that attribute\n and then make the appropriate change to the contact\n \"\"\"\n if attribute_type == 'first_name':\n self.first_name = attribute_update\n elif attribute_type == 'last_name':\n self.last_name = attribute_update\n elif attribute_type == 'email':\n self.email = attribute_update\n elif attribute_type == 'note':\n self.note = attribute_update\n\n\n @classmethod\n def find_by(cls, attribute_type, search_by):\n \"\"\"This method should work similarly to the find method above\n but it should allow you to search for a contact using attributes other than id\n by specifying both the name of the attribute and the value\n eg. searching for 'first_name', 'Betty' should return the first contact named Betty\n \"\"\"\n for contact in Contact.contacts:\n if attribute_type == 'first_name' and contact.first_name == search_by:\n return contact\n elif attribute_type == 'last_name' and contact.last_name == search_by:\n return contact\n elif attribute_type == 'email' and contact.email == search_by:\n return contact\n elif attribute_type == 'note' and contact.note == search_by:\n return contact\n print(f\"Cannot find user based on {attribute_type}, please check that you have the right identifier\")\n return False\n\n @classmethod\n def delete_all(cls):\n \"\"\"This method should delete all of the contacts\"\"\"\n Contact.contacts.clear()\n\n\n def full_name(self):\n \"\"\"Returns the full (first and last) name of the contact\"\"\"\n return f'{self.first_name} {self.last_name}'\n\n\n def delete(self):\n \"\"\"This method should delete the contact\n HINT: Check the Array class docs for built-in methods that might be useful here\n \"\"\"\n Contact.contacts.remove(self)\n # Feel free to add other methods here, if you need them.\n\n\n# # Creating new contacts and checking create function\n# contact1 = Contact.create('Betty', 'Maker', 'bettymakes@bitmakerlabs.com', 'Loves Pokemon')\n# contact2 = Contact.create('Bit', 'Bot', 'bitbot@bitmakerlabs.com', 'beep boop')\n# print(contact1.first_name)\n# print(contact1.id)\n# print('')\n# print(contact2.first_name)\n# print(contact2.id)\n# print('')\n\n# # Testing all and find functions\n# print(len(Contact.contacts))\n# Contact.all()\n# print(Contact.find(1))\n# print('')\n\n# # Testing update function\n# print(contact1.update('full name'))\n# contact1.update('first_name', 'stanley')\n# print(contact1.first_name)\n# print('')\n\n# # Testing find by\n# print(Contact.find_by('first_name', 'stanley'))\n# print(Contact.find_by('first_name', 'betty'))\n# print('')\n\n# # Testing delete all\n# # Contact.delete_all() # Commented out so that delete function below works\n# print(Contact.contacts) # Should return an empty list\n# print('')\n\n# # Testing full name function\n# print(contact1.full_name())\n# print('')\n\n# # Testing delete\n# contact1.delete()\n# print(Contact.contacts)\n","sub_path":"contact.py","file_name":"contact.py","file_ext":"py","file_size_in_byte":4853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"295716237","text":"# # ⚠ Warning\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT\n# LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN\n# NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,\n# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\n# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n#\n# [🥭 Mango Markets](https://mango.markets/) support is available at:\n# [Docs](https://docs.mango.markets/)\n# [Discord](https://discord.gg/67jySBhxrg)\n# [Twitter](https://twitter.com/mangomarkets)\n# [Github](https://github.com/blockworks-foundation)\n# [Email](mailto:hello@blockworks.foundation)\n\n\nimport mango\nimport traceback\nimport typing\n\nfrom datetime import datetime\nfrom decimal import Decimal\n\nfrom .hedger import Hedger\n\n\n# # 🥭 PerpToSpotHedger class\n#\n# A hedger that hedges perp positions using a spot market.\n#\nclass PerpToSpotHedger(Hedger):\n def __init__(self, group: mango.Group, underlying_market: mango.PerpMarket,\n hedging_market: mango.SpotMarket, market_operations: mango.MarketOperations,\n max_price_slippage_factor: Decimal, max_hedge_chunk_quantity: Decimal):\n super().__init__()\n if (underlying_market.base != hedging_market.base) or (underlying_market.quote != hedging_market.quote):\n raise Exception(\n f\"Market {hedging_market.symbol} cannot be used to hedge market {underlying_market.symbol}.\")\n\n self.underlying_market: mango.PerpMarket = underlying_market\n self.hedging_market: mango.SpotMarket = hedging_market\n self.market_operations: mango.MarketOperations = market_operations\n self.buy_price_adjustment_factor: Decimal = Decimal(\"1\") + max_price_slippage_factor\n self.sell_price_adjustment_factor: Decimal = Decimal(\"1\") - max_price_slippage_factor\n self.max_hedge_chunk_quantity: Decimal = max_hedge_chunk_quantity\n self.market_index: int = group.find_perp_market_index(underlying_market.address)\n\n def pulse(self, context: mango.Context, model_state: mango.ModelState):\n try:\n perp_account: typing.Optional[mango.PerpAccount] = model_state.account.perp_accounts[self.market_index]\n if perp_account is None:\n raise Exception(\n f\"Could not find perp account at index {self.market_index} in account {model_state.account.address}.\")\n\n basket_token: typing.Optional[mango.AccountBasketToken] = model_state.account.basket_tokens[self.market_index]\n if basket_token is None:\n raise Exception(\n f\"Could not find basket token at index {self.market_index} in account {model_state.account.address}.\")\n\n token_balance: mango.TokenValue = basket_token.net_value\n perp_position: mango.TokenValue = perp_account.base_token_value\n\n # We're interested in maintaining the right size of hedge lots, so round everything to the hedge\n # market's lot size (even though perps have different lot sizes).\n perp_position_rounded: Decimal = self.hedging_market.lot_size_converter.round_base(perp_position.value)\n token_balance_rounded: Decimal = self.hedging_market.lot_size_converter.round_base(token_balance.value)\n\n # When we add the rounded perp position and token balances, we should get zero if we're delta-neutral.\n delta: Decimal = perp_position_rounded + token_balance_rounded\n self.logger.debug(\n f\"Delta from {self.underlying_market.symbol} to {self.hedging_market.symbol} is {delta:,.8f} {basket_token.token_info.token.symbol}\")\n\n if delta != 0:\n side: mango.Side = mango.Side.BUY if delta < 0 else mango.Side.SELL\n up_or_down: str = \"up to\" if side == mango.Side.BUY else \"down to\"\n price_adjustment_factor: Decimal = self.sell_price_adjustment_factor if side == mango.Side.SELL else self.buy_price_adjustment_factor\n\n adjusted_price: Decimal = model_state.price.mid_price * price_adjustment_factor\n quantity: Decimal = abs(delta)\n if (self.max_hedge_chunk_quantity > 0) and (quantity > self.max_hedge_chunk_quantity):\n self.logger.debug(\n f\"Quantity to hedge ({quantity:,.8f}) is bigger than maximum quantity to hedge in one chunk {self.max_hedge_chunk_quantity:,.8f} - reducing quantity to {self.max_hedge_chunk_quantity:,.8f}.\")\n quantity = self.max_hedge_chunk_quantity\n order: mango.Order = mango.Order.from_basic_info(side, adjusted_price, quantity, mango.OrderType.IOC)\n self.logger.info(\n f\"Hedging perp position {perp_position} and token balance {token_balance} with {side} of {quantity:,.8f} at {up_or_down} {adjusted_price:,.8f} on {self.hedging_market.symbol}\\n\\t{order}\")\n try:\n self.market_operations.place_order(order)\n except Exception:\n self.logger.error(\n f\"[{context.name}] Failed to hedge on {self.hedging_market.symbol} using order {order} - {traceback.format_exc()}\")\n raise\n\n self.pulse_complete.on_next(datetime.now())\n except (mango.RateLimitException, mango.NodeIsBehindException, mango.BlockhashNotFoundException, mango.FailedToFetchBlockhashException) as common_exception:\n # Don't bother with a long traceback for these common problems.\n self.logger.error(f\"[{context.name}] Hedger problem on pulse: {common_exception}\")\n self.pulse_error.on_next(common_exception)\n except Exception as exception:\n self.logger.error(f\"[{context.name}] Hedger error on pulse:\\n{traceback.format_exc()}\")\n self.pulse_error.on_next(exception)\n\n def __str__(self) -> str:\n return f\"« 𝙿𝚎𝚛𝚙𝚃𝚘𝚂𝚙𝚘𝚝𝙷𝚎𝚍𝚐𝚎𝚛 for underlying '{self.underlying_market.symbol}', hedging on '{self.hedging_market.symbol}' »\"\n","sub_path":"mango/hedging/perptospothedger.py","file_name":"perptospothedger.py","file_ext":"py","file_size_in_byte":6252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"288678834","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport urllib.request\nimport re\nimport time\nimport pygame\nimport datetime\nimport threading\n#获取整个网页内容\ndef getHtml(url):\n page = urllib.request.urlopen(url) # urllib.urlopen()方法用于打开一个URL地址\n html = page.read() # read()方法用于读取URL上的数据，向getHtml()函数传递一个网址，并把整个页面下载下来。\n return html\n #正则匹配数据\ndef getdata(html):\n reg =r'= 90:\n if pygame.mixer.music.get_busy(): #判断： humidity大于90播放音乐\n x=1\n else:\n print('music start')\n pygame.mixer.music.play(loops=10)\n waitinput()\n if flag1==0:\n print('Time over! Stop keying')\n else: #判断： 小于90停止音乐\n if pygame.mixer.music.get_busy():\n pygame.mixer.music.stop()\n else:\n return\n return\n\n\nmainurl = 'http://www.xinglong-naoc.org/weather/weatherchart1.jsp' #目标网站\npygame.mixer.init()\npygame.mixer.music.load('C:\\\\Monitor_Weather\\\\sound.mp3') #加载音乐流\nwhile 1:\n flag1 = 0\n flag2 = 0\n main(mainurl)\n if flag1==1: #监控停止\n flag2=remonit()\n print('\\n' + 'Monitoring!')\n if flag2==0:\n time.sleep(40) # 每60秒访问网站一次","sub_path":"XingLong_Weather.py","file_name":"XingLong_Weather.py","file_ext":"py","file_size_in_byte":3859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"355582849","text":"import time, datetime, numpy, cv2\nfrom dateutil.relativedelta import relativedelta\n\nclass Trackerino:\n\t# 4x4 Grid Printer Frames\n\tdef print_frames(self, video, timecode, frame_idx):\n\t\tframes = []\n\t\tfor i in range(16):\n\t\t\tvideo.set(cv2.CAP_PROP_POS_FRAMES, frame_idx-8+i)\n\t\t\t_, img = video.read()\n\t\t\timg = cv2.resize(img, None, fx=0.3, fy=0.3, interpolation = cv2.INTER_LINEAR)\n\t\t\timg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\t\t\tframes.append(img)\n\n\t\taux_list = []\n\t\tfor i in range(0, 16, 4):\n\t\t\tvis = numpy.concatenate((frames[i], frames[i+1]), axis=1)\n\t\t\tvis = numpy.concatenate((vis, frames[i+2]), axis=1)\n\t\t\tvis = numpy.concatenate((vis, frames[i+3]), axis=1)\n\t\t\taux_list.append(vis)\n\n\t\tvis = numpy.concatenate((aux_list[0], aux_list[1]), axis=0)\n\t\tvis = numpy.concatenate((vis, aux_list[2]), axis=0)\n\t\tvis = numpy.concatenate((vis, aux_list[3]), axis=0)\n\t\tcv2.imwrite('./boundary_frames/' + str(timecode.timecode) + '.png', vis)\n\n\tdef track(self, timecode, video, fps, frame_idx, last_frame, reverse):\n\t\t# Init things we need to track objects\n\t\ttracker = cv2.TrackerKCF_create()\n\t\txa, xb, ya, yb = timecode.bbox\n\t\tbboxes = tuple([xa, ya, xb - xa, yb - ya])\n\t\ttrack_phase = 0\n\n\t\t'''\n\t\ttrack_phase = 0 --> Quick search until we reach a near region over the desired boundary\n\t\ttrack_phase = 1 --> Slow tracker who gets the exact frame\n\t\t'''\n\t\twhile track_phase < 2:\n\t\t\tif track_phase == 1:\n\t\t\t\tframe_idx = frame_idx + 15*fps if reverse else frame_idx - 15*fps\n\t\t\t\tincrement = -1 if reverse else 1\n\t\t\t\titeration = 0\n\t\t\telse:\n\t\t\t\tincrement = -self.step if reverse else self.step\n\t\t\t\titeration = 0\n\t\t\t\tretries = 0\n\n\t\t\twhile video.isOpened():\n\t\t\t\tvideo.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)\n\t\t\t\tret, frame = video.read()\n\t\t\t\tif ret is False or frame_idx == last_frame or frame_idx == 0:\n\t\t\t\t\tprint(f'ret is False or frame_idx reach the last frame of the video, this is no-bueno at all')\n\t\t\t\t\tbreak\n\t\t\t\tframe = cv2.resize(frame, None, fx=self.scale, fy=self.scale, interpolation = cv2.INTER_LINEAR)\n\t\t\t\tframe = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\t\t\t\tif iteration == 1:\n\t\t\t\t\ttracker.init(frame, bboxes)\n\t\t\t\telif iteration > 1:\n\t\t\t\t\t(success, box) = tracker.update(frame)\n\t\t\t\t\tif success:\n\t\t\t\t\t\t(x, y, w, h) = [int(v*self.scale) for v in box]\n\t\t\t\t\t\tcv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 5)\n\t\t\t\t\t\tif self.show_frames:\n\t\t\t\t\t\t\tcv2.imshow(\"Trackerino\", frame)\n\t\t\t\t\t\tif retries > 0 and track_phase == 0:\n\t\t\t\t\t\t\tprint(f'Object detected again!')\n\t\t\t\t\t\t\tretries = 0\n\t\t\t\t\telif not success and track_phase == 0:\n\t\t\t\t\t\tretries += 1\n\t\t\t\t\t\tif retries == 1:\n\t\t\t\t\t\t\tprint(f'Missing object! I\\'m going to retry 9 more times')\n\t\t\t\t\t\tif retries > 9:\n\t\t\t\t\t\t\tframe_idx -= 10\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\telse:\n\t\t\t\t\t\t# Pack it up guys, we are done here. -Tracker, 2019\n\t\t\t\t\t\tbreak\n\t\t\t\tkey = cv2.waitKey(1) & 0xFF\n\t\t\t\tframe_idx += increment\n\t\t\t\titeration += 1\n\t\t\ttrack_phase += 1\n\n\t\tself.print_frames(video, timecode, frame_idx)\n\t\tboundary = timecode.video[1]+relativedelta(seconds=(frame_idx // fps))+timecode.offset\n\n\t\tvideo.release()\n\t\tcv2.destroyAllWindows()\n\t\tprint(f'Tracking done at {boundary}')\n\t\treturn boundary\n\n\tdef multi_file(self, timecode_in, timecode_out):\n\t\tvideo = cv2.VideoCapture(timecode_in.video[0])\n\t\tfps = round(video.get(cv2.CAP_PROP_FPS))\n\t\tlength = int(video.get(cv2.CAP_PROP_FRAME_COUNT))\n\t\ttcin_detection = self.track(timecode_in, video, fps, length-1, 0, True)\t\t# <---\n\n\t\tvideo = cv2.VideoCapture(timecode_out.video[0])\n\t\tfps = round(video.get(cv2.CAP_PROP_FPS))\n\t\tlength = int(video.get(cv2.CAP_PROP_FRAME_COUNT))\n\t\ttcout_detection = self.track(timecode_out, video, fps, 1, length, False)\t# --->\n\n\t\treturn (tcin_detection, tcout_detection)\n\n\tdef single_file(self, timecode_in, timecode_out):\n\t\tvideo = cv2.VideoCapture(timecode_in.video[0])\n\t\tfps = round(video.get(cv2.CAP_PROP_FPS))\n\t\tlength = int(video.get(cv2.CAP_PROP_FRAME_COUNT))\n\n\t\ttcin = timecode_in.timecode - timecode_in.offset - timecode_in.video[1]\t# Seconds\n\t\ttcin = tcin.seconds * fps\t# Frames\n\t\ttcout = timecode_out.timecode - timecode_out.offset - timecode_out.video[1]\t# Seconds\n\t\ttcout = tcout.seconds * fps\t# Frames\n\t\tmid = tcin + (tcout - tcin)/2\n\n\t\ttcin_detection = self.track(timecode_in, video, fps, mid, length, True)\t\t# <---\n\t\ttcout_detection = self.track(timecode_in, video, fps, 1, mid, False)\t\t# --->\n\n\t\treturn (tcin_detection, tcout_detection)\n\n\tdef __init__(self, show_frames):\n\t\tself.show_frames, self.scale, self.step = show_frames, 1.0, 25","sub_path":"modules/trackerino.py","file_name":"trackerino.py","file_ext":"py","file_size_in_byte":4439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"435578329","text":"from Card import Card\nfrom Player import *\nfrom ComputerPlayer import ComputerPlayer\nimport time\nimport pygame\n\n\nclass Game:\n def __init__(self):\n self.players = [] # list of 4 players\n self.deck = None # stores the deck ID\n self.cards_left = 32\n self.discard_pile = []\n self.rapping_player = None\n self.pot = 0\n self.current_bet = 0\n self.raise_amount = 0\n self.msgs = []\n self.turn_num = 0\n\n def check_reshuffle(self): # shuffles the deck when the draw pile has been used up\n if self.cards_left == 0:\n deck_cards = [card.__str__() for card in self.discard_pile]\n self.discard_pile = []\n to_shuffle = \",\".join(deck_cards)\n\n shuffled_deck = session.get(f\"https://deckofcardsapi.com/api/deck/new/shuffle/?cards={to_shuffle}\")\n while shuffled_deck.status_code != 200:\n shuffled_deck = session.get(f\"https://deckofcardsapi.com/api/deck/new/shuffle/?cards={to_shuffle}\")\n self.deck = shuffled_deck.json()[\"deck_id\"]\n self.cards_left = shuffled_deck.json()[\"remaining\"]\n\n # discard a single card\n self.discard_pile.append(self.draw_card())\n self.players[0].add_message(\"The deck has been reshuffled.\")\n # reset the display\n self.display_all()\n\n # handles resets and initial setup\n def deal(self):\n self.discard_pile.clear()\n self.cards_left = 52 - len(self.players) * 5\n for player in self.players:\n player.reset()\n self.rapping_player = None\n self.pot = 0\n self.current_bet = 0\n self.raise_amount = 0\n self.turn_num = 0\n self.msgs = []\n\n new_deck = session.get(\"https://deckofcardsapi.com/api/deck/new/shuffle/?deck_count=1\")\n while new_deck.status_code != 200:\n new_deck = session.get(\"https://deckofcardsapi.com/api/deck/new/shuffle/?deck_count=1\")\n deck_id = new_deck.json()[\"deck_id\"]\n\n for i in range(len(self.players)):\n # draw 5 cards\n draw_hands = session.get(f\"https://deckofcardsapi.com/api/deck/{deck_id}/draw/?count=5\")\n while draw_hands.status_code != 200:\n draw_hands = session.get(f\"https://deckofcardsapi.com/api/deck/{deck_id}/draw/?count=5\")\n draw_hands = draw_hands.json()\n\n self.deck = draw_hands[\"deck_id\"]\n cards = draw_hands[\"cards\"]\n hand = []\n\n for card in cards: # add the cards to the player's hand\n new_card = Card(card[\"image\"], card[\"value\"], card[\"suit\"], session)\n hand.append(new_card)\n self.players[i].hand = hand\n\n # discard a single card before the game starts\n self.discard_pile.append(self.draw_card())\n # reset the display\n self.display_all()\n\n # draws a single card from the deck\n def draw_card(self):\n card_request = session.get(f\"https://deckofcardsapi.com/api/deck/{self.deck}/draw/?count=1\")\n while card_request.status_code != 200:\n card_request = session.get(f\"https://deckofcardsapi.com/api/deck/{self.deck}/draw/?count=1\")\n card_json = card_request.json()\n\n t = card_json[\"cards\"][0]\n self.deck = card_json[\"deck_id\"]\n self.cards_left = card_json[\"remaining\"]\n\n new_card = Card(t[\"image\"], t[\"value\"], t[\"suit\"], session)\n return new_card\n\n def display_hand_values(self):\n nonfolded_players = [player for player in self.players if not player.has_folded]\n for index, player in enumerate(nonfolded_players):\n def val_to_string(points):\n if points == 0:\n high_card = max(player.hand).value\n high_card = high_card[0] + high_card[1:].lower()\n string = f\"{high_card} high\"\n elif points == 1:\n string = \"One pair\"\n elif points == 2:\n string = \"Two pair\"\n elif points == 3:\n string = \"Three of a kind\"\n elif points == 4:\n string = \"Straight\"\n elif points == 5:\n string = \"Flush\"\n elif points == 6:\n string = \"Full house\"\n elif points == 7:\n string = \"Four of a kind\"\n else:\n string = \"Straight flush\" if max(player.hand).value != \"KING\" else \"Royal flush\"\n return string\n \n points = self.hand_val(player.hand)[0]\n string = val_to_string(points)\n \n name_str = SIDE_BAR_FONT.render(f\"{player.name}: {string}\", True, BLACK)\n pos = (10, 10 + index * 25)\n window.blit(name_str, pos)\n\n # check, raise, and fold, calls already_bet_buttons()\n def display_bet_buttons(self):\n self.display_already_bet_buttons()\n mouse_x, mouse_y = pygame.mouse.get_pos()\n\n # draw the raise button\n raise_color = LIGHT_GRAY if is_over(raise_rect, mouse_x, mouse_y) else GRAY\n pygame.draw.rect(window, raise_color, raise_rect)\n pygame.draw.rect(window, BLACK, raise_rect, 2)\n window.blit(raise_msg, (47, 638))\n\n def display_already_bet_buttons(self):\n mouse_x, mouse_y = pygame.mouse.get_pos()\n self.display_current_bet()\n\n # draw the check button\n check_color = LIGHT_GRAY if is_over(check_rect, mouse_x, mouse_y) else GRAY\n pygame.draw.rect(window, check_color, check_rect)\n pygame.draw.rect(window, BLACK, check_rect, 2)\n window.blit(check_msg, (45, 593))\n\n # draw the fold button\n fold_color = LIGHT_GRAY if is_over(fold_rect, mouse_x, mouse_y) else GRAY\n pygame.draw.rect(window, fold_color, fold_rect)\n pygame.draw.rect(window, BLACK, fold_rect, 2)\n window.blit(fold_msg, (55, 683))\n\n # check, rap, and bet\n def display_prebet_buttons(self):\n self.display_final()\n mouse_x, mouse_y = pygame.mouse.get_pos()\n\n # draw the rap button\n rap_color = LIGHT_GRAY if is_over(rap_rect, mouse_x, mouse_y) else GRAY\n pygame.draw.rect(window, rap_color, rap_rect)\n pygame.draw.rect(window, BLACK, rap_rect, 2)\n window.blit(rap_msg, (58, 638))\n\n # plus, minus, place_bet, and $ amount of bet\n def display_plus_minus(self, bet_amount, button=\"place\"):\n mouse_x, mouse_y = pygame.mouse.get_pos()\n\n plus_color = LIGHT_GRAY if is_over(plus_rect, mouse_x, mouse_y) else GRAY\n pygame.draw.rect(window, plus_color, plus_rect)\n pygame.draw.rect(window, BLACK, plus_rect, 2)\n window.blit(plus_msg, (175, 661))\n\n minus_color = LIGHT_GRAY if is_over(minus_rect, mouse_x, mouse_y) else GRAY\n pygame.draw.rect(window, minus_color, minus_rect)\n pygame.draw.rect(window, BLACK, minus_rect, 2)\n window.blit(minus_msg, (178, 704))\n if button == \"place\":\n place_color = LIGHT_GRAY if is_over(place_rect, mouse_x, mouse_y) else GRAY\n pygame.draw.rect(window, place_color, place_rect)\n pygame.draw.rect(window, BLACK, place_rect, 2)\n window.blit(place_msg, (22, 683))\n word = \"Bet\"\n self.display_current_bet()\n else:\n ante_color = LIGHT_GRAY if is_over(ante_rect, mouse_x, mouse_y) else GRAY\n pygame.draw.rect(window, ante_color, ante_rect)\n pygame.draw.rect(window, BLACK, ante_rect, 2)\n window.blit(ante_msg, (28, 683))\n word = \"Ante\"\n\n bet_choice = MONEY_FONT.render(f\"{word}: ${bet_amount}\", True, BLACK)\n offset = bet_choice.get_size()[0] / 2\n window.blit(bet_choice, (90 - offset, 625))\n\n # check and bet\n def display_final(self):\n mouse_x, mouse_y = pygame.mouse.get_pos()\n self.display_current_bet()\n\n # draw the check button\n check_color = LIGHT_GRAY if is_over(check_rect, mouse_x, mouse_y) else GRAY\n pygame.draw.rect(window, check_color, check_rect)\n pygame.draw.rect(window, BLACK, check_rect, 2)\n window.blit(check_msg, (45, 593))\n\n # draw the bet button\n bet_color = LIGHT_GRAY if is_over(bet_rect, mouse_x, mouse_y) else GRAY\n pygame.draw.rect(window, bet_color, bet_rect)\n pygame.draw.rect(window, BLACK, bet_rect, 2)\n window.blit(bet_msg, (62, 683))\n\n def display_current_bet(self):\n bet = MONEY_FONT.render(f\"Current Bet: ${self.current_bet}\", True, BLACK)\n offset = bet.get_size()[0] / 2\n window.blit(bet, (375 - offset, 450))\n \n # only display method that updates display\n def display_all(self, to_display=None, bet_amount=0):\n def display_pot(): # shows the amount of money in the pot\n text = MONEY_FONT.render(f\"Pot: ${self.pot}\", True, BLACK)\n x_length, y_height = text.get_size()\n pos = (725 - x_length, 704)\n window.blit(text, pos)\n\n def display_pile(): # displays the draw and discard piles\n dp = self.discard_pile\n # display the face-up card\n if len(dp) >= 1:\n top_card = dp[len(dp) - 1].image\n window.blit(top_card, (375, 300))\n # display the drawing pile\n if self.cards_left >= 1:\n window.blit(dp_card_back, (270, 300))\n \n def display_msgs():\n def display_msg(msg, line_num):\n line_one = SIDE_BAR_FONT.render(msg, True, BLACK)\n line_two = \"\"\n words = msg.split()\n x_pos, y_pos = 515, 15 + line_num * 25\n\n while line_one.get_size()[0] > max_length: # checks if the message needs to be displayed on a new line\n line_two += (words[-1] + \" \")\n words = words[:-1]\n line_one = \"\".join([word + \" \" for word in words])\n line_one = SIDE_BAR_FONT.render(line_one, True, BLACK)\n\n if len(line_two) == 0:\n window.blit(line_one, (x_pos, y_pos))\n return True\n else: # two lines are needed to display the message\n reversed_list = []\n for word in line_two.split():\n reversed_list.insert(0, word)\n line_two = \"\".join(word + \" \" for word in reversed_list) # reversed(line_two)\n line_two = SIDE_BAR_FONT.render(line_two, True, BLACK)\n\n window.blit(line_one, (x_pos, y_pos))\n window.blit(line_two, (x_pos, y_pos + 25))\n return False\n line_num = 0\n max_length = 222\n num_lines = 0\n messages = self.msgs\n \n pygame.draw.rect(window, WHITE, side_board_rect)\n pygame.draw.rect(window, BLACK, side_board_rect, 2)\n\n for msg in messages: # remove old messages\n line_one = SIDE_BAR_FONT.render(msg, True, BLACK) \n if line_one.get_size()[0] > max_length:\n num_lines += 1\n extra_lines = -7 + len(messages) + num_lines\n if extra_lines > 0:\n for i in range(extra_lines):\n messages.pop(0)\n\n for msg in messages: # display current messages\n if not display_msg(msg, line_num): # if it is two lines long, add 2\n line_num += 1\n line_num += 1\n\n window.fill(GREEN)\n display_pile()\n display_pot()\n for player in self.players:\n player.display_hand()\n player.display_name_and_money()\n display_msgs()\n \n disp = to_display # display the amount of the current betting round\n if disp == \"betting\":\n self.display_bet_buttons()\n elif disp == \"already bet\":\n self.display_already_bet_buttons()\n elif disp == \"discarded\":\n self.display_prebet_buttons()\n elif disp == \"final\":\n self.display_final()\n elif disp == \"placing bet\":\n self.display_plus_minus(bet_amount)\n elif disp == \"ante\":\n self.display_plus_minus(bet_amount, button=\"ante\")\n\n pygame.display.update()\n\n def hand_finished(self):\n # determine the winner of the hand and transfer money\n winner = self.determine_winner()\n winner.add_message(f\"{winner.name} won ${self.pot}.\")\n winner.transfer_money(-self.pot)\n\n # show everyone's hand and the winner\n self.display_all()\n for player in self.players:\n player.display_hand(show_front=True)\n self.display_hand_values()\n pygame.display.update()\n check_for_quit(60, skip=True) # if player clicks they can skip the wait time, otherwise 15 seconds\n\n # remove players who don't have enough money\n money_list = [p.money for p in self.players]\n num_removed = 0\n for i in range(len(money_list)):\n if money_list[i] <= 0:\n winner.add_message(f\"{self.players[i - num_removed].name} has run out of money.\")\n self.players.pop(i - num_removed)\n num_removed += 1\n check_for_quit(4)\n \n def hand_val(self, hand): # returns a tuple representing the value of the hand\n royals = \"JQKA\"\n suits = set()\n values = []\n values_dict = {}\n def search_dict(value, exclude=0):\n for k, v in values_dict.items():\n if v == value and k != exclude:\n return k\n\n for card in hand:\n suit, val = card.suit, card.value\n suits.add(suit)\n if val.isalpha():\n val = val[0]\n if val in royals:\n val = 11 + royals.index(val)\n values.append(int(val))\n values.sort()\n\n for val in values:\n if val not in values_dict:\n values_dict[val] = 1\n else:\n values_dict[val] += 1\n\n # check for value of hand\n is_flush = False\n if len(suits) == 1:\n is_flush = True\n \n is_straight = True\n for i in range(4):\n if values[i + 1] - values[i] != 1:\n is_straight = False\n break\n \n one_pair = True if len(values_dict) == 4 else False\n if one_pair:\n pair_val = search_dict(2)\n\n three_of_kind = False\n two_pair = False\n if len(values_dict) == 3:\n if 3 in values_dict.values():\n three_of_kind = True\n pair_val = search_dict(3)\n else:\n two_pair = True\n first_pair = search_dict(2)\n second_pair = search_dict(2, exclude=first_pair)\n high = max(first_pair, second_pair)\n low = min(first_pair, second_pair)\n pair_val = (high, low)\n \n four_of_kind = False\n full_house = False\n if len(values_dict) == 2:\n if 4 in values_dict.values():\n four_of_kind = True\n pair_val = search_dict(4)\n else:\n full_house = True\n pair_val = (search_dict(3), search_dict(2))\n\n # add points for type of hand\n if one_pair:\n total_value = 1, pair_val\n elif two_pair:\n total_value = 2, pair_val\n elif three_of_kind:\n total_value = 3, pair_val\n elif is_straight and not is_flush:\n total_value = 4, None\n elif is_flush and not is_straight:\n total_value = 5, None\n elif full_house:\n total_value = 6, pair_val\n elif four_of_kind:\n total_value = 7, pair_val\n elif is_straight and is_flush:\n total_value = 8, None\n else:\n total_value = 0, None\n\n return total_value # always a tuple\n\n def determine_winner(self):\n hands = []\n temp_players = []\n for player in self.players:\n if not player.has_folded:\n hands.append(player.hand)\n temp_players.append(player)\n\n best_hand = hands[0]\n best_hand_val = self.hand_val(hands[0]) # a tuple with score, pair\n best_high = max(hands[0])\n index = 0\n\n for i in range(1, len(hands)): # put the winner/tied in a list\n cur_val = self.hand_val(hands[i])\n cur_high = max(hands[i])\n if cur_val > best_hand_val or (cur_val == best_hand_val and best_high < cur_high):\n best_hand = hands[i]\n best_hand_val = cur_val\n best_high = cur_high\n index = i\n\n elif cur_val == best_hand_val and cur_high == best_high: # still equal\n best_tuple = tuple(sorted(best_hand, reverse=True))\n cur_tuple = tuple(sorted(hands[i], reverse=True))\n if cur_tuple > best_tuple:\n best_hand = hands[i]\n index = i\n\n return temp_players[index]\n\n def handle_all_bets(self, start_index):\n cur_index = start_index + 1 if start_index != (len(self.players) - 1) else 0\n already_moved = [self.players[start_index]] # list of players who moved already\n\n while cur_index != start_index:\n cur_player = self.players[cur_index]\n\n if cur_player.has_folded:\n cur_index = cur_index + 1 if cur_index != (len(self.players) - 1) else 0 # next player\n continue\n cur_player.has_folded, raise_amount = (cur_player.handle_bet())\n\n if cur_player.has_folded:\n continue\n\n elif raise_amount != 0: # determine if the other players will stay in\n self.raise_amount = raise_amount\n self.current_bet += raise_amount\n for player in already_moved:\n player.has_folded = player.handle_already_bet()\n\n if player.has_folded:\n already_moved.remove(player) \n\n already_moved.insert(0, cur_player)\n cur_index = cur_index + 1 if cur_index != (len(self.players) - 1) else 0 # next player\n\n self.current_bet = 0\n self.raise_amount = 0\n check_for_quit(1)\n\n def play_hand(self):\n self.players = self.players[1:] + [self.players[0]] # switch the dealer\n self.players[len(self.players) - 1].add_message(f\"{self.players[len(self.players) - 1].name} is dealing.\")\n self.deal()\n\n dealer = self.players[len(self.players) - 1]\n ante = dealer.set_ante()\n for player in self.players[:len(self.players) - 1]:\n player.transfer_money(ante)\n\n next_player_index = None\n run = True\n while run:\n self.display_all()\n self.turn_num += 1\n\n for player in self.players:\n check_for_quit(1)\n if player.has_rapped:\n run = False # initialize final betting round\n break\n elif player.has_folded:\n if next_player_index == self.players.index(player):\n next_player_index = next_player_index + 1 if next_player_index != (len(self.players) - 1) else 0\n continue\n if next_player_index and self.players.index(player) != next_player_index:\n continue\n if player.has_rapped:\n run = False # initialize final betting round\n break\n\n player.is_turn = True\n player.handle_draw()\n player.handle_discard()\n\n can_rap = False if self.rapping_player else True\n bet = player.set_bet(can_rap=can_rap)\n if bet != 0:\n index = self.players.index(player)\n next_player_index = index + 1 if index != (len(self.players) - 1) else 0\n better_index = self.players.index(player)\n\n self.handle_all_bets(better_index)\n else:\n next_player_index = None\n\n\n player.is_turn = False\n player.add_message((f'{player.name} has finished their turn.'))\n self.check_reshuffle()\n\n # final betting round\n for player in self.players:\n self.is_turn = False\n\n better_index = -1 # wait for one of the players to bet\n cur_index = self.players.index(self.rapping_player)\n for i in range(len(self.players)):\n cur_player = self.players[cur_index]\n if cur_player.has_folded:\n continue\n bet = cur_player.set_bet(can_rap=False)\n if bet != 0:\n self.current_bet = bet\n better_index = cur_index\n break\n else:\n cur_index = cur_index + 1 if cur_index != (len(self.players) - 1) else 0\n\n # handle bets for the rest of the players\n if better_index != -1:\n self.handle_all_bets(better_index)\n # transfer money, remove players who lost, display results\n self.hand_finished()\n # determine if there is a final winner\n return len(self.players) != 1\n\n\ndef check_for_quit(cycles, skip=False): # checks for quitting every .25 seconds, if skip is True a click will exit the method\n for i in range(cycles):\n time.sleep(.25)\n outer_break = False # break out of both loops if clicked\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n elif i > 1 and skip and event.type == pygame.MOUSEBUTTONDOWN:\n outer_break = True\n\n if outer_break:\n break\n\n","sub_path":"Game.py","file_name":"Game.py","file_ext":"py","file_size_in_byte":22157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"576421825","text":"#! /usr/bin/env python3\n\nimport sys\nimport socket\nimport threading\n#import hexdump\n\ndef server_loop(local_host,local_port,remote_host,remote_port,receive_first):\n\n server=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n\n try:\n server.bind((local_host,local_port))\n except:\n print('[!!] failed to listen on %s:%d'%(local_host,local_port))\n print('[!!] check for other listening sockets or correct permissions')\n sys.exit(0)\n print('listening on %s:%d'%(local_host,local_port))\n\n server.listen(5)\n\n while True:\n client_socket,addr=server.accept()\n\n #print the local connection information\n print('[==>] received incoming connection from %s:%d '%(addr[0],addr[1]))\n\n #open a thread to communicate with remote host\n proxy_thread=threading.Thread(target=proxy_handler,args=(client_socket,remote_host,remote_port,receive_first))\n\n proxy_thread.start()\n\n#十六进制转换\ndef hexdump(src,length=16):\n result=[]\n #python2中的Unicode已经被python3的str代替，python2中的str被python3中的bytes代替\n digits=4 if isinstance(src,str) else 2\n\n for i in range(0,len(src),length):\n s=src[i:i+length]\n #ord()转为ascii码,%0*x表示十六进制，digits表示占的位数，例如：print('%0*x'%(6,ord('z'))==>00007a\n #再比如：s=b'alc' print('%0*x'%(4,s[1]))==>,并且s[1]还是个整数，例如：print(s[1])==>108,但是chr(b'l')是错的\n #并且0x20和s[1]是可以直接比较的，但0x20和b'l'是不能直接比较的。并且b'l'还不能直接转换为int,\n #例如：a=input() 输入b,int(a)会报错，但输入10之类的数字时可以的\n #一个str字符有多个字节bytes,ascii ,unicode,utf-8编码只是编码不同，用不同个数的字节表示同一个字符\n hexa=' '.join(['%0*x'% (digits,x) for x in s])\n text=''.join([chr(x) if 0x20<=x<0x7F else '.' for x in s])\n #%-*s表示输入一个字符串，-号表示左对齐，*表示对齐宽度由输入时决定,这里不能把length*(digits+1)和hexa括起来\n result.append('%04x %-*s %s'%(i,length*(digits+1),hexa,text))\n print('\\n'.join(result))\n\n#从一个连接中接收数据并返回\ndef receive_from(connection):\n buffer=b''\n\n #设置两秒的超时，取决于目标情况，可以调整\n connection.settimeout(2)\n\n try:\n #持续从缓存中读取数据直到没有数据或超时\n while True:\n data=connection.recv(1024)\n if not data:\n break\n buffer+=data\n except:\n pass\n\n return buffer\n\n#可以在该函数中修改传送到远程主机的数据\ndef request_handler(buffer):\n #修改数据（没做）\n return buffer\n\n#对本地主机的响应数据进行修改\ndef response_handler(buffer):\n #修改数据（没做）\n return buffer\n\n#handle the proxy\ndef proxy_handler(client_socket,remote_host,remote_port,receive_first):\n\n #connect to the remote host\n remote_socket=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n remote_socket.connect((remote_host,remote_port))\n\n #是否需要从远程主机接收数据\n if receive_first:\n\n remote_buffer=receive_from(remote_socket)\n hexdump(remote_buffer)\n\n #send to the response_handler\n remote_buffer=response_handler(remote_buffer)\n\n #if we need to transmit the data to the local host, send it\n if len(remote_buffer):\n print('[<==] sending %d bytes to localhost.'%len(remote_buffer))\n client_socket.send(remote_buffer)\n\n #从本地循环读取数据，发送给远程主机和本地主机\n while True:\n #从本地读取数据\n local_buffer=receive_from(client_socket)\n\n if len(local_buffer):\n print('[==>] received %d bytes from localhost.'%len(local_buffer))\n hexdump(local_buffer)\n\n #发送本地请求\n local_buffer=request_handler(local_buffer)\n\n #向远程主机发送数据\n remote_socket.send(local_buffer)\n print('[==>] sent to remote')\n\n #receive the response\n remote_buffer=receive_from(remote_socket)\n\n if len(remote_buffer):\n print('[<==] received %d bytes from remote.'%len(remote_buffer))\n hexdump(remote_buffer)\n\n #send to the response_handler\n remote_buffer=response_handler(remote_buffer)\n\n #将响应发送到本地\n client_socket.send(remote_buffer)\n print('[<==] sent to the localhost')\n #如果两边都没有数据，则关闭连接\n if not len(local_buffer) or not len(remote_buffer):\n client_socket.close()\n remote_socket.close()\n print('[*] no more data, closing connections')\n\n break\n\n\ndef main():\n\n if len(sys.argv[1:])!=5:\n print('usage:./proxy.py [localhost] [localport] [remotehost] [remoteport] [receive_first]')\n print('example: ./proxy.py 127.0.0.1 9000 10.12.132.1 9000 True')\n sys.exit(0)\n\n #set the local listening parameter\n local_host=sys.argv[1]\n local_port=int(sys.argv[2])\n\n #set the remote target\n remote_host=sys.argv[3]\n remote_port=int(sys.argv[4])\n\n #use proxy whether or not\n receive_first=sys.argv[5]\n\n if \"True\" in receive_first:\n receive_first=True\n else:\n receive_first=False\n\n server_loop(local_host,local_port,remote_host,remote_port,receive_first)\n\nif __name__=='__main__':\n main()\n\n","sub_path":"tcpproxy.py","file_name":"tcpproxy.py","file_ext":"py","file_size_in_byte":5564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"462034681","text":"from ftw.builder import Builder\nfrom ftw.builder import create\nfrom jsonschema import Draft4Validator\nfrom opengever.propertysheets.assignment import DOCUMENT_DEFAULT_ASSIGNMENT_SLOT\nfrom opengever.propertysheets.assignment import DOCUMENT_TYPE_ASSIGNMENT_SLOT_PREFIX\nfrom opengever.propertysheets.exportimport import dottedname\nfrom opengever.propertysheets.field import PropertySheetField\nfrom opengever.propertysheets.testing import dummy_default_factory_fr\nfrom opengever.propertysheets.tests.fixture import fixture_assignment_factory\nfrom opengever.testing import FunctionalTestCase\nfrom plone import api\nfrom plone.restapi.types.interfaces import IJsonSchemaProvider\nfrom zope.component import getMultiAdapter\n\n\nclass TestPropertySheetFieldSchemaProvider(FunctionalTestCase):\n\n maxDiff = None\n\n def setUp(self):\n super(TestPropertySheetFieldSchemaProvider, self).setUp()\n\n self.field = PropertySheetField(\n \"unused_request_key\",\n \"unused_attribute\",\n DOCUMENT_TYPE_ASSIGNMENT_SLOT_PREFIX,\n fixture_assignment_factory,\n DOCUMENT_DEFAULT_ASSIGNMENT_SLOT\n )\n\n @property\n def schema_provider(self):\n return getMultiAdapter(\n (self.field, self.portal, self.request), IJsonSchemaProvider\n )\n\n def test_returns_json_schema_only_for_assignment_slots(self):\n create(\n Builder(\"property_sheet_schema\")\n .named(\"schema\")\n .assigned_to_slots(\n u\"IDocumentMetadata.document_type.question\",\n u\"IDocumentMetadata.document_type.offer\", # not in factory\n )\n .with_field(\"text\", u\"foo\", u\"some input\", u\"\", True)\n )\n create(\n Builder(\"property_sheet_schema\")\n .named(\"not_inlcuded\")\n .assigned_to_slots(u\"IDocumentMetadata.document_type.request\")\n .with_field(\"text\", u\"bar\", u\"discard me\", u\"\", False)\n )\n\n json_schema = self.schema_provider.get_schema()\n expected = {\n \"type\": \"object\",\n \"title\": u\"Custom properties\",\n \"description\": u\"Contains data for user defined custom properties.\",\n \"properties\": {\n u\"IDocumentMetadata.document_type.question\": {\n \"assignments\": [\n u\"IDocumentMetadata.document_type.question\",\n u\"IDocumentMetadata.document_type.offer\",\n ],\n \"fieldsets\": [\n {\n \"behavior\": \"plone\",\n \"fields\": [\"foo\"],\n \"id\": \"default\",\n \"title\": \"Default\",\n }\n ],\n \"properties\": {\n u\"foo\": {\n u\"description\": u\"\",\n u\"factory\": u\"Text\",\n u\"title\": u\"some input\",\n u\"type\": u\"string\",\n u\"widget\": u\"textarea\",\n }\n },\n \"required\": [\"foo\"],\n \"title\": \"schema\",\n \"type\": \"object\",\n }\n },\n }\n self.assertEqual(expected, json_schema)\n\n # smoke-test to validate the schema\n Draft4Validator.check_schema(json_schema)\n\n def test_sheet_assigned_to_multiple_slots_is_serialized_for_each_slot(\n self,\n ):\n create(\n Builder(\"property_sheet_schema\")\n .named(\"schema\")\n .assigned_to_slots(\n u\"IDocumentMetadata.document_type.contract\",\n u\"IDocumentMetadata.document_type.question\",\n )\n .with_field(\"text\", u\"foo\", u\"some input\", u\"\", True)\n )\n\n json_schema = self.schema_provider.get_schema()\n schema_properties = json_schema[\"properties\"]\n\n self.assertIn(\n \"IDocumentMetadata.document_type.contract\",\n schema_properties,\n )\n self.assertIn(\n \"IDocumentMetadata.document_type.question\",\n schema_properties,\n )\n self.assertEqual(\n schema_properties[\"IDocumentMetadata.document_type.contract\"],\n schema_properties[\"IDocumentMetadata.document_type.question\"],\n )\n\n def test_returns_empty_dict_when_no_schemas_are_available(self):\n json_schema = self.schema_provider.get_schema()\n self.assertEqual(\n {\n \"description\": u\"Contains data for user defined custom properties.\",\n \"title\": u\"Custom properties\",\n \"type\": \"null\",\n },\n json_schema,\n )\n\n # smoke-test to validate the schema\n Draft4Validator.check_schema(json_schema)\n\n def test_returns_static_default_in_json_schema(self):\n choices = [u'de', u'fr', u'en']\n create(\n Builder(\"property_sheet_schema\")\n .named(\"schema\")\n .assigned_to_slots(u\"IDocument.default\")\n .with_field(\"choice\", u\"language\", u\"Language\", u\"\", True,\n values=choices, default=u'fr')\n )\n\n json_schema = self.schema_provider.get_schema()\n assignment = json_schema['properties']['IDocument.default']\n prop = assignment['properties']['language']\n self.assertEqual(u'fr', prop['default'])\n\n # smoke-test to validate the schema\n Draft4Validator.check_schema(json_schema)\n\n def test_returns_default_factory_in_json_schema(self):\n choices = [u'de', u'fr', u'en']\n create(\n Builder(\"property_sheet_schema\")\n .named(\"schema\")\n .assigned_to_slots(u\"IDocument.default\")\n .with_field(\"choice\", u\"language\", u\"Language\", u\"\", True,\n values=choices,\n default_factory=dottedname(dummy_default_factory_fr))\n )\n\n json_schema = self.schema_provider.get_schema()\n assignment = json_schema['properties']['IDocument.default']\n prop = assignment['properties']['language']\n\n self.assertEqual(u'fr', prop['default'])\n self.assertEqual(\n dottedname(dummy_default_factory_fr),\n prop['default_factory'])\n\n # smoke-test to validate the schema\n Draft4Validator.check_schema(json_schema)\n\n def test_returns_default_expression_in_json_schema(self):\n choices = [u'de', u'fr', u'en']\n create(\n Builder(\"property_sheet_schema\")\n .named(\"schema\")\n .assigned_to_slots(u\"IDocument.default\")\n .with_field(\"choice\", u\"language\", u\"Language\", u\"\", True,\n values=choices,\n default_expression=\"portal/language\")\n )\n\n json_schema = self.schema_provider.get_schema()\n assignment = json_schema['properties']['IDocument.default']\n prop = assignment['properties']['language']\n\n self.assertEqual(u'en', prop['default'])\n self.assertEqual(\n \"portal/language\",\n prop['default_expression'])\n\n # smoke-test to validate the schema\n Draft4Validator.check_schema(json_schema)\n\n def test_returns_default_from_member_in_json_schema(self):\n member = api.user.get_current()\n member.setProperties({'location': 'Berlin'})\n\n default_from_member_options = {\n 'property': 'location',\n 'fallback': 'CH',\n 'mapping': {\n 'Bern': 'CH',\n 'St. Gallen': 'CH',\n 'Berlin': 'DE'}\n }\n\n create(\n Builder(\"property_sheet_schema\")\n .named(\"schema\")\n .assigned_to_slots(u\"IDocument.default\")\n .with_field(\"textline\", u\"location\", u\"Location\", u\"\", True,\n default_from_member=default_from_member_options)\n )\n\n json_schema = self.schema_provider.get_schema()\n assignment = json_schema['properties']['IDocument.default']\n prop = assignment['properties']['location']\n\n self.assertEqual(u'DE', prop['default'])\n self.assertEqual(\n default_from_member_options,\n prop['default_from_member'])\n\n # smoke-test to validate the schema\n Draft4Validator.check_schema(json_schema)\n","sub_path":"opengever/propertysheets/tests/test_field_schema_provider.py","file_name":"test_field_schema_provider.py","file_ext":"py","file_size_in_byte":8454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"327949154","text":"#!/usr/bin/env python3\n# coding=utf-8\n\n\"\"\"\nhuman.py is to showcase agps3.py, a Python 2.7-3.5 GPSD interface\nDefaults host='127.0.0.1', port=2947, gpsd_protocol='json'\n\nToggle Lat/Lon form with '0', '1', '2', '3' for RAW, DDD, DMM, DMS\n\nToggle units with '0', 'm', 'i', 'n', for 'raw', Metric, Imperial, Nautical\n\nQuit with 'q' or '^c'\n\npython[X] human.py --help for list of commandline options.\n\"\"\"\n\nimport argparse\nimport curses\nimport sys\nfrom datetime import datetime\nfrom math import modf\nfrom time import sleep\n\nfrom gps3 import agps3\n\n__author__ = 'Moe'\n__copyright__ = \"Copyright 2015-2016 Moe\"\n__license__ = \"MIT\"\n__version__ = \"0.20\"\n\nCONVERSION = {'raw': (1, 1, 'm/s', 'meters'),\n 'metric': (3.6, 1, 'kph', 'meters'),\n 'nautical': (1.9438445, 1, 'kts', 'meters'),\n 'imperial': (2.2369363, 3.2808399, 'mph', 'feet')}\n\n\ndef add_args():\n \"\"\"Adds commandline arguments and formatted Help\"\"\"\n parser = argparse.ArgumentParser()\n\n parser.add_argument('-host', action='store', dest='host', default='127.0.0.1', help='DEFAULT \"127.0.0.1\"')\n parser.add_argument('-port', action='store', dest='port', default='2947', help='DEFAULT 2947', type=int)\n parser.add_argument('-json', dest='gpsd_protocol', const='json', action='store_const', default='json', help='DEFAULT JSON objects */')\n parser.add_argument('-device', dest='devicepath', action='store', help='alternate devicepath e.g.,\"-device /dev/ttyUSB4\"')\n # Infrequently used options\n parser.add_argument('-nmea', dest='gpsd_protocol', const='nmea', action='store_const', help='*/ output in NMEA */')\n parser.add_argument('-rare', dest='gpsd_protocol', const='rare', action='store_const', help='*/ output of packets in hex */')\n parser.add_argument('-raw', dest='gpsd_protocol', const='raw', action='store_const', help='*/ output of raw packets */')\n parser.add_argument('-scaled', dest='gpsd_protocol', const='scaled', action='store_const', help='*/ scale output to floats */')\n parser.add_argument('-timing', dest='gpsd_protocol', const='timing', action='store_const', help='*/ timing information */')\n parser.add_argument('-split24', dest='gpsd_protocol', const='split24', action='store_const', help='*/ split AIS Type 24s */')\n parser.add_argument('-pps', dest='gpsd_protocol', const='pps', action='store_const', help='*/ enable PPS JSON */')\n\n args = parser.parse_args()\n return args\n\n\ndef satellites_used(feed):\n \"\"\"Counts number of satellites use in calculation from total visible satellites\n Arguments:\n feed feed=dot.satellites\n Returns:\n total_satellites(int):\n used_satellites (int):\n \"\"\"\n total_satellites = 0\n used_satellites = 0\n\n if not isinstance(feed, list):\n return 0, 0\n\n for satellites in feed:\n total_satellites += 1\n if satellites['used'] is True:\n used_satellites += 1\n return total_satellites, used_satellites\n\n\ndef make_time(gps__datetime_str):\n \"\"\"Makes datetime object from string object\"\"\"\n if not 'n/a' == gps__datetime_str:\n datetime_string = gps__datetime_str\n datetime_object = datetime.strptime(datetime_string, \"%Y-%m-%dT%H:%M:%S\")\n return datetime_object\n\n\ndef elapsed_time_from(start_time):\n \"\"\"calculate time delta from latched time and current time\"\"\"\n time_then = make_time(start_time)\n time_now = datetime.utcnow().replace(microsecond=0)\n if time_then is None:\n return\n delta_t = time_now - time_then\n return delta_t\n\n\ndef unit_conversion(thing, units, length=False):\n \"\"\"converts base data between metric, imperial, or natical units\"\"\"\n if 'n/a' == thing:\n return 'n/a'\n try:\n thing = round(thing * CONVERSION[units][0 + length], 2)\n except:\n thing = 'fubar'\n return thing, CONVERSION[units][2 + length]\n\n\ndef sexagesimal(sexathang, tag, form='DDD'):\n \"\"\"\n Arguments:\n sexathang: (float), -15.560615 (negative = South), -146.241122 (negative = West) # Apataki Carenage\n tag: (str) 'lat' | 'lon'\n form: (str), 'DDD'|'DMM'|'DMS', decimal Degrees, decimal Minutes, decimal Seconds\n Returns:\n latitude: e.g., '15°33'38.214\" S'\n longitude: e.g., '146°14'28.039\" W'\n \"\"\"\n cardinal = 'O'\n if not isinstance(sexathang, float):\n sexathang = 'n/a'\n return sexathang\n\n if tag == 'lon':\n if sexathang > 0.0:\n cardinal = 'E'\n if sexathang < 0.0:\n cardinal = 'W'\n\n if tag == 'lat':\n if sexathang > 0.0:\n cardinal = 'N'\n if sexathang < 0.0:\n cardinal = 'S'\n\n if form == 'RAW':\n sexathang = '{0:4.6f}°'.format(sexathang)\n return sexathang\n\n if form == 'DDD':\n sexathang = '{0:3.6f}°'.format(abs(sexathang))\n\n if form == 'DMM':\n _latlon = abs(sexathang)\n minute_latlon, degree_latlon = modf(_latlon)\n minute_latlon *= 60\n sexathang = '{0}° {1:2.5f}\\''.format(int(degree_latlon), minute_latlon)\n\n if form == 'DMS':\n _latlon = abs(sexathang)\n minute_latlon, degree_latlon = modf(_latlon)\n second_latlon, minute_latlon = modf(minute_latlon * 60)\n second_latlon *= 60.0\n sexathang = '{0}° {1}\\' {2:2.3f}\\\"'.format(int(degree_latlon), int(minute_latlon), second_latlon)\n\n return sexathang + cardinal\n\n\ndef show_human():\n \"\"\"Curses terminal with standard outputs \"\"\"\n args = add_args()\n gps_connection = agps3.GPSDSocket(args.host, args.port, args.gpsd_protocol, args.devicepath)\n dot = agps3.Dot()\n form = 'RAW'\n units = 'raw'\n # units = 'metric'\n screen = curses.initscr()\n screen.clear()\n screen.scrollok(True)\n curses.noecho()\n curses.curs_set(0)\n curses.cbreak()\n\n data_window = curses.newwin(19, 39, 1, 1)\n sat_window = curses.newwin(19, 39, 1, 40)\n device_window = curses.newwin(6, 39, 14, 40)\n packet_window = curses.newwin(20, 78, 20, 1)\n\n try:\n for new_data in gps_connection:\n if new_data:\n dot.unpack(new_data)\n\n screen.nodelay(1)\n event = screen.getch()\n\n if event == ord('q'):\n shut_down(gps_connection)\n elif event == ord('0'): # raw\n form = 'RAW'\n units = 'raw'\n data_window.clear()\n elif event == ord(\"1\"): # DDD\n form = 'DDD'\n data_window.clear()\n elif event == ord('2'): # DMM\n form = 'DMM'\n data_window.clear()\n elif event == ord(\"3\"): # DMS\n form = 'DMS'\n data_window.clear()\n elif event == ord(\"m\"): # Metric\n units = 'metric'\n data_window.clear()\n elif event == ord(\"i\"): # Imperial\n units = 'imperial'\n data_window.clear()\n elif event == ord(\"n\"): # Nautical\n units = 'nautical'\n data_window.clear()\n\n data_window.box()\n data_window.addstr(0, 2, 'GPS3 Python {}.{}.{} GPSD Interface'.format(*sys.version_info), curses.A_BOLD)\n data_window.addstr(1, 2, 'Time: {} '.format(dot.time))\n data_window.addstr(2, 2, 'Latitude: {} '.format(sexagesimal(dot.lat, 'lat', form)))\n data_window.addstr(3, 2, 'Longitude: {} '.format(sexagesimal(dot.lon, 'lon', form)))\n data_window.addstr(4, 2, 'Altitude: {} {}'.format(*unit_conversion(dot.alt, units, length=True)))\n data_window.addstr(5, 2, 'Speed: {} {}'.format(*unit_conversion(dot.speed, units)))\n data_window.addstr(6, 2, 'Heading: {}° True'.format(dot.track))\n data_window.addstr(7, 2, 'Climb: {} {}'.format(*unit_conversion(dot.climb, units, length=True)))\n data_window.addstr(8, 2, 'Status: {:<}D '.format(dot.mode))\n data_window.addstr(9, 2, 'Latitude Err: +/-{} {}'.format(*unit_conversion(dot.epx, units, length=True)))\n data_window.addstr(10, 2, 'Longitude Err: +/-{} {}'.format(*unit_conversion(dot.epy, units, length=True)))\n data_window.addstr(11, 2, 'Altitude Err: +/-{} {}'.format(*unit_conversion(dot.epv, units, length=True)))\n data_window.addstr(12, 2, 'Course Err: +/-{} '.format(dot.epc), curses.A_DIM)\n data_window.addstr(13, 2, 'Speed Err: +/-{} {}'.format(*unit_conversion(dot.eps, units)), curses.A_DIM)\n data_window.addstr(14, 2, 'Time Offset: +/-{} '.format(dot.ept), curses.A_DIM)\n data_window.addstr(15, 2, 'gdop:{} pdop:{} tdop:{}'.format(dot.gdop, dot.pdop, dot.tdop))\n data_window.addstr(16, 2, 'ydop:{} xdop:{} '.format(dot.ydop, dot.xdop))\n data_window.addstr(17, 2, 'vdop:{} hdop:{} '.format(dot.vdop, dot.hdop))\n\n sat_window.clear()\n sat_window.box()\n sat_window.addstr(0, 2, 'Using {0[1]}/{0[0]} satellites (truncated)'.format(satellites_used(dot.satellites)))\n sat_window.addstr(1, 2, 'PRN Elev Azimuth SNR Used')\n line = 2\n if isinstance(dot.satellites, list): # Nested lists of dictionaries are strings before data is present\n for sats in dot.satellites[0:10]:\n sat_window.addstr(line, 2, '{PRN:>2} {el:>6} {az:>5} {ss:>5} {used:}'.format(**sats))\n line += 1\n\n # device_window.clear()\n device_window.box()\n if not isinstance(dot.devices, list):\n gps_connection.send('?DEVICES;') # Local machines need a 'device' kick start to have valid data I don't know why.\n\n if isinstance(dot.devices, list):\n for gizmo in dot.devices:\n start_time, _uicroseconds = gizmo['activated'].split('.') # Remove '.000Z'\n elapsed = elapsed_time_from(start_time)\n\n device_window.addstr(1, 2, 'Activated: {}'.format(gizmo['activated']))\n device_window.addstr(2, 2, 'Host:{0.host}:{0.port} {1}'.format(args, gizmo['path']))\n device_window.addstr(3, 2, 'Driver:{driver} BPS:{bps}'.format(**gizmo))\n device_window.addstr(4, 2, 'Cycle:{0} Hz {1!s:>14} Elapsed'.format(gizmo['cycle'], elapsed))\n\n packet_window.clear()\n packet_window.border(0)\n packet_window.scrollok(True)\n packet_window.addstr(0, 0, '{}'.format(new_data))\n\n sleep(.4)\n\n data_window.refresh()\n sat_window.refresh()\n device_window.refresh()\n packet_window.refresh()\n\n except KeyboardInterrupt:\n shut_down(gps_connection)\n\ndef shut_down(gps_connection):\n \"\"\"Closes connection and restores terminal\"\"\"\n curses.nocbreak()\n curses.echo()\n curses.endwin()\n gps_connection.close()\n print(\"Keyboard interrupt received\\nTerminated by user\\nGood Bye.\\n\")\n sys.exit(1)\n\n\nif __name__ == '__main__':\n try:\n\n if 'json' in add_args().gpsd_protocol:\n show_human()\n if 'nmea' in add_args().gpsd_protocol:\n show_nmea()\n\n except KeyboardInterrupt:\n shut_down(show_human().gps_connection)\n#\n# Someday a cleaner Python interface will live here\n#\n# End\n","sub_path":"gps3/gps3-master/ahuman.py","file_name":"ahuman.py","file_ext":"py","file_size_in_byte":11648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"416504085","text":"# Created by PyCharm Pro Edition \n# User: Kaushik Talukdar \n# Date: 31-03-2017 \n# Time: 01:48 PM\n\n# dictionary containing a list\n\nfood_available = {\n \"fastfood\" : [\"momo\", \"roll\", \"chow\", \"pizza\"],\n \"slowfood\" : [\"rice\", \"dal\", \"aloo fry\"]\n}\n\nprint(\"You have ordered\")\nfor food in food_available[\"slowfood\"]:\n print(food.title())","sub_path":"5. Dictionary/13.py","file_name":"13.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"296306469","text":"from .base_api import BaseAPI\n\nclass UserGroupsUsers(BaseAPI):\n \"\"\"Follows the Slack UserGroupUsers API. See https://api.slack.com/methods\"\"\"\n\n def list(self, usergroup, include_disabled=False):\n \"\"\"\n Lists all users in a usergroup\n\n :param usergroup: The usergroup ID\n :type usergroup: str\n :param include_disabled: Include disabled users\n :type include_disabled: bool\n :return: A response object to run the API request.\n :rtype: :class:`Response ` object\n \"\"\"\n if isinstance(include_disabled, bool):\n include_disabled = str(include_disabled).lower()\n\n yield self.get('usergroups.users.list', params={\n 'usergroup': usergroup,\n 'include_disabled': include_disabled,\n })\n\n def update(self, usergroup, users, include_count=False):\n \"\"\"\n Updates the list of users for a usergroup\n\n :param usergroup: The usergroup ID\n :type usergroup: str\n :param users: CSV of user IDs to add\n :type users: list[str]\n :param include_count: Include a count of users\n :type include_count: bool\n :return: A response object to run the API request.\n :rtype: :class:`Response ` object\n \"\"\"\n if isinstance(users, (tuple, list)):\n users = ','.join(users)\n\n return self.post('usergroups.users.update', data={\n 'usergroup': usergroup,\n 'users': users,\n 'include_count': str(include_count).lower(),\n })\n\n","sub_path":"slackest/user_group_users.py","file_name":"user_group_users.py","file_ext":"py","file_size_in_byte":1563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"350798977","text":"\n\nfrom xai.brain.wordbase.nouns._emission import _EMISSION\n\n#calss header\nclass _EMISSIONS(_EMISSION, ):\n\tdef __init__(self,): \n\t\t_EMISSION.__init__(self)\n\t\tself.name = \"EMISSIONS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"emission\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_emissions.py","file_name":"_emissions.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"} +{"seq_id":"17911426","text":"# -*- coding: utf-8 -*-\n\nimport scrapy\nimport io\nfrom HTMLParser import HTMLParser\n\n\nclass ObservadorSpider(scrapy.Spider):\n name = \"observador\"\n allowed_domains = [\"observador.pt\"]\n start_urls = [\n \"http://observador.pt/\",\n # \"http://observador.pt/2015/11/10/pcp/\",\n # \"http://observador.pt/opiniao/acabou-a-austeridade-reformados-vao-ter-aumento-de-18-euros/\",\n\n ]\n def parse(self, response):\n for href in response.css(\"a::attr('href')\"):\n url = response.urljoin(href.extract())\n yield scrapy.Request(url, callback=self.parse_content)\n\n def parse_content(self, response):\n\n content = None\n\n for sel in response.xpath('//title'):\n title = HTMLParser().unescape(sel.xpath('text()').extract()[0])\n # print \"#Title:\", HTMLParser().unescape(title[0])\n t = sel.xpath('text()').extract()[0]\n\n for sel in response.xpath('//*[contains(concat(\" \", normalize-space(@class), \" \"), \" content \")]'):\n content = sel.xpath('*').extract()\n # print HTMLParser().unescape(''.join(content))\n\n filename = \"output/\" + response.url.split(\"/\")[-2] + '.raw'\n\n print(response.url)\n page = title + \"\\n\"\n\n if content:\n for cont in content:\n\n if \"